Intelligent water affair management method and system based on Internet of Things

Abstract

The invention relates to the technical field of intelligent water affair management, in particular to an intelligent water affair management method and system based on the Internet of Things. Node flow velocity, pressure, temperature and turbidity signals are obtained, fluctuation and deviation rates are synchronously compared and analyzed, hydraulic dynamic data are recorded, and a flow velocity and pressure linkage trend is extracted to generate a hydraulic response level matrix; calculating path energy consumption difference, screening an optimal water supply path, determining node response delay sequencing to form a scheduling priority list, adjusting water outlet proportion according to priority, updating flow distribution, and generating a dynamic scheduling result set. Water source pipe network flow node parameters are synchronously collected and compared to generate hydraulic dynamic data, fluctuation abnormity is identified to improve response capability, flow velocity and pressure linkage quantification stability is analyzed, energy consumption and flow distribution are optimized to ensure energy efficiency balance, and an execution sequence is adjusted according to response delay to reduce fluctuation. And the dispatching flexibility and efficiency are improved by dynamically adjusting the water yield of the valve and the pump station.

Description

Technical Field

[0001] This invention relates to the field of smart water management technology, and in particular to a smart water management method and system based on the Internet of Things. Background Technology

[0002] The field of smart water management technology encompasses comprehensive technologies for the digital, information-based, and automated management of urban water supply systems, drainage systems, and related water resources. Core components include water quality monitoring, water quantity control, pipeline network operation management, leakage detection, and equipment operation status monitoring. Through the collaborative efforts of sensor terminals, communication networks, and information platforms, it achieves real-time perception, data collection, and remote control of water management operations. Smart water management technology typically integrates IoT communication, data acquisition terminals, environmental monitoring sensors, and information integration systems to form a dynamic monitoring and refined management system for water operations, thus providing the technological foundation for the scientific allocation and efficient scheduling of water resources.

[0003] Among them, the IoT-based smart water management method and system refers to a technical solution that utilizes IoT sensing networks and data interaction mechanisms to collect, transmit, and process key data in the water supply and drainage process. This primarily targets technical aspects such as water quality testing, water pressure monitoring, pipeline flow measurement, and equipment operating status identification. It employs sensor nodes to collect water operation parameters and transmits multi-source data to an information processing terminal for centralized management via wireless communication. The system classifies and stores monitoring data using predefined rules and combines this with a visualization platform to achieve unified presentation and dynamic updates of water operation data, thereby constructing a data-driven smart water management model centered on IoT communication.

[0004] Existing technologies rely on periodic data acquisition, failing to capture dynamic fluctuations in water systems in real time, resulting in the failure to detect instantaneous changes and anomalies promptly. Node data is typically monitored at single points, lacking temporal correlations between multiple nodes, making it impossible to effectively identify patterns in flow rate and pressure changes. System analysis is relatively static, unable to make real-time adjustments to load fluctuations, leading to untimely correction of anomalies. Energy loss and flow allocation assessments rely on rough estimations, failing to accurately quantify energy efficiency differences between paths, impacting system efficiency. The unclear node execution order during scheduling leads to pressure fluctuations and system response delays. Summary of the Invention

[0005] To address the technical problems existing in the prior art, embodiments of the present invention provide a smart water management method and system based on the Internet of Things. The technical solution is as follows:

[0006] A smart water management method based on the Internet of Things includes the following steps:

[0007] S1: Acquire the velocity, pressure, temperature and turbidity signals of water source nodes, pipeline nodes and flow nodes, compare them according to the node time sequence, detect the sampling fluctuation amplitude and signal offset rate, analyze the time change relationship of velocity and pressure of each node, record the synchronization difference and delay, and generate a node hydraulic dynamic record set.

[0008] S2: Used to extract the linkage trend of flow velocity and pressure based on the hydraulic dynamic record set of the node, call the response time and stabilization time of the node under the same period, compare the difference between the two and judge the stable performance of each node under load fluctuation, classify the level according to the response time distribution, and generate a hydraulic response level matrix.

[0009] S3: Used to call the pressure and flow velocity data of the pipeline water conveyance section based on the hydraulic response level matrix, detect the pressure difference and velocity difference between adjacent sections, calculate the energy loss change and compare the energy consumption difference detected by the path, analyze the flow distribution and energy consumption ratio of the path, screen the path with energy consumption ratio not exceeding the preset ratio threshold and with balanced flow, and generate water supply path optimization results.

[0010] S4: Based on the water supply path optimization results, analyze the pressure and flow rate change rate in the node signals, measure the response delay time and sort them, determine the execution order of the nodes in the overall scheduling, establish a node operation order table, and generate a node scheduling priority list.

[0011] S5: Used to call valve opening and pump station water output information based on the node scheduling priority list, compare the total water supply with the load change trend, adjust the water output ratio according to node priority and update the flow allocation, coordinate the path water supply relationship, and generate a smart water dynamic scheduling result set.

[0012] As a further aspect of the present invention,

[0013] The node hydraulic dynamics record set includes node flow velocity changes, node pressure fluctuations, node temperature records, node turbidity changes, synchronization differences, and delay time.

[0014] The hydraulic response level matrix includes node response time, node stabilization time, stability level, response time distribution, and level interval division;

[0015] The water supply path optimization results include optimized path, path energy loss, path flow distribution, path energy efficiency, and low-energy-consumption path;

[0016] The node scheduling priority list includes node execution order, node response delay, node operation priority, and node scheduling order;

[0017] The smart water dynamic scheduling result set includes total scheduling volume, node water output, flow ratio adjustment, path water supply coordination, and node priority adjustment.

[0018] As a further aspect of the present invention, the step of obtaining the node hydraulic dynamic record set is as follows:

[0019] S101: Acquire the velocity, pressure, temperature and turbidity signals of water source nodes, pipeline nodes and flow nodes, synchronously compare the signals of each node in time sequence, detect the fluctuation amplitude and offset rate of signal sampling, perform synchronous verification of the time series data of each node, and obtain the synchronization difference data of each node.

[0020] S102: Based on the synchronous difference data of each node, analyze the time relationship between flow velocity and pressure, establish a time series model of flow velocity and pressure changes between nodes, calculate the correlation coefficient between flow velocity and pressure, and obtain the flow velocity-pressure correlation coefficient.

[0021] S103: Based on the flow velocity-pressure correlation coefficient and time change analysis, detect and record the synchronization differences and delays of each node, calculate the delay time and synchronization error between nodes, and generate a node hydraulic dynamic record set.

[0022] As a further aspect of the present invention, the step of obtaining the hydraulic response level matrix is ​​as follows:

[0023] S201: Based on the node hydraulic dynamic record set, extract the linkage change trend of flow velocity and pressure, obtain the response time and stabilization time of each node under the same period, compare the response differences of each node under load fluctuation, and obtain node response time difference data.

[0024] S202: Based on the node response time difference data, analyze the response time and stabilization time of each node, divide the level intervals according to the distribution of response time, and obtain response time level interval data;

[0025] S203: Based on the response time level range data, evaluate the stability performance of each node to obtain the hydraulic response level matrix.

[0026] As a further aspect of the present invention, the step of obtaining the water supply path optimization result is as follows:

[0027] S301: Based on the hydraulic response level matrix, obtain the pressure and velocity change data of the pipeline water conveyance section, detect the pressure difference and velocity difference between adjacent sections, calculate the energy loss change between each section, and obtain the energy loss data of each section.

[0028] S302: Based on the energy loss data of each section, compare the differences in energy consumption of different paths, analyze the flow distribution and energy consumption ratio of each path, and obtain the path flow distribution and energy consumption ratio data.

[0029] S303: Based on the path flow distribution and energy consumption ratio data, select paths with lower energy consumption and balanced flow distribution to generate water supply path optimization results.

[0030] As a further aspect of the present invention, the step of obtaining the node scheduling priority list is as follows:

[0031] S401: Based on the water supply path optimization results, obtain the pressure and flow rate change rates in the node signals, measure the response delay time of each node, sort the nodes according to the delay time, and obtain the node response delay time data.

[0032] S402: Based on the node response delay time data, determine the execution order of each node in the overall scheduling, determine the order of node execution, and obtain node execution order data;

[0033] S403: Based on the node execution order data, generate a node operation order table to obtain a node scheduling priority list.

[0034] As a further aspect of the present invention, the steps for obtaining the smart water management dynamic scheduling result set are as follows:

[0035] S501: Based on the node scheduling priority list, obtain valve opening and pump station water output information, and combine the total water supply and load change trend to compare the water supply situation of each path and obtain the difference data between the total water supply and load change.

[0036] S502: Based on the difference data between the total water supply and the load change, adjust the water output ratio of each node according to the node priority, update the flow distribution of the nodes, and obtain the updated flow distribution data.

[0037] S503: Based on the updated flow allocation data, coordinate the water supply relationship between each path and generate a smart water dynamic scheduling result set.

[0038] A smart water management system based on the Internet of Things, the system comprising:

[0039] The data acquisition and comparison module acquires the velocity, pressure, temperature and turbidity signals of water source nodes, pipeline nodes and flow nodes, performs synchronous comparison according to the node time sequence, detects the fluctuation amplitude and signal offset between samples, analyzes the time change relationship of velocity and pressure of each node, records the synchronization difference and delay, and generates a node hydraulic dynamic record set.

[0040] The node stability analysis module extracts the linkage trend of flow velocity and pressure from the hydraulic dynamic records of the nodes, analyzes the response time and stabilization time under the same period, compares the differences and evaluates the stability of each node under load fluctuations, classifies the levels according to the response time distribution, and generates a hydraulic response level matrix.

[0041] The energy efficiency optimization module, based on the hydraulic response level matrix, analyzes the pressure and flow velocity changes in the pipeline water conveyance section, detects the pressure difference and velocity difference between adjacent sections, calculates energy loss and compares the energy consumption differences between paths, analyzes the path flow distribution and energy consumption ratio, selects paths with lower energy consumption and balanced flow distribution, and generates water supply path optimization results.

[0042] The scheduling priority analysis module analyzes the pressure and flow rate change rates in the node signals based on the water supply path optimization results, measures and sorts the response delays, determines the execution order of the nodes in the overall scheduling, and generates a node scheduling priority list.

[0043] The dynamic scheduling optimization module adjusts valve opening and pump station water output information according to the node scheduling priority list, compares the total water supply with the load change trend, adjusts the water output ratio according to node priority and updates the flow distribution, coordinates the water supply relationship between paths, and generates a set of smart water dynamic scheduling results.

[0044] The beneficial effects of the technical solutions provided by the embodiments of the present invention include at least the following:

[0045] In this invention, by synchronously collecting parameters such as flow velocity, pressure, temperature, and turbidity from water sources, pipelines, and flow nodes, and performing time-series comparisons, a hydraulic dynamic record set is generated, providing accurate time-series data for system analysis. This data helps identify fluctuations and anomalies between nodes, improving the system's response capability to sudden changes. Analysis of the linkage between flow velocity and pressure quantifies node stability, providing a basis for response time and stabilization time under load fluctuations. Combining pressure and velocity differences in pipeline water conveyance sections optimizes energy consumption and flow distribution, ensuring balanced energy efficiency across paths. Analysis of node response delays and execution sequences effectively reduces pressure fluctuations, enhancing system stability. Dynamic adjustment of valve opening and pump station output optimizes water supply scheduling, improving system flexibility and efficiency. Attached Figure Description

[0046] Figure 1 This is a flowchart of the method of the present invention;

[0047] Figure 2 This is a flowchart illustrating the process of obtaining the node hydraulic dynamic record set in this invention.

[0048] Figure 3 This is a flowchart illustrating the process of obtaining the hydraulic response level matrix of this invention.

[0049] Figure 4 This is a flowchart illustrating the process of obtaining the water supply path optimization results of this invention.

[0050] Figure 5 This is a flowchart illustrating the process of obtaining the node scheduling priority list in this invention.

[0051] Figure 6 This is a flowchart illustrating the process of obtaining the smart water dynamic scheduling result set of this invention. Detailed Implementation

[0052] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0053] refer to Figures 1 to 6 A smart water management method based on the Internet of Things includes the following steps:

[0054] S1: Acquire the velocity, pressure, temperature and turbidity signals of water source nodes, pipeline nodes and flow nodes, perform synchronous comparison according to node time sequence, detect the fluctuation amplitude and signal offset rate between continuous sampling, analyze the time change relationship of velocity and pressure of each node, record the synchronization difference and delay, and generate a node hydraulic dynamic record set.

[0055] S2: Extract the linkage trend of flow velocity and pressure based on the node hydraulic dynamic record set, call the response time and stabilization time of the node under the same period, compare the differences and judge the stability performance of each node in load fluctuation, divide the level interval according to the response time distribution, and generate a hydraulic response level matrix.

[0056] S3: Based on the hydraulic response level matrix, call the pressure and flow velocity change data of the pipeline water conveyance section, detect the pressure difference and velocity difference between adjacent sections, calculate the energy loss change and compare the energy consumption difference between paths, analyze the flow distribution and energy consumption ratio of each path, screen the paths with energy consumption ratio not exceeding the preset ratio threshold and balanced flow distribution, and generate water supply path optimization results.

[0057] S4: Based on the water supply path optimization results, analyze the pressure and flow rate change rate in the node signals, measure the response delay time and arrange them in order, determine the execution order of the nodes in the overall scheduling, form a node operation sequence table, and generate a node scheduling priority list.

[0058] S5: Based on the node scheduling priority list, the valve opening and pump station water output information are called, the total water supply and load change trend are compared, the water output ratio is adjusted according to the node priority and the flow distribution is updated, the water supply relationship between paths is coordinated, and a smart water dynamic scheduling result set is generated.

[0059] The node hydraulic dynamics record set includes node flow velocity changes, node pressure fluctuations, node temperature records, node turbidity changes, synchronization differences, and delay times. The hydraulic response level matrix includes node response time, node stabilization time, stability level, response time distribution, and level interval division. The water supply path optimization results include optimized paths, path energy loss, path flow distribution, path energy efficiency, and low-energy-consumption paths. The node scheduling priority list includes node execution order, node response delay, node operation priority, and node scheduling order. The smart water dynamic scheduling result set includes total scheduling volume, node water output, flow ratio adjustment, path water supply coordination, and node priority adjustment.

[0060] Please see Figure 2 The steps for obtaining the nodal hydraulic dynamics record set are as follows:

[0061] S101: Acquire the velocity, pressure, temperature and turbidity signals of water source nodes, pipeline nodes and flow nodes, synchronously compare the signals of each node in time sequence, detect the fluctuation amplitude and offset rate of signal sampling, perform synchronous verification of the time series data of each node, and obtain the synchronization difference data of each node.

[0062] The process involves acquiring velocity, pressure, temperature, and turbidity signals from water source nodes, pipeline nodes, and flow nodes. This process includes selecting water source nodes. Pipeline relay nodes and traffic consumption nodes The original signal data frames are acquired within a continuous sampling period. Taking the data from two consecutive sampling times as an example: (node) (Timestamp 1666321000.000s, Flow velocity 2.5m / s, Pressure 0.50MPa, Temperature 15.1°C, Turbidity 0.8NTU), (Node) (Timestamp 1666321000.100s, Flow velocity 2.2m / s, Pressure 0.45MPa, Temperature 15.3°C, Turbidity 0.9NTU), (Node) (Timestamp 1666321000.050s, flow velocity 2.1m / s, pressure 0.44MPa, temperature 15.3°C, turbidity 0.9NTU), and data at the second time point: ( , 1666321001.000s, 2.5m / s, 0.51MPa), ( ,1666321001.100s, 2.3m / s, 0.46MPa), ( (1666321001.050s, 2.2m / s, 0.45MPa), then, according to the time sequence, synchronously compare the signals of each node and select... The first data timestamp of the node As a global comparison benchmark and will timestamp and timestamp and The comparison is performed to detect the fluctuation amplitude and offset rate of the sampled signal. Taking the pressure signal of a node as an example, it is in The value collected at any time is MPa, in The value collected at any time is MPa, calculate its signal offset rate And calculate the fluctuation range. MPa, set the pressure signal offset rate threshold and fluctuation range threshold The MPa thresholds were set based on statistical analysis of pressure signal samples from 5000 historical pipeline network operating conditions under stable conditions. The 98th percentile of the sample data changes within a 1-second period was selected. and Based on the comparison results, it is determined that... Node at The pressure signal at any given time is a stable signal. Synchronization verification is performed on the time series data of each node, and calculation is performed. Synchronization differences ,calculate Synchronization differences , As a benchmark, its To obtain the synchronization difference data of each node.

[0063] S102: Based on the synchronous difference data of each node, analyze the time relationship between flow velocity and pressure, establish a time series model of flow velocity and pressure changes between nodes, calculate the correlation coefficient between flow velocity and pressure, and obtain the flow velocity-pressure correlation coefficient.

[0064] Based on the synchronization difference data of each node, the obtained , and The analysis begins by examining the temporal relationship between flow velocity and pressure. This process involves analyzing the original timestamps. Apply its corresponding synchronization differences The correction is performed to obtain a corrected timestamp under a unified time base. ,For example of , of This achieves precise time alignment of data from all nodes. Using this aligned data, a time-series model of flow velocity and pressure changes between nodes is established. The model establishment process involves selecting upstream nodes. stress time series and downstream nodes Flow velocity time series Calculate the translation of these two sequences at different times. Cross-correlation value ,like exist When it reaches its maximum peak value, then identified as Pressure fluctuations are transmitted to The hydraulic delay time that produces the flow velocity response, given the determined optimal delay Under the premise of calculating the correlation coefficient between flow velocity and pressure, this calculation uses Sequence and time shift Sequence, selection The example demonstrates the calculation using data from a series of consecutive points. sequence The values ​​are [0.50, 0.51, 0.49, 0.52]. (Corresponding sequence after 10 seconds of translation) Given the sequence [2.2, 2.3, 2.1, 2.35], calculate the average of the two sequences. MPa and m / s, then calculate and Standard deviation (based on) ) MPa and m / s, then calculate and covariance (based on) ) Calculate the correlation coefficient Obtain the correlation coefficient between flow velocity and pressure.

[0065] S103: Based on the correlation coefficient of flow velocity and pressure and the analysis of time changes, detect and record the synchronization differences and delays of each node, calculate the delay time and synchronization error between nodes, and generate a node hydraulic dynamic record set.

[0066] Based on the analysis of the correlation coefficient between flow velocity and pressure and time variation, the calculated... (Stress) and Correlation coefficient between (flow velocity) and hydraulic delay The process begins by detecting and recording the synchronization differences and latency of each node. This step involves setting a baseline value for the validity of the correlation coefficient. , The setting is based on: conducting 500 simulation experiments on the pipeline network model, when the correlation coefficient... When it is below 0.75, the calculated hydraulic delay Compared with the actual delay in simulation relative error If the probability exceeds 15% and is greater than 90%, the system will... same Comparison, because Therefore, it is determined that... and The correlation analysis between them was effective, and the delay was recorded. If there is another pair of nodes and Calculated correlation coefficient Therefore, The latency of this node pair This will be marked as "invalid". Then, the delay time between nodes and the synchronization error are calculated. Use the result of S101 directly, that is , , Delay time That is, the hydraulic delay determined by S102 , ,right Flow velocity time series and Flow velocity time series Repeat the time-series relationship mapping and correlation coefficient calculation steps in S102, assuming the calculation yields... and Optimal delay between Corresponding correlation coefficient ,Will and Compare, Determine it to be valid and record it. , all nodes All associated node pairs and Values ​​are aggregated into structured data, such as (nodes) Synchronization error 0.000s, upstream correlation -, hydraulic delay 0s, correlation coefficient 1.000), (node) Synchronization error 0.100s, related to upstream (hydraulic delay 10s, correlation coefficient 0.905), (node) Synchronization error 0.050s, related to upstream (Hydraulic delay 5s, correlation coefficient 0.880), generate a set of node hydraulic dynamic records.

[0067] Please see Figure 3 The steps for obtaining the hydraulic response level matrix are as follows:

[0068] S201: Based on the node hydraulic dynamic record set, extract the linkage change trend of flow velocity and pressure, obtain the response time and stabilization time of each node under the same period, compare the response differences of each node under load fluctuation, and obtain node response time difference data.

[0069] Based on the nodal hydraulic dynamics record set, including , , and its synchronization error, hydraulic delay time and The data is used to extract the correlation trend between flow rate and pressure. This process monitors a specific load fluctuation event, such as... Time, node The increased valve opening caused the required flow rate to jump from 2.1 m / s to 3.0 m / s within 2 seconds. The system continuously collected data. , and Flow velocity and pressure signals are used to obtain the response time and settling time of each node under the same period. Defined from The pressure change at the node first exceeded the fluctuation threshold defined by S101. MPa at time, stabilization time Defined from The node pressure enters a new stable state (i.e., the fluctuation range of 5 consecutive seconds). All less than At that moment, Taking a node as an example, its pressure is Time detected MPa (change) MPa> ), determine its The pressure will continue in The pressure remained stable at 0.35 MPa. 0.04 MPa, determine its ,for Nodes, fluctuations at Pressure changes exceeding ,Sure Its pressure is It remained stable at 0.40 MPa. 0.04MPa, confirmed. ,for Nodes, fluctuations at Pressure changes exceeding ,Sure Its pressure is It remained stable at 0.48 MPa. 0.04MPa, confirmed. Compare the response differences of each node under load fluctuations and calculate Compared to Response time difference ,calculate Compared to Response time difference and will With S103 Comparison, With S103 By comparison, the difference in node response time is obtained.

[0070] S202: Based on the node response time difference data, analyze the response time and stabilization time of each node, divide the level intervals according to the distribution of response time, and obtain the response time level interval data;

[0071] Based on the node response time difference data, the response time of each node was obtained. , , and recovery time , , This analysis examines the response time and stabilization time of each node, summarizing the performance of all monitored nodes during this load fluctuation event. and The measured values ​​are divided into grade intervals based on the distribution of response times. This division is based on statistical analysis of 1000 load fluctuation events of different scales recorded in the historical database of the pipeline network, extracting the response times of all nodes. and recovery time The 25th percentile (P25) and 75th percentile (P75) values ​​of these 1000 events were generated through simulation using a calibrated hydraulic model and compared with data from 50 field-controlled scheduling experiments. The errors were all less than 5%, verifying the validity of the data. Statistical results show... P25 takes 5 seconds, and P75 takes 20 seconds. P25 is 40s and P75 is 60s. Based on these statistical results, the grade intervals are set: for response time [Level 1 (Fast)] is defined as (less than or equal to P25), [Level 2 (Intermediate)] is defined as (Between P25 and P75), [Level 3 (Slow)] is defined as... (Greater than P75), regarding stabilization time [Level A (Excellent)] is defined as (Less than or equal to P25), [Level B (Good)] is defined as (Between P25 and P75), [Level C (Poor)] is defined as... (Greater than P75), these intervals are defined and solidified as evaluation benchmarks to obtain response time level interval data.

[0072] S203: Based on the response time level range data, the stability performance of each node is evaluated to obtain the hydraulic response level matrix.

[0073] Based on response time level range data, i.e., the defined Level range [Level 1:] [Level 2:] [Level 3:] ]and Level range [Level A:] [Level B:] [LevelC:] The stability performance of each node is then evaluated. This evaluation process involves taking out the nodes measured in S201 one by one. and The value was compared with the grade range of S202. The node is evaluated: ,fall into The interval is rated as Level 2. ,fall into The interval is rated as Level B, and the overall grade is recorded as 2B. The node is evaluated: ,fall into The interval is rated as Level 1. ,fall into The interval is rated as Level A, and the overall grade is recorded as 1A. The node is evaluated: ,fall into The interval is rated as Level 1. ,fall into The interval is rated as Level A, and the overall grade is recorded as 1A. All node identifiers, Measured values Rating, Measured values The assessment levels and overall levels are summarized to form a structured dataset, for example (nodes). Response time 15.0s, response level Level 2; stabilization time 45.0s, stabilization level Level B; overall level 2B. (Node) Response time 5.0s, response level Level 1, stabilization time 40.0s, stabilization level Level A, overall level 1A), (node) : Response time 1.0s, response level Level 1, stabilization time 36.0s, stabilization level Level A, overall level 1A), generate hydraulic response level matrix.

[0074] Please see Figure 4 The steps for obtaining the water supply path optimization results are as follows:

[0075] S301: Based on the hydraulic response level matrix, obtain the pressure and velocity change data of the pipeline water conveyance section, detect the pressure difference and velocity difference between adjacent sections, calculate the energy loss change between each section, and obtain the energy loss data of each section.

[0076] Based on the hydraulic response level matrix, including nodes , , The dataset for the grading assessment, along with the original stable operating condition data of S101, is used to obtain pressure and velocity variation data for the water conveyance section of the pipeline, and two data points from the water source node are defined. to downstream confluence point Parallel water conveyance path, path one includes segment SG1 ( ), Path 2 includes segment SG2 ( The pipeline in section SG1 is 1500 meters long and 0.8 meters in diameter, while the pipeline in section SG2 is 1800 meters long and 0.7 meters in diameter. Retrieve stable operating data before load fluctuations from S101: Node pressure MPa Node pressure MPa Node pressure MPa, and retrieve the operating condition data after the load fluctuation in S201 has stabilized: Node pressure MPa Node pressure MPa, flow rate m / s, Node pressure MPa, flow rate m / s, detecting the pressure difference between adjacent sections, taking section SG1 as an example, the pressure difference before the fluctuation. MPa, pressure difference after fluctuation MPa, calculate the energy loss variation between different sections, energy loss The calculation process for (kilowatt-hours) is as follows: calculate the head loss through the pressure difference. (meters), combined with flow rate Calculate power loss (cubic meters per second) (kW), multiplied by time (1 hour), the head loss of section SG1 after fluctuation. Meters, flow rate m³ / s, power loss kW, energy loss kWh, energy loss of SG1 before fluctuation kWh, its energy loss variation kWh, similarly, calculate the pressure difference after the fluctuation of section SG2. MPa, head loss Meters, flow rate m³ / s, power loss kW, energy loss kWh, summarizing the calculation results of each section, to obtain the energy loss data of each section.

[0077] S302: Based on the energy loss data of each section, compare the differences in energy consumption of different paths, analyze the flow distribution and energy consumption ratio of each path, and obtain the path flow distribution and energy consumption ratio data.

[0078] Based on the energy loss data of each section, namely the energy loss of section SG1 kWh and SG2 energy loss in section kWh, comparing the energy consumption differences of different paths, path one ( ) and path two ( The total energy consumption under steady state after fluctuation is respectively kWh and kWh, calculate the absolute difference in energy consumption between the two paths. kWh, and relative percentage difference Analyze the flow distribution and energy consumption ratio of each path, and extract the flow calculated in S301, including the flow of path one. m³ / s, flow rate of path two m³ / s, total system flow m³ / s, calculate the proportion of traffic to the total traffic for each path, and the proportion of traffic for path one. Traffic proportion of path two Calculate the proportion of energy consumption of each path to the total energy consumption, and the total energy loss of the system. kWh, energy consumption ratio of path one Energy consumption ratio of path two By combining data such as path identifier, energy consumption value, flow rate, flow rate ratio, and energy consumption ratio, the following data are generated: (Path 1: Energy consumption 112.5 kWh, flow rate 1.407 m³ / s, flow rate ratio 55.8%, energy consumption ratio 54.6%) and (Path 2: Energy consumption 93.6 kWh, flow rate 1.116 m³ / s, flow rate ratio 44.2%, energy consumption ratio 45.4%), thus obtaining the path flow distribution and energy consumption ratio data.

[0079] S303: Based on the path flow distribution and energy consumption ratio data, select paths with lower energy consumption and balanced flow distribution to generate water supply path optimization results.

[0080] Based on the path traffic distribution and energy consumption ratio data, for the datasets of Path 1 (energy consumption ratio 54.6%, traffic ratio 55.8%) and Path 2 (energy consumption ratio 45.4%, traffic ratio 44.2%), paths with lower energy consumption and more balanced traffic distribution were selected, and two criteria were set: energy efficiency benchmark. and flow balance benchmark Energy efficiency benchmark Defined as the specific energy consumption (kWh / m³) consumed in transporting a unit volume of water, the 75th percentile of the specific energy consumption value was obtained through analysis of historical operating data of the pipeline network over the past 6 months. kWh / m³, this value is set as the energy consumption judgment threshold, compared to energy consumption lower than... kWh / m³ is defined as "low energy consumption", a benchmark for flow balance. Defined as the flow ratio of two parallel paths, this was determined through 1000 hydraulic simulations of the pipe network. The system terminal pressure qualification rate is highest when the pressure is in the range [0.8, 1.25]. This range [0.8, 1.25] is set as the criterion for "balanced flow distribution". Each path is evaluated, and the specific energy consumption of each path is calculated. The specific energy consumption of path one is... kWh / m³, specific energy consumption of path two kWh / m³, comparing the calculated results with the energy efficiency benchmark. kWh / m³, and Both paths are determined to meet the "low energy consumption" condition, and the flow ratio under the current operating conditions is calculated. Compare this ratio with the flow balance benchmark. The interval is [0.8, 1.25]. If the flow rate is not within the range of [0.8, 1.25], it is determined that the current flow distribution is unbalanced. Given that the energy consumption of both paths is low, the operating parameters are adjusted. Since the flow rate of path one is relatively high, a control command is generated to reduce the opening of regulating valve V-SG1 on path one by 3%, while increasing the opening of regulating valve V-SG2 on path two by 2%. After adjustment, the predicted flow ratio is 1.20, which falls into the equilibrium range. This control command is the result of water supply path optimization, and the water supply path optimization result is generated.

[0081] Please see Figure 5 The steps to obtain the node scheduling priority list are as follows:

[0082] S401: Based on the water supply path optimization results, obtain the pressure and flow rate change rates in the node signals, measure the response delay time of each node, sort the nodes according to the delay time, and obtain the node response delay time data.

[0083] Based on the water supply path optimization results, extract the specified node set {N1, N2, N5, N8} and its pressure and flow velocity signals, and calculate the signal change values. and To determine the response delay time, a response identification threshold was experimentally verified based on historical stable operating data, and a pressure threshold was set. Flow rate threshold Taking node N5 as an example, when the system sends an operation command... Then, continuously monitor the pressure signal of N5 until its value changes. First time not less than ( Record the response time at this point. Calculate the response delay of N5. Obtain the measured value Using the same method, when the flow rate of N1 changes (satisfy When ), it was measured When the pressure of N8 changes (satisfy When ), it was measured When the flow rate of N2 changes (satisfy When ), it was measured The measurement result set {N5:500ms, N1:600ms, N8:800ms, N2:900ms} is sorted in ascending order of response delay time to form an ordered list [(N5,500ms),(N1,600ms),(N8,800ms),(N2,900ms)].

[0084] S402: Based on node response delay time data, determine the execution order of each node in the overall scheduling, determine the order of node execution, and obtain node execution order data;

[0085] Based on the ordered list [(N5, 500ms), (N1, 600ms), (N8, 800ms), (N2, 900ms)], a batch time window is introduced. Group the nodes. The value is referenced to the 5th percentile of the node settling time in S2 (based on experimental analysis of 1000 scheduling events, this value is...). ),set up for The first batch (Batch 1) is created starting with the smallest delay time, N5 (500ms), and its time interval is set as follows. The list is defined as [500ms, 700ms]. N1 (600ms) falls within this range and is added to Batch 1. N8 (800ms) and N2 (900ms) are outside this range and are not added. Batch 1 is then defined as {N5, N1}. From the remaining nodes, N8 (800ms) with the smallest delay is selected to create the second batch (Batch 2), with its time interval set to [500ms, 700ms]. That is, [800ms, 1000ms]. Traverse the remaining N2 (900ms), which is located in this interval and is included in Batch2. Batch2 is determined as {N8, N2}. According to the batch order (Batch1 takes precedence over Batch2) and the original delay order within the batch (N5 takes precedence over N1, N8 takes precedence over N2), assign a unique execution sequence table identifier to each node. Assign N5 1.1, N1 1.2, N8 2.1, and N2 2.2 to obtain the set of node execution order [(N5, 1.1), (N1, 1.2), (N8, 2.1), (N2, 2.2)].

[0086] S403: Generate a node operation order table based on the node execution order data to obtain a node scheduling priority list.

[0087] Based on the node execution order set [(N5,1.1),(N1,1.2),(N8,2.1),(N2,2.2)], the specific operation instructions corresponding to each node {N5,N1,N8,N2} are retrieved from the water supply path optimization results of S3. These instructions are part of the optimization result data structure. In the example, the operation instruction for N5 is "Set pump station N5 speed = 80%", for N1 it is "Set valve N1 opening = 45%", for N8 it is "Set pump station N8 speed = 75%", and for N2 it is "Set valve N2 opening = 60%". The node identifier, execution order, and operation instructions are combined to construct a node operation order table with the execution order as the primary key, as shown in Table 1.

[0088] Table 1 Node Operation Sequence Table

[0089] Execution order Node identifier Node type Operation instructions 1.1 N5 pumping station Set pump station N5 speed to 80%. 1.2 N1 valve Set valve N1 opening degree = 45% 2.1 N8 pumping station Set pump station N8 speed to 75%. 2.2 N2 valve Set valve N2 opening degree to 60%.

[0090] As shown in Table 1, the node operation order table is solidified into structured data, resulting in a node scheduling priority list.

[0091] Please see Figure 6 The steps for obtaining the smart water management dynamic scheduling result set are as follows:

[0092] S501: Based on the node scheduling priority list, obtain valve opening and pump station water output information, combine the total water supply and load change trends, compare the water supply situation of each path, and obtain the difference data between the total water supply and load changes.

[0093] Based on the node scheduling priority list, extract the current cycle instruction information, namely, pump station N5 in path A has an operating speed of 80% and valve N1 has an opening of 45%, and pump station N8 in path B has an operating speed of 75% and valve N2 has an opening of 60%. Use the pump station characteristic curves to obtain the water output of N5. for With N8 water output for Calculate the total water supply The load change trend data retrieved from the node hydraulic dynamic record set specifies the current predicted total load demand. for Compare the two and calculate the difference value. ,this The difference is the water supply gap, including the water supply from path A. Water supply via route B The records together form the data on the difference between total water supply and load changes {difference value: +150}. Water supply via route A: 1500 Water supply via route B: 1200 }

[0094] S502: Based on the difference between total water supply and load changes, adjust the water output ratio of each node according to node priority, update the flow distribution of the nodes, and obtain the updated flow distribution data.

[0095] By using the difference between total water supply and load changes, the total amount to be adjusted is extracted. The allocation of this adjustment amount must refer to the node priority determined by the node scheduling priority list, and introduce a set of priority adjustment weights based on the S2 hydraulic response level matrix and the preset S4 node response delay time, with Batch 1 (high priority) weights. Set as Batch2 (low priority) weight Set as , and The sum is 1.0, and the adjustment flow rate allocated to Batch 1 (executed by pump station N5) is calculated according to this weight. Adjusted flow rate allocated to Batch 2 (executed by pump station N8) Update node traffic allocation, new target traffic for N5. N8's new target traffic The updated traffic allocation data {N5:} is obtained N8: }

[0096] S503: Based on the updated flow distribution data, coordinate the water supply relationship between each path and generate a set of smart water dynamic scheduling results.

[0097] Based on the updated traffic allocation data {N5: N8: The system coordinates the water supply relationships across different paths and converts the target flow rate into equipment commands. It then invokes a pre-calibrated node status-flow-command mapping set (based on data from 200 joint control experiments). For path A, based on... Target traffic query mapping set to determine the new speed of N5 (Originally 80%) and valve N1 coordinated opening. (Originally 45%), for path B, based on Target traffic query mapping set to determine the new speed of N8 (Originally 75%) and valve N2 coordinated opening. (Originally 60%), the new instructions {N5:83%, N1:46.8%, N8:78%, N2:61.2%} are combined according to the execution order {1.1,1.2,2.1,2.2} determined by S402 to generate an ordered instruction list [(1.1,N5,'Set pump station N5 speed=83.0%'),(1.2,N1,'Set valve N1 opening=46.8%'),(2.1,N8,'Set pump station N8 speed=78.0%'),(2.2,N2,'Set valve N2 opening=61.2%')]. This list is the result set of the smart water dynamic scheduling.

[0098] A smart water management system based on the Internet of Things, the system includes:

[0099] The data acquisition and comparison module acquires the velocity, pressure, temperature and turbidity signals of water source nodes, pipeline nodes and flow nodes, performs synchronous comparison according to the node time sequence, detects the fluctuation amplitude and signal offset between samples, analyzes the time change relationship of velocity and pressure of each node, records the synchronization difference and delay, and generates a node hydraulic dynamic record set.

[0100] The node stability analysis module extracts the linkage trend of flow velocity and pressure from the node hydraulic dynamic record set, analyzes the response time and stabilization time under the same period, compares the differences and evaluates the stability of each node under load fluctuations, classifies the level according to the response time distribution, and generates a hydraulic response level matrix.

[0101] The energy efficiency optimization module, based on the hydraulic response level matrix, analyzes the pressure and flow velocity changes in the pipeline water conveyance section, detects the pressure and velocity differences between adjacent sections, calculates energy loss and compares the energy consumption differences between paths, analyzes the path flow distribution and energy consumption ratio, selects paths with lower energy consumption and balanced flow distribution, and generates water supply path optimization results.

[0102] The scheduling priority analysis module analyzes the pressure and flow rate change rates in node signals based on the water supply path optimization results, measures and sorts the response delays, determines the execution order of nodes in the overall scheduling, and generates a node scheduling priority list.

[0103] The dynamic scheduling optimization module adjusts valve opening and pump station output information according to the node scheduling priority list, compares the total water supply with the load change trend, adjusts the output ratio according to node priority and updates the flow distribution, coordinates the water supply relationship between paths, and generates a set of smart water dynamic scheduling results.

[0104] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A smart water management method based on Internet of Things, characterized in that, Comprise the following steps: S1: Obtain the flow rate, pressure, temperature and turbidity signals of the water source node, pipe network node and flow node, compare the signals in time sequence, detect the sampling fluctuation amplitude and signal offset rate, analyze the time variation relationship of the flow rate and pressure of each node, record the synchronization difference and delay, and generate a node hydraulic dynamic record set; S2: used for extracting the linkage trend of flow rate and pressure according to the node hydraulic dynamic record set, calling the response time and back stable time of the node under the same period, comparing the difference between the two and judging the stable performance of each node under load fluctuation, dividing the level according to the response time distribution, and generating a hydraulic response level matrix; S3: used for calling the pressure and flow rate data of the pipeline water conveying section based on the hydraulic response level matrix, detecting the pressure difference and velocity difference between adjacent sections, calculating the energy loss change and comparing the energy consumption difference of the path, analyzing the flow distribution and energy consumption ratio of the path, screening the path with energy consumption ratio not exceeding the preset proportion threshold and balanced flow, and generating a water supply path optimization result; S4: used for analyzing the pressure and flow rate change rate in the node signal according to the water supply path optimization result, determining the response delay time and arranging and sorting, judging the execution order of the node in the overall scheduling, establishing a node operation order table, and generating a node scheduling priority list.

2. The intelligent water management method based on Internet of Things according to claim 1, characterized in that: the node hydraulic dynamic record set comprises node flow rate change, node pressure fluctuation, node temperature record, node turbidity change, synchronization difference, delay time; the hydraulic response level matrix comprises node response time, node back stable time, stability level, response time distribution, and grade interval division; the water supply path optimization result comprises optimized path, path energy loss, path flow distribution, path energy efficiency, and low energy consumption path; the node scheduling priority list comprises node execution order, node response delay, node operation priority, and node scheduling order. 3.The Internet of Things based smart water management method according to claim 1, wherein: The acquisition step of the node hydraulic dynamic record set is: S101: Obtain the flow rate, pressure, temperature and turbidity signals of the water source node, pipe network node and flow node, compare the signals in time sequence, detect the sampling fluctuation amplitude and offset rate, and synchronize the time sequence data of each node for synchronous verification, and obtain the synchronization difference data of each node; S102: based on the synchronization difference data of each node, analyze the time variation relationship of flow rate and pressure, establish a time sequence model of flow rate and pressure change between nodes, calculate the correlation coefficient of flow rate and pressure, and obtain the flow rate pressure correlation coefficient; S103: according to the flow rate pressure correlation coefficient and time variation analysis, detect and record the synchronization difference and delay of each node, calculate the delay time and synchronization error between nodes, and generate a node hydraulic dynamic record set. 4.The Internet of Things based smart water management method according to claim 1, wherein: The acquisition step of the hydraulic response level matrix is: S201: according to the node hydraulic dynamic record set, extract the linkage change trend of flow rate and pressure, obtain the response time and back stable time of each node under the same period, compare the response difference of each node in load fluctuation, and obtain the node response time difference data; S202: Based on the node response time difference data, analyze the response time and the back-to-steady time of each node, divide the level interval according to the distribution of the response time, and obtain response time level interval data; S203: According to the response time level interval data, evaluate the stability performance of each node, and obtain a hydraulic response level matrix. 5.The Internet of Things based smart water management method according to claim 1, wherein: The acquisition step of the water supply path optimization result is: S301: According to the hydraulic response level matrix, acquire the pressure and flow rate change data of the pipe water conveying section, detect the pressure difference and velocity difference of adjacent sections, calculate the energy loss change between each section, and obtain each section energy loss data; S302: Based on the energy loss data of each section, compare the energy consumption difference of different paths, analyze the flow distribution and energy consumption ratio of each path, and obtain path flow distribution and energy consumption ratio data; S303: According to the path flow distribution and energy consumption ratio data, filter out the path with lower energy consumption and balanced flow distribution, and generate the water supply path optimization result. 6.The Internet of Things based intelligent water management method according to claim 1, wherein: The acquisition step of the node scheduling priority list is: S401: According to the water supply path optimization result, acquire the pressure and flow rate change rate in the node signal, determine the response delay time of each node, sort the nodes according to the delay time, and obtain node response delay time data; S402: Based on the node response delay time data, determine the execution order of each node in the overall scheduling, determine the order of node execution, and obtain node execution order data; S403: According to the node execution order data, generate a node operation sequence table, and obtain a node scheduling priority list. 7.The Internet of Things based intelligent water management method according to claim 1, wherein, Further comprising: S5: A node scheduling priority list is used to call valve opening and pump station water discharge information, compare total water supply and load change trend, adjust water discharge proportion according to node priority and update flow distribution, coordinate path water supply relationship, and generate a set of intelligent water management dynamic scheduling results; The set of intelligent water management dynamic scheduling results includes scheduling total amount, node water discharge amount, flow proportion adjustment, path water supply coordination, and node priority adjustment. 8.The IoT-based intelligent water management method according to claim 7, characterized in that: The acquisition step of the set of intelligent water management dynamic scheduling results is: S501: According to the node scheduling priority list, acquire valve opening and pump station water discharge information, combine total water supply and load change trend, compare water supply of each path, and obtain water supply total amount and load change difference data; S502: Based on the water supply total amount and load change difference data, adjust the water discharge proportion of each node according to the node priority, update the flow distribution of the node, and obtain updated flow distribution data; S503: According to the updated flow distribution data, coordinate the water supply relationship between each path, and generate a set of intelligent water management dynamic scheduling results.

9. A smart water management system based on Internet of Things, characterized in that, The system is used for the intelligent water management method based on Internet of Things in any one of claims 1-8, and the system comprises: The data acquisition and comparison module acquires flow rate, pressure, temperature and turbidity signals of the water source node, the pipe network node and the flow node, synchronously compares the signals according to the node time sequence, detects fluctuation amplitude and signal offset between sampling, analyzes time variation relationship of flow rate and pressure of each node, records synchronization difference and delay, and generates a node hydraulic dynamic record set; The node stability analysis module extracts linkage variation trend of flow rate and pressure from the node hydraulic dynamic record set, analyzes response time and back-to-stable time under the same period, compares the difference and evaluates stability of each node in load fluctuation, divides levels according to response time distribution, generates a hydraulic response level matrix, and The energy efficiency optimization module analyzes pressure and flow rate variation of a pipe water conveying section based on the hydraulic response level matrix, detects pressure difference and velocity difference of adjacent sections, calculates energy loss and compares energy consumption difference between paths, analyzes path flow distribution and energy consumption proportion, selects a path with lower energy consumption and balanced flow distribution, and generates a water supply path optimization result; The scheduling priority analysis module analyzes pressure and flow rate change rate in the node signal according to the water supply path optimization result, determines response delay and sorts, judges execution order of the node in overall scheduling, and generates a node scheduling priority list; The dynamic scheduling optimization module adjusts valve opening degree and pump station water discharge information according to the node scheduling priority list, compares total water supply and load variation trend, adjusts water discharge proportion according to node priority and updates flow distribution, coordinates water supply relationship between paths, and generates a smart water affair dynamic scheduling result set.

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