Low-power-consumption electromagnetic water meter system with dynamic excitation and self-calibration functions

Abstract

The invention discloses a low-power-consumption electromagnetic water meter system with dynamic excitation and self-calibration functions, which comprises a sensor layer, a metering board, a main control board and a communication board, and is characterized in that the sensor layer comprises an excitation coil, an electrode pair, a temperature sensor and a pressure sensor; the metering board comprises an excitation driving circuit, a signal conditioning circuit, a high-precision ADC and a register block, the main control board comprises an embedded processor, an RTC clock, a data storage module and a display screen, and the embedded processor is used for receiving signals and processing and executing tasks according to a pre-written program; the RTC clock is used for providing an accurate and continuous time reference for the embedded processor; the communication board comprises an NB-IoT / 485 / PWM multimode communication interface, when water flow passes through the measuring pipe, the electrode pair generates an induced voltage signal, after the induced voltage signal is converted by the signal conditioning circuit and the high-precision ADC, the main control board optimizes excitation parameters in real time by combining environmental parameters and a cloud platform strategy, and meanwhile, triple calibration is periodically executed to ensure the precision of a signal chain.

Description

Technical Field

[0001] This invention belongs to the field of electromagnetic water meter technology, specifically relating to a low-power electromagnetic water meter system with dynamic excitation and self-calibration functions. Background Technology

[0002] Traditional electromagnetic water meters employ a design with fixed excitation current and fixed sampling interval, which has the following drawbacks: (1) A contradiction between wide range ratio and low power consumption: a large excitation current can improve the accuracy of small flow measurement but increases power consumption, while a small excitation current reduces power consumption but introduces noise. (2) A contradiction between response speed and power consumption: extending the excitation interval can save power but causes a lag in response when the flow changes abruptly. (3) Insufficient long-term stability: factors such as electrode polarization and coil aging cause signal reference drift, affecting the accuracy of trade settlement. Summary of the Invention

[0003] In view of the above-mentioned problems, the present invention provides a low-power electromagnetic water meter system with dynamic excitation and self-calibration functions.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0005] A low-power electromagnetic water meter system with dynamic excitation and self-calibration functions includes a sensor layer, a metering board, a main control board, and a communication board. The sensor layer includes an excitation coil, electrode pairs, a temperature sensor, and a pressure sensor. The metering board includes an excitation drive circuit, a signal conditioning circuit, a high-precision ADC, and a register set. The main control board includes an embedded processor, an RTC clock, a data storage module, and a display screen. The embedded processor receives signals, processes them according to a pre-written program, and executes tasks. The RTC clock provides a precise and continuous time reference for the embedded processor. The communication board includes an NB-IoT / 485 / PWM multi-mode communication interface. When water flows through the measuring tube, the electrode pairs generate an induced voltage signal. After conversion by the signal conditioning circuit and the high-precision ADC, the main control board optimizes the excitation parameters in real time based on environmental parameters and cloud platform strategies, while periodically performing triple calibration to ensure signal chain accuracy.

[0006] In one possible implementation, when the electromagnetic water meter system is powered on, it first executes a power-on self-test program, including hardware self-test, parameter loading, and system initialization. Then, the system enters a continuous main loop, detecting and responding to water flow status. The presence of water flow is determined by monitoring the flow rate. When water flow is detected, dynamic excitation control is initiated. The system acquires the empty pipe detection signal via an ADC and inputs it into a pre-designed optimization model to select the optimal excitation parameters, thereby achieving adaptive optimization. The induced voltage signal acquired by the ADC is converted into flow velocity and flow rate results. The flow velocity response is adjusted based on the rate of change of flow velocity, and the sampling and excitation frequencies are adjusted according to different rates of change of flow velocity. After the flow rate data is uploaded to the platform, the time-period strategy issued by the platform is loaded, and graded excitation current control is executed.

[0007] In one possible implementation, when zero flow is continuously detected, a drift self-test mechanism is triggered. The signal collected by the ADC is used to perform triple calibration to determine the parameters, and then drift compensation calculation is performed. The compensated data is used for subsequent flow calculation. The data results are then saved to the memory in real time and transmitted out according to different communication modes. Here, only energy-saving mode and real-time mode are used. When energy-saving mode is used at a specified time, local storage and timed upload are performed. When there is a sudden change in flow, real-time mode is used to upload the data via NB-IoT.

[0008] In one possible implementation, the water usage period determination in dynamic magnetic excitation control includes: setting a day / night excitation strategy based on historical flow big data analysis. The strategy is as follows: the metering board provides excitation current to the coil of the electromagnetic water meter sensor through the excitation drive circuit. When water flows through the measuring section, an induced voltage signal is generated on the electrode. After the signal conditioning circuit of the metering board amplifies and filters the signal, it enters the high-precision ADC. The chip MCU processes the raw signal collected by the ADC and calculates data results including instantaneous flow rate, flow rate, cumulative amount, and time. The main control board saves the data results to the data storage module through a low-power serial port, and displays the data results required by the user through the display screen. The communication board reads the data results stored in the main control board's memory and sends them to the platform through the wireless long-distance communication module.

[0009] In one possible implementation, the platform uses the following algorithm to analyze the data obtained from different time periods:

[0010] The data is preprocessed, and water consumption and flow rate change are statistically analyzed according to the window.

[0011] Cluster analysis was used to automatically divide the water consumption into three clusters based on water usage and change rate. The center points of cluster 1 (high flow rate - high change rate), cluster 2 (medium flow rate - medium change rate), and cluster 3 (low flow rate - low change rate) were respectively. Standardization was then performed using the following formula:

[0012] Where is the mean, is the standard deviation, and is the original data;

[0013] The algorithm generates a control strategy, with cluster 1 corresponding to the large excitation current, cluster 2 corresponding to the medium excitation current, and cluster 3 corresponding to the small excitation current. The three clusters are assigned to their respective time periods. Instructions are received through the communication board, and the control strategy is transmitted to the main control board, which then controls the excitation current of the metering board.

[0014] In one possible implementation, the adaptive optimization achieved by acquiring the empty pipe detection signal via ADC and substituting it into a pre-designed optimization model to select the optimal excitation parameters includes: using the amplitude of the empty pipe detection signal to infer the water conductivity and dynamically selecting the excitation current level.

[0015] The conductivity range measured by the electromagnetic water meter is as follows: The signal amplitude generated by the empty pipe detection circuit has the following relationship with the magnitude of the excitation current and the excitation frequency:

[0016] Where is the excitation current, is the excitation frequency, and is the conductivity. The adaptive processing steps include:

[0017] Basic data acquisition: With constant flow rate and pipe diameter, adjust the conductivity;

[0018] When the excitation current is 5μS / cm, the excitation current I is 10-100mA and the excitation frequency f is 3-50Hz.

[0019] When the excitation current is 50 μS / cm, the excitation current I is 10-50 mA and the excitation frequency f is 6.25-25 Hz;

[0020] When the excitation current is 500 μS / cm, the excitation current I is 5-20 mA and the excitation frequency f is 25.5-100 Hz;

[0021] Establishing a parameter optimization model includes:

[0022] Establish the objective function:

[0023] Where is the weighting coefficient, used to balance accuracy and energy consumption;

[0024] The data was then fitted by adjusting the conductivity as follows:

[0025] Low conductivity: The objective function relates to a weak signal due to low conductivity. Increasing the current improves the SNR while reducing interference, thus meeting the objective. The coefficients are determined by fitting experimental data to ensure the optimal solution is found under the constraints of the objective function. High conductivity:

[0026] Similarly, with the objective function, a high conductivity results in a strong signal, reduces current, and meets the requirements, while simultaneously improving noise suppression; adaptive optimization is achieved through the above mapping relationship between conductivity and excitation parameters.

[0027] One possible implementation further includes: in areas with wireless long-range signal coverage, control is performed at the water meter terminal, and the water meter terminal control algorithm strategy is as follows: data is classified into multiple or binary categories based on preset or dynamically calculated thresholds, using the following formula:

[0028] Where is the mean flow rate within the time window, is the standard deviation, and is the sensitivity coefficient;

[0029] If the current measurement value is determined to be a high rate of change, adjust the excitation frequency to high and the excitation current to high; for the current measurement value, use a medium current and adjust the excitation frequency to medium; for the current measurement value, further reduce the excitation current and adjust the excitation frequency to minimum.

[0030] In one possible implementation, upon entering the drift self-test subsystem, it is determined again whether the current flow rate remains 0. If the flow rate is still 0, the acquired ΔV0 is evaluated to determine whether to enter the triple calibration subroutine. If the evaluation result is "yes," the ADC reference calibration task is performed first to obtain the reference voltage Vref. If it deviates from the threshold range, compensation calculation is performed, and the compensation coefficient is updated. If not, the coil parameters are measured. The three coil parameters acquired and calculated are: coil resistance, decay time, and inductance. If the calculated result indicates that the phase exceeds the threshold range, the required compensation delay time Δt is calculated, and then the compensation parameters are updated. If the phase offset does not exceed the threshold, gain calibration is performed. The sensor is disconnected, a standard voltage is injected, and the output signal Vout is acquired. The gain error is calculated, and finally, the compensation coefficient is updated, written to the EEPROM, and the parameters are applied to the flow rate calculation. The system then returns to normal operation. The specific algorithm for triple calibration is as follows:

[0031] During periods of zero flow, the induced voltage V0 is continuously sampled. If ΔV0 > the threshold, a self-test process is triggered.

[0032] First, the ADC reference voltage is detected, and the voltage division of the reference voltage is collected. If the voltage is not found, it is considered that the reference voltage has drifted, and the new offset value is compensated into the subsequent calculation.

[0033] Secondly, the coil impedance, coil resistance R, and coil inductance L are measured separately. The coil resistance is calculated by measuring the coil current and coil voltage, and then applying Ohm's law. For the inductance L, the constant current source is briefly turned off to collect and analyze the current decay time constant, and combined with the calculated resistance value, the inductance L is finally obtained. If the change in inductance L causes the current phase to shift, the trigger delay time of the ADC must be compensated. The formula is: where is the difference between the current phase measured at the moment and the current phase measured at the factory, and is the excitation frequency.

[0034] Finally, the gain of the induced voltage measurement amplification circuit changes during operation due to ambient temperature and aging, leading to distortion of the original signal. The hardware design and online detection process are as follows: The excitation current is turned off, the sensor has no magnetic field, and there is no induced voltage generated by water flow. The main control board MCU outputs a control signal to the analog switch, disconnecting the measurement circuit from the sensor electrodes. The constant pressure calibration circuit on the main control board is activated and connected to the measurement circuit via the switch. The main control board MCU controls the constant pressure source enable pin through the I / O port, causing the constant pressure source output signal to enter the measurement circuit as an 80ms pulse signal. Assuming the circuit is drift-free, before leaving the factory, this output signal is [value missing], and after use, the output signal is [value missing]. Then, [value missing] is the current circuit amplification gain that needs calibration, [value missing] is the standard measurement circuit amplification gain at the factory, [value missing] is the standard amplified signal at the factory, and [value missing] is the currently measured amplified standard signal. The parameters in the online calibration program can be used to compensate for the fluid induced voltage signal.

[0035] The present invention has the following beneficial effects:

[0036] (1) Power consumption optimization: Compared with the fixed excitation scheme, dynamic adjustment can reduce power consumption by 30%-60%;

[0037] (2) Improved range ratio: By switching between high and low excitation current, Qmin / Qmax≥250:1 can be achieved, which can provide smaller flow measurement for specific application environments.

[0038] (3) Improved anti-drift capability: Multi-parameter self-testing reduces the annual drift rate to <0.5%, while traditional water meters have a drift rate of 2-3%.

[0039] (4) Improved response speed: When the flow rate changes abruptly, the sampling interval can be shortened to 100ms, which is 5 times faster than the fixed interval scheme. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of a low-power electromagnetic water meter system with dynamic excitation and self-calibration functions according to an embodiment of the present invention.

[0041] Figure 2 This is a block diagram of a drift self-test circuit in a specific application example.

[0042] Figure 3 This is a circuit diagram showing the excitation current being turned off in a specific application example. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Reference Figure 1 The diagram shows a principle block diagram of a low-power electromagnetic water meter system with dynamic excitation and self-calibration functions according to an embodiment of the present invention. It includes a sensor layer, a metering board, a main control board, and a communication board. The sensor layer includes an excitation coil, electrode pairs, a temperature sensor, and a pressure sensor. The metering board further includes an excitation drive circuit, a signal conditioning circuit, a high-precision ADC, and a register group. The main control board further includes an embedded processor, an RTC clock, a data storage module, and a display screen. The embedded processor receives signals, processes them according to a pre-written program, and executes tasks. The RTC clock provides a precise and continuous time reference for the embedded processor. The communication board includes an NB-IoT / 485 / PWM multi-mode communication interface. When water flows through the measuring tube, the electrode pairs generate an induced voltage signal. After the induced voltage signal is converted by the signal conditioning circuit and the high-precision ADC, the main control board optimizes the excitation parameters in real time based on environmental parameters and cloud platform strategies, while periodically performing triple calibration to ensure signal chain accuracy.

[0045] When the electromagnetic water meter system configured above is powered on, it first executes a power-on self-test program, including hardware self-test, parameter loading, and system initialization. Then, the system enters a continuous main loop, detecting and responding to water flow status. The presence of water flow is determined by monitoring the flow rate. When water flow is detected, dynamic excitation control is initiated. The system acquires the empty pipe detection signal via an ADC and inputs it into a pre-designed optimization model to select the optimal excitation parameters, thus achieving adaptive optimization. The induced voltage signal acquired by the ADC is converted into flow velocity and flow rate results. The flow velocity response is adjusted based on the rate of change of flow velocity, adjusting the sampling and excitation frequencies according to different flow velocity change rates. After the flow rate data is uploaded to the platform, the time-period strategy issued by the platform is loaded, and graded excitation current control (matching water usage patterns) is executed.

[0046] Furthermore, when zero flow is continuously detected, a drift self-check mechanism is triggered. The signal acquired by the ADC undergoes triple calibration to determine parameters, followed by drift compensation calculation. The compensated data is used for subsequent flow calculations, and the results are then saved to memory in real time. The data is then transmitted according to different communication modes, specifically energy-saving mode and real-time mode. Energy-saving mode is activated at a specified time (e.g., 2:00 AM), performing local storage and periodic uploads. In the event of sudden flow changes, real-time mode is used, uploading data via NB-IoT. Simultaneously, RS-485 / PWM is set as the external communication mode, allowing data transmission via either RS-485 or PWM, in addition to NB-IoT. Another embodiment of the present invention discloses a low-power electromagnetic water meter system with dynamic excitation and self-calibration functions. The water usage period determination in the dynamic excitation control includes: setting a day / night excitation strategy through historical flow big data analysis. The strategy is set as follows: the metering board provides excitation current to the coil of the electromagnetic water meter sensor through the excitation drive circuit. When the water flows through the measuring section, an induced voltage signal is generated on the electrode. After the signal conditioning circuit of the metering board amplifies and filters the signal, it enters the high-precision ADC. The chip MCU processes the raw signal collected by the ADC and calculates data results including instantaneous flow rate, flow rate, cumulative amount, and time. The main control board saves the data results to the data storage module through a low-power serial port. At the same time, the data results required by the user are displayed on the display screen. The communication board reads the data results stored in the memory of the main control board and sends them to the platform through the wireless long-distance communication module.

[0047] The platform uses the following algorithm to analyze the data obtained from different time periods:

[0048] The data is preprocessed, and water consumption and flow rate change are statistically analyzed according to the window.

[0049] Cluster analysis was used to automatically divide the water consumption into three clusters based on water usage and change rate. The center points of cluster 1 (high flow rate - high change rate), cluster 2 (medium flow rate - medium change rate), and cluster 3 (low flow rate - low change rate) were respectively. Standardization was then performed using the following formula:

[0050] Where is the mean, is the standard deviation, and is the original data;

[0051] The algorithm generates a control strategy, with cluster 1 corresponding to the large excitation current, cluster 2 corresponding to the medium excitation current, and cluster 3 corresponding to the small excitation current. The three clusters are assigned to their respective time periods. Instructions are received through the communication board, and the control strategy is transmitted to the main control board, which then controls the excitation current of the metering board.

[0052] Adaptive conductivity control in dynamic magnetic excitation control includes: selecting the optimal excitation parameters by ADC acquisition of empty pipe detection signals and inputting them into a pre-designed optimization model, thereby achieving adaptive optimization; and using the amplitude of empty pipe detection signals to infer water conductivity and dynamically select the excitation current level.

[0053] The conductivity range measured by the electromagnetic water meter is as follows: The signal amplitude generated by the empty pipe detection circuit has the following relationship with the magnitude of the excitation current and the excitation frequency:

[0054] Where is the excitation current, is the excitation frequency, and is the conductivity. The adaptive processing steps include:

[0055] Basic data acquisition: With constant flow rate and pipe diameter, adjust the conductivity;

[0056] When the excitation current is 5μS / cm, the excitation current I is 10-100mA and the excitation frequency f is 3-50Hz.

[0057] When the excitation current is 50 μS / cm, the excitation current I is 10-50 mA and the excitation frequency f is 6.25-25 Hz;

[0058] When the excitation current is 500 μS / cm, the excitation current I is 5-20 mA and the excitation frequency f is 25.5-100 Hz;

[0059] Establishing a parameter optimization model includes:

[0060] Establish the objective function:

[0061] Where is the weighting coefficient, used to balance accuracy and energy consumption;

[0062] The data was then fitted by adjusting the conductivity as follows:

[0063] Low conductivity: The objective function relates to a weak signal due to low conductivity. Increasing the current improves the SNR while reducing interference, thus meeting the objective. The coefficients are determined by fitting experimental data to ensure the optimal solution is found under the constraints of the objective function. High conductivity:

[0064] Similarly, with the objective function, a high conductivity results in a strong signal, reduces current, and meets the requirements, while simultaneously improving noise suppression; adaptive optimization is achieved through the above mapping relationship between conductivity and excitation parameters.

[0065] The aforementioned determination of water usage time periods involves uploading data to a platform for analysis and the provision of control strategies. However, in certain specific scenarios, such as areas with wireless long-range signal coverage, another embodiment of the present invention provides a low-power electromagnetic water meter system with dynamic excitation and self-calibration functions, further comprising: in areas with wireless long-range signal coverage, control is performed at the water meter end. The water meter end control algorithm strategy is as follows: based on a preset or dynamically calculated threshold, the data is classified into multiple or binary categories, using the following formula:

[0066] Where is the mean flow rate within the time window, is the standard deviation, and is the sensitivity coefficient;

[0067] If the current measurement value is determined to be a high rate of change, adjust the excitation frequency to high and the excitation current to high; for the current measurement value, use a medium current and adjust the excitation frequency to medium; for the current measurement value, further reduce the excitation current and adjust the excitation frequency to minimum.

[0068] Another embodiment of the present invention provides a low-power electromagnetic water meter system with dynamic excitation and self-calibration functions. The drift self-check mechanism includes: upon entering the drift self-check subsystem, it checks again whether the current flow rate remains 0. If the flow rate is still 0, it checks the acquired ΔV0 to determine whether to enter the triple calibration subroutine. If the check result is "yes," it first performs ADC reference calibration to obtain the reference voltage Vref. If it deviates from the threshold range, it performs compensation calculation and updates the compensation coefficient. If not, it measures the coil parameters. The acquired and calculated coil parameters are three: coil resistance, attenuation time, and inductance. If the calculated result indicates that the phase exceeds the threshold range, it calculates the required compensation delay time Δt and then updates the compensation parameters. If the phase offset does not exceed the threshold, it performs gain calibration by disconnecting the sensor, injecting a standard voltage, acquiring the output signal Vout, calculating the gain error, updating the compensation coefficient, writing it to the EEPROM, and applying the parameters to the flow calculation, returning to normal operation. The specific algorithm for triple calibration is as follows:

[0069] During periods of zero flow, the induced voltage V0 is continuously sampled. If ΔV0 > the threshold, a self-test process is triggered.

[0070] First, such as Figure 2 As shown, the ADC reference voltage is detected, and the voltage division of the reference voltage is collected. If the value of the voltage division is not found, it is considered that the reference voltage has drifted, and the new offset value is compensated into the subsequent calculation.

[0071] Secondly, the coil impedance, coil resistance R, and coil inductance L are measured separately. The coil resistance is calculated by measuring the coil current and coil voltage, and then applying Ohm's law. For the inductance L, the constant current source is briefly turned off to collect and analyze the current decay time constant, and combined with the calculated resistance value, the inductance L is finally obtained. If the change in inductance L causes the current phase to shift, the trigger delay time of the ADC must be compensated. The formula is: where is the difference between the current phase measured at the moment and the current phase measured at the factory, and is the excitation frequency.

[0072] Finally, the gain of the induced voltage measurement amplifier circuit changes during operation due to ambient temperature and aging, leading to distortion of the original signal. The hardware design and online detection process are as follows: Figure 3 As shown, with the excitation current off, the sensor has no magnetic field and no induced voltage from the water flow. The main control board MCU outputs a control signal to the analog switch, disconnecting the measurement circuit from the sensor electrodes. The constant voltage calibration circuit on the main control board is activated and connected to the measurement circuit via the switch. The main control board MCU controls the constant voltage source enable pin through the I / O port, causing the constant voltage source output signal to enter the measurement circuit as an 80ms pulse signal. Assuming the circuit is drift-free, before leaving the factory, this output signal is [value], and after use, the output signal is [value]. Then, we have [value].

[0073] Among them are the amplification gain of the current circuit that needs to be calibrated, the amplification gain of the standard measurement circuit at the factory, the amplified standard signal at the factory, and the amplified standard signal measured currently. The parameters in the online calibration program can be used to compensate for the induced voltage signal of the fluid.

[0074] The low-power electromagnetic water meter system with dynamic excitation and self-calibration functions described above achieves intelligent metering through a multi-level collaborative working mechanism, solving the problems of wide range and low power consumption, response speed and energy consumption, and insufficient long-term stability of traditional water meters.

[0075] It should be understood that the exemplary embodiments described herein are illustrative and not restrictive. Although one or more embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art will understand that various changes in form and detail may be made without departing from the spirit and scope of the invention as defined by the appended claims.

Claims

1. A low-power electromagnetic water meter system with dynamic excitation and self-calibration functions, characterized in that, The system comprises a sensor layer, a metering board, a main control board, and a communication board. The sensor layer includes an excitation coil, electrode pairs, a temperature sensor, and a pressure sensor. The metering board includes an excitation drive circuit, a signal conditioning circuit, a high-precision ADC, and a register set. The main control board includes an embedded processor, an RTC clock, a data storage module, and a display screen. The embedded processor receives signals, processes them according to a pre-written program, and executes tasks. The RTC clock provides a precise and continuous time reference for the embedded processor. The communication board includes an NB-IoT / 485 / PWM multi-mode communication interface. When water flows through the measuring tube, the electrode pairs generate an induced voltage signal. After the induced voltage signal is converted by the signal conditioning circuit and the high-precision ADC, the main control board optimizes the excitation parameters in real time based on environmental parameters and cloud platform strategies, while periodically performing triple calibration to ensure signal chain accuracy.

2. The low-power electromagnetic water meter system with dynamic excitation and self-calibration function as described in claim 1, characterized in that, When the electromagnetic water meter system is powered on, it first executes a power-on self-test program, including hardware self-test, parameter loading, and system initialization. Then, the system enters a continuous main loop, detecting and responding to water flow status. The presence of water flow is determined by monitoring the flow rate. When water flow is detected, dynamic excitation control is initiated. The system uses an ADC to collect the empty pipe detection signal and inputs it into a pre-designed optimization model to select the optimal excitation parameters, thus achieving adaptive optimization. The induced voltage signal collected by the ADC is converted into flow velocity and flow rate results. The flow velocity response is adjusted based on the rate of change of flow velocity, and the sampling and excitation frequencies are adjusted according to different rates of change of flow velocity. After the flow rate data is uploaded to the platform, the time-period strategy issued by the platform is loaded, and graded excitation current control is executed.

3. The low-power electromagnetic water meter system with dynamic excitation and self-calibration function as described in claim 2, characterized in that, When zero flow is continuously detected, a drift self-test mechanism is triggered. The signal collected by the ADC is used to perform triple calibration to determine the parameters, and then drift compensation calculation is performed. The compensated data is used for subsequent flow calculation. The data results are then saved to the memory in real time and transmitted out according to different communication modes. Here, we only distinguish between power saving mode and real-time mode. When power saving mode is used at a specified time, local storage and timed uploading are performed. When there is a sudden change in flow, real-time mode is used to upload the data via NB-IoT.

4. The low-power electromagnetic water meter system with dynamic excitation and self-calibration function as described in claim 1, characterized in that, The water usage period determination in dynamic magnetic excitation control includes: setting day / night excitation strategies based on historical flow big data analysis. The strategy is as follows: the metering board provides excitation current to the coil of the electromagnetic water meter sensor through the excitation drive circuit. When water flows through the measuring section, an induced voltage signal is generated on the electrode. After the signal conditioning circuit of the metering board amplifies and filters the signal, it enters the high-precision ADC. The chip MCU processes the raw signal collected by the ADC and calculates data results including instantaneous flow rate, flow rate, cumulative amount, and time. The main control board saves the data results to the data storage module through a low-power serial port. At the same time, the data results required by the user are displayed on the screen. The communication board reads the data results stored in the main control board's memory and sends them to the platform through the wireless long-distance communication module.

5. The low-power electromagnetic water meter system with dynamic excitation and self-calibration function as described in claim 4, characterized in that, The platform uses the following algorithm to analyze the data obtained from different time periods: The data is preprocessed, and water consumption and flow rate change are statistically analyzed according to the window. Cluster analysis was used to automatically divide the water consumption into three clusters based on water usage and change rate. The center points of cluster 1 (high flow rate - high change rate), cluster 2 (medium flow rate - medium change rate), and cluster 3 (low flow rate - low change rate) were respectively. Standardization was then performed using the following formula: Where is the mean, is the standard deviation, and is the original data; The algorithm generates a control strategy, with cluster 1 corresponding to the large excitation current, cluster 2 corresponding to the medium excitation current, and cluster 3 corresponding to the small excitation current. The three clusters are assigned to their respective time periods. Instructions are received through the communication board, and the control strategy is transmitted to the main control board, which then controls the excitation current of the metering board.

6. The low-power electromagnetic water meter system with dynamic excitation and self-calibration function as described in claim 1, characterized in that, The adaptive optimization by acquiring the empty pipe detection signal by ADC and substituting it into the pre-designed optimization model to select the optimal excitation parameters includes: using the amplitude of the empty pipe detection signal to back-calculate the water conductivity and dynamically selecting the excitation current level. The conductivity range measured by the electromagnetic water meter is as follows: The signal amplitude generated by the empty pipe detection circuit has the following relationship with the magnitude of the excitation current and the excitation frequency: Where is the excitation current, is the excitation frequency, and is the conductivity. The steps of adaptive processing include: Basic data acquisition: With constant flow rate and pipe diameter, adjust the conductivity; When the excitation current is 5μS / cm, the excitation current I is 10-100mA and the excitation frequency f is 3-50Hz. When the excitation current is 50 μS / cm, the excitation current I is 10-50 mA and the excitation frequency f is 6.25-25 Hz; When the excitation current is 500 μS / cm, the excitation current I is 5-20 mA and the excitation frequency f is 25.5-100 Hz; Establishing a parameter optimization model includes: Establish the objective function: Where is the weighting coefficient, used to balance accuracy and energy consumption; The data was then fitted by adjusting the conductivity as follows: Low electrical conductivity: The objective function is associated with a weak signal due to low conductivity. Increasing the current can improve the SNR while reducing interference, which meets the objective. The coefficients are fitted with experimental data to ensure that the optimal solution is found under the constraints of the objective function. High electrical conductivity: Similarly, with the objective function, a high conductivity results in a strong signal, reduces current, and meets the requirements, while simultaneously improving noise suppression; adaptive optimization is achieved through the above mapping relationship between conductivity and excitation parameters.

7. The low-power electromagnetic water meter system with dynamic excitation and self-calibration function as described in claim 1, characterized in that, Further includes: In areas with wireless long-range signal coverage, control is implemented at the water meter. The water meter control algorithm strategy is as follows: Data is classified into multiple or binary categories based on preset or dynamically calculated thresholds, using the following formula: Where is the mean flow rate within the time window, is the standard deviation, and is the sensitivity coefficient; If the current measurement value is determined to be a high rate of change, adjust the excitation frequency to high and the excitation current to high; for the current measurement value, use a medium current and adjust the excitation frequency to medium; for the current measurement value, further reduce the excitation current and adjust the excitation frequency to minimum.

8. The low-power electromagnetic water meter system with dynamic excitation and self-calibration functions as described in claim 1, characterized in that, Upon entering the drift self-test subsystem, it again checks if the current flow rate remains 0. If the flow rate is still 0, it assesses the acquired ΔV0 to determine whether to proceed to the triple calibration subroutine. If the assessment result is "yes," it first performs ADC reference calibration to obtain the reference voltage Vref. If it deviates from the threshold range, it performs compensation calculations and updates the compensation coefficients. If not, it measures the coil parameters. The three coil parameters acquired and calculated are coil resistance, decay time, and inductance. If the calculated result indicates that the phase exceeds the threshold range, it calculates the required compensation delay time Δt and then updates the compensation parameters. If the phase offset does not exceed the threshold, it performs gain calibration by disconnecting the sensor, injecting a standard voltage, acquiring the output signal Vout, calculating the gain error, updating the compensation coefficients, writing them to the EEPROM, and applying the parameters to the flow rate calculation. It then returns to normal operation. The specific algorithm for triple calibration is as follows: During periods of zero flow, the induced voltage V0 is continuously sampled. If ΔV0 > the threshold, a self-test process is triggered. First, the ADC reference voltage is detected, and the voltage division of the reference voltage is collected. If the voltage is not found, it is considered that the reference voltage has drifted, and the new offset value is compensated into the subsequent calculation. Secondly, the coil impedance, coil resistance R, and coil inductance L are measured separately. The coil resistance is calculated by measuring the coil current and coil voltage and then applying Ohm's law. For the inductance L, the constant current source is briefly turned off, the current decay time constant is collected and analyzed, and combined with the calculated resistance value, the inductance L is finally obtained. If the change in inductance L causes the current phase to shift, the trigger delay time of the ADC needs to be compensated. The formula is: where is the difference between the current phase measured at present and the current phase measured at the factory, and is the excitation frequency. Finally, the gain of the induced voltage measurement amplifier circuit will change during operation due to ambient temperature and aging, resulting in distortion of the original signal. The hardware design and online detection process are as follows: With the excitation current off, the sensor has no magnetic field, resulting in no induced voltage from water flow. The main control board MCU outputs a control signal to the analog switch, disconnecting the measurement circuit from the sensor electrodes. The constant pressure calibration circuit on the main control board is activated and connected to the measurement circuit via the switch. The main control board MCU controls the constant pressure source enable pin through the I / O port, causing the constant pressure source output signal to enter the measurement circuit as an 80ms pulse signal. Assuming the circuit is drift-free, before leaving the factory, this output signal is [value missing], and after use, the output signal is [value missing]. Then, [value missing] is the amplification gain of the current circuit that needs calibration, [value missing] is the standard amplification gain of the measurement circuit at the factory, [value missing] is the standard amplified signal at the factory, and [value missing] is the currently measured amplified standard signal. The induced voltage signal of the fluid can be compensated by using parameters in the online calibration program.

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