Ultrasonic water meter flow curve fitting system

Abstract

The invention relates to the technical field of water meter flow, in particular to an ultrasonic water meter flow curve fitting system, which is characterized in that an edge computing device is used for primarily processing original data at a data acquisition end, a mean filtering algorithm is used for removing noise data, and the data after acquisition is subjected to filtering, denoising, abnormal value elimination and normalization processing; a dynamic evaluation model is constructed by adopting a time sequence prediction algorithm, the model calculates and predicts various data of future ultrasonic water meter flow by continuously inputting real-time data, and the model is optimized according to a prediction result, so that errors of the model are continuously reduced in the training process, and the prediction accuracy of the model is improved; modeling and fitting are conducted on the flow curves, calculated by the neural network model, of the ultrasonic water meter in different working states through the fitting model, the fitted curves can serve as a basic data source of a user water consumption behavior and system load characteristic advanced analysis model, and energy-saving analysis and fault positioning are facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of water meter flow, and particularly relates to an ultrasonic water meter flow curve fitting system. Background Art

[0002] With the rapid development of smart city and Internet of Things technologies, ultrasonic water meters, as a new generation of intelligent metering devices, are gradually replacing traditional mechanical water meters due to their non-mechanical structure, high accuracy, fast response, and long service life, and are widely used in the real-time monitoring and metering of residential, commercial, and industrial water use. In the actual application of ultrasonic water meters, the accuracy of flow measurement not only depends on the accuracy of the sensor itself, but is also affected by various factors such as fluid disturbance, pipeline installation method, water temperature change, and electronic noise. In order to achieve accurate evaluation of the water meter performance and improve its metering reliability under complex working conditions, it is necessary to perform curve fitting on the measurement data of the water meter at different flow points, extract the water meter characteristic curve or error curve, so as to be used for subsequent calibration, correction, grading management, and data modeling;

[0003] However, current flow curve fitting mostly relies on manual modeling or single algorithm processing methods, and often ignores the influence of environmental variables such as water temperature and pressure in the water meter on flow measurement, resulting in errors in the fitting results. Summary of the Invention

[0004] Technical problems to be solved: The problem that flow curve fitting mostly relies on manual modeling or single algorithm processing methods, and often ignores the influence of environmental variables such as water temperature and pressure in the water meter on flow measurement, resulting in errors in the fitting results.

[0005] In view of the deficiencies of the prior art, the present invention provides an ultrasonic water meter flow curve fitting system, thereby solving the technical problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions:

[0007] Including the following: a data acquisition module, which uses sensors to collect real-time instantaneous flow, cumulative flow data, water temperature data, water pressure data, and voltage data in the ultrasonic water meter;

[0008] A data preprocessing / prediction module, which filters, denoises, removes outliers, and normalizes the data collected by the data acquisition module to improve the data quality, and constructs a dynamic evaluation model with the help of advanced deep learning algorithms;

[0009] A data calculation / curve fitting module, which is used to construct a calculation model based on the preprocessed flow data and generate a fitting curve according to the calculated data model;

[0010] An error analysis and visualization module, which is used to calculate the error metrics between the fitted curve and the actual collected data, and graphically display the fitting result and the actual flow data;

[0011] An optimization module, which is used to automatically adjust and optimize the parameters of the fitting model based on the error analysis result to improve the fitting accuracy.

[0012] In a possible implementation manner, the data acquisition module respectively uses an ultrasonic transducer, a pressure sensor, a temperature sensor, and a voltage / current sensor to collect data on water flow velocity, water pressure in the pipeline, water supply temperature, and the power supply state of the ultrasonic water meter.

[0013] In a possible implementation manner, the data preprocessing and prediction module uses an edge computing device to perform preliminary processing on the raw data at the data acquisition end. After the data acquisition is completed, a mean filtering algorithm is used to remove noise data, and the data is filtered, denoised, outliers are removed, and normalization processing is performed. A time series prediction algorithm is used to construct a dynamic evaluation model. The model calculates by continuously inputting real-time data, predicts various data of the future ultrasonic water meter flow, and optimizes the model according to the prediction result.

[0014] In a possible implementation manner, the data calculation and curve fitting module uses a neural network model to model and calculate the data preliminarily processed by the data preprocessing and prediction module. It can deeply explore the complex coupling relationship between the input flow velocity data, pressure data, temperature data and the actual flow. It receives multi-dimensional input features such as temperature, pressure, and power supply state, and completes feature extraction and fusion analysis under a unified model framework. A fitting model is used to model and fit the flow curves of the ultrasonic water meter in different working states calculated by the neural network model. The fitted curve can be used as the basic data source for the high-level analysis model of user water use behavior and system load characteristics for energy-saving analysis and fault location.

[0015] In a possible implementation manner, the error analysis and visualization module uses the mean absolute error (MAE) to calculate the error of the calculated and fitted curve, and uses a chart dashboard to display the measurement and fitting results under different time periods and different working conditions. The intuitive curve graph and error graph display methods are used, and the flow curve and the fitting trend line are continuously drawn in real time.

[0016] In a possible implementation manner, the optimization module, the iterative training, parameter fine-tuning, and structure update mechanism of the model optimization module can continuously reduce the prediction error of the model, make the flow fitting result closer to the real water flow state, regularly perform data deduplication, cleaning and preprocessing, and optimize the stored information.

[0017] Beneficial effects compared with the prior art:

[0018] 1. In this solution, by using edge computing devices, the raw data is preliminarily processed at the data acquisition end. The mean filtering algorithm is adopted to remove noise. After the data acquisition is completed, the data is filtered, denoised, outliers are removed, and normalization processing is carried out. And the time series prediction algorithm is used to construct a dynamic evaluation model. The model continuously calculates by inputting real-time data, predicts various data of the future ultrasonic water meter flow, and optimizes the model according to the prediction results, so that the error of the model is continuously reduced during the training process, and the accuracy of the model prediction is improved. By using a fitting model to model and fit the flow curves of the ultrasonic water meter calculated by the neural network model under different working states, the fitted curve can be used as the basic data source for the advanced analysis model of the user's water use behavior and system load characteristics, which helps energy-saving analysis and fault location.

[0019] 2. In this solution, a temperature sensor is used to monitor the water temperature in real time, assist in temperature compensation for the flow velocity calculation results, improve the measurement accuracy, and avoid the influence of temperature on the ultrasonic water meter propagation speed. Since in actual applications, the water supply temperature may change greatly due to seasonal changes, hot water system fluctuations or pipeline transmission conditions, the traditional fixed-value sound speed method is difficult to adapt to the operating environment with large temperature differences. Through the intuitive graph display methods of curve graphs and error graphs, the complex flow fitting and diagnosis results are made clearer and easier to understand, facilitating users to quickly judge and understand the measurement results. At the same time, the flow curve and the fitting trend line can be refreshed in real time and continuously drawn, enabling users to observe the change trend of the flow over time, and predicting potential problems of the ultrasonic water meter, such as leakage, blockage or abnormal water use, according to the data predicted by the prediction module.

[0020] 3. In this solution, through the iterative training, parameter fine-tuning, and structure update mechanism of the model optimization module, the prediction error (such as MAE) of the model can be continuously reduced, making the flow fitting result closer to the real water flow state and improving the measurement credibility. By regularly performing data deduplication, cleaning, and preprocessing, it is ensured that the data input into the system is of high quality, redundant and error information is reduced, and the accuracy and efficiency of data analysis are improved. The data is encrypted during transmission to ensure the confidentiality, integrity, and availability of information during data transmission and storage, and the stored information is optimized regularly. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly and implement it according to the content of the specification, the following takes the preferred embodiments of the present invention and combines with the drawings to describe in detail as follows.

[0022] Figure 1 It is a schematic diagram of the module structure of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "axial", "radial", "circumferential" is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0024] In addition, the terms "first" and "second" are only used for descriptive purposes, and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" explicitly or implicitly include one or more of such features. In the description of the present invention, the meaning of "a plurality" is two or more, unless otherwise specifically defined.

[0025] In the present invention, unless otherwise clearly specified and limited, the terms "mounted", "connected", "coupled", "fixed" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection, an electrical connection, or a communication connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the internal communication of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0026] In the present invention, unless otherwise clearly specified and limited, when the first feature is "above" or "below" the second feature, it means that the first and second features are in direct contact, or the first and second features are indirectly in contact through an intermediate medium. In the description of this specification, the description with reference to the terms "one solution", "some solutions", "examples", "specific examples" or "some examples" means that the specific features, structures, materials or characteristics described in connection with the solution or example are included in at least one solution or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same solution or example. Moreover, the specific features, structures, materials or characteristics described are combined in a suitable manner in any one or more solutions or examples;

[0027] To more clearly and completely illustrate the technical solution of the present invention, the present invention will be further described below with reference to the drawings:

[0028] Example 1

[0029] Please refer to Figure 1 As shown, this example introduces an ultrasonic water meter flow curve fitting system, including the following:

[0030] Data acquisition module

[0031] This module uses various sensors to collect instantaneous flow, cumulative flow, water temperature, water pressure, and voltage in the ultrasonic water meter in real time;

[0032] Implementation steps of data acquisition module

[0033] Data acquisition, by using ultrasonic transducers and their ability to send and receive ultrasonic signals, can achieve accurate measurement of the time difference of ultrasonic wave propagation in water bodies, and then calculate the water flow velocity, and obtain flow data based on the product relationship between flow velocity and cross-sectional area, without the need for direct contact with the water flow or the use of mechanical rotating parts, effectively avoiding the accuracy attenuation caused by long-term use, thereby improving the service life; by using pressure sensors to monitor the water pressure in the pipeline, it can assist in judging whether the water flow state is stable and identify turbulence or abnormal conditions. Since small fluctuations in water pressure in the pipeline are often closely related to the water flow state, stable water pressure usually corresponds to laminar flow or uniform flow, while violent fluctuations or sudden changes in pressure may indicate turbulence, backflow or local disturbances in the water flow. By analyzing the dynamic characteristics of water pressure, it can help identify which time periods are in a stable flow state, thereby selecting more representative flow data for fitting and improving the accuracy and stability of the flow curve; using temperature sensors to monitor water temperature in real time, assist in temperature compensation of flow rate calculation results, improve measurement accuracy, and avoid the ultrasonic water meter propagation speed being affected by temperature. In actual applications, the water supply temperature may change significantly due to seasonal changes, fluctuations in the hot water system, or pipe network transmission conditions. The traditional constant sound velocity method is difficult to adapt to operating environments with large temperature differences. Through real-time temperature collection and feedback adjustment, it can be applied to cold water, hot water and variable temperature scenarios to maintain consistent measurement performance;

[0034] Voltage / current monitoring: The voltage / current sensor monitors the power supply status of the ultrasonic water meter in real time, including the power supply voltage, current, and power supply stability information. Abnormal power supply status may cause sensor drift, data loss, or abnormal sampling cycle. Since power supply fluctuations or voltage drops of ultrasonic water meters may cause signal processing unit reset, sampling interruption, or data abnormality, the setting of voltage / current sensors can achieve full tracking and timely response to the power supply status, ensuring uninterrupted data collection.

[0035] Data preprocessing\prediction module

[0036] This module filters, denoises, removes outliers and normalizes the data collected by the data acquisition module to improve the quality of the data. It also uses advanced deep learning algorithms to build a dynamic evaluation model. By continuously learning and analyzing real-time data, it can quickly and accurately predict subsequent data information.

[0037] Data preprocessing\prediction module implementation steps

[0038] Data preprocessing: By using edge computing devices, the original data is preliminarily processed at the data acquisition end. After the data acquisition is completed, the data is filtered, denoised, outliers are removed, and normalization processing is performed using the mean filter algorithm to remove noise data.

[0039] Since edge computing devices have data caching and preliminary aggregation functions, they can temporarily store data when the network transmission is interrupted or congested. After the network returns to normal, the cached data is aggregated according to certain rules and then transmitted to the backend data analysis platform, effectively reducing the data transmission volume and transmission delay, and improving the real-time response ability of the system.

[0040] Data prediction: By using the time series prediction algorithm to construct a dynamic evaluation model, the model continuously calculates by inputting real-time data, predicts various data of the future ultrasonic water meter flow, and optimizes the model according to the prediction results, so that the model continuously reduces the error during the training process and improves the accuracy of model prediction.

[0041] Data calculation\Curve fitting module

[0042] This module is used to construct a calculation model based on the preprocessed flow data and generate a fitting curve according to the calculated data model.

[0043] Implementation steps of the data calculation\Curve fitting module

[0044] Data calculation: By using a neural network model to model and calculate the data preliminarily processed by the data preprocessing\prediction module, it can deeply explore the complex coupling relationship between the input flow velocity data, pressure data, temperature data and the actual flow. Compared with traditional linear or polynomial fitting methods, it has higher fitting accuracy when dealing with non-uniform flow velocity, instantaneous fluctuations or turbulent regions. At the same time, it receives multi-dimensional input features such as temperature, pressure, and power supply status, and completes feature extraction and fusion analysis under a unified model framework, avoiding the complex process of manually setting weights or selecting features in traditional algorithms, thereby improving the overall intelligent level and processing efficiency of the system.

[0045] Since the neural network automatically learns the complex mapping relationship between data through training, without the need for manual formula construction or preset functions, it significantly reduces the modeling workload and can quickly adapt to the measurement characteristics of different types of water meters and different installation environments. Through continuous training and online learning, the model parameters can be continuously optimized through subsequent data, realizing dynamic adaptation to actual working conditions, especially suitable for urban and park application scenarios with complex flow usage patterns and large on-site interferences.

[0046] Curve fitting: By using a fitting model to model and fit the flow curves of ultrasonic water meters calculated by the neural network model under different working conditions, the fitted curve can be used as the basic data source for the advanced analysis models of user water use behavior and system load characteristics, which helps with energy-saving analysis and fault location.

[0047] The fitting model can adapt to the characteristics of low flow rate, high flow rate, start / stop instant, and pulsating flow rate changes of ultrasonic water meters under various operating conditions, significantly improving the stability and accuracy of measurement data under dynamic fluctuation conditions.

[0048] Error Analysis \ Visualization Module

[0049] Used to calculate the error index between the fitted curve and the actual collected data, and graphically display the fitting result and the actual flow data for easy manual verification and evaluation.

[0050] Implementation Steps of the Error Analysis \ Visualization Module

[0051] Error calculation: By using the mean absolute error (MAE) to calculate the error of the calculated and fitted curve, and using the absolute difference to measure the error size, it has good interpretability, can clearly reflect the overall deviation level of the fitting model, helps to quickly evaluate the measurement accuracy of the water meter under different working conditions. At the same time, it is not affected by the cancellation of positive and negative errors and is applicable to the fitting accuracy evaluation of any form of error distribution.

[0052] Visualization display: Use a chart dashboard to display the measurement and fitting results under different time periods and different working conditions. Through intuitive curve graphs and error graphs, the graphical display method makes the complex flow fitting and diagnostic results clearer and easier to understand, facilitating users to quickly judge and understand the measurement results. At the same time, it can be refreshed in real-time and continuously plot the flow curve and the fitting trend line, enabling users to observe the change trend of the flow rate over time and predict potential problems of the ultrasonic water meter, such as leakage, blockage, or abnormal water use, based on the data predicted by the prediction module.

[0053] Optimization Module

[0054] Used to automatically adjust and optimize the parameters of the fitting model based on the error analysis results to improve the fitting accuracy.

[0055] Implementation Steps of the Optimization Module

[0056] Model optimization: Through the iterative training, parameter fine-tuning, and structure update mechanisms of the model optimization module, the prediction error (such as MAE) of the model can be continuously reduced, making the flow fitting result closer to the real water flow state and improving the measurement credibility.

[0057] Data optimization: By regularly removing duplicates, cleaning, and preprocessing data, ensure the high quality of the data input into the system, reduce redundant and error information, improve the accuracy and efficiency of data analysis. Encrypt the data during transmission to ensure the confidentiality, integrity, and availability of information during data transmission and storage, and regularly optimize the stored information.

[0058] Finally, it should be noted that: Obviously, the above embodiments are merely examples for clearly illustrating the present invention, rather than limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation manners here. And the obvious changes or modifications derived therefrom are still within the protection scope of the present invention.

Claims

1. An ultrasonic water meter flow curve fitting system, characterized in that It includes the following: A data acquisition module that uses sensors to collect real-time instantaneous flow rate, cumulative flow rate data, water temperature data, water pressure data, and voltage data in the ultrasonic water meter; A data preprocessing \ prediction module that filters, denoises, removes outliers, and normalizes the data collected by the data acquisition module to improve the data quality, and constructs a dynamic evaluation model with the help of advanced deep learning algorithms; A data calculation \ curve fitting module that is used to construct a calculation model based on the preprocessed flow rate data and generate a fitting curve according to the calculated data model; An error analysis \ visualization module that calculates the error index between the fitting curve and the actual collected data and graphically displays the fitting result and the actual flow rate data; An optimization module that automatically adjusts and optimizes the parameters of the fitting model based on the error analysis results to improve the fitting accuracy.

2. The ultrasonic water meter flow curve fitting system according to claim 1, characterized in that, The data acquisition module respectively uses ultrasonic transducers, pressure sensors, temperature sensors, and voltage / current sensors to collect data on water flow velocity, water pressure in the pipeline, water supply temperature, and the power supply status of the ultrasonic water meter.

3. The ultrasonic water meter flow curve fitting system according to claim 1, characterized in that, The data preprocessing \ prediction module uses edge computing devices to preliminarily process the raw data at the data acquisition end, adopts a mean filtering algorithm to remove noise, filters, denoises, removes outliers, and normalizes the data after data collection is completed, adopts a time series prediction algorithm to construct a dynamic evaluation model, and the model calculates by continuously inputting real-time data to predict various data of the future ultrasonic water meter flow rate, and optimizes the model according to the prediction results.

4. The ultrasonic water meter flow curve fitting system according to claim 1, wherein The data calculation \ curve fitting module uses a neural network model to model and calculate the data preliminarily processed by the data preprocessing \ prediction module, can deeply explore the complex coupling relationship between the input flow velocity data, pressure data, temperature data and the actual flow rate, receives multi-dimensional input features such as temperature, pressure, and power supply status, completes feature extraction and fusion analysis under a unified model framework, and uses a fitting model to model and fit the flow rate curves of the ultrasonic water meter in different working states calculated by the neural network model. The fitted curve can be used as the basic data source for the advanced analysis model of user water use behavior and system load characteristics for energy conservation analysis and fault location.

5. The ultrasonic water meter flow curve fitting system according to claim 1, characterized in that The error analysis \ visualization module uses the mean absolute error MAE to calculate the error of the calculated and fitted curve, and uses a chart dashboard to display the measurement and fitting results under different time periods and different working conditions, with an intuitive curve graph and error graph display method, and continuously refreshes and continuously draws the flow rate curve and the fitting trend line in real time.

6. The ultrasonic water meter flow curve fitting system according to claim 1, characterized in that, The optimization module, with the iterative training, parameter fine-tuning, and structure update mechanism of the model optimization module, can continuously reduce the prediction error of the model, make the flow rate fitting result closer to the real water flow state, regularly perform data deduplication, cleaning, and preprocessing, and optimize the stored information.

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