Water pressure control optimization method for non-negative pressure water supply equipment

Abstract

The invention relates to the technical field of secondary water supply equipment control, in particular to a water pressure control optimization method for non-negative pressure water supply equipment, which comprises the following steps: S1, collecting pressure data at the tail end of a pipe network; s2, constructing a negative pressure pre-judgment-micro fluctuation suppression double-target linkage adjustment model; and S3, layered pressure data and pipe network instantaneous flow data are obtained in real time, and the flow change rate and the main pipeline pressure change rate are calculated. S4, the tail end target pressure value is corrected and substituted into the double-target linkage adjustment model to calculate the basic adjustment rotating speed of the variable frequency pump; s5, the negative pressure risk is judged, and the final adjusting rotating speed is obtained; s6, the variable frequency pump is controlled to operate according to the final adjustment rotating speed, and single-time water pressure adjustment is completed; and S7, the steps S3 to S6 are repeated, and water pressure dynamic control under the super high-rise precise water consumption scene is achieved. Aiming at the problem of slow adjustment response caused by long super high-rise pipe network and pressure wave transmission lag, the problem of tail end overshoot caused by pressure wave lag is effectively solved through depth correlation of all units.

Description

Technical Field

[0001] This invention relates to the field of secondary water supply equipment control technology, and in particular to an optimization method for water pressure control in a negative pressure-free water supply equipment. Background Technology

[0002] During peak water usage periods in ultra-high-rise precision water use scenarios (such as ultra-high-rise scientific research laboratories and operating rooms on the top floor of medical centers), there are higher requirements for the water pressure control accuracy and the ability to maintain a negative pressure state of the negative pressure-free water supply equipment.

[0003] Traditional negative pressure water supply equipment uses a closed-loop regulation method based on variable frequency speed regulation combined with real-time feedback from pressure sensors. By detecting the pressure signal of the main water supply pipeline, the speed of the variable frequency pump is dynamically adjusted to achieve stable control of the water supply pressure.

[0004] For example, Chinese patent application number CN201910344165.3 discloses a water pressure control method for a negative pressure-free water supply device. This method collects water supply pipeline pressure data by setting a main pressure sensor and a backup pressure sensor, processes the pressure signal based on a weighted average algorithm, and outputs an adjustment command to control the speed of the variable frequency pump, so as to avoid negative pressure in the pipeline network and maintain stable water supply pressure.

[0005] As can be seen from the above description, it has the following drawbacks when used:

[0006] First, it cannot meet the water pressure micro-fluidity control requirements of ultra-high-rise precision water use scenarios: The pressure regulation accuracy of the above solutions can only meet the macro-pressure stabilization under normal working conditions. It does not consider the fluid pressure transmission characteristics at the end of the ultra-high-rise pipeline network. When there is a sudden change in instantaneous flow during peak water use, it is difficult to control the water pressure fluctuation at the end of the pipeline network, which may affect the accuracy of experimental data or the operational safety of medical equipment.

[0007] Second, the above scheme adjusts the pump speed through real-time pressure feedback. In the scenario of ultra-high-rise long-distance pipe network, when the speed is excessively adjusted to suppress micro-fluctuations at the end, it is impossible to predict the pressure change trend at the inlet of the main pipeline in advance, which may easily lead to a sudden drop in the pressure of the main pipeline and trigger the negative pressure protection shutdown. If the adjustment sensitivity is reduced to maintain a state without negative pressure, the stability of the water pressure at the end will be sacrificed.

[0008] Therefore, it is necessary to design an optimization method that can solve the dynamic balance problem of suppressing micro-fluctuations in water pressure and maintaining the negative pressure state in ultra-high-rise precision water use scenarios during peak periods. Summary of the Invention

[0009] To solve one of the aforementioned technical problems, the present invention provides a method for optimizing water pressure control in a negative pressure-free water supply system, comprising the following steps:

[0010] S1 collects pressure data from the inlet of the main water supply pipeline, the middle layer of the ultra-high-rise pipeline at 150m, and the end of the pipeline at 300m or more.

[0011] S2, construct a dual-objective linkage regulation model of negative pressure prediction and micro-fluctuation suppression, and associate pressure, flow rate change rate and pipeline dynamic characteristics of multiple nodes.

[0012] S3 acquires real-time stratified pressure data and instantaneous flow data of the pipeline network, and calculates the flow rate change rate and the main pipeline pressure change rate.

[0013] S4, dynamically corrects the target pressure value at the end based on the dynamic characteristics of the pipeline network, and substitutes it into the dual-target linkage regulation model to calculate the basic regulation speed of the variable frequency pump.

[0014] S5 determines the risk of negative pressure based on the main pipeline pressure prediction result. If there is a risk, the negative pressure prediction compensation mechanism is activated to correct the basic regulating speed and obtain the final regulating speed.

[0015] S6 controls the variable frequency pump to run at the final adjusted speed to complete a single water pressure adjustment.

[0016] S7. Repeat steps S3 to S6 to achieve dynamic water pressure control in ultra-high-rise precision water use scenarios.

[0017] Based on any of the above technical solutions, a further optimization is made in step S2, where the dual-objective linkage regulation model establishes a predictive formula for the main pipeline pressure: .

[0018] Where k1 and k2 are correction coefficients based on the calibration of ultra-high-rise pipeline network parameters, P1 is the main pipeline pressure value, P2 is the middle layer pressure value, P3 is the terminal pressure value, and P... 30 Here, k is the initial target pressure value at the terminal, and k3 is the influence coefficient of sudden flow change. This represents the instantaneous rate of change of flow rate.

[0019] The above scheme is based on the linear correlation characteristics of vertical pressure transmission in ultra-high-rise pipe networks. It uses the mid-level pressure P2 as the core benchmark parameter and corrects its influence weight on the main pipeline pressure using k1. It also introduces the difference between the terminal pressure and the initial target pressure (P3−P). 30 The method quantifies the feedback impact of terminal pressure deviation on main pipeline pressure using k2; simultaneously, it considers the disturbance effect of sudden flow changes on main pipeline pressure, and corrects this disturbance effect using the product term of k3 and instantaneous flow rate change rate. Finally, it constructs a main pipeline pressure prediction formula that can comprehensively reflect the correlation of multiple parameters, realize accurate prediction of main pipeline pressure change trend, provide quantitative basis for subsequent negative pressure risk assessment, and functionally realize multi-parameter collaborative prediction of main pipeline pressure, overcoming the technical problem of insufficient accuracy of traditional single-parameter prediction.

[0020] By quantifying the impact weights of mid-level pressure, terminal pressure deviation, and flow mutation on main pipeline pressure, the prediction results are made more consistent with the actual pressure transmission patterns of ultra-high-rise pipe networks. Secondly, the correction coefficient based on pipe network parameter calibration ensures the adaptability of the prediction formula in different ultra-high-rise projects and avoids the prediction deviation of general formulas. The introduction of instantaneous flow rate change rate can capture the impact of flow disturbance on pressure in advance and improve the foresight of the prediction.

[0021] The multi-parameter collaborative prediction method described above significantly improves the accuracy of main pipeline pressure prediction, providing a reliable basis for the early identification of negative pressure risks and avoiding untimely or excessive negative pressure prevention and control due to prediction deviations.

[0022] Based on any of the above technical solutions, a further optimization is made in step S4: a compensation and correction formula for micro-fluctuations in terminal water pressure is established based on the elastic water hammer effect of ultra-high-rise pipe networks, and the target pressure value P at the terminal is adjusted accordingly. 30 Dynamic correction: .

[0023] Where β is the water hammer compensation coefficient, ranging from 0.02 to 0.08, L is the total length of the ultra-high-rise pipe network, and c is the propagation speed of the pressure wave in the pipe network. The main pipeline pressure change rate.

[0024] The elastic water hammer effect specifically refers to the physical phenomenon in ultra-high-rise pipe networks where a sudden change in flow rate causes a rapid change in the water velocity within the pipe, triggering a pressure wave that propagates along the pipe and interacts with the elastic deformation of the pipe. This phenomenon can lead to hysteretic fluctuations in the end pressure, which need to be offset by compensation and correction.

[0025] Because of the large vertical height and long pipe sections of ultra-high-rise pipe networks, the elastic water hammer effect caused by changes in flow rate will lead to hysteretic fluctuations in the terminal pressure, and the fluctuation amplitude is directly related to the pressure wave propagation characteristics and the pressure change rate of the main pipeline.

[0026] This scheme analyzes the influence mechanism of elastic water hammer effect and constructs a compensation correction formula that includes the water hammer compensation coefficient β, the total pipeline length L, the pressure wave propagation velocity c, and the pressure change rate of the main pipeline to quantify the degree of influence of water hammer effect on the terminal pressure. This quantified influence value is then superimposed onto the initial terminal target pressure value P. 30 The dynamically corrected end target pressure value is obtained. This design allows the target pressure value to adapt in advance to pressure changes caused by water hammer, avoiding overshoot or undershoot in the terminal pressure; it also achieves proactive compensation for the elastic water hammer effect in ultra-high-rise pipe networks. The advance compensation design offsets the lag in pressure wave propagation, avoiding lag fluctuations in terminal pressure and meeting the needs of precision water use scenarios; at the same time, it enables the compensation correction to follow changes in operating conditions in real time, maintaining good compensation effect even under extreme conditions such as sudden changes in flow, thus improving the robustness of the system.

[0027] Based on any of the above technical solutions, a further optimization is made to the negative pressure prediction and compensation mechanism in step S5, which is as follows: .

[0028] Wherein, n1 is the basic regulating speed, n2 is the final regulating speed after compensation, α is the negative pressure compensation coefficient with a value range of 0.05-0.2, P0 is the negative pressure warning threshold of the main pipeline, γ is the end fluctuation damping coefficient with a value range of 0.03-0.1, ΔP3′ is the actual fluctuation value of the end pressure, and ΔP0 is the end pressure fluctuation threshold.

[0029] The value of the negative pressure compensation coefficient α needs to be determined based on the minimum allowable pressure of the municipal water supply network. When the minimum allowable pressure of the municipal water supply network is low (e.g., 0.2 MPa), α should be 0.15-0.2 (to increase the compensation level); when the pressure of the municipal water supply network is sufficient (e.g., above 0.3 MPa), α should be 0.05-0.1 (to reduce the compensation level) to avoid excessive compensation leading to increased energy consumption. The determination of P0 needs to be confirmed by the municipal water supply department to ensure compliance with water supply specifications.

[0030] In addition, the threshold value of pressure fluctuation at the end point, ΔP0, needs to be determined according to the requirements of the precision water use scenario. For conventional precision scenarios, ΔP0 is set to 0.02MPa, and for ultra-high precision scenarios, ΔP0 is set to 0.01MPa.

[0031] This scheme uses the base regulating speed calculated by the dual-objective linkage regulation model as the benchmark, and introduces a negative pressure compensation term and an end-wave damping term for secondary correction; through (P0−P 1预 ) / P0 quantifies the degree of negative pressure risk in the main pipeline, when P 1预 The closer to P0 (the higher the risk of negative pressure), the larger this ratio, and the stronger the correction force of the negative pressure compensation term, resulting in a more significant increase in the speed of the variable frequency pump, thereby increasing the pressure in the main pipeline and avoiding the risk of negative pressure. At the same time, the fluctuation of the terminal pressure is quantified by |ΔP3′| / ΔP0. The greater the fluctuation, the stronger the correction force of the terminal fluctuation damping term. By appropriately reducing the speed adjustment range, further fluctuations in the terminal pressure are suppressed. The two correction terms work together to finally obtain the final adjustment speed that takes into account both negative pressure prevention and terminal pressure stabilization.

[0032] The above-mentioned scheme achieves synergistic correction of negative pressure risk prevention and control and end pressure fluctuation suppression, breaking through the problem of neglecting one aspect due to traditional single-dimensional compensation; the synergistic design of the dual correction terms enables the final adjustment speed to respond quickly to negative pressure risk and effectively suppress end fluctuations.

[0033] In addition, the introduction of the end-point fluctuation damping term suppresses end-point pressure fluctuations in real time, ensuring precise water demand; moreover, the design of the end-point fluctuation damping term suppresses the spread of pressure fluctuations in advance, avoiding end-point pressure runaway caused by the superposition of fluctuations, and improving the reliability of the system under complex operating conditions.

[0034] Based on any of the above technical solutions, the following optimization is made: In step S1, the sampling frequency of the pressure data is 10-50Hz, and the sampling timing of the pressure data at the inlet of the main pipeline and the pressure data at the outlet is synchronized, with a synchronization error of no more than 50ms.

[0035] Given the lag in pressure transmission within ultra-high-rise pipeline networks, time-series synchronization of pressure data between the main pipeline and the terminal pressure points is a prerequisite for ensuring the accuracy of multi-node pressure correlation analysis. By setting a sampling frequency suitable for the operating conditions, the integrity of data acquisition is ensured while avoiding data redundancy and increased computational load caused by excessively high sampling frequencies. Furthermore, through time-series synchronization design, the time-series deviation between the main pipeline and terminal pressure sampling is eliminated, ensuring that the collected pressure data accurately reflects the pressure status of different nodes in the pipeline network at the same time. This provides a time-consistent data source for the accurate calculation of the subsequent dual-objective linkage regulation model.

[0036] Based on any of the above technical solutions, the following optimization is made: In step S2, the method for calibrating the correction coefficients k1, k2, and k3 based on the parameters of the ultra-high-rise pipe network is as follows: collect historical data of P1, P2, P3, and Q under different flow conditions and different pipe material resistance coefficients of the ultra-high-rise pipe network, and obtain the optimal value range of k1, k2, and k3 through computational fluid dynamics simulation fitting; k1 is positively correlated with the vertical height of the pipe network, k2 is positively correlated with the fluctuation amplitude of the end flow, and k3 is negatively correlated with the pipe diameter of the pipe network.

[0037] The correction coefficients k1, k2, and k3 are the core parameters for the accurate calculation of the dual-objective linkage adjustment model, and their values ​​directly determine the degree of adaptation of the model to the characteristics of ultra-high-rise pipe networks.

[0038] By collecting historical pressure and flow data from multiple nodes under different flow conditions and pipe material resistance coefficients, a basic dataset covering various operating conditions is obtained. A precise pipe network model is constructed using computational fluid dynamics simulation software to simulate the pressure distribution patterns of the pipe network under different parameter combinations, supplementing the deficiencies of measured data. Based on a multivariate nonlinear regression algorithm, the measured and simulated data are fitted to obtain the optimal value ranges for k1, k2, and k3. Simultaneously, the correlation between the coefficients and the core parameters of the pipe network (vertical height, flow fluctuation amplitude, and pipe diameter) is clarified to ensure that the coefficients can be dynamically adjusted according to different pipe network characteristics, improving the model's versatility and accuracy.

[0039] Based on any of the above technical solutions, the following optimization is made: the method is further improved by adding a step of real-time acquisition of the instantaneous flow of the pipeline network. When the fluctuation range exceeds ±30% of the rated flow, it is determined as a sudden change in flow, and the calculation frequency of the dual-target linkage adjustment model is automatically increased to 1.5-2 times the original frequency.

[0040] Sudden flow rate changes are the core cause of drastic pressure fluctuations and negative pressure risks in ultra-high-rise pipeline networks. Adding a flow rate change identification feature enables real-time monitoring of this cause. During operation, a high-precision flow sensor collects the instantaneous flow rate of the pipeline network in real time. After signal preprocessing, a continuous multi-cycle verification mechanism is used to accurately determine flow rate changes. When a valid change is detected, an interrupt response in data processing is immediately triggered, increasing the calculation frequency of the dual-target linkage regulation model by 1.5-2 times and accelerating the output frequency of regulation commands. This allows the variable frequency pump to quickly respond to pressure changes caused by flow rate changes, preventing the expansion of pressure fluctuations and the generation of negative pressure risks.

[0041] Based on any of the above technical solutions, the following optimization is made: the flow change identification module establishes a data communication link with the pressure data acquisition module. After the flow change signal is triggered, the sampling frequency of the pressure data is synchronously increased to a value that matches the operation frequency of the adjustment model.

[0042] When a sudden change in flow is triggered, the pipeline pressure will change rapidly and drastically. At this time, higher frequency pressure data acquisition is required to accurately capture the pressure change trend and provide accurate real-time data for model calculation.

[0043] Through the data interconnection link, the rapid transmission of sudden change signals is realized; when the flow sudden change signal is triggered, the data processing unit synchronously issues the instruction to increase the pressure sampling frequency and the model calculation frequency, so that the pressure sampling frequency and the calculation frequency are precisely matched, ensuring that each model calculation is based on the latest pressure data; when the sudden change signal is removed, the initial frequency is synchronously restored to balance the adjustment accuracy and system energy consumption.

[0044] Based on any of the above technical solutions, the method is further optimized as follows: the method also includes the following steps: collecting the terminal pressure fluctuation value ΔP3′ after the variable frequency pump is adjusted; when ΔP3′ exceeds the range of ΔP0, automatically adjusting the correction coefficients k1 and k2 of the dual-target linkage adjustment model, with the adjustment range being ±5% of the original value, and the adjusted value not exceeding the optimal value range.

[0045] The terminal pressure fluctuation value ΔP3′ after the variable frequency pump is adjusted is the core indicator reflecting the adjustment effect of the dual-objective linkage adjustment model. When ΔP3′ exceeds the threshold ΔP0, it indicates that the correction coefficients k1 and k2 of the current model can no longer adapt to the current pipeline network conditions and need to be dynamically adjusted.

[0046] The adjustment effect feedback unit periodically collects ΔP3′ and compares it with ΔP0 to determine whether the adjustment effect meets the standard. When it does not meet the standard, the direction and magnitude of coefficient adjustment are determined according to the pressure status of the main pipeline (whether there is a risk of negative pressure), ensuring that the adjusted coefficient can improve the end pressure stabilization effect without affecting the negative pressure prevention and control function. Through a closed-loop mechanism of multiple adjustments and effect verification, the model coefficients always adapt to the changes in pipeline network conditions and maintain the stability of the adjustment effect.

[0047] The present invention also provides a negative pressure-free water supply device, comprising: a layered pressure detection unit for collecting pressure data of nodes at different heights in the pipeline network.

[0048] The flow change identification module is used to identify instantaneous flow change states in the pipeline network in real time.

[0049] The data processing unit is used to receive and process pressure data and flow data.

[0050] The data processing unit incorporates the aforementioned optimized water pressure control method for negative pressure-free water supply equipment.

[0051] The variable frequency pump control unit is used to receive adjustment commands from the data processing unit and output variable frequency pump speed control signals. The signal output terminal of the variable frequency pump control unit is electrically connected to the control terminal of the variable frequency pump.

[0052] The variable frequency pump is used to adjust the water supply pressure according to the speed control signal. The inlet is connected to the municipal water supply network, and the outlet is connected to the high-rise building network.

[0053] The adjustment effect feedback unit is used to collect the end pressure fluctuation value ΔP3′ after the variable frequency pump is adjusted. The signal acquisition end is electrically connected to the signal output end of the third pressure sensor, and the signal output end is electrically connected to the third signal input end of the data processing unit.

[0054] When the equipment is working, the stratified pressure detection unit collects pressure data at different heights of the pipeline network, and the flow change identification module monitors the flow status in real time. Both data are transmitted to the data processing unit. The data processing unit processes and calculates the data using a built-in optimization method to generate variable frequency pump adjustment commands. After receiving the adjustment commands, the variable frequency pump control unit converts them into corresponding speed control signals to drive the variable frequency pump to adjust its operating speed, thereby adjusting the water supply pressure. The adjustment effect feedback unit collects the adjusted end pressure data through a third pressure sensor, extracts the pressure fluctuation value ΔP3′, and feeds it back to the data processing unit. The data processing unit judges the adjustment effect based on the feedback data. If the target is not met, the model coefficient is dynamically adjusted to form a closed-loop control system of acquisition, processing, adjustment, feedback, and optimization at the equipment level, realizing precision water supply without negative pressure in ultra-high-rise buildings.

[0055] Based on any of the above technical solutions, a further optimization is made as follows: the layered pressure detection unit includes a first pressure sensor, a second pressure sensor, and a third pressure sensor sequentially installed at the inlet end of the main water supply pipeline, at a height of 150m in the ultra-high-rise pipeline network, and at the end of the pipeline network above 300m. It also includes a signal preprocessing module. The signal output terminals of the first pressure sensor, the second pressure sensor, and the third pressure sensor are all electrically connected to the input terminal of the pressure signal preprocessing module, and the output terminal of the signal preprocessing module is electrically connected to the first signal input terminal of the data processing unit.

[0056] The flow change identification module includes a flow sensor installed in the main water supply pipeline and a signal preprocessing module. The signal output terminal of the flow sensor is electrically connected to the input terminal of the flow signal preprocessing module, and the output terminal of the signal preprocessing module is electrically connected to the second signal input terminal of the data processing unit.

[0057] The adjustment effect feedback unit is used to collect the terminal pressure fluctuation value ΔP3′ after the variable frequency pump is adjusted. It includes a signal acquisition component and a pressure fluctuation value extraction module. The signal acquisition end of the signal acquisition component is electrically connected to the signal output end of the third pressure sensor, and the signal output end of the pressure fluctuation value extraction module is electrically connected to the third signal input end of the data processing unit.

[0058] The three pressure sensors in the layered pressure detection unit collect pressure signals from different key nodes in the pipeline network. After filtering, amplification, and analog-to-digital conversion by an independent pressure signal preprocessing module, the signals are transmitted to the data processing unit through the first signal input terminal. The flow sensor in the flow mutation identification module collects the instantaneous flow signal of the main pipeline. After processing by the existing flow signal preprocessing module, the signal is transmitted to the data processing unit through the second signal input terminal. The signal acquisition component of the regulation effect feedback unit obtains the end pressure signal from the third pressure sensor. After calculation by the existing pressure fluctuation value extraction module, ΔP3′ is obtained and transmitted to the data processing unit through the third signal input terminal. The signals from the three units are transmitted to different input terminals of the data processing unit to achieve independent signal acquisition and transmission and avoid crosstalk. The data processing unit performs collaborative processing of the three signals based on the built-in optimization method to generate regulation commands and achieve precise control.

[0059] Based on any of the above technical solutions, a further optimization is made: the sampling frequency of each pressure sensor in the layered pressure detection unit is 10-50Hz, and the sampling timing synchronization is achieved through the timing synchronization module built into the data processing unit, with a synchronization error of no more than 50ms.

[0060] The three pressure sensors of the stratified pressure detection unit need to collect pressure data from nodes at different heights at the same time to accurately reflect the pressure distribution status of the ultra-high-rise pipe network.

[0061] Based on any of the above technical solutions, the following optimization is made: the sampling frequency of the flow sensor in the flow mutation identification module is 20-60Hz, and the flow mutation identification module and the hierarchical pressure detection unit establish a data communication link through the data processing unit. After the flow mutation signal is triggered, the data processing unit automatically sends a sampling frequency increase command to the hierarchical pressure detection unit.

[0062] Based on any of the above technical solutions, a further optimization is made: the feedback period of the adjustment effect feedback unit is 0.5-2s. When the terminal pressure fluctuation value ΔP3′ exceeds the threshold ΔP0, the unit sends a coefficient adjustment command to the data processing unit. The data processing unit adjusts the correction coefficients k1 and k2 of the dual-target linkage adjustment model according to the command.

[0063] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0064] 1. This invention addresses the problem of slow regulation response caused by the long length of ultra-high-rise pipelines and the lag in pressure wave transmission. It effectively solves the problem of end-point overshoot caused by pressure wave lag by using precise data acquisition of layered pressure detection, quantification of pressure wave transmission time by dynamic correction formula, and deep correlation of early warning for flow change identification.

[0065] 2. The control method in this invention solves the problem of lag in traditional single-point pressure acquisition and adjustment: the layered pressure detection and time-series synchronization design reduces the error of multi-node pressure data correlation analysis, providing data support for precise adjustment.

[0066] 3. The control method in this invention achieves the synergistic effect of negative pressure control and micro-fluctuation suppression: the combination of the dual-objective linkage model and the compensation mechanism ensures that the main pipeline pressure is stably maintained above P0, and the pressure fluctuation amplitude at the end is adapted to the precision water demand.

[0067] 4. Improved response capability under sudden operating conditions: The design of flow change identification and frequency linkage improves the regulation response time and reduces the peak water hammer pressure.

[0068] 5. Enhanced adaptability to operating conditions: The model coefficients are dynamically and adaptively adjusted, enabling the system to adapt to operating conditions such as pipe aging and water load changes, improving the compliance rate of terminal pressure fluctuations, and eliminating the need for frequent manual intervention. Attached Figure Description

[0069] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or components are generally identified by similar reference numerals. In the drawings, the elements or components are not necessarily drawn to scale.

[0070] Figure 1 This is a flowchart illustrating the water pressure control optimization method for the negative pressure-free water supply equipment of the present invention.

[0071] Figure 2 This is a schematic diagram of the module connection of the negative pressure-free water supply equipment of the present invention. Detailed Implementation

[0072] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore merely examples and should not be used to limit the scope of protection of the present invention. The specific structure of the present invention is as follows: Figures 1-2 As shown in the image.

[0073] Example 1: A method for optimizing water pressure control in a negative pressure-free water supply system, comprising the following steps:

[0074] S1 collects pressure data from the inlet of the main water supply pipeline, the middle layer of the ultra-high-rise pipeline at 150m, and the end of the pipeline at 300m or more.

[0075] After the equipment is started, the three pressure sensors of the stratified pressure detection unit collect pressure data at a sampling frequency of 10-50Hz. Interrupt-triggered sampling and DMA data transmission are used to avoid data transmission delays. The sampling timestamps of the three sensors are compared every 1 second, and a delay compensation algorithm is used to correct deviations, ensuring that the pressure data sampling sequence at the main pipeline inlet and outlet is synchronized, with a synchronization error not exceeding 50ms. The sampled data is filtered, amplified, and converted from analog to digital by the pressure signal preprocessing module before being transmitted to the data processing unit for buffering.

[0076] S2, construct a dual-objective linkage regulation model of negative pressure prediction and micro-fluctuation suppression, and associate pressure, flow rate change rate and pipeline dynamic characteristics of multiple nodes.

[0077] Specifically, based on the current characteristics of ultra-high-rise pipeline networks, a dual-objective linkage regulation model is constructed that relates the pressure and flow rate change rate of multiple nodes and the dynamic characteristics of the pipeline network. The core of this model is to establish a predictive formula for the main pipeline pressure and a corrective formula for the target pressure at the end.

[0078] The specific scheme for calibrating the correction coefficient is as follows:

[0079] ① Collect parameters of the target super high-rise building's pipe network: The pipe material is stainless steel (elastic modulus E=2.0×10). 11 The main pipeline has a diameter of DN150, the middle layer pipeline has a diameter of DN125, and the end pipeline has a diameter of DN65. The total vertical height is 320m, and the total length of the pipeline section is L=450m.

[0080] ② Build an ANSYS Fluent 1:1 three-dimensional simulation model, set the inlet pressure (0.2-0.4MPa) and outlet flow (0-50m³ / h), simulate 11 sets of flow conditions (0-100% rated flow, 10% interval), run each set of conditions stably for 5 minutes, and collect P1, P2, P3 and Q data (one set every 100ms, 3000 sets of data per set).

[0081] ③ Based on the least squares method, a multivariate nonlinear regression fitting was performed to obtain the optimal range of correction coefficients: k1=0.6-0.9 (positively correlated with vertical height), k2=0.3-0.5 (positively correlated with terminal flow fluctuation), k3=0.02-0.06 (negatively correlated with pipe diameter), and the fitting determination coefficient R²≥0.95 was controlled.

[0082] ④ The main pipeline pressure prediction formula is: Among them, P 30 This represents the initial target pressure value at the end point.

[0083] S3 acquires real-time stratified pressure data and instantaneous flow data of the pipeline network, and calculates the flow rate change rate and the main pipeline pressure change rate.

[0084] The data processing unit receives real-time stratified pressure data and instantaneous flow data transmitted by the flow change identification module (flow sensor sampling frequency 20-60Hz), and calculates the flow rate change and the main pipeline pressure rate change. The specific scheme is as follows: a sliding window method is used for calculation, with a window length of 0.1s. =(Q n -Q n-1 ) / 0.1 (Q n Q represents the average flow rate of the current window. n-1 (The average flow rate of the previous window). The calculation methods are the same, ensuring that the time scales of both are consistent.

[0085] S4, dynamically corrects the target pressure value at the end based on the dynamic characteristics of the pipeline network, and substitutes it into the dual-target linkage regulation model to calculate the basic regulation speed of the variable frequency pump.

[0086] In step S4, based on the elastic water hammer effect of ultra-high-rise pipe networks, a compensation and correction formula for micro-fluctuations in terminal water pressure is established, and the target pressure value P at the terminal is adjusted accordingly. 30 Dynamic correction: .

[0087] Where β is the water hammer compensation coefficient, ranging from 0.02 to 0.08, L is the total length of the ultra-high-rise pipe network, and c is the propagation speed of the pressure wave in the pipe network. The main pipeline pressure change rate.

[0088] The logical connection between the elastic water hammer effect and this modified formula: Sudden changes in flow rate in ultra-high-rise pipe networks cause a rapid change in water velocity, and the pressure wave propagates along the pipe network (at velocity c), requiring passage through… The time required for the signal to reach the end point results in a lag in end-point pressure fluctuations; this formula uses... Quantify the propagation lag time of the pressure wave, combined with the pressure change rate of the main pipeline. This compensates for the lag effect of water hammer on the terminal pressure in advance, avoiding overshoot of the terminal pressure under the traditional fixed target pressure.

[0089] The specific value and determination method of L: L is the total length of all pipe sections (including vertical and horizontal pipe sections) from the water inlet of the main water supply pipeline to the end water node. The length of the pipe section needs to be measured on-site with a laser rangefinder and verified in conjunction with the pipeline network as-built drawings.

[0090] Steps for calculating and verifying c: c is calculated using the formula Calculate; where E is the bulk modulus of water (taken as 2.1 × 10⁻⁶ at room temperature). 9 Pa), ρ are the densities of water, D is the diameter of the main pipe, E P The elastic modulus of the pipe material is determined with reference to the structural design code for water supply and drainage pipelines; for stainless steel, it is 2.0 × 10¹¹ Pa, and for PE pipe, it is 0.8 × 10¹¹ Pa.9 Pa and δ represent the pipe wall thickness.

[0091] Calibration details of β: β needs to be calibrated in conjunction with the pipe network and pipe material parameters. The specific formula is as follows: ;

[0092] The pipe material correction factor (dimensionless, 1.0 for stainless steel, 1.5 for PE pipe, and 0.8 for ductile iron pipe, adapting to the water hammer response characteristics of different pipe materials) is used to correct for the deviation in water hammer wave velocity caused by the elasticity differences of different pipe materials, making the calculated value closer to the actual working conditions. This ensures that the pipe material can effectively withstand water hammer pressure fluctuations during the operation of equipment without negative pressure, preventing pipe network rupture, leakage, or negative pressure generation, and guaranteeing the safety and stability of the water supply system. By using the pipe material correction factor to differentiate the water hammer response characteristics of different pipe materials, the formula can cover commonly used pipe materials in ultra-high-rise buildings such as stainless steel, PE pipe, and ductile iron pipe, improving its versatility.

[0093] Wherein, k is an empirical correction coefficient (dimensionless, ranging from 0.8 to 1.2, calibrated according to the "Outdoor Water Supply Design Standard" GB50013-2018), which is determined based on the parameter requirements, design specifications, and operating condition statistics of current national / industry standards such as the construction and acceptance specifications for water supply and drainage pipeline engineering, the outdoor water supply design standard, and the structural design specifications for water supply and drainage pipelines.

[0094] As a dimensional normalization coefficient, it is used to balance the order of magnitude of parameters. All formula parameters are calibrated based on current national standards for pipe materials, meeting the operational requirements of technical personnel.

[0095] The sliding window method is used for calculation, with the window length set to 0.1s (consistent with the flow rate change calculation window). The ratio of the difference in the main pipeline pressure data within the window to the window duration is taken, and high-frequency interference in the pressure signal needs to be eliminated by digital filtering (such as Kalman filtering).

[0096] The pressure data collected by the stratified pressure detection unit provides the basic data source for calculating the pressure change rate of the main pipeline. The data processing unit then continues to filter and calculate the pressure change rate of the main pipeline. Based on the hysteresis characteristics of the elastic water hammer in ultra-high-rise pipe networks, this scheme... The time delay of pressure wave transmission is quantified, and the influence of water hammer effect on the terminal pressure is quantified into a compensation value and superimposed on the initial target pressure P by adapting the elastic deformation characteristics of different pipe materials through the β coefficient. 30 Dynamically corrected This correction process, in conjunction with the dual-target linkage adjustment model, enables the terminal target pressure to adapt to the lag effect of water hammer in advance, providing an accurate target benchmark for calculating the base adjustment speed of the variable frequency pump. Functionally, it realizes the active lag compensation of the elastic water hammer effect in ultra-high-rise pipe networks, solving the problem that the traditional fixed terminal target pressure cannot adapt to the lag of water hammer.

[0097] The above-mentioned solution links the real-time correction of the main pipeline pressure change rate, enabling the terminal target pressure to dynamically adjust according to operating conditions and improve pressure stabilization accuracy; it quantifies the pressure wave transmission lag time to avoid terminal pressure overshoot caused by water hammer effect, and can adapt to the fluctuation requirements of precision water use scenarios.

[0098] Compared to traditional uncompensated target pressure control, this scheme reduces the fluctuation amplitude of terminal pressure caused by water hammer effect, while avoiding the risk of negative pressure in the main pipeline due to overcompensation, and can produce the technical effects of hysteresis compensation and precise adaptation.

[0099] The introduction of this feature allows the correction logic to be deeply integrated with the pressure wave transmission characteristics. For super high-rise projects of different heights, only the value of L needs to be updated to directly apply the formula without the need to readjust the compensation strategy.

[0100] It can be seen that the synergy between the modified formula and the dual-objective linkage regulation model allows the variable frequency pump regulation to simultaneously take into account the negative pressure control of the main pipeline and the pressure stabilization at the end, solving the problem of traditional single-dimensional regulation that fails to address both aspects, and improving the water supply stability in ultra-high-rise precision water use scenarios.

[0101] Will Substituting into the dual-objective linkage regulation model, and combining it with the pump characteristic curve formula... ; Calculate the basic adjustment speed n1 of the variable frequency pump.

[0102] S5, based on the main pipeline pressure prediction results, determine the negative pressure risk. If a risk exists, activate the negative pressure prediction compensation mechanism to correct the basic regulating speed n1, thus obtaining the final regulating speed n2; for example, the specific scheme is as follows:

[0103] Set the negative pressure warning threshold for the main pipeline to P0 = 0.16MPa (lower than the minimum allowable pressure of the municipal pipeline network by 0.2MPa, with a 0.04MPa response margin).

[0104] The compensation algorithm is as follows: Wherein, α is the negative pressure compensation coefficient, and its value range is derived from current standards, adapting to municipal pipeline pressures of 0.2-0.4 MPa; the maximum value of α is set to 0.2 (to avoid excessive compensation leading to a sudden increase in main pipeline pressure, exceeding the fluctuation threshold); the minimum value is 0.05 (to ensure sufficient compensation to cope with minor negative pressure risks, avoiding insufficient compensation leading to terminal pressure below 0.14 MPa). γ is the terminal fluctuation damping coefficient, with a value range of 0.03-0.1; its core function is to control terminal pressure fluctuations within the standard allowable range by suppressing the variable frequency pump speed adjustment amplitude, while avoiding excessive damping leading to slow system response. This value range and determination process strictly follow current national / industry standards to ensure that the damping effect meets the specifications and adapts to the dynamic characteristics of ultra-high-rise water supply systems; according to building water supply and drainage design standards, it ensures that the terminal pressure fluctuation after speed correction does not exceed the standard threshold of ±0.02 MPa; the lower limit of 0.03 ensures that the damping force is sufficient to suppress high-frequency small fluctuations (such as pressure fluctuations caused by user water switching), avoiding fluctuation accumulation exceeding the standard allowable range. When fluctuations exceed limits, a damping adjustment of 3%-10% is provided to control the fluctuations within ±0.01MPa; at the same time, the damping force is moderate, which will not cause the pressure recovery time to be too long, thus meeting the standard requirements for the dynamic performance of the system.

[0105] If P1 < P0 (there is a risk of negative pressure), then If the value is greater than 1, increase the rotation speed to avoid negative pressure; if , If the value is less than 1, the speed adjustment range is reduced to suppress fluctuations, thus achieving synergy between the two objectives.

[0106] S6 controls the variable frequency pump to run at the final adjusted speed to complete a single water pressure adjustment.

[0107] The data processing unit converts the final adjusted speed n2 into a standardized control command and sends it to the variable frequency pump control unit. The variable frequency pump control unit outputs a speed control signal of the corresponding frequency to drive the variable frequency pump to run at n2, thus completing a single water pressure adjustment.

[0108] S7. Repeat steps S3 to S6 to achieve dynamic water pressure control in ultra-high-rise precision water use scenarios.

[0109] Flow fluctuation identification: The flow fluctuation identification module adopts a verification mechanism of three consecutive sampling cycles (0.02s / cycle). When the flow fluctuation exceeds ±30% of the rated flow, it is determined to be a flow fluctuation, and a high-level interrupt signal is immediately sent to the data processing unit. The data processing unit responds within 10ms, increases the calculation frequency of the dual-target linkage adjustment model from 10Hz to 20Hz, and at the same time sends a sampling frequency increase command to the hierarchical pressure detection unit through the data interconnection link, increasing the pressure sampling frequency from 20Hz to 24Hz (pressure sampling frequency = model calculation frequency × 1.2), ensuring that the data and calculation match.

[0110] Dynamic adjustment of model coefficients: The adjustment effect feedback unit collects end pressure data at a 1-second feedback cycle and calculates ΔP3′. When ΔP3′ > ΔP0 for two consecutive feedback cycles, a coefficient adjustment command (including ΔP3′, ΔP0, and P1 values) is sent to the data processing unit. Based on the result, k1 and k2 are adjusted by ±5%, without exceeding the optimal value range. After adjustment, the system continuously monitors for three feedback cycles. If ΔP3′ returns to the threshold, the new coefficient is maintained; otherwise, the adjustment is repeated (up to three times). If the target is still not met, a fault alarm is triggered.

[0111] In the above scheme, the 150m mid-level of the ultra-high-rise pipe network is defined as the vertical pipe segment node corresponding to 150m in the height direction of the ultra-high-rise building. This node must avoid local resistance components such as pipe branch manifolds and pressure reducing valves to ensure that the pressure data can reflect the true pressure status of the straight pipe section of the network. The end of the pipe network above 300m refers to the pipe segment node 0.5-1m before the user's water supply end at the very top of the ultra-high-rise pipe network, to avoid interference from the start and stop of water appliances at the user end on pressure acquisition.

[0112] The dynamic characteristics of the pipeline network specifically include core parameters such as the elastic modulus of the pipe materials, the friction parameter of the pipe diameter, the local resistance coefficient of the pipe section, the water volume of the pipeline network, and the pressure wave propagation characteristics. These parameters are determined through a combination of on-site measurements and computational fluid dynamics simulations. The dual-objective linkage regulation model is constructed by pre-establishing a multi-parameter correlation database, containing pressure distribution data under different flow conditions and vertical heights, as well as correlation data between flow rate change rate and pressure fluctuations. The model is then built based on existing technologies such as multiple linear regression algorithms and dynamic weight allocation strategies. The negative pressure prediction target accounts for 60%-70% of the weight, while the micro-fluctuation suppression target accounts for 30%-40%, which can be dynamically adjusted according to actual operating conditions.

[0113] The precision water use scenarios adapted in this invention specifically refer to water use scenarios with high requirements for water pressure fluctuation amplitude, such as laboratories, medical equipment, and precision manufacturing workshops in ultra-high-rise buildings, which are different from the fluctuation requirements of ordinary residential buildings.

[0114] In this method, pressure benchmark data of different vertical nodes of ultra-high-rise pipe network are first obtained through stratified pressure acquisition to provide a basic data source for subsequent model construction; then, a dual-objective model integrating negative pressure prediction and micro-fluctuation suppression is constructed to establish quantitative correlation between multiple parameters; by collecting pressure and flow data in real time, core derived parameters (flow rate of change, main pipeline pressure rate of change) are calculated and substituted into the model to complete the dynamic correction of the terminal target pressure, thereby obtaining the basic regulating speed.

[0115] Based on the main pipeline pressure prediction results, a negative pressure compensation mechanism is activated to make a secondary correction to the base speed, ensuring that the risk of negative pressure is avoided while suppressing the pressure fluctuation at the end. Finally, through the cyclic iterative adjustment logic, continuous and precise control under dynamic operating conditions is achieved, forming a closed-loop control link of acquisition-modeling-computation-adjustment-feedback-iteration.

[0116] This method achieves dual-objective coordinated control of negative pressure avoidance and precise pressure stabilization in ultra-high-rise water supply, overcoming the technical challenge of traditional single-objective regulation failing to address both issues.

[0117] The layered pressure acquisition in the above scheme can accurately capture the differences in vertical pressure distribution in the pipeline network, providing data support for the non-uniform pressure regulation of ultra-high-rise pipeline networks and solving the regulation lag problem caused by traditional single-point pressure acquisition; the dynamically corrected terminal target pressure value adapts to the real-time changes in pipeline network conditions, avoiding over-regulation or under-regulation under fixed target pressure; the negative pressure prediction and compensation mechanism avoids the risk of negative pressure suction from the municipal pipeline network in advance, ensuring the coordinated and stable operation of the water supply system and the municipal pipeline network; finally, the iterative regulation logic ensures the continuous suppression of pressure fluctuations in precision water use scenarios, improving water use stability.

[0118] Based on any of the above technical solutions, the following optimization is made: In step S1, the sampling frequency of the pressure data is 10-50Hz, and the sampling timing of the pressure data at the inlet of the main pipeline and the pressure data at the outlet is synchronized, with a synchronization error of no more than 50ms.

[0119] Based on any of the above technical solutions, the following optimization is made: In step S2, the method for calibrating the correction coefficients k1, k2, and k3 based on the parameters of the ultra-high-rise pipe network is as follows: collect historical data of P1, P2, P3, and Q under different flow conditions and different pipe material resistance coefficients of the ultra-high-rise pipe network, and obtain the optimal value range of k1, k2, and k3 through computational fluid dynamics simulation fitting; k1 is positively correlated with the vertical height of the pipe network, k2 is positively correlated with the fluctuation amplitude of the end flow, and k3 is negatively correlated with the pipe diameter of the pipe network.

[0120] Based on any of the above technical solutions, the following optimization is made: the method is further improved by adding a step of real-time acquisition of the instantaneous flow of the pipeline network. When the fluctuation range exceeds ±30% of the rated flow, it is determined as a sudden change in flow, and the calculation frequency of the dual-target linkage adjustment model is automatically increased to 1.5-2 times the original frequency.

[0121] Based on any of the above technical solutions, the following optimization is made: the flow change identification module establishes a data communication link with the pressure data acquisition module. After the flow change signal is triggered, the sampling frequency of the pressure data is synchronously increased to a value that matches the operation frequency of the adjustment model.

[0122] The units of the physical quantities involved in the formulas in this invention shall be selected by those skilled in the art as needed based on the conventional usage habits of specific engineering application scenarios (e.g., pressure is commonly used in MPa / Pa, length is commonly used in m / mm, rotation speed is commonly used in r / min, etc.) to achieve the requirement of dimensional homogeneity of the formulas; the subsequent descriptions of the units of physical quantities in the formulas shall follow this principle and will not be repeated separately.

[0123] Example 2: The difference between this example and Example 1 is that:

[0124] The present invention also provides a negative pressure-free water supply device, comprising: a layered pressure detection unit for collecting pressure data of nodes at different heights in the pipeline network.

[0125] The flow change identification module is used to identify instantaneous flow change states in the pipeline network in real time.

[0126] The data processing unit is used to receive and process pressure data and flow data.

[0127] The data processing unit incorporates the aforementioned optimized water pressure control method for negative pressure-free water supply equipment.

[0128] For example, the data processing unit receives and processes data from various units. Its specific design employs an ARM Cortex-A9 architecture industrial control board with a 1GHz clock speed, 512MB of memory, and 4GB of storage. It is equipped with multiple serial ports, multiple analog input interfaces, and an Ethernet interface, supporting RS485 and Ethernet communication. The optimization method is implemented through modular programming in C language, and parameter settings and data queries can be performed via a touchscreen human-machine interface.

[0129] In addition, when applying this solution in practice, the data processing unit will pre-store the coefficient value tables under different unit systems. You only need to select the corresponding value according to the unit system used in the project, and you can directly substitute it into the formula for calculation without additional dimensional conversion. In actual calculation, direct calculation can be achieved by unifying the unit system and calibrating the coefficients under the corresponding units.

[0130] The variable frequency pump control unit is used to receive adjustment commands from the data processing unit and output variable frequency pump speed control signals. The signal output terminal of the variable frequency pump control unit is electrically connected to the control terminal of the variable frequency pump.

[0131] The variable frequency pump is used to adjust the water supply pressure according to the speed control signal. The inlet is connected to the municipal water supply network, and the outlet is connected to the high-rise building network.

[0132] For example, the variable frequency pump control unit receives adjustment commands from the data processing unit and drives the variable frequency pump to operate; the variable frequency pump is used to regulate the water supply pressure to achieve negative pressure-free water supply. The specific solution is as follows: the variable frequency pump control unit includes a command receiving module, a power variable frequency drive module, and a protection module. The command receiving module is electrically isolated from the data processing unit, and the signal output terminal is electrically connected to the variable frequency pump control terminal via a cable; the protection module sets the overcurrent threshold to 1.5 times the rated current, the overvoltage threshold to 450V, and the overheat threshold to 85℃ to ensure equipment safety.

[0133] The variable frequency pump is a vertical multistage centrifugal pump. The rated flow rate is determined according to the total water consumption of the super high-rise building. It has a soft start function, and the inlet is connected to the municipal water supply network, while the outlet is connected to the super high-rise building's water supply network.

[0134] The adjustment effect feedback unit is used to collect the end pressure fluctuation value ΔP3′ after the variable frequency pump is adjusted. The signal acquisition end is electrically connected to the signal output end of the third pressure sensor, and the signal output end is electrically connected to the third signal input end of the data processing unit.

[0135] Based on any of the above technical solutions, a further optimization is made as follows: the layered pressure detection unit includes a first pressure sensor, a second pressure sensor, and a third pressure sensor sequentially installed at the inlet end of the main water supply pipeline, at a height of 150m in the ultra-high-rise pipeline network, and at the end of the pipeline network above 300m. It also includes a signal preprocessing module. The signal output terminals of the first pressure sensor, the second pressure sensor, and the third pressure sensor are all electrically connected to the input terminal of the pressure signal preprocessing module, and the output terminal of the signal preprocessing module is electrically connected to the first signal input terminal of the data processing unit.

[0136] For example, a layered pressure detection unit is used to collect pressure data from nodes at different heights in the pipeline network, providing a precise data source for subsequent control decisions. The specific design is as follows: three pressure sensors can be installed using conventional methods, all of which are diffused silicon piezoresistive sensors. The first pressure sensor has a measurement range of 0-1.0 MPa and is installed on a straight pipe section 1.5m downstream of the inlet valve of the main water supply pipeline (distance from local resistance components ≥ 5 times the pipe diameter). The second pressure sensor has a measurement range of 0-1.6 MPa and is connected via a flange to the side of a vertical pipe section at a height of 150m in the ultra-high-rise pipeline network, with the installation direction perpendicular to the water flow. The third pressure sensor has a measurement range of 0-2.5 MPa and is connected via a threaded connection 0.8m upstream of the user's water supply end at the end of the pipeline network above 300m.

[0137] The pressure signal preprocessing module is powered by DC24V and has a built-in RC low-pass filter circuit, adjustable gain amplifier circuit and 16-bit analog-to-digital converter unit. The signal output terminals of the three pressure sensors are electrically connected to the input terminal of the preprocessing module through shielded cables. The output terminal of the preprocessing module is electrically connected to the first signal input terminal of the data processing unit through an RS485 bus.

[0138] The flow change identification module includes a flow sensor installed in the main water supply pipeline and a signal preprocessing module. The signal output terminal of the flow sensor is electrically connected to the input terminal of the flow signal preprocessing module, and the output terminal of the signal preprocessing module is electrically connected to the second signal input terminal of the data processing unit.

[0139] For example, the flow change identification module is used to identify instantaneous flow changes in the pipeline network in real time and provide early warning of pressure fluctuation risks. The specific solution is as follows: an electromagnetic flowmeter is selected as the flow sensor, with a measurement accuracy of 0.5 class, a response time ≤5ms, a measurement range of 0-1.2 times the rated flow, and an output of 4-20mA analog signal; it is installed according to standard procedures on a straight pipe section 0.8m downstream of the first pressure sensor on the main water supply pipeline. The upstream straight pipe section length is ≥10 times the pipe diameter, and the downstream length is ≥5 times the pipe diameter to ensure measurement accuracy.

[0140] The flow signal preprocessing module also uses DC24V power supply and has a built-in RC low-pass filter circuit, amplifier circuit and 16-bit analog-to-digital converter unit. The signal output terminal of the flow sensor is electrically connected to the input terminal of the preprocessing module, and the output terminal of the preprocessing module is electrically connected to the second signal input terminal of the data processing unit through RS485 bus.

[0141] It should be noted that the pressure and flow signal preprocessing modules are two independent hardware modules with power isolation design to avoid signal crosstalk.

[0142] The adjustment effect feedback unit is used to collect the terminal pressure fluctuation value ΔP3′ after the variable frequency pump is adjusted. It includes a signal acquisition component and a pressure fluctuation value extraction module. The signal acquisition end of the signal acquisition component is electrically connected to the signal output end of the third pressure sensor, and the signal output end of the pressure fluctuation value extraction module is electrically connected to the third signal input end of the data processing unit. For example, a specific scheme includes a signal acquisition component and a pressure fluctuation value extraction module. The signal acquisition component uses a differential amplifier circuit and is electrically connected to the signal output end of the third pressure sensor via a shielded cable. The pressure fluctuation value extraction module is integrated into the expansion board of the data processing unit, communicates with the core board via an SPI interface, and has built-in conventional fluctuation value calculation logic. The output end of this unit is electrically connected to the third signal input end of the data processing unit, and the feedback period can be set to 0.5-2s through the human-machine interface.

[0143] Based on any of the above technical solutions, a further optimization is made: the sampling frequency of each pressure sensor in the layered pressure detection unit is 10-50Hz, and the sampling timing synchronization is achieved through the timing synchronization module built into the data processing unit, with a synchronization error of no more than 50ms.

[0144] Based on any of the above technical solutions, the following optimization is made: the sampling frequency of the flow sensor in the flow mutation identification module is 20-60Hz, and the flow mutation identification module and the hierarchical pressure detection unit establish a data communication link through the data processing unit. After the flow mutation signal is triggered, the data processing unit automatically sends a sampling frequency increase command to the hierarchical pressure detection unit.

[0145] The data interconnection link uses a twisted-pair shielded RS485 bus connection, with the shielding layer grounded at one end. The communication protocol is Modbus-RTU, ensuring stable transmission of sudden change signals and frequency adjustment commands.

[0146] Based on any of the above technical solutions, a further optimization is made: the feedback period of the adjustment effect feedback unit is 0.5-2s. When the terminal pressure fluctuation value ΔP3′ exceeds the threshold ΔP0, the unit sends a coefficient adjustment command to the data processing unit. The data processing unit adjusts the correction coefficients k1 and k2 of the dual-target linkage adjustment model according to the command.

[0147] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention. For those skilled in the art, any alternative improvements or transformations made to the implementation of the present invention fall within the protection scope of the present invention.

[0148] Any aspects of this invention not described in detail are well-known to those skilled in the art.

Claims

1. A method for optimizing water pressure control in a negative pressure-free water supply system, characterized in that, Includes the following steps: S1 collects pressure data from the inlet of the main water supply pipeline, the middle layer of the ultra-high-rise pipe network at 150m, and the end of the pipe network above 300m. S2, construct a dual-objective linkage regulation model for negative pressure prediction and micro-fluctuation suppression, and associate multi-node pressure, flow rate change rate and pipeline dynamic characteristics; S3 acquires stratified pressure data and instantaneous flow data of the pipeline network in real time, and calculates the flow rate change rate and the main pipeline pressure change rate. S4, dynamically correct the target pressure value at the end based on the dynamic characteristics of the pipeline network, and substitute it into the dual-target linkage regulation model to calculate the basic regulation speed of the variable frequency pump; S5, judge the negative pressure risk based on the main pipeline pressure prediction result. If there is a risk, start the negative pressure prediction compensation mechanism to correct the basic regulating speed and obtain the final regulating speed. S6 controls the variable frequency pump to run at the final adjusted speed to complete a single water pressure adjustment; S7. Repeat steps S3 to S6 to achieve dynamic water pressure control in ultra-high-rise precision water use scenarios.

2. The method for optimizing water pressure control in a negative pressure-free water supply system according to claim 1, characterized in that, In step S2, the dual-objective linkage regulation model establishes a predictive formula for the main pipeline pressure: ; Where k1 and k2 are correction coefficients based on the calibration of ultra-high-rise pipeline network parameters, P1 is the main pipeline pressure value, P2 is the middle layer pressure value, P3 is the terminal pressure value, and P... 30 Here, k is the initial target pressure value at the terminal, and k3 is the influence coefficient of sudden flow change. This represents the instantaneous flow rate change.

3. The method for optimizing water pressure control in a negative pressure-free water supply system according to claim 1, characterized in that, In step S4, a compensation and correction formula for micro-fluctuations in terminal water pressure is established based on the elastic water hammer effect of ultra-high-rise pipe networks. For the target pressure value P at the end 30 Dynamic correction: ; Where β is the water hammer compensation coefficient, ranging from 0.02 to 0.08, L is the total length of the ultra-high-rise pipe network, and c is the propagation speed of the pressure wave in the pipe network. The main pipeline pressure change rate.

4. The method for optimizing water pressure control in a negative pressure-free water supply system according to claim 1, characterized in that, In step S5, the correction algorithm for the negative pressure prediction and compensation mechanism is as follows: ; Wherein, n1 is the basic regulating speed, n2 is the final regulating speed after compensation, α is the negative pressure compensation coefficient with a value range of 0.05-0.2, P0 is the negative pressure warning threshold of the main pipeline, γ is the end fluctuation damping coefficient with a value range of 0.03-0.1, ΔP3′ is the actual fluctuation value of the end pressure, and ΔP0 is the end pressure fluctuation threshold.

5. The water pressure control optimization method for a negative pressure-free water supply equipment according to claim 1, characterized in that: In step S1, the sampling frequency of the pressure data is 10-50Hz, and the sampling timing of the pressure data at the inlet of the main pipeline and the pressure data at the outlet is synchronized, with a synchronization error of no more than 50ms.

6. The method for optimizing water pressure control in a negative pressure-free water supply system according to claim 1, characterized in that, The method further includes the following steps: collecting the terminal pressure fluctuation value ΔP3′ after the variable frequency pump is adjusted; when ΔP3′ exceeds the range of ΔP0, automatically adjusting the correction coefficients k1 and k2 of the dual-target linkage adjustment model, with the adjustment range being ±5% of the original value, and the adjusted value not exceeding the optimal value range.

7. A negative pressure-free water supply device, said device being used to implement the water pressure control optimization method for a negative pressure-free water supply device as described in claim 6, characterized in that, The device includes: a layered pressure detection unit, used to collect pressure data of nodes at different heights in the pipeline network; The flow change identification module is used to identify instantaneous flow change states in the pipeline network in real time. The data processing unit is used to receive and process pressure data and flow data; The data processing unit incorporates the aforementioned water pressure control optimization method for negative pressure-free water supply equipment; The variable frequency pump control unit is used to receive adjustment commands from the data processing unit and output variable frequency pump speed control signals. The signal output terminal of the variable frequency pump control unit is electrically connected to the control terminal of the variable frequency pump. The variable frequency pump is used to adjust the water supply pressure according to the speed control signal. The inlet is connected to the municipal water supply network, and the outlet is connected to the high-rise building network.

8. The device according to claim 7, characterized in that, The tiered pressure detection unit includes a first pressure sensor, a second pressure sensor, and a third pressure sensor, which are sequentially installed at the inlet of the main water supply pipeline, at a height of 150m in the ultra-high-rise pipeline network, and at the end of the pipeline network above 300m. It also includes a signal preprocessing module. The signal output terminals of the first pressure sensor, the second pressure sensor, and the third pressure sensor are all electrically connected to the input terminal of the pressure signal preprocessing module. The output terminal of the signal preprocessing module is electrically connected to the first signal input terminal of the data processing unit.

9. The device according to claim 7, characterized in that, The flow change identification module includes a flow sensor installed in the main water supply pipeline and a signal preprocessing module. The signal output terminal of the flow sensor is electrically connected to the input terminal of the flow signal preprocessing module, and the output terminal of the signal preprocessing module is electrically connected to the second signal input terminal of the data processing unit.

10. The device according to claim 7, characterized in that, The adjustment effect feedback unit is used to collect the terminal pressure fluctuation value ΔP3′ after the variable frequency pump is adjusted. It includes a signal acquisition component and a pressure fluctuation value extraction module. The signal acquisition end of the signal acquisition component is electrically connected to the signal output end of the third pressure sensor, and the signal output end of the pressure fluctuation value extraction module is electrically connected to the third signal input end of the data processing unit.

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