Droplet metering method for ultrasonic water meter

Abstract

The application provides a kind of ultrasonic water meter dripping water metering method, by accurately measuring the slight change in water flow, especially dripping event, improve the accuracy of water metering, first, the sensor of ultrasonic water meter real-time acquisition water flow signal data, including flow information, flow fluctuation and water flow state, data transmission to signal processing module, after noise removal, signal gain adjustment and waveform amplitude detection etc., subsequently, the system compares with the set threshold, identifies dripping event, and calculates the flow fluctuation difference, judges the occurrence of dripping behavior, finally, the system records the detailed information of event and stores in memory, for subsequent query, the application through accurate signal processing and dripping event identification method, effectively improves the identification accuracy of low flow dripping event, avoids false negative and false positive, while supporting detailed information recording and efficient data storage, ensures data integrity and accuracy, improves the intelligent level and practicability of water meter.

Description

Technical Field

[0001] This invention relates to the field of water meter technology, specifically to an ultrasonic water meter drip metering method. Background Technology

[0002] With the rapid development of intelligent technology, water meters, as a common device in daily life, have gradually received widespread attention for their intelligent transformation. Traditional water meters mainly rely on mechanical pointers or electronic components to measure water flow, which has certain limitations. In particular, traditional technology has failed to fully meet the needs for measuring and monitoring minute dripping behavior. To solve this problem, ultrasonic water meters have emerged. Ultrasonic water meters use ultrasonic technology to calculate the flow rate of water by the time difference of propagation, thereby accurately reflecting the water flow.

[0003] Despite some progress in water meter technology, existing ultrasonic water meters generally suffer from difficulty in identifying dripping events. They rely on fluctuations in overall flow to determine whether a dripping event has occurred, but this often fails to accurately distinguish between normal flow changes and minute dripping behavior. Minor changes in water flow are often masked by noise, leading to missed or false alarms of dripping events. Furthermore, current technology lacks effective algorithms to accurately identify dripping behavior, especially when flow fluctuations are extremely small and highly periodic. In addition, existing technologies are also insufficient in the accuracy of dripping event data storage and subsequent analysis, particularly in the efficiency of data storage and retrieval during long-term operation. Therefore, a new ultrasonic water meter dripping measurement method is urgently needed to solve the above problems and improve the accuracy of dripping event identification and data processing efficiency. Summary of the Invention

[0004] To address the technical problems mentioned in the background section regarding the underreporting or false alarms in low-flow dripping events, the inability to accurately identify minute dripping behavior, and the insufficient efficiency in data storage and retrieval of dripping event data, this invention proposes an ultrasonic water meter dripping measurement method.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for measuring water dripping using an ultrasonic water meter, comprising:

[0007] S1. The water flow signal data flowing through the water pipe is collected in real time by the sensor of the ultrasonic water meter. The water flow signal data includes water flow information, flow fluctuation and time sequence information of water flow status. The sensor collects water flow signal data based on the ultrasonic reflection principle.

[0008] S2. Process the water flow signal data. The processed water flow signal data is used to calculate and extract water flow parameters. Water flow parameter extraction includes water flow velocity calculation and flow fluctuation calculation. At the same time, the periodic fluctuation of water flow parameters is further analyzed through water flow state analysis, and the frequency and amplitude of water flow change are calculated.

[0009] S3. Based on the comparison between the extracted water flow parameters and the set dripping event detection threshold, dripping events are identified. The threshold setting includes amplitude threshold setting and frequency threshold setting. The identification of dripping events includes the comparison of flow fluctuation amplitude and flow fluctuation frequency, which is used for further confirmation and identification of the dripping events.

[0010] S4. After the dripping event is identified, the difference in flow rate fluctuation in the water flow parameters is calculated to reflect the change in water flow. This difference is then compared with the detection threshold in S3 to determine the occurrence of dripping behavior.

[0011] S5. Once the dripping behavior is determined, record the detailed information of the dripping event, including the start time, end time, duration, and cumulative drip volume, and store this information in the water meter's memory for subsequent querying and analysis.

[0012] Furthermore, the water flow signal processing includes noise removal, signal gain adjustment, and waveform amplitude detection. The noise filtering module is used to remove external interference from the water flow signal, the gain adjustment module is used to adjust the amplitude of the water flow signal, and the waveform analysis module is used to analyze the signal waveform.

[0013] Furthermore, the flow rate fluctuation in the flow parameters is calculated through the change in flow velocity, and the flow rate Q and flow velocity v have the following relationship:

[0014]

[0015] Where A is the cross-sectional area of ​​the water pipe, and Δv is the change in flow velocity.

[0016] Furthermore, the periodic fluctuation analysis in S2 quantifies the water flow signal using three methods: RMS calculation, Fourier transform, and autocorrelation function.

[0017] RMS calculation is used to quantify the amplitude changes of water flow signals. The calculation formula is as follows:

[0018]

[0019] Where RMS is the root mean square value of the water flow signal. For each sampling point of the water flow signal, N represents the value of the water flow signal, and N is the total number of sampling points of the water flow signal.

[0020] The Fourier transform analyzes the frequency components of periodic fluctuations by converting a time-domain signal to the frequency domain. The calculation formula is as follows:

[0021]

[0022] in, Let f(t) be the frequency domain representation of the water flow signal, f(t) be the time domain representation of the water flow signal, and ω be the angular frequency of the water flow signal.

[0023] The autocorrelation function is used to detect periodic fluctuations in water flow signals. The calculation formula is as follows:

[0024]

[0025] Where R(τ) is the autocorrelation function of the water flow signal, and τ is the delay time. and These are two sampling points of the time-domain signal.

[0026] Furthermore, the variation in water flow in S4 can be reflected by calculating the difference between the maximum and minimum values ​​of flow fluctuation in the water flow parameters. The calculation formula is as follows:

[0027]

[0028] in, This represents the difference in flow fluctuations. This represents the maximum value within the traffic fluctuation. This represents the minimum value within the flow fluctuation.

[0029] Furthermore, the cumulative drip volume in S5 refers to the cumulative flow fluctuation during the dripping event, calculated by combining the amplitude of the flow fluctuation and the duration of the event. By calculating the cumulative flow fluctuation during the dripping event, the system is provided with detailed data on the impact of the dripping event on water consumption. The calculation formula is as follows:

[0030]

[0031] in, Let Q(t) be the cumulative drip volume, and Q(t) be the instantaneous flow rate during the event. and These represent the start and end times of the event, respectively.

[0032] Furthermore, the information storage in S5 is generally performed using data fields, specifically including:

[0033] 1) Event ID: A unique identifier for the dripping event;

[0034] 2) Start time: The start time of the dripping event, recorded as a timestamp;

[0035] 3) End time: The end time of the dripping event, recorded as a timestamp;

[0036] 4) Duration: The duration of the dripping event;

[0037] 5) Total drip volume: The cumulative amount of dripping water during the dripping event;

[0038] 6) Additional data: supplementary information.

[0039] Furthermore, the data transmission method in S1 is divided into wired transmission and wireless transmission. Wired transmission means that the data is transmitted to the signal processing module through a wired connection, while wireless transmission means that in the case of flexible deployment or remote monitoring, the collected water flow signal data can be transmitted to the remote signal processing module through the wireless communication module.

[0040] Furthermore, the water meter supports quick retrieval of dripping event data, with query methods including querying by time interval, event type, and cumulative dripping volume.

[0041] Compared with the prior art, the advantages of the present invention are as follows:

[0042] 1. This invention addresses the problem of missed or false alarms in existing technologies for low-flow dripping events. This invention uses a sensor based on an ultrasonic water meter to collect water flow signal data in real time. By comparing the amplitude and frequency of flow fluctuations, it can accurately identify minute changes in water flow. Through the extraction of water flow parameters and the analysis of periodic fluctuations, this invention can effectively distinguish between normal water flow fluctuations and dripping behavior, thereby greatly improving the accuracy of dripping event identification and avoiding missed or false alarms.

[0043] 2. To address the problem that existing technologies cannot accurately identify minute dripping behavior, this invention introduces a method that compares the difference in flow fluctuation with a set detection threshold. After identifying a dripping event, the difference between the maximum and minimum values ​​of flow fluctuation in the water flow parameters is further calculated to accurately reflect the amplitude of water flow changes. This method can effectively eliminate external interference and noise, accurately determine the occurrence of dripping behavior, and provide a more accurate basis for subsequent data recording and analysis of dripping events.

[0044] 3. This invention addresses the shortcomings of existing technologies in the efficiency of data storage and retrieval for dripping events. It precisely records detailed data about dripping events (such as start time, end time, duration, and cumulative drip volume) and stores it in non-volatile memory. Furthermore, this invention supports rapid data retrieval, allowing queries by time interval, event type, and cumulative drip volume, significantly improving the efficiency of data storage and retrieval during long-term operation of the water meter, ensuring data integrity and ease of use. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart of the ultrasonic water meter drip metering method of the present invention;

[0047] Figure 2 This is a schematic diagram of the water flow state analysis method of the present invention;

[0048] Figure 3 This is a schematic diagram illustrating the steps for determining the dripping behavior according to the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0050] To achieve the above objectives, the present invention provides an ultrasonic water meter drip metering method, such as... Figure 1 As shown, the method includes:

[0051] S1. The water flow signal data flowing through the water pipe is collected in real time by the sensor of the ultrasonic water meter. The water flow signal data includes water flow information, flow fluctuation and time sequence information of water flow status. The sensor collects water flow signal data based on the ultrasonic reflection principle.

[0052] The ultrasonic water meter's sensor collects real-time water flow signal data through the water pipe. The specific data collection process is as follows:

[0053] Water flow information: Water flow information mainly refers to the flow velocity of water. The propagation speed of ultrasonic waves in water flow is closely related to the flow velocity. In areas with faster flow, the propagation time of ultrasonic signals is shorter; while in areas with slower flow, the propagation time is longer. By measuring the difference in signal propagation time, the sensor can detect changes in water flow velocity.

[0054] The specific acquisition steps are as follows: The sensor emits an ultrasonic signal. When the signal passes through the water flow, it is affected by the water flow speed, and the propagation time changes. By using the propagation time difference of the reflected signal, the sensor can acquire the flow information of the water flow, that is, the flow velocity.

[0055] Flow fluctuation: Flow fluctuation refers to the changes in water flow over a certain period of time. Fluctuations in water flow rate will lead to changes in water flow. The propagation time of ultrasonic signals changes with fluctuations in water flow rate. Sensors identify water flow fluctuations by capturing these changes in propagation time.

[0056] The specific acquisition steps are as follows: When the flow velocity of water changes, the propagation time of ultrasonic signals also fluctuates. The fluctuation of flow velocity manifests as a periodic change in signal propagation time, reflecting the flow fluctuation. The sensor monitors these fluctuations, and by analyzing the time domain characteristics of the ultrasonic signals, the flow fluctuation information of the water can be acquired.

[0057] Temporal information of water flow state: The temporal information of water flow state reflects the different dynamic characteristics of water flow, such as steady flow, turbulent flow, dripping, etc. The state of water flow will affect the amplitude and frequency of ultrasonic signal, and thus affect the temporal characteristics of the signal.

[0058] The specific acquisition steps are as follows: The sensor can acquire the time sequence information of the water flow state by monitoring the periodic changes of the ultrasonic signal. For example, under a stable flow rate, the sensor signal changes smoothly, with regular and small amplitude. Under turbulent conditions, the irregularity of the water flow velocity makes the ultrasonic signal change more violently, with large amplitude and irregular frequency. When dripping water, the signal shows periodic fluctuations with small amplitude.

[0059] Data transmission methods are divided into wired transmission and wireless transmission: wired transmission means that data is transmitted to the signal processing module through a wired connection (such as serial port, Ethernet). This method provides more stable and low-latency data transmission and is suitable for short-distance application scenarios. Wireless transmission, on the other hand, is used when flexible deployment or remote monitoring is required. The collected water flow signal data can be transmitted to the remote signal processing module through a wireless communication module (such as Wi-Fi, Bluetooth, and Zigbee). This method is suitable for large-scale application scenarios and can reduce the complexity of wiring.

[0060] Meanwhile, regardless of whether it is wired or wireless transmission, the collected water flow signal data undergoes necessary formatting and encoding to ensure the data integrity and stability during transmission, and is finally transmitted to the signal processing module.

[0061] S2. Process the water flow signal data. The processed water flow signal data is used to calculate and extract water flow parameters. Water flow parameter extraction includes water flow velocity calculation and flow fluctuation calculation. At the same time, the periodic fluctuation of water flow parameters is further analyzed through water flow state analysis, and the frequency and amplitude of water flow change are calculated.

[0062] Water flow signal processing includes noise removal, signal gain adjustment, and waveform amplitude detection;

[0063] Noise Removal: Water flow signals are affected by external environmental factors (such as electromagnetic interference, temperature changes, etc.) and the noise of the sensor itself. In order to improve signal quality, noise removal is required. Commonly used methods include low-pass filtering, high-pass filtering, and band-pass filtering. These methods can effectively remove high-frequency or low-frequency noise and retain the effective signal related to water flow. Noise removal is a key step to ensure the accuracy of subsequent steps, especially when performing dripping event analysis, noise removal can improve the accuracy of analysis.

[0064] Signal gain adjustment: Since water flow signals may be attenuated or amplified under different environmental conditions, signal gain adjustment is used to ensure that the signal amplitude is within a suitable processing range. Through gain adjustment, information loss due to excessively weak signals or signal distortion due to excessively strong signals can be avoided. This process makes the signal amplitude more balanced in subsequent processing stages, which is beneficial to improving data stability.

[0065] Waveform amplitude detection: Waveform amplitude detection analyzes the amplitude of a signal waveform, paying particular attention to changes in the amplitude of water flow fluctuations. In special cases such as dripping events, the amplitude changes of the signal are small and occur frequently. Waveform amplitude detection can accurately identify subtle changes in water flow, ensuring that key fluctuation information of the signal is not ignored.

[0066] After data preprocessing, the signal has been purified. The next step is to extract water flow parameters, which is the core task of water flow signal data processing. It will directly provide key data support for subsequent dripping event analysis, flow fluctuation calculation, etc. Water flow parameter extraction includes water flow velocity calculation, flow fluctuation calculation, and water flow state analysis.

[0067] Flow velocity calculation: The sensor of an ultrasonic water meter emits ultrasonic signals. As the signal propagates in the water flow, it is affected by factors such as water flow rate and temperature. Since the water flow rate directly determines the propagation speed of the ultrasonic signal, a faster flow rate results in a faster propagation speed, and vice versa. Therefore, the propagation speed of the ultrasonic signal can be calculated using the time difference in its propagation. The formula for calculating the flow velocity of water is:

[0068]

[0069] in, Let be the velocity of the water flow, and c be the speed at which ultrasound propagates in water (a constant, affected by water flow and temperature). For the time difference of signal propagation, This represents the distance from the sensor to the water flow.

[0070] Flow fluctuation calculation: Flow fluctuation is caused by changes in the flow velocity of water. Fluctuations in flow velocity will cause changes in the amount of water flowing through the pipe per unit time (flow rate). Therefore, flow fluctuation is calculated by changing the flow velocity. The flow rate Q and the flow velocity v have the following relationship:

[0071]

[0072] Where A is the cross-sectional area of ​​the water pipe. This refers to the change in flow velocity;

[0073] By monitoring fluctuations in water flow velocity, the sensor can extract the magnitude and frequency of fluctuations in water flow rate.

[0074] Water flow state analysis: The state of water flow (such as steady flow, turbulent flow, dripping, etc.) affects the amplitude and frequency of ultrasonic signals. The changes in water flow state can be comprehensively calculated and quantified using the following formula:

[0075] ①RMS (Root Mean Square) is a method for measuring signal strength and the degree of change. It is used to quantify changes in signal amplitude. In water flow, the RMS value is small and stable during steady flow, while dripping and turbulent flow exhibit larger and irregularly changing RMS values. The calculation formula is:

[0076]

[0077] Where RMS is the root mean square value of the water flow signal. For each sampling point of the water flow signal, N represents the value of the water flow signal, and N is the total number of sampling points of the water flow signal.

[0078] The RMS value helps quantify the amplitude changes of the water flow signal and can be used to identify the occurrence of dripping events. When the RMS value of the water flow signal amplitude is small and fluctuates frequently, it indicates a dripping phenomenon.

[0079] ② The Fourier transform is a mathematical method for converting time-domain signals to the frequency domain. It can help extract the frequency components of periodic fluctuations from water flow signals. For dripping water, this manifests as high-frequency, low-amplitude periodic fluctuations, while turbulence manifests as irregular fluctuations in frequency. The calculation formula is as follows:

[0080]

[0081] in, Let f(t) be the frequency domain representation of the water flow signal, f(t) be the time domain representation of the water flow signal, and ω be the angular frequency of the water flow signal (unit: radians / second).

[0082] Fourier transform can convert time-domain signals into frequency-domain representations, thereby analyzing the main frequency components in water flow signals. In dripping water, frequency analysis can reveal periodic changes, while turbulence does not have a clear period and its frequency changes are more irregular.

[0083] ③ The autocorrelation function is a tool used to analyze whether a signal has periodicity. By calculating the autocorrelation function of a signal, it is possible to detect whether there are regular fluctuations in the signal. In the phenomenon of dripping water, the signal will exhibit periodic fluctuations, and the value of the autocorrelation function will show a higher peak at the periodic position. The calculation formula is:

[0084]

[0085] Where R(τ) is the autocorrelation function of the water flow signal, and τ is the delay time (i.e., the time interval between two water flow signals). and These are two sampling points of the time-domain signal;

[0086] The autocorrelation function is used to identify periodicity in a signal. When a dripping phenomenon occurs, the signal will exhibit periodic fluctuations with small amplitudes. At the same time, the autocorrelation function will produce significant peaks on these periodic fluctuations. Through the autocorrelation function, periodic fluctuations can be quantitatively detected, thereby helping to identify dripping events.

[0087] S3. Based on the comparison between the extracted water flow parameters and the set dripping event detection threshold, dripping events are identified. The threshold setting includes amplitude threshold setting and frequency threshold setting. The identification of dripping events includes the comparison of flow fluctuation amplitude and flow fluctuation frequency, which is used for further confirmation and identification of the dripping events.

[0088] To accurately identify dripping events, a dripping event detection threshold needs to be set. This threshold is determined based on the amplitude and frequency of flow fluctuations. By setting an appropriate threshold, normal flow changes can be effectively distinguished from dripping events.

[0089] Threshold settings include amplitude threshold settings and frequency threshold settings;

[0090] Amplitude threshold: The flow fluctuation of a dripping event is relatively small, so an amplitude threshold needs to be set. When the flow fluctuation amplitude is less than the threshold and the fluctuation has a certain periodicity, it can be identified as a dripping event. This threshold is set based on the characteristics of dripping events. After certain experiments and debugging, it is ensured that dripping events can be correctly identified.

[0091] Frequency threshold: Drip events are characterized by high-frequency, low-amplitude periodic fluctuations. Therefore, a frequency threshold can be set. When the frequency of flow fluctuations exceeds this threshold, the occurrence of a drip event is further confirmed. Drip events are generally more frequent, so the threshold is lower to capture these frequent, small-amplitude changes.

[0092] By setting these thresholds, it is possible to effectively distinguish between normal flow fluctuations and dripping events, thereby enabling accurate identification.

[0093] Identifying dripping events involves comparing the amplitude and frequency of flow fluctuations.

[0094] Flow fluctuation amplitude comparison: In S2, the amplitude of flow fluctuation has been calculated. Then, the extracted flow fluctuation amplitude is compared with the set amplitude threshold. If the flow fluctuation amplitude is less than the set threshold and has the characteristics of periodic fluctuation, it can be identified as a dripping event.

[0095] Flow fluctuation frequency comparison: In S2, the frequency of flow fluctuation has been extracted, and then the frequency of flow fluctuation is compared with the set frequency threshold. If the frequency is higher than the set threshold and the amplitude is small, it meets the characteristics of a dripping event and is used to further confirm the identification of the dripping event.

[0096] S4. After the dripping event is identified, the difference in flow rate fluctuation in the water flow parameters is calculated to reflect the change in water flow. This difference is then compared with the detection threshold in S3 to determine the occurrence of the dripping behavior.

[0097] Drip behavior is characterized by small, periodic fluctuations in flow rate. These fluctuations often reflect minute changes in water flow. To accurately identify drip behavior, we can use flow fluctuation difference calculation, difference comparison with threshold, and drip behavior judgment to assist in precise identification.

[0098] In S3, a threshold comparison was used to determine whether a dripping event had occurred. However, the flow fluctuations in a dripping event may be quite small, making it impossible to definitively confirm dripping behavior through simple amplitude and frequency comparisons. Therefore, in S4, the difference between the maximum and minimum values ​​of the flow fluctuations in the flow parameters can reflect the actual amplitude of the flow fluctuations, thus enabling a more accurate confirmation of whether dripping behavior has occurred. The specific steps are as follows:

[0099] Difference between maximum and minimum values: This calculates the difference between the maximum and minimum values ​​of flow fluctuations in the water flow signal. This difference reflects the amplitude of water flow variation. The difference is smaller for dripping behavior. The calculation formula is as follows:

[0100]

[0101] in, This represents the difference in traffic flow. This represents the maximum value within the traffic fluctuation. This represents the minimum value within the flow fluctuation.

[0102] Comparison of the difference with the threshold: Using the dripping event detection threshold set in S3, the flow fluctuation difference calculated in S4 is compared with this threshold. When the flow fluctuation difference is less than the threshold and exhibits periodic fluctuation characteristics, the dripping behavior is confirmed. It should be noted that S4 mainly verifies the dripping event by calculating the difference, while S3 has already completed the initial judgment of flow fluctuation. Therefore, S4 is only a confirmation step.

[0103] Determining the dripping behavior: If the difference... If the difference is small and exhibits periodic fluctuation characteristics, it is determined to be the occurrence of dripping behavior. If the water flow is large, the possibility of dripping water is ruled out, and other abnormalities in the water flow are considered to exist.

[0104] S5. Once the dripping behavior is determined, record the detailed information of the dripping event, including the start time, end time, duration, and cumulative drip volume, and store this information in the water meter's memory for subsequent querying and analysis.

[0105] Event start time: The start time of a dripping event refers to the moment when the characteristics of a dripping event are first detected in the water flow signal. This time is recorded by the system based on the change point of the periodic fluctuation or the flow rate fluctuation amplitude. The recording of the start time ensures that the starting moment of the dripping event can be traced and provides basic data for subsequent event analysis.

[0106] End time: The end time of a dripping event refers to the moment when the dripping phenomenon stops, the fluctuations disappear, or the characteristics of dripping behavior no longer hold true. Recording the end time is crucial for accurately calculating the duration of a dripping event. Generally speaking, the end time is triggered when the flow fluctuation of the dripping event returns to the normal range, or when the water flow state changes.

[0107] Duration: The duration of a dripping event refers to the time difference between the start and end times of the event, reflecting the length of time the event lasts. This information helps quantify the impact of the dripping event and assess whether it has exceeded the system's preset warning time limit.

[0108] Cumulative drip rate: The cumulative drip rate refers to the cumulative flow fluctuation during a dripping event. It is calculated by combining the amplitude of the flow fluctuation and the duration of the event. By calculating the cumulative flow fluctuation during the dripping event, the system is provided with detailed data on the impact of the dripping event on water consumption. The formula for calculating the cumulative drip rate is as follows:

[0109]

[0110] in, Let Q(t) be the cumulative drip volume, and Q(t) be the instantaneous flow rate during the event. and These are the start and end times of the event, respectively.

[0111] Information storage structure: When storing dripping event information, it is first necessary to choose a suitable storage format. Storage formats include JSON, CSV, or binary formats. For embedded devices (such as water meters), the binary format is more efficient because it occupies less memory space and has a faster read and write speed.

[0112] The stored data fields include, but are not limited to, the following fields:

[0113] event id: A unique identifier for the dripping event;

[0114] start time: The start time of the dripping event, recorded as a timestamp;

[0115] end time: The end time of the dripping event, recorded as a timestamp;

[0116] duration: The duration of the dripping event, in minutes or hours;

[0117] Total drip volume: The total amount of dripping water during the dripping event, expressed in liters (L).

[0118] Additional data: This may include other supplementary information, such as event type, abnormal water flow conditions, etc.

[0119] To optimize storage space and memory usage, water meters may employ compressed storage or a circular buffer structure for data storage efficiency.

[0120] Compressed storage: By using compression techniques (such as data deduplication and data compression algorithms), the storage space occupied is reduced, especially for dripping event data recorded over a long period of time.

[0121] Circular buffer: When the water meter has limited memory, a circular buffer can be used to overwrite old records with newly recorded dripping events, ensuring that only a certain amount of the latest dripping event data is stored in memory. In this way, as many dripping events as possible can be recorded with limited memory space.

[0122] In subsequent queries and analysis, the water meter will support quick retrieval of dripping event data, with query methods including by time interval, event type, and cumulative dripping volume:

[0123] Query by time interval: Users or the system can view relevant data by specifying a query interval (for example, querying dripping events within the past month);

[0124] Search by event type: Quickly find specific types of dripping events by filtering by fields such as event identifier and event type;

[0125] Search by cumulative drip volume: Filter drip events within a certain range based on cumulative drip volume;

[0126] The stored data not only needs to be recorded, but also needs to be maintained regularly. Drip event data may be modified, deleted, or updated (for example, due to some abnormal situations, the cumulative drip volume needs to be recalculated). The update operation is an incremental update or an overwrite update to ensure the accuracy of the data.

[0127] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0128] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for measuring water flow using an ultrasonic water meter, characterized in that, include: S1. The water flow signal data flowing through the water pipe is collected in real time by the sensor of the ultrasonic water meter. The water flow signal data includes water flow information, flow fluctuation and time sequence information of water flow status. The sensor collects water flow signal data based on the ultrasonic reflection principle. S2. Process the water flow signal data. The processed water flow signal data is used to calculate and extract water flow parameters. Water flow parameter extraction includes water flow velocity calculation and flow fluctuation calculation. At the same time, the periodic fluctuation of water flow parameters is further analyzed through water flow state analysis, and the frequency and amplitude of water flow change are calculated. S3. Based on the comparison between the extracted water flow parameters and the set dripping event detection threshold, dripping events are identified. The threshold setting includes amplitude threshold setting and frequency threshold setting. The identification of dripping events includes the comparison of flow fluctuation amplitude and flow fluctuation frequency, which is used for further confirmation and identification of the dripping events. S4. After the dripping event is identified, the difference in flow rate fluctuation in the water flow parameters is calculated to reflect the change in water flow. This difference is then compared with the detection threshold in S3 to determine the occurrence of dripping behavior. S5. Once the dripping behavior is determined, record the detailed information of the dripping event, including the start time, end time, duration, and cumulative dripping volume, and store this information in the water meter's memory for subsequent querying and analysis. The periodic fluctuation analysis in S2 quantifies the water flow signal using three methods: RMS calculation, Fourier transform, and autocorrelation function. RMS calculation is used to quantify the amplitude changes of water flow signals. The calculation formula is as follows: ； Where RMS is the root mean square value of the water flow signal. For each sampling point of the water flow signal, N represents the value of the water flow signal, and N is the total number of sampling points of the water flow signal. The Fourier transform analyzes the frequency components of periodic fluctuations by converting a time-domain signal to the frequency domain. The calculation formula is as follows: ； in, Let f(t) be the frequency domain representation of the water flow signal, f(t) be the time domain representation of the water flow signal, and ω be the angular frequency of the water flow signal. The autocorrelation function is used to detect periodic fluctuations in water flow signals. The calculation formula is as follows: ； Where R(τ) is the autocorrelation function of the water flow signal, and τ is the delay time. and These are two sampling points of the time-domain signal; The variation in water flow in S4 can be reflected by calculating the difference between the maximum and minimum values ​​of flow fluctuation in the water flow parameters. The calculation formula is as follows: ； in, This represents the difference in flow fluctuations. This represents the maximum value within the traffic fluctuation. This represents the minimum value within the flow fluctuation.

2. The ultrasonic water meter drip metering method according to claim 1, characterized in that, The water flow signal processing includes noise removal, signal gain adjustment, and waveform amplitude detection. The noise filtering module is used to remove external interference from the water flow signal, the gain adjustment module is used to adjust the amplitude of the water flow signal, and the waveform analysis module is used to analyze the signal waveform.

3. The ultrasonic water meter drip metering method according to claim 1, characterized in that, The flow rate fluctuation in the flow parameters is calculated by the change in flow velocity, and the flow rate Q and flow velocity v have the following relationship: ； Where A is the cross-sectional area of ​​the water pipe. This represents the change in flow velocity.

4. The ultrasonic water meter drip metering method according to claim 1, characterized in that, The cumulative drip volume in S5 refers to the cumulative flow fluctuation during a dripping event. It is calculated by combining the amplitude of the flow fluctuation and the duration of the event. By calculating the cumulative flow fluctuation during the dripping event, the system is provided with detailed data on the impact of the dripping event on water consumption. The calculation formula is as follows: ； in, Let Q(t) be the cumulative drip volume, and Q(t) be the instantaneous flow rate during the event. and These represent the start and end times of the event, respectively.

5. The ultrasonic water meter drip metering method according to claim 1, characterized in that, The information storage in S5 is generally done in the form of data fields, and the specific data storage fields include: 1) Event ID: A unique identifier for the dripping event; 2) Start time: The start time of the dripping event, recorded as a timestamp; 3) End time: The end time of the dripping event, recorded as a timestamp; 4) Duration: The duration of the dripping event; 5) Total drip volume: The cumulative amount of dripping water during the dripping event; 6) Additional data: supplementary information.

6. The ultrasonic water meter drip metering method according to claim 1, characterized in that, The data transmission methods in S1 are divided into wired transmission and wireless transmission. Wired transmission means that the data is transmitted to the signal processing module through a wired connection, while wireless transmission means that in the case of flexible deployment or remote monitoring, the collected water flow signal data can be transmitted to the remote signal processing module through the wireless communication module.

7. The ultrasonic water meter drip metering method according to claim 1, characterized in that, The water meter supports quick retrieval of dripping event data, and the query methods include querying by time interval, event type, and cumulative dripping volume.

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