Intelligent water meter position health monitoring and fault-tolerant switching method and system

Abstract

The invention discloses an intelligent water meter position health monitoring and fault-tolerant switching method and system, and relates to the field of fluid pressure measurement. The method comprises the steps that pressure disturbance waves are applied to one end of a verification pipeline, pressure waveform data in the pressure disturbance wave propagation process are collected, abnormal distortion characteristics in the pressure waveform data are determined, and the abnormal distortion characteristics are calculated; positioning a target meter position associated with the abnormal distortion feature, and determining a fault mode of the target meter position; when the fault mode is connection leakage, generating a mechanical adjustment instruction for controlling a mechanical clamp corresponding to the target meter position; after the mechanical adjustment instruction is executed and the pipeline pressure is recovered through verification, the pressure disturbance waves are applied again, and new pressure waveform data are collected to determine the repair effect; and when the repairing effect does not reach the preset standard, generating a virtual shielding mask for isolating the target meter position, and eliminating the measurement data of the target meter position. By implementing the application, the timeliness of detecting the meter position fault of the water meter can be improved.

Description

Technical Field

[0001] This application relates to the field of fluid pressure measurement, and in particular to a method and system for health monitoring and fault-tolerant switching of smart water meter positions. Background Technology

[0002] With the rapid development of smart water management and the increasing market for smart water meters, extremely high demands are being placed on the capacity and quality control of water meter calibration production lines. Large-scale series-connected water meter calibration devices have become the industry mainstream due to their high efficiency. These devices typically consist of dozens of consecutive test positions along a calibration pipeline. These positions, as the core physical carriers, are responsible for mechanically clamping the water meters, forming a sealed water circuit, and conducting calibration tests and data exchange during the calibration process. Therefore, the operational health of each individual position directly determines the production efficiency of the entire calibration line and the reliability of the final calibration data.

[0003] In this technology, the calibration system employs a combination of online global monitoring and offline manual inspection. For online monitoring, pressure sensors are installed at the inlet and outlet of the calibration pipeline. The main control system collects and monitors the total pressure and total pressure drop of the entire pipeline in real time. During the calibration process, when the system detects that the total pressure or total pressure drop deviates abnormally from the preset normal operating range, it triggers a global shutdown alarm. For offline maintenance, the production line regularly schedules manual inspections, using standard gauges or specialized tooling to perform offline tests on the sealing performance of each gauge location, either individually or in sections.

[0004] However, series-connected calibration pipelines have strong pressure buffering and fluid volume. When a small leak occurs at a connection of a gauge due to poor sealing, the resulting local flow loss and pressure change, after being distributed across the entire long pipeline, will result in a very small change in the total pressure drop. Furthermore, related technologies cannot detect the existence of such local faults in real time, leading to reliance on inefficient offline manual inspections for fault detection, or passive response only when the leak expands to the point of affecting the overall pressure. Summary of the Invention

[0005] This application provides a method and system for health monitoring and fault-tolerant switching of smart water meter positions, which can improve the timeliness of water meter position fault detection.

[0006] Firstly, this application provides a method for health monitoring and fault-tolerant switching of smart water meter positions, applied to a calibration system. The calibration system includes multiple meter positions arranged in series along a calibration pipeline, each equipped with a mechanical clamp. The method includes: applying a pressure disturbance wave to one end of the calibration pipeline and collecting pressure waveform data during the propagation of the pressure disturbance wave based on pressure sensors at at least two different locations on the pipeline; comparing and analyzing the pressure waveform data with a reference waveform data to determine abnormal distortion features in the pressure waveform data; locating the target meter position associated with the abnormal distortion features based on the time delay and waveform change type of the abnormal distortion features, and determining the fault mode of the target meter position; when the fault mode is a connection leak, generating a mechanical adjustment command to control the mechanical clamp corresponding to the target meter position to perform a loosening and re-clamping operation; after the mechanical adjustment command is executed and the pipeline pressure is restored, applying the pressure disturbance wave again and collecting new pressure waveform data to determine the repair effect; when the repair effect does not meet a preset standard, generating a virtual shielding mask to isolate the target meter position and removing the metering data of the target meter position.

[0007] In the above embodiments, the calibration system applies pressure disturbance waves and analyzes their distortion to locate, attempt to repair, and isolate faults in individual meter positions, ensuring the timeliness of water meter position fault detection and improving the operational efficiency and data reliability of the calibration pipeline.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the step of comparing and analyzing pressure waveform data and reference waveform data to determine abnormal distortion features in the pressure waveform data specifically includes: decomposing the pressure waveform data into incident wave components and reflected wave components that propagate forward along the test pipeline based on a wave separation algorithm; calculating the difference signal between the reflected wave component and the reference reflected waveform; and extracting the waveform abrupt change at the position corresponding to the signal amplitude when the amplitude of the difference signal exceeds a preset difference threshold, as an abnormal distortion feature caused by pipeline impedance discontinuity.

[0009] In the above embodiments, the verification system can extract weak reflection signals caused by pipeline impedance discontinuity from waveform data through wave separation algorithm and difference signal analysis, thereby improving the detection accuracy of abnormal distortion features.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the step of locating the target tabletop associated with the abnormal distortion feature based on the time delay of the abnormal distortion feature occurrence and the waveform change type, and determining the fault mode of the target tabletop, specifically includes: calculating the friction distance of the abnormal source on the calibration pipeline based on the propagation speed of the pressure disturbance wave in the current medium environment and the transit time difference of the abnormal distortion feature relative to the incident wave front; mapping and matching the friction distance with the installation position coordinates of each tabletop to determine the target tabletop; and determining the fault mode of the target tabletop matching in the fault feature database based on the waveform polarity, waveform amplitude, and spectral characteristics of the abnormal distortion feature.

[0011] In the above embodiments, the verification system combines the transit time difference to calculate the distance and the waveform characteristics to match the fault mode, thereby realizing a complete diagnosis from detecting the anomaly to locating the specific table position and determining the cause of the fault.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, before the step of applying a pressure disturbance wave to one end of the calibration pipeline, the method further includes: acquiring the static fluid pressure and fluid temperature in the calibration pipeline; substituting the static fluid pressure and fluid temperature into the fluid state equation to calculate the theoretical sound velocity of the fluid; and determining the calibration compensation parameter for the propagation speed of the pressure disturbance wave based on the theoretical sound velocity of the fluid.

[0013] In the above embodiments, the verification system eliminates the influence of environmental changes on wave velocity by measuring fluid parameters in real time and calibrating the sound velocity, ensuring the accuracy of fault location based on transit time calculation and avoiding location errors.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, before the step of collecting the static fluid pressure and fluid temperature in the calibration pipeline, the method further includes: when the calibration pipeline is in operation, collecting background noise pressure data and performing spectrum analysis to determine the environmental noise frequency band of the calibration pipeline; adjusting the excitation frequency and frequency bandwidth of the pressure disturbance wave so that the main energy spectrum of the pressure disturbance wave avoids the environmental noise frequency band of the calibration pipeline.

[0015] In the above embodiments, the verification system avoids environmental noise frequency bands through spectrum analysis, so that the main energy of the excitation signal is concentrated in the frequency band with a high signal-to-noise ratio, which enhances the identification of weak fault signals and reduces the probability of system misjudgment or missed judgment.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, after generating a virtual shielding mask to isolate the target tabletop and removing the measurement data of the target tabletop, the method further includes: updating the valid tabletop mapping map of the current verification batch according to the virtual shielding mask; adjusting the verification result judgment logic of the verification system based on the valid tabletop mapping map; generating an abnormal status report containing the location of the faulty tabletop, the fault type, and the repair failure record, and sending the abnormal status report to the remote monitoring terminal.

[0017] In the above embodiments, after a repair failure, the verification system updates the valid epitope mapping diagram and generates a report, thereby achieving intelligent fault tolerance management at the system level, ensuring the continuous operation of the verification process, and providing clear fault records for manual maintenance.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, the step of adjusting the verification result judgment logic of the verification system based on the effective tabletop mapping map specifically includes: acquiring target pressure waveform data at the target tabletop, calculating the exponential decay coefficient of the target pressure waveform data to determine the equivalent leakage orifice diameter at the target tabletop; calculating the equivalent leakage rate based on the equivalent leakage orifice diameter and the current pipeline static pressure value; terminating the current verification process when the equivalent leakage rate exceeds a preset safety threshold; determining the leakage flow rate value of the target tabletop based on the equivalent leakage rate when the equivalent leakage rate does not exceed the preset safety threshold; and using the corrected flow rate data after deducting the leakage flow rate value as the verification result data for effective tabletops located downstream of the target tabletop.

[0019] In the above embodiments, the verification system quantifies the leakage rate and corrects the downstream flow data, so that even if an irreparable minor leak occurs at a certain station, the system can still provide accurate verification results for other normal stations, ensuring verification continuity.

[0020] In a second aspect, embodiments of this application provide a testing system comprising: one or more processors and a memory; the memory is coupled to the one or more processors and is used to store computer program code, the computer program code including computer instructions, wherein the one or more processors invoke the computer instructions to cause the testing system to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a testing system, cause the testing system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a testing system, cause the testing system to perform the method described in the first aspect and any possible implementation thereof.

[0023] Understandably, the verification system provided in the second aspect, the computer storage medium provided in the third aspect, and the computer program product provided in the fourth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0025] 1. By employing a technical solution that applies a pressure disturbance wave to one end of the calibration pipeline, analyzes waveform data collected by a pressure sensor to locate and determine the fault mode, generates a mechanical adjustment command to attempt automatic repair after identifying a connection leak, and generates a virtual shielding mask to isolate the fault location after repair failure, this application can detect local anomalies at any location in the pipeline with high sensitivity, ensuring the continuity of the entire calibration production line and the reliability of the calibration results, thereby improving production efficiency and quality control.

[0026] 2. By employing a technical solution that calculates the distance along the path of the abnormal source based on the propagation speed of the pressure disturbance wave in the current medium environment and the transit time difference of the abnormal distortion characteristics, and then mapping and matching this distance with the table installation coordinates to determine the target table position, and comparing the polarity, amplitude, and spectral characteristics of the distorted waveform with the fault feature database to determine the specific fault mode, this application can accurately associate the detected abnormal signal with the specific fault point and fault type in the physical world. This provides clear instructions for the system to take targeted follow-up measures (such as re-clamping, shielding, or alarm), shortens the fault handling time, and reduces maintenance costs.

[0027] 3. Because the technical solution of collecting static fluid pressure and temperature in the pipeline before applying the pressure disturbance wave, and calculating the theoretical sound velocity using the fluid state equation to determine the calibration compensation parameters for the propagation speed of the pressure disturbance wave is adopted, this application uses the accurate wave velocity value corrected for the current environmental conditions when performing fault location calculations. This ensures the reliability of the target position location and is a necessary prerequisite for achieving subsequent accurate operations (such as repair and isolation), thereby improving the robustness and reliability of the entire monitoring system. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating the smart water meter position health monitoring and fault-tolerant switching method in the embodiments of this application;

[0029] Figure 2 This is another flowchart illustrating the smart water meter position health monitoring and fault-tolerant switching method in the embodiments of this application;

[0030] Figure 3 This is a schematic diagram of the physical device structure of a verification system in the embodiments of this application. Detailed Implementation

[0031] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions a, an, the above, the, and this are intended to also include the plural expressions unless the context clearly indicates otherwise. It should also be understood that the terms used in this application refer to any or all possible combinations that include one or more of the listed items.

[0032] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0033] In the implementation scenario of this application, the calibration pipeline refers to a closed pipeline system used to install multiple water meters to be calibrated in series. A meter position refers to a specific location or workstation on the calibration pipeline pre-set for installing a single water meter; each meter position is equipped with a mechanical clamp for mechanically fixing and sealing the water meter into the calibration pipeline. A pressure disturbance wave is a brief pressure pulse or wave packet generated at one end of the pipeline by an excitation source, which propagates along the pipeline as a detection signal. Abnormal distortion characteristics refer to the morphological changes of reflected or transmitted waves generated when the pressure disturbance wave encounters discontinuities in pipeline characteristics (such as leaks or blockages) during propagation; it is a direct basis for judging the existence of a fault. A virtual shielding mask is a software-level logical marker used to identify a meter position as faulty in the system and instruct subsequent data processing flows to ignore the data of that meter position. This application transforms previously difficult-to-detect localized minor faults into identifiable and locatable waveform features through detection and signal analysis. This enables precise monitoring of the health status of each station in a large-scale serial verification system, solving the problem of traditional methods failing to detect localized faults in a timely manner and ensuring the continuity and reliability of verification work.

[0034] The following describes the process of the method provided in this implementation. Please refer to [link / reference]. Figure 1 This is a flowchart illustrating a method for health monitoring and fault-tolerant switching of smart water meter positions in an embodiment of this application.

[0035] S101. Apply a pressure disturbance wave to one end of the test pipeline, and collect pressure waveform data during the propagation of the pressure disturbance wave based on pressure sensors at at least two different locations on the test pipeline.

[0036] Pressure disturbance waves refer to transient pressure changes that serve as diagnostic signals. Pressure sensors are devices used to convert pressure signals into electrical signals; at least two sensors are used to distinguish between incident and reflected waves. Pressure waveform data refers to a sequence of pressure values ​​that change over time.

[0037] Specifically, before each calibration batch begins or during equipment idle periods, the calibration system controls an excitation source (such as a fast-switching solenoid valve or piezoelectric actuator) located at the inlet of the calibration pipeline to generate a pressure pulse of a preset waveform. This pulse propagates along the pipeline as a pressure disturbance wave. Simultaneously, the calibration system synchronously acquires the output signals of two or more pressure sensors installed near the excitation source and at another location in the pipeline at a high sampling rate, forming a time-series pressure waveform data that records the propagation and reflection process of the disturbance wave.

[0038] In some embodiments, this step can be implemented in several ways: Optionally, the calibration system controls a solenoid valve at the pipeline inlet to rapidly open and close once within a short period of time (e.g., milliseconds), generating an approximately step pressure wave; the calibration system drives a piezoelectric ceramic oscillator coupled to the pipe wall to generate a swept-frequency signal or Gaussian pulse signal with a specific center frequency and bandwidth. Optionally, the calibration system installs a pressure sensor at both the beginning and end of the pipeline, and comprehensively monitors the wave propagation process by analyzing the waveforms recorded by the two sensors. It is understood that other methods can also be used to apply the pressure disturbance wave and acquire data, which are not limited here.

[0039] S120. Compare and analyze the pressure waveform data and the reference waveform data to determine the abnormal distortion characteristics in the pressure waveform data.

[0040] The reference waveform data refers to the pressure waveform data collected and stored when the test pipeline is in a confirmed healthy state (i.e., without any leaks or blockages). Abnormal distortion characteristics refer to the additional peaks, troughs, or morphological changes that appear at a specific point in time compared to the reference waveform.

[0041] Specifically, the verification system retrieves pre-stored reference waveform data from the memory. Then, it aligns and compares the newly acquired pressure waveform data from step S101 with this reference data in the time domain. The verification system calculates the difference signal between the two and checks the amplitude of this difference signal. When the amplitude of the difference signal at a certain time point exceeds a preset fluctuation threshold, the verification system marks the original waveform segment near that time point as having abnormal distortion characteristics.

[0042] In some embodiments, slow drift in the overall pressure or temperature of the calibration pipeline may cause a shift in the entire waveform baseline, leading to misjudgment. To address this, the calibration system preprocesses the currently acquired waveform data and the reference waveform data before comparative analysis, for example, by performing mean removal or baseline correction operations, to eliminate the interference of slowly changing environmental factors on anomaly detection.

[0043] S103. Based on the time delay and waveform change type of the abnormal distortion features, locate the target table position associated with the abnormal distortion features and determine the fault mode of the target table position.

[0044] In this context, time delay refers to the time difference between the arrival of the abnormal distortion feature (usually the wavefront of the reflected wave) and the initial incident wavefront at the sensor. Waveform change types include the polarity (positive or negative), amplitude, and attenuation rate of the reflected wave. The target position refers to the specific position of the meter that is identified as the source of the fault. The fault mode refers to the specific type of fault, such as a connection leak or a valve not fully open.

[0045] Specifically, the calibration system first calculates the distance from the point of impedance discontinuity that causes the abnormal reflection to the sensor based on the time delay and the known propagation speed of the pressure wave. Then, the system compares this calculated distance with the pre-stored precise installation coordinates of each gauge position, identifying the gauge position with the best distance match as the target gauge position. Next, the system analyzes the detailed waveform of the abnormal distortion characteristic; for example, negative pulses typically correspond to leakage (pressure release), and positive pulses correspond to blockage (pressure increase), and matches this waveform with a pre-established fault characteristic mode database to determine the fault mode of the target gauge position.

[0046] In some embodiments, this step can be implemented in several ways: Optionally, the verification system maintains a hash table, with the key being the table position number and the value being its distance coordinates along the path. The matching table position is directly queried using the distance calculation result. Fault mode determination is achieved through a pre-trained classifier (such as a support vector machine or neural network), with the input being the feature vector (amplitude, width, spectrum, etc.) of the abnormal waveform, and the output being the label of the most likely fault mode. Optionally, the verification system employs multi-sensor data fusion positioning, combining the time difference of reflected waves received by multiple sensors (TDOA) to calculate a more accurate fault source location using a hyperbolic positioning algorithm. It is understood that other positioning and pattern recognition algorithms can also be used, and are not limited here.

[0047] S104. When the fault mode is connection leakage, generate a mechanical adjustment command for the mechanical fixture corresponding to the control target position to perform a loosening and re-clamping operation.

[0048] Among them, the mechanical adjustment command is a specific command code sent to the lower-level machine or actuator controller of the calibration system. The release and re-clamping operation refers to controlling the mechanical clamp to first completely release the clamping force on the water meter, and then re-clamp the water meter with a preset clamping force.

[0049] Specifically, when the fault mode determined in step S103 is a connection leak, the verification system determines that the fault may be caused by improper initial installation of the water meter or uneven compaction of the sealing ring. Therefore, the verification system generates a mechanical adjustment command for the target meter position. This command includes the address code of the target meter position and the operation code for releasing the clamp. This command is sent via the internal bus to the drive module responsible for controlling the mechanical clamp of the meter position, triggering an automated repair attempt.

[0050] In some embodiments, this step can be implemented in several ways: Optionally, the instruction includes detailed motion parameters, such as the release stroke, the time to maintain the release state, and the target pressure or torque value for re-clamping; the calibration system can first execute a fine-tuning instruction, that is, slightly reduce the clamping force and then increase it again; if leakage persists, then perform a complete release and re-clamping operation. Optionally, the instruction includes a callback flag, and the actuator will return a confirmation signal to the calibration system after completing the operation, indicating that the instruction has been executed. It is understood that other forms of adjustment instructions can also be generated depending on the type of fixture, which are not limited here.

[0051] In some embodiments, the mechanical fixture itself may malfunction (e.g., motor jamming, drive failure), making it difficult to execute instructions correctly. To address this, the calibration system starts a timeout timer after sending the instruction and monitors the fixture's status sensors (e.g., position or pressure sensors). If a completion signal is not received within a preset time or the sensor status does not change as expected, the calibration system determines the repair operation has failed and records the fixture execution failure.

[0052] S105. After the mechanical adjustment command is executed and the pipeline pressure is restored, apply a pressure disturbance wave again and collect new pressure waveform data to determine the repair effect.

[0053] Restoring pipeline pressure refers to re-pressurizing the calibration pipeline with water to its normal calibration working pressure after loosening the clamping operation. Determining the repair effectiveness involves verifying, through re-testing, whether the previous repair operation successfully eliminated the leak.

[0054] Specifically, after the mechanical clamp at the target position completes the release and re-clamping operation and returns a confirmation signal, the calibration system first executes the pipeline pressure recovery procedure. Once the pipeline pressure stabilizes, the calibration system repeats step S101, which involves applying the same pressure disturbance wave to the same end of the pipeline and acquiring a new set of pressure waveform data. This new data reflects the health status of the calibration pipeline after the repair operation.

[0055] In some embodiments, this step can be implemented in several ways: Optionally, the verification system compares the newly acquired pressure waveform data with the reference waveform data used in step S102. If the difference signal amplitude between the two falls back to within a preset normal threshold, the repair is deemed successful. Optionally, the verification system compares the newly acquired waveform data with the waveform data before repair (i.e., acquired in step S101). If the abnormal distortion features previously appearing at the target surface location are significantly reduced or disappear, the repair is deemed successful. It is understood that other evaluation methods can also be used to determine the repair effect, and this is not limited here.

[0056] S106. If the repair effect does not meet the preset standard, generate a virtual mask to isolate the target table position and remove the measurement data of the target table position.

[0057] Among these, the preset standard refers to the quantitative indicator for determining whether the repair is successful; for example, the amplitude of abnormal distortion characteristics must be below a certain threshold. A virtual mask is a data marker used at the software level to mark the target meter position as invalid. Excluding measurement data means that in subsequent verification processes, the system will ignore all flow, pressure, and other measurement data collected from the target meter position.

[0058] Specifically, if the repair effect determined in step S105 does not meet the preset standard, meaning the leakage problem still exists, the verification system determines that the automatic repair has failed. In this case, to prevent the faulty tabletop from affecting the accuracy of the entire verification batch, the verification system sets a virtual mask for the target tabletop in memory or the database (for example, setting its status to INVALID). Subsequently, during the verification process, the data processing module checks this mask; any data from the masked tabletop will be directly discarded and will not participate in the final verification result calculation and pass / fail determination.

[0059] It should be noted that the virtual mask is a highly efficient data filtering mechanism at the software level. Its core processing logic is to set a Boolean status flag for each table position in the system and to perform a logical judgment to check the flag before all processes involving table position data.

[0060] Specifically, the verification system can maintain a boolean array or bitmap that corresponds one-to-one with each table position, such as boolisPositionValid[N], where N is the total number of table positions, and all elements are initially true. When the system determines that the i-th table position (e.g., table position 12) needs to be masked, it will perform a simple assignment operation: isPositionValid

[11] =false (assuming the array index starts from 0). After this mask is generated, it will be called on multiple subsequent data processing nodes:

[0061] 1) Data Acquisition and Parsing Layer: When the main control program of the verification system receives metering data packets from various water meters on the data bus, it first parses the meter position address i from which the data packet originates. Before storing the data in memory or the database, the program checks the value of isPositionValid[i-1]. If it is false, the data packet will be discarded directly without any further processing, preventing invalid data from flowing in at the source.

[0062] 2) Verification Result Calculation Layer: After the verification process is completed, the system needs to calculate statistical indicators such as the pass rate and average error of the entire batch. At this time, the calculation program will not simply traverse all table positions, but will iterate based on the isPositionValid array.

[0063] For example, the pseudocode logic for calculating the total number of qualified candidates is as follows:

[0064] passedCount=0;

[0065] for (int i = 0; i <N;i++)

[0066] {if(isPositionValid[i]==true&&meterResults[i].isPassed==true)

[0067] {passedCount++;}

[0068] }

[0069] This ensures that data from faulty locations are not included in any statistical calculations, guaranteeing the accuracy of the final report.

[0070] 3) User Interface (GUI) Presentation Layer: To provide operators with intuitive feedback, the GUI reads the isPositionValid array when drawing the calibration pipeline status diagram. For positions with a value of false, it renders them with different colors (such as gray or red) or special icons, along with fault information prompts, clearly informing the operator that the position has been automatically isolated by the system.

[0071] Through this multi-layered application, the virtual mask achieves full-process isolation of faulty positions from data generation to final presentation, ensuring the robustness of the verification system and the reliability of the data.

[0072] To further improve the accuracy of fault location and enhance the robustness of the method, the monitoring method in the above embodiments can be optimized. For example, environmental factors such as changes in fluid temperature and pressure can affect the propagation speed of pressure waves, thereby affecting the location accuracy. Furthermore, background noise in the pipeline may also interfere with the identification of abnormal features. The method provided in this embodiment will now be described in more detail. Please refer to... Figure 2 This is another flowchart illustrating the smart water meter position health monitoring and fault-tolerant switching method in this application embodiment.

[0073] S201. Collect the static fluid pressure and fluid temperature in the test pipeline.

[0074] Static fluid pressure refers to the baseline pressure of the fluid (usually water) within a pipeline when there are no flow or pressure disturbances. Fluid temperature refers to the real-time temperature of the fluid within the pipeline. These are key physical parameters affecting the velocity of sound in fluids.

[0075] Specifically, before executing the health monitoring process, the calibration system reads the values ​​from the pressure and temperature sensors connected to the calibration pipeline. These sensors are typically part of the standard configuration of the calibration system. The calibration system acquires and records the current static pressure value (e.g., P_static) and temperature value (e.g., T_static) for subsequent sound velocity calculations. This acquisition process can be performed during system initialization or at the start of each monitoring task to obtain parameters closest to real-time operating conditions.

[0076] In some embodiments, this step can be implemented in several ways: Optionally, the calibration system can install multiple temperature sensors along the long pipeline and collect their average value as the overall fluid temperature of the pipeline to address potential temperature gradients. Optionally, when collecting pressure and temperature data, the calibration system can perform multiple samplings and take the average value to filter out random noise from the sensors themselves and improve the accuracy of the parameters. It is understood that other methods can also be used to obtain fluid state parameters, which are not limited here.

[0077] In some embodiments, sensor malfunctions or abnormal readings (such as exceeding the measurement range or a constant value) may occur. In response, the calibration system performs validity checks after data acquisition. For example, it checks whether the reading is within a reasonable physical range or compares it with historical data. If an anomaly is found, the system triggers a sensor fault alarm and can use a preset default value or the previous valid value for subsequent calculations, while also prompting for maintenance.

[0078] S202. Substitute the static fluid pressure and fluid temperature into the fluid state equation to calculate the theoretical speed of sound of the fluid.

[0079] The fluid state equation is a mathematical model that describes the relationship between the physical properties of a fluid (such as density and sound velocity) and its state parameters (such as pressure and temperature). For water, there are several recognized empirical formulas or international standard equations (such as IAPWS-IF97). The theoretical speed of sound refers to the theoretical speed at which sound waves propagate in the fluid under the current pressure and temperature, calculated according to this equation.

[0080] Specifically, the calibration system incorporates one or more equations of state applicable to water. The system uses the static pressure P_static and temperature T_static collected in step S201 as input variables, substituting them into the equation for calculation. The output of the equation is the theoretical speed of sound C_theory under the current operating conditions. This calculation is performed by the calibration system's processor, and the result is temporarily stored as a key parameter for use in subsequent steps.

[0081] In some embodiments, this step can be implemented in several ways: Optionally, the verification system uses a simplified polynomial fitting formula to calculate the speed of sound, which has sufficient accuracy and low computational cost within a specific temperature and pressure range. Optionally, the verification system employs a lookup table method, pre-calculating and storing a three-dimensional lookup table of pressure-temperature-speed of sound covering common operating conditions based on authoritative state equations. During actual calculations, the speed of sound is quickly obtained through interpolation, balancing accuracy and efficiency. It is understood that other mathematical models or methods can also be used to calculate the theoretical speed of sound, and this is not limited here.

[0082] In some embodiments, trace amounts of air may be introduced into the pipeline, causing the actual sound velocity of the fluid to be lower than the theoretical sound velocity of pure water. To address this, the calibration system can introduce an adjustable air content correction coefficient into the state equation. This coefficient can be experimentally determined during system calibration or adaptively adjusted during operation based on waveform attenuation characteristics, thereby making the calculated sound velocity closer to the actual sound velocity of the mixed fluid.

[0083] S203. Determine the calibration compensation parameters for the propagation speed of the pressure disturbance wave based on the theoretical sound velocity of the fluid.

[0084] The calibration compensation parameter is a factor or bias used to correct the theoretical sound velocity, aiming to make the final wave velocity value closer to the effective propagation speed of pressure disturbance waves in the actual pipeline system.

[0085] Specifically, the calibration system uses the theoretical sound velocity C_theory calculated in step S202 as a benchmark. Considering that factors such as the elasticity of the actual pipeline wall (fluid-structure interaction effect) may affect the wave velocity, the system will use a preset or adaptive calibration coefficient α to fine-tune the theoretical sound velocity, obtaining the final effective wave velocity C_eff = α * C_theory used for positioning calculation. This calibration coefficient α can be accurately determined during the initial installation and calibration of the system by measuring the actual propagation time of the wave between two points at a known distance.

[0086] In some embodiments, this step can be implemented in several ways: Optionally, the calibration compensation parameter is an additive bias, i.e., C_eff = C_theory + ΔC, where ΔC is the compensation value. Optionally, the calibration system establishes a physical model of parameters such as pipe material, wall thickness, and pipe diameter, calculates the influence of pipe wall elasticity on sound velocity based on these parameters, and dynamically generates the calibration compensation parameter to achieve higher accuracy in wave velocity determination. It is understood that the methods for determining the calibration compensation parameter can be diverse and are not limited here.

[0087] In some embodiments, aging of the pipeline or structural changes may cause the preset calibration compensation parameters to become inaccurate. To address this, the calibration system can be designed with a self-calibration process. For example, using the echo generated by a fixed reflector (such as a valve) at the end of the pipeline or a known location, the system can calculate the actual wave propagation velocity in reverse and compare it with the currently calculated effective wave velocity. If the difference exceeds a threshold, the calibration compensation parameters are automatically updated.

[0088] S204. Apply a pressure disturbance wave to one end of the test pipeline, and collect pressure waveform data during the propagation of the pressure disturbance wave based on pressure sensors at at least two different locations on the test pipeline.

[0089] Refer to step S101, which will not be repeated here.

[0090] S205. Compare and analyze the pressure waveform data and the reference waveform data to determine the abnormal distortion characteristics in the pressure waveform data.

[0091] Refer to step S102, which will not be repeated here.

[0092] S206. Based on the propagation speed of the pressure disturbance wave in the current medium environment, and combined with the transit time difference between the abnormal distortion characteristics and the incident wave front, calculate the distance along the calibration pipeline from the source of the anomaly.

[0093] The propagation speed in the current medium environment is the effective wave velocity C_eff determined in step S203. The transit time difference (Δt) refers to the time interval between the point in time when the incident wavefront passes the sensor and the point in time when the wavefront of the reflected wave (i.e., the anomalous distortion feature) passes the same sensor again. The path distance refers to the length of the pipe from the sensor location to the anomalous source that produces the reflection.

[0094] Specifically, after identifying the abnormal distortion feature in step S205, the verification system first accurately determines the timestamps of the incident wavefront and the abnormal feature wavefront. The difference between the two is the transit time difference Δt. Since the wave travels twice the path distance from the sensor to the anomalous source and back to the sensor, the verification system uses the formula L=(C_eff*Δt) / 2 to calculate the path distance L of the anomalous source.

[0095] In some embodiments, this step can be implemented in several ways: Optionally, the verification system employs a cross-correlation algorithm to accurately calculate the transit time difference, that is, performing a cross-correlation operation between the incident waveform segment and the reflected waveform segment containing anomalous distortion features, with the time delay corresponding to the correlation peak being Δt. Optionally, when using two sensors (located at x1 and x2 respectively), the verification system can calculate the transit time differences Δt1 and Δt2 recorded by the two sensors separately, and solve the distance using a system of equations to improve the robustness of positioning. It is understood that other time delay estimation algorithms and distance calculation models can also be used, and are not limited here.

[0096] S207. Map and match the distance along the route with the installation position coordinates of each meter position to determine the target meter position.

[0097] The installation location coordinates are pre-measured and stored in the system as precise pipe length values ​​relative to the sensor position for each gauge position's centerline. Mapping matching involves comparing the calculated anomaly source distance L from the previous step with this coordinate list to find the closest gauge position.

[0098] Specifically, the verification system maintains a table position coordinate database, which records the distance coordinates (L1, L2, ..., LN) along the path for each table position (e.g., 1, 2... N). The verification system compares the distance L calculated in step S206 with all table position coordinates in the database, and calculates the absolute difference |L-Li| between L and each Li. The verification system selects the table position i that minimizes the difference as the candidate target table position.

[0099] In some embodiments, this step can be implemented in several ways: Optionally, the verification system sets a matching tolerance ΔL, and only when |L-Li|≤ΔL is the match considered successful; otherwise, it is judged as an anomaly at an unknown location. This tolerance ΔL can be set according to the uncertainty of the wave velocity calculation and the accuracy of the tabletop installation. Optionally, if the calculated distance L falls exactly between the coordinates of the two tabletops, Li and L(i+1), the verification system can list both tabletops as suspect targets and further distinguish them through subsequent failure mode analysis. It is understood that other matching strategies can also be used to determine the target tabletop, which are not limited here.

[0100] In some embodiments, a calculated distance L may be very close to the coordinate Li of a certain instrument position, but also very close to the coordinate L_feature of other structures on the pipeline (such as elbows or reducers). To address this, the calibration system stores not only the instrument position location in its coordinate database, but also the locations of all pipeline structural components that may cause reflections. During matching, if L matches both an instrument position and a structural component, the system will first check whether the structural component is a known normal structure, thus eliminating false positives.

[0101] S208. Based on the waveform polarity, waveform amplitude, and spectral characteristics of the abnormal distortion features, determine the fault mode of the target position that matches the fault feature library.

[0102] The fault feature library is a pre-built database that stores characteristic parameters of typical abnormal distortion waveforms corresponding to different fault types (such as different degrees of leakage, blockage, valve not opening, etc.). Waveform polarity refers to whether the reflected wave is positive (pressure increase) or negative (pressure decrease). Spectral characteristics refer to the energy distribution of the waveform in the frequency domain.

[0103] Specifically, after determining the target location, the verification system extracts multiple characteristic parameters of the abnormally distorted waveform corresponding to that location, forming a feature vector. For example, it extracts the peak amplitude of the reflected wave, the amplitude ratio relative to the incident wave, the pulse width, and the dominant frequency component. Then, the verification system compares this feature vector with records in the fault feature database, using nearest neighbor, minimum distance, or other pattern recognition algorithms to find the best-matching fault mode, and uses this as the diagnostic result for the target location.

[0104] In some embodiments, this step can be implemented in several ways: Optionally, the fault feature library is constructed using machine learning methods. During the system debugging phase, various known faults are artificially introduced, corresponding waveforms are collected, features are extracted, and used to train a multi-classification model (such as a decision tree, random forest, or neural network). This model can directly classify the input feature vector into specific fault modes. Optionally, the verification system employs analysis based on physical models, for example, estimating the size of the leakage aperture through the attenuation rate (exponential attenuation coefficient) of the reflected wave, thereby performing a more refined quantitative classification of the leakage fault. It is understood that the methods for determining fault modes can be diverse and are not limited here.

[0105] It should be noted that the construction of the fault feature database is essentially a data-driven pattern recognition problem. Its data processing logic is divided into two stages: offline training and online diagnosis. In the offline training stage, i.e., when building the fault feature database, technicians will artificially and controllably introduce various typical, known faults into a healthy calibration system. For example, by connecting a precision metering valve to the sealing ring of a gauge position, different levels of connection leakage faults (such as 0.5L / h, 2L / h, 5L / h) can be simulated; or by partially closing the valves before and after the gauge position, different degrees of blockage faults can be simulated. For each introduced known fault, the system will execute a complete health monitoring process, collecting pressure waveform data and extracting the corresponding abnormal distortion feature waveform through the aforementioned waveform separation and difference calculation steps. Subsequently, the system will extract a series of quantified feature parameters from this abnormal waveform, forming a multi-dimensional feature vector.

[0106] These features may include:

[0107] 1) Waveform polarity (-1 represents negative pulse / leakage, +1 represents positive pulse / blockage).

[0108] 2) Normalized amplitude (the ratio of the peak value of the reflected wave to the peak value of the incident wave, reflecting the severity of impedance mismatch).

[0109] 3) Pulse width (the time span of the reflected wave from start to end);

[0110] 4) Energy decay coefficient (for oscillating reflections, the exponential decay rate of its envelope is related to leakage damping).

[0111] 5) Spectral centroid (the point where the reflected wave energy is concentrated in the frequency domain), etc.

[0112] Each feature vector is stored along with its corresponding known fault type (e.g., minor leak - seal, severe blockage - valve), forming a record in the fault feature library. During online diagnosis, when the system detects an unknown abnormal distortion, it also extracts the feature vector V_unknown for that abnormality. Then, the system uses a distance metric algorithm (e.g., Euclidean distance or Mahalanobis distance) to calculate the distance between V_unknown and all pre-stored feature vectors V_i in the feature library. The fault label corresponding to the pre-stored vector V_k with the smallest distance is considered the most probable mode of the current unknown fault. For example, if online monitoring detects an abnormality with a feature vector calculated as [-1, 0.08, 15ms, ...], the system finds in the feature library that the closest record to this vector is [-1, 0.07, 13ms, ...], with the label "Connection Leak - O-ring - 1.5L / h". Therefore, the system diagnoses the fault mode of the current target position as a connection leak of approximately 1.5L / h caused by an O-ring.

[0113] S209. When the fault mode is connection leakage, generate a mechanical adjustment command for the mechanical fixture corresponding to the control target position to perform a loosening and re-clamping operation.

[0114] Refer to step S104, which will not be repeated here.

[0115] S210. After the mechanical adjustment command is executed and the pipeline pressure is restored, apply a pressure disturbance wave again and collect new pressure waveform data to determine the repair effect.

[0116] Refer to step S105, which will not be repeated here.

[0117] S211. When the repair effect does not meet the preset standard, generate a virtual mask to isolate the target table position and remove the measurement data of the target table position.

[0118] Refer to step S106, which will not be repeated here.

[0119] To achieve more accurate extraction of abnormal distortion features, in some embodiments, the verification system further refines the process of determining abnormal distortion features. Specifically, the verification system will: decompose the pressure waveform data into incident wave components and reflected wave components that propagate forward along the verification pipeline based on the wave separation algorithm; calculate the difference signal between the reflected wave component and the reference reflected waveform; and extract the waveform abrupt change at the position corresponding to the signal amplitude when the amplitude of the difference signal exceeds a preset difference threshold, as an abnormal distortion feature caused by pipeline impedance discontinuity.

[0120] Wave separation algorithm is a technique that uses data from at least two sensors to distinguish between waves propagating in opposite directions simultaneously in a pipeline. The incident wave component refers to the original disturbance wave generated by the excitation source and propagating forward along the pipeline. The reflected wave component refers to the wave reflected back after the incident wave encounters an impedance discontinuity. The reference reflected waveform refers to the reflected waveform of the pipeline itself under healthy conditions, typically caused by normal structural elements such as pipe joints and bends.

[0121] Specifically, the calibration system uses pressure data P1(t) and P2(t) from two sensors located at a known distance Δx apart to calculate the incident wave f(t) and reflected wave g(t) in real time by solving the wave equation. Then, the calibration system retrieves the reference reflected wave g_ref(t) from memory under healthy conditions. By calculating the difference signal Δg(t) = g(t) - g_ref(t) between the current reflected wave g(t) and the reference reflected wave g_ref(t), background reflections caused by normal pipeline structure can be effectively eliminated, retaining only abnormal reflections caused by new faults (such as leaks). When the amplitude of Δg(t) exceeds a threshold, abnormal distortion is considered detected.

[0122] It should be noted that the wave separation algorithm utilizes data collected by at least two pressure sensors at a known distance along the pipeline to calculate two waves—the incident wave and the reflected wave—that propagate in opposite directions at the same time and location. Its basic physical principle is that the pressure P(t) measured by any sensor at time t is a linear superposition of the incident wave pressure f(t) and the reflected wave pressure g(t) at that point. By establishing the spatiotemporal relationship between the two sensors, a system of equations can be constructed to solve for these two unknown waveforms.

[0123] A common and efficient implementation method is the frequency domain-based two-sensor method. First, the system collects pressure data P1(t) and P2(t) from two sensors located at positions x1 and x2 as they change over time, with the distance between the two sensors being Δx = x2 - x1.

[0124] Next, the system performs a Fast Fourier Transform (FFT) on the two sets of time series data, transforming them to the frequency domain to obtain complex-form spectra P1(ω) and P2(ω), where ω is the angular frequency. According to wave theory, a wave propagating in the forward direction (incident direction) will produce a phase delay of e^(-iωτ) in the frequency domain after propagating a distance Δx, while a wave propagating in the reverse direction (reflection direction) will produce a phase lead of e^(+iωτ), where the propagation delay τ = Δx / C_eff, and C_eff is the calibrated effective wave velocity.

[0125] Therefore, the following frequency domain equations can be established: P1(ω) = F(ω) + G(ω) and P2(ω) = F(ω)e^(-iωτ) + G(ω)e^(+iωτ), where F(ω) and G(ω) are the spectra of the incident and reflected waves, respectively. This is a system of two linear equations in two variables, F(ω) and G(ω), which can be solved independently at each frequency point ω. After solving, the system obtains the incident wave spectrum F(ω) and the reflected wave spectrum G(ω) over the entire frequency range.

[0126] Finally, the system performs inverse fast Fourier transform (IFFT) on F(ω) and G(ω) respectively to obtain the pure incident wave component f(t) and reflected wave component g(t) in the time domain. In this way, even if the weak reflected wave is completely submerged in the strong incident wave, it can be accurately separated and extracted, providing a high-quality data foundation for subsequent difference signal analysis and anomaly feature identification.

[0127] To improve signal quality, in some embodiments, the calibration system avoids noise interference before applying the disturbance wave. Specifically, the calibration system will: collect background noise pressure data and perform spectrum analysis when the calibration pipeline is in operation to determine the environmental noise frequency band of the calibration pipeline; and adjust the excitation frequency and frequency bandwidth of the pressure disturbance wave so that the main energy spectrum of the pressure disturbance wave avoids the environmental noise frequency band of the calibration pipeline.

[0128] Background noise pressure data refers to pipeline pressure fluctuations generated solely by the normal operation of equipment such as pumps and valves without the application of disturbance waves. Environmental noise frequency bands refer to the frequency range where noise energy is concentrated, as identified through spectral analysis. The main energy spectrum refers to the frequency interval where the pressure disturbance wave signal energy is most concentrated.

[0129] Specifically, before conducting health monitoring, the calibration system silently collects pressure signals for a period of time and performs Fast Fourier Transform (FFT) or Power Spectral Density (PSD) analysis on the signals to identify the main noise frequencies under the current operating conditions. For example, it might detect narrowband noise around 50Hz caused by the pump's rotational speed. Subsequently, when generating pressure disturbance waves, the calibration system designs the waveform so that its spectral energy is mainly distributed in quieter frequency bands with lower noise. For example, if the noise is concentrated in low frequencies, a disturbance wave with a higher center frequency is generated.

[0130] In some embodiments, this step can be implemented in several ways: Optionally, the verification system generates a swept-frequency signal as a disturbance wave, the start and end frequencies of which are set to precisely skip the identified noise bands. Optionally, the verification system employs the concept of orthogonal frequency division multiplexing (OFDM) to distribute the disturbance wave energy across multiple discrete subcarriers located in quiet frequency bands to maximize the signal-to-noise ratio. It is understood that other spectrum shaping techniques can also be used to avoid noise, and are not limited here.

[0131] In some embodiments, changes in the operating status of the verification system (such as changes in flow rate or the addition of new equipment) may cause dynamic changes in the frequency band of environmental noise. To address this, the verification system incorporates background noise spectrum analysis as a routine, periodic procedure. Before each health monitoring task, the noise environment is reassessed, and the spectral parameters of the disturbance wave are dynamically adjusted to ensure that the monitoring method maintains a high signal-to-noise ratio and robustness under different operating conditions.

[0132] To achieve intelligent fault-tolerant management, in some embodiments, the verification system will execute a series of fault-tolerant switching and reporting processes after a repair failure. Specifically, the verification system will: update the valid tabletop mapping map of the current verification batch according to the virtual mask; adjust the verification result judgment logic of the verification system based on the valid tabletop mapping map; generate an abnormal status report containing the location of the faulty tabletop, the fault type, and the repair failure record, and send the abnormal status report to the remote monitoring terminal.

[0133] The effective epitope mapping diagram is a data structure that records a list of all epitopes in the current verification batch that are in normal condition and whose data are acceptable. The verification result judgment logic refers to the algorithmic rules used to calculate the final verification data (such as total flow rate and pass rate) and determine whether the entire batch is qualified. The abnormal status report is a structured data file containing detailed information about the fault.

[0134] Specifically, when a tabletop is virtually masked, the verification system removes its ID from the valid tabletop mapping. In subsequent verification result statistics, all calculations, such as the average error and the number of nonconforming items, will be based on the updated valid tabletop mapping, meaning only data from valid tabletops will be considered. Simultaneously, the verification system automatically generates a report detailing the time of the fault, the located tabletop number, the diagnosed fault mode, the failed repair attempts, and the final masking measures taken. This report is sent via network interface to the remote monitoring terminal in the central control room or to the Manufacturing Execution System (MES).

[0135] In some embodiments, this step can be implemented in several ways: Optionally, adjustments to the effective tabletop mapping diagram trigger dynamic updates to the verification system interface (GUI), marking the masked tabletops on the piping diagram with gray or special icons. Optionally, abnormal status reports use standardized formats such as XML or JSON for easy parsing and archiving by other systems. The report may also include a severity level field to trigger maintenance work orders of different levels. It is understood that fault tolerance management and reporting mechanisms can be customized according to specific application scenarios, and are not limited here.

[0136] In some embodiments, multiple consecutive tablets may be masked, resulting in an insufficient number of valid tablets and rendering the current verification batch statistically meaningless. To address this, the verification system sets a minimum threshold for the number of valid tablets. After updating the valid tablet mapping, if the number of valid tablets falls below this threshold, the system determines the current batch is invalid, automatically terminates the verification process, and generates a higher-level systemic fault alarm, indicating the need for large-scale maintenance.

[0137] To maximize data value after fault tolerance, in some embodiments, the verification system quantifies and compensates for the impact of leakage faults. Specifically, the verification system will: acquire target pressure waveform data at the target location; calculate the exponential decay coefficient of the target pressure waveform data to determine the equivalent leakage orifice diameter at the target location; calculate the equivalent leakage rate based on the equivalent leakage orifice diameter and the current pipeline static pressure; terminate the current verification process when the equivalent leakage rate exceeds a preset safety threshold; determine the leakage flow rate value of the target location based on the equivalent leakage rate when the equivalent leakage rate does not exceed the preset safety threshold; and for effective locations downstream of the target location, use the corrected flow rate data after deducting the leakage flow rate value as the verification result data.

[0138] The exponential decay coefficient is a parameter describing the rate of decay of the reflected waveform oscillations and is related to leakage damping. The equivalent leakage orifice diameter and velocity are physical quantifications of the leakage severity. Corrected flow data refers to subtracting the flow lost due to upstream fault points from the original measured flow rate at the downstream meter position to obtain a flow rate value that more closely approximates the actual situation.

[0139] Specifically, for a shielded but not severely leaking meter, the calibration system analyzes the oscillation attenuation envelope of its reflected wave and fits an exponential attenuation coefficient. Based on a pre-defined physical model, this coefficient can be converted into an equivalent leakage orifice diameter. Then, combined with the current pipeline pressure, the equivalent leakage rate (i.e., leakage flow rate) is calculated using fluid dynamics formulas such as Bernoulli's equation. If this value is less than a safety threshold, the calibration system will not terminate the process but will treat the leakage flow rate as a known system deviation. For all valid meters downstream of the faulty meter, the calibration system will deduct this leakage flow rate from the total upstream flow when calculating their calibration results, thereby compensating for their readings.

[0140] It should be noted that the conversion process of the equivalent leakage orifice diameter is based on fluid dynamics and acoustic damping theory. When a pressure wave encounters a leak point, some fluid medium will be ejected from the leak orifice. This process consumes the wave's energy and dampens its propagation. For a reflected wave caused by a leak, its shape is often not a single pulse, but an oscillating decay process, similar to a bell that gradually quiets down after being struck. The energy dissipation rate of this oscillation decay is directly related to the severity of the leak. The data processing logic is as follows: First, the system accurately locates the oscillating waveform segment caused by the leak at the target surface from the separated reflected wave signal. Then, through a peak detection algorithm, the amplitudes A1, A2, A3, ... of a series of consecutive peaks (or troughs) of this oscillating waveform are identified. Theoretically, these peak amplitudes decay exponentially, i.e., An ≈ A0 * e^(-ktn), where tn is the time of the nth peak, and k is the exponential decay coefficient to be determined. The system can accurately calculate the value of k by performing a linear regression fit on (tn, ln(An)), with the slope being -k. A larger k value means faster energy decay, i.e., greater damping and more severe leakage. The next crucial conversion step is as follows: the system has a pre-stored calibration model (which can be a mathematical formula or a lookup table obtained through experimental calibration) that describes the nonlinear relationship between the exponential decay coefficient k and the equivalent leakage orifice diameter A_leak. This model was obtained during the system development phase through extensive experiments using standard leaking components with known orifice diameters. With the k value, the system can calculate the corresponding equivalent leakage orifice diameter A_leak (usually in square millimeters) by querying the model or substituting it into the formula. For example, the system calculates the decay coefficient k = 35.2 for a certain leak. By consulting the built-in calibration curve, it is found that k = 35.2 corresponds to A_leak = 0.2 mm². Once the equivalent leakage orifice diameter is obtained, the system can combine the current static pressure value P of the pipeline with the standard orifice outflow formula (such as Q=C_d*A_leak*sqrt(2P / ρ), where C_d is the flow coefficient and ρ is the fluid density) to estimate the real-time leakage flow rate Q, providing an accurate quantitative basis for subsequent flow compensation or safe shutdown decisions.

[0141] In this embodiment, by employing pressure wave reflection measurement technology for fault diagnosis and combining an automated repair attempt with a closed-loop control strategy of intelligent fault-tolerant isolation, this application can accurately perceive and process the health status of individual meter positions in a series-connected calibration pipeline in real time. This solution effectively solves the problems of traditional monitoring methods being insensitive to minor local faults, having delayed response times, and being inefficient due to reliance on manual inspections. It thus achieves online self-monitoring, self-repair, and adaptive fault tolerance of the physical health status of the water meter calibration production line, significantly improving production automation, data reliability, and overall equipment operating efficiency.

[0142] The verification system in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference]. Figure 3 This is a schematic diagram of the physical device structure of a verification system in an embodiment of this application.

[0143] It should be noted that, Figure 3 The structure of the verification system shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0144] like Figure 3 As shown, the verification system includes a CPU 301, which can perform various appropriate actions and processes according to a program stored in ROM 302 or a program loaded into RAM 303 from storage section 308, such as executing the methods described in the above embodiments. RAM 303 also stores various programs and data required for system operation. CPU 301, ROM 302, and RAM 303 are interconnected via bus 304. I / O interface 305 is also connected to bus 304.

[0145] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including hard disks, etc.; and communication section 309 including network interface cards such as LAN (Local Area Network) cards, modems, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0146] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by CPU 301, it performs the various functions defined in the present invention.

[0147] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0148] Specifically, the verification system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the smart water meter position health monitoring and fault-tolerant switching method provided in the above embodiment.

[0149] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the verification system described in the above embodiments; or it may exist independently and not be assembled into the verification system. The storage medium carries one or more computer programs, which, when executed by a processor of the verification system, cause the verification system to implement the smart water meter position health monitoring and fault-tolerant switching method provided in the above embodiments.

[0150] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application 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 of the technical features. 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 this application.

[0151] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning if... or after... or in response to determining... or in response to detecting... Similarly, depending on the context, the phrase "when determining... or if (the stated condition or event) is interpreted as meaning if determining... or in response to determining... or in response to detecting (the stated condition or event)" or in response to detecting (the stated condition or event).

Claims

1. A method for health monitoring and fault-tolerant switching of smart water meter positions, characterized in that, Applied to a calibration system, the calibration system comprising a plurality of gauge positions arranged in series along a calibration pipeline, each gauge position being equipped with a mechanical clamp, the method comprising: A pressure disturbance wave is applied to one end of the calibration pipeline, and pressure waveform data during the propagation of the pressure disturbance wave is collected based on pressure sensors at at least two different locations on the calibration pipeline. By comparing and analyzing the pressure waveform data with the reference waveform data, abnormal distortion features in the pressure waveform data are determined. Based on the time delay and waveform change type of the abnormal distortion feature, locate the target tabletop associated with the abnormal distortion feature and determine the fault mode of the target tabletop. When the fault mode is connection leakage, a mechanical adjustment command is generated to control the mechanical clamp corresponding to the target position to perform a loosening and re-clamping operation; After the mechanical adjustment command is executed and the pressure in the calibration pipeline is restored, the pressure disturbance wave is applied again and new pressure waveform data is collected to determine the repair effect. If the repair effect does not meet the preset standard, a virtual mask is generated to isolate the target tabletop, and the measurement data of the target tabletop is removed.

2. The method according to claim 1, characterized in that, The step of comparing and analyzing the pressure waveform data and the reference waveform data to determine the abnormal distortion features in the pressure waveform data specifically includes: Based on the wave separation algorithm, the pressure waveform data is decomposed into incident wave components and reflected wave components that propagate forward along the test pipeline; Calculate the difference signal between the reflected wave component and the reference reflected waveform; When the amplitude of the difference signal exceeds a preset difference threshold, the waveform abrupt change at the position corresponding to the signal amplitude is extracted as an abnormal distortion feature caused by the discontinuity of pipeline impedance.

3. The method according to claim 1, characterized in that, The step of locating the target position associated with the abnormal distortion feature based on the time delay and waveform change type of the abnormal distortion feature, and determining the fault mode of the target position, specifically includes: Based on the propagation speed of the pressure disturbance wave in the current medium environment, and combined with the transit time difference of the abnormal distortion characteristics relative to the incident wave front, the travel distance of the abnormal source on the calibration pipeline is calculated. The target table position is determined by mapping and matching the distance along the route with the installation position coordinates of each table position. Based on the waveform polarity, waveform amplitude, and spectral characteristics of the abnormal distortion features, the fault mode of the target epitope that matches in the fault feature library is determined.

4. The method according to claim 1, characterized in that, Prior to the step of applying a pressure disturbance wave to one end of the calibration pipeline, the method further includes: Collect the static fluid pressure and fluid temperature in the calibration pipeline; Substituting the static fluid pressure and the fluid temperature into the fluid state equation, the theoretical speed of sound of the fluid is calculated. Based on the theoretical sound velocity of the fluid, the calibration compensation parameters for the propagation velocity of the pressure disturbance wave are determined.

5. The method according to claim 4, characterized in that, Prior to the step of collecting the static fluid pressure and fluid temperature within the calibration pipeline, the method further includes: When the test pipeline is in operation, background noise pressure data is collected and spectrum analysis is performed to determine the environmental noise frequency band of the test pipeline. Adjust the excitation frequency and frequency bandwidth of the pressure disturbance wave so that the main energy spectrum of the pressure disturbance wave avoids the environmental noise frequency band of the calibration pipeline.

6. The method according to claim 1, characterized in that, After the steps of generating a virtual mask to isolate the target tabletop and removing the measurement data of the target tabletop, the method further includes: Update the valid epitope mapping diagram of the current verification batch based on the virtual masking mask; Based on the effective epitope mapping diagram, the verification result judgment logic of the verification system is adjusted. Generate an abnormal status report containing the location of the fault location, the fault type, and the repair failure record, and send the abnormal status report to the remote monitoring terminal.

7. The method according to claim 6, characterized in that, The step of adjusting the verification result judgment logic of the verification system based on the effective epitope mapping specifically includes: Acquire the target pressure waveform data at the target location, calculate the exponential decay coefficient of the target pressure waveform data, and determine the equivalent leakage orifice diameter at the target location. Calculate the equivalent leakage rate based on the equivalent leakage orifice diameter and the current pipeline static pressure. When the equivalent leakage rate exceeds a preset safety threshold, the current verification process is terminated. When the equivalent leakage rate does not exceed the preset safety threshold, the leakage flow rate value of the target surface is determined based on the equivalent leakage rate; For valid substations located downstream of the target substation, the corrected flow rate data after deducting the leakage flow rate value is used as the verification result data.

8. A verification system, characterized in that, The verification system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the verification system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the verification system, the verification system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the verification system, the verification system performs the method as described in any one of claims 1-7.

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