An online self-checking method for ultrasonic metering instrument based on waveform compensation

Abstract

The application provides an online self-checking method of an ultrasonic metering instrument based on waveform compensation, comprising the following steps: before the instrument is shipped, standard waveform feature point parameters serving as a reference are calibrated and stored under standard working conditions; during the operation of the instrument, self-checking is triggered remotely or locally, real-time waveform feature point parameters and current real-time environmental parameters are collected; meanwhile, a bivariate compensation function model which comprehensively considers temperature and device aging is introduced, the collected real-time waveform feature point parameters are compensated and calculated by using the model, and the real-time waveform feature point parameters are converted into equivalent waveform feature point parameters under the standard working conditions; the equivalent waveform feature point parameters are compared with the pre-stored standard waveform feature point parameters, if the difference is within a preset tolerance range, the instrument state is determined to be qualified. Through high-precision online self-checking, the application realizes preventive maintenance and state monitoring of the ultrasonic metering instrument.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to an online self-testing method for ultrasonic measuring instruments based on waveform compensation. Background Technology

[0002] With the deep integration of IoT, big data, and AI technologies in industrial monitoring, online monitoring and predictive maintenance of equipment status have become crucial aspects of intelligent manufacturing and smart city management. As widely deployed sensing terminals, the long-term health status of ultrasonic metrology instruments directly impacts the accuracy of metrological data and the reliability of business operations.

[0003] Current verification methods primarily involve on-site testing, either online or offline. Testing personnel must bring specialized equipment to the site, connect standard gauges and the gauges under test in series, and conduct comparative tests at different flow rates. This method has the following significant drawbacks: 1. Simple Remote Monitoring Based on Rule-Based Thresholds: Existing technologies typically read the instrument's cumulative flow, error codes, or simple signal strength values ​​remotely via wireless networks and set fixed thresholds in the background to trigger over-limit alarms. This method can only be triggered after a fault has occurred or performance has severely deteriorated, failing to provide early warning and lacking the ability to deeply process raw sensor data.

[0004] 2. Shallow data analysis lacking environmental coupling modeling: Environmental variables have a coupled effect on sensor signals. Changes in water temperature simultaneously alter the sound velocity and the absorption and attenuation of sound waves by the medium. Existing methods ignore this effect, leading to the failure of monitoring standards in different seasons; or they use simple linear compensation, which is insufficient in accuracy over a wide temperature range, has poor environmental robustness, and a high false alarm rate.

[0005] 3. Static models that fail to consider equipment lifecycle evolution: Existing monitoring models generally treat sensing terminals as static devices, assuming their performance parameters remain constant throughout their lifecycle. In reality, key components such as ultrasonic transducers undergo a natural aging process over time, causing the signal reference to drift slowly. Current software solutions lack the ability to model the time-varying characteristics of the equipment itself, failing to distinguish between the slow drift caused by normal aging and the rapid changes caused by sudden failures, thus leading to missed or false alarms.

[0006] Therefore, in the field of computer technology, especially in the direction of industrial IoT device health management, there is an urgent need to break through the following technical bottlenecks: how to design a highly robust embedded signal processing algorithm that can extract robust features from raw sensor data and build a dynamic compensation model that simultaneously decouples environmental interference and device aging, so as to realize automated assessment of the status of terminal devices at the edge or in the cloud. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of existing technologies by proposing an online self-testing method for ultrasonic metrology instruments based on waveform compensation.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: The online self-testing method for ultrasonic metrology instruments based on waveform compensation includes the following steps: S1: At the preset standard temperature T std The standard waveform feature point parameters of the ultrasonic metrology instrument are calibrated and stored, and the waveform feature point parameters include the standard transit time t. pkstd and standard peak voltage V pkstd ; S2: Self-test is triggered via a remote wireless communication network or according to an internally preset timing strategy, and the current real-time medium temperature T is collected. rt The instrument has been running for t age And real-time waveform feature point parameters, wherein the real-time waveform feature point parameters include the real-time transit time t pkrt and real-time peak voltage V pkrt ; Furthermore, the self-test is triggered in step S2 by receiving a self-test command from the central server via an NB-IoT, LoRaWAN, or GPRS wireless communication network.

[0009] The central server is connected to the ultrasonic measuring instrument via a communication network, and is used to send self-test commands to the ultrasonic measuring instrument and receive the self-test results and related data reported by the ultrasonic measuring instrument.

[0010] S3: To eliminate the real-time medium temperature T rt Compared with standard temperature T std To address the differences in performance and the impact of transducer aging over time, this invention constructs a bivariate compensation function model.

[0011] The bivariate compensation function model described in this invention converts the waveform feature point parameters measured in real time into values ​​at a standard temperature T. std The model of the equivalent waveform characteristic point parameters under the operating condition is derived from the calculation of the equivalent transit time t. pkcomp And calculate the equivalent peak voltage V pkcomp The model consists of the following components; its function is to convert the waveform feature point parameters measured in real time into values ​​at a standard temperature T. std Equivalent waveform characteristic point parameters under operating conditions.

[0012] Applying the bivariate compensation function model, based on the standard temperature T std The real-time medium temperature T rt and the instrument has been running for t ageThe real-time waveform feature point parameters are compensated and converted into equivalent waveform feature point parameters under standard temperature conditions.

[0013] Furthermore, in step S3, the equivalent transit time t is calculated. pkcomp The model is as follows: t pkcomp =t pkrt *S1 S1=[1+α(T rt -T std )+β(T rt -T std )²] in: α is the first-order influence coefficient of the medium's sound velocity on temperature; β is the second-order influence coefficient of the medium's sound velocity on temperature; t pkcomp For equivalent transit time; t pkrt For real-time transit time; T rt T represents the real-time temperature of the medium. std S1 is the preset standard temperature; S1 is the compensation factor 1.

[0014] Furthermore, α and β are determined as follows: At different temperature points Measuring transit time After each temperature point stabilizes, the transit time is measured and averaged, and recorded as follows: The transit time t is inversely proportional to the speed of sound c over a fixed distance L, as shown by the following formula: ; in: For temperature, Speed ​​of sound; for The speed of sound at temperature.

[0015] The constructor is as follows: ;

[0016] ;

[0017] ;

[0018] in for transit time at temperature; Temperature under the i-th measurement The time it takes to cross over.

[0019] From the formula Constructor Then the relationship between α and β is: ; in: Solving for the expansion, we get: ; ; Where: n is the number of times the transit time measurement value is collected; for transit time at temperature Temperature under the i-th measurement The time it takes to cross over; It should be noted that this is reverse compensation; when the real-time temperature is lower than the standard temperature, the speed of sound slows down. pkrt It will get longer. At this time (T) rt -T std) If the value is negative, the compensation factor S1 is less than 1, which will lengthen the t. pkrt Multiplying by this factor will "shorten" it back to the theoretical value that should be at the standard temperature. The quadratic polynomial ensures the compensation accuracy over a wide temperature range.

[0020] Furthermore, in step S3, the equivalent peak voltage V is calculated. pkcomp The model is as follows: V pkcomp =V pkrt / S2; S2=([1+γ(T rt -T std )]*e^(-δ*t age )) ; Where: V pkcomp V is the equivalent peak voltage; pkrt For real-time peak voltage; T rt T represents the real-time temperature of the medium. std The preset standard temperature; γ is the voltage-temperature combined influence coefficient; δ is the transducer performance aging degradation coefficient; t age is the instrument's running time; e is the base of the natural logarithm; S2 is the compensation factor 2.

[0021] Furthermore, the method for determining γ is as follows: Peak voltage was collected at each temperature point, and the average value was taken and recorded. ; The formula is constructed based on voltage V and temperature T as follows: ; in: for Peak voltage at temperature.

[0022] Constructor: ; ; ; in: Let i be the dependent variable for the i-th data collection point. Let i be the independent variable for the i-th data collection point; Let be the peak voltage from the i-th acquisition; for Peak voltage at temperature; Let be the temperature of the i-th sample.

[0023] Solving The expression is as follows: ; in: Let i be the dependent variable for the i-th data collection point. Let be the independent variable for the i-th collection point.

[0024] Furthermore, the method for determining δ is as follows: Select n transducer samples from the same batch and place them in a high-temperature environment. Periodically remove them and measure the peak voltage at a standard temperature T0. Record the results as (time). ,Voltage ); The aging function is constructed as follows: ; Logarithmic linearization is as follows: ; in: This represents the peak voltage measured at temperature T0. It is the natural logarithm; Standard time.

[0025] Constructor: Let ,but Seeking The expansion is as follows: ; in: The number of times a measurement is taken periodically; For the j-th measurement; Let be the logarithmic relative voltage value of the j-th measurement; Let be the time of the j-th measurement.

[0026] It should be noted that this is reverse compensation, and the real-time voltage V pkrt After experiencing the effects of temperature and t ageThe result after aging. To convert it back to its original factory state, it needs to be divided by these two influencing factors. If e^(-δ*t) age If the value is less than 1, it represents signal attenuation due to aging. Dividing by the real-time voltage is equivalent to restoring the signal amplitude to the level before aging. Incorporating both environmental variables and device lifecycle factors into the compensation model is a significant innovation that distinguishes this invention from existing technologies.

[0027] V generated in step S3 pkcomp Equivalent peak voltage, V pkrt Real-time peak voltages constitute time-series data describing the trajectory of equipment performance degradation. This data, stripped of environmental noise, directly reflects the evolution of equipment health and serves as a valuable data foundation for advanced artificial intelligence applications such as remaining useful life prediction and reliability analysis. Step S3 constructs a bivariate compensation function model and solidifies it into an executable algorithm. This algorithm integrates physical mechanisms and data-driven principles at the mathematical model level, achieving normalization of the original sensor data and improving the robustness of state assessment.

[0028] S4: Compare the equivalent waveform feature point parameters with the standard waveform feature point parameters. When the absolute value of the difference between the two is within their respective preset tolerance range, the instrument status is judged to be qualified; otherwise, it is judged to be abnormal.

[0029] Furthermore, the method for comparing the equivalent waveform feature point parameters with the standard waveform feature point parameters is as follows: |t pkcomp -t pkrt |≤εt; |V pkcomp -V pkrt |≤εV; Where: t pkcomp For equivalent transit time; t pkrt For real-time transit time; V pkcomp V is the equivalent peak voltage; pkrt ε is the real-time peak voltage; εt is the transit time tolerance threshold; εV is the peak voltage tolerance threshold.

[0030] If all the compared feature points satisfy the above two inequalities, the ultrasonic measuring instrument is judged to be "qualified"; otherwise, it is judged to be "abnormal".

[0031] Furthermore, step S4 also includes a result reporting step: the judgment result obtained from the comparison and judgment step, together with the collected real-time parameters and the converted equivalent waveform feature point parameters, is reported to the central server through the wireless communication network.

[0032] Step S4 transforms the state identification problem into feature analysis and decision-making based on an algorithm model. By comparing the compensated equivalent features with the factory baseline features, the cause of performance degradation is identified, achieving a leap from whether an alarm is triggered to understanding the cause of the anomaly, thus providing a basis for precise operation and maintenance.

[0033] Furthermore, the present invention also provides the following technical solution: an ultrasonic metering instrument, comprising: an ultrasonic transducer pair, a temperature sensor, a non-volatile memory, a communication module, and a microcontroller unit; the non-volatile memory is used to store standard waveform feature point parameters and standard temperature; the microcontroller unit is configured to execute an online self-test method.

[0034] Furthermore, the microcontroller unit is configured to: based on the acquired real-time transit time t pkrt Real-time peak voltage V pkrt Real-time medium temperature T rt and the instrument has been running for t age Calculate the equivalent peak voltage V pkcomp And compare it with the standard peak voltage V stored in the non-volatile memory. pkstd Compare them.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention constructs a bivariate compensation function model and solidifies it into an executable algorithm. This algorithm integrates physical mechanisms and data-driven laws at the mathematical model level, achieving normalization of the original sensor data and improving the robustness of state assessment.

[0036] 2. This solution transforms the state identification problem into feature analysis and decision-making based on an algorithm model. By comparing the compensated equivalent features with the factory baseline features, the cause of performance degradation is identified, achieving a leap from whether an alarm is triggered to understanding the cause of the anomaly, thus providing a basis for precise operation and maintenance.

[0037] 3. The equivalent waveform characteristic parameter sequence generated by each self-test constitutes time-series data describing the trajectory of equipment performance degradation. This data, stripped of environmental noise, directly reflects the evolution of equipment health and serves as a valuable data foundation for advanced artificial intelligence applications such as remaining service life prediction and reliability analysis.

[0038] 4. Through software algorithms and remote system capabilities, this invention enables high-frequency, automated, in-depth inspection of a massive number of devices, changing the high-cost model that relies on manual on-site verification. Detailed Implementation

[0039] To provide a further understanding of the purpose, structure, features, and functions of the present invention, detailed descriptions are provided below with reference to specific embodiments.

[0040] Specifically, this invention provides an online self-testing method for ultrasonic metrology instruments based on waveform compensation, the method comprising the following steps: S1: Calibration and storage of standard waveform feature point parameters: At the preset standard temperature T std The standard waveform feature point parameters of the ultrasonic metrology instrument are calibrated and stored, and the waveform feature point parameters include the standard transit time t. pkstd and standard peak voltage V pkstd The standard waveform characteristic point parameters and the standard temperature T during calibration are used. std It is stored in the instrument's non-volatile memory.

[0041] S2: Self-test is triggered via a remote wireless communication network or according to an internally preset timing strategy, and the current real-time medium temperature T is collected. rt The instrument has been running for t age And real-time waveform feature point parameters, wherein the real-time waveform feature point parameters include the real-time transit time t pkrt and real-time peak voltage V pkrt ; Furthermore, the self-test is triggered in step S2 by receiving a self-test command from the central server via an NB-IoT, LoRaWAN, or GPRS wireless communication network.

[0042] The central server is connected to the ultrasonic measuring instrument via a communication network, and is used to send self-test commands to the ultrasonic measuring instrument and receive the self-test results and related data reported by the ultrasonic measuring instrument.

[0043] S3: To eliminate the real-time medium temperature T rt Compared with standard temperature T std To address the differences in waveform characteristics and the impact of transducer aging over time, this invention constructs a bivariate compensation function model. This model converts the real-time measured waveform feature point parameters into values ​​measured at a standard temperature T. std Equivalent waveform characteristic point parameters under operating conditions; applying the bivariate compensation function model, based on the standard temperature T std The real-time medium temperature T rt and the instrument has been running for t age The real-time waveform feature point parameters are compensated and converted into equivalent waveform feature point parameters under standard temperature conditions.

[0044] Furthermore, in step S3, the equivalent transit time t is calculated. pkcomp The model is as follows: t pkcomp =t pkrt *[1+α(T rt-T std )+β(T rt -T std )²] in: α is the first-order influence coefficient of the medium's sound velocity on temperature; β is the second-order influence coefficient of the medium's sound velocity on temperature; t pkcomp For equivalent transit time; t pkrt For real-time transit time; T rt T represents the real-time temperature of the medium. std This is the preset standard temperature.

[0045] It should be noted that this is reverse compensation; when the real-time temperature is lower than the standard temperature, the speed of sound slows down. pkrt It will get longer. At this time (T) rt -T std If the sum is negative, the compensation factor S1 is less than 1, which will lengthen the t. pkrt Multiplying by this factor will "shorten" it back to the theoretical value that should be at the standard temperature. The quadratic polynomial ensures the compensation accuracy over a wide temperature range.

[0046] Furthermore, in step S3, the equivalent peak voltage V is calculated. pkcomp The model is as follows: V pkcomp =V pkrt / ([1+γ(T rt -T std )]*e^(-δ*t age )) in: V pkcomp V is the equivalent peak voltage; pkrt For real-time peak voltage; T rt T represents the real-time temperature of the medium. std The preset standard temperature; γ is the voltage-temperature combined influence coefficient; δ is the transducer performance aging degradation coefficient; t age is the instrument's running time; e is the base of the natural logarithm.

[0047] It should be noted that this is reverse compensation, and the real-time voltage V pkrt After experiencing the effects of temperature and t age The result after aging. To convert it back to its original factory state, it needs to be divided by these two influencing factors. If e^(-δ*t) age If the value is less than 1, it represents signal attenuation due to aging. Dividing by the real-time voltage is equivalent to restoring the signal amplitude to its pre-aging level. Incorporating both environmental variables and device lifecycle factors into the compensation model is a significant innovation that distinguishes this invention from existing technologies.

[0048] S4: Compare the equivalent waveform feature point parameters with the standard waveform feature point parameters. When the absolute value of the difference between the two is within their respective preset tolerance range, the instrument status is judged to be qualified; otherwise, it is judged to be abnormal.

[0049] Furthermore, the method for comparing the equivalent waveform feature point parameters with the standard waveform feature point parameters is as follows: |t pkcomp -t pkrt |≤εt; |V pkcomp -V pkrt |≤εV; in: t pkcomp For equivalent transit time; t pkrt For real-time transit time; V pkcomp V is the equivalent peak voltage; pkrt ε is the real-time peak voltage; εt is the transit time tolerance threshold; εV is the peak voltage tolerance threshold.

[0050] If all the compared feature points satisfy the above two inequalities, the ultrasonic measuring instrument is deemed to be in "qualified" condition; otherwise, it is deemed to be "abnormal" or "suspected malfunction".

[0051] Furthermore, step S4 also includes a result reporting step: the judgment result obtained from the comparison and judgment step, together with the collected real-time parameters and the converted equivalent parameters, is reported to the central server via a wireless communication network.

[0052] Example 1

[0053] Take an ultrasonic water meter deployed in a residential community as an example.

[0054] Step S1: Factory-calibrated standard water temperature T std =20.0°C. The standard parameters of the first main peak were obtained through calibration and stored: t pkstd =30.00µs, V pkstd =8.00mV. The compensation model coefficients pre-stored in the instrument firmware are (all example values, calibrated experimentally): α = 4.5E - 3 = 4.5 * 10 - 3; β = -5.0E-6 = -5.0 * 10-6; γ = 0.01 (assuming experimental calibration, as temperature decreases, attenuation increases, and voltage decreases, so the term 1 + γ(ΔT) should be less than 1); The tolerance threshold δ = 0.02 (unit: 1 / year) is set as follows: εt = 0.5µs; εV = 0.8mV.

[0055] Step S2: Online self-test and data acquisition Three years later (t) age =3.0 years), on a winter night, the water meter performed a self-test. The measured water temperature T rt =5.0°C. At this point, due to scaling on the inner wall of the pipe, the signal attenuation is abnormal, and the acquired real-time waveform characteristic point parameters are: t pkrt =32.15µs, V pkrt =5.50mV.

[0056] Step S3: Parameter conversion calculation Based on the collected real-time data, the compensation model is invoked for conversion: ΔT=T rt -T std =5.0-20.0=-15.0°C; Calculate the equivalent transit time t pkcomp : t pkcomp =32.15*[1+(4.5E-3)(-15.0)+(-5.0E-6)(-15.0)²]=32.15*[1-0.0675-0.001125]=32.15*0.931375≈29.95µs; Calculate the equivalent peak voltage V pkcomp : V pkcomp =5.50 / ([1+0.01*(-15.0)]*e^(-0.02*3.0))=5.50 / ([1-0.15]*e^(-0.06))=5.50 / (0.85*0.94176)=5.50 / 0.8005≈6.87mV.

[0057] Step S4: State Comparison Comparing transit times: |t pkcomp -t pkstd The time parameter |=|29.95-30.00|=0.05µs. Since 0.05µs≤ε_t(0.5µs), the time parameter meets the requirements. (Time is mainly affected by the speed of sound; scaling has a relatively small impact, and after compensation, the result is basically consistent.) Compare peak voltages: |V pkcomp -V pkstd |=|6.87-8.00|=1.13mV Since 1.13mV > ε_V (0.8mV), the voltage parameter does not meet the requirements.

[0058] Conclusion: Since the deviation between the equivalent peak voltage and the standard peak voltage exceeds the allowable threshold, the water meter is determined to be in an "abnormal" state.

[0059] Step S5: Report the "abnormal" conclusion and related data. After receiving the report, the central server analyzes the data and finds that the compensated equivalent voltage (6.87mV) is much lower than the factory standard voltage (8.00mV), indicating that there are additional signal attenuation factors besides normal aging and temperature effects. Based on this, the system generates a maintenance work order. Maintenance personnel go to the site to verify and confirm that it is caused by pipe scaling, proving the accuracy of the online self-test.

[0060] Additionally, based on the above technical solution, the present invention also provides an ultrasonic metering instrument, comprising: an ultrasonic transducer pair, a temperature sensor, a non-volatile memory, a communication module, and a microcontroller unit. Non-volatile memory is used to store standard waveform feature point parameters and standard temperature; The microcontroller unit is configured to perform an online self-test method.

[0061] Furthermore, based on the collected real-time transit time t pkrt Real-time peak voltage V pkrt Real-time medium temperature T rt and the instrument has been running for t age Calculate the equivalent peak voltage V pkcomp And compare it with the standard peak voltage V stored in non-volatile memory. pkstd Compare them.

[0062] Additionally, based on the above technical solution, the present invention also provides an online self-testing system for ultrasonic metrology instruments, comprising: at least one ultrasonic metrology instrument; and a central server; The central server connects to the ultrasonic measuring instrument via a communication network to send self-test commands to it and receive the self-test results and related data reported by it.

[0063] To verify the beneficial effects of the present invention, the following verification experiments were conducted: 1. Experimental objective: This experiment aims to verify the effectiveness and advancement of the "online self-testing method for ultrasonic metrology instruments based on waveform compensation" proposed in this invention. The specific objectives are as follows: 1.1. Verification of accuracy under healthy conditions: This demonstrates that the bivariate compensation model of the present invention can effectively eliminate the effects of changes in ambient temperature and normal aging of devices. Under healthy conditions, the real-time parameters measured under different operating conditions are highly consistent with the factory standard parameters after compensation and conversion.

[0064] 1.2. Verify the reliability of fault identification: Prove that this method can accurately identify waveform abnormalities caused by abnormal factors and distinguish them from normal temperature changes and aging effects.

[0065] 1.3. Verification of the advanced nature of the scheme, i.e., comparative experiment: to prove that the "temperature + aging" bivariate compensation model adopted in this invention has significantly higher accuracy and lower misjudgment rate compared with the simple model of "no compensation" or "temperature compensation only".

[0066] 2. Experimental equipment and materials: Test samples: 3 brand new ultrasonic water meters from the same batch, using the technology of this invention, numbered #1, #2, and #3.

[0067] High-precision constant temperature water bath: temperature control range 0-50°C, accuracy ±0.1°C.

[0068] Metering standard device: Certified static volumetric water meter calibration device.

[0069] Data acquisition and monitoring system: used to receive and record self-inspection data reported by water meters, including the central server software of this invention.

[0070] Fault simulation material: Epoxy resin mixed with quartz powder coating, used to simulate a uniform layer of scale on the inner wall of the pipe, causing additional attenuation of the sound signal.

[0071] Artificial aging device: High temperature and high humidity test chamber, used to accelerate the aging of the transducer of water meter #3 to simulate its state after long-term operation. In this experiment, we directly substituted t into the calculation. age Parameters are used to simulate natural aging.

[0072] 3. Experimental Design and Procedures: Step 1: Calibration of sample reference parameters: Three brand-new ultrasonic water meters (#1, #2, #3) were installed on the calibration device, and the temperature of the constant temperature water bath was set to the standard temperature T of this invention. std =20.0°C. After the water temperature stabilized, a water flow test was performed on the three water meters, and their self-test program was triggered. The parameters of their factory standard waveform characteristic points were recorded. Assuming that the parameters of their first main wave peak are basically the same, the records are as follows.

[0073] Table 1: Sample Reference Parameters Table Water meter number <![CDATA[Standard temperature T std (°C)]]> <![CDATA[Standard transit time t pkstd (µs)]]> <![CDATA[Standard peak voltage V pkstd (mV)]]> #1 20.0 30.00 8.00 #2 20.0 30.02 7.98 #3 20.0 30.01 8.01 Tolerance threshold - εt=0.5µs εV=0.8mV Step Two: Self-inspection and verification under different operating conditions in a healthy state: This step simulates the working conditions of a water meter under different water temperatures after 3 years of normal use. We will calculate the meter's operating time as t. age =3.0 Input into the calculation model.

[0074] Operating Condition A: Low Temperature in Winter: Place water meter #1 in a constant temperature water bath and set the water temperature to T. rt =5.0°C.

[0075] Operating Condition B: High Temperature in Summer: Place water meter #2 in a constant temperature water bath and set the water temperature to T. rt =35.0°C.

[0076] Operating condition C: Aging at room temperature: Place water meter #3 in a constant temperature water bath and set the water temperature to T. rt =20.0°C, which is the same as the standard temperature, to verify the aging compensation effect separately.

[0077] Once the temperature of each water meter stabilizes, it triggers a self-test, records real-time parameters, and automatically performs compensation calculations and judgments internally.

[0078] Table 2: Self-test verification data under different operating conditions in a healthy state Water meter number <![CDATA[Test condition (T rt , t age )]]> <![CDATA[Real-time parameter (t rt , V rt )]]> <![CDATA[Equivalent parameters after compensation conversion (t comp , V comp )]]> Deviation from the standard (|Δt|,|ΔV|) Single-item judgment Final result #1 5.0°C, 3 years (32.15µs, 6.41mV) (29.95µs, 8.01mV) (0.05µs, 0.01mV) (t:√,V:√) qualified #2 35.0°C, 3 years (29.20µs, 5.85mV) (30.03µs, 7.99mV) (0.01µs, 0.01mV) (t:√,V:√) qualified #3 20.0°C, 3 years (30.01µs, 7.53mV) (30.01µs, 7.99mV) (0.00µs, 0.02mV) (t:√,V:√) qualified Data analysis (Table 2): Under three distinct operating conditions, the real-time measured transit times (32.15µs, 29.20µs) and peak voltages (6.41mV, 5.85mV) differed significantly from the standard values ​​(approximately 30µs, 8mV).

[0079] However, after conversion using the bivariate compensation model of this invention, the equivalent parameter t obtained is... comp and V comp All of them are remarkably close to their respective factory standard values.

[0080] The calculated deviations |Δt| and |ΔV| are both much smaller than the preset tolerance thresholds εt and εV.

[0081] Conclusion: Experiments have shown that the method of the present invention can accurately compensate for the effects of temperature and aging, and will not produce false alarms when the instrument is in good condition, verifying its high accuracy in a healthy state.

[0082] Step 3: Self-test verification simulating fault conditions: This step verifies the method's ability to identify real-world faults. We took water meter #1 and, without altering its electronic components and transducer, uniformly coated the inner wall of its pipe with a layer of epoxy resin quartz powder coating approximately 0.5 mm thick to simulate the additional sound attenuation caused by pipe scaling. Then, it was placed back at a standard temperature T. rt The test was conducted in a water bath at 20.0°C, with the temperature t set accordingly. age =3.0 years.

[0083] Table 3: Self-test verification data under simulated fault conditions Water meter number Test status <![CDATA[Real-time parameter (t rt , V rt )]]> <![CDATA[Equivalent parameters after compensation conversion ( tcomp , V comp )]]> Deviation from the standard (|Δt|,|ΔV|) Single-item judgment Final result #1 Healthy (Control) (30.01µs, 7.53mV) (30.01µs, 7.99mV) (0.01µs, 0.01mV) (t:√,V:√) qualified #1 Fault (scaling) (30.05µs, 4.50mV) (30.05µs, 4.78mV) (0.05µs, 3.22mV) (t:√,V:×) abnormal Data analysis (Table 3): After the scaling fault is introduced, since the sound path remains unchanged, the transit time t rt The change is minimal, and after compensation, t comp It is still within the allowable tolerance.

[0084] However, due to the sharp increase in signal attenuation, the real-time voltage V... rt It dropped sharply from 7.53mV when healthy (after aging) to 4.50mV.

[0085] After aging compensation according to the present invention (no temperature compensation in this operating condition), the equivalent voltage V comp It is 4.78mV.

[0086] Compare this equivalent voltage with the standard voltage V pkstd Compared to (8.00mV), the deviation |ΔV| is as high as 3.22mV, far exceeding the tolerance threshold εV (0.8mV).

[0087] Conclusion: Experiments have shown that the present invention can successfully identify signal anomalies caused by physical faults (such as scaling) and distinguish them from normal aging, triggering an "abnormal" judgment and verifying the reliability of its fault identification.

[0088] Step 4: Comparative Experiment of the Effects of Different Compensation Methods This step is crucial in demonstrating the advancement of this invention. We use the same set of real-time data (t) from operating condition A (water meter #1, 5.0°C, 3 years) in step two. rt =32.15µs,V rt =6.41mV), and was processed and judged using three different methods. The standard value is V. pkstd =8.00mV.

[0089] Table 4: Comparison of the effects of different compensation methods (taking peak voltage as an example) Comparison Methods Calculation process <![CDATA[Calculation result V result (mV)]]> <![CDATA[Relative error with respect to the standard value |V result -8.00| / 8.00]]> Conclusion (based on εV=0.8mV) Method 1: No compensation <![CDATA[V result =V rt ]]> 6.41 19.88% Abnormal (misjudgment) Method 2: Temperature compensation only <![CDATA[V result =V rt / [1+γ(ΔT)]=6.41 / 0.85]]> 7.54 5.75% Abnormal (misjudgment) Method 3: This invention <![CDATA[V result =V rt / ([1+γ(ΔT)]e^(-δt age ))]]> 8.01 0.13% Qualified (Correct) Data analysis (Table 4): Method 1 (Uncompensated): Directly comparing real-time values ​​with standard values. Due to temperature differences and aging, this introduces a huge error of nearly 20%, causing the system to misclassify a healthy table as "abnormal". This is the most primitive and unreliable method.

[0090] Method 2 (Temperature Compensation Only): After considering only the effect of temperature, the error was significantly reduced from 19.88% to 5.75%. Although there was an improvement, the calculated voltage deviation |7.54-8.00|=0.46mV would still produce misjudgments if the tolerance εV was set more strictly (e.g., 0.5mV) or the aging time was longer. It cannot explain the degradation caused by aging.

[0091] Method 3 (this invention): After compensating for both temperature and aging, the calculated result is 8.01mV, with a relative error of only 0.13% compared to the standard value of 8.00mV, almost perfectly replicating the factory condition. The deviation of only 0.01mV is well within the tolerance range, leading to a correct "qualified" judgment.

[0092] 4. Experiment Summary: Based on the above series of experiments and data comparisons, the following conclusions can be drawn: The online self-testing method of this invention, with its innovative temperature + aging dual-variable compensation model, can accurately convert the real-time waveform characteristic point parameters of the health instrument over a wide temperature range and a long service life, making them highly consistent with the factory standard values, thus proving its excellent accuracy.

[0093] This invention can effectively identify abnormal signal attenuation caused by physical faults such as pipe scaling and distinguish it from normal, predictable attenuation, demonstrating its reliable fault diagnosis capability.

[0094] Compared with schemes without compensation or with only single-variable compensation, the present invention reduces the false positive rate from possible to almost zero, and its superiority and advancement in technical effect have been fully demonstrated.

[0095] In summary, the present invention addresses the shortcomings of existing technologies in balancing environmental changes and device aging, providing a high-precision and high-reliability remote self-testing method with significant engineering application value.

[0096] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0097] The present invention has been described in the above-described embodiments; however, these embodiments are merely examples for implementing the present invention. It must be noted that the disclosed embodiments do not limit the scope of the present invention. Conversely, any modifications and refinements made without departing from the spirit and scope of the present invention are within the scope of patent protection of the present invention.

Claims

1. A method for online self-testing of ultrasonic measuring instruments based on waveform compensation, characterized in that, Includes the following steps: S1: At the preset standard temperature T std The standard waveform feature point parameters of the ultrasonic metrology instrument are calibrated and stored, and the waveform feature point parameters include the standard transit time t. pkstd and standard peak voltage V pkstd ; S2: Self-test is triggered via a remote wireless communication network or according to an internally preset timing strategy, and the current real-time medium temperature T is collected. rt The instrument has been running for t age And real-time waveform feature point parameters, wherein the real-time waveform feature point parameters include the real-time transit time t pkrt and real-time peak voltage V pkrt ; S3: Applying a bivariate compensation function model, which is the method used in this invention to convert the waveform feature point parameters measured in real time into values ​​at a standard temperature T. std The model of the equivalent waveform characteristic point parameters under the operating condition is derived from the calculation of the equivalent transit time t. pkcomp And calculate the equivalent peak voltage V pkcomp The model composition; based on the standard temperature T std The real-time medium temperature T rt and the instrument has been running for t age The real-time waveform feature point parameters are compensated and converted into equivalent waveform feature point parameters under standard temperature conditions. S4: Compare the equivalent waveform feature point parameters with the standard waveform feature point parameters. When the absolute value of the difference between the two is within their respective preset tolerance range, the instrument status is judged to be qualified; otherwise, it is judged to be abnormal.

2. The online self-testing method for ultrasonic metrology instruments based on waveform compensation as described in claim 1, characterized in that, In step S3, the equivalent transit time t is calculated. pkcomp The model is as follows: t pkcomp =t pkrt *[1+α(T rt -T std )+β(T rt -T std )²] in: α is the first-order influence coefficient of the medium's sound velocity on temperature; β is the second-order influence coefficient of the medium's sound velocity on temperature; t pkcomp For equivalent transit time; t pkrt For real-time transit time; T rt T represents the real-time temperature of the medium. std This is the preset standard temperature.

3. The online self-testing method for ultrasonic metrology instruments based on waveform compensation as described in claim 1, characterized in that, In step S3, the equivalent peak voltage V is calculated. pkcomp The model is as follows: V pkcomp =V pkrt / ([1+γ(T rt -T std )]*e^(-δ*t age )); in: V pkcomp V is the equivalent peak voltage; pkrt For real-time peak voltage; T rt T represents the real-time temperature of the medium. std The preset standard temperature; γ is the voltage-temperature combined influence coefficient; δ is the transducer performance aging degradation coefficient; t age is the instrument's running time; e is the base of the natural logarithm.

4. The online self-testing method for ultrasonic metrology instruments based on waveform compensation as described in claim 1, characterized in that, The self-test triggering method for step S2 is as follows: receiving a self-test command issued by the central server through an NB-IoT, LoRaWAN, or GPRS wireless communication network; the central server is connected to the ultrasonic meter through a communication network to send a self-test command to the ultrasonic meter and receive the self-test results and related data reported by it.

5. The online self-testing method for ultrasonic metrology instruments based on waveform compensation as described in claim 1, characterized in that, The method for comparing the equivalent waveform feature point parameters with the standard waveform feature point parameters in step S4 is as follows: |t pkcomp -t pkrt |≤εt; |V pkcomp -V pkrt |≤εV; in: t pkcomp For equivalent transit time; t pkrt For real-time transit time; V pkcomp V is the equivalent peak voltage; pkrt εt is the real-time peak voltage; εt is the transit time tolerance threshold; εV is the peak voltage tolerance threshold. If all the compared feature points satisfy the above two inequalities, the ultrasonic measuring instrument is judged to be "qualified"; otherwise, it is judged to be "abnormal".

6. The online self-testing method for ultrasonic metrology instruments based on waveform compensation as described in claim 4, characterized in that, Step S4 also includes a result reporting step: the judgment result obtained in step S4, together with the collected real-time parameters and the converted equivalent parameters, is reported to the central server through a wireless communication network.

7. An ultrasonic measuring instrument configured to perform the waveform compensation-based online self-testing method for ultrasonic measuring instruments according to any one of claims 1-6, characterized in that, include: The system includes an ultrasonic transducer pair, a temperature sensor, a non-volatile memory, a communication module, and a microcontroller unit. The non-volatile memory is used to store standard waveform feature point parameters and standard temperature; The microcontroller unit is configured to perform an online self-test method.

8. The ultrasonic measuring instrument as described in claim 7, characterized in that, The microcontroller unit is configured to: based on the acquired real-time transit time t pkrt Real-time peak voltage V pkrt Real-time medium temperature T rt and the instrument has been running for t age Calculate the equivalent peak voltage V pkcomp And compare it with the standard peak voltage V stored in the non-volatile memory. pkstd Compare them.