CN117893358A - Intelligent management and control method for urban water supply system integrating big data and cloud computing - Google Patents

Abstract

An intelligent management and control method for an urban water supply system integrating big data and cloud computing relates to the technical field of environmental engineering. The invention aims to solve the problems of unstable water supply pressure and low utilization rate of booster pump stations in the conventional urban water supply scheduling. The invention establishes a multisource data monitoring network integrating weather-water source-pipe network-user information, performs data intercommunication and visual modeling, performs simulation and decision making based on full-element monitoring data, supports a plan model coping with different events, and enables intelligent management and control of the urban water supply system through big data, cloud computing and digital twin engine driving.

Description

A smart management and control method for urban water supply system integrating big data and cloud computing

Technical Field

The invention belongs to the technical field of environmental engineering, and in particular to the field of urban water supply.

Background technique

In the process of urban municipal planning and infrastructure construction, urban water systems occupy an important position and are an important guarantee for satisfying people's happy life. However, with the continuous development of urban scale, it is impossible to comprehensively consider the water demand of regions, enterprises and residents, resulting in unbalanced water supply. Users with large water demand cannot get sufficient water, users with small water demand waste resources, and the efficiency of water supply management continues to decline.

The scientific scheduling and effective operation of the water supply system are important factors to ensure the quality of daily tap water supply management in cities. At present, there are two difficulties in the specific water supply scheduling. On the one hand, there is the problem of water supply pressure. The water supply pressure is directly related to the transmission efficiency of daily tap water in cities and is the guarantee for the smooth operation of urban water supply systems. If the stability of water supply pressure cannot be effectively managed, it is very easy to cause water supply pipeline rupture and water resource waste. On the other hand, for booster pump stations, the utilization rate of pump rooms is not enough. The utilization rate is an important guarantee for improving the working efficiency of water supply systems. However, in specific work, there are problems such as lack of professional technology and insufficient allocation of funds, which have led to the lack of scientific planning and management in most water supply systems, which has had an adverse impact on the benefits of water supply management.

Summary of the invention

The present invention aims to solve the problems of unstable water supply pressure and low utilization rate of booster pump stations in the current urban water supply scheduling, and now provides an intelligent management and control method for urban water supply system integrating big data and cloud computing.

A smart management and control method for urban water supply system integrating big data and cloud computing, comprising:

S1: Establish a multi-source monitoring network for urban water supply systems that integrates meteorological, water source, pipe network and user information;

S2: Using the multi-source data monitored by the multi-source monitoring network of the urban water supply system to build a multi-source monitoring data fusion and analysis system, the multi-source monitoring data fusion and analysis system is used to perform feature fusion on the multi-source data and obtain the corresponding water supply through water demand;

S3: Build a water demand prediction model using the multi-source data monitored by the multi-source monitoring network of the urban water supply system. The water demand prediction model is used to obtain the predicted water demand based on time, geographical location and climate factors through the seasonal water consumption variation law and the relationship between temperature, climate factors and regional water consumption. The predicted water demand is used as the input of the multi-source monitoring data fusion analysis system to obtain the water supply based on time, geographical location and climate factors, and the real-time water supply is stored in the cloud platform;

S4: Build a digital twin model of the urban water supply system based on the basic distribution data of the urban water supply system to simulate the operation scenario of the urban water supply system;

S5: Use the multi-source data monitored by the multi-source monitoring network of the urban water supply system to establish a fault analysis model and a water resource allocation model for the urban water supply system. The fault analysis model is used to analyze and detect the cause of the fault, and the water resource allocation model is used to generate water resource allocation plans for different scenarios based on the water supply volume. S6: Put the water supply volume, fault cause and water resource allocation plan stored in real time on the cloud platform into the digital twin model of the urban water supply system to obtain the water resource allocation plan for different scenarios and realize the intelligent management and control of the urban water supply system.

A monitoring area is delineated according to the service scope of the urban water supply system, and a multi-source monitoring network of the urban water supply system is established within the monitoring area.

The multi-source monitoring network of the urban water supply system includes: an urban water supply system pipeline network monitoring subsystem, an urban water supply plant monitoring subsystem and a user water demand monitoring subsystem; the urban water supply system pipeline network monitoring subsystem is used to monitor the water pressure, flow and water quality of the urban water supply system pipeline network in real time; the urban water supply plant monitoring subsystem is used to monitor the urban weather, water source, water quality and pump group working status in real time; the user water demand monitoring subsystem is used to monitor the water consumption of each user in different areas in real time.

The multi-source data is subjected to feature fusion, including:

The temporal and spatial features of multi-source data are extracted, the features belonging to the same subsystem are clustered, and then the multi-source data belonging to the same cluster are normalized to achieve feature fusion of multi-source data.

The method of obtaining the corresponding water supply according to the water demand includes:

A training model is constructed based on a restricted Boltzmann machine, wherein the input and output of the training model are water demand and water supply, respectively.

The regional distribution characteristics of daily and monthly water consumption were analyzed by time series and correlation, and the seasonal water consumption variation patterns as well as the relationship between temperature, climate factors and regional water consumption were obtained.

The construction of the digital twin model of the urban water supply system includes:

The basic distribution data of water supply plants, water supply networks and users in the comprehensive urban water supply system are constructed with water supply plant module, water supply network module and user module;

The digital twin model summarizes the output information of the water supply plant module, the water supply network module and the user module to obtain the water supply plant production capacity, the network status, the user's predicted water demand, the user's water consumption and water use behavior that take into account the mutual influence.

The water supply plant module uses a time series model to predict the future production capacity of the water supply plant by analyzing the water production capacity and water quality and quantity data of the water supply plant;

The water supply network module reflects the network status based on the network topology data and the network hydraulic parameters;

The user module predicts the user's future water demand based on the user's water use behavior, habits, and location, and provides the user's water consumption and water use behavior.

The fault analysis model obtains abnormal behaviors in the water supply system by analyzing historical multi-source data, and detects the causes of the faults.

The water resource allocation model adjusts the water supply ratio of the water source by analyzing the water resource usage and the operating status of the water supply system in the historical data, and obtains a water resource allocation plan that can maintain the stability of the water supply system.

The present invention proposes a smart management and control method for urban water supply systems that integrates big data and cloud computing, establishes a multi-source data monitoring network that integrates meteorological-water source-pipeline network-user information, conducts data interoperability and visualization modeling, performs simulation and decision-making based on full-factor monitoring data, and is supported by emergency plan models for responding to different events. It is driven by big data, cloud computing and digital twin engines to empower smart management and control of urban water supply systems.

The core invention effects of the present invention are mainly reflected in the following five points:

1. Improve the visualization, dataization and intelligence of urban water resources management through information technology, realize refined management of water resources and intelligent water supply scheduling services, and provide important technical support for the sustainable use of water resources;

2. Establish a multi-scale and all-round monitoring network to systematically perceive weather-water source-pipeline network-user data, provide massive and accurate data for the central decision-making system, and improve the scientific nature of decision-making;

3. Establish a digital twin scenario for smart urban water supply based on basic data of urban pipe networks, multi-source monitoring data and network shared data;

4. Based on the established refined simulation model of the urban water supply network, simulate the impact of factors such as meteorology, water sources and pipe network performance on the stability of the urban water supply system;

5. Integrate multiple plans to deal with different events, use cloud computing platforms to automatically analyze monitoring data, predict water demand in different periods of the city, and put forward targeted treatment suggestions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a smart management and control method for an urban water supply system integrating big data and cloud computing.

Detailed ways

The following will be combined with the accompanying 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, rather than 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 belong to the scope of protection of the present invention. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

This embodiment is described in detail with reference to FIG1. This embodiment describes a smart management and control method for urban water supply systems that integrates big data and cloud computing. A multi-source data monitoring network that integrates meteorological-water source-pipeline network-user information is established to conduct data intercommunication and visualization modeling. Simulation and decision-making are performed based on all-factor monitoring data, supported by emergency response models for different events, and driven by big data, cloud computing, and digital twin engines to enable smart management and control of urban water supply systems. The specific steps are as follows:

S1: Establish a multi-source monitoring network for urban water supply systems that integrates meteorological, water source, pipe network and user information.

The monitoring area is delineated according to the service scope of the urban water supply system, and the urban water supply system pipe network monitoring subsystem, the urban water supply plant monitoring subsystem and the user water demand monitoring subsystem are established within the monitoring area, including:

The urban water supply system pipe network monitoring subsystem is used to monitor the water pressure, flow, water quality, etc. of the urban water supply system pipe network in real time.

The urban water supply plant monitoring subsystem is used to monitor the city’s weather, water sources, water quality, pump group working status, etc. in real time.

The user water demand monitoring subsystem is used to monitor the water consumption of each user in different areas in real time.

S2: Receive data monitored by the multi-source monitoring network of the urban water supply system, and use the monitoring data to build a multi-source monitoring data fusion and analysis system based on the cloud platform.

① Integrate the Internet of Things (such as NB-lot, LoRa) wireless transmission module in the monitoring sensors corresponding to each monitoring data to realize the wireless transmission of monitoring data and store it on the cloud computing platform;

②Data verification and quality improvement: Pre-process the monitoring data stored in the cloud computing platform, including cleaning and interpolation of "pseudo data";

③ Multi-source data fusion: Analyze the structural characteristics of monitoring data (including source, format and accuracy, etc.), extract time and space characteristics from the sources of each monitoring data, cluster the characteristics belonging to the same subsystem, and then normalize the monitoring data in each cluster to achieve unified representation of the structural characteristics of multi-source heterogeneous data under different time and space conditions, and complete the deep feature fusion of multi-source monitoring data of urban water supply system;

④ Multi-source data analysis: Based on the restricted Boltzmann machine, we deeply explore the distribution patterns of water supply and demand in different periods and regions. That is, based on the restricted Boltzmann machine, we establish a training model for the water supply of the urban water supply system and the water demand of users, so as to obtain the corresponding water supply through the predicted water demand, and then adjust the water supply conditions.

S3: Receive data monitored by the multi-source monitoring network of the urban water supply system, and combine it with the multi-source monitoring data fusion analysis system to obtain the urban water supply based on time, geographical location, climate and other factors.

① Conduct time series and correlation analysis on the regional distribution characteristics of daily and monthly water consumption to obtain the seasonal water consumption variation pattern and the relationship between factors such as temperature and climate and regional water consumption, and then establish a regional water demand prediction model based on time, geographical location, climate and other factors.

② The output of the regional water demand prediction model is used as the input of the training model to obtain the urban water supply based on factors such as time, geographical location, and climate, and store it on the cloud computing platform.

S4: Establish a digital twin model of the urban water supply system and realize data visualization.

① Based on the basic distribution data of water supply plants, water supply networks and users in the urban water supply system, a digital twin model of the urban water supply system based on water supply plant modules, water supply network modules and user modules is constructed:

The water supply plant module uses a time series model to model basic information such as the water production capacity, water quality and quantity data of the water supply plant, and predicts the production capacity of the water supply plant in the future through real-time monitoring data;

The water supply network module uses a hydraulic model to reflect the status of the network by learning parameters such as the network topology data and the network hydraulic parameters. The status includes possible leakage and other problems.

The user module predicts the user's future water demand and provides data such as the user's water consumption and water behavior by analyzing basic information such as the user's water use behavior, habits, and location.

The digital twin model summarizes the output information of the water supply plant module, the water supply network module and the user module to obtain the water supply plant production capacity, the network status, the user's predicted water demand, the user's water consumption and water use behavior that take into account the mutual influence, simulates the operation scenario of the urban water supply system, and realizes the output of the digital twin model of the urban water supply system.

② Call the urban water supply stored in the cloud computing platform in real time, update the urban water supply system operation scenario, provide real-time feedback on the urban water supply process, and build an online monitoring platform for the urban water supply system.

S5: Establishing a fault analysis model and a water resource allocation model for the urban water supply system using the multi-source data monitored by the multi-source monitoring network of the urban water supply system.

① Based on the historical multi-source data stored on the cloud computing platform, establish the urban water supply system fault analysis model and water resources allocation model;

The water supply system fault analysis model is to identify abnormal behaviors in the water supply system and infer the possible impact of different fault types by analyzing historical multi-source data;

The water resource allocation model optimizes the water supply plan and adjusts the water supply ratio of water sources to maintain the stability of the water supply system by analyzing the water resource usage and water supply system operation status in historical data.

② Combined with the fault causes detected by the urban water supply system fault analysis model, modify the input of the urban water supply system digital twin model to simulate the operating status of the urban water supply system under the influence of uncertain fault factors.

S6: Put the water supply volume, fault causes and water resource allocation plans stored in real time on the cloud platform into the digital twin model of the urban water supply system to obtain water resource allocation plans under different scenarios and realize intelligent management and control of the urban water supply system.

① Based on the digital twin model of the urban water supply system and combined with the water resources allocation model, the water resources allocation process is simulated according to the water supply in different time periods and different regions.

② Based on the simulation results of the digital twin model of the urban water supply system, establish water resource allocation plans corresponding to different events.

In summary, this implementation method uses information technology to improve water operation efficiency and water resource utilization. By applying technologies such as the Internet of Things, big data, and artificial intelligence, it can realize intelligent monitoring of water resources, intelligent management of water consumption, and intelligent monitoring of water quality, and provide intelligent services such as smart water meters and smart water equipment to improve efficiency, save resources, and improve service quality for water companies. At the same time, it can improve the visualization, dataization, and intelligence of urban water resource management through information technology, realize refined management of water resources and intelligent water supply scheduling, and provide important technical support for the sustainable use of water resources.

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 will also be understood that the features described in conjunction with a single embodiment may be used in other described embodiments.

Claims (10)

1. An intelligent management and control method for an urban water supply system integrating big data and cloud computing is characterized by comprising the following steps:

s1: establishing a multi-source monitoring network of the urban water supply system integrating weather, water sources, pipe networks and user information;

s2: constructing a multi-source monitoring data fusion analysis system by utilizing multi-source data monitored by the multi-source monitoring network of the urban water supply system, wherein the multi-source monitoring data fusion analysis system is used for carrying out characteristic fusion on the multi-source data and obtaining corresponding water supply quantity through water demand;

s3: constructing a water demand prediction model by utilizing multi-source data monitored by the multi-source monitoring network of the urban water supply system, wherein the water demand prediction model is used for obtaining predicted water demand based on time, geographic position and climate factors through the change rule of seasonal water consumption and the relation between air temperature, climate factors and regional water consumption, taking the predicted water demand as the input of the multi-source monitoring data fusion analysis system, obtaining water supply based on time, geographic position and climate factors, and storing the water supply obtained in real time to a cloud platform;

s4: building a digital twin model of the urban water supply system according to the distributed basic data of the urban water supply system, and simulating the running scene of the urban water supply system;

s5: utilizing multi-source data monitored by the multi-source monitoring network of the urban water supply system to establish a fault analysis model and a water resource allocation model of the urban water supply system, wherein the fault analysis model is used for analyzing and detecting fault reasons, and the water resource allocation model is used for generating water resource allocation schemes in different scenes according to water supply quantity;

s6: and (3) putting the water supply quantity, the fault reasons and the water resource allocation scheme stored in real time by the cloud platform into a digital twin model of the urban water supply system to obtain the water resource allocation scheme under different scenes, so as to realize intelligent management and control of the urban water supply system.

2. The intelligent management and control method for the urban water supply system integrating big data and cloud computing according to claim 1, wherein a monitoring area is defined according to the service range of the urban water supply system, and a multi-source monitoring network of the urban water supply system is established within the monitoring area.

3. The intelligent management and control method for the urban water supply system integrating big data and cloud computing as claimed in claim 2, wherein the urban water supply system multi-source monitoring network comprises: a city water supply system pipe network monitoring subsystem, a city global water supply plant monitoring subsystem and a user water demand monitoring subsystem;

the urban water supply system pipe network monitoring subsystem is used for monitoring the water pressure, flow and water quality of the urban water supply system pipe network in real time;

the urban global water supply plant monitoring subsystem is used for monitoring urban weather, water sources, water quality and pump set working states in real time;

the user water demand monitoring subsystem is used for monitoring the water consumption of each user in different areas in real time.

4. The intelligent urban water supply system control method integrating big data and cloud computing according to claim 3, wherein the feature integration of the multi-source data comprises the following steps:

extracting time and space characteristics of multi-source data, clustering the characteristics belonging to the same subsystem, and normalizing the multi-source data belonging to the same cluster to realize the characteristic fusion of the multi-source data.

5. The intelligent control method for the urban water supply system integrating big data and cloud computing according to claim 1 or 4, wherein the obtaining the corresponding water supply amount by the water demand comprises the following steps:

and building a training model based on the limited Boltzmann mechanism, wherein the input quantity and the output quantity of the training model are respectively water demand and water supply.

6. The intelligent management and control method for the urban water supply system integrating big data and cloud computing according to claim 1, wherein time sequence and correlation analysis are carried out on regional distribution characteristics of daily water consumption and monthly water consumption, and a change rule of seasonal water consumption, air temperature, climate factors and regional water consumption are obtained.

7. The intelligent management and control method for the urban water supply system integrating big data and cloud computing as claimed in claim 1, wherein the building of the digital twin model of the urban water supply system comprises the following steps:

the method comprises the steps of integrating distribution basic data of a water supply plant, a water supply pipe network and users in a city water supply system, and constructing a water supply plant module, a water supply pipe network module and a user module;

and the digital twin model gathers the output information of the water supply plant module, the water supply network module and the user module to obtain the water supply plant production capacity, the network state, the predicted water demand of the user, the water consumption of the user side and the water consumption behavior which are considered to be mutually influenced.

8. The intelligent management and control method for the urban water supply system integrating big data and cloud computing as claimed in claim 7, wherein,

the water supply plant module predicts the future water supply plant production capacity by using a time sequence model through data of the water supply plant water production capacity and water quality and water quantity;

the water supply network module reflects the network state to the network topology structure data and the network hydraulic parameters;

the user module predicts the future water demand of the user through the water consumption behavior, habit and position of the user side and provides the water consumption and water consumption behavior of the user side.

9. The intelligent management and control method for the urban water supply system integrating big data and cloud computing as claimed in claim 1, wherein,

the fault analysis model obtains abnormal behaviors in the water supply system by analyzing historical multi-source data, and detects and obtains fault reasons.

10. The intelligent management and control method for the urban water supply system integrating big data and cloud computing according to claim 1 or 9, wherein the water resource allocation model is used for obtaining a water resource allocation scheme capable of maintaining the stability of the water supply system by analyzing the water resource use condition and the water supply system operation condition in the historical data and adjusting the water supply proportion of a water source.