Intelligent water distribution control method based on time sequence prediction

Abstract

The invention discloses an intelligent water distribution control method based on time sequence prediction, and belongs to the technical field of urban water supply control. The invention aims to solve the problems that the existing water supply pressure and water quantity control depends on the change of the pipe diameter of a pipeline, the construction difficulty is high and the sudden water use condition cannot be dealt with. Comprising the following steps: collecting average water consumption data and average water pressure data of each household per hour; carrying out abnormal value identification based on quartile, and adjusting an abnormal value to a normal value; obtaining daily hour average water consumption original data and average water pressure original data of each household in each season of the adjacent previous year according to the adjusted data; obtaining daily hour average water consumption reference data and average water pressure reference data of each household in the current season of the current year; based on the original data and the reference data, using an SARIMA model to obtain average water consumption prediction data and average water pressure prediction data per hour of each household in the next day of the current year; and adjusting the valve of the water segregator according to the predicted data. The method is used for urban water supply and distribution control.

Description

Technical Field

[0001] The invention relates to an intelligent water distribution control method based on time series prediction, belonging to the technical field of urban water supply control. Background Art

[0002] In the urban water supply system, water supply pipes usually need to pass through residential areas and enter various buildings. Inside the building, the water supply pipes are first distributed to each floor through a riser. The water supply system on each floor distributes water to each household through a water distributor. However, due to differences in water use habits and sanitary ware, different households have different water consumption and peak water use, which may cause certain problems in this water distribution method. Specifically, when the residents on the ground floor have no water demand during a certain period of time, the residents on the upper floors may face an increase in water demand, which will cause the water pressure on the ground floor to be too high and the water pressure on the upper floors to be insufficient. This problem of uneven water pressure distribution not only affects the daily quality of life of the residents, but also puts higher requirements on how the water distributor distributes water. Therefore, smart water distributors are gradually gaining attention.

[0003] At present, many technologies have made significant progress in the field of long-distance data transmission. For example, by using multimodal sensors to monitor and transmit the water pressure and water volume of each household in real time, we can obtain rich environmental data and water use information. In addition, as various machine learning algorithms gradually mature, the application of machine learning algorithms in processing large-scale, multi-dimensional data is also becoming more and more extensive. Machine learning algorithms can extract valuable information and patterns from massive data.

[0004] However, existing remote monitoring devices are mainly used to count the water consumption of each user in order to calculate water charges. In terms of scheduling water pressure and flow, traditional methods usually rely on changing the diameter of the pipe to achieve this. This method requires a detailed analysis of the water consumption of each household and the re-laying of pipes, which is not only complicated to handle but also difficult to construct. In addition, when abnormal water usage occurs, traditional methods may also cause safety hazards such as pipe bursts, thereby affecting the overall stability and reliability of the water supply system. Summary of the invention

[0005] In view of the problem that the existing water supply water pressure and water volume control depends on the change of pipeline diameter, is difficult to construct and cannot cope with sudden water use situations, the present invention provides an intelligent water distribution control method based on time series prediction.

[0006] The present invention provides an intelligent water distribution control method based on time series prediction, comprising:

[0007] Multimodal sensors are used to collect average water consumption data and average water pressure data for each household every hour; outliers are identified based on quartiles for average water consumption data and average water pressure data for each household, and outliers are adjusted to normal values;

[0008] The average water consumption data and average water pressure data of all households in the previous year after adjustment for abnormal values ​​are taken as the original data, and the original data of the average daily and hourly water consumption and average water pressure of each household in each season of the previous year are calculated based on the original data of each season; the reference data of the average daily and hourly water consumption and average water pressure of each household in the current season of the year are calculated from the average water consumption data and average water pressure data of each household in each hour of the dates that have occurred in the current season of the year after adjustment for abnormal values;

[0009] The SARIMA model is used to obtain the predicted data of average hourly water consumption and average water pressure for each household on the second day of the current year based on the original data of average hourly water consumption and average water pressure for each household in each season of the previous year and the reference data of average hourly water consumption and average water pressure for each household in the current season of the current year;

[0010] The valve of the water distributor is adjusted based on the predicted data of average hourly water consumption and average water pressure for each household on the second day of the year to achieve intelligent water distribution control.

[0011] According to the intelligent water distribution control method based on time series prediction of the present invention, the method for identifying outliers based on quartiles includes:

[0012] The average water consumption data and average water pressure data of each household in each hour of each season collected by the multimodal sensor are sorted from small to large by hour to obtain hourly time series data;

[0013] The first quartile Q1, the third quartile Q3 and the interquartile range IQR are determined from the hourly time series data;

[0014] Determine the lower limit of the average water consumption and average water pressure data of each household per hour as Q1-1.5*IQR; the upper limit is Q3+1.5*IQR; where IQR=Q3-Q1;

[0015] The data of average water consumption per household or average water pressure per hour that exceeds the corresponding lower and upper limits are regarded as outliers.

[0016] According to the intelligent water distribution control method based on time series prediction of the present invention, the method of adjusting the abnormal value to the normal value is:

[0017] The abnormal values ​​are replaced by the average of all normal values ​​in the average water consumption data or average water pressure data of each household per hour on different days of the same season, and the abnormal values ​​are adjusted to normal values.

[0018] According to the intelligent water distribution control method based on time series prediction of the present invention, the model parameters of the SARIMA model include seasonal cycle, difference order, regression parameter and moving average order;

[0019] The model parameters are determined by autocorrelation graphs and partial autocorrelation graphs, and the autocorrelation graphs and partial autocorrelation graphs are obtained by deep learning or grid search.

[0020] According to the intelligent water distribution control method based on time series prediction of the present invention, the method for obtaining the average hourly water consumption prediction data and average water pressure prediction data of each household on the second day of the year by the SARIMA model is:

[0021] Input the original data of daily and hourly average water consumption and average water pressure of each household in each season of the previous year and the reference data of daily and hourly average water consumption and average water pressure of each household in the current season into the SARIMA model to make the initial prediction of the average water consumption data and average water pressure data;

[0022] If the initial forecast result is within the fluctuation range of the box plot generated by historical data, the initial forecast result will be used as the forecast data for the average hourly water consumption and average water pressure per household on the second day of the year;

[0023] Otherwise, set the input data threshold, delete the data that exceeds the input data threshold in the SARIMA model, input the retained data into the SARIMA model again, and re-predict the average water consumption data and the average water pressure data until the prediction result is within the fluctuation range of the box plot generated by the historical data. Use the current prediction result as the predicted data for the average water consumption and the average water pressure per household per hour on the second day of the year.

[0024] According to the intelligent water distribution control method based on time series prediction of the present invention, if the predicted data of average water consumption per household per hour on the second day of the year is greater than the average water consumption per household per hour on the same day, the opening of the water distributor valve is increased accordingly, thereby increasing the user's water inlet pressure to the average water pressure predicted data;

[0025] If the predicted average water consumption per household per hour on the second day of the year is less than the average water consumption per household per hour on the same day, the water distributor valve opening will be reduced accordingly, thereby reducing the user's water inlet pressure to the average water pressure predicted data.

[0026] According to the intelligent water distribution control method based on time series prediction of the present invention, a controller is used to calculate original data and reference data based on the collected average water consumption data and average water pressure data of each household per hour; and the SARIMA model is called to predict the average water consumption forecast data and average water pressure forecast data of each household per hour on the second day of the year; and the control instructions of the water distribution valves are obtained based on the average water consumption forecast data and average water pressure forecast data of each household per hour on the second day of the year and the current valve status of each household's water distribution device.

[0027] According to the intelligent water distribution control method based on time series prediction of the present invention, an actuator is used to adjust the opening of the water distributor valve based on the control instruction of the water distributor valve.

[0028] According to the intelligent water distribution control method based on time series prediction of the present invention, the controller, multimodal sensor and actuator transmit data based on a communication network; the communication protocol of the communication network includes industrial Ethernet, field bus and wireless network.

[0029] According to the intelligent water distribution control method based on time series prediction of the present invention, the controller is powered by an uninterruptible power supply UPS and a backup power supply.

[0030] Beneficial effects of the present invention: The present invention controls the water diversion of urban water by controlling the water diverter valve based on historical water use data, improves the water diversion efficiency of the water diverter by online control of the water diverter valve, and can respond to various water use emergencies by adjusting the opening of the water diverter valve, thereby reducing the possibility of danger and ensuring the overall stability and reliability of the water supply system.

[0031] The present invention realizes the intelligent water distribution of urban water supply, can optimize water volume scheduling, effectively improve the intelligence level of the water supply system, and optimize the allocation of water supply resources.

[0032] The method of the present invention predicts water consumption data in hours, which can improve the decision-making prediction efficiency of smart water distribution. The cause of abnormal water consumption is generally due to abnormal water consumption by users and sensor damage. The method of the present invention adjusts the abnormal value after identifying it, and uses the average value of normal water consumption to replace the abnormal value, which can improve the accuracy of the prediction and further improve the prediction efficiency of smart water distribution. The data is recycled at one-hour intervals, and the prediction model can be continuously optimized, thereby further improving the accuracy of water volume prediction and the overall efficiency of water distribution control. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a flow chart of the intelligent water distribution control method based on time series prediction described in the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0036] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but they are not intended to limit the present invention.

[0037] Specific implementation method 1. Combination Figure 1 As shown, the present invention provides an intelligent water distribution control method based on time series prediction, comprising:

[0038] Multimodal sensors are used to collect average water consumption data and average water pressure data for each household every hour; outliers are identified based on quartiles for average water consumption data and average water pressure data for each household, and outliers are adjusted to normal values;

[0039] The average water consumption data and average water pressure data of all households in the previous year after adjustment for abnormal values ​​are taken as the original data, and the original data of the average daily and hourly water consumption and average water pressure of each household in each season of the previous year are calculated based on the original data of each season; the reference data of the average daily and hourly water consumption and average water pressure of each household in the current season of the year are calculated from the average water consumption data and average water pressure data of each household in each hour of the dates that have occurred in the current season of the year after adjustment for abnormal values;

[0040] The SARIMA model is used to obtain the predicted data of average hourly water consumption and average water pressure for each household on the second day of the current year based on the original data of average hourly water consumption and average water pressure for each household in each season of the previous year and the reference data of average hourly water consumption and average water pressure for each household in the current season of the current year;

[0041] The valve of the water distributor is adjusted based on the predicted data of average hourly water consumption and average water pressure for each household on the second day of the year to achieve intelligent water distribution control.

[0042] The data collected by the multimodal sensor can be transmitted to the controller for data analysis and processing. The real sensor data corresponding to the average water consumption prediction data and average water pressure prediction data obtained in each prediction will continue to be stored for subsequent predictions.

[0043] The multimodal sensor selects a high-sensitivity, multimodal water volume and water pressure sensor to ensure its measurement accuracy and data transmission stability. The measurement process takes each hour as a time period, processes the collected water volume data, calculates the average water flow in each time period, and obtains the hourly average flow.

[0044] The measurement data of this embodiment is in hours, for example, average water consumption data within one hour between one o'clock and two o'clock.

[0045] Further, the method of outlier identification based on quartiles includes:

[0046] The average water consumption data and average water pressure data of each household in each hour of each season collected by the multimodal sensor are sorted from small to large by hour to obtain hourly time series data;

[0047] The first quartile Q1, the third quartile Q3 and the interquartile range IQR are determined from the hourly time series data;

[0048] Determine the lower limit of the average water consumption and average water pressure data of each household per hour as Q1-1.5*IQR; the upper limit is Q3+1.5*IQR; where IQR=Q3-Q1;

[0049] The data of average water consumption per household or average water pressure per hour that exceeds the corresponding lower and upper limits are regarded as outliers.

[0050] The first quartile Q1 corresponds to the 25% of the data in the time series data, and the third quartile Q3 corresponds to the 75% of the data in the time series data; the gap between the third quartile and the first quartile is called the interquartile range.

[0051] Time series data analysis includes a series of methods for analyzing time series data, the purpose of which is to extract meaningful statistical information from the data and make predictions for the future. In this implementation, flow data is used to perform time series analysis, collect the user's hourly water consumption in a day, and analyze the data for the same period of time each day.

[0052] In this implementation, the method for adjusting the abnormal value to the normal value is:

[0053] The abnormal values ​​are replaced by the average of all normal values ​​in the average water consumption data of each household or the average water pressure data per hour on different days of the same season, and the abnormal values ​​are adjusted to normal values ​​to ensure the continuity and rationality of the data.

[0054] In this implementation manner, the calculation and prediction of data are performed based on data of the same season but different dates and the same time period.

[0055] Considering the seasonal periodic changes in user water consumption, the SARIMA algorithm is used to predict future water consumption, which can support time series data with seasonal components. Four seasonal parameters can be added on the basis of the existing SARIMA model. The model parameters of the SARIMA model include seasonal cycle, difference order, regression parameter and moving average order;

[0056] The model parameters are determined by autocorrelation graphs and partial autocorrelation graphs, which are obtained by deep learning or grid search. The performance of each combination is evaluated by cross-validation, and the optimal parameters are finally selected to improve the accuracy of decision-making predictions, thereby further improving the prediction efficiency of smart water distribution. This implementation method collects water consumption data and predicts water consumption data at intervals of one hour, which can realize the recycling of data and the continuous optimization of the prediction model, thereby further improving the accuracy of water volume prediction and the overall efficiency of the system.

[0057] The autocorrelation graph and partial autocorrelation graph are drawn through the autocorrelation and partial autocorrelation functions, and the parameter size is selected through the tailing or truncation characteristics.

[0058] Furthermore, the method for obtaining the average hourly water consumption forecast data and average water pressure forecast data for each household on the second day of the year by the SARIMA model is as follows:

[0059] Input the original data of daily and hourly average water consumption and average water pressure of each household in each season of the previous year and the reference data of daily and hourly average water consumption and average water pressure of each household in the current season into the SARIMA model to make the initial prediction of the average water consumption data and average water pressure data;

[0060] If the initial forecast result is within the fluctuation range of the box plot generated by historical data, the initial forecast result will be used as the forecast data for the average hourly water consumption and average water pressure per household on the second day of the year;

[0061] Otherwise, set the input data threshold, delete the data that exceeds the input data threshold in the SARIMA model, input the retained data into the SARIMA model again, and re-predict the average water consumption data and the average water pressure data until the prediction result is within the fluctuation range of the box plot generated by the historical data. Use the current prediction result as the predicted data for the average water consumption and the average water pressure per household per hour on the second day of the year.

[0062] The box plot is drawn based on the quartile principle and is generated through historical data, and the boundaries represent the normal fluctuation range of the data.

[0063] In this implementation, the hourly water consumption of the user is collected during the day, and the data of the same period of each day are analyzed separately. Using single-step prediction, the value of a future time can be predicted using historical data to ensure the accuracy of the prediction. The actual value of water consumption each day is then added to the data value in turn to predict the value of the next day, which greatly improves the reliability of the prediction.

[0064] If the prediction result is within the bounds of the box plot, it means that the prediction result is good and the prediction process is complete. If the prediction result is not within the bounds, it is believed that there may be abnormal data or the model parameters need to be adjusted. For the prediction results that are not within the bounds, data adjustment and re-prediction are required. Eliminate the data with abnormal water consumption (too large or too small) on some days and retrain the remaining data. Reconstruct the SARIMA model and make predictions.

[0065] Forecast data that exceeds the time limit can be removed as invalid data.

[0066] The intelligent water distribution control of this embodiment can realize personalized services and decision-making services for users, thereby improving the service efficiency and decision-making prediction efficiency of water distribution.

[0067] The steps of identification, fitting and testing of SARIMA model are as follows:

[0068] 1) Observe the sequence diagram of the original time series and analyze the basic change characteristics;

[0069] 2) The observation results are converted into correlation diagrams and partial correlation diagrams of stationary series by difference, so as to identify and determine the parameters of the SARIMA model;

[0070] 3) Estimate model parameters using the maximum likelihood estimation method for the initially established model;

[0071] 4) Use the established model to predict future water consumption data.

[0072] The user's water usage habits are related to the season, and the SARIMA model has a high sensitivity to periodic attributes such as seasonal changes in time series data; the method of the present invention adopts a single-step prediction method, adding the actual value of daily water consumption to the training set to predict the value of the next day, and only predicting one day greatly improves the reliability; and for the prediction of water consumption in the first few days of each quarter, historical real data at the same time point in the same quarter in different years can be added as a training set for training, which increases the richness of the training set and can improve the accuracy of the prediction.

[0073] If the average hourly water consumption forecast data for each household on the second day of the year is greater than the average hourly water consumption data for each household on the same day, the water distributor valve opening will be increased accordingly, thereby increasing the user's water inlet pressure to the average water pressure forecast data;

[0074] If the predicted average water consumption per household per hour on the second day of the year is less than the average water consumption per household per hour on the same day, the water distributor valve opening will be reduced accordingly, thereby reducing the user's water inlet pressure to the average water pressure predicted data.

[0075] The opening degree of the water distributor valve is directly related to the user's water inlet pressure. By predicting the water consumption once every hour, the opening degree of the valve can be accurately controlled.

[0076] Furthermore, the controller is used to calculate the original data and reference data based on the collected average water consumption data and average water pressure data of each household per hour; and the SARIMA model is called to predict the average water consumption forecast data and average water pressure forecast data of each household per hour on the second day of the year; the control instructions of the water distributor valves are obtained based on the average water consumption forecast data and average water pressure forecast data of each household per hour on the second day of the year and the current valve status of each household's water distributor.

[0077] The data collected by the multimodal sensor is converted into electrical signals for processing by the controller. As the core part of data processing, the controller receives the data from the sensor and performs calculations and decisions according to the predetermined control algorithm. The controller can be implemented as a programmable logic controller (PLC), and users can write control logic using a programming language.

[0078] The actuator is used to adjust the opening of the manifold valve based on the control command of the manifold valve. The actuator converts the control signal into physical action.

[0079] The controller, multimodal sensor and actuator transmit data based on the communication network to ensure the coordination between various links; the communication protocols of the communication network include industrial Ethernet, fieldbus and wireless network.

[0080] In this embodiment, data transmission can use wireless transmission technology to ensure the reliability and real-time nature of data transmission, avoid the lag of monitoring information caused by transmission delay or data loss, and transmit data once every hour. The transmitted data is used to prepare the water consumption forecast for the same time period of the next day.

[0081] This embodiment may also include a human-machine interface HMI: a graphical interface for operators to monitor and control the system, providing real-time data, alarm information and operation instruction input functions.

[0082] It can also include data storage and management: storage system operation data, control history, alarm records, etc. It is convenient for later analysis, fault diagnosis and system optimization.

[0083] In order to ensure stable power supply of the system and avoid system failure due to power supply problems, the controller is powered by an uninterruptible power supply UPS and a backup power supply.

[0084] The method of the present invention can be implemented through the following modules:

[0085] Data receiving module: used to receive real-time data transmitted by multi-analog sensors;

[0086] Data analysis module: used to analyze the data of the data receiving module and identify abnormal values ​​using the box plot method;

[0087] Data correction module: used to correct abnormal values, the data used is the average value of the same season and the same time period;

[0088] Model building module: used to build time series models, using the SMRIMA algorithm;

[0089] Result prediction module: used to import user data into the model and output prediction results;

[0090] Decision execution module: used to control the valve of the water distributor according to the prediction results.

[0091] Although the present invention is described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. It should therefore be understood that many modifications may be made to the exemplary embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the various dependent claims and features described herein may be combined in a manner different from that described in the original claims. It should also be understood that the features described in conjunction with a single embodiment may be used in other described embodiments.

Claims

1. An intelligent water distribution control method based on time series prediction, characterized in that: include: Use multimodal sensors to collect average water consumption and average water pressure data for each household every hour; The average water consumption data and average water pressure data of each household were used to identify outliers based on quartiles, and the outliers were adjusted to normal values; The average water consumption data and average water pressure data of all households in the previous year after adjustment for abnormal values ​​are taken as the original data, and the original data of the daily and hourly average water consumption and average water pressure of each household in each season of the previous year are calculated based on the original data of each season; the reference data of the daily and hourly average water consumption and average water pressure of each household in the current season of the year are calculated from the average water consumption data and average water pressure data of each household in each hour of the dates that have occurred in the current season of the year after adjustment for abnormal values; The SARIMA model is used to obtain the predicted data of average hourly water consumption and average water pressure for each household on the second day of the current year based on the original data of average hourly water consumption and average water pressure for each household in each season of the previous year and the reference data of average hourly water consumption and average water pressure for each household in the current season of the current year; The valve of the water distributor is adjusted based on the predicted data of average hourly water consumption and average water pressure for each household on the second day of the year to achieve intelligent water distribution control.

2. The intelligent water distribution control method based on time series prediction according to claim 1 is characterized in that: Methods for outlier identification based on quartiles include: The average water consumption data and average water pressure data of each household in each hour of each season collected by the multimodal sensor are sorted from small to large by hour to obtain hourly time series data; The first quartile Q1, the third quartile Q3 and the interquartile range IQR are determined from the hourly time series data; Determine the lower limit of the average water consumption and average water pressure data of each household per hour as Q1-1.5*IQR; the upper limit is Q3+1.5*IQR; where IQR=Q3-Q1; The data of average water consumption per household or average water pressure per hour that exceeds the corresponding lower and upper limits are regarded as outliers.

3. The intelligent water distribution control method based on time series prediction according to claim 2 is characterized in that: The method to adjust outliers to normal values ​​is: The abnormal values ​​are replaced by the average of all normal values ​​in the average water consumption data or average water pressure data of each household per hour on different days of the same season, and the abnormal values ​​are adjusted to normal values.

4. The intelligent water distribution control method based on time series prediction according to claim 3 is characterized in that: The model parameters of the SARIMA model include seasonal period, difference order, regression parameter, and moving average order; The model parameters are determined by autocorrelation graphs and partial autocorrelation graphs, and the autocorrelation graphs and partial autocorrelation graphs are obtained by deep learning or grid search.

5. The intelligent water distribution control method based on time series prediction according to claim 3 is characterized in that: The method for the SARIMA model to obtain the average hourly water consumption forecast data and average water pressure forecast data for each household on the second day of the year is: Input the original data of daily and hourly average water consumption and average water pressure of each household in each season of the previous year and the reference data of daily and hourly average water consumption and average water pressure of each household in the current season into the SARIMA model to make the initial prediction of the average water consumption data and average water pressure data; If the initial forecast result is within the fluctuation range of the box plot generated by historical data, the initial forecast result will be used as the forecast data for the average hourly water consumption and average water pressure per household on the second day of the year; Otherwise, set the input data threshold, delete the data that exceeds the input data threshold in the SARIMA model, input the retained data into the SARIMA model again, and re-predict the average water consumption data and the average water pressure data until the prediction result is within the fluctuation range of the box plot generated by the historical data. Use the current prediction result as the predicted data for the average water consumption and the average water pressure per household per hour on the second day of the year.

6. The intelligent water distribution control method based on time series prediction according to claim 5 is characterized in that: If the average hourly water consumption forecast data for each household on the second day of the year is greater than the average hourly water consumption data for each household on the same day, the water distributor valve opening will be increased accordingly, thereby increasing the user's water inlet pressure to the average water pressure forecast data; If the predicted average water consumption per household per hour on the second day of the year is less than the average water consumption per household per hour on the same day, the water distributor valve opening will be reduced accordingly, thereby reducing the user's water inlet pressure to the average water pressure predicted data.

7. The intelligent water distribution control method based on time series prediction according to claim 5 is characterized in that: The controller is used to calculate the original data and reference data based on the collected average water consumption data and average water pressure data of each household per hour; and the SARIMA model is called to predict the average water consumption forecast data and average water pressure forecast data of each household per hour on the second day of the year; the control instructions of the water distributor valves are obtained based on the average water consumption forecast data and average water pressure forecast data of each household per hour on the second day of the year and the current valve status of each household's water distributor.

8. The intelligent water distribution control method based on time series prediction according to claim 7 is characterized in that: The actuator is used to adjust the opening of the water distributor valve based on the control instruction of the water distributor valve.

9. The intelligent water distribution control method based on time series prediction according to claim 8 is characterized in that: The controller, multimodal sensor and actuator transmit data based on a communication network; the communication protocols of the communication network include industrial Ethernet, field bus and wireless network.

10. The intelligent water distribution control method based on time series prediction according to claim 7, characterized in that: The controller is powered by an uninterruptible power supply UPS and a backup power supply.

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