Data transmission method and system of non-magnetic dry type internet of things water meter

Abstract

The present application relates to the technical field of data processing, in particular to a data transmission method and system of a non-magnetic dry type Internet of Things water meter. The method comprises: screening micro-flow time periods based on instantaneous flow data collected by the non-magnetic dry type water meter; determining micro-flow feature vectors and obtaining regularity scores according to the continuous distribution of the micro-flow time periods in each sub-time period, the fluctuation characteristics of the instantaneous flow data, and the similarity of the micro-flow feature vectors of each sub-time period and the corresponding sub-time period in the adjacent date; obtaining a target credibility based on the regularity scores of each sub-time period and the sub-time period before it; obtaining a corresponding correlation degree according to the similarity of the instantaneous flow data of each water meter and the associated water meter in the same sub-time period in the same pipe network topology; and screening a to-be-reported period and performing data transmission according to the target credibility and the correlation degree. The present application reduces the number of invalid communication wake-up times and network bandwidth occupation while ensuring the timely reporting of real abnormal micro-flow.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a data transmission method and system for a non-magnetic dry-type Internet of Things (IoT) water meter. Background Technology

[0002] With the continuous improvement of digitalization and refined management of urban water supply systems, IoT water meters are gradually becoming an important basic equipment for smart water management. Compared with traditional mechanical water meters, IoT water meters integrate flow metering units, data acquisition modules, and wireless communication modules to achieve functions such as remote reading, centralized data management, and water usage behavior analysis. In recent years, non-magnetic dry-type water meters, due to their non-magnetic metering structure, have strong anti-magnetic interference capabilities, high metering accuracy, and suitability for complex electromagnetic environments, and have been widely used in residential buildings, commercial complexes, and industrial water supply systems. At the same time, low-power wide-area communication technologies (such as NB-IoT and LoRa) provide a reliable communication foundation for large-scale water meter terminal access to cloud platforms, enabling water supply companies to achieve remote monitoring, water usage data analysis, and refined operation management.

[0003] In existing IoT water meter systems, a combined transmission mode of "daily fixed-time reading + abnormal event-triggered reporting" is commonly used. Fixed-period reporting typically occurs 1-2 times daily, uploading only summary data such as cumulative water consumption. Abnormal reporting primarily targets obvious anomalies such as continuous high-flow-rate water use, reverse flow, or valve malfunction. With the widespread application of non-magnetic dry-type water meters, their metering structure possesses low-flow-velocity, high-sensitivity characteristics, capable of detecting flows as low as 0.001m. 3 Microflow variations of / h and below are considered. However, existing anomaly detection mechanisms still rely on fixed flow thresholds, lacking the comprehensive analytical capabilities to assess the stability and regularity of microflows. This makes it difficult to distinguish between genuine low-speed water use or chronic leaks and non-water-use factors such as pipeline pressure fluctuations and transient disturbances. In microflow scenarios, the false alarm rate is high, affecting not only the accuracy of anomaly detection but also increasing the number of terminal communication wake-ups and network bandwidth usage. Summary of the Invention

[0004] To address the problem that existing methods lack specificity when transmitting data from non-magnetic dry-type IoT water meters, leading to an increase in the number of terminal communication wake-ups, the present invention aims to provide a data transmission method and system for non-magnetic dry-type IoT water meters. The specific technical solution adopted is as follows: In a first aspect, the present invention provides a data transmission method for a non-magnetic dry-type Internet of Things (IoT) water meter, the method comprising the following steps: Acquire instantaneous flow data collected by a non-magnetic dry water meter; Microflow time periods that conform to a preset microflow interval are selected based on instantaneous flow data; a regularity score for each sub-time period is obtained based on the continuous distribution of microflow time periods in each sub-time period, the fluctuation characteristics of instantaneous flow data, and the similarity of the microflow feature vectors of each sub-time period and its corresponding sub-time period on adjacent dates; the microflow feature vectors are determined based on the continuous distribution, the fluctuation characteristics, and the instantaneous flow data. Based on the regularity scores of each sub-time period and the sub-time periods before it, the target credibility of each sub-time period is obtained; based on the similarity of the instantaneous flow data of each water meter and the associated water meter in the same sub-time period in the same pipeline topology, the correlation degree of each water meter is obtained. Based on the target credibility and the correlation, the reporting period is selected and data is transmitted.

[0005] Preferably, the step of filtering microflow time periods that conform to a preset microflow interval based on instantaneous flow data includes: For any sampling time: if the instantaneous flow data at any sampling time is within the preset microflow interval, then the sampling time is considered a valid microflow time; The consecutive effective microflow moments constitute a microflow time period.

[0006] Preferably, the step of obtaining a regularity score for each sub-time period based on the continuous distribution of micro-flow time periods within each sub-time period, the fluctuation characteristics of instantaneous flow data, and the similarity of the micro-flow feature vectors of each sub-time period with its corresponding sub-time period on adjacent dates includes: The ratio of the number of valid microfluidic moments within a candidate sub-time period to the total number of all sampling moments within the candidate sub-time period is used as the continuity characteristic value of the candidate sub-time period. Based on the instantaneous flow data of adjacent moments within the candidate sub-time period, the change amplitude of the instantaneous flow data is calculated; according to the change amplitude of all the instantaneous flow data within the candidate sub-time period and the distribution of the instantaneous flow data, the volatility characteristic value of the candidate sub-time period is obtained. The data period repetition factor of the candidate sub-time period is obtained based on the similarity between the micro-flow feature vector of the candidate sub-time period and the micro-flow feature vector of the corresponding sub-time period in the adjacent date, as well as the similarity between the micro-flow feature vectors of the corresponding sub-time periods in the adjacent date; the micro-flow feature vector includes continuous feature value, fluctuation feature value and the average value of instantaneous flow data at all times in the sub-time period; By integrating the continuity feature value, the volatility feature value, and the data periodicity repetition factor, a regularity score for the candidate sub-time period is obtained; The candidate sub-time period can be any sub-time period.

[0007] Preferably, obtaining the volatility characteristic value of the candidate sub-time period based on the variation amplitude and distribution of all instantaneous flow data within the candidate sub-time period includes: Calculate the standard deviation of the variation range of all instantaneous flow data within the candidate sub-time period, and the average value of the instantaneous flow data at all times within the candidate sub-time period; The volatility characteristic values ​​of candidate sub-time periods are determined based on the standard deviation and the mean.

[0008] Preferably, the step of fusing the continuous characteristic value, the volatility characteristic value, and the data periodicity repetition factor to obtain the regularity score of the candidate sub-time period includes: The continuous characteristic value, the reverse normalized value of the volatility characteristic value, and the data periodicity repetition factor are assigned weight coefficients and then summed in a weighted manner to obtain the regularity score of the candidate sub-time period.

[0009] Preferably, obtaining the target credibility of each sub-time period based on the regularity scores of each sub-time period and the sub-time periods before it includes: Initialize the confidence level to zero; For candidate sub-time periods: if the regularity score of the candidate sub-time period is greater than or equal to the regularity threshold, then the target reliability of the candidate sub-time period is obtained by increasing the preset increment step size based on the reliability of the previous sub-time period; if the regularity score of the candidate sub-time period is less than the regularity threshold, then the target reliability of the candidate sub-time period is obtained by decreasing the preset decrement step size based on the reliability of the previous sub-time period.

[0010] Preferably, the step of obtaining the correlation degree of each water meter based on the similarity of the instantaneous flow data of each water meter and its associated water meter within the same sub-time period in the same pipeline network topology includes: For any water meter: Obtain the associated water meters that belong to the same pipeline topology as any of the water meters mentioned above; Calculate the correlation coefficient between any water meter and each associated water meter in the same sub-time period for the flow data sequence; select the maximum value of the correlation coefficient as the correlation degree corresponding to any water meter; wherein, the flow data sequence in the sub-time period consists of the instantaneous flow data at all times in the sub-time period.

[0011] Preferably, the step of filtering the reporting period based on the target credibility and the relevance includes: When the correlation degree is greater than or equal to a preset correlation degree threshold, the target credibility is multiplied by a preset first adjustment coefficient to obtain a credibility factor; when the correlation degree is less than the preset correlation degree threshold, the target credibility is multiplied by a preset second adjustment coefficient to obtain a credibility factor; the preset first adjustment coefficient is less than 1, and the preset second adjustment coefficient is greater than 1. The reporting period is selected based on the value of the credibility factor.

[0012] Preferably, the step of filtering the reporting period based on the value of the credibility factor includes: If the credibility factor is greater than or equal to the event reporting threshold, the corresponding sub-time period will be used as the reporting period.

[0013] Secondly, the present invention provides a data transmission system for a non-magnetic dry-type Internet of Things (IoT) water meter, the system being used to implement the method described in the first aspect, the system comprising: The data acquisition module is used to acquire instantaneous flow data collected by the non-magnetic dry water meter; The evaluation module is used to filter microflow time periods that conform to a preset microflow interval based on instantaneous flow data; and to obtain a regularity score for each sub-time period based on the continuous distribution of microflow time periods in each sub-time period, the fluctuation characteristics of instantaneous flow data, and the similarity of the microflow feature vectors of each sub-time period and its corresponding sub-time period on adjacent dates; the microflow feature vectors are determined based on the continuous distribution, the fluctuation characteristics, and the instantaneous flow data. The feature analysis module is used to obtain the target credibility of each sub-time period based on the regularity score of each sub-time period and the sub-time periods before it; and to obtain the correlation degree of each water meter based on the similarity of the instantaneous flow data of each water meter and the associated water meter in the same sub-time period in the same pipeline topology. The transmission module is used to filter the reporting period and transmit the data based on the target credibility and the correlation.

[0014] The present invention has at least the following beneficial effects: This invention first acquires instantaneous flow data collected by a non-magnetic dry-type water meter. Based on the instantaneous flow data, micro-flow time periods conforming to a preset micro-flow interval are selected. The regularity of each sub-time period is evaluated by combining the continuous distribution of micro-flow time periods, the fluctuation characteristics of instantaneous flow data, and the similarity of micro-flow feature vectors of corresponding sub-time periods on adjacent dates, resulting in a regularity score. Furthermore, the target credibility is obtained based on the regularity score and its historical trend, and the correlation is obtained by combining the similarity of instantaneous flow data between water meters in the same pipeline network topology. Finally, the reporting period is selected based on the target credibility and correlation, and data transmission is performed. This method can accurately distinguish between real micro-flow events and non-water-use factors such as pipeline pressure fluctuations and instantaneous disturbances, avoiding false alarms triggered by occasional micro-flow data from a single moment or a single water meter. Simultaneously, by introducing a dual judgment mechanism of credibility and correlation, it can significantly reduce the number of invalid communication wake-ups and network bandwidth consumption while ensuring timely reporting of real abnormal micro-flows, improving the targeting of data transmission and the overall system efficiency. It is suitable for data hierarchical reporting scenarios of non-magnetic dry-type IoT water meters in complex pipeline network environments. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating a data transmission method for a non-magnetic dry-type IoT water meter provided in an embodiment of the present invention; Figure 2 This is a structural block diagram of a data transmission system for a non-magnetic dry-type Internet of Things (IoT) water meter provided in an embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the following detailed description of a data transmission method and system for a non-magnetic dry-type Internet of Things water meter based on the present invention is provided in conjunction with the accompanying drawings and preferred embodiments.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of the data transmission method and system of the non-magnetic dry-type Internet of Things water meter provided by the present invention.

[0020] An embodiment of a data transmission method for a non-magnetic dry-type IoT water meter: This embodiment proposes a data transmission method for a non-magnetic dry-type IoT water meter, such as... Figure 1 As shown, a data transmission method for a non-magnetic dry-type IoT water meter in this embodiment includes the following steps: Step S1: Obtain the instantaneous flow data collected by the non-magnetic dry water meter.

[0021] Non-magnetic dry-type water meters acquire impeller rotation signals through Hall effect sensors or magnetoresistive sensors to achieve high-resolution measurement of minute flow rates. Since microflow states are typically characterized by short durations and small flow fluctuations, a fixed-period continuous sampling method is required to obtain complete flow time-series data, thereby ensuring the accuracy and continuity of subsequent analysis.

[0022] Specifically, the sampling frequency of the non-magnetic dry water meter is set to once every 10 seconds, and instantaneous flow data is collected once every 10 seconds. The unit of instantaneous flow data is m³. 3 / h; The collected instantaneous flow data is first stored in the water meter's RAM for caching, and then written to non-volatile Flash every 5 minutes for backup to avoid data loss due to power failure.

[0023] Step S2: Filter microflow time periods that conform to the preset microflow interval based on instantaneous flow data; obtain the regularity score of each sub-time period based on the continuous distribution of microflow time periods in each sub-time period, the fluctuation characteristics of instantaneous flow data, and the similarity of the microflow feature vectors of each sub-time period and its corresponding sub-time period on adjacent dates; the microflow feature vector is determined based on the continuous distribution, the fluctuation characteristics, and the instantaneous flow data.

[0024] The monitoring process can be divided into multiple sub-time periods, with each sub-time period lasting one hour. In practical applications, implementers can set the duration of each sub-time period according to specific circumstances.

[0025] Furthermore, based on the range characteristics of non-magnetic dry water meters, the microflow range is predefined as the flow range below the threshold of normal domestic water use. The flow within this range needs to be carefully distinguished between real events and disturbances.

[0026] Specifically, for any sampling time: if the instantaneous flow rate data at that sampling time is within a preset microflow interval, then that sampling time is considered a valid microflow time. In this embodiment, the lower limit of the preset microflow interval is... m 3 / h, maximum value m 3 / h, where m 3 / h is the minimum measurable flow rate of the water meter. m 3 / h is the minimum flow threshold for regular domestic water use. In specific applications, implementers can set the lower and upper limits of the preset microflow interval according to the specific circumstances.

[0027] By using the above methods to determine each sampling moment during the monitoring process, multiple valid microflow moments are selected. Continuous valid microflow moments constitute a microflow time period.

[0028] Considering that real low-velocity water use or leakage typically manifests as continuous microflows, while random disturbances are short-lived, determining continuity by the proportion of microflows within a sub-time period and extracting patterns from the raw data allows for the identification of the temporal structure characteristics of microflow behavior from continuous sampling data, rather than simply determining whether a small flow exists at a particular moment. By analyzing the continuous distribution of flow time periods within a single sub-time period, different operating conditions such as occasional instantaneous water use, normal background seepage, and abnormal continuous microflows can be distinguished, providing a structured feature basis for subsequent anomaly identification, leakage early warning, or behavior determination.

[0029] The following embodiment uses a sub-time period as an example for illustration. The method provided in this embodiment can be used to process other sub-time periods.

[0030] Any sub-time period is designated as a candidate sub-time period. The ratio of the number of valid microflow moments within a candidate sub-time period to the total number of all sampling moments within that candidate sub-time period is used as the continuity characteristic value of the candidate sub-time period. If there are no valid microflow moments within a candidate sub-time period, the continuity characteristic value of the candidate sub-time period is 0. The larger the value of the continuity characteristic value, the stronger the microflow continuity, indicating that the flow is not an occasional pulse.

[0031] Considering that relying solely on continuity indicators cannot distinguish between stable microflows and microflow pulses with frequent starts and stops—for example, multiple short microflows accumulating for a relatively long time within a sub-time period may also yield high continuity characteristic values—the following section evaluates the volatility of microflows.

[0032] Specifically, within each candidate sub-time period, the difference between the instantaneous flow data of the latter and the instantaneous flow data of the former is calculated, and this difference is taken as the variation range of the instantaneous flow data between the two adjacent moments. Further, based on the variation range and distribution of all instantaneous flow data within the candidate sub-time period, the volatility characteristic value of the candidate sub-time period is obtained. Specifically, the standard deviation of the variation range of all instantaneous flow data within the candidate sub-time period and the average value of the instantaneous flow data at all moments within the candidate sub-time period are calculated respectively; the ratio of the standard deviation to the average value is taken as the volatility characteristic value of the candidate sub-time period. It should be noted that if the average value is 0, the volatility characteristic value is directly set to 0.

[0033] Since continuity and volatility primarily reflect the flow pattern characteristics within a single sub-time period, they cannot identify whether microflows exhibit periodic repetition. Regular water use or long-term leakage will result in similar microflow patterns repeating across multiple identical sub-time periods, while random disturbances show no repeatability.

[0034] Based on the above characteristics, the periodic repetition of microflows will be analyzed. By comparing across sub-time periods, the regularity of microflow patterns will be assessed to distinguish between predictable normal behavior and random abnormal microflows.

[0035] Specifically, the corresponding sub-time periods within adjacent dates of the candidate sub-time period are obtained. These obtained sub-time periods and the candidate sub-time periods are designated as the first sub-time period. For example, if the candidate sub-time period is 1:00 AM to 2:00 AM on March 7th, then the first sub-time periods are 1:00 AM to 2:00 AM on March 6th, 1:00 AM to 2:00 AM on March 7th, and 1:00 AM to 2:00 AM on March 8th. For each first sub-time period, a micro-flow feature vector is constructed using the continuity feature value, the volatility feature value, and the average instantaneous flow data of all times within the sub-time period. The continuity feature value characterizes the continuous distribution of micro-flow time periods within the sub-time period, and the volatility feature value characterizes the volatility characteristics. Each first sub-time period has a corresponding micro-flow feature vector, which can be represented as: ,in, , representing the The first sub-time period; Indicates the first The microfluidic feature vector of the first sub-time period, Indicates the first The continuous characteristic value of the first sub-time period, Indicates the first The average value of instantaneous flow data at all times within the first sub-time period. Indicates the first The volatility characteristic value of the first sub-time period.

[0036] The cosine similarity between the microfluidic feature vectors of each pair of the first sub-time periods is calculated, and this cosine similarity is used as the similarity between each pair of the first sub-time periods. Then, the average of the similarities between all pairs of the first sub-time periods is used as the data period repetition factor for the candidate sub-time periods. The larger the value of the data period repetition factor, the stronger the repeatability of the microfluidic pattern.

[0037] True low-velocity leaks or continuous micro-water usage are typically characterized by long durations, small flow rate fluctuations, and recurrence within the same time period, thus exhibiting high consistency in terms of continuity, volatility, and periodic repetition. In contrast, non-water-using factors such as pipeline pressure fluctuations, transient disturbances, or mechanical rebound are usually short-lived, highly volatile, and lack temporal repetition. By jointly analyzing these three characteristics, true stable microflows can be distinguished from random disturbances from three perspectives: temporal persistence, numerical stability, and historical consistency, thereby improving the reliability of single-meter microflow event assessment.

[0038] Based on the above characteristics, the regularity score of the candidate sub-time period is obtained by integrating continuous characteristic values, volatility characteristic values, and data period repetition factors. Specifically, weight coefficients are assigned to the inverse normalized values ​​of continuous characteristic values, volatility characteristic values, and data period repetition factors, and then a weighted sum is performed to obtain the regularity score of the candidate sub-time period.

[0039] In the comprehensive regularity score, continuous characteristic values ​​reflect whether microflows persist over a long period and have the greatest impact on identifying actual leaks, thus receiving a higher weight. Fluctuation characteristic values ​​measure flow stability and play an important auxiliary role in distinguishing disturbances, with a lower weight. The data period repetition factor is mainly used to identify regular water use or long-term trends, and has a relatively small impact on short-term judgments, therefore receiving a slightly lower weight. As a specific example, the weights of continuous characteristic values, fluctuation characteristic values, and data period repetition factors are set manually; for instance, the weight of continuous characteristic values ​​could be 0.4, the weight of fluctuation characteristic values ​​could be 0.3, and the weight of the data period repetition factor could be 0.3.

[0040] As a concrete example, the specific formula for calculating the regularity score is given. The regularity score of the candidate sub-time period can be expressed as: in, This indicates the regularity score of the candidate sub-time period. The continuous characteristic value representing the candidate sub-time period. This represents the volatility characteristic value of the candidate sub-time period. This indicates the maximum value of all fluctuating characteristic values ​​of the water meter within a preset time window. This represents the data period repetition factor for candidate sub-time periods. The weights represent the continuous eigenvalues. The weights representing the volatility eigenvalues This indicates the weight of the data periodicity factor. This represents the function that takes the maximum value.

[0041] This represents the inverse normalized value of the volatility characteristic. The preset time window can be the past seven days.

[0042] By using the above methods, the fusion of continuous characteristic values, volatile characteristic values, and data periodic repetition factors can be achieved, thus obtaining a regularity score for each sub-time period.

[0043] Step S3: Based on the regularity scores of each sub-time period and the sub-time periods before it, obtain the target credibility of each sub-time period; based on the similarity of the instantaneous flow data of each water meter and the associated water meter in the same sub-time period in the same pipeline topology, obtain the correlation degree corresponding to each water meter.

[0044] Considering that the scores of a single sub-time period may be subject to random disturbances, and to prevent misjudgment in a single window from directly triggering anomaly reporting, it is necessary to improve the reliability of the judgment through continuous verification over multiple sub-time periods. Therefore, a reliability increment mechanism is adopted.

[0045] Specifically, the initial confidence level is zero. The following explanation uses a candidate sub-time period as an example: If the regularity score of the candidate sub-time period is greater than or equal to the regularity threshold, a preset increment step is added to the confidence level of the previous sub-time period to obtain the target confidence level of the candidate sub-time period. If the regularity score of the candidate sub-time period is less than the regularity threshold, a preset decrement step is reduced to the confidence level of the previous sub-time period to obtain the target confidence level of the candidate sub-time period. In this embodiment, the regularity threshold is 0.6. In specific applications, the implementer can set this threshold according to specific circumstances.

[0046] As a concrete example, the specific formula for calculating target credibility is given. The target credibility of a candidate sub-time period can be expressed as: in, Indicates the target credibility of the candidate sub-time period. This indicates the credibility of the previous sub-time period of the candidate sub-time period. This indicates the regularity score of the candidate sub-time period. Indicates a regularity threshold. This represents the function that takes the minimum value. This represents the function that takes the maximum value.

[0047] In the formula for calculating the target credibility, 0.2 represents the preset increment step size, and 0.1 represents the preset decrement step size.

[0048] Periodic fluctuations in pipeline pressure, nighttime pressure regulation operations in the region, or the start-up and shutdown of upstream pumping stations can simultaneously trigger similar minute flow changes in multiple water meters. In such cases, even if a single water meter exhibits a high continuity and stability score, it may not represent a genuine user-side leak, but rather a systemic disturbance. Therefore, relying solely on single-meter characteristics for reliability assessment can easily misjudge network-level disturbances as individual anomalies. A comprehensive analysis, incorporating information from related meters, is necessary to improve the accuracy of the analysis results.

[0049] Specifically, for any given water meter: For each sub-time period, the instantaneous flow data at all sampling times within the sub-time period are arranged in chronological order to obtain the flow data sequence for each sub-time period. Related water meters belonging to the same pipe network topology as this water meter are identified; these are water meter units located downstream of the same branch pipe or riser, sharing the same upstream main meter or the same zone. The correlation coefficient between this water meter and each related water meter within the same sub-time period is calculated; the maximum value of the correlation coefficient is selected as the correlation degree corresponding to this water meter in the corresponding sub-time period. It should be noted that the correlation coefficient here is the Pearson correlation coefficient, and the calculation method for the Pearson correlation coefficient is existing technology and will not be elaborated further here. If the microflow changes of multiple water meters are consistent, it indicates pipe network-level interference, and the reliability of a single meter should be reduced; if only the current water meter is abnormal, the reliability should be increased.

[0050] Step S4: Based on the target credibility and the correlation, filter the reporting period and transmit the data.

[0051] In step S3 of this embodiment, the target credibility and relevance are obtained. Next, these two will be combined to filter the reporting period.

[0052] Specifically, when the correlation degree is greater than or equal to the preset correlation degree threshold, it indicates that network interference has occurred. In this case, the product of the target credibility and the preset first adjustment coefficient is used as the credibility factor. When the correlation degree is less than the preset correlation degree threshold, it indicates that a single table anomaly has occurred. In this case, the product of the target credibility and the preset second adjustment coefficient is calculated, and the minimum value between this product and the constant 1 is used as the credibility factor. The preset first adjustment coefficient is less than 1, and the preset second adjustment coefficient is greater than 1. In this embodiment, the preset correlation degree threshold is 0.7, the preset first adjustment coefficient is 0.5, and the preset second adjustment coefficient is 1.2. In specific applications, implementers can set these values ​​according to specific circumstances.

[0053] If the credibility factor is greater than or equal to the event reporting threshold, the micro-stream event is considered highly authentic and requires timely event-level reporting. In this case, the corresponding sub-time period is designated as the reporting cycle. If the credibility factor is less than the event reporting threshold, the corresponding data is included in the fixed-cycle reporting system without immediate communication wake-up; it is only uploaded uniformly at a fixed cycle (e.g., 0:00 daily). This mechanism enables differentiated processing of micro-stream events with different credibility levels, avoiding false alarms or delays caused by a single strategy.

[0054] At the data reporting execution level, for event-level reporting scenarios, the system wakes up the wireless communication module to upload complete event information, including water meter identification, microflow time period, regularity score, correlation degree, and related flow data. After the transmission is completed, the communication module is immediately shut down to reduce power consumption. For periodic reporting scenarios, the system uploads the cumulative water volume and cached microflow data at a preset fixed time to achieve data integration and transmission. For low-priority microflow disturbances, the relevant data is only stored locally and the communication module is not triggered to wake up.

[0055] Thus, by using the method provided in this embodiment, the hierarchical reporting of data from non-magnetic dry IoT water meters has been achieved.

[0056] This embodiment first acquires instantaneous flow data collected by a non-magnetic dry-type water meter. Based on the instantaneous flow data, micro-flow time periods that conform to a preset micro-flow interval are selected. Combining the continuous distribution of micro-flow time periods, the fluctuation characteristics of instantaneous flow data, and the similarity of micro-flow feature vectors of corresponding sub-time periods on adjacent dates, the regularity of each sub-time period is evaluated, and a regularity score is obtained. Furthermore, the target credibility is obtained based on the regularity score and its historical trend, and the correlation is obtained by combining the similarity of instantaneous flow data between water meters in the same pipeline network topology. Finally, the reporting period is selected based on the target credibility and correlation, and data transmission is performed. This method can accurately distinguish between real micro-flow events and non-water-use factors such as pipeline pressure fluctuations and instantaneous disturbances, avoiding false alarms triggered by occasional micro-flow data from a single moment or a single water meter. At the same time, by introducing a dual judgment mechanism of credibility and correlation, it can significantly reduce the number of invalid communication wake-ups while ensuring timely reporting of real abnormal micro-flows, improving the targeting of data transmission and the overall energy efficiency of the system. It is suitable for data hierarchical reporting scenarios of non-magnetic dry-type IoT water meters in complex pipeline network environments.

[0057] An embodiment of a data transmission system for a non-magnetic dry-type IoT water meter: See Figure 2 The diagram illustrates a structural block diagram of a data transmission system for a non-magnetic dry-type IoT water meter according to an embodiment of the present invention. The system may include a data acquisition module, an evaluation module, a feature analysis module, and a transmission module.

[0058] The data acquisition module is used to acquire instantaneous flow data collected by the non-magnetic dry water meter. The evaluation module is used to filter microflow time periods that conform to a preset microflow interval based on instantaneous flow data; and to obtain a regularity score for each sub-time period based on the continuous distribution of microflow time periods in each sub-time period, the fluctuation characteristics of instantaneous flow data, and the similarity of the microflow feature vectors of each sub-time period and its corresponding sub-time period on adjacent dates; the microflow feature vectors are determined based on the continuous distribution, the fluctuation characteristics, and the instantaneous flow data. The feature analysis module is used to obtain the target credibility of each sub-time period based on the regularity score of each sub-time period and the sub-time periods before it; and to obtain the correlation degree of each water meter based on the similarity of the instantaneous flow data of each water meter and the associated water meter in the same sub-time period in the same pipeline topology. The transmission module is used to filter the reporting period and transmit the data based on the target credibility and the correlation.

[0059] It should be understood that Figure 2 The structural block diagram and modules of the data transmission system for a non-magnetic dry-type IoT water meter shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by appropriate instructions, such as a microprocessor or dedicated hardware. Those skilled in the art will understand that the above-described methods and systems can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The systems and modules of this specification can be implemented not only by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., but also by software, for example, executed by various types of processors, or by a combination of the above-described hardware circuits and software (e.g., firmware).

[0060] For more details about the above modules, please refer to other parts of this manual; they will not be repeated here.

[0061] It should be noted that 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 principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A data transmission method of a non-magnetic dry-type Internet of Things water meter, characterized in that, The method includes the following steps: Acquire instantaneous flow data collected by a non-magnetic dry water meter; Microflow time periods that conform to a preset microflow interval are selected based on instantaneous flow data; a regularity score for each sub-time period is obtained based on the continuous distribution of microflow time periods in each sub-time period, the fluctuation characteristics of instantaneous flow data, and the similarity of the microflow feature vectors of each sub-time period and its corresponding sub-time period on adjacent dates; the microflow feature vectors are determined based on the continuous distribution, the fluctuation characteristics, and the instantaneous flow data. Based on the regularity scores of each sub-time period and the sub-time periods before it, the target credibility of each sub-time period is obtained; based on the similarity of the instantaneous flow data of each water meter and the associated water meter in the same sub-time period in the same pipeline topology, the correlation degree of each water meter is obtained. Based on the target credibility and the correlation, the reporting period is selected and data is transmitted.

2. The data transmission method of the non-magnetic dry type water meter of the Internet of Things according to claim 1, characterized in that, The step of filtering microflow time periods that conform to a preset microflow interval based on instantaneous flow data includes: For any sampling time: if the instantaneous flow data at any sampling time is within the preset microflow interval, then the sampling time is considered a valid microflow time; The consecutive effective microflow moments constitute a microflow time period.

3. The data transmission method of the non-magnetic dry type water meter of the Internet of Things according to claim 2, characterized in that, The regularity score for each sub-time period is obtained based on the continuous distribution of micro-flow time periods within each sub-time period, the fluctuation characteristics of instantaneous flow data, and the similarity of micro-flow feature vectors between each sub-time period and its corresponding sub-time period on adjacent dates. This includes: The ratio of the number of valid microfluidic moments within a candidate sub-time period to the total number of all sampling moments within the candidate sub-time period is used as the continuity characteristic value of the candidate sub-time period. Based on the instantaneous flow data of adjacent moments within the candidate sub-time period, the change amplitude of the instantaneous flow data is calculated; according to the change amplitude of all the instantaneous flow data within the candidate sub-time period and the distribution of the instantaneous flow data, the volatility characteristic value of the candidate sub-time period is obtained. The data period repetition factor of the candidate sub-time period is obtained based on the similarity between the micro-flow feature vector of the candidate sub-time period and the micro-flow feature vector of the corresponding sub-time period in the adjacent date, as well as the similarity between the micro-flow feature vectors of the corresponding sub-time periods in the adjacent date; the micro-flow feature vector includes continuous feature value, fluctuation feature value and the average value of instantaneous flow data at all times in the sub-time period; By integrating the continuity feature value, the volatility feature value, and the data periodicity repetition factor, a regularity score for the candidate sub-time period is obtained; The candidate sub-time period can be any sub-time period.

4. The data transmission method of the non-magnetic dry type water meter of the Internet of Things according to claim 3, characterized in that, The step of obtaining the volatility characteristic value of the candidate sub-time period based on the change amplitude and distribution of all instantaneous flow data within the candidate sub-time period includes: Calculate the standard deviation of the variation range of all instantaneous flow data within the candidate sub-time period, and the average value of the instantaneous flow data at all times within the candidate sub-time period; The volatility characteristic values ​​of candidate sub-time periods are determined based on the standard deviation and the mean.

5. The data transmission method of the non-magnetic dry type water meter of the Internet of Things according to claim 3, characterized in that, The process of fusing the continuous characteristic value, the volatility characteristic value, and the data periodicity repetition factor to obtain the regularity score of the candidate sub-time period includes: The continuous characteristic value, the reverse normalized value of the volatility characteristic value, and the data periodicity repetition factor are assigned weight coefficients and then summed in a weighted manner to obtain the regularity score of the candidate sub-time period.

6. The data transmission method for a non-magnetic dry-type IoT water meter according to claim 3, characterized in that, The method of obtaining the target credibility of each sub-time period based on the regularity scoring of each sub-time period and the preceding sub-time periods includes: Initialize the confidence level to zero; For candidate sub-time periods: if the regularity score of the candidate sub-time period is greater than or equal to the regularity threshold, then the target reliability of the candidate sub-time period is obtained by increasing the preset increment step size based on the reliability of the previous sub-time period; if the regularity score of the candidate sub-time period is less than the regularity threshold, then the target reliability of the candidate sub-time period is obtained by decreasing the preset decrement step size based on the reliability of the previous sub-time period.

7. The data transmission method for a non-magnetic dry-type IoT water meter according to claim 1, characterized in that, The method of obtaining the correlation degree of each water meter based on the similarity of the instantaneous flow data of each water meter and its associated water meter in the same sub-time period within the same pipeline network topology includes: For any water meter: Obtain the associated water meters that belong to the same pipeline topology as any of the water meters mentioned above; Calculate the correlation coefficient between any water meter and each associated water meter in the same sub-time period for the flow data sequence; select the maximum value of the correlation coefficient as the correlation degree corresponding to any water meter; wherein, the flow data sequence in the sub-time period consists of the instantaneous flow data at all times in the sub-time period.

8. The data transmission method for a non-magnetic dry-type IoT water meter according to claim 1, characterized in that, The step of filtering reporting periods based on the target credibility and the correlation includes: When the correlation degree is greater than or equal to a preset correlation degree threshold, the target credibility is multiplied by a preset first adjustment coefficient to obtain a credibility factor; when the correlation degree is less than the preset correlation degree threshold, the target credibility is multiplied by a preset second adjustment coefficient to obtain a credibility factor; the preset first adjustment coefficient is less than 1, and the preset second adjustment coefficient is greater than 1. The reporting period is selected based on the value of the credibility factor.

9. The data transmission method for a non-magnetic dry-type IoT water meter according to claim 8, characterized in that, The process of filtering the reporting period based on the value of the credibility factor includes: If the credibility factor is greater than or equal to the event reporting threshold, the corresponding sub-time period will be used as the reporting period.

10. A data transmission system for a non-magnetic dry-type Internet of Things (IoT) water meter, the system being used to implement the method of claim 1, characterized in that, The system includes: The data acquisition module is used to acquire instantaneous flow data collected by the non-magnetic dry water meter; The evaluation module is used to filter microflow time periods that conform to a preset microflow interval based on instantaneous flow data; and to obtain a regularity score for each sub-time period based on the continuous distribution of microflow time periods in each sub-time period, the fluctuation characteristics of instantaneous flow data, and the similarity of the microflow feature vectors of each sub-time period and its corresponding sub-time period on adjacent dates; the microflow feature vectors are determined based on the continuous distribution, the fluctuation characteristics, and the instantaneous flow data. The feature analysis module is used to obtain the target credibility of each sub-time period based on the regularity score of each sub-time period and the sub-time periods before it; and to obtain the correlation degree of each water meter based on the similarity of the instantaneous flow data of each water meter and the associated water meter in the same sub-time period in the same pipeline topology. The transmission module is used to filter the reporting period and transmit the data based on the target credibility and the correlation.

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