Method for rapidly correcting wave velocity of negative pressure wave of long-distance hot water delivery pipe network based on temperature and pressure look-up table

Abstract

The invention belongs to the technical field of leakage monitoring of long-distance hot water transmission pipe networks, and particularly relates to a method for quickly correcting the wave velocity of negative pressure waves of a long-distance hot water transmission pipe network based on temperature and pressure table look-up, which comprises the following steps of: S1, deploying a pressure sensing assembly and a data acquisition unit on a specified pipe section of the long-distance hot water transmission pipe network, the pressure sensing assembly is used for capturing negative pressure wave signals in the pipe network, and the data acquisition unit is used for acquiring temperature parameters and pressure parameters in the operation process of the pipe network. Through the flow method of deploying the sensing and collecting assembly, constructing the associated data table, interpolating and correcting the wave velocity in real time and calculating the leakage position, the industrial pain point that high precision depends on high cost and low cost and cannot be corrected in real time in the field of leakage monitoring of the long-distance hot water transmission pipe network is fundamentally solved, and the leakage monitoring accuracy of the long-distance hot water transmission pipe network is improved. And wave velocity correction is realized only through the incidence relation of the temperature, the pressure and the wave velocity without depending on complex prior parameters such as pipe network topology and friction resistance coefficients, so that the technical scheme is greatly simplified.

Description

Technical Field

[0001] This invention belongs to the field of leakage monitoring technology for long-distance hot water pipelines, and particularly relates to a method for rapid correction of negative pressure wave velocity in long-distance hot water pipelines based on temperature and pressure lookup tables. Background Technology

[0002] In the field of long-distance hot water pipeline leakage monitoring, existing technologies generally rely on "mechanism-data hybrid twin models": first, a hydraulic-thermal coupling model is established based on prior parameters such as pipeline topology, friction coefficient, and soil thermal properties; then, SCADA or CFD is used to iteratively correct the wave velocity. This involves a large amount of computation, requires workstation or cloud GPU support, resulting in leakage location taking seconds to minutes and significant alarm delays. Although the negative pressure wave method has a simple structure, the wave velocity is difficult to obtain accurately in real time due to changes in water temperature and pressure. The error of traditional empirical values ​​or offline CFD estimates can reach ±5% to ±8%, with a location deviation of tens of meters. While adding fiber optic DTS / DAS or wireless sensor nodes can improve accuracy, it also multiplies the equipment and maintenance costs. Therefore, the industry has long faced a gap where "high accuracy requires high cost, while low cost cannot be corrected in real time."

[0003] A search revealed Chinese patent CN117028869A, which discloses a method for identifying and locating leaks in heating pipe networks, relating to the safe operation of heating pipe networks. This method combines negative pressure wave detection with 1D-IResNet. First, a data acquisition system is used to obtain raw data on the negative pressure wave pressure and water temperature at the beginning and end stations of the pipeline. Then, wavelet denoising using hard threshold processing is applied to the raw pressure signals to distinguish between normal, leaking, and valve-regulating operating conditions, and to capture the singularities of the leaking condition. Next, the pressure data under the three operating conditions are used as the training and testing sets for 1D-IResNet, and learning and identification are performed on the one-dimensional data features. Finally, for the identified leaking condition, an improved negative pressure wave location method (i.e., the bisection interval method) is used to locate the leak point. This invention has the advantages of low cost and high sensitivity, while reducing the false alarm rate, thereby improving the reliability of leak identification and location in heating pipe networks.

[0004] However, the aforementioned patented technology has the following drawbacks: 1. Complex correction methods: Some solutions rely on mechanism-data hybrid twin models or online CFD iteration to update wave velocity, which requires a large number of prior parameters such as pipeline topology and friction, resulting in large computational load, which edge PLCs cannot handle, and high engineering implementation costs. 2. Poor real-time performance: Complex models take seconds for each iteration, making it impossible to provide wave velocity within milliseconds, thus delaying leakage alarms; 3. Inconvenient on-site operation: Existing technologies lack simplified solutions, and maintenance personnel cannot complete on-site calibration within 30 seconds, relying on servers or external software, which increases the cost of manual intervention and training. Summary of the Invention

[0005] The purpose of this invention is to address the aforementioned technical problems by providing a method for rapid correction of negative pressure wave velocity in long-distance hot water pipelines based on temperature and pressure lookup tables.

[0006] In view of this, the present invention provides a method for rapid correction of negative pressure wave velocity in long-distance hot water pipelines based on temperature and pressure lookup tables, comprising the following steps: S1: Deploy pressure sensing components and data acquisition units on designated sections of the long-distance hot water pipeline network. The pressure sensing components are used to capture negative pressure wave signals within the pipeline network, and the data acquisition units are used to acquire temperature and pressure parameters during the operation of the pipeline network. S2: Construct a correlation data table of negative pressure wave velocity, temperature and pressure. The correlation data table is generated by triggering micro-leakage events under preset steady-state conditions, collecting negative pressure wave propagation data and then discretizing and interpolating the data. S3: The data acquisition unit acquires the current temperature and pressure parameters of the pipeline network in real time, and combines them with the associated data table to obtain the real-time corrected negative pressure wave velocity through interpolation calculation; S4: Based on the real-time corrected negative pressure wave velocity and the arrival time difference of the negative pressure wave captured by the pressure sensing component, calculate the location of the pipeline leak point and complete the leak location.

[0007] Preferably, in step S1, the designated pipe section is the main inlet pipe section of the pressure reducing station of the long-distance hot water pipeline network, and the pressure sensing component includes at least two high-frequency pressure sensors. The sampling rate of the high-frequency pressure sensors is not less than 1kHz, and the high-frequency pressure sensors are arranged at a preset distance L along the axial direction of the main pipe section.

[0008] Preferably, the data acquisition unit includes a microcontroller, which establishes a communication connection with the SCADA system of the pipeline network to obtain temperature and pressure parameters, and is also connected to a high-frequency pressure sensor to receive negative pressure wave signals.

[0009] Preferably, the specific steps for constructing the associated data table in step S2 include: S21: Using the heat exchanger and pressure regulating valve in the pressure isolation station, multiple sets of steady-state operating conditions are built. The steady-state operating conditions are formed by a cross combination of different temperature values ​​and different pressure values. S22: For each group of steady-state operating conditions, after the operating conditions stabilize, a micro-leakage event is triggered through the branch actuator of the main section; S23: The arrival times t1 and t2 of the negative pressure wave at their respective positions are collected by two high-frequency pressure sensors. The actual negative pressure wave velocity v_real corresponding to each steady-state condition is calculated according to the formula v_real=L / (t2-t1). S24: Set the value range and step size of the temperature parameter and the value range and step size of the pressure parameter, and construct a discrete grid based on the value range and step size; S25: Using the real negative pressure wave velocity v_real obtained in S23, fill the blank cells in the discrete grid with an interpolation algorithm to form a complete association data table; S26: After verifying the associated data table, store it in the on-chip memory unit of the microcontroller.

[0010] Preferably, the multiple steady-state conditions in step S21 include nine sets of conditions formed by cross-combining temperatures of 70℃, 90℃, and 110℃ with pressures of 0.8MPa, 1.4MPa, and 1.8MPa, respectively.

[0011] Preferably, in step S22, the leakage flow rate of the micro-leakage event is less than 1% of the main flow rate.

[0012] Preferably, in step S24, the temperature parameter ranges from 50 to 120°C with a step size of 5°C, the pressure parameter ranges from 0.8 to 2.0 MPa with a step size of 0.2 MPa, and the discrete grid has a size of 15×7.

[0013] Preferably, step S3 includes the following steps: S31: The microcontroller reads the current temperature and pressure parameters of the pipeline network from the SCADA system in 1ms cycles; S32: Determine the target grid cell in the corresponding discrete grid of the current temperature and pressure parameters in the associated data table, and extract the wave velocity values ​​corresponding to the four vertices of the target grid cell; S33: Calculate the normalized distance between the temperature direction and the pressure direction. The normalized distance is calculated based on the difference between the current parameter and the vertex parameter of the target mesh cell and the corresponding step size. S34: Based on the wave velocity value at the vertex of the target grid cell and the normalized distance, the negative pressure wave velocity is obtained in real time through interpolation.

[0014] Preferably, in step S34, the interpolation operation uses a bilinear interpolation algorithm.

[0015] Preferably, step S4 includes the following steps: S41: When a leak occurs in the pipeline, the microcontroller captures and latches the arrival times of the negative pressure waves detected by two high-frequency pressure sensors, and calculates the time difference. ; S42: The microcontroller transmits the real-time corrected negative pressure wave velocity and time difference to the host computer via Ethernet; S43: The host computer combines the interval distance between the two high-frequency pressure sensors, real-time correction of wave velocity and time difference, and substitutes them into the positioning formula to calculate the distance x from the leak point to the first sensor, thus completing the leak location. .

[0016] The beneficial effects of this invention are: This invention fundamentally solves the industry pain points in the field of long-distance hot water pipeline network leakage monitoring, namely, high precision relies on high cost and low cost cannot be corrected in real time, by deploying sensing and acquisition components, constructing a correlation data table, interpolating and correcting wave velocity in real time, and calculating the leakage location. It does not rely on complex prior parameters such as pipeline topology and friction coefficient, but only achieves wave velocity correction through the correlation between temperature, pressure and wave velocity. This greatly simplifies the technical solution and lays the foundation for subsequent improvements in real-time performance and cost reduction. It can be widely adapted to the leakage location needs of long-distance hot water pipeline networks of different specifications and has strong versatility. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall system architecture of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0019] It should be noted that all directional and positional terms used in this invention, such as "up," "down," "left," "right," "front," "back," "vertical," "horizontal," "inner," "outer," "top," "lower," "lateral," "longitudinal," and "center," are only used to explain the relative positional relationships and connections between components in a specific state (as shown in the accompanying drawings). They are merely for the convenience of describing the invention and do not require the invention to be constructed and operated in a specific orientation; therefore, they should not be construed as limitations on the invention. Furthermore, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated.

[0020] In the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0021] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0022] like Figure 1 As shown, the method for rapid correction of negative pressure wave velocity in long-distance hot water pipelines based on temperature and pressure lookup tables includes the following steps: S1: Deploy pressure sensing components and data acquisition units on designated sections of the long-distance hot water pipeline network. The pressure sensing components are used to capture negative pressure wave signals within the pipeline network, and the data acquisition units are used to acquire temperature and pressure parameters during the operation of the pipeline network. S2: Construct a correlation data table of negative pressure wave velocity, temperature and pressure. The correlation data table is generated by triggering micro-leakage events under preset steady-state conditions, collecting negative pressure wave propagation data and then discretizing and interpolating the data. S3: The data acquisition unit acquires the current temperature and pressure parameters of the pipeline network in real time, and combines them with the associated data table to obtain the real-time corrected negative pressure wave velocity through interpolation calculation; S4: Based on the real-time corrected negative pressure wave velocity and the arrival time difference of the negative pressure wave captured by the pressure sensing component, calculate the location of the pipeline leak point and complete the leak location.

[0023] This application fundamentally solves the industry pain point in the field of long-distance hot water pipeline network leakage monitoring: high accuracy relies on high cost, and low cost cannot be corrected in real time. It solves the problem of high cost and inability to correct in real time in the field of long-distance hot water pipeline network leakage monitoring by deploying sensing and acquisition components, building associated data tables, interpolating and correcting wave velocity in real time. It does not rely on complex a priori parameters such as pipeline topology and friction coefficient, but only achieves wave velocity correction through the correlation between temperature, pressure and wave velocity. It greatly simplifies the technical solution and lays the foundation for subsequent improvement of real-time performance and reduction of cost. It can be widely adapted to the leakage location needs of long-distance hot water pipeline networks of different specifications and has strong versatility.

[0024] In the example of this application, in step S1, the designated pipe section is the main inlet pipe section of the pressure reducing station of the long-distance hot water pipeline network, the pressure sensing component includes at least two high-frequency pressure sensors, the sampling rate of the high-frequency pressure sensors is not less than 1kHz, and the high-frequency pressure sensors are arranged at a preset distance L along the axial direction of the main pipe section.

[0025] As a preferred example of this application, the main inlet section of the pressure relief station in the long-distance hot water pipeline network is designated as the main pipe section. This section is a critical node for changes in pipeline pressure and temperature, and has a high risk of leakage. Targeted deployment of sensing components can improve monitoring accuracy. At the same time, the sampling rate of the high-frequency pressure sensor is set to be no less than 1kHz to ensure that it can accurately capture subtle changes in the negative pressure wave signal and avoid misjudgment of the arrival time of the negative pressure wave due to insufficient sampling rate. The sensor is deployed at preset intervals along the axial direction of the main pipe section to provide a stable spatial reference for subsequent calculation of the leak location through time difference, further ensuring the reliability of the positioning data.

[0026] In the example of this application, the data acquisition unit includes a microcontroller. The microcontroller establishes a communication connection with the SCADA system of the pipeline network to obtain temperature and pressure parameters, and simultaneously connects to a high-frequency pressure sensor to receive negative pressure wave signals, realizing integrated acquisition and processing of temperature, pressure parameters, and negative pressure wave signals. No additional server or cloud equipment is required; data interaction is completed directly through the microcontroller, significantly reducing hardware deployment costs. Furthermore, the localized processing characteristics of the microcontroller reduce data transmission latency, providing hardware support for subsequent millisecond-level wave speed correction and leak location, solving the problem of poor real-time performance caused by reliance on cloud computing power in existing technologies.

[0027] In the example of this application, the specific steps for constructing the associated data table in step S2 include: S21: Using the heat exchanger and pressure regulating valve in the pressure isolation station, multiple sets of steady-state operating conditions are built. The steady-state operating conditions are formed by a cross combination of different temperature values ​​and different pressure values. S22: For each group of steady-state operating conditions, after the operating conditions stabilize, a micro-leakage event is triggered through the branch actuator of the main section; S23: The arrival times t1 and t2 of the negative pressure wave at their respective positions are collected by two high-frequency pressure sensors. The actual negative pressure wave velocity v_real corresponding to each steady-state condition is calculated according to the formula v_real=L / (t2-t1). S24: Set the value range and step size of the temperature parameter and the value range and step size of the pressure parameter, and construct a discrete grid based on the value range and step size; S25: Using the real negative pressure wave velocity v_real obtained in S23, fill the blank cells in the discrete grid with an interpolation algorithm to form a complete association data table; S26: After verifying the associated data table, store it in the on-chip memory unit of the microcontroller.

[0028] As a preferred example of this application, the above scheme ensures the accuracy and availability of the associated data table. It utilizes the existing heat exchange units and pressure regulating valves of the pressure relief station to build the operating conditions without additional equipment investment, thus reducing calibration costs. It collects real wave velocity data through actual micro-leakage events, avoiding errors caused by theoretical calculations. Discrete grid construction and interpolation filling extend the data from limited operating conditions to full operating condition coverage, ensuring that accurate wave velocity references can be obtained under different temperature and pressure conditions. The verification and storage steps further ensure the stability of the data table in the microcontroller for storage and retrieval.

[0029] In the example of this application, the multiple steady-state conditions in step S21 include nine sets of conditions formed by cross-combining temperatures of 70℃, 90℃, and 110℃ with pressures of 0.8MPa, 1.4MPa, and 1.8MPa, respectively. As a preferred example of this application, the above-mentioned operating conditions cover the mainstream temperature and pressure ranges of daily operation of long-distance hot water pipelines, ensuring that the core data source of the associated data table is consistent with the actual operating scenario and avoiding wave velocity correction deviations caused by operating conditions deviating from reality; the cross-combination of the 9 sets of operating conditions not only ensures the representativeness of the data, but also eliminates the need for too many experiments, achieving a balance between accuracy and calibration efficiency, and reducing the time cost and operational complexity of on-site calibration.

[0030] In the example of this application, in step S22, the leakage flow of the micro-leakage event is less than 1% of the flow of the main pipe section. This flow scale is highly matched with the micro-leakage scenarios commonly seen in the actual operation of the pipeline network, ensuring that the real wave velocity data collected in this way can directly reflect the wave velocity characteristics under actual leakage conditions, avoiding wave velocity anomalies caused by excessive leakage flow, further improving the practicality and correction accuracy of the associated data table, and reducing leakage location errors caused by data deviation.

[0031] In other examples of this application, the method of triggering the micro-leakage event in step S22 can be replaced by creating a step negative pressure wave by rapidly closing the ball valve or rupturing the diaphragm; In other examples of this application, the storage and updating method of the associated data table in S26 can be replaced by: storing the associated data table in the EEPROM or FRAM storage unit of the microcontroller, and the host computer periodically back-calculating the actual negative pressure wave velocity under the corresponding working condition based on the monitoring data of historical leakage events, and dynamically updating the wave velocity value of the corresponding grid cell in the associated data table to achieve online self-calibration.

[0032] In the example of this application, in step S24, the temperature parameter ranges from 50 to 120°C with a step size of 5°C, the pressure parameter ranges from 0.8 to 2.0 MPa with a step size of 0.2 MPa, and the discrete grid has a size of 15×7. As a preferred example of this application, the above settings achieve a scientific match between the parameter range and the grid density. This range covers the temperature and pressure variation range of the entire process of starting, operating, and shutting down the long-distance hot water pipeline network. The step size setting achieves a balance between data accuracy and storage cost, avoiding correction errors caused by excessively large step sizes while preventing excessively small step sizes from increasing the storage burden on the microcontroller. The 15×7 grid size ensures that the data table can be completely stored in the on-chip storage unit of the microcontroller without the need for additional storage modules, thus reducing hardware costs.

[0033] In other examples of this application, the discrete mesh may be replaced with any of the following forms: Form A: Reduce the step size of temperature and pressure parameters to form a discrete grid with a subdivided step size of 25×11 or 31×15; Form B: Add the fluid velocity parameter or Reynolds number parameter in the pipeline network as a third dimension to construct a three-dimensional discrete grid. The step size of the three-dimensional discrete grid satisfies that the wave velocity calculation time using the trilinear interpolation algorithm does not exceed 100µs.

[0034] In the example of this application, step S3 specifically includes the following steps: S31: The microcontroller reads the current temperature and pressure parameters of the pipeline network from the SCADA system in 1ms cycles; S32: Determine the target grid cell in the corresponding discrete grid of the current temperature and pressure parameters in the associated data table, and extract the wave velocity values ​​corresponding to the four vertices of the target grid cell; S33: Calculate the normalized distance between the temperature direction and the pressure direction. The normalized distance is calculated based on the difference between the current parameter and the vertex parameter of the target mesh cell and the corresponding step size. S34: Based on the wave velocity value at the vertex of the target grid cell and the normalized distance, the real-time corrected negative pressure wave velocity is obtained through interpolation. As a preferred example of this application, the 1ms acquisition cycle ensures timely capture of dynamic changes in pipeline temperature and pressure, avoiding wave velocity correction deviations caused by parameter lag; the determination of target grid cells and the calculation of normalized distance provide a basis for accurate interpolation, ensuring that even if the parameters are between discrete grid nodes, accurate wave velocity values ​​can be obtained through interpolation, solving the positioning deviation problem caused by the use of fixed wave velocity in existing technologies, and providing real-time and accurate wave velocity data support for subsequent accurate leak location.

[0035] In the example of this application, the interpolation operation in step S34 uses a bilinear interpolation algorithm; As a preferred example of this application, the algorithm has a small computational load, and its execution time in a microcontroller can be controlled within 20µs, which is fully compatible with the 1ms real-time correction cycle and ensures the timeliness of wave velocity correction. At the same time, the bilinear interpolation algorithm can realize the smooth transition of wave velocity between discrete grid nodes, avoid the sudden change of wave velocity caused by improper interpolation method, further improve the accuracy of wave velocity correction, and provide algorithmic support for controlling the leakage location error within 5m.

[0036] In the example of this application, step S4 specifically includes the following steps: S41: When a leak occurs in the pipeline, the microcontroller captures and latches the arrival times of the negative pressure waves detected by two high-frequency pressure sensors, and calculates the time difference. ; S42: The microcontroller transmits the real-time corrected negative pressure wave velocity and time difference to the host computer via Ethernet; S43: The host computer combines the interval distance between the two high-frequency pressure sensors, real-time correction of wave velocity and time difference, and substitutes them into the positioning formula to calculate the distance x from the leak point to the first sensor, thus completing the leak location. ; As a preferred example of this application, in the specific settings of step S4, the microcontroller directly captures the arrival time of the negative pressure wave and calculates the time difference, avoiding time difference errors caused by data transmission delay; Ethernet transmission ensures that the real-time correction of wave velocity and time difference can be quickly uploaded to the host computer, and the host computer can calculate the leak location through a simple formula without complex iterative calculations. The entire positioning process can be controlled within 2ms, solving the alarm delay problem caused by the second to minute positioning time of the prior art, realizing rapid detection and positioning of leaks, and reducing economic losses and safety risks caused by leaks.

[0037] Example 1: Application of leakage monitoring at the inlet section of a pressure relief station in a long-distance hot water pipeline network in a certain city: At the inlet of the pressure relief station in the long-distance hot water pipeline network, two high-frequency pressure sensors, PT1 and PT2, with a range of 0-2.5MPa and a sampling rate of 1kHz, have been installed on the DN600 main pipe, with a spacing of L=107m. They are connected to an STM32H7 microcontroller via a 4-20mA to 0-5V signal. The STM32H7 reads the current water temperature T and pressure P from the SCADA system in the station in real time (sampling period 1s). The chip's internal Flash memory stores a 15×7 two-dimensional table of v(T,P), with each entry representing the experimentally calibrated wave velocity value. Bilinear interpolation is performed every 1ms to map the real-time (T,P) to a corrected wave velocity v_corr, which is then output to the host computer / server via the Ethernet RMII interface. The host computer simultaneously receives the arrival times t1 and t2 of the negative pressure waves from PT1 and PT2, and uses the corrected negative pressure wave velocity v_corr and the time difference Δt measured at both ends to deduce the distance x from the leak point to the first-end sensor. The result is then written to the alarm system. The entire link achieves millisecond-level closed-loop operation without the need for an additional server.

[0038] Leakage location calculation formula: x=(L-v_corr·Δt) / 2 in: L: The actual pipe section length from the first sensor to the last sensor (unit: m).

[0039] v_corr: Real-time negative pressure wave velocity (unit: m / s) obtained by looking up a table for temperature T and pressure P and then bilinearly interpolating.

[0040] Δt: The difference between the time t1 when the negative pressure wave propagates from the leak point to both the beginning and end points, and the time t2 when it arrives at the sensor at the beginning and the sensor at the end, respectively. (Unit: s).

[0041] X: Distance from the leak point to the sensor at the beginning (unit: m).

[0042] The experimental procedure for generating the two-dimensional v(T, P) table is as follows: At the DN600 main pipe section at the entrance of the pressure relief station, nine steady-state operating conditions were established using the heat exchanger and pressure regulating valve within the station: temperatures of 70℃, 90℃, and 110℃ were cross-combined with pressures of 0.8MPa, 1.4MPa, and 1.8MPa. After each operating condition stabilized, a micro-leakage (leakage flow rate <1% of the main pipe flow rate) was created by momentarily opening the solenoid valve of the DN100 branch for 1 second. 1kHz pressure sensors at both ends continuously collected the arrival times t1 and t2 of the negative pressure wave, calculating the true wave velocity v_real = L / (t2-t1). Subsequently, the 50-120℃ range was divided into 15×7 grids with a step size of 5℃, and the 0.8-2.0MPa range with a step size of 0.2MPa. Bilinear interpolation was used to fill the blank cells, forming a two-dimensional table v(T,P). This table was burned into the internal Flash of the STM32H7 within 30 seconds, with CRC verification, completing the one-time on-site calibration.

[0043] The real-time operation process of STM32H7 is as follows: The STM32H7 main loop operates on a 1ms cycle. It reads the current temperature T and pressure P from the SCADA register. On the discrete grid of the two-dimensional wave velocity table v(T,P), given any pair of real-time temperature T and pressure P, it uses the wave velocity values ​​at the four vertices of the rectangular cell containing the given pair. Through two linear interpolations, it calculates a continuous and smooth corrected wave velocity v_corr, thus eliminating wave velocity drift caused by temperature and pressure changes. This is then written to an Ethernet UDP frame. When a negative pressure wave interrupt is triggered, the STM32H7 hardware captures the arrival times t1 and t2 of the first and last stations. The host computer reads v_corr and Δt via Ethernet to calculate the leak location. The entire process takes less than 2ms and requires no server intervention. The formula for calculating v_corr is: v_corr=v[i]j(1-β)+v[i+1][j]α(1-β)+v[i]j+1β+v[i+1][j+1]αβ (where α=(T-T_i) / 5, β=(P-P_j) / 0.2) in: v[i][j]: Wave velocity value at the bottom left corner of the grid cell, corresponding to temperature T_i and pressure P_j.

[0044] v[i+1][j]: Wave velocity value in the lower right corner, corresponding to temperature T_{i+1} and pressure P_j.

[0045] v[i][j+1]: Wave velocity value in the upper left corner, corresponding to temperature T_i and pressure P_{j+1}.

[0046] v[i+1][j+1]: Wave velocity value in the upper right corner, corresponding to temperature T_{i+1} and pressure P_{j+1}.

[0047] α: Normalized distance in the direction of temperature, α= / 5℃, where 5℃ is the temperature step.

[0048] β: Normalized distance in the direction of pressure, β= / 0.2MPa, where 0.2MPa is the pressure step.

[0049] The formula first performs two linear interpolations along the temperature direction under a fixed pressure, and then performs one linear interpolation along the pressure direction to obtain the accurate wave velocity v_corr on the continuous surface v(T,P).

[0050] The principle of double-line interpolation is as follows: The discrete (T,P) points obtained from the experiment are arranged into a 15×7 grid, with each grid cell being a rectangle of 5 ℃ × 0.2 MPa. Given an arbitrary working condition (T,P), it is determined that it falls within the cell (i,j). Then, bilinear weighting is performed using the four vertex wave velocities v[i][j], v[i+1][j], v[i][j+1], and v[i+1][j+1]: two linear interpolations are performed in the T direction to obtain two straight lines in the P direction, and then one linear interpolation is performed in the P direction to obtain the continuous surface v(T,P). This method requires only 4 multiplications and 3 additions, and the execution time on the STM32H7 is <20 µs, satisfying a 1 ms real-time cycle.

[0051] Furthermore, in the above embodiment 1, the wave velocity v(T,P) table can be replaced in terms of data dimensions as follows: Expand the "15×7" grid to "25×11" or "31×15" with a finer step size; or change it to a non-uniform grid (for higher temperature areas). Alternatively, a third dimension can be added: flow velocity (or Reynolds number Re) → a three-dimensional lookup table v(T,P,Re) can be used, and trilinear interpolation can still be completed within 100µs.

[0052] The interpolation method can be replaced by: Bilinear → bicubic B-spline, or Shepard-based inverse distance weighting, improves surface smoothness; Change the lookup table + interpolation method to online fitting: run a 3rd-order polynomial regression v=aT²+bP²+cTP+d in the MCU, with the coefficients updated by the host computer every 10 minutes.

[0053] The calibration method can be replaced by: Instead of using solenoid valves to prevent leakage, quick-closing ball valves or bursting diaphragms are used to generate step negative pressure waves, resulting in higher wave velocity measurement accuracy. The calibration conditions have been changed from "9 groups" to "online self-calibration": the host computer back-calculates v_real based on historical leakage events and dynamically updates the corresponding grid values ​​in the table (stored in EEPROM or FRAM).

[0054] The "all-fiber optic" negative pressure wave positioning system can be replaced by: Distributed fiber acoustic sensing (DAS) is laid along the DN600 main pipe, with the optical fiber itself serving as the sensor; The negative pressure wave generated by the leak causes strain and phase change in the optical fiber. The DAS host directly provides the leak location without the need for MCU, table lookup, or interpolation.

[0055] In summary, this application addresses the 20–30m error in existing heating network negative pressure wave positioning caused by fixed wave velocity by proposing a "two-dimensional temperature-pressure lookup table + millisecond-level interpolation" technical solution: Utilizing two 1kHz high-frequency pressure sensors and a SCADA temperature / pressure interface already installed on-site, a 15×7 v(T,P) two-dimensional table is embedded within the PLC / ARM edge box; during operation, the PLC reads the current T and P every 1ms → performs bilinear interpolation → outputs the real-time wave velocity v_corr; the host computer then uses v_corr and the arrival time difference Δt between the first and last negative pressure waves to complete the positioning. The system requires no pipeline topology, prior friction or server iteration, has a positioning error of ≤5m, and can be recalibrated on site in 30s; To address the problem of "complex correction methods" in existing technologies, a wave velocity correction method is provided that can be completed solely based on existing temperature and pressure SCADA data on-site. This method completely eliminates the dependence on prior parameters such as pipeline topology and friction coefficient, as well as mechanism-data twin models, reducing the computational load to "two-dimensional table + bilinear interpolation" and can be executed directly within an edge PLC / MCU.

[0056] To address the issue of "poor real-time performance" in existing technologies, the wave velocity correction time is compressed from the existing second-level model iteration to the millisecond level, enabling the output of real-time wave velocity in just "2-step table lookup". This ensures that the negative pressure wave positioning algorithm is completed within a millisecond-level closed loop, avoiding leakage alarm delays.

[0057] To address the issue of "inconvenient on-site operation" in existing technologies, a 15×7 v(T,P) two-dimensional lookup table structure is established. On-site calibration or updates can be completed within 30 seconds simply by reading the current T and P, without the need for servers or external software, significantly reducing maintenance complexity and training costs. Achieved: Reduce wave velocity correction costs: The two-dimensional table is generated entirely from 27 sets of field experiments in one go, without the need to solve prior parameters such as pipeline topology and friction coefficient; the interpolation operation is completed in one go within the STM32H7, reducing the "mechanism-data hybrid twin model" to "one table + one formula", which can be handled by PLC or microcontroller, reducing the engineering implementation cost by an order of magnitude.

[0058] Reduce alarm latency: The time for completing wave velocity correction and positioning is reduced from the existing "second-level iteration" to "millisecond-level closed loop", avoiding leakage alarm latency.

[0059] The embodiments of this application have been described above with reference to the accompanying drawings. Unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other. This application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for rapid correction of negative pressure wave velocity in long-distance hot water pipelines based on temperature and pressure lookup tables, characterized in that: Includes the following steps: S1: Deploy pressure sensing components and data acquisition units on designated sections of the long-distance hot water pipeline network. The pressure sensing components are used to capture negative pressure wave signals within the pipeline network, and the data acquisition units are used to acquire temperature and pressure parameters during the operation of the pipeline network. S2: Construct a correlation data table of negative pressure wave velocity, temperature and pressure. The correlation data table is generated by triggering micro-leakage events under preset steady-state conditions, collecting negative pressure wave propagation data and then discretizing and interpolating the data. S3: The data acquisition unit acquires the current temperature and pressure parameters of the pipeline network in real time, and combines them with the associated data table to obtain the real-time corrected negative pressure wave velocity through interpolation calculation; S4: Based on the real-time corrected negative pressure wave velocity and the arrival time difference of the negative pressure wave captured by the pressure sensing component, calculate the location of the pipeline leak point and complete the leak location.

2. The method for rapid correction of negative pressure wave velocity in long-distance hot water pipelines based on temperature and pressure lookup tables as described in claim 1, characterized in that: In step S1, the designated pipe section is the main inlet pipe section of the pressure reducing station of the long-distance hot water pipeline network. The pressure sensing component includes at least two high-frequency pressure sensors. The sampling rate of the high-frequency pressure sensors is not less than 1kHz, and the high-frequency pressure sensors are arranged at a preset distance L along the axial direction of the main pipe section.

3. The method for rapid correction of negative pressure wave velocity in long-distance hot water pipelines based on temperature and pressure lookup tables as described in claim 2, characterized in that: The data acquisition unit includes a microcontroller, which establishes a communication connection with the SCADA system of the pipeline network to obtain temperature and pressure parameters, and is also connected to a high-frequency pressure sensor to receive negative pressure wave signals.

4. The method for rapid correction of negative pressure wave velocity in long-distance hot water pipelines based on temperature and pressure lookup tables as described in claim 3, characterized in that: The specific steps for constructing the related data table in step S2 include: S21: Using the heat exchanger and pressure regulating valve in the pressure isolation station, multiple sets of steady-state operating conditions are built. The steady-state operating conditions are formed by a cross combination of different temperature values ​​and different pressure values. S22: For each group of steady-state operating conditions, after the operating conditions stabilize, a micro-leakage event is triggered through the branch actuator of the main section; S23: The arrival times t1 and t2 of the negative pressure wave at their respective positions are collected by two high-frequency pressure sensors. The actual negative pressure wave velocity v_real corresponding to each steady-state condition is calculated according to the formula v_real=L / (t2-t1). S24: Set the value range and step size of the temperature parameter and the value range and step size of the pressure parameter, and construct a discrete grid based on the value range and step size; S25: Using the real negative pressure wave velocity v_real obtained in S23, fill the blank cells in the discrete grid with an interpolation algorithm to form a complete association data table; S26: After verifying the associated data table, store it in the on-chip memory unit of the microcontroller.

5. The method for rapid correction of negative pressure wave velocity in long-distance hot water pipelines based on temperature and pressure lookup tables as described in claim 4, characterized in that: In step S21, the multiple steady-state operating conditions include nine sets of operating conditions formed by cross-combining temperatures of 70℃, 90℃, and 110℃ with pressures of 0.8MPa, 1.4MPa, and 1.8MPa, respectively.

6. The method for rapid correction of negative pressure wave velocity in long-distance hot water pipelines based on temperature and pressure lookup tables as described in claim 4, characterized in that: In step S22, the leakage flow rate of the micro-leakage event is less than 1% of the main flow rate.

7. The method for rapid correction of negative pressure wave velocity in long-distance hot water pipelines based on temperature and pressure lookup tables as described in claim 4, characterized in that: In step S24, the temperature parameter ranges from 50 to 120°C with a step size of 5°C, the pressure parameter ranges from 0.8 to 2.0 MPa with a step size of 0.2 MPa, and the discrete grid has a size of 15×7.

8. The method for rapid correction of negative pressure wave velocity in long-distance hot water pipelines based on temperature and pressure lookup tables as described in claim 3, characterized in that: The specific steps of step S3 include: S31: The microcontroller reads the current temperature and pressure parameters of the pipeline network from the SCADA system in 1ms cycles; S32: Determine the target grid cell in the corresponding discrete grid of the current temperature and pressure parameters in the associated data table, and extract the wave velocity values ​​corresponding to the four vertices of the target grid cell; S33: Calculate the normalized distance between the temperature direction and the pressure direction. The normalized distance is calculated based on the difference between the current parameter and the vertex parameter of the target mesh cell and the corresponding step size. S34: Based on the wave velocity value at the vertex of the target grid cell and the normalized distance, the negative pressure wave velocity is obtained in real time through interpolation.

9. The method for rapid correction of negative pressure wave velocity in long-distance hot water pipelines based on temperature and pressure lookup tables as described in claim 8, characterized in that: In step S34, the interpolation operation uses the bilinear interpolation algorithm.

10. The method for rapid correction of negative pressure wave velocity in long-distance hot water pipelines based on temperature and pressure lookup tables according to claim 9, characterized in that: The specific steps of step S4 include: S41: When a leak occurs in the pipeline, the microcontroller captures and latches the arrival time of the negative pressure wave detected by two high-frequency pressure sensors, and calculates the time difference Δt, where Δt = ; S42: The microcontroller transmits the real-time corrected negative pressure wave velocity and time difference to the host computer via Ethernet; S43: The host computer combines the interval distance between the two high-frequency pressure sensors, real-time correction of wave velocity and time difference, and substitutes them into the positioning formula to calculate the distance x from the leak point to the first sensor, thus completing the leak location, where x = .

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