Water resource intelligent management and control system and method based on multi-parameter internet-of-things perception

Abstract

The invention belongs to the field of water resource intelligent management and control, and particularly relates to a water resource intelligent management and control system and method based on multi-parameter internet-of-things perception. A water resource intelligent management and control method based on multi-parameter internet-of-things perception comprises the following steps that S10, water flow monitoring devices are installed at exposed nodes which can be approached by a public part of a building, the exposed nodes comprise nodes of the building, units, floors and a portal trunk, and the positions of the water flow monitoring devices are set as monitoring nodes; and combining the rigid corresponding relationship between each water flow monitoring device and the building structure, determining the upstream and downstream relationship of the pipe sections of the pipe network, and establishing an Internet of Things sensing network. According to the scheme, the sensing network is established through the public exposed nodes of the building, and installation limitation is broken through without relying on missing parameters; the leakage is positioned by measuring the pressure difference in the low-load period, and the pressure increasing valve drop is misjudged; the water flow silt flushing and dredging assist leakage exposure, sediment can be discharged to protect the pipeline, and integration of accurate leakage detection and maintenance of the old pipe network is achieved.

Description

TECHNICAL FIELD

[0001] The present solution belongs to the field of intelligent management and control of water resources, and specifically relates to a water resource intelligent management and control system and method based on multi-parameter Internet of Things sensing. BACKGROUND

[0002] Under the dual background of urbanization process and water resource supply and demand contradiction, the digital intelligent management and control of water supply system as the lifeline of the city is crucial. The stable operation of the water supply network directly affects the efficient use of water resources, the livelihood guarantee of residents and the economic development of the city. Pipe leakage not only causes a large amount of water resources waste, but also may cause ground subsidence, pipe rupture and other safety accidents. Therefore, relying on digital technology to realize accurate identification and positioning of leakage has become the core goal of digital intelligent management and control of the water supply industry.

[0003] Currently, the detection technology represented by the public technical solution with the application publication number CN118623240A has become the mainstream solution for water supply network leakage detection. The core logic of this solution is to lay collection points based on the structure of the network, and to collect data from the collection points and the network structure to analyze and detect water leakage. Specifically, according to the actual pipe distribution structure of the water supply network, Internet of Things detection devices are deployed on each pipe to lay collection points. The collection points collect pipe state data at a predetermined period, and store these data in the water pipe state table of the time series database. At the same time, the collected data is integrated with the pipe basic structure and mechanism information related to the network structure, and the water pipe association information table recording the connection relationship between the upstream and downstream pipes (reflecting the network topology) to determine the prediction parameters of the current pipe (i.e. the state data of the current pipe and the upstream and downstream pipes connected thereto). Finally, through the trained pipe leakage model, the prediction parameters are used to predict the leakage of each pipe, and the target prediction information table is generated to realize the systematic analysis and detection of water leakage.

[0004] However, the above mainstream solution can accurately capture the leakage characteristics and effectively ensure the timeliness and accuracy of leakage detection in the context of well-maintained conventional networks (where the network structure is clear and the collection points can be laid according to the network structure). In old communities, the application of this mainstream technology faces significant limitations.

[0005] In old communities (such as stair rooms with a building age of more than 20 years), pipes are often embedded in brick-concrete walls in stairwells (bearing building load-bearing functions) or buried under narrow ground in the community (surrounded by residents' living sundries), and when collecting points are laid according to pipe network structures, walls need to be chiseled or ground needs to be excavated, which not only easily damages the integrity of the building structure and affects the living environment of residents, but also faces extremely high construction coordination difficulties, resulting in insufficient coverage of collection points and the inability to completely obtain the state data corresponding to the pipe network structure; at the same time, old pipes generally have serious corrosion and internal wall scaling problems after long-term use, and most of the leakage is "progressive small leakage" (such as slow leakage caused by corrosion perforation), which has a small pressure fluctuation amplitude and a short duration, and when combining pipe network structure collection data, the key leakage signal is easily missed, resulting in a significant deviation of the monitoring result from the actual state of the pipe network and a high misjudgment rate of the pipe network leakage. SUMMARY

[0006] The purpose of the present scheme is to provide a water resource intelligent management and control system and method based on multi-parameter Internet of Things sensing, to solve the problem of high misjudgment rate of pipe network leakage in the detection of pipe network leakage in old communities.

[0007] In order to achieve the above-mentioned purpose, the present scheme provides a water resource intelligent management and control method based on multi-parameter Internet of Things sensing, comprising the following steps: S10: installing a water flow monitoring device at a bare node accessible in a public part of a building, the bare node including a node at a building, a unit, a floor, and a portal main pipe, setting the position of the water flow monitoring device as a monitoring node; combining the rigid relationship of each water flow monitoring device with the building structure to determine the upstream and downstream relationships of the pipe network pipe section, and establishing an Internet of Things sensing network; S20: collecting monitoring data under a highest node water flow data less than a preset useless water threshold through the Internet of Things sensing network, the detection parameters corresponding to the monitoring data including real-time water flow data, building height of each monitoring node, upstream and downstream levels between monitoring nodes, and collection time of monitoring data, the water flow data including differential pressure and flow; performing multi-dimensional comprehensive analysis on the monitoring data with each monitoring parameter as a dimension, thereby obtaining the differences of water flow data at different building heights between unit nodes and floor nodes, and the differences of each item of water flow data between portal nodes and floor nodes at different positions; combining the differences of water flow data with the Internet of Things sensing network to construct a network sensing model of pipe network water flow state; S30: using the network sensing model to combine multi-dimensional parameter analysis of real-time collected water flow data to calculate the differential pressure between the monitoring node and other monitoring nodes, and compare it with the differential pressure in the network sensing model, when the comparison result exceeds a preset differential pressure threshold, the pipe section between the corresponding monitoring nodes is regarded as an abnormal pipe section; S40: The booster valve is integrated in the water flow monitoring device, when an abnormal pipe section is obtained, the single-point water pressure is adjusted through the booster valve, the pressure difference of the abnormal pipe section before and after the water pressure change is obtained as the detection pressure difference; the comparison result of the detection pressure difference and the preset pressure difference threshold is taken as the detection result, and the leakage loss of the abnormal pipe section is evaluated according to the detection result.

[0008] And a multi-parameter Internet of Things sensing-based intelligent water resource management system using a multi-parameter Internet of Things sensing-based intelligent water resource management method.

[0009] The principle and technical effect of the scheme are that: the scheme deploys monitoring devices at building public exposed nodes, determines the upstream and downstream of the pipe network according to the rigid correspondence relationship between the building structure and the device, constructs an adaptive sensing network that does not need to rely on missing parameters such as pipe diameter and pipe material, effectively breaks through the installation restrictions caused by complex building structures, and significantly improves the coverage rate of collection points; collect data in the low-load period when the highest node water flow data is lower than the useless water threshold, avoid resident water interference, combine multi-dimensional analysis to construct a network sensing model with inter-node pressure difference as the core, locate the local pressure loss anomaly caused by leakage through the comparison of real-time pressure difference and model benchmark, accurately capture subtle pressure fluctuations, and significantly improve the leakage signal capture sensitivity; at the same time, the water pressure of the suspected abnormal pipe section is adjusted through the booster valve, and the detection pressure difference before and after the pressure change is compared, which effectively distinguishes leakage from natural fluctuations of pipe aging, reduces the risk of misjudgment, and still achieves the leakage positioning accuracy that meets the maintenance requirements in the scene where the basic data is incomplete, greatly shortens the whole process time, fully solves the leakage detection pain points of old pipe network, and significantly improves the precision and practicality of management and control.

[0010] Secondly, the local water flow caused by the change of local pressure difference can also form targeted optimization for the common silt problem in old pipelines: old pipelines are prone to accumulate silt such as water scale and impurities after long-term use, which not only may cover up leakage points, but also may interfere with the accuracy of pressure difference measurement, leading to further weakening of the leakage signal; the flushing effect of local water flow can gradually remove the silt around the leakage point, reduce the interference of silt on pressure difference data, and make it easier to expose the small leakage point that is blocked by silt, so that the pressure difference anomaly characteristics caused by leakage are more clear and identifiable. This linkage effect of water flow flushing, silt removal and leakage exposure can complement the secondary verification mechanism of the booster valve, further improve the accuracy of leakage identification in old pipelines, and is especially suitable for old pipeline scenes with serious silt and deeply covered leakage signals, so that leakage detection not only breaks through the installation and interference restrictions, but also actively resolves the detection obstacles caused by silt, and fully adapts to the complex working conditions of old community water supply pipelines.

[0011] Moreover, in the process of flushing the silt around the leakage point, the local water flow will simultaneously loosen the long-term deposits attached to the inner wall of the pipeline; during the daytime peak period of residential water use, the water flow power in the pipeline network is enhanced, and these loosened deposits will be discharged from the pipeline along with the normal water flow, avoiding problems such as the reduction of the inner diameter of the pipeline and the increase of water flow resistance caused by the long-term attachment of silt. This detection process, accompanied by the characteristics of flushing and silt maintenance, not only eliminates the need for additional pipeline dredging costs, but also reduces the hidden dangers of accelerated pipeline corrosion and local blockage caused by silt, indirectly prolongs the service life of old pipelines, and provides an integrated management effect for the detection and maintenance of old community water supply networks.

[0012] In summary, the present scheme builds a sensing network through building public exposed nodes in buildings, breaks through the installation restrictions without relying on missing parameters; locates leaks during low-load periods by measuring pressure difference, reduces misjudgment by using pressure increasing valves; water flow flushing and silt removal assist in exposing leaks, and can also remove sediments to protect pipelines, achieving integrated detection and maintenance of old pipeline leaks.

[0013] Further, the water flow monitoring device is also used to collect water flow, and according to the sum of the flow data of each branch outlet under the portal node and the change rate of the sum of the flow data, different levels of water demand are evaluated; for different levels of water demand, the flow change law of the branch outlet and its correlation with the total flow deviation are analyzed to generate corresponding water fluctuation characteristic factors; when evaluating the leakage of abnormal pipe sections, the water fluctuation characteristic factor is integrated into the fusion process of flow deviation rate, pressure gradient and topological parameters to dynamically correct the influence of flow deviation on leakage.

[0014] The water flow monitoring device collects flow and evaluates water demand, and generates water fluctuation characteristic factors, which can accurately adapt to the characteristics of old community water use period concentration (such as morning and evening peak) and significant flow fluctuation. On the one hand, by comparing and analyzing the change rate of branch and total flow, the real water demand and abnormal flow caused by leakage can be clearly distinguished, avoiding misjudgment of normal water fluctuation such as morning peak as a leakage signal; on the other hand, by integrating this factor into the multi-parameter fusion process of leakage evaluation, it can dynamically filter the interference of normal water fluctuation on flow deviation rate and pressure gradient, such as correcting temporary flow deviation caused by residents' concentrated water use, avoiding masking or false triggering of leakage warning, further improving the accuracy of leakage judgment; at the same time, the clear water fluctuation law can also provide demand basis for subsequent pipeline operation and maintenance, reduce invalid maintenance caused by misjudgment of leakage, and balance the accuracy of leakage detection and the economy of operation and maintenance.

[0015] Further, the branch water outlet is also provided with a faucet, the faucet comprising an electric control group and a mechanical group, the mechanical group comprising a switch component for opening and closing the faucet, a connection component for connecting and disconnecting the faucet and the branch water outlet, and an electromagnetic coupling component for coupling when the faucet is closed and separating when the faucet is opened; the electric control group is electrically connected with the electromagnetic coupling component and the connection component respectively, and is used for identifying the opening and closing state of the faucet according to the current change of the electromagnetic coupling component, and is also used for controlling the current of the connection component to connect and disconnect the faucet and the branch water outlet, and is electrically connected with the water flow monitoring device of the branch water outlet, and is used for monitoring the water flow and the water pressure between the faucet and the branch water outlet through the water flow monitoring device; the electric control group controls the current of the connection component according to the current change of the electromagnetic coupling component or the water flow and the water pressure, and is used for uploading the water flow and the water pressure to the cloud, and the regulation unit is used for receiving the regulation instruction of the cloud and controlling the current of the connection component according to the regulation instruction.

[0016] When the user operates the switch component to open and close the faucet, the electromagnetic coupling component generates a current signal through the change of the coupling and disengaging states, and the electric control group identifies the opening and closing state of the faucet according to the current signal, and controls the current of the connection component to connect and disconnect the faucet and the branch water outlet, so as to ensure the accurate connection and disconnection of the faucet and the branch water outlet, for example, in the household water scene, after the user closes the faucet, the electromagnetic coupling component is separated, the electric control group detects the current change and controls the connection component to cut off the water flow, so as to avoid the dripping phenomenon. In addition, the water flow monitoring device monitors the flow and the water pressure in real time, for example, in the farmland irrigation scene, when the water flow exceeds the preset threshold, the electric control group can automatically adjust the current of the connection component according to the parameter, reduce the connection and disconnection resistance to reduce the flow, and upload the data to the cloud, and the management personnel can control the water flow through the remote instruction, for example, in the urban water supply network, when the cloud finds that the water pressure of a certain area is abnormal, the electric control group controls the current of the connection component to adjust the connection and disconnection state of the water flow, so as to realize the dynamic allocation and energy-saving management of water resources, which not only improves the water use efficiency, but also realizes the real-time response and remote management of the abnormal state through the Internet of Things sensing technology.

[0017] Further, when the electric control group monitors the water pressure, the connection component is controlled to block the faucet and the branch water outlet when the water pressure is lower than the preset low pressure threshold or a water cut instruction sent by the cloud is received, and the current of the electromagnetic coupling component is monitored as a trigger current after a water supply instruction sent by the cloud is received or the water pressure is not lower than the preset low pressure threshold, and the connection component is controlled to open the faucet and the branch water outlet when the change period of the trigger current within the preset trigger time exceeds the preset trigger times.

[0018] When the water pressure is lower than the threshold value due to water stoppage or pipe repair, the electric control group automatically blocks the faucet to avoid dripping waste. When the water supply is restored, the program identifies the user's repeated on-off behavior by monitoring the current change of the electromagnetic coupling component. Only when the number of current change cycles exceeds the preset number, the water is turned on to avoid waste caused by the user forgetting to turn off the faucet. For example, after the water is stopped in the family, the user opens and closes the faucet several times to test. After confirming the user's continuous operation, the water source is connected to respond to the demand and prevent direct discharge of water when unattended, achieving energy saving and intelligent control.

[0019] In addition, in the early stage of water supply restoration, if the user does not repeatedly operate the faucet (i.e., the number of current change cycles does not exceed the preset number), the program will maintain the blocked state of the faucet to avoid pipe impact or water splashing caused by sudden water supply when the water pressure is unstable. At the same time, the dual control logic of cloud command and local water pressure monitoring can adapt to remote emergency water stop scenes (such as remote issuance of water stop commands in emergency fire fighting), improving the flexibility and safety of water resource management. For example, when municipal pipe network leaks, the management party can quickly block the faucets in the region through cloud commands to reduce water loss. After the fault is eliminated, the water supply is remotely restored to realize cross-scene intelligent scheduling.

[0020] Further, the electric control group receives the water supply instruction sent by the cloud or the water pressure is not lower than the preset low pressure threshold. According to the current water pressure, the water outlet time is set. According to the water outlet time, the connecting component is controlled to connect the faucet and the branch water outlet, and then the faucet and the branch water outlet are blocked to obtain the water flow change. When the water flow changes, the connecting component is controlled to connect the faucet and the branch water outlet.

[0021] The scheme realizes fine control of water resources and optimization of user experience by dynamically setting the water outlet time and flow monitoring. The old community water supply network often has problems such as large water pressure fluctuation and sudden pressure rise during water transmission due to pipeline aging. When traditional direct water transmission is used, water flow is easily sprayed from the faucet at high speed due to pressure impact, causing water splashing. The scheme sets the water outlet time according to the current water pressure, temporarily connects the faucet and the branch outlet, and allows the pipeline to retain a certain amount of water. This water forms a transition buffer section in the pipeline, and when the water is officially turned on, the water flow will first push the stored water to flow out, greatly reducing the pressure impact of sudden water transmission, so that the water flow from the faucet always maintains a smooth state, completely avoiding the situation of water splashing wetting user's clothes, kitchen countertop or bathroom floor, improving the comfort of daily water use, and reducing the cleaning burden and the risk of slipping due to water splashing. In addition, the scheme blocks the water flow after temporary water outlet and monitors the flow change. If a flow change (such as manual operation of the faucet) is detected, the water source is connected again to realize on-demand water supply. If no change is detected, the blocking state is maintained to avoid water waste when no one is using it, especially suitable for public places or unattended scenes. This intelligent control mechanism not only improves water safety, but also achieves water saving through precise flow management.

[0022] Further, the mechanical group further comprises a sound detection device, the sound detection device being electrically connected with the electric control group, when the electric control group controls the connecting component to open the faucet and the branch outlet, if a flow change is identified, sound information collected by the sound detection device is obtained, and the connecting component is controlled to block the faucet and the branch outlet according to the change of the sound information and the time of collecting the sound information; if no flow change is identified, the faucet and the branch outlet are blocked according to the water outlet time.

[0023] The scheme realizes intelligent anti-overflow and water saving of water storage scene through sound detection and water pressure cooperative control. When water flow change is identified within the water outlet time, the water storage speed is predicted according to the water pressure, the container full overflow state is judged through sound change, and the water flow is automatically blocked when the water quantity is close to the upper limit to avoid overflow waste. If no water flow is identified within the water outlet time, the water is cut off according to the water outlet time. For example, after the household water is stopped, the user uses a bucket to collect water. When the water pressure is high, the water flow is fast. The scheme discovers that the bucket is full by sound change and closes the water in time. When the water pressure is low, the monitoring is prolonged to ensure that the bucket is full and does not overflow, which meets the water storage demand and prevents water overflow, reduces overflow waste compared with the traditional way, and is suitable for temporary water storage scene. In addition, the scheme can also identify abnormal water flow sound when there is no water storage container, such as continuous abnormal sound generated by pipeline idling or water leakage. The water flow is blocked in time through sound feature analysis to reduce the risk of water leakage. For example, the faucet is not connected to the container, but the continuous water flow sound is detected, which is judged as abnormal water leakage. The automatic closing of the connecting part avoids the hidden loss of water resources, reduces the damage of facilities caused by water leakage, and improves the safety and equipment maintenance efficiency of water resource management.

[0024] Further, when the electric control group detects the change of water flow, the sound information collected by the sound detection device, the water flow, the collection time and the local address are uploaded to the cloud; the cloud processes the sound information in layers according to the sound frequency characteristics, the cloud establishes a sediment scouring sound model, and inputs the sound characteristics into the sediment scouring model to calculate the sediment accumulation degree corresponding to the local address.

[0025] The water flow rate change directly corresponds to the scene of water flow activity in the pipeline (such as the user opening the faucet, the pipe network water replenishment), which can naturally exclude the environmental noise (such as the corridor voice, the equipment running sound) when there is no water flow, and ensure the effectiveness of the subsequent data collection; when the trigger signal is generated, the electric control group will link the sound detection device to capture the vibration sound when the water flow flows in the pipeline, impacts the pipe wall or washes the sediment (such as the vibration characteristics of the sound caused by whether there is sediment in the pipeline, the sediment accumulation degree is different, which will present different vibration characteristics), and at the same time, the sound information is bound with the real-time water flow (different water flow intensity will cause different scouring force, and then change the frequency and amplitude of the sound), the collection time (distinguish the peak or valley period of water use, the water flow stability is different in different periods, and the sediment scouring rule is different), and the local address (precisely corresponding to the specific building, unit or pipe section, avoiding the disconnection of data and the actual pipeline), forming a four-dimensional data set of sound, flow, time and location and uploading to the cloud. The principle of this association design is to confirm each other through multiple dimensions, to provide complete data support for subsequent accurate analysis, and to avoid the judgment deviation caused by single data; after receiving the data from the cloud, the sound information is first layered according to the frequency interval and the sound attribute, and according to the unique frequency characteristics of different types of sound (such as sediment scouring sound with lower frequency and larger amplitude, and pure water flow sound with more stable frequency), the layered sound characteristics can filter out environmental noise and extract the core sound characteristics directly related to sediment, and exclude non-target signal interference; then the cloud inputs the layered sound characteristics into the pre-built model of sediment accumulation degree and sound characteristics. The model is based on a large number of experimental data (scouring sound samples under different sediment accumulation thickness and different water flow velocity), and establishes the corresponding relationship between the accumulation degree and the sound frequency, the amplitude change period, and the sound wave attenuation rate. By comparing the matching degree of the input characteristics and the standard characteristic library in the model, the sediment accumulation degree of the local address corresponding to the pipe section is inversely calculated, realizing the quantitative transformation from sound signal to pipeline internal state.

[0026] Further, the cloud compares the real-time flow data with the historical flow curve through the data comparison of the sound information association, locates the abnormal pipe section according to the identification result, synchronously collects the water flow, sound information and water pressure data of the abnormal pipe section, and establishes a dynamic correlation model. According to the dynamic correlation model, the abnormal type is distinguished, and different levels of warning information are generated according to the distinguishing result.

[0027] The scheme compares real-time flow with historical data to lock abnormal pipe sections, synchronously collects flow, sound and water pressure data to construct a correlation model, distinguishes problems such as pipe damage and sediment blockage according to data change characteristics, and generates different levels of early warnings to help operation and maintenance personnel quickly locate and handle faults. This not only reduces the blindness of manual inspection, but also prevents problems such as water leakage and insufficient water pressure caused by pipe abnormalities in advance, improving water resource transportation efficiency and pipe network safety. For example, if a pipe in a certain community is damaged due to aging, an increase in flow and a decrease in water pressure are detected in the area, combined with the high-frequency water flow impact sound captured by the sound detection device, the model analyzes and determines that it is a pipe damage, and immediately issues a red warning. After receiving the information, maintenance personnel quickly repair it to avoid wasting a large amount of water resources and damaging surrounding facilities.

[0028] Further, the detection data of the water flow monitoring device is combined with the network perception model to form a space-time correlation model, the cloud superimposes the water flow, sound information and water pressure data into the space-time correlation model, and labels the abnormal pipe section in the space-time correlation model, divides the influence range of the abnormal pipe section in the three-dimensional water flow monitoring network according to the pressure wave, and sends a preset verification signal to the corresponding address of the electric control group according to the influence range; the electric control group receives the signal and controls the switching of the connecting component; the cloud collects the water flow, sound information and water pressure data during the on-off switching process again, and calculates the abnormal pipe section as a verification section again, compares the abnormal pipe section with the verification section, and inputs the comparison result into the dynamic correlation model to distinguish the abnormal type.

[0029] The scheme realizes efficient re-inspection and accurate positioning of the abnormal pipe section by controlling the on-off switching of the connecting component through the electric control group. The on-off operation causes the flow rate in the pipe to change regularly, forming variable speed water flow, which produces a dynamic scouring effect on the sediment, prompting the sediment to move or fall off, thereby changing the water flow vibration characteristics; at the same time, frequent on-off switching enhances the vibration intensity of the pipe and amplifies the water flow sound signal, so that the sound detection device can capture more obvious abnormal acoustic characteristics. The cloud can effectively exclude interference factors such as environmental noise by comparing the data collected before and after on-off, and accurately identify the real problem of the abnormal pipe section. Such a method significantly improves the accuracy and reliability of abnormal positioning, not only reduces the risk of misjudgment, but also quickly locates the positions of pipe damage, blockage and other hidden dangers, providing accurate basis for subsequent maintenance work, and greatly improving the operation efficiency of the water resource intelligent management and control system. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 The flowchart of the water resource intelligent management and control method based on multi-parameter Internet of Things perception in the embodiments of the present application.

[0031] Figure 2 The functional structure diagram among the faucet, water flow monitoring device and cloud in the embodiments of the present application. Detailed Implementation

[0032] The following will describe the concept and technical effects of the present invention clearly and completely with reference to embodiments, so as to fully understand the purpose, features and effects of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the scope of protection of the present invention. like Figure 1 As shown, a method for intelligent water resource management based on multi-parameter IoT sensing includes the following steps: S10: Install water flow monitoring devices at accessible exposed nodes in the common areas of the building. The exposed nodes include nodes at the building, unit, floor, and main pipe entrance. Set the locations of the water flow monitoring devices as monitoring nodes. Combine the rigid correspondence between each water flow monitoring device and the building structure to determine the upstream and downstream relationship of the pipe network segment and establish an Internet of Things sensing network. S20: Collect monitoring data when the water flow data of the highest node is less than a preset no-water threshold through the IoT sensing network. The detection parameters corresponding to the monitoring data include real-time water flow data, building height of each monitoring node, upstream and downstream levels between monitoring nodes, and data collection time. The water flow data includes pressure difference and flow rate. Perform multi-dimensional comprehensive analysis on the monitoring data based on each monitoring parameter to obtain the differences in water flow data at different floor heights between unit nodes and floor nodes, as well as the differences in various water flow data between portal nodes and floor nodes at different locations. Combine the differences in water flow data with the IoT sensing network to construct a network sensing model of the pipe network water flow status. S30: Using a network sensing model, combined with multi-dimensional parameter analysis of real-time collected water flow data, calculate the pressure difference between the monitoring node and other monitoring nodes, and compare it with the pressure difference in the network sensing model. When the comparison result exceeds the preset pressure difference threshold (determined by the administrator based on the pressure difference under no-water conditions and the position of the two monitoring nodes), the pipe segment between the corresponding monitoring nodes is regarded as an abnormal pipe segment. S40: Integrate a booster valve into the water flow monitoring device. When an abnormal pipe section is detected, adjust the water pressure at a single point through the booster valve to obtain the pressure difference of the abnormal pipe section before and after the water pressure change as the detection pressure difference. Compare the detection pressure difference with the preset pressure difference threshold as the detection result, and evaluate the leakage of the abnormal pipe section based on the detection result.

[0033] In step S10, a water flow monitoring device is installed at an exposed node accessible in a public part of a building, the exposed node including a building main pipe node, a unit entrance pipe node, a floor riser pipe node, and a portal branch pipe node, each water flow monitoring device installation position is set as a unique monitoring node; the vertical distance between each monitoring node and the building datum plane (such as the ground floor) is measured by a laser range finder, a rigid correspondence between each water flow monitoring device and the building structure is established (such as node coding is bound to building elevation and pipe segment number), the pipe segment connection relationship is combed along the water flow direction, the upstream and downstream relationship of the pipe network pipe segment is determined (such as the unit entrance pipe node is the superior of the floor riser pipe node, and the floor riser pipe node is the superior of the portal branch pipe node), each monitoring node is connected to the same Internet of Things gateway by using LoRa wireless communication protocol, an Internet of Things sensing network is established, for example, the gateway sampling frequency is set to 5 minutes / time, and the data upload delay is set to ≤10 seconds.

[0034] In step S20, the monitoring data is collected through the Internet of Things sensing network, and the useless water state is determined before collection. For example, the flow data of the highest node (such as the roof tank outlet node) in the last 3 sampling periods is ≤0.1m³ / h, and the pressure fluctuation of the node is ≤0.01MPa, it is determined that the highest node water flow data is less than the preset useless water threshold.

[0035] The detection parameters corresponding to the monitoring data include real-time water flow data (for example, the differential pressure is collected by using a diffusion silicon pressure sensor, the range is 0-1.6MPa, and the accuracy is ±0.2%FS; the flow is collected by using an ultrasonic Doppler flowmeter, the range is 0-5m³ / h, and the accuracy is ±1%FS, wherein FS is “full scale”, which is a common term in the field of measuring instruments or sensors, indicating the maximum upper limit of the range that can be measured by the instrument or sensor), the building height of each monitoring node (for example, the building datum plane is 0 point, and the accuracy is 0.5 meters), the upstream and downstream level between monitoring nodes (for example, according to the building main pipe, the unit pipe, the floor riser pipe, and the portal branch, which are divided into 4 levels, marked as L1-L4), and the collection time of the monitoring data (for example, accurate to seconds).

[0036] The multi-dimensional comprehensive analysis mode is specifically: (1) According to the building height layering. For example, according to the calculation accuracy, the preset number of layers (such as every 3 layers) is divided into 1 height layering unit (such as 1-3 layers, 4-6 layers), the differential pressure difference ΔP1=P_L2-P_L3 between the unit main pipe node (L2 level) and the floor stand pipe node (L3 level) in the same layering unit is calculated, wherein P_L2 is the water pressure of the L2 level node, and P_L3 is the water pressure of the L3 level node. The flow ratio Q1=Q_L2 / Q_L3 is calculated, Q_L2 is the flow of the L2 level node, and Q_L3 is the flow of the L3 level node. The pressure difference decay rate η=ΔP_ upper layer / ΔP_ lower layer between adjacent layers is calculated (for example, ΔP is the pressure difference between the L3 level node and the L4 level node in the layering unit).

[0037] (2) According to the upstream and downstream level association. The data transmission chain between L1, L2, L3 and L4 is established, and the flow transmission efficiency ε=Q_ this level / Q_ upper level and the pressure difference loss rate θ=(P_ upper level-P_ this level) / P_ upper level between the nodes of this level and the nodes of the upper level are calculated.

[0038] (3) According to the collection time screening. For example, 22:00-6:00 is defined as the core useless water period, which is determined according to the water flow velocity collected at the highest level (such as the building main pipe node) monitoring node, and the ΔP1, Q1, η, ε, θ data in the period are counted for a continuous preset number of days (such as 7 days, which is determined according to the service life of the pipe network), and the abnormal values are removed by using the 3σ criterion (i.e. mean±3 times standard deviation), and the stable mean μ and variation coefficient CV (CV=standard deviation / mean, which is assumed to be CV≤0.05 as stable data) of each parameter are calculated.

[0039] (4) Data fusion modeling: the stable ΔP1, Q1, η, ε, θ are taken as input variables, the pipe segment number is taken as output label, the network perception model of the pipe network water flow state is constructed by using the multiple linear regression algorithm (the formula is y=α1x1+α2x2+…+α5x5+β, wherein α1-α5 are regression coefficients, and β is a constant term, which is determined by least square fitting), and the pressure difference fluctuation range (μ_ΔP±0.02MPa) and the flow fluctuation range (μ_Q±0.05m³ / h) of each pipe segment under normal working condition are output.

[0040] In step S30, the network perception model is used to combine multi-dimensional parameter analysis to collect water flow data in real time. The differential pressure ΔPreal=P_upstream node-P_downstream node and the flow Qreal=Q_upstream node-Q_downstream node between the monitoring nodes are calculated in real time. The normal pressure difference fluctuation range [μ_ΔPlow, μ_ΔPhigh] (specifically set by the administrator) and the normal flow fluctuation range [μ_Qlow, μ_Qhigh] (specifically set by the administrator) of the corresponding pipe segment in the network perception model are called.

[0041] Wherein, the preset differential pressure threshold is determined by the administrator in the following way: based on the model output μ_ΔP high, superimpose two monitoring node height difference correction coefficients (for example, correction coefficient k = 0.01 MPa / 10 meters, k = height difference x 0.01 / 10), that is, the preset differential pressure threshold T_ΔP = μ_ΔP high + k.

[0042] Suppose, if ΔP real > T_ΔP, and continuously 2 sampling periods (10 minutes in total) remain in this state, and Q real > μ_Q high + 0.05 m³ / h, then the pipe segment between the corresponding monitoring nodes is regarded as an abnormal pipe segment, and an abnormality label (such as an abnormality label in the form of "pipe segment number- abnormality type-trigger time") is generated.

[0043] In step S40, an electromagnetic pressure increasing valve (for example, working pressure 0.1-1.0 MPa, adjustment accuracy ±0.01 MPa) is integrated in the water flow monitoring device, and the pressure increasing valve is connected with the monitoring device through RS485 communication; for example, after the abnormal pipe segment is obtained, the electric control unit operates in the following steps: First adjustment, the pressure of the pressure increasing valve at the upstream node of the abnormal pipe segment is increased by 0.05 MPa, and maintained for 3 sampling periods, and the differential pressure ΔP measurement of the abnormal pipe segment in this process is collected; Second adjustment, the pressure of the pressure increasing valve is adjusted back to the original pressure, maintained for 3 sampling periods, and the differential pressure ΔP measurement is collected; Detection differential pressure calculation, taking the average value of ΔP measurement 1 and ΔP measurement 2 as the detection differential pressure ΔP measurement; Leakage evaluation, if ΔP measurement > T_ΔP and ΔP measurement 1-ΔP measurement 2>0.03 MPa, it is determined as "severe leakage"; if ΔP measurement > T_ΔP and 0.01 MPa<ΔP measurement 1-ΔP measurement 2≤0.03 MPa, it is determined as "moderate leakage"; if ΔP measurement ≤T_ΔP, it is determined as "false judgment, no leakage", and the parameter mean value μ of the corresponding pipe segment in the network perception model is updated.

[0044] Specifically, the water flow monitoring device is also used to collect water flow, evaluate different levels of water demand according to the sum of the flow data of each branch outlet under the portal node and the change rate of the sum of the flow data, generate corresponding water fluctuation characteristic factors by analyzing the flow change law of the branch outlet and the relevance of the total flow deviation, evaluate the leakage condition of the abnormal pipe segment, and integrate the water fluctuation characteristic factors into the fusion process of the flow deviation rate, the pressure gradient and the topological parameters to dynamically correct the influence of the flow deviation on the leakage condition.

[0045] For example, the operation and maintenance team cooperates with the property, on the first day, uses a pipeline detector and a laser range finder to survey the target unit (a 6-story community building No. 1, Unit 2, built in a city in 2000), locates the 1st floor indoor main pipe (the door node L4) and the installation points of 24 indoor branch pipes (L5 level), records the height of each point, and collects 24-hour water pressure with a pressure gauge; the next day, according to the survey data, purchase and accept the appropriate equipment (1 TDS-100 type flow meter, 24 FS-05 type micro flow meters, etc.). Equipment installation (1 day): close the unit valve and indoor branch valve, clean the pipeline, then fix the door node flow meter on the indoor main pipe with a clamp (avoiding the elbow, leaving a straight pipe section in front and back), install the indoor branch flow meter under the sink / basin (with a waterproof box), connect the power supply and RS485 line, test for no leakage, and then turn on the water. Network deployment (4 hours): place the LoRa gateway in the 1st floor power distribution room, configure the sampling period of 5 minutes, the upload address, connect each flow meter, add repeaters on the 5th-6th floors, test the transmission delay ≤8 seconds, and calibrate the flow meter with a standard measuring cup (deviation exceeding ±2% micro-adjustment coefficient). Data operation (long-term): the cloud automatically calculates the branch flow total ΣQ and the change rate K every 5 minutes, divides the low (ΣQ≤0.3m³ / h), medium, and high demand levels, calculates the water fluctuation characteristic factor F every hour, and adjusts the σ, γ, τ weights according to F to calculate the leakage index L. If the threshold is exceeded, the APP will alarm, and the operation and maintenance will be reviewed and repaired within 2 hours. Operation and maintenance guarantee (once a month): check the equipment status, calibrate the parameters, backup the data at the end of the month, and ensure the stable operation of the system.

[0046] The branch outlet is also provided with a faucet, as shown in Figure 2 The faucet includes an electric control group and a mechanical group. The mechanical group includes a switch component for opening and closing the faucet, a connection component for connecting and disconnecting the faucet and the branch outlet, and an electromagnetic coupling component that is coupled when the faucet is closed and separated when the faucet is opened. The electric control group is electrically connected with the electromagnetic coupling component and the connection component. The electric control group is used to identify the opening and closing state of the faucet according to the current change of the electromagnetic coupling component, and to control the current of the connection component to connect and disconnect the faucet and the branch outlet. The electric control group is electrically connected with the flow monitoring device of the branch outlet, and is used to monitor the flow rate and water pressure between the faucet and the branch outlet through the flow monitoring device. The electric control group controls the current of the connection component according to the current change of the electromagnetic coupling component or the flow rate and water pressure. The electric control group is used to upload the flow rate and water pressure to the cloud, and the control unit is used to receive the control instruction from the cloud and control the current of the connection component according to the control instruction.

[0047] The electric control group monitors the water pressure, and when the water pressure is lower than a preset low pressure threshold or a water cutoff instruction sent by the cloud is received, the connecting component is controlled to block the faucet and the branch water outlet. After receiving a water supply instruction sent by the cloud or the water pressure is not lower than the preset low pressure threshold, the current of the electromagnetic coupling component is monitored as a trigger current. When the change period of the trigger current exceeds the preset trigger times within the preset trigger time, the connecting component is controlled to open the faucet and the branch water outlet.

[0048] In an embodiment of the present scheme, when the water supply is notified to be stopped or the water pressure is lowered to below the preset low pressure threshold due to local pipe repair, the electric control group immediately controls the connecting component to block the faucet and the branch water outlet, so as to avoid dripping or invalid water supply caused by insufficient water pressure. For example, when the water supply in a community is stopped for repair, the electric control group automatically blocks the faucet of each branch water outlet to prevent waste of residual water in the pipeline. When the water supply is restored to normal and the water pressure rises to not lower than the low pressure threshold, the electric control group identifies user behavior by monitoring the current change of the electromagnetic coupling component. If the user repeatedly turns on and off the faucet in a short time (such as repeatedly operating the faucet to check whether the water supply is restored), the number of current change periods exceeds the preset number of times, and then the electric control group controls the connecting component to supply water. In this way, it can be avoided that water is directly discharged due to the user forgetting to turn off the faucet when the water supply is restored. For example, in a home scenario, the user may turn on and off the faucet multiple times after the water supply is stopped. The present scheme will connect the water source only after confirming that the user continuously operates, which not only responds to the user's demand, but also prevents water loss when the water supply is restored without supervision, thereby achieving the dual effects of energy saving and intelligent control.

[0049] Specifically, after receiving the water supply instruction sent by the cloud or the water pressure being not lower than the preset low pressure threshold, the electric control group sets the water outlet time according to the current water pressure, controls the connecting component to connect the faucet and the branch water outlet according to the water outlet time, then blocks the faucet and the branch water outlet, and obtains the water flow change. When the water flow changes, the connecting component is controlled to connect the faucet and the branch water outlet.

[0050] More specifically, the water pressure is negatively correlated with the water outlet time, that is, the higher the water pressure, the shorter the water outlet time, and vice versa. In an embodiment of the present scheme, the higher the water pressure, the faster the water flow speed, and enough water can be retained in the pipeline in a short time; the lower the water pressure, the longer the water outlet time needs to be extended to ensure that the pipeline retains an appropriate amount of water, so as to avoid the subsequent water supply from being unable to trigger the user perception due to insufficient water. Secondly, when the water pressure is high, the water flow speed is fast and the impact force is strong, and if the faucet is suddenly fully opened, it is easy to cause water splashing due to excessive water pressure (such as the water flow injection phenomenon commonly seen when high-rise residents open the faucet in the initial stage of water supply after municipal pipe network repair). By shortening the water outlet time, the present scheme only allows the water flow to pass through the faucet for a short time (for example, set a very short water outlet time in a high water pressure scenario), so that only a small amount of water is retained in the pipeline, which not only avoids water waste caused by long-time water supply, but also reduces the water pressure impact by limiting the duration of the water flow. For example, if the water outlet time is set to 1 second under high water pressure, the impact force is greatly weakened due to the limited amount of stored water when the water flow just flows out, and the water splashing phenomenon is significantly reduced. This way can ensure the user's perception of incoming water while achieving the dual effects of water saving and impact prevention through dynamic time control.

[0051] More specifically, the mechanical group further includes a sound detection device, which is electrically connected to the electric control group. When the electric control group controls the connection component to open the faucet and the branch water outlet, if a change in water flow rate is identified, the sound detection device acquires sound information, and the electric control group controls the connection component to block the faucet and the branch water outlet according to the change in sound information and the time at which the sound information is collected. If no change in water flow rate is identified, the electric control group controls the connection component to block the faucet and the branch water outlet according to the water outlet time.

[0052] In an embodiment of the present scheme, the higher the water pressure, the faster the water flow speed, and the faster the water volume in the water storage container increases; the lower the water pressure, the slower the water storage speed. The electric control group predicts the water volume change rate of the water storage container according to the current measured water pressure value (for example, the water storage speed is fast by default when the water pressure is high, and the water storage speed is slow by default when the water pressure is low). During the water outlet time, if a water storage container is placed under the faucet, the sound of the water flow impacting the container wall or the water surface will change with the increase in the water storage volume. When the water volume is small at the beginning, the water flow sound presents a "crisp, high-frequency" feature; as the water volume increases, the water surface rises, and the sound of the water flow impacting the water surface gradually changes to "muffled, low-frequency"; when the container is close to full, the water flow continuously impacts the edge of the water surface, and the sound tends to be stable (such as a continuous "whoosh" sound), at which time the sound change amplitude significantly decreases or even no longer changes.

[0053] The electric control group analyzes the time domain characteristics (such as frequency, amplitude change) of the sound information and the collection time to determine the state of the water storage container: when the sound information is detected to have no obvious change for a long time (i.e., reaching a preset "sound stability threshold"), combined with the water pressure corresponding to the water storage speed model, it is determined that the container may have approached full overflow, and the connecting component is immediately controlled to block the water flow to avoid waste caused by water overflow. For example, in a high water pressure scenario, when the water storage container quickly approaches full overflow, the sound changes tend to stabilize in advance, and the present scheme blocks the water flow in advance; in a low water pressure scenario, the water storage process is slow, and the monitoring time is dynamically extended according to the delay characteristics of the sound change to ensure that the container is fully water-stored but not overflowed.

[0054] In a more specific embodiment of the present scheme, the collection frequency of the sound detection device is dynamically adjusted according to the water pressure, for example, the greater the water pressure, the higher the collection frequency, and the smaller the water pressure, the lower the collection frequency. The greater the water pressure, the faster the water storage time, and the faster the sound change speed. In this way, the accuracy of controlling the water storage time can be improved, and the water saving effect can be further improved. The smaller the water pressure, the slower the water storage time, and the slower the sound change speed. The collection frequency can adapt to the water storage speed while increasing the difference between the sound information, avoiding false positives of "water storage completion" caused by small sound differences.

[0055] When the electric control group detects a change in water flow, it obtains the sound information collected by the sound detection device, the water flow, the collection time, and the local address associated with the cloud; the cloud processes the sound information in layers according to the sound frequency characteristics, and the cloud establishes a sediment scouring sound model to calculate the sediment accumulation degree corresponding to the local address according to the input of the sound characteristics into the sediment scouring model.

[0056] Specifically, when the electric control group detects a change in water flow, it triggers a multi-parameter synchronous collection mechanism: the sound detection device captures water flow sound signals in real time, the flow sensor records the water flow size, the clock module synchronously marks the collection time, and the geographic position module associates the local address (such as the building number, unit number, and branch pipe of a certain number). These multi-dimensional data are uploaded to the cloud server through the Internet of Things to form a four-dimensional data set containing sound characteristics, flow changes, time series, and spatial positions. The cloud server decomposes the original audio based on sound frequency characteristics, and divides it into characteristic audio tracks of different frequency bands such as water flow impact sound (such as the sound of water flow impacting a faucet or a water storage container), water flow vibration sound (vibration signals generated by water flow and pipe friction), and environmental background sound (such as pipe resonance and air flow sound).

[0057] The cloud preloads the sediment scouring model, which is trained based on historical monitoring data and contains a sound feature library under different accumulation levels. By comparing the real-time collected sound features with the model library, the sediment-related characteristic frequency bands in the current water flow vibration sound (such as specific frequency vibration attenuation or abnormal fluctuation) are identified. Combined with the correlation between flow data and sound features (such as under the same flow, the stronger the vibration sound, the more serious the sediment accumulation), the cloud estimates the sediment accumulation level of the local address corresponding to the pipeline, and generates a visual report (such as mild silting, moderate blockage, and severe blockage).

[0058] The cloud compares real-time flow data with historical flow curves through sound information correlation, locates abnormal pipe sections according to the identification results, synchronously collects water flow, sound information, and water pressure data of the abnormal pipe sections, and establishes a dynamic correlation model. According to the dynamic correlation model, the abnormal type is distinguished, and different levels of warning information are generated according to the distinguishing results.

[0059] Specifically, the cloud receives sound information, water flow, collection time, and local address data uploaded by the electronic control group, compares real-time flow data with historical flow curves. The historical flow curve is generated from long-term monitoring data, reflecting the flow variation law of the pipeline in each region under normal working conditions. When the real-time flow deviates significantly from the historical curve, such as a sudden large increase or decrease in flow, and the change amplitude exceeds the normal fluctuation range, it is determined that the pipeline section corresponding to the flow is abnormal. For the identified abnormal pipe section, the cloud immediately starts the multi-parameter synchronous collection mechanism to again obtain the water flow, sound information, and water pressure data of the region.

[0060] The cloud integrates the three collected data (water flow, sound information, and water pressure data) to establish a dynamic correlation model. In the process of constructing the model, the cloud analyzes the change relationship of the three under different working conditions: when the pipeline is damaged, the water flow usually increases sharply, the water pressure decreases, and the water flow impact on the damaged part produces high-frequency and sharp sound; when the pipeline is blocked by sediment, the water flow decreases, the water pressure fluctuates unstably, and the sound is a low-frequency and dull vibration. Through learning a large amount of historical abnormal data, the model forms a feature combination corresponding to different abnormal types. The newly collected data is input into the dynamic correlation model for matching analysis to determine the abnormal type. If it is determined that the pipeline is damaged, a high-level red warning information is generated; if it is determined that the pipeline is blocked by sediment, a yellow or orange warning is generated according to the blockage degree. Different levels of warning information will be timely pushed to the terminal of the operation and maintenance personnel, with detailed information such as abnormal position and type, so that the operation and maintenance personnel can quickly respond and handle.

[0061] The detection data of the water flow monitoring device is combined with the network perception model to form a space-time correlation model, the cloud superimposes the water flow, sound information and water pressure data into the space-time correlation model, and labels the abnormal pipe section in the space-time correlation model, divides the influence range of the abnormal pipe section according to the pressure wave and the three-dimensional water flow monitoring network, and sends a preset verification signal to the corresponding address of the electric control group according to the influence range; the electric control group receives the signal and controls the switching of the connecting component; the cloud collects the water flow, sound information and water pressure data in the on-off switching process again, and calculates the abnormal pipe section again as a verification section, compares the abnormal pipe section with the verification section, and inputs the comparison result into the dynamic correlation model to distinguish the abnormal type.

[0062] Specifically, after receiving the cloud verification signal, the electric control group first acquires the real-time water flow data of the abnormal pipe section, and dynamically adjusts the on-off frequency according to the flow size.

[0063] In an embodiment of the present scheme, when the flow is greater than a preset high threshold (such as 3 m³ / h), it is determined that the user's water demand is high, and a low-frequency switching mode (such as on-off 2 times every 5 minutes) is adopted, for example, the duration of each switching is not more than 10 seconds, which ensures that the short water flow impact is sufficient to cause pipe response, while avoiding obvious impact on the user; when the flow is less than a preset low threshold (such as 0.5 m³ / h), it is determined that the user's water demand is low, and a high-frequency switching mode (such as on-off 3 times every minute) is adopted, which enhances the pipe vibration effect through continuous impact and improves the recognition of sound characteristics; when the flow is in the middle interval, a step frequency adjustment (such as the flow decreases by 0.5 m³ / h, and the on-off frequency increases by 1 time / minute) is adopted to balance the flow and verification efficiency.

[0064] Suppose that the electric control group has a built-in water peak period database (such as 7:00-9:00 in the morning and 18:00-20:00 in the evening), and automatically reduces the on-off frequency to 50% of the base value during the peak period, and compresses the single switching time to within 5 seconds; combined with the real-time flow trend prediction of the cloud, if it is detected that the flow of the abnormal pipe section will rise (such as an increase of 20%), the verification operation is suspended, and the flow is restored to the safe interval before restarting.

[0065] During the on-off switching process, the electric control group synchronously collects the switch state data of the connecting component, and compares it with the verification signal parameters sent by the cloud to ensure that the execution accuracy error is not more than ±5%; the cloud analyzes the smoothness of the flow change curve after each switching cycle, and if there is an unexpected fluctuation (such as the flow recovery time exceeds the preset threshold, which is set by the administrator according to the actual working condition of the pipe network), the next round of switching parameters is automatically adjusted to form a closed loop optimization.

[0066] If abnormal feedback of the user end (such as water pressure fluctuation complaint) is received in the verification process, the electric control group immediately terminates the current operation and sends an interruption signal to the cloud end; the cloud end re-evaluates the influence range according to the interruption information, adjusts the verification strategy (such as reducing the frequency, shortening the time length), and increases the compensatory silent period (such as pausing for 2 minutes every 5 minutes of verification) in the subsequent verification.

[0067] The above is only an embodiment of the present application, and the well-known specific structures and characteristics in the scheme are not described in detail. It should be pointed out that for those skilled in the art, without departing from the structure of the present application, a number of modifications and improvements can be made, which should also be considered as the protection scope of the present application, which will not affect the effect and practicality of the patent. The protection scope claimed in the present application should be subject to the content of its claims, and the specific implementation mode and the like recorded in the specification can be used to explain the content of the claims.

Claims

1. A water resource intelligent management and control method based on multi-parameter Internet of Things perception, characterized in that, The method comprises the following steps: S10: installing a water flow monitoring device at a naked node accessible in a public part of a building, the naked node comprising a node at a building, a unit, a floor, and a portal main pipe, setting the position of the water flow monitoring device as a monitoring node, combining each water flow monitoring device with a rigid correspondence relationship of the building structure to determine an upstream and downstream relationship of a pipe network pipe section, and establishing an Internet of Things sensing network; S20: collecting monitoring data under a preset useless water threshold value through the Internet of Things sensing network, the monitoring data corresponding to detection parameters including real-time water flow data, building heights of each monitoring node, upstream and downstream levels between monitoring nodes, and collection times of monitoring data, the water flow data including pressure difference and flow; performing multi-dimensional comprehensive analysis on the monitoring data with each monitoring parameter as a dimension, thereby obtaining differences in different water flow data at different building heights between unit nodes and floor nodes, and differences in each water flow data between different position portal nodes and floor nodes; combining the differences in water flow data with the Internet of Things sensing network to construct a network sensing model of pipe network water flow state; S30: using the network sensing model to combine multi-dimensional parameter analysis of real-time collected water flow data to calculate pressure differences between monitoring nodes and other monitoring nodes, and comparing the pressure differences with pressure differences in the network sensing model, when a comparison result exceeds a preset pressure difference threshold value, a pipe section between corresponding monitoring nodes is regarded as an abnormal pipe section; S40: integrating a pressure boosting valve in the water flow monitoring device, when an abnormal pipe section is obtained, adjusting single-point water pressure through the pressure boosting valve, obtaining a pressure difference of the abnormal pipe section before and after water pressure change as a detection pressure difference; taking a comparison result of the detection pressure difference with the preset pressure difference threshold value as a detection result, and evaluating a leakage condition of the abnormal pipe section according to the detection result.

2. The multi-parameter Internet of Things perception-based water resource intelligent management and control method according to claim 1, characterized in that: The water flow monitoring device is also used to collect water flow, evaluate different levels of water demand according to a sum of flow data of each branch outlet under a portal node and a change rate of the sum of flow data, generate corresponding water fluctuation characteristic factors by analyzing a flow change rule of the branch outlet and relevance of the flow change rule to total flow deviation for different levels of water demand, and integrate the water fluctuation characteristic factors into a fusion process of flow deviation rate, pressure gradient, and topological parameters when evaluating the leakage condition of the abnormal pipe section to dynamically correct an influence of flow deviation on the leakage condition.

3. The water resource intelligent management and control method based on multi-parameter Internet of Things sensing according to claim 2, characterized in that: The branch water outlet is also provided with a faucet, the faucet comprises an electric control group and a mechanical group, the mechanical group comprises a switch component for opening and closing the faucet and a connection component for connecting and disconnecting the faucet and the branch water outlet, the switch component comprises an electromagnetic coupling component coupled when the faucet is closed and separated when the faucet is opened; the electric control group is electrically connected with the electromagnetic coupling component and the connection component respectively, the electric control group is used for identifying the opening and closing state of the faucet according to the current change of the electromagnetic coupling component, the electric control group is also used for controlling the current of the connection component to connect and disconnect the faucet and the branch water outlet, the electric control group is electrically connected with a water flow monitoring device of the branch water outlet, the electric control group is used for monitoring the water flow and the water pressure between the faucet and the branch water outlet through the water flow monitoring device; the electric control group controls the current of the connection component according to the current change of the electromagnetic coupling component or the water flow and the water pressure, the electric control group is used for uploading the water flow and the water pressure to the cloud, and a regulation unit is used for receiving the regulation instruction of the cloud and controlling the current of the connection component according to the regulation instruction.

4. The water resource intelligent management and control method based on multi-parameter Internet of Things sensing according to claim 3, characterized in that: When the electric control group monitors the water pressure, the connection component is controlled to block the faucet and the branch water outlet when the water pressure is lower than a preset low pressure threshold or a water cut instruction sent by the cloud is received, and the current of the electromagnetic coupling component is monitored as a trigger current after a water supply instruction sent by the cloud is received or the water pressure is not lower than the preset low pressure threshold, the connection component is controlled to open the faucet and the branch water outlet when the change period of the trigger current exceeds a preset trigger number within a preset trigger time.

5. The water resource intelligent management and control method based on multi-parameter Internet of Things sensing according to claim 4, characterized in that: After the electric control group receives the water supply instruction sent by the cloud or the water pressure is not lower than the preset low pressure threshold, the water outlet time is set according to the current water pressure, the connection component is connected to the faucet and the branch water outlet according to the water outlet time, then the faucet and the branch water outlet are blocked, the water flow change is obtained, and the connection component is connected to the faucet and the branch water outlet when the water flow changes.

6. The water resource intelligent management and control method based on multi-parameter Internet of Things sensing according to claim 5, characterized in that: The mechanical group further comprises a sound detection device, the sound detection device is electrically connected with the electric control group, when the electric control group controls the connection component to open the faucet and the branch water outlet, if the water flow change is identified, the sound information collected by the sound detection device is obtained, the connection component is controlled to block the faucet and the branch water outlet according to the change of the sound information and the time of collecting the sound information; If the water flow change is not identified, the faucet and the branch water outlet are blocked according to the water outlet time.

7. The water resource intelligent management and control method based on multi-parameter Internet of Things sensing according to claim 6, characterized in that: When the electric control group detects the water flow change, the sound information collected by the sound detection device, the water flow, the collection time and the local address are associated and uploaded to the cloud; The cloud performs hierarchical processing on the sound information according to the sound frequency characteristics, establishes a sediment scouring sound model, and calculates the sediment accumulation degree corresponding to the local address according to inputting the sound characteristics into the sediment scouring model.

8. The water resource intelligent management and control method based on multi-parameter Internet of Things sensing according to claim 7, characterized in that: The cloud compares real-time flow data with historical flow curves through each data associated with the sound information, identifies abnormal pipe sections, synchronously collects the water flow, sound information and water pressure data of the abnormal pipe sections, establishes a dynamic correlation model, distinguishes the types of abnormalities according to the dynamic correlation model, and generates early warning information of different levels according to the distinguishing results.

9. The water resource intelligent management and control method based on multi-parameter Internet of Things sensing according to claim 8, characterized in that: The detection data of the water flow monitoring device is combined with the network perception model to form a space-time correlation model, the cloud superimposes the water flow, sound information and water pressure data into the space-time correlation model, and labels the abnormal pipe section in the space-time correlation model, divides the influence range of the abnormal pipe section according to the pressure wave and in the three-dimensional water flow monitoring network, and sends a preset verification signal to the corresponding address of the electric control group according to the influence range; the electric control group controls the switching of the connection component after receiving the signal; The cloud collects the water flow, sound information and water pressure data again during the on-off switching process, and calculates the abnormal pipe section again as a verification section, compares the abnormal pipe section with the verification section, and inputs the comparison result into the dynamic correlation model to distinguish the abnormal type.

10. The water resource intelligent management and control system based on multi-parameter Internet of Things sensing, characterized in that, The water resource intelligent management and control method based on multi-parameter Internet of Things perception is used.

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