Abnormal sound checking treatment method for water supply thin-wall stainless steel pipe

Abstract

The invention provides a water supply thin-wall stainless steel pipe abnormal sound checking and processing method, and belongs to the technical field of building water supply and drainage, and the method comprises the following steps: installing a first pressure gauge on the inlet side of a water supply thin-wall stainless steel main pipe, and installing a second pressure gauge on the inlet side of a layered water supply thin-wall stainless steel branch pipe; acquiring monitoring values P1 and P2, and calculating a pressure reduction ratio pi; if pi is greater than 2, judging that the water supply thin-wall stainless steel main pipe is an abnormal sound pipeline; a main pressure reducing valve is installed on the abnormal sound pipeline, and branch pressure reducing valves are installed on multiple user pipelines on the same layer; the pressure reduction ratio pi is adjusted through the main pressure reducing valve and the branch pressure reducing valve; in the peak period of water consumption, the pressure reduction ratio pi is adjusted to be 2; and adjusting the pressure reduction ratio pi to be less than 2 in a water conventional period. According to the abnormal sound checking and processing method for the water supply thin-wall stainless steel pipe, by means of quantitative management of layering and household pressure data, the system pressure is dynamically adjusted according to different water use time periods, and the problem of repeated occurrence of the abnormal sound problem is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of water supply and drainage for buildings, and more specifically, relates to a method for troubleshooting abnormal noises in thin-walled stainless steel water supply pipes. Background Art

[0002] In recent years, thin-walled stainless steel pipes have become the preferred material for water supply systems in high-rise buildings and high-end residential buildings due to their excellent corrosion resistance, sanitary properties, and long service life. Compared to traditional plastic or galvanized steel pipes, stainless steel pipes effectively prevent water pollution and reduce maintenance costs, meeting the safety and environmental requirements of modern buildings.

[0003] However, in actual engineering applications, it was found that thin-walled stainless steel water supply pipes often make abnormal noises during operation. This abnormal noise not only interferes with users' daily lives, but may also cause concerns about the stability of the pipeline system. After research and analysis, one of the main reasons for abnormal noise in pipelines is the uneven pressure distribution in the water supply system, especially when the pressure reduction ratio between the main pipe and the branch pipe is too large, the water flow impacts the pipe wall, pipe fittings and valves to produce vibration noise. At present, the industry's investigation and treatment of abnormal noises in thin-walled stainless steel water supply pipes mostly adopts empirical local inspections or replacement of pipe fittings, and lacks systematic detection and adjustment methods. These traditional methods cannot accurately locate the root cause of the pressure abnormality, and it is difficult to dynamically adjust the system pressure according to different water use periods, resulting in repeated abnormal noise problems and poor treatment effects. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for troubleshooting abnormal noises in thin-walled stainless steel water supply pipes, aiming to solve the problem that traditional means cannot accurately locate the source of abnormal pressure, and it is difficult to dynamically adjust the system pressure according to different water usage periods, resulting in repeated abnormal noise problems and poor treatment effects.

[0005] To achieve the above object, the technical solution adopted by the present invention is to provide a method for troubleshooting abnormal noise in thin-walled stainless steel water supply pipes, comprising the following steps: S1: Install a first pressure gauge on the inlet side of the thin-walled stainless steel main pipe for water supply, and install a second pressure gauge on the inlet side of the thin-walled stainless steel branch pipe for stratified water supply; S2: Obtain the monitoring value P1 of the first pressure gauge and the monitoring value P2 of the second pressure gauge, and calculate the pressure reduction ratio π of the water supply thin-walled stainless steel main pipe, π=P1 / P2; If π>2, it is determined that the water supply thin-walled stainless steel main pipe is an abnormal noise pipe; If π≤2, it is determined that the water supply thin-walled stainless steel main pipe is a non-abnormal noise pipe; S3: Install a main pressure reducing valve on the thin-walled stainless steel water supply main pipe corresponding to the abnormal noise pipe, and install sub-pressure reducing valves on multiple user pipes on the same floor corresponding to the thin-walled stainless steel water supply main pipe; S4: Adjust the pressure reduction ratio π through the main pressure reducing valve and the sub-pressure reducing valve; During peak water usage periods, adjust the pressure reduction ratio to π = 2; During normal water use period, adjust the pressure reduction ratio π to 2.

[0006] In one possible implementation, in step S1, an ultrasonic leak detector is used to scan the thin-walled stainless steel water supply main pipe and the thin-walled stainless steel layered water supply branch pipe to mark the locations where potential leakage risks may exist. Based on historical water use data, a correlation model between the location of leakage risks and water flow and pressure changes is established, and the weight of the impact of leakage risks on pressure in different water use periods is analyzed. High-risk pipe sections, medium-risk pipe sections and low-risk pipe sections are marked in order of impact weight from large to small, parallel spare branches are installed in high-risk pipe sections, and first pressure gauges and second pressure gauges are installed in low-risk pipe sections.

[0007] In one possible implementation, in step S1, an auxiliary pressure gauge is installed 10-15 m away from the inlet side of the thin-walled stainless steel water supply pipe. Based on the pressure difference and distance between the first pressure gauge and the auxiliary pressure gauge, the along-the-line resistance coefficient of the water flow in the pipeline is calculated, and a pressure stability evaluation model is established in combination with historical data to adjust the installation position of the first pressure gauge.

[0008] In one possible implementation, when adjusting the installation position of the first pressure gauge, the pressure data spectrum output by the pressure stability evaluation model is used to determine the location of the noise source based on the noise frequency and amplitude: If there is a valve or elbow at the noise source, move the installation location of the first pressure gauge to a branch pipe section that is ≥10 times the pipe diameter upstream or ≥5 times the pipe diameter downstream; If there is a pulse device at the noise source, move the installation location of the first pressure gauge to a position ≥ 2 meters away from the pulse device.

[0009] In a possible implementation, in step S2, when calculating the decompression ratio π, the values ​​of P1 and P2 are corrected in combination with the pressure stability evaluation model. The corrected decompression ratio calculation formula is: Among them, k1 is the pressure correction coefficient corresponding to the installation position of the first pressure gauge, and k2 is the pressure correction coefficient corresponding to the installation position of the second pressure gauge. By performing machine learning training on historical leakage data and pressure change data, the k1 and k2 values ​​under different leakage scenarios are determined to obtain the corrected pressure reduction ratio.

[0010] In one possible implementation, in step S2, an abnormal noise prediction model based on machine learning is established, and historical pressure reduction ratio data, pipeline operation time, and ambient temperature parameters are input into the model to perform abnormal prediction on the currently calculated pressure reduction ratio π to assist in determining whether the thin-walled stainless steel water supply main pipe is an abnormal noise pipe.

[0011] In a possible implementation, in step S3, when installing the main pressure reducing valve and the sub-pressure reducing valve, a shock-absorbing rubber pad is used to wrap the connection.

[0012] 8. The method for troubleshooting and treating abnormal noise in thin-walled stainless steel water supply pipes as described in claim 1 is characterized in that the sub-pressure reducing valve is an electric pressure reducing valve with an automatic adjustment function, which is connected to a smart water meter on the user's pipeline and automatically adjusts the valve opening of the electric pressure reducing valve according to the user's real-time water consumption to control the water flow pressure.

[0013] In one possible implementation, in step S4, the main pressure reducing valve and the sub-pressure reducing valve are adjusted by the pressure fluctuation frequency, and a pressure fluctuation frequency threshold is pre-set. When the pressure fluctuation frequency of the water supply system exceeds the threshold, the main pressure reducing valve and the sub-pressure reducing valve are controlled to be adjusted according to a preset step-by-step pressure reducing curve, so that the pressure reduction ratio π during peak water use periods gradually approaches 2, and the pressure reduction ratio π during normal water use periods gradually becomes less than 2.

[0014] In one possible implementation, IoT devices are used to correlate the main and sub-pressure reducing valves with the elevator operating status and lighting power load data within the building, establishing a pressure reduction ratio adjustment model based on multi-source data. When the frequency of elevator operation increases and the lighting power load rises, it is determined to be the peak water consumption period. The dynamic adjustment parameters are calculated through the model to control the main pressure reducing valve and the sub-pressure reducing valve to adjust the pressure reducing ratio π to 2; When the elevator operation frequency decreases and the lighting power load decreases, it is determined to be a normal water usage period and the pressure reduction ratio π is adjusted to be less than 2.

[0015] The beneficial effect of the method for troubleshooting abnormal noises in thin-walled stainless steel water supply pipes provided by the present invention is that, compared with the prior art, a main pipe + layered pressure data acquisition network is constructed by adding a first pressure gauge to the inlet side of the thin-walled stainless steel water supply main pipe and simultaneously installing a second pressure gauge on the inlet side of the water supply branch pipe on each floor. On this basis, the pressure reduction ratio π=P1 / P2 is used as a quantitative diagnostic scale. When π>2, the main pipe section of the specific abnormal noise floor can be locked, avoiding the blind troubleshooting of pipe fittings and valves in the entire building in the traditional way, and solving the drawbacks of the traditional method of lacking data support and ambiguous diagnosis. After determining the abnormal noise main pipe, a main pressure reducing valve is installed on the main pipe to realize the overall pressure control of the floor. At the same time, a sub-pressure reducing valve is installed on each user pipeline on the same floor, forming a layered + household double-layer pressure control system. During peak water usage periods, the main pressure reducing valve and the sub-pressure reducing valves work together to adjust the pressure reduction ratio π to 2, which can not only meet the flow demand of multiple households on the same floor using water at the same time, but also avoid the noise caused by the impact of water flow; during normal water usage periods, π is controlled to <2, effectively reducing the internal pressure of the pipe under low-flow conditions in each household, and reducing cavitation and vibration noise. The present invention provides a method for troubleshooting abnormal noises in thin-walled stainless steel water supply pipes, which uses a pressure gauge and pressure reducing valve that can operate stably in main, layered, and household water supply systems. Through the quantitative management of layered and household pressure data, not only can the problem of abnormal noises be accurately solved, but the source of the pressure anomaly can also be accurately located, and the system pressure can be dynamically adjusted according to different water usage periods to solve the problem of recurring abnormal noises. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 This is a flow chart of a method for troubleshooting abnormal noise in thin-walled stainless steel water supply pipes provided by the present invention; Figure 2 A schematic diagram of the structure of a method for troubleshooting abnormal noise in thin-walled stainless steel water supply pipes provided by the present invention; Figure 3 Flowchart for establishing a decompression ratio adjustment model based on multi-source data.

[0018] In the figure: 1. Thin-walled stainless steel water supply main pipe; 2. Thin-walled stainless steel branch pipe for stratified water supply; 3. User pipeline; 4. First pressure gauge; 5. Second pressure gauge; 6. Main pressure reducing valve; 7. Sub-pressure reducing valve. DETAILED DESCRIPTION

[0019] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] Unless otherwise explicitly defined, the use of terms such as "first," "second," or "third," etc., are intended to distinguish different objects rather than to describe a specific order.

[0021] Unless otherwise expressly defined, directional words such as the terms "center", "lateral", "longitudinal", "horizontal", "vertical", "top", "bottom", "inside", "outside", "up", "down", "front", "back", "left", "right", "clockwise", "counterclockwise", "high", "low" and the like indicating directions or positional relationships are based on the directions and positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction or be constructed and operated in a specific direction, so they cannot be understood as limiting the specific scope of protection of the present invention.

[0022] See also Figure 1 and Figure 2 Now, a method for troubleshooting abnormal noise in thin-walled stainless steel water supply pipes provided by the present invention is described. The method for troubleshooting abnormal noises in thin-walled stainless steel water supply pipes comprises the following steps: S1: installing a first pressure gauge 4 on the inlet side of the thin-walled stainless steel water supply pipe 1, and installing a second pressure gauge 5 on the inlet side of the thin-walled stainless steel stratified water supply branch pipe 2; S2: obtaining the monitoring value P1 of the first pressure gauge 4 and the monitoring value P2 of the second pressure gauge 5, and calculating the pressure reduction ratio π of the thin-walled stainless steel water supply pipe 1, π=P1 / P2; if π>2, the thin-walled stainless steel water supply pipe 1 is judged to be an abnormal noise pipe; if π≤2, the thin-walled stainless steel water supply pipe 1 is judged to be a non-abnormal noise pipe; S3: installing a main pressure reducing valve 6 on the thin-walled stainless steel water supply pipe 1 corresponding to the abnormal noise pipe, and respectively installing sub-pressure reducing valves 7 on multiple user pipes 3 on the same layer corresponding to the thin-walled stainless steel water supply pipe 1; S4: adjusting the pressure reduction ratio π by the main pressure reducing valve 6 and the sub-pressure reducing valve 7; during peak water use periods, adjusting the pressure reduction ratio π=2; during normal water use periods, adjusting the pressure reduction ratio π<2.

[0023] The present invention provides a method for troubleshooting and treating abnormal noises in thin-walled stainless steel water supply pipes. Compared with the prior art, the method constructs a main pipe + layered pressure data acquisition network by adding a first pressure gauge 4 to the inlet side of the thin-walled stainless steel water supply main pipe 1 and simultaneously installing a second pressure gauge 5 on the inlet side of the water supply branch pipe on each floor. On this basis, the pressure reduction ratio π=P1 / P2 is used as a quantitative diagnostic scale. When π>2, the main pipe section of the specific abnormal noise floor can be locked, avoiding the blind inspection of pipes and valves of the entire building in the traditional way, and solving the drawbacks of the traditional method of lacking data support and ambiguous diagnosis. After determining the abnormal noise main pipe, a main pressure reducing valve 6 is installed on the main pipe to realize the overall control of the floor pressure. At the same time, a sub-pressure reducing valve 7 is installed on each user pipe 3 on the same floor, forming a layered + household double-layer pressure control system. During peak water usage periods, the main pressure reducing valve 6 and the sub-pressure reducing valve 7 work together to adjust the pressure reduction ratio π to 2, which can not only meet the flow demand of multiple households on the same floor using water at the same time, but also avoid the noise caused by the impact of water flow; during normal water usage periods, π is controlled to <2, effectively reducing the internal pressure of the pipe under low-flow conditions of each household, and reducing cavitation and vibration noise. The present invention provides a method for troubleshooting abnormal noises in thin-walled stainless steel water supply pipes, which uses a pressure gauge and a pressure reducing valve that can operate stably in main, layered, and household water supply systems. Through the quantitative management of layered and household pressure data, not only can the problem of abnormal noises be accurately solved, but the source of the pressure anomaly can also be accurately located, and the system pressure can be dynamically adjusted according to different water usage periods to solve the problem of recurring abnormal noises.

[0024] During the inspection and maintenance of thin-walled stainless steel water supply pipelines, inspectors first don protective gear and, equipped with a highly sensitive ultrasonic leak detector, slowly move along the main pipe and branch pipes at a speed of 0.5-1 meter per minute. The leak detector emits 40-100kHz ultrasonic waves. If a tiny crack, pinhole, or seal failure is detected in the pipeline, the turbulence caused by air leakage will change the frequency, amplitude, and phase of the reflected wave, generating an electronic file containing coordinates, detection time, and signal strength.

[0025] After completing the initial inspection, the water supply management system database was retrieved for the past 1-3 years, including hourly water flow and minute-by-minute pressure fluctuation data, as well as water usage characteristics during special periods such as holidays and extreme weather. Using a big data analysis platform, the hidden danger location data was spatially and temporally aligned with the water usage data. Using machine learning regression analysis algorithms, a multidimensional correlation model was established. Through iterative training on large amounts of data, the model was able to accurately predict pressure changes under different leak scenarios.

[0026] Based on the established correlation model, the analytic hierarchy process and entropy weight method were used to comprehensively consider factors such as water flow rate, pressure changes, pipeline age, and the surrounding environment to calculate the impact weight of each hidden danger location during different water use periods. Pipe sections with a weight greater than 0.7 were classified as high-risk, those with a weight between 0.3 and 0.7 as medium-risk, and those with a weight less than 0.3 as low-risk. These sections were visually marked in red, yellow, and blue in the 3D pipeline model.

[0027] When installing a parallel backup branch pipe in a high-risk section, hydraulic cutting equipment was used to precisely open the pipe without damaging the existing main structure. Subsequently, a double-jointed copper ball valve was installed, which flexibly controls the opening and closing of the backup branch pipe, facilitating subsequent maintenance and emergency use. Finally, argon arc welding was used to securely connect the backup branch pipe to the main pipeline. This welding method not only ensures a good seal at the joint to prevent water leakage, but also ensures sufficient strength at the joint.

[0028] In low-risk pipe sections, construction workers use mounting brackets to secure the first and second pressure gauges 4 and 5. Both gauges are equipped with a built-in LoRa wireless transmission module, enabling real-time monitoring of pipeline pressure. The collected pressure data is wirelessly transmitted and instantly uploaded to the cloud management platform. This allows managers to remotely access pressure data for low-risk pipe sections at any time, providing reliable data support for subsequent pressure analysis, abnormal noise detection, and overall water supply system control.

[0029] After completing the aforementioned inspection, analysis, and pipe section processing, inspectors will install an auxiliary pressure gauge 10-15 meters from the inlet side of the thin-walled stainless steel water supply main pipe 1, in a relatively straight pipe with no obvious elbows or valve interference. During installation, a pipe hole-cutting tool is used to drill a hole in the main pipe. The auxiliary pressure gauge is then connected to the main pipe via a sealing joint, ensuring a tight connection to prevent water or pressure leaks.

[0030] After the auxiliary pressure gauge is installed, the system will collect the pressure data of the first pressure gauge 4 and the auxiliary pressure gauge in real time, and use the Darcy-Weisberg formula based on the pressure difference between the two and the distance between the two gauges. , where h ƒ is the head loss along the pipeline, corresponding to the pressure difference; ƒ is the longitudinal resistance coefficient; L is the length of the pipe between the two meters; D is the pipe inner diameter; v is the water velocity; and g is the acceleration due to gravity. Calculate the longitudinal resistance coefficient of water flow in the pipeline. By repeatedly measuring the pressure difference at different time periods and water flow rates, we can obtain multiple sets of longitudinal resistance coefficient data.

[0031] Subsequently, a pressure stability assessment model was developed using data analysis software and machine learning algorithms, combining historical pressure data from the water supply system, including pressure variations across seasons and during peak and trough periods. This model uses factors such as the resistance coefficient along the pipeline, historical pressure fluctuations, and changes in water flow as input variables. By learning and training from a large amount of historical data, it can simulate pipeline pressure trends under different operating conditions and assess pressure stability.

[0032] Based on the output results of the pressure stability assessment model, the installation position of the first pressure gauge 4 is adjusted. If the model shows that the pressure fluctuations in a certain area are abnormally frequent or are subject to many interference factors, when there is a valve or elbow at the noise source, the installation position of the first pressure gauge 4 is moved to a branch pipe section ≥10 times the pipe diameter upstream or ≥5 times the pipe diameter downstream to avoid the influence of local resistance on pressure measurement; if there is a pulse device (pump) at the noise source, the installation position of the first pressure gauge 4 is moved to a position ≥2 meters away from the pulse device to reduce the interference of the pulse device on the pressure data, thereby obtaining more accurate and stable pressure monitoring data. Through this series of operations, the accuracy and reliability of pressure monitoring can be effectively improved, providing more accurate data support for the subsequent abnormal noise investigation and pressure regulation of thin-walled stainless steel water supply pipes, and further ensuring the stable operation of the water supply system.

[0033] In an actual water supply system, the pressure in the pipeline will be affected by many factors, such as local pipeline resistance, leakage, water flow changes, etc. In order to more accurately determine whether the thin-walled stainless steel water supply main pipe 1 is an abnormal noise pipeline, it is necessary to combine the pressure stability evaluation model to correct the value P1 of the first pressure gauge 4 and the value P2 of the second pressure gauge 5.

[0034] The system first retrieves data on pressure-influencing factors related to the installation locations of the first and second pressure gauges 4 and 5 from the pressure stability assessment model, including the drag coefficient along the path and historical pressure fluctuation characteristics at those locations. It also retrieves historical leakage and pressure change data, which covers pressure variations at each pressure gauge location under various leakage scenarios (such as leaks from minor cracks and loose connections).

[0035] Based on this data, a machine learning algorithm is trained. Using supervised learning regression analysis, the algorithm uses known leak scenarios and corresponding pressure change data as training samples. Factors such as the pressure gauge installation location, along-the-line resistance coefficient, and historical pressure fluctuations are used as input features, and the pressure correction factor corresponding to the actual correction required is used as the output label. Through iterative training on a large amount of data, the machine learning model learns the mapping between different leak scenarios, different installation locations, and pressure correction factors.

[0036] After training is complete, when the pressure reduction ratio π needs to be calculated, the model will automatically output the pressure correction coefficient k1 corresponding to the installation position of the first pressure gauge 4 and the pressure correction coefficient k2 corresponding to the installation position of the second pressure gauge 5 based on the current leakage scenario judgment results (obtained through ultrasonic leak detection and correlation model analysis). Substituting k1 and k2 into the revised pressure reduction ratio calculation formula, P1 and P2 are corrected to obtain a revised pressure reduction ratio that more accurately reflects the actual pressure changes in the pipeline. .

[0037] This correction eliminates pressure measurement deviations caused by pipeline interference, making the reduction ratio calculation more accurate and reliably determining whether there is a risk of abnormal noise in the pipeline. The corrected reduction ratio provides a more accurate basis for subsequent treatment measures such as valve adjustment, effectively improving the scientific nature and effectiveness of troubleshooting abnormal noise in thin-walled stainless steel water supply pipes.

[0038] In order to establish an abnormal noise prediction model based on machine learning and use it to assist in determining whether the thin-walled stainless steel water supply main pipe 1 is an abnormal noise pipe, data preparation, model building and prediction application must be carried out in an orderly manner.

[0039] The first is the data preparation stage. The system will retrieve the calculation results of the pressure reduction ratio under different time periods and working conditions, especially focusing on collecting the numerical values ​​corresponding to confirmed cases of abnormal noise, providing the model with basic characteristic data of pressure changes, so as to facilitate the study of the correlation pattern between abnormal noise and pressure reduction ratio; at the same time, the total length of time the pipeline has been put into use to date, the cumulative full-load operation time, etc., are counted to reflect the degree of pipeline aging, which is a key factor affecting the occurrence of abnormal noise; in addition, the pipeline environmental temperature data in different seasons and weather conditions throughout the year will be sorted out, fully considering the impact of temperature on the physical properties of pipeline materials, and improving the dimensions of factors affecting abnormal noise.

[0040] The random forest algorithm in ensemble learning is selected to improve the accuracy of model prediction by virtue of its advantages in resisting overfitting and processing high-dimensional data. The historical pressure reduction ratio, pipeline operation time, and ambient temperature are clearly defined as input variables, and whether abnormal noise occurs is set as a binary output label to clearly define the variable relationship between model learning and prediction. Subsequently, optimization methods such as cross-validation and grid search are used to adjust the model parameters to effectively avoid overfitting or underfitting of the model and further improve the prediction accuracy.

[0041] Once the model is built, it can be put into predictive applications. The model inputs the currently calculated corrected pressure reduction ratio, along with data such as the pipeline's real-time operating time and ambient temperature, to generate predictions based on the current pipeline status. If the model outputs a predicted probability of abnormal noise greater than a set threshold (e.g., 0.7), the pressure reduction ratio is considered abnormal, confirming that the water supply thin-walled stainless steel main pipe 1 is an abnormal noise pipe. Otherwise, it is considered normal. Compared to traditional single-threshold judgment methods, this model significantly improves the reliability of abnormal noise detection by integrating multiple factors.

[0042] When installing the main pressure-reducing valve 6 and the sub-pressure-reducing valve 7, wrapping the joints with shock-absorbing rubber pads significantly reduces vibration and noise in the piping system. This effectively improves the user's water environment and avoids the annoyance caused by pipe noise. It also reduces vibration-induced damage to pipes and valves, extending the service life of the piping system and reducing subsequent maintenance costs.

[0043] The sub-pressure reducing valve 7 is an electric pressure reducing valve with automatic adjustment. After installation, the construction personnel connect the electric pressure reducing valve to the designated location on the user's pipeline 3 via a dedicated data cable or wireless communication module (such as NB-IoT or LoRa), establishing a data exchange bridge between the electric pressure reducing valve and the smart water meter. As a front-end sensing device, the smart water meter collects user water flow data in real time at a high frequency of minutes or even seconds, accurately capturing subtle changes in user water use behavior. Whether it's a short burst of high-flow water during morning showers or sporadic small-flow water use throughout the day, these data are accurately recorded and converted into digital signals.

[0044] This real-time water usage data is immediately transmitted to the electric pressure reducing valve's built-in microcontroller. Equipped with intelligent algorithms designed based on fluid mechanics and control theory, the microcontroller rapidly analyzes the received data. When it detects an increase in user water usage, such as when multiple people in the household are using the bathroom simultaneously, the algorithm automatically generates instructions to drive the electric pressure reducing valve's motor, increasing the valve opening to allow more water to flow through, ensuring stable and sufficient water pressure at the user's end. Conversely, when a user's water usage decreases, such as simply opening the faucet for simple cleaning, the microcontroller controls the motor to decrease the valve opening, reducing water pressure and avoiding water waste and pipe damage caused by excessive pressure.

[0045] After completing the coordinated control of the sub-pressure reducing valve 7 and the smart water meter, to further optimize the pressure stability of the water supply system, it is necessary to dynamically adjust the main pressure reducing valve 6 and the sub-pressure reducing valve 7 based on the pressure fluctuation frequency. This process uses a pre-set pressure fluctuation frequency threshold as a trigger condition and combines it with a stepped pressure reduction curve to achieve precise control of the pressure reduction ratio during different water use periods.

[0046] First, based on the historical operating data of the water supply system and the characteristics of the pipeline, technicians set a reasonable pressure fluctuation frequency threshold, for example, setting the threshold to 8 times per minute. The setting of this threshold comprehensively considers the pressure fluctuation range during normal water use and the critical value of abnormal fluctuations, which can not only avoid frequent false triggering of adjustments, but also respond to abnormal pressure changes in a timely manner. At the same time, a preset stepped pressure reduction curve was developed. This curve is based on the principles of fluid mechanics and a large amount of experimental data. It clarifies the adjustment range and sequence of the main and sub-pressure reducing valves 7 under different pressure fluctuation frequencies, ensuring a smooth and orderly pressure reduction process.

[0047] When the water supply system is operating, pressure sensors installed on the pipelines monitor pressure changes in real time with a millisecond sampling frequency and transmit the data to the central control system. The system processes the pressure data using algorithms such as the Fast Fourier Transform (FFT) to calculate the current pressure fluctuation frequency. If the system detects a pressure fluctuation frequency exceeding 8 times per minute during peak water usage hours (e.g., 7:00 AM to 9:00 PM), it immediately initiates a regulation process. Based on a stepped pressure reduction curve, the main pressure reducing valve 6 is first slowly opened to initially reduce the main line pressure. Subsequently, the sub-pressure reducing valves 7 are proportionally adjusted based on the real-time pressure conditions in each user pipeline 3, gradually bringing the overall system pressure reduction ratio π closer to 2. During this process, the system reviews the pressure fluctuation frequency and pressure reduction ratio every 10 seconds and dynamically adjusts the valve opening to ensure pressure stability within a reasonable range.

[0048] During normal water usage periods, when the frequency of pressure fluctuations exceeds a threshold, the central control system, likewise based on the stepped pressure reduction curve, prioritizes adjusting the sub-pressure reducing valves 7 to appropriately reduce the pressure in each user's pipeline 3. It then fine-tunes the main pressure reducing valve 6 to gradually reduce the pressure reduction ratio π to less than 2. For example, in one office park, concentrated water use on individual floors during lunch break caused the frequency of pressure fluctuations to rise to 10 times per minute. The system responded swiftly, adjusting the pressure reduction ratio from 2.3 to 1.8 within three minutes, subsequently reducing the frequency of pressure fluctuations to normal levels and effectively preventing abnormal pipe noise and equipment damage caused by excessive pressure.

[0049] See also Figure 3 In order to realize the intelligent dynamic adjustment of the water supply system's pressure reduction ratio and make full use of the potential correlation between multi-source data in the building, the main and sub-pressure reducing valves 7 can be deeply integrated with the elevator operation status and lighting power load data through the Internet of Things devices to build an accurate pressure reduction ratio adjustment model.

[0050] First, an IoT sensor network is deployed within the building. An operating status acquisition module is integrated into the elevator control system to capture real-time data such as elevator start and stop times and operating hours. Smart meters are installed along the lighting distribution lines to accurately monitor the real-time power consumption and trends of lighting loads in each area. Furthermore, communication modules are installed on the main pressure-reducing valve (6) and the sub-pressure-reducing valve (7), enabling data transmission and reception. These modules are then connected to the central management platform via a wireless network, forming a closed data exchange loop.

[0051] Based on the acquired multi-source data, a machine learning algorithm was used to establish a pressure reduction ratio adjustment model. Based on historical water consumption data, combined with data such as elevator operation frequency and lighting power load, correlation analysis was used to uncover the inherent connections between the data. For example, analysis revealed that elevator operation frequency in a certain office building peaked around 9:00 a.m. on weekdays, accompanied by a significant increase in lighting power load. This period also coincided with peak water consumption. Using this data, the model was trained to identify characteristic combinations of multi-source data in different scenarios, enabling accurate determination of water consumption periods.

[0052] When the model detects a continuous increase in elevator operation frequency and a rapid rise in lighting power load—for example, on a weekend afternoon in a shopping mall, when a large influx of customers causes frequent elevator starts and stops, lights on every floor are fully on, and power load reaches its peak—the model immediately identifies this as a peak water usage period. Then, based on pre-defined rules and algorithms, it calculates the dynamic adjustment parameters of the main pressure-reducing valve 6 and the sub-pressure-reducing valve 7, including the change in valve opening and the adjustment interval. It then sends instructions to the valves, gradually adjusting the pressure reduction ratio π to 2. This ensures stable pipeline pressure and meets user water needs even when water demand surges.

[0053] However, when elevator operation frequency decreases significantly and lighting power load decreases, such as when only a small number of people are on duty in an office building late at night, the model identifies this as a normal water usage period. At this point, the model recalculates the adjustment parameters and controls the operation of the main and sub-pressure reducing valves 7, adjusting the pressure reduction ratio π to less than 2. This reduces pipeline pressure while ensuring basic water use, thereby minimizing energy consumption and equipment wear.

[0054] By establishing a pressure reduction ratio adjustment model based on multi-source data, the limitations of traditional reliance on experience or single data to judge water use periods have been broken, enabling the water supply system to actively sense changes in human activities in the building and achieve intelligent and precise adjustment of the pressure reduction ratio. This not only effectively reduces the probability of abnormal noise in pipelines, but also improves the efficiency of water resource utilization.

[0055] Example 1: High-rise residential area scenario A high-rise residential community has 30 floors and an occupancy rate of 90%. Recently, residents have frequently reported unusual noises coming from the water supply pipes.

[0056] Step S1: Install a first pressure gauge 4 on the inlet side of the thin-walled stainless steel main pipe 1 for the community water supply, and a second pressure gauge 5 on the inlet side of each thin-walled stainless steel branch pipe 2 for the stratified water supply. Use an ultrasonic leak detector to scan all pipes and mark 12 locations where potential leaks may exist. Retrieve the community's historical water consumption data for the past six months, and establish a correlation model between the location of potential leaks and changes in water flow and pressure. After analysis and determining the impact weights, three high-risk, five medium-risk, and four low-risk pipe sections are identified. Install parallel backup branches in the high-risk sections, and install a first pressure gauge 4 and a second pressure gauge 5 in the low-risk sections. Furthermore, install an auxiliary pressure gauge 12 meters from the inlet side of the thin-walled stainless steel main pipe 1. Calculate the longitudinal resistance coefficient of the water flow in the pipe based on the pressure difference (0.15 MPa) between the first pressure gauge 4 and the auxiliary pressure gauge and the distance. A pressure stability assessment model was established based on historical pressure data. Through spectral analysis of the pressure data, it was found that there was a valve at the noise source. Therefore, the installation location of the first pressure gauge 4 was moved to an upstream branch pipe section 12 times the pipe diameter.

[0057] Step S2: Obtain the monitored value P1 (0.4 MPa) from the first pressure gauge 4 and the monitored value P2 (0.15 MPa) from the second pressure gauge 5. Combined with the pressure stability assessment model, the values ​​of P1 and P2 are corrected. Machine learning training based on the community's historical leakage and pressure change data determines that k1 = 1.2 and k2 = 0.9. The corrected pressure reduction ratio π is calculated to be (0.4 × 1.2) / (0.15 × 0.9) ≈ 3.56 > 2, thus determining that the thin-walled stainless steel water supply main pipe 1 is an abnormal noise pipe. Simultaneously, the community's historical pressure reduction ratio data, the pipeline's operating time (five years), and the ambient temperature (average 28°C in summer) are input into the machine learning-based abnormal noise prediction model to further verify the judgment.

[0058] Step S3: Install a main pressure-reducing valve 6 on the thin-walled stainless steel water supply main 1 corresponding to the noise-producing pipe. Install sub-pressure-reducing valves 7 on each of the ten user pipes 3 on the same floor, wrapping all joints with shock-absorbing rubber pads. The sub-pressure-reducing valves 7 are electric pressure-reducing valves with automatic adjustment functions. They connect to the smart water meters on the user pipes 3, automatically adjusting the valve opening based on the user's real-time water consumption.

[0059] Step S4: A pressure fluctuation frequency threshold of 10 times / minute is preset. When the water supply system pressure fluctuation frequency exceeds the threshold, the main pressure-reducing valve 6 and the sub-pressure-reducing valve 7 are controlled to adjust according to a preset stepped pressure-reducing curve. During peak water usage hours (7:00-9:00 AM and 5:00-7:00 PM), the IoT device links elevator operating status and lighting power load data. Once peak usage is determined, the pressure reduction ratio π is adjusted to gradually approach 2. During normal water usage hours, the pressure reduction ratio π is adjusted to less than 2. After this treatment, residents reported a significant reduction in abnormal noise and stable pipeline pressure.

[0060] Example 2: Commercial office building scenario A 25-story commercial office building houses many companies with high water demand during daytime on weekdays. Recently, the problem of abnormal noise in pipes has become prominent.

[0061] Step S1: Install a first pressure gauge 4 at the inlet of the thin-walled stainless steel main pipe 1 for the office building's water supply, and a second pressure gauge 5 at the inlet of the thin-walled stainless steel branch pipe 2 for the stratified water supply. An ultrasonic leak detector was used to scan the pipeline, marking eight potential leak locations. Based on the office building's historical water usage data for the past year, a correlation model was established between leak potential locations and water flow and pressure fluctuations. Two high-risk, three medium-risk, and three low-risk sections were identified, and corresponding equipment was installed as required. An auxiliary pressure gauge was installed 10 meters from the inlet of the thin-walled stainless steel main pipe 1. The longitudinal resistance coefficient of the water flow in the pipeline was calculated based on the pressure difference (0.12 MPa) and distance, and a pressure stability assessment model was established. Spectral analysis of the pressure data revealed the presence of a pulse device at the noise source. The first pressure gauge 4 was relocated 2.5 meters away from the pulse device.

[0062] Step S2: Obtain the monitoring value P1 (0.35 MPa) from the first pressure gauge 4 and the monitoring value P2 (0.12 MPa) from the second pressure gauge 5. Using the pressure stability assessment model, P1 and P2 are modified. Machine learning training determines k1 = 1.1 and k2 = 1.0. The modified pressure reduction ratio π is calculated as (0.35 × 1.1) / (0.12 × 1.0) ≈ 3.21 > 2, thus determining the main pipe as an abnormal noise pipe. The abnormal noise prediction model is then fed with the office building's historical pressure reduction ratio data, the pipe's operating time (three years), and the ambient temperature (average 30°C in summer) to aid in the identification process.

[0063] Step S3: Install the main and sub-pressure reducing valves 7 and process them as required. The sub-pressure reducing valves 7 are connected to the smart water meter to achieve automatic adjustment.

[0064] Step S4: A pressure fluctuation frequency threshold is set. When the threshold is exceeded, the control valve is adjusted according to a stepped pressure reduction curve. During peak water usage hours (weekdays, 9:00-11:00 AM and 2:00-4:00 PM), the pressure reduction ratio π is adjusted to approach 2 by correlating elevator operation frequency with lighting power load data. During normal water usage hours, the pressure reduction ratio π is adjusted to less than 2. After this treatment, abnormal pipe noise in the office building has been largely eliminated, and the water supply pressure is stable and meets office needs.

[0065] Example 3: School scenario For a middle school with 3,000 students, peak water usage is concentrated during breaks and lunch breaks, and abnormal noises from pipes affect the teaching environment.

[0066] Step S1: Install a first pressure gauge 4 at the inlet of the school's thin-walled stainless steel water supply main pipe 1, and a second pressure gauge 5 at the inlet of the thin-walled stainless steel branch pipe 2 for stratified water supply. An ultrasonic leak detector was used to scan the pipeline, marking 10 potential leak locations. Based on the school's historical water usage data over the past three years, a correlation model was established between leak potential locations and water flow and pressure changes. Two high-risk, four medium-risk, and four low-risk pipe sections were identified, and the corresponding branch pipes and pressure gauges were installed. An auxiliary pressure gauge was installed 15 meters from the inlet of the thin-walled stainless steel water supply main pipe 1. The flow resistance coefficient along the pipeline was calculated based on the pressure difference (0.1 MPa) and distance, and a pressure stability assessment model was established. Based on the noise source type (presence of elbows), the installation location of the first pressure gauge 4 was moved to a branch pipe section six pipe diameters downstream.

[0067] Step S2: Obtain the monitoring value P1 (0.3 MPa) from the first pressure gauge 4 and the monitoring value P2 (0.1 MPa) from the second pressure gauge 5. Using the pressure stability assessment model, P1 and P2 are modified. Machine learning training determines k1 = 1.15 and k2 = 0.95. The modified pressure reduction ratio π is calculated as (0.3 × 1.15) / (0.1 × 0.95) ≈ 3.53 > 2, thus determining the main pipe as an abnormal noise pipe. The school's historical pressure reduction ratio data, the pipeline's operating time (four years), and the ambient temperature (average 27°C in summer) are input into the abnormal noise prediction model to assist in the identification.

[0068] Step S3: Install the main and sub-pressure reducing valves 7 and process them as required. The sub-pressure reducing valves 7 are connected to the smart water meter to achieve automatic adjustment.

[0069] Step S4: Set a pressure fluctuation frequency threshold. When the threshold is exceeded, the control valve is adjusted according to a stepped pressure reduction curve. During peak water usage periods (breaks and lunchtime), the pressure reduction ratio π is adjusted to approach 2 by correlating elevator operating status (if any) with lighting load data. During normal water usage periods, the pressure reduction ratio π is adjusted to less than 2. This process has significantly reduced abnormal noise in the school's pipes, ensuring normal teaching and learning.

[0070] The three examples above demonstrate that while the core steps of the method for troubleshooting abnormal noise in thin-walled stainless steel water pipes remain the same, the specific implementation details vary in different application scenarios due to differences in water usage patterns, building structures, and other factors. To more clearly and intuitively demonstrate the similarities and differences between the various examples and help readers quickly grasp the key points of applying the method in different scenarios, the following example comparison table is provided.

[0071]

[0072] Comparative Table of Examples The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for troubleshooting abnormal noise in thin-walled stainless steel water supply pipes, characterized in that: The following steps are involved: S1: Install a first pressure gauge (4) on the inlet side of the thin-walled stainless steel main pipe (1) for water supply, and install a second pressure gauge (5) on the inlet side of the thin-walled stainless steel branch pipe (2) for stratified water supply; S2: Obtain the monitoring value P1 of the first pressure gauge (4) and the monitoring value P2 of the second pressure gauge (5), and calculate the pressure reduction ratio π of the water supply thin-walled stainless steel main pipe (1), π=P1 / P2; If π>2, it is determined that the water supply thin-walled stainless steel main pipe (1) is an abnormal noise pipe; If π≤2, it is determined that the water supply thin-walled stainless steel main pipe (1) is a non-abnormal noise pipe; S3: Install a main pressure reducing valve (6) on the thin-walled stainless steel water supply main pipe (1) corresponding to the abnormal noise pipe, and install sub-pressure reducing valves (7) on multiple user pipes (3) on the same floor corresponding to the thin-walled stainless steel water supply main pipe (1); S4: Adjust the pressure reduction ratio π through the main pressure reducing valve (6) and the sub-pressure reducing valve (7); During peak water usage periods, adjust the pressure reduction ratio to π = 2; During normal water use period, adjust the pressure reduction ratio π to 2.

2. A method for troubleshooting abnormal noise in thin-walled stainless steel water supply pipes according to claim 1, characterized in that: In step S1, an ultrasonic leak detector is used to scan the thin-walled stainless steel main pipe (1) and the layered water supply thin-walled stainless steel branch pipe (2) to mark the locations where potential leakage hazards may exist. Based on historical water use data, a correlation model between the location of leakage hazards and water flow and pressure changes is established. The weight of the impact of leakage hazards on pressure in different water use periods is analyzed. High-risk pipe sections, medium-risk pipe sections and low-risk pipe sections are marked in descending order of the weight of the impact. A parallel spare branch pipe is installed in the high-risk pipe section, and a first pressure gauge (4) and a second pressure gauge (5) are installed in the low-risk pipe section.

3. The method for troubleshooting abnormal noise in thin-walled stainless steel water supply pipes according to claim 1, characterized in that: In step S1, an auxiliary pressure gauge is installed 10-15 m away from the inlet side of the water supply thin-walled stainless steel main pipe (1). Based on the pressure difference and distance between the first pressure gauge (4) and the auxiliary pressure gauge, the resistance coefficient of the water flow in the pipeline is calculated. Combined with historical data, a pressure stability evaluation model is established, and the installation position of the first pressure gauge (4) is adjusted.

4. A method for troubleshooting abnormal noise in thin-walled stainless steel water supply pipes as claimed in claim 3, characterized in that: When adjusting the installation position of the first pressure gauge (4), the pressure data spectrum output by the pressure stability evaluation model is used to determine the location of the noise source based on the noise frequency and amplitude: If there is a valve or elbow at the noise source, move the installation position of the first pressure gauge (4) to a branch pipe section that is ≥10 times the pipe diameter upstream or ≥5 times the pipe diameter downstream; If there is a pulse device at the noise source, the installation position of the first pressure gauge (4) is moved to a position ≥ 2 meters away from the pulse device.

5. The method for troubleshooting abnormal noise in thin-walled stainless steel water supply pipes according to claim 3, characterized in that: In step S2, when calculating the decompression ratio π, the values ​​of P1 and P2 are corrected in combination with the pressure stability evaluation model. The corrected decompression ratio calculation formula is: ; Among them, k1 is the pressure correction coefficient corresponding to the installation position of the first pressure gauge (4), and k2 is the pressure correction coefficient corresponding to the installation position of the second pressure gauge (5). By performing machine learning training on historical leakage data and pressure change data, the k1 and k2 values ​​under different leakage scenarios are determined to obtain the corrected pressure reduction ratio .

6. A method for troubleshooting abnormal noise in thin-walled stainless steel water supply pipes according to claim 1, characterized in that: In step S2, an abnormal noise prediction model based on machine learning is established, and historical pressure reduction ratio data, pipeline operation time and ambient temperature parameters are input into the model to perform abnormal prediction on the currently calculated pressure reduction ratio π, thereby assisting in determining whether the water supply thin-walled stainless steel main pipe (1) is an abnormal noise pipe.

7. A method for troubleshooting abnormal noise in thin-walled stainless steel water supply pipes according to claim 1, characterized in that: In step S3, when installing the main pressure reducing valve (6) and the sub-pressure reducing valve (7), a shock-absorbing rubber pad is used to wrap the connection.

8. The method for troubleshooting abnormal noise in thin-walled stainless steel water supply pipes according to claim 1, characterized in that: The pressure reducing valve (7) is an electric pressure reducing valve with an automatic adjustment function. By connecting to the smart water meter on the user pipeline (3), the valve opening of the electric pressure reducing valve is automatically adjusted according to the user's real-time water consumption to control the water flow pressure.

9. A method for troubleshooting abnormal noise in thin-walled stainless steel water supply pipes according to claim 1, characterized in that: In step S4, the main pressure reducing valve (6) and the sub-pressure reducing valve (7) are adjusted by the pressure fluctuation frequency, and a pressure fluctuation frequency threshold is preset. When the pressure fluctuation frequency of the water supply system exceeds the threshold, the main pressure reducing valve (6) and the sub-pressure reducing valve (7) are controlled to be adjusted according to a preset step-type pressure reducing curve, so that the pressure reducing ratio π during the peak water use period gradually approaches 2, and the pressure reducing ratio π during the normal water use period gradually becomes less than 2.

10. A method for troubleshooting abnormal noise in thin-walled stainless steel water supply pipes according to claim 1, characterized in that: In step S4, the main pressure reducing valve (6) and the sub-pressure reducing valve (7) are associated with the elevator operation status and lighting power load data in the building using an Internet of Things device, and a pressure reducing ratio adjustment model based on multi-source data is established; When the elevator operation frequency increases and the lighting power load rises, it is determined to be the peak water consumption period. The dynamic adjustment parameters are calculated through the model to control the main pressure reducing valve (6) and the sub-pressure reducing valve (7) to adjust so that the pressure reduction ratio π reaches 2; When the elevator operation frequency decreases and the lighting power load decreases, it is determined to be a normal water usage period and the pressure reduction ratio π is adjusted to be less than 2.

Images