Water meter measurement monitoring and calibration method based on Internet of Things

Abstract

The invention relates to the field of water meter measurement monitoring, and discloses a water meter measurement monitoring and calibration method based on the Internet of Things, which comprises the following steps of: firstly, acquiring parameters of a water meter and environmental parameters in real time through the Internet of Things; secondly, comparing each parameter of the water meter with a preset parameter threshold value in real time, and if at least one exceeding parameter of the water meter exists, preliminarily judging that the current water meter has a metering problem; backtracking for a plurality of preset time periods by taking the current moment as an end point to obtain a plurality of time gradients, and calculating each parameter of the water meter per se in each time gradient to obtain a water meter anomaly index; the water meter abnormal index is compared with a preset water meter abnormal threshold value, if the water meter abnormal index is larger than or equal to the water meter abnormal threshold value, it is determined that the current water meter has the metering problem again, then an automatic calibration signal is triggered, and finally whether the automatic calibration signal is sent or not is determined according to the analysis result of the environment parameters.

Description

Technical Field

[0001] The present invention relates to the field of water meter measurement and monitoring, and particularly to a method for water meter measurement monitoring and calibration based on the Internet of Things. Background Art

[0002] After long-term use of traditional mechanical water meters, component wear and aging frequently occur. For example, the rotational resistance of the impeller increases, and the gear meshing accuracy decreases, which will cause the water meter reading deviation to become larger and larger. Such deviations will at least affect the fairness of residents' water fee accounting, and at most interfere with the accuracy of the revenue accounting of water supply enterprises and the macro-allocation of urban water resources.

[0003] Traditional mechanical water meters rely on manual meter reading at regular intervals, and the meter reading cycle is often long, so they cannot provide real-time feedback of water usage data. Once sudden abnormal water usage situations such as water pipe bursts or illegal water use occur, it is difficult to detect and handle them in a timely manner, resulting in unnecessary waste of water resources, and also increasing the operation and maintenance costs and management difficulties of water supply enterprises.

[0004] With the development of Internet of Things technology, with the support of sensor technology, water meters can achieve rapid data transfer, and various parameters of water meters can be efficiently monitored, providing richer parameter bases for accurate measurement. However, there are still some defects. Wireless signals are affected by factors such as the environment, equipment performance, and signal interference. If the signal is unstable or interference occurs during transmission, it may cause data delay or transmission errors. For example, during thunderstorm weather or when there are large electromagnetic devices nearby, the signal transmission may be affected, resulting in the reading displayed by the system being inconsistent with the actual reading on the dial. At this time, if the reading displayed by the system exceeds the calibration set threshold, it will lead to high-frequency invalid calibrations, affecting the accurate measurement of the water meter. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for water meter measurement monitoring and calibration based on the Internet of Things to solve the above technical problems.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A method for water meter measurement monitoring and calibration based on the Internet of Things includes:

[0008] S1. Detect the water meters in the target area based on sensors and environmental monitoring devices, and collect the water meter's own parameters and environmental parameters in real time through the Internet of Things;

[0009] S2. Compare each water meter's own parameter with the preset parameter threshold in real time. If there is at least one water meter's own parameter that exceeds the threshold, it is initially determined that there is a measurement problem with the current water meter; otherwise, return to step S1;

[0010] S3. Taking the current moment as the end point, trace back multiple preset time periods backward to obtain multiple time gradients, and then calculate each water meter's own parameter within each time gradient to obtain the water meter anomaly index.

[0011] S4. Compare the water meter anomaly index with the preset water meter anomaly threshold. If the water meter anomaly index ≥ the water meter anomaly threshold, confirm again that there is a measurement problem with the current water meter and enter step S5; otherwise, return to step S1.

[0012] S5. Trigger an automatic calibration signal, and then determine whether to send the automatic calibration signal according to the analysis result of the environmental parameters.

[0013] Through the above technical solution, in order to solve the problem that in the traditional mode, the parameter comparison at a single time point is easily interfered by temporary fluctuations, resulting in frequent misjudgments, the time gradient backtracking is introduced to calculate the water meter anomaly index, comprehensively considering the operation trend of the water meter over a period of time, which can screen out parameter anomalies caused by accidental factors and accurately lock the water meters with real measurement problems. In addition, combined with the analysis of environmental parameters, the impact of the environment on measurement is judged before automatic calibration, avoiding ineffective calibration caused by sudden environmental conditions such as heavy rain and strong electromagnetic interference during calibration, making the calibration action targeted and reducing the overall measurement error.

[0014] As a further technical solution, the S5 includes:

[0015] S51. Obtain the environmental parameters of each time gradient and calculate to obtain the environmental anomaly index.

[0016] S52. Compare the environmental anomaly index with the preset environmental anomaly threshold. If the environmental anomaly index ≥ the environmental anomaly threshold, enter step S53; if the environmental anomaly index < the environmental anomaly threshold, enter step S54.

[0017] S53. Judge that the measurement problem of the current water meter is affected by the environment and stop sending the automatic calibration signal.

[0018] S54. Confirm that there is a measurement problem with the current water meter and immediately send the automatic calibration signal.

[0019] Through the above technical solution, once the measurement problem is accurately identified, the system automatically triggers the calibration signal, eliminating the need for manual inspection and calibration one by one, greatly reducing the workload of water service operation and maintenance personnel.

[0020] As a further technical solution, the method for obtaining the water meter anomaly index is:

[0021] Substitute each water meter's own parameter into the formula:

[0022]

[0023] Calculate the water meter anomaly index Q;

[0024] Among them, β i (t) is the change curve of the i-th water meter's own parameter over time, t0 to t1 is any time gradient, α i is the weight coefficient corresponding to the i-th time gradient, m is the number of time gradients, q r is the change amount of the r-th water meter's own parameter, n is the number of samples within each time gradient, β ij is the observed value of the water meter's own parameter corresponding to the j-th sampling point, is the corresponding average value of the water meter's own parameter, N is the number of the water meter's own parameters, γ r is the weight coefficient corresponding to the r-th water meter's own parameter.

[0025] Through the above technical solution, first calculate the cumulative change amount within each time gradient through the formula. If the change of the water meter's own parameter within each time gradient is greater, it means the probability of the water meter having problems is higher. Then calculate the water meter anomaly index. The larger the water meter anomaly index, the higher the probability that the water meter has metering problems. Then calculate all the water meter's own parameters by the way of cumulative summation, and then obtain a more accurate water meter anomaly assessment status to achieve the purpose of fitting the actual state of the water meter, providing accurate data support for the subsequent goal of whether to trigger automatic calibration.

[0026] As a further technical solution, the process of obtaining the environmental anomaly index is as follows:

[0027] Substitute each environmental parameter into the formula:

[0028]

[0029] Calculate the environmental anomaly index H;

[0030] Among them, k is any environmental parameter, M is the number of environmental parameters, θk is the weight coefficient corresponding to the k-th environmental parameter, R k is the real-time value of the k-th environmental parameter.

[0031] As a further technical solution, the expression of the weight coefficient corresponding to the k-th environmental parameter is:

[0032]

[0033] Among them, S is the number of samples within multiple time gradients, R kμ is the value of the k-th environmental parameter corresponding to any sampling point, is the average value of the k-th environmental parameter, μ is the μ-th sampling point.

[0034] As a further technical solution, compare the calculated environmental anomaly index H with a preset environmental anomaly threshold H0;

[0035] If H≥H0, it is determined that the environment where the current water meter is located is abnormal;

[0036] If H<H0, it is determined that the environment where the current water meter is located is normal.

[0037] As a further technical solution, the method further includes:

[0038] S6. After the calibration is completed, start the monitoring cycle again, and re-collect the water meter's own parameters and environmental parameters based on the sensors and environmental monitoring devices, and repeat the judgment process of steps S2 - S4 to verify whether the calibrated water meter has returned to the normal metering state.

[0039] S6 further includes the following steps:

[0040] If there are still metering problems, mark the water meter as a difficult fault state, and at the same time push detailed fault information to the operation and maintenance personnel. The fault information includes the water meter number, abnormal parameters, and multiple calibration records.

[0041] Advantages of the present invention:

[0042] (1) In order to solve the problem that in the traditional mode, the parameter comparison at a single time point is easily interfered by temporary fluctuations, resulting in frequent misjudgments, the present invention introduces time gradient backtracking to calculate the water meter anomaly index, comprehensively considers the operation trend of the water meter over a period of time, can screen out parameter anomalies caused by accidental factors, and accurately lock the water meters with real metering problems; in addition, combined with environmental parameter analysis, it judges the impact of the environment on metering before automatic calibration, avoids ineffective calibration caused by sudden environmental conditions such as heavy rain and strong electromagnetic interference during the period, makes the calibration action targeted, and reduces the overall metering error.

[0043] (2) Once the metering problem is accurately identified in the present invention, the system automatically triggers a calibration signal, eliminating the need for manual individual inspections and manual calibrations, greatly reducing the workload of water service operation and maintenance personnel; the long-term accumulated data of the water meter's own parameters and environmental parameters provides rich materials for subsequent system optimization. With the help of big data analysis and machine learning algorithms, various preset thresholds can be continuously optimized to make the judgment criteria fit the dynamic changes of the actual water use scenario, ensuring that the water meter always maintains stable and accurate metering performance in different seasons and different pipe network conditions, and extending the service life of the water meter.

[0044] (3) In the present invention, the cumulative change amount within each time gradient is first calculated through a formula. If the change in the water meter's own parameters within each time gradient is greater, it indicates a higher probability that the water meter has a problem. Then, the water meter anomaly index is calculated. The larger the water meter anomaly index, the higher the probability that the water meter has a metering problem. Next, all the water meter's own parameters are calculated by cumulative summation, thereby obtaining a more accurate water meter anomaly assessment status to achieve the purpose of conforming to the actual state of the water meter and providing accurate data support for the subsequent goal of whether to trigger automatic calibration. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The present invention will be further described below with reference to the accompanying drawings.

[0046] Figure 1 is a step diagram of a water meter metering monitoring and calibration method based on the Internet of Things according to the present invention;

[0047] Figure 2 is Figure 1 the method step diagram of S5 in DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0049] Please refer to Figures 1 - 2 as shown. The present invention is a water meter metering monitoring and calibration method based on the Internet of Things, including:

[0050] S1. Detect the water meters in the target area based on sensors and environmental monitoring devices, and collect the water meter's own parameters and environmental parameters in real time through the Internet of Things;

[0051] S2. Compare each water meter's own parameter with the preset parameter threshold in real time. If there is at least one water meter's own parameter that exceeds the threshold, it is initially determined that the current water meter has a metering problem; otherwise, return to step S1. It should be noted that the water meter's own parameters include flow rate, pressure, water turbine rotation speed, sensor working voltage and current; the environmental parameters include temperature, rainfall and electromagnetic intensity;

[0052] S3. Taking the current moment as the end point, look back multiple preset time periods backward to obtain multiple time gradients, and then calculate each water meter's own parameter within each time gradient to obtain the water meter anomaly index;

[0053] S4. Compare the water meter anomaly index with the preset water meter anomaly threshold. If the water meter anomaly index ≥ the water meter anomaly threshold, confirm again that there is a metering problem with the current water meter and proceed to step S5; otherwise, return to step S1.

[0054] S5. Trigger an automatic calibration signal, and then determine whether to send the automatic calibration signal based on the analysis result of the environmental parameters.

[0055] The above S5 includes:

[0056] S51. Obtain the environmental parameters of each time gradient and calculate to obtain the environmental anomaly index.

[0057] S52. Compare the environmental anomaly index with the preset environmental anomaly threshold. If the environmental anomaly index ≥ the environmental anomaly threshold, proceed to step S53; if the environmental anomaly index < the environmental anomaly threshold, proceed to step S54.

[0058] S53. Determine that the metering problem of the current water meter is affected by the environment and stop sending the automatic calibration signal.

[0059] S54. Confirm that there is a metering problem with the current water meter and immediately send the automatic calibration signal.

[0060] In this embodiment, a method for monitoring and calibrating water meter metering based on the Internet of Things is provided. To solve the problem that in the traditional mode, the parameter comparison at a single time point is easily interfered by temporary fluctuations, resulting in frequent misjudgments, the time gradient is introduced to retrospectively calculate the water meter anomaly index, comprehensively consider the operation trend of the water meter over a period of time, and can screen out parameter anomalies caused by accidental factors, accurately lock the water meters with real metering problems; in addition, combined with the analysis of environmental parameters, judge the impact of the environment on metering before automatic calibration, avoid ineffective calibration caused by sudden environmental conditions such as heavy rain and strong electromagnetic interference during calibration, make the calibration action targeted, and reduce the overall metering error.

[0061] At the same time, once a metering problem is accurately identified, the system automatically triggers a calibration signal, eliminating the need for manual individual inspections and manual calibrations, greatly reducing the workload of water service operation and maintenance personnel. The long-term accumulated data of the water meter's own parameters and environmental parameters provides rich materials for subsequent system optimization. With the help of big data analysis and machine learning algorithms, various preset thresholds can be continuously optimized, enabling the judgment criteria to adapt to the dynamic changes of the actual water use scenario, ensuring that the water meter maintains stable and accurate metering performance in different seasons and different pipe network conditions, and extending the service life of the water meter.

[0062] The method for obtaining the water meter anomaly index is as follows:

[0063] Substitute each water meter's own parameter into the formula:

[0064]

[0065] Calculate the water meter anomaly index Q;

[0066] Among them, β i (t) is the change curve of the i-th water meter's own parameter over time, t0 to t1 is any time gradient, α i is the weight coefficient corresponding to the i-th time gradient, m is the number of time gradients, q r is the change amount of the r-th water meter's own parameter, n is the number of samples within each time gradient, β ij is the observed value of the water meter's own parameter corresponding to the j-th sampling point, is the corresponding average value of the water meter's own parameter, N is the number of the water meter's own parameters, γ r is the weight coefficient corresponding to the r-th water meter's own parameter, which is determined comprehensively based on historical data and experimental data.

[0067] In this embodiment, first, calculate the cumulative change amount within each time gradient through the formula Obviously, if the change of the water meter's own parameter within each time gradient is greater, it means the probability that the water meter has problems is higher. Therefore, it is more necessary to focus on monitoring this water meter. Subsequently, substitute the calculated q r into the formula to calculate the water meter anomaly index Q; It can be seen from the formula that if the cumulative change amount of each water meter's own parameter is greater and the fluctuation degree within each time gradient is greater, it means that the water meter's own parameter is more abnormal. Therefore, the probability that the water meter has measurement problems is higher. Then, calculate all the water meter's own parameters by the way of cumulative summation, and further obtain a more accurate water meter anomaly assessment status to achieve the purpose of conforming to the actual state of the water meter and provide accurate data support for the subsequent goal of whether to trigger automatic calibration.

[0068] The process of obtaining the environmental anomaly index is as follows:

[0069] Substitute each environmental parameter obtained into the formula:

[0070]

[0071] Calculate the environmental anomaly index H;

[0072] Among them, k is any environmental parameter, M is the number of environmental parameters, θk is the weight coefficient corresponding to the k-th environmental parameter, R k is the real-time value of the k-th environmental parameter.

[0073] The expression of the weight coefficient corresponding to the k-th environmental parameter is:

[0074]

[0075] where S is the number of samples within multiple time gradients, and R kμ is the value of the k-th environmental parameter corresponding to any sampling point, is the average value of the k-th environmental parameter, and μ is the μ-th sampling point.

[0076] Compare the calculated environmental anomaly index H with the preset environmental anomaly threshold H0;

[0077] If H≥H0, it is determined that the environment where the current water meter is located is abnormal;

[0078] If H<H0, it is determined that the environment where the current water meter is located is normal.

[0079] In this embodiment, the analysis of environmental parameters is added. By calculating the environmental anomaly index and comparing it with the threshold, it is determined whether the water meter measurement problem is affected by the environment, avoiding the situation of blindly triggering calibration when the water meter parameters exceed the threshold due to environmental anomalies. For example, when encountering environmental interference such as thunderstorm weather that causes abnormal system readings, if the calculated environmental anomaly index exceeds the threshold, the automatic calibration signal is stopped, thereby reducing the occurrence probability of high-frequency ineffective calibrations and helping to maintain the accuracy of water meter measurement; and a method for obtaining the environmental anomaly index is given. Specifically, first obtain each environmental parameter,

[0080] Then substitute it into the formula to calculate the environmental anomaly index obtained by multiplying each real-time environmental parameter. By multiplying and adding the exponential increase method for each environmental parameter, the purpose of dynamically adjusting the weight of each environmental parameter is achieved, and the weight of each environmental parameter is calculated through the formula Obviously, if the fluctuation of this environmental difference parameter within multiple historical time gradients is greater, it indicates that the probability of this environmental parameter having a drastic change to affect the water meter's own parameters is higher, so the corresponding weight is larger, in order to achieve a dynamic assessment of the overall environmental anomaly index, and then avoid unnecessary calibrations caused by environmental anomalies and affect the normal measurement of the water meter.

[0081] The method further includes:

[0082] S6. After calibration is completed, start the monitoring loop again, and re-collect the water meter's own parameters and environmental parameters based on the sensors and environmental monitoring devices, and repeat the judgment process of steps S2 - S4 to verify whether the calibrated water meter has returned to the normal measurement state.

[0083] S6 further includes the following steps:

[0084] If there are still measurement problems, mark the water meter as in a difficult fault state and at the same time push detailed fault information to the operation and maintenance personnel. The fault information includes the water meter number, abnormal parameters, and multiple calibration records.

[0085] In this embodiment, after calibration, by immediately restarting the monitoring loop, that is, repeating the judgment process of S2 - S4, the calibration results can be quickly quantitatively evaluated, so that the measurement status of the water meter is under continuous and dynamic monitoring, avoiding potential problems left after calibration being ignored, ensuring the accuracy of water meter measurement to the greatest extent, and thus providing reliable water usage data for water supply enterprises and users in the long term, stabilizing the cornerstones of work such as water fee accounting and water resource allocation. Compared with the traditional mode lacking immediate review after calibration, this method can reduce the probability of recurrent measurement errors in the short term after calibration, significantly improving the long-term stability of measurement; when there are still measurement problems with the calibrated water meter, it is timely marked as in a difficult fault state and detailed fault information is pushed, realizing efficient fault location and information flow; thus enabling the operation and maintenance personnel to no longer spend a lot of time troubleshooting and sorting out the root causes of problems. According to the water meter number pushed by the system, the target water meter can be quickly locked; with the help of abnormal parameters, the problem can be intuitively known, for example, whether the flow deviation is too large or the pressure reading is abnormal; referring to multiple calibration records can better grasp the evolution context of the problem and judge whether it is a newly emerging fault or a long-term intractable problem.

[0086] It should be noted that: the calculation formulas and each parameter participating in the operation in the present invention have been pre-dimensionless processed, and the process of dimensionless processing is well-known in the industry and will not be described here.

[0087] The above has described a detailed description of an embodiment of the present invention, but the content described is only the preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A water meter measurement monitoring and calibration method based on the Internet of Things, characterized in that: It includes the following steps: S1. Detect the water meter in the target area based on sensors and environmental monitoring devices, and collect the water meter's own parameters and environmental parameters in real time through the Internet of Things; S2. Compare each water meter's own parameter with the preset parameter threshold in real time. If there is at least one water meter's own parameter that exceeds the threshold, preliminarily determine that there is a metering problem with the current water meter; otherwise, return to step S1; S3. Taking the current moment as the end point, trace back multiple preset time periods backward to obtain multiple time gradients, and then calculate each water meter's own parameter within each time gradient to obtain the water meter anomaly index; S4. Compare the water meter anomaly index with the preset water meter anomaly threshold. If the water meter anomaly index ≥ the water meter anomaly threshold, then confirm again that there is a metering problem with the current water meter and enter step S5; otherwise, return to step S1; S5. Trigger an automatic calibration signal, and then determine whether to send the automatic calibration signal according to the analysis result of the environmental parameters.

2. The water meter measurement monitoring and calibration method based on the Internet of Things according to claim 1 is characterized in that: The said S5 includes: S51. Obtain the environmental parameters of each time gradient and calculate to obtain the environmental anomaly index; S52. Compare the environmental anomaly index with the preset environmental anomaly threshold. If the environmental anomaly index ≥ the environmental anomaly threshold, then enter step S53; if the environmental anomaly index < the environmental anomaly threshold, then enter step S54; S53. Judge that the metering problem of the current water meter is affected by the environment and stop sending the automatic calibration signal; S54. Confirm that there is a metering problem with the current water meter and immediately send the automatic calibration signal.

3. The water meter measurement monitoring and calibration method based on the Internet of Things according to claim 2 is characterized in that: The method for obtaining the water meter anomaly index is: Substitute each water meter's own parameter into the formula: Calculate to obtain the water meter anomaly index Q; Among them, β i (t) is the time-varying curve of the i-th water meter parameter, t0~t1 is any time gradient, α i is the weight coefficient corresponding to the i-th time gradient, m is the number of time gradients, q r is the change of the rth water meter parameter, n is the number of samples in each time gradient, β ij is the observed value of the water meter's own parameters corresponding to the jth sampling point, is the corresponding mean value of the water meter’s own parameters, N is the number of water meter’s own parameters, γ r is the weight coefficient corresponding to the rth water meter parameter.

4. The water meter measurement monitoring and calibration method based on the Internet of Things according to claim 2 or 3, characterized in that: The process for obtaining the environmental anomaly index is: After obtaining each environmental parameter, substitute it into the formula: Calculate to obtain the environmental anomaly index H; Where k is any environmental parameter, M is the number of environmental parameters, θk is the weight coefficient corresponding to the kth environmental parameter, R k is the real-time value of the kth environmental parameter.

5. The water meter measurement monitoring and calibration method based on the Internet of Things according to claim 4 is characterized in that: The expression of the weight coefficient corresponding to the kth environmental parameter is: Among them, S is the number of samples in multiple time gradients, R kμ is the kth environmental parameter value corresponding to any sampling point, is the average value of the kth environmental parameter, and μ is the μth sampling point.

6. The water meter measurement monitoring and calibration method based on the Internet of Things according to claim 5 is characterized in that: Compare the calculated environmental anomaly index H with the preset environmental anomaly threshold H0; If H≥H0, then judge that the environment where the current water meter is located is abnormal; If H<H0, then judge that the environment where the current water meter is located is normal.

7. The water meter measurement monitoring and calibration method based on the Internet of Things according to claim 1 or 2, characterized in that: The said method further includes: S6. After calibration is completed, start the monitoring cycle again, and re-collect the water meter's own parameters and environmental parameters based on sensors and environmental monitoring devices, and repeat the judgment process of steps S2 - S4 to verify whether the calibrated water meter has returned to the normal metering state.

8. The water meter measurement monitoring and calibration method based on the Internet of Things according to claim 7 is characterized in that: The said S6 further includes the following steps: If there is still a metering problem, mark the water meter as a difficult fault state, and at the same time push detailed fault information to the operation and maintenance personnel. The fault information includes the water meter number, abnormal parameters, and multiple calibration records.

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