Monitoring system applied to remote acquisition of water meter data

Abstract

The invention discloses a monitoring system applied to remote acquisition of water meter data, and relates to the technical field of water affair monitoring. The acquisition and preprocessing module is used for acquiring and purifying volume increment and pressure difference data of the water meter; the volume factor calculation module is used for calculating a volume mutation factor based on the volume increment and the pressure difference in combination with the pipeline deposition influence; the jump degree calculation module is used for calculating the disturbance resistance jump degree based on the volume increment time response and the differential pressure frequency domain characteristics; the abnormal score calculation module is used for constructing space-time correlation features and generating abnormal scores; the alarm generation module is used for triggering an alarm signal according to a dynamic alarm threshold value and an abnormal score comparison result; and the fault diagnosis module is used for performing fault type diagnosis when the alarm signal is triggered. By combining the pipeline deposition effect, the eddy current attenuation spectrum and the time-frequency domain characteristics, the volume mutation factor and the disturbance resistance jump degree are calculated, the alarm threshold value is dynamically corrected, and the water meter fault early warning accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of water affairs monitoring, and in particular to a remote water meter data collection and monitoring system. Background Art

[0002] With the development of smart water meters and the Internet of Things (IoT), remote data collection and monitoring has become a key technology in water management. Traditional water meter monitoring relies primarily on manual inspections, which can lead to issues such as delayed data collection, poor accuracy, and high labor costs. Modern water meter monitoring systems utilize remote data collection technology combined with advanced data analysis methods to obtain key information such as water volume increments and pressure differentials in real time. Intelligent algorithms are then used to process and analyze water meter data, enabling real-time monitoring, fault warnings, and performance optimization.

[0003] Currently, in the process of collecting and monitoring water meter data, the precise measurement and processing of information such as volume increments and pressure differentials has become a core technical challenge for improving system stability and accuracy. Traditional water meter monitoring technology has certain limitations, making it difficult to accurately identify and predict various fault types such as leaks, backflow, and pipe bursts. To address this issue, it is necessary to combine multiple sensor technologies with advanced signal processing algorithms to improve the processing accuracy of water meter data and effectively identify potential faults. Summary of the Invention

[0004] Based on the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a remote data acquisition and monitoring system for water meters to solve the above-mentioned technical problems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: applied to a water meter data remote acquisition and monitoring system, comprising: Collection and preprocessing module: used to collect and preprocess the volume increment and pressure difference data of the water meter to obtain the purified volume increment and pressure difference; Volume factor calculation module: used to calculate the volume mutation factor based on volume increment and pressure difference, combined with the effect of pipeline deposition on pressure difference attenuation; Transition degree calculation module: used to calculate the disturbance resistance transition degree based on the time response characteristics of volume increment and the frequency domain response characteristics of pressure difference; Anomaly score calculation module: used to construct the spatiotemporal correlation characteristics of volume mutation factor and disturbance resistance jump degree to generate anomaly score; Alarm generation module: used to generate dynamic alarm thresholds based on the historical anomaly score average and periodic corrections. When the anomaly score exceeds the dynamic alarm threshold, an alarm signal is triggered. Fault diagnosis module: used to diagnose the fault type according to the preset judgment logic when the alarm signal is triggered.

[0006] The present invention is further configured such that the acquisition and preprocessing module includes: The sensor collects the volume increment and pressure difference data of the water meter in real time, monitoring the volume change of water flowing through the water meter and the pressure difference on both sides of the water meter; Performing Gaussian filtering on the collected volume increment data to obtain a purified volume increment; The collected pressure difference data is subjected to a hyperbolic tangent compensation algorithm based on the instantaneous pressure change rate to obtain the purified pressure difference.

[0007] The present invention is further configured such that the volume factor calculation module includes: Calculate the volume-pressure coupling coefficient based on the volume increment and pressure difference after purification; The pipeline standing wave is excited by emitting ultrasonic waves of a preset frequency, and the pipe wall standing wave resonance factor is calculated based on the phase offset of the transmitting and receiving signals and the acoustic impedance ratio of the pipe wall to water. The volume mutation factor is calculated based on the volume pressure difference coupling coefficient and the tube wall standing wave resonance factor.

[0008] The present invention is further configured such that the transition degree calculation module includes: By applying a preset constant alternating magnetic field to induce eddy current, the eddy current decay spectrum coefficient is calculated based on the initial current and instantaneous current, combined with the magnetic field strength and water conductivity parameters; The purified volume increment is subjected to high-order time-domain difference processing, and the time-domain sharpness feature is obtained using inverse hyperbolic sine transform; The spectrum energy of the pressure difference change rate within the preset frequency band is extracted, and the time gradient modulus of the eddy-electric attenuation spectrum coefficient is used as the suppression factor for attenuation weighting; The disturbance resistance jump degree is calculated based on the time domain sharpness characteristics and the attenuation weighted spectrum energy.

[0009] The present invention is further configured such that the abnormality score calculation module includes: Perform one-dimensional convolution kernel processing on the volume mutation factor and disturbance resistance jump degree to extract local temporal correlation features; Perform element-wise product operation on the correlation feature and the disturbance resistance jump degree; The suppression denominator is constructed based on the time gradient norm of the difference between the volume mutation factor and the disturbance resistance jump degree; Calculate the anomaly score based on the element-wise product and the suppressed denominator.

[0010] The present invention is further configured such that the alarm generation module includes: Perform a sliding average integral operation on the anomaly score within a preset time window to generate a historical baseline offset; The historical baseline offset is superimposed on the preset baseline initial threshold, and the periodic working condition sign function is introduced to correct the superposition result to output the dynamic alarm threshold; The real-time generated anomaly score is compared with the dynamic alarm threshold at the corresponding moment. When the anomaly score is greater than the dynamic alarm threshold, an alarm signal is triggered.

[0011] The present invention is further configured such that the fault diagnosis module includes: When the volume mutation factor exceeds the preset leakage threshold, and the spectrum energy of the disturbance resistance jump degree within the preset frequency band is less than the preset low-frequency vibration energy threshold, it is determined to be a leakage fault; When the negative time gradient of the volume mutation factor is less than the preset critical fluctuation rate, and the real-time pressure difference is greater than the preset reflux pressure difference threshold, it is determined to be a reflux fault; When the maximum value of the time-domain derivative of the disturbance resistance jump degree is greater than the preset pipe burst threshold, it is determined to be a pipe burst fault.

[0012] The present invention is further configured such that when the absolute deviation between the volume mutation factor and the disturbance resistance jump degree is greater than a preset deviation threshold, and the duration of the absolute deviation state is greater than a preset holding time, a sensor drift compensation mechanism is triggered, and the compensation mechanism includes: The pipeline is placed in a static flow state by remotely controlling the solenoid valve, and the differential pressure zero point offset is collected under static flow conditions; Based on the collected pressure difference zero point offset and the preset time modulation compensation factor, the current pressure difference sensor data is drift compensated to obtain the compensated pressure difference, and the compensation time mark is updated to the current moment.

[0013] The present invention is further configured such that the system further includes a visualization module for synchronously rendering the volume increment and pressure difference after purification, and a time series curve of the abnormality score.

[0014] The present invention is further configured such that the system also includes a data storage and feedback module for uploading the collected original water meter volume increment and pressure difference data, anomaly scores and fault diagnosis results to a remote server according to a preset period.

[0015] The present invention provides a remote data acquisition and monitoring system for water meters, which includes an acquisition and preprocessing module for acquiring and preprocessing volume increment and pressure differential data of water meters to obtain purified volume increment and pressure differential; a volume factor calculation module for calculating a volume mutation factor based on the volume increment and pressure differential, combined with the influence of pipeline deposition on pressure differential attenuation; a transition degree calculation module for calculating a disturbance resistance transition degree based on the time response characteristics of the volume increment and the frequency domain response characteristics of the pressure differential; an anomaly score calculation module for constructing spatiotemporal correlation characteristics of the volume mutation factor and the disturbance resistance transition degree to generate an anomaly score; an alarm generation module for generating a dynamic alarm threshold based on the mean of historical anomaly scores superimposed with periodic corrections, and triggering an alarm signal when the anomaly score is greater than the dynamic alarm threshold; and a fault diagnosis module for diagnosing the fault type according to a preset decision logic when the alarm signal is triggered. The beneficial effects produced include: 1. Accurate calculation of volume mutation factor: The volume mutation factor is calculated by combining the pressure differential attenuation effect of pipeline deposition. Through technologies such as pipeline standing wave and eddy electric decay spectrum, it can reflect the dynamic changes of water flow in the pipeline and timely capture possible fault hazards, thereby providing more accurate fault warning for the water meter monitoring system; 2. Accurate calculation of disturbance resistance jump degree: By combining time-domain and frequency-domain features and using innovative technologies such as eddy-electric attenuation spectrum coefficients and time-domain sharpness characteristics, it can efficiently extract and evaluate the disturbance resistance of water flow, monitor subtle changes in the fluid state in the pipeline in real time, and provide timely and accurate state assessment to help detect potential faults. 3. Anomaly score generation and alarm mechanism: Anomaly scores are generated based on the spatiotemporal correlation characteristics of the volume mutation factor and the disturbance resistance jump degree. Combined with the dynamic correction of historical data, an adaptive alarm threshold is provided. This method can avoid false alarms caused by environmental changes, improve system reliability, and trigger alarms in a timely manner when real faults occur, ensuring the safe operation of the water meter.

[0016] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] In the attached figure:

[0019] Figure 1 The structure diagram of a remote water meter data collection and monitoring system according to an exemplary embodiment of the present invention is shown. DETAILED DESCRIPTION

[0020] The following describes the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art will readily appreciate the other advantages and benefits of the present invention from the disclosure herein. The present invention may also be implemented or applied through various other specific embodiments, and the various details in this specification may be modified or altered based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are intended only to illustrate the present invention and are not intended to limit the scope of protection of the present invention.

[0021] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0022] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the embodiments of the present invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring the embodiments of the present invention.

[0023] Applied to water meter data remote collection and monitoring system, such as Figure 1 Shown, including: Collection and preprocessing module: used to collect and preprocess the volume increment and pressure difference data of the water meter to obtain the purified volume increment and pressure difference; Volume factor calculation module: used to calculate the volume mutation factor based on volume increment and pressure difference, combined with the effect of pipeline deposition on pressure difference attenuation; Transition degree calculation module: used to calculate the disturbance resistance transition degree based on the time response characteristics of volume increment and the frequency domain response characteristics of pressure difference; Anomaly score calculation module: used to construct the spatiotemporal correlation characteristics of volume mutation factor and disturbance resistance jump degree to generate anomaly score; Alarm generation module: used to generate dynamic alarm thresholds based on the historical anomaly score average and periodic corrections. When the anomaly score exceeds the dynamic alarm threshold, an alarm signal is triggered. Fault diagnosis module: used to diagnose the fault type according to the preset judgment logic when the alarm signal is triggered.

[0024] The present invention is further configured such that the acquisition and preprocessing module includes: The sensor collects the volume increment and pressure difference data of the water meter in real time, monitoring the volume change of water flowing through the water meter and the pressure difference on both sides of the water meter; Performing Gaussian filtering on the collected volume increment data to obtain a purified volume increment; The collected pressure difference data is subjected to a hyperbolic tangent compensation algorithm based on the instantaneous pressure change rate to obtain the purified pressure difference. Specifically, the volume increment data and pressure difference data of the water meter are collected in real time by the sensor. During the collection process, the data are affected by external factors such as environmental noise, electromagnetic interference or temperature fluctuations, and need to be pre-processed and noise suppressed. In order to improve the credibility of the volume increment data, this embodiment uses Gaussian filtering to smooth it, and the original volume increment is smoothed. Apply Gaussian filter for weighted smoothing to remove random noise and instantaneous fluctuations in the data and obtain the purified volume increment , is the current moment; the filter has a width parameter , used to control the smoothness of the filter, The specific value of is determined by the data acquisition frequency, noise characteristics and the frequency of the retained signal. The value range is [0.1,5]. Values ​​are suitable for scenes with less noise and faster changes, while larger It is suitable for occasions with strong noise or slow changes; through Gaussian filtering, the accuracy and stability of volume increment data can be enhanced, and the error caused by instantaneous fluctuations can be reduced, thereby reflecting the stable change trend of water flow; for the collected pressure difference data, this embodiment adopts a hyperbolic tangent compensation algorithm based on the instantaneous pressure change rate to eliminate the measurement error caused by environmental changes, for the original pressure difference data , calculate the time derivative of the pressure change rate, and correct it with the hyperbolic tangent function to get the pressure difference after purification The compensation algorithm can remove the influence of external interference such as temperature change and vibration, making the pressure difference data more accurate and reflecting the state of water flow in the pipeline more realistically. Through the above data preprocessing and noise suppression steps, the purified volume increment data is obtained. And the pressure difference data after purification , providing more stable and reliable basic data for subsequent calculations. The purified data can better reflect the actual situation in the pipeline, especially when water flow fluctuations or pressure differences are abnormal, and can provide more accurate potential fault warnings.

[0025] The present invention is further configured such that the volume factor calculation module includes: Calculate the volume-pressure coupling coefficient based on the volume increment and pressure difference after purification; The pipeline standing wave is excited by emitting ultrasonic waves of a preset frequency, and the pipe wall standing wave resonance factor is calculated based on the phase offset of the transmitting and receiving signals and the acoustic impedance ratio of the pipe wall to water. The volume mutation factor is calculated based on the volume-pressure differential coupling coefficient and the pipe wall standing wave resonance factor. Specifically, in this embodiment, the volume mutation factor is calculated by combining the volume increment after purification, the pressure differential after purification, and the pressure differential attenuation correction caused by pipeline deposition to reflect the sudden change characteristics of the water flow and eliminate the interference of pipeline deposits on the measurement results, such as biofilm and scale, thereby enhancing the accuracy and reliability of the data. The calculation logic of the volume mutation factor is: , is the volume mutation factor, is the adjustment coefficient, is the sensitivity coefficient, is the attenuation factor, is the tube wall standing wave resonance factor; Used to adjust the effect of pressure difference on volume pressure difference coupling coefficient, the value range is [0.1,5]; It is used to adjust the nonlinear relationship between volume increment and pressure difference to ensure that the interaction between the two can be reasonably adjusted under different pipeline and environmental conditions. The value range is [1,2]; Used to control the pressure difference decay rate caused by pipeline sediments, the value range is [0.1,1]; is the volume-pressure coupling coefficient. It reflects the dynamic changes of water flow by combining volume increment and pressure difference. The volume increment reflects the change of water flow per unit time, while the pressure difference reflects the resistance or flow state of water flow. Combining the two helps to capture the overall change trend of water flow. To eliminate the interference of pipeline deposits, in this embodiment, a preset frequency ultrasonic transmitter is used to excite standing waves in the pipeline. The thickness of biofilm or scale on the pipe wall is quantified based on the phase shift of the received signal. The pipe wall standing wave resonance factor is calculated in combination with the acoustic impedance ratio of the water in the pipe. By monitoring the reaction of the ultrasonic standing wave to the pipe deposits, the water flow condition inside the pipe is indirectly reflected. The calculation logic of the pipe wall standing wave resonance factor is as follows: , is the pipe diameter, is the phase difference between the transmitted wave and the received wave, is the wavelength of ultrasound in water, is the acoustic impedance ratio between the pipe wall and water, is the density of water, is the speed of sound in water, is the density of the pipe wall material, is the sound velocity of the pipe wall material; according to the change of temperature, the wavelength of ultrasound under different temperature conditions will be different. The instantaneous wavelength of ultrasound in water calculated according to the ambient temperature is The calculation logic is: , is the speed of sound in water after temperature adjustment, It is a preset frequency ultrasonic wave; Reflects the effect of water temperature on ultrasonic propagation characteristics, the sound speed in water after temperature adjustment The calculation logic is: , is the speed of sound in water at the reference temperature, is the temperature variation coefficient, is the current temperature of the water, is the reference temperature of water; This parameter reflects the effect of temperature on the speed of sound in water, with a value range of [0.017, 0.020] and units of degrees Celsius. By combining analysis of the pipe wall standing wave resonance factor, volume increment data, and pressure difference data, it quantifies the impact of pipeline sediments on water flow, improving the accuracy and reliability of water meter monitoring systems in complex environments.

[0026] The present invention is further configured such that the transition degree calculation module includes: By applying a preset constant alternating magnetic field to induce eddy current, the eddy current decay spectrum coefficient is calculated based on the initial current and instantaneous current, combined with the magnetic field strength and water conductivity parameters; The purified volume increment is subjected to high-order time-domain difference processing, and the time-domain sharpness feature is obtained using inverse hyperbolic sine transform; The spectrum energy of the pressure difference change rate within the preset frequency band is extracted, and the time gradient modulus of the eddy-electric attenuation spectrum coefficient is used as the suppression factor for attenuation weighting; The disturbance resistance jump degree is calculated based on the time domain sharpness characteristics and attenuation weighted spectrum energy. Specifically, this step extracts multi-scale jump information by combining time domain and frequency domain characteristics, thereby improving the response capability to sudden events. The disturbance resistance jump degree is calculated based on the time response characteristics of volume increment and the frequency domain response characteristics of pressure difference, combined with the eddy-electric attenuation spectrum coefficient, to evaluate the jump characteristics and anti-interference capability of water flow dynamics. The calculation logic of the disturbance resistance jump degree is as follows: , For disturbance resistance jump degree, is the inverse hyperbolic sine function, is the time domain sharpness adjustment coefficient, is the frequency domain suppression adjustment coefficient, The spectral energy of the pressure difference change rate within the preset frequency band, is the eddy-electric decay spectrum coefficient, is the time gradient operator, is the time gradient of the eddy-electric decay spectrum coefficient, is the attenuation coefficient, It is the time domain sharpness characteristic term in the disturbance resistance jump degree calculation logic, Frequency domain suppression term in the disturbance resistance jump calculation logic; inverse hyperbolic sine function Used to enhance the response to small changes and suppress over-sensitivity to large fluctuations; For suppressing presets to High-frequency noise or interference signals in the Hz frequency band; In this embodiment, a preset constant alternating magnetic field is used to induce eddy currents. The metal ion concentration in the water is inverted by combining the real-time current and magnetic field strength with the water conductivity, pipeline material and sediment, thereby improving the sensitivity to small disturbances in the water flow and the eddy current attenuation spectrum coefficient. The calculation logic is: , is the time constant, is the initial current, is the instantaneous current, is the eddy current effect intensity factor; It is used to reflect the influence of water conductivity, magnetic field strength and pipe surface sediment on eddy current effect, and to describe the changes of eddy current under different materials and pipe conditions. The calculation logic is: , is the relative magnetic permeability, is the vacuum permeability, is the water conductivity, is the magnetic field intensity; the time gradient of the eddy-electric decay spectrum coefficient It is used to describe the influence of eddy effects in water flow on water quality and flow disturbance. It represents the rate of change of eddy currents and is related to the change of metal ion concentration. It is used to adjust the contribution of the time domain response characteristics to the disturbance resistance jump degree and control the response of the time domain sharpness to the sudden change of water flow. The value range is [0.1, 2]. It is used to adjust the contribution of frequency domain response characteristics to the disturbance resistance jump degree, and the value range is [0.1, 2]; Used to control the influence of eddy-electric attenuation spectrum coefficient on frequency domain suppression, the value range is [0,1]; It is used to describe the decay rate of eddies and reflect the impact of sediments on eddy decay. Its value range is [0.1, 10] and the unit is seconds. The time domain sharpness feature item focuses on the instantaneous change of the water volume increment. By calculating the third-order derivative and logarithmic inverse function of the volume increment, it captures the instantaneous fluctuation of the water flow and reveals drastic changes in a short period of time. Frequency domain suppression reduces noise interference and enhances the ability to identify real signals by analyzing pressure difference changes and eddy effects. The combination of the two can more accurately detect disturbances and potential faults in the water flow. Through eddy effects and frequency domain suppression technology, efficient monitoring of water flow status can be achieved in complex water quality and pipeline environments, improving the ability to identify sudden changes in water flow and potential faults, and enhancing the robustness and stability of the system.

[0027] The present invention is further configured such that the abnormality score calculation module includes: Perform one-dimensional convolution kernel processing on the volume mutation factor and disturbance resistance jump degree to extract local temporal correlation features; Perform element-wise product operation on the correlation feature and the disturbance resistance jump degree; The suppression denominator is constructed based on the time gradient norm of the difference between the volume mutation factor and the disturbance resistance jump degree; An anomaly score is calculated based on the element-wise product operation result and the suppression denominator. Specifically, in this embodiment, the anomaly score is obtained by analyzing the dynamic change characteristics between the volume mutation factor and the disturbance resistance jump degree, combined with feature extraction using a convolutional neural network. The anomaly score is used to quantify the abnormal behavior of the water flow and provide a basis for subsequent fault warnings. The calculation logic of the anomaly score is as follows: , Score anomaly, is the adjustment coefficient, is the element-by-element multiplication symbol; Used to weight the gradient difference to enhance the influence of significant fluctuations. For example, when the water flow is fast, a larger value should be selected to amplify the influence of fluctuations, while for a more stable flow, a smaller value should be selected to reduce the influence of noise. The value range is [0.1, 2]; The differential gradient represents the time gradient difference between the volume mutation factor and the disturbance resistance jump degree, which is used to reflect the difference in their dynamic behavior in the time dimension. By performing time differentiation on the difference between the volume mutation factor and the disturbance resistance jump degree, the instantaneous change of water flow dynamics is captured. This difference reflects the mutation and disturbance of water flow at different time points. Gradient amplitude adjustment is used to enhance the response to significant changes. It helps to amplify the change amplitude when the water flow changes sharply, so as to accurately capture sudden events. This is a convolution operation. One-dimensional convolution is used to smooth the volume mutation factor and disturbance resistance jump degree to extract local features. The convolution operation helps to discover local fluctuation trends in time series data and integrate these feature information into a more stable result. In this embodiment, the convolution kernel size is preset to 3, mainly based on its advantage in capturing small fluctuations and instantaneous mutations in water flow, avoiding excessive smoothing of large fluctuations while maintaining a high temporal resolution. is an activation function used to make nonlinear adjustments to the disturbance resistance jump degree, suppress negative values ​​to zero, enhance sensitivity to positive changes, and avoid interference from negative disturbances; the anomaly score is obtained through the above calculation , reflects the degree of abnormality of the current water flow state. The higher the anomaly score value, the more obvious the abnormal characteristics of the current water flow, and there may be risks of leakage, backflow or pipe burst. By combining gradient difference and convolution operations, it can capture instantaneous mutations and subtle changes in water flow dynamics, thereby enhancing the ability to identify water flow anomalies, especially suitable for small disturbances in complex environments.

[0028] The present invention is further configured such that the alarm generation module includes: Perform a sliding average integral operation on the anomaly score within a preset time window to generate a historical baseline offset; The historical baseline offset is superimposed on the preset baseline initial threshold, and the periodic working condition sign function is introduced to correct the superposition result to output the dynamic alarm threshold; The real-time generated anomaly score is compared with the dynamic alarm threshold at the corresponding moment. When the anomaly score is greater than the dynamic alarm threshold, an alarm signal is triggered. Specifically, in this embodiment, the calculation of the dynamic alarm threshold is adaptively adjusted by introducing a time window and a nighttime adjustment mechanism, thereby optimizing the accuracy and timeliness of water flow anomaly detection and fault warning. The dynamic alarm threshold is adjusted by combining the weighted integral of the historical anomaly score and the nighttime adjustment coefficient to ensure that the system can adapt to environmental changes in different time periods and reduce the possibility of false alarms at night. When the real-time generated anomaly score is greater than the dynamic alarm threshold, the system triggers an alarm signal. The calculation logic of the dynamic alarm threshold is: , is the dynamic alarm threshold, is the preset baseline initial threshold, is the adjustment coefficient, For the time window, Score anomaly, is the threshold correction constant, is the adjustment coefficient, is a night indication function; in this embodiment, the time window If set to 24 hours, it means that the weighted average of the abnormal scores of the past 24 hours is used when calculating the dynamic alarm threshold, which smooths the abnormal behavior in the recent time period and enhances the system's adaptability to long-term trends; Night indicator function It is used to indicate whether the current time belongs to the night time period. In this embodiment, the night time period is defined as 00:00 to 05:00. When the time is in this period, The value of 1 indicates that it is at night. In other time periods, The value of is 0. The function is used to adjust the abnormal response at night to reduce the probability of false alarms caused by changes in the night environment. It is used to determine the influence of the time-weighted average of the anomaly score on the dynamic threshold adjustment, with a value range of [0.05, 0.3]; Used to control the influence of the weighted integral term on the threshold, the value range is [1,3]; It is used to adjust the alarm threshold during the night time period to ensure that minor anomalies detected at night do not falsely trigger the alarm. The value range is [0.1, 0.3]. Dynamic adjustment of the alarm threshold enables the system to adaptively adjust the alarm trigger conditions under different environmental conditions, thereby improving the accuracy of the alarm. By considering past anomaly scores, it can reduce false alarms caused by environmental changes and changes in pipeline conditions.

[0029] The present invention is further configured such that the fault diagnosis module includes: When the volume mutation factor exceeds the preset leakage threshold, and the spectrum energy of the disturbance resistance jump degree within the preset frequency band is less than the preset low-frequency vibration energy threshold, it is determined to be a leakage fault; When the negative time gradient of the volume mutation factor is less than the preset critical fluctuation rate, and the real-time pressure difference is greater than the preset reflux pressure difference threshold, it is determined to be a reflux fault; When the maximum value of the time-domain derivative of the disturbance resistance jump degree exceeds the preset pipe burst threshold, a pipe burst fault is determined. Specifically, after the alarm signal is triggered, the system uses the preset judgment logic to diagnose the water flow anomaly to help identify the potential fault type. Fault type diagnosis includes leakage judgment, backflow judgment, and pipe burst judgment. The judgment of leakage fault is based on the combination of volume mutation factor and disturbance resistance jump degree. The judgment logic of leakage fault is as follows: ,in, is the volume mutation factor, Preset leakage threshold, For disturbance resistance jump degree in preset to Spectral energy in the Hz frequency band, To preset the low-frequency vibration energy threshold; Refers to leakage judgment, which is used to judge whether there is leakage fault in the pipeline system. When it is detected, it means leakage fault. When , it means that no leakage fault is detected; the leakage judgment method can identify small leaks in the pipeline. By combining the characteristics of volume change and low-frequency vibration, the accuracy of leak detection is improved and the influence of background noise or external interference is reduced; the judgment of backflow fault is based on the time gradient of volume mutation factor and pressure difference change. The judgment logic of backflow fault is: ,in, is the time gradient of the volume mutation factor, For the preset critical volatility, is the real-time pressure difference, is the return pressure differential threshold; Refers to leakage judgment, which is used to judge whether backflow occurs in the pipeline. When a backflow fault is detected, When , it means that no backflow fault is detected; the backflow determination method can quickly identify and handle possible backflow problems, avoiding safety hazards caused by water backflow; the determination of pipe burst fault is based on the sudden change rate of the disturbance resistance jump degree. The determination logic of pipe burst fault is: ,in, is the preset pipe burst threshold, is the time domain derivative of the disturbance resistance jump degree, which represents the mutation rate of the disturbance resistance jump degree; Refers to pipe burst judgment, which is used to judge whether a pipe burst occurs. When a burst fault is detected, When , it means that no pipe burst fault is detected; the pipe burst determination method can detect pipe burst in the early stage in time, reduce the scope of pipeline damage and improve the system response capability; by implementing the above fault diagnosis steps, an efficient fault determination method is provided, which can identify abnormal conditions in water meter data in real time, such as leakage, backflow and pipe burst faults, and issue timely alarms. The application of this method can improve the fault response capability of the water meter monitoring system, reduce the false alarm rate, and improve the reliability and stability of the water meter detection system.

[0030] The present invention is further configured such that when the absolute deviation between the volume mutation factor and the disturbance resistance jump degree is greater than a preset deviation threshold, and the duration of the absolute deviation state is greater than a preset holding time, a sensor drift compensation mechanism is triggered, and the compensation mechanism includes: The pipeline is placed in a static flow state by remotely controlling the solenoid valve, and the differential pressure zero point offset is collected under static flow conditions; Based on the collected pressure difference zero point offset, combined with the preset time modulation compensation factor, the current pressure difference sensor data is drift compensated to obtain the compensated pressure difference, and the compensation time mark is updated to the current moment; specifically, since the sensor may be affected by long-term use, environmental changes or other factors, causing its readings to drift, the system needs to regularly perform drift calibration on the sensor to ensure the accuracy of the measurement. In this embodiment, the sensor is calibrated, including the following steps: when the difference between the volume mutation factor and the disturbance resistance jump degree is greater than the preset deviation threshold, it indicates that there is a significant change in the water flow state or the sensor is unstable. When drift occurs and this state lasts longer than the preset holding time, the compensation calibration process is triggered. When the sensor drift compensation mechanism is triggered, the remote control solenoid valve puts the pipeline into a static flow state, that is, the water flow stops. By closing the water valve, calibration is ensured in a static environment without water flow to avoid measurement errors caused by dynamic water flow. In the static flow state, the current differential pressure data is recorded and stored as the zero offset. The zero offset represents the current reference value of the sensor when there is no water flow, providing basic data for the compensation calculation after calibration. Compensation calculation is performed based on the zero offset to correct the drift error of the sensor. The calculation logic of the differential pressure compensation is as follows: , is the pressure difference after compensation, is the original pressure difference at the current moment, is the zero point offset, is the adjustment coefficient, The timestamp of the last compensation calibration time; Used to control the rate of sensor drift compensation, with a value range of [0.1, 1]. Through the above compensation calculation formula, based on the historical zero offset and the current time, the current pressure difference data is gradually corrected to reduce the drift error of the sensor. After each compensation calibration is completed, the timestamp is updated. The current time is used to mark the moment of this calibration and provide a reference time for the next compensation calibration; the pressure difference after output compensation is The compensated and calibrated differential pressure data eliminates the drift error of the sensor, provides more accurate measurement results, and ensures that subsequent analysis and decision-making are based on reliable data; when the sensor drifts, calibration can adjust the measurement results in time to prevent the accumulation of errors and avoid long-term measurement errors caused by drift.

[0031] The present invention is further configured such that the system further includes a visualization module for synchronously rendering the volume increment and pressure difference after purification, as well as a time series curve of anomaly score; specifically, the present embodiment provides a visualization module for a remote water meter data acquisition and monitoring system, the main function of which is to synchronously render and display the time series curve of the volume increment, pressure difference and anomaly score after purification in real time; the volume increment and pressure difference data of the water meter are collected at fixed time intervals, such as 5 seconds, and these data are pre-processed and transmitted to the visualization module for real-time display; the synchronous update of the data is completed through the WebSocket protocol, and the D3.js library in JavaScript is used to render the purified data into a clear time series curve; the volume increment and pressure difference curves are displayed in blue and green respectively, while the anomaly score is displayed in red. The system presents three curves in different colors to distinguish different types of data. In the user interface, the timeline is located at the top, and users can slide the timeline to select a specific time period for viewing. The data display area shows three curves: volume increment, pressure difference, and abnormality score after purification. Users can also freely choose to display or hide these curves. For abnormality scores, when they exceed the dynamic alarm threshold, the system will automatically mark a warning mark on the curve and pop up a warning box to inform the user of the abnormality type and specific fault data. Users can click the View Details button to further view relevant fault diagnosis information to help them quickly identify problems and respond. In this way, users can intuitively understand the working status of the water meter, monitor the system operation in real time, and take timely measures when abnormalities occur, thereby improving the response speed and accuracy of the entire monitoring system.

[0032] The present invention is further configured such that the system also includes a data storage and feedback module for uploading the collected original water meter volume increment and pressure difference data, anomaly scores and fault diagnosis results to a remote server according to a preset period; specifically, the data storage and feedback module in the present invention is used to realize the continuous collection, storage and periodic feedback functions of water meter data. The module can collect the volume increment, pressure difference, anomaly score and fault diagnosis results of the water meter from the sensor and calculation module in real time, and store them in a local cache unit; in order to ensure that data is not lost, the data cache unit supports temporary storage of data during data transmission and provides seamless caching function when data is uploaded; the core function of the module is to transmit data back to the remote server according to a preset period for subsequent data analysis and historical data tracing; data transmission is completed through a standard communication protocol, and different network communication methods can be selected according to specific application requirements, such as Wi-Fi, cellular network or Bluetooth, etc., in order to ensure To ensure the security of data, the module uses encryption technology to encrypt the transmitted data, such as AES or TLS protocol, to ensure the integrity and confidentiality of data during transmission; in actual applications, the data storage and return module automatically performs data collection, storage and upload tasks according to the preset cycle. The collected data will be stored after preprocessing, and the integrity and security of the data will be ensured during upload; when a network interruption occurs during the upload process, the data will be automatically cached and the upload request will be re-initiated after the network is restored to ensure that the data can eventually be successfully returned to the remote server; this module has high reliability and flexibility, and can adjust the data upload cycle and select the appropriate communication method according to actual needs. Through caching mechanism and encryption measures, it can ensure data security and improve work efficiency, reduce manual intervention, and improve the automation level of the system; the above design can support the stable operation of the water meter data remote monitoring system, and provide important historical data support and fault tracing functions.

[0033] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. Applied to water meter data remote collection and monitoring system, characterized by: include: Collection and preprocessing module: used to collect and preprocess the volume increment and pressure difference data of the water meter to obtain the purified volume increment and pressure difference; Volume factor calculation module: used to calculate the volume mutation factor based on volume increment and pressure difference, combined with the effect of pipeline deposition on pressure difference attenuation; Transition degree calculation module: used to calculate the disturbance resistance transition degree based on the time response characteristics of volume increment and the frequency domain response characteristics of pressure difference; Anomaly score calculation module: used to construct the spatiotemporal correlation characteristics of volume mutation factor and disturbance resistance jump degree to generate anomaly score; Alarm generation module: used to generate dynamic alarm thresholds based on the historical anomaly score average and periodic corrections. When the anomaly score exceeds the dynamic alarm threshold, an alarm signal is triggered. Fault diagnosis module: used to diagnose the fault type according to the preset judgment logic when the alarm signal is triggered.

2. The remote data acquisition and monitoring system for water meters according to claim 1 is characterized in that: The acquisition and preprocessing module includes: The sensor collects the volume increment and pressure difference data of the water meter in real time, monitoring the volume change of water flowing through the water meter and the pressure difference on both sides of the water meter; Performing Gaussian filtering on the collected volume increment data to obtain a purified volume increment; The collected pressure difference data is subjected to a hyperbolic tangent compensation algorithm based on the instantaneous pressure change rate to obtain the purified pressure difference.

3. The remote data acquisition and monitoring system for water meters according to claim 1 is characterized in that: The volume factor calculation module includes: Calculate the volume-pressure coupling coefficient based on the volume increment and pressure difference after purification; The pipeline standing wave is excited by emitting ultrasonic waves of a preset frequency, and the pipe wall standing wave resonance factor is calculated based on the phase offset of the transmitting and receiving signals and the acoustic impedance ratio of the pipe wall to water. The volume mutation factor is calculated based on the volume pressure difference coupling coefficient and the tube wall standing wave resonance factor.

4. The remote data acquisition and monitoring system for water meters according to claim 1 is characterized in that: The transition degree calculation module includes: By applying a preset constant alternating magnetic field to induce eddy current, the eddy current decay spectrum coefficient is calculated based on the initial current and instantaneous current, combined with the magnetic field strength and water conductivity parameters; The purified volume increment is subjected to high-order time-domain difference processing, and the time-domain sharpness feature is obtained using inverse hyperbolic sine transform; The spectrum energy of the pressure difference change rate within the preset frequency band is extracted, and the time gradient modulus of the eddy-electric attenuation spectrum coefficient is used as the suppression factor for attenuation weighting; The disturbance resistance jump degree is calculated based on the time domain sharpness characteristics and the attenuation weighted spectrum energy.

5. The remote data acquisition and monitoring system for water meters according to claim 1 is characterized in that: The abnormality score calculation module includes: Perform one-dimensional convolution kernel processing on the volume mutation factor and disturbance resistance jump degree to extract local temporal correlation features; Perform element-wise product operation on the correlation feature and the disturbance resistance jump degree; The suppression denominator is constructed based on the time gradient norm of the difference between the volume mutation factor and the disturbance resistance jump degree; Calculate the anomaly score based on the element-wise product and the suppressed denominator.

6. The remote data acquisition and monitoring system for water meters according to claim 1 is characterized in that: The alarm generation module includes: Perform a sliding average integral operation on the anomaly score within a preset time window to generate a historical baseline offset; The historical baseline offset is superimposed on the preset baseline initial threshold, and the periodic working condition sign function is introduced to correct the superposition result to output the dynamic alarm threshold; The real-time generated anomaly score is compared with the dynamic alarm threshold at the corresponding moment. When the anomaly score is greater than the dynamic alarm threshold, an alarm signal is triggered.

7. The remote data acquisition and monitoring system for water meters according to claim 1 is characterized in that: The fault diagnosis module includes: When the volume mutation factor exceeds the preset leakage threshold, and the spectrum energy of the disturbance resistance jump degree within the preset frequency band is less than the preset low-frequency vibration energy threshold, it is determined to be a leakage fault; When the negative time gradient of the volume mutation factor is less than the preset critical fluctuation rate, and the real-time pressure difference is greater than the preset reflux pressure difference threshold, it is determined to be a reflux fault; When the maximum value of the time-domain derivative of the disturbance resistance jump degree is greater than the preset pipe burst threshold, it is determined to be a pipe burst fault.

8. The remote data acquisition and monitoring system for water meters according to claim 1 is characterized in that: When the absolute deviation between the volume mutation factor and the disturbance resistance jump degree is greater than the preset deviation threshold, and the duration of the absolute deviation state is greater than the preset holding time, the sensor drift compensation mechanism is triggered. The compensation mechanism includes: The pipeline is placed in a static flow state by remotely controlling the solenoid valve, and the differential pressure zero point offset is collected under static flow conditions; Based on the collected pressure difference zero point offset and the preset time modulation compensation factor, the current pressure difference sensor data is drift compensated to obtain the compensated pressure difference, and the compensation time mark is updated to the current moment.

9. The remote data acquisition and monitoring system for water meters according to claim 1 is characterized in that: The system further includes a visualization module for synchronously rendering the volume increment and pressure difference after purification, and a time series curve of the anomaly score.

10. The remote data acquisition and monitoring system for water meters according to claim 1 is characterized in that: The system also includes a data storage and feedback module for uploading the collected original water meter volume increment and pressure difference data, anomaly scores and fault diagnosis results to a remote server according to a preset period.

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