A gas flow sensor with automatic calibration function

Abstract

This invention proposes a gas flow sensor with automatic calibration function, comprising a sensing chip module installed on a pipeline, and an ultrasonic transducer group disposed on one side of the sensing chip module. The ultrasonic transducer group includes ultrasonic transducer I and ultrasonic transducer II, both of which are fixedly connected to the pipeline and correspond to each other. The sensing chip module, ultrasonic transducer I, and ultrasonic transducer II are all connected to a circuit module. This invention solves the problems of wear and aging of the sensor mechanism after long-term use, resulting in reduced measurement accuracy. It ensures the authenticity and reliability of the measurement process and has practical significance in gas metering and trade settlement applications.

Description

Technical Field

[0001] This invention relates to the technical field of gas flow detection, and more particularly to a gas flow sensor with automatic calibration function. Background Technology

[0002] In recent years, the domestic gas meter market has reached tens of millions of units, with an estimated total market size exceeding 40 billion yuan, indicating a promising future. Currently, over 90% of the gas meters on the market are diaphragm-type mechanical meters, while ultrasonic and thermal gas meters account for less than 5% combined, each with its own technological drawbacks.

[0003] As diaphragm gas meters are used, issues such as mechanical wear and aging gradually reduce their measurement accuracy; however, their advantage lies in their low cost. Ultrasonic gas meters use the time-of-flight method for measurement, which has the advantage of no moving parts and high long-term reliability. However, because the narrower the pipe, the shorter the sound propagation time difference, the higher the systematic error, resulting in lower measurement accuracy at low flow rates and in narrow pipes. Thermal gas meters also have the characteristics of no moving parts and no pressure loss. Their advantage is that they are good at measuring small flow rates, and the heat transfer method directly measures the mass flow rate of the gas, without introducing secondary errors. Therefore, the combination of these two types of sensors, with their respective advantages and disadvantages complementing each other and mutual calibration, has practical significance in gas metering and trade settlement applications.

[0004] For example, application number 201710976469.2 discloses an ultrasonic gas flow meter and its flow measurement method. The flow meter includes a fluid transport pipeline and ultrasonic transducers in the forward direction (transmitting signal), a forward direction (receiving signal), and a reverse direction (transmitting signal), as well as an MCU chip connected to a power supply, a user button setting module, a temperature compensation module, a pressure compensation module, an LCD display, a serial port and wireless communication module, and a pulse output and a standard 4-20mA output module. This ultrasonic gas flow meter exhibits strong noise interference resistance, reduces measurement errors, and features high measurement accuracy and excellent stability. In gas field production processes, it can reliably, stably, and accurately measure the volumetric flow rate of natural gas. Summary of the Invention

[0005] To address the technical problem of low measurement accuracy of gas flow sensors after prolonged use, this invention proposes a gas flow sensor with automatic calibration function, which has a simple structure and high reliability.

[0006] To achieve the above objectives, the technical solution of the present invention is implemented as follows: A gas flow sensor with automatic calibration function includes a sensing chip module disposed on a pipeline, an ultrasonic transducer group disposed on one side of the sensing chip module, the ultrasonic transducer group including ultrasonic transducer I and ultrasonic transducer II, both ultrasonic transducer I and ultrasonic transducer II are fixedly connected to the pipeline, ultrasonic transducer I and ultrasonic transducer II correspond to each other, and the sensing chip module, ultrasonic transducer I and ultrasonic transducer II are all connected to a circuit module.

[0007] The sensing chip module includes a flow measurement unit, a pressure sensor, a temperature sensor, and a MEMS chip, all of which are connected to the MEMS chip.

[0008] The flow measurement unit includes a resistance wire I, a heating wire, and a resistance wire II arranged sequentially along the airflow direction. Resistance wire I and resistance wire II are arranged symmetrically about the heating wire. All three resistance wires are connected to a MEMS chip.

[0009] The ultrasonic transducers I and II are respectively staggered on both sides of the pipe. The output end of the ultrasonic transducer I and the input end of the ultrasonic transducer II are arranged on the same straight line, and the straight line is set at an angle with the pipe axis.

[0010] The angle between the straight line and the pipeline axis is 45°.

[0011] The circuit module includes a power supply circuit, a drive circuit, a signal detection circuit, a buffer, a DAC module, an EEPROM module, and a microcontroller. The signal detection circuit and the drive circuit are both connected to the power supply circuit. The drive circuit is connected to the MEMS chip. The power supply circuit, ultrasonic transducer I, ultrasonic transducer II, and the MEMS chip are all connected to the signal detection circuit. The signal detection circuit, the buffer, the DAC module, and the EEPROM module are all connected to the microcontroller. The microcontroller contains a neural network.

[0012] The method of use is as follows: First, the airflow passes through the inside of the pipe. The sensor chip module and the ultrasonic transducer group collect the gas flow rate, pressure and temperature, and transmit the gas flow rate, pressure and temperature to the circuit module. The neural network trained in the MCU controller of the circuit module analyzes the collected flow rate, temperature and pressure information, and corrects the flow rate information in real time based on the flow rate, temperature and pressure information to realize the measurement of gas volume.

[0013] The method for training a neural network is as follows:

[0014] S1: Place the gas flow sensor inside the flow experiment device, and statistically analyze the temperature signal T, pressure signal P, thermal flow signal q1, and ultrasonic flow signal q2 collected by the gas flow sensor, and record the corresponding standard flow rate Q that is actually output by the flow experiment device.

[0015] S2: The corresponding temperature signal T, pressure signal P, thermal flow signal q1, ultrasonic flow signal q2 and standard flow Q are compiled into a set of data. Multiple experiments are conducted on the thermal gas flow sensor to obtain multiple sets of data and form a data matrix.

[0016] S3: The neural network consists of an input layer, a hidden layer, and an output layer. The data matrix is ​​input into the neural network through the input layer to calculate the standard flow rate Qm1 and the standard flow rate Qm2.

[0017] S4: The trained neural network is obtained using standard flow rates Qm1, Qm2, and Cp of the gas in actual applications.

[0018] The method for calculating the standard flow rate Qm1 and standard flow rate Qm2 in step S3 is as follows:

[0019] The standard flow rates Qm1 and Qm2 are obtained by converting a set of data to standard conditions using the formulas Qm=K*△T / Cp and Qv=v*s=(t2*c-t1*c) / (t1*cos45°+ t2*cos45°)*s.

[0020] The specific method for obtaining the trained neural network in step S4 is as follows:

[0021] When Cp changes, Qm1 / Qm2=f. Since Qm2 is not affected by the change in Cp, when f≠1 within 1 minute, the output Qm=Qm2; when f=1 within 1 minute, Qm=Qm1, thus obtaining the trained neural network.

[0022] The beneficial effects of the present invention are as follows: 1. The present invention utilizes a sensor chip module and an ultrasonic flow sensor to collect gas flow, pressure and temperature information respectively, and achieves high-precision measurement and wide application range through mutual calibration of the two measurement principles.

[0023] 2. This invention uses two different measurement principles, thermal and ultrasonic, both of which have the characteristic of having no moving parts and a long service life. The mutual calibration of the two principles has higher reliability.

[0024] 3. This invention analyzes the signal evolution law and outputs a data model by using the data matrix between temperature signal, pressure signal, MEMS flow signal, ultrasonic time difference signal and standard flow.

[0025] 4. The pressure signal used in the ultrasonic principle of this invention is directly taken from the MEMS chip, which has advantages in signal timeliness and application speed.

[0026] 5. The signal output circuit of this invention uses a bidirectional buffer to isolate the MCU from external applications, thereby avoiding the influence of external applications on the sensor.

[0027] 6. This invention has a raw signal output interface, which facilitates the measurement and establishment of data models. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a schematic diagram of the structure of the present invention.

[0030] Figure 2 This is a circuit diagram of the circuit module of the present invention.

[0031] Figure 3 This is a technical roadmap for the present invention. Detailed Implementation

[0032] 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 embodiments of the present invention, and not all embodiments. 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.

[0033] like Figure 1 As shown, a gas flow sensor with automatic calibration function includes a sensing chip module disposed on a pipeline. Downstream of the sensing chip module is an ultrasonic transducer group, comprising ultrasonic transducer I and ultrasonic transducer II. Both ultrasonic transducer I and ultrasonic transducer II are fixedly connected to the pipeline and correspond to each other. The sensing chip module, ultrasonic transducer I, and ultrasonic transducer II are all connected to a circuit module. The ultrasonic transducer group and the sensing chip module are located at different positions in the same flow field space, forming a closed airflow path, enabling the gas flow sensor to simultaneously obtain a data matrix of flow rate, thermal signals, and ultrasonic signals in the same flow field.

[0034] The sensor chip module primarily collects temperature, pressure, and flow rate information of the airflow in the pipeline and transmits this information to the circuit module. The ultrasonic transducer array is mainly used to obtain time-difference signals at different flow velocities to calculate real-time volumetric flow rate. The circuit module primarily drives the sensor chip module and the ultrasonic transducer array, and simultaneously performs calculations based on the information collected by these components.

[0035] Specifically, the sensor chip module includes a flow measurement unit, a pressure sensor, a temperature sensor, and a MEMS chip. The flow measurement unit, pressure measurement unit, and temperature measurement unit are all connected to the MEMS chip. The flow measurement unit includes a resistance wire I, a heating wire, and a resistance wire II arranged sequentially along the airflow direction. Resistance wires I and II are symmetrically arranged about the heating wire. All three are connected to the MEMS chip. Resistance wires I and II are PT platinum resistance wires. Utilizing the principle of convective heat transfer, when there is no airflow, the two resistance wires are at the same temperature gradient, and their temperature difference ΔT is zero. When airflow passes through, the temperature gradient shifts downstream, and the two resistance wires generate a signal difference due to the temperature change. The real-time mass flow rate signal Q can be obtained from this signal difference. Specifically, the formula for the mass flow rate signal Q is: Qm = K * ΔT / Cp; where Cp is the isobaric specific heat of the measured gas, K is the instrument coefficient generated during calibration, and ΔT is the temperature difference measured by the PT platinum resistance wire. Pressure sensors are mainly used to monitor the pressure of the medium inside a pipeline in real time and output a pressure signal P as an electrical signal. Temperature sensors are mainly used to monitor the temperature of the medium inside a pipeline in real time and output a temperature signal T as an electrical signal.

[0036] Ultrasonic transducers I and II are staggered on both sides of the pipe. The output end of ultrasonic transducer I and the input end of ultrasonic transducer II are aligned on the same straight line, with an angle of 45° between the straight line and the pipe axis. In use, one of the ultrasonic transducers I and II emits an ultrasonic signal, while the other receives the ultrasonic signal inside the pipe.

[0037] When the ultrasonic signal propagates in the direction of the current, the sound wave transmission time is:

[0038] t1 = L / (c + v * cos45°);

[0039] When the ultrasonic signal is transmitted in the opposite direction, the transmission time of the sound wave is:

[0040] t2 = L / (cv*cos45°);

[0041] Where L is the straight-line distance between transducers, c is the speed of sound in air, t1 is the propagation time with the current, t2 is the propagation time against the current, and v is the speed of ultrasonic wave propagation in air.

[0042] Combining the two formulas, we get:

[0043] v=(t2*c-t1*c) / (t1*cos45°+ t2*cos45°).

[0044] Operating flow rate Q obtained by ultrasonic principle v The calculation formula is:

[0045] Q v =v*s=(t2*c-t1*c) / (t1*cos45°+ t2*cos45°)*s;

[0046] Where s is the cross-sectional area of ​​the pipe at the location of the transducer.

[0047] Based on operating flow rate Q v Obtain the standard flow rate Q m :

[0048] Specifically, the formula is the standard volumetric flow rate converted to 0℃ and 101325kPa conditions. Since the density of the measured medium is constant under these standard conditions, Qv*ρ=Q m This is equivalent to mass flow rate.

[0049] The circuit module includes a power supply circuit, a drive circuit, a signal detection circuit, a buffer, a DAC module, an EEPROM module, and a microcontroller. The signal detection circuit and drive circuit are both connected to the power supply circuit. The drive circuit is connected to the MEMS chip. The power supply circuit, ultrasonic transducers I and II, and the MEMS chip are all connected to the signal detection circuit. The signal detection circuit, buffer, DAC module, and EEPROM module are all connected to the microcontroller. The power supply circuit primarily powers the drive circuit and converts the external 5V voltage to 3.3V to power the signal detection circuit. The drive circuit primarily drives the MEMS chip. The signal detection circuit primarily converts and corrects the electrical signals collected by the sensor chip module and the ultrasonic transducer group, thereby enabling the measurement of gas flow rate, pressure, and temperature. The buffer primarily isolates the MCU from external applications. The DAC module primarily converts the gas flow rate, pressure, and temperature information from digital signals to analog signals. The EEPROM module primarily stores the gas flow rate, pressure, and temperature information. The microcontroller, specifically an MCU controller, is primarily used to correlate real-time pressure, temperature, flow, and time difference signals. It employs a dynamic compensation algorithm to reduce the impact of environmental factors on flow measurement results. The MCU controller incorporates a trained neural network that analyzes the collected flow, temperature, and pressure information and corrects the flow rate in real-time based on these data. Simultaneously, the signal detection circuit includes a raw signal output interface to facilitate data model measurement and establishment.

[0050] In use, the airflow first passes through the inside of the pipe. The sensor chip module and ultrasonic transducer group collect the gas flow rate, pressure and temperature, and transmit the gas flow rate, pressure and temperature to the circuit module. The neural network trained in the MCU controller of the circuit module analyzes the collected flow rate, temperature and pressure information, and corrects the flow rate information in real time based on the flow rate, temperature and pressure information to realize the measurement of gas volume.

[0051] The specific method for training a neural network is as follows:

[0052] S1: Place the gas flow sensor inside the flow experiment device, and statistically analyze the temperature signal T, pressure signal P, thermal flow signal q1, and ultrasonic flow signal q2 collected by the gas flow sensor, and record the corresponding standard flow rate Q that is actually output by the flow experiment device.

[0053] S2: Group the corresponding temperature signal T, pressure signal P, thermal flow signal q1, ultrasonic flow signal q2 and standard flow Q into a set, conduct multiple experiments on the thermal gas flow sensor, obtain multiple sets of data and form the data matrix.

[0054] S3: Utilizing neural networks, based on operating condition flow rates

[0055] The neural network consists of an input layer, a hidden layer, and an output layer. The data matrix is ​​input into the neural network through the input layer, and the standard flow rates Qm1 and Qm2 are obtained by converting them to standard conditions using the formulas: Qm=K*△T / Cp and Qv=v*s=(t2*c-t1*c) / (t1*cos45°+ t2*cos45°)*s.

[0056] S4: In practical applications, the Cp of the gas is affected by the composition and pressure changes of the measured medium. When Cp changes, Qm1 / Qm2=f. Since Qm2 is not affected by the Cp change, when f≠1 within 1 minute, the output Qm=Qm2; when f=1 within 1 minute, Qm=Qm1, thus obtaining the trained neural network.

[0057] This invention utilizes a sensor chip module and an ultrasonic flow sensor to collect gas flow, pressure, and temperature information, respectively. Through mutual calibration of the two measurement principles, it achieves high-precision measurement with a wide range of applications.

[0058] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A gas flow sensor with automatic calibration function, characterized in that, The system includes a sensor chip module installed on the pipeline, an ultrasonic transducer group on one side of the sensor chip module, and an ultrasonic transducer group including ultrasonic transducer I and ultrasonic transducer II. Both ultrasonic transducer I and ultrasonic transducer II are fixedly connected to the pipeline and correspond to each other. The sensor chip module, ultrasonic transducer I and ultrasonic transducer II are all connected to the circuit module. The sensing chip module includes a flow measurement unit, a pressure sensor, a temperature sensor, and a MEMS chip, all of which are connected to the MEMS chip. The method for training a neural network is as follows: S1: Place the gas flow sensor inside the flow experiment device, and statistically analyze the temperature signal T, pressure signal P, thermal flow signal q1, and ultrasonic flow signal q2 collected by the gas flow sensor, and record the corresponding standard flow rate Q that is actually output by the flow experiment device. S2: Compile the corresponding temperature signal T, pressure signal P, thermal flow signal q1, ultrasonic flow signal q2 and standard flow Q into a set of data models, conduct multiple experiments on the thermal gas flow sensor, obtain multiple sets of data, and form the data into a data matrix. S3: The neural network consists of an input layer, a hidden layer, and an output layer. The data matrix is ​​input into the neural network through the input layer to calculate the standard flow rate Qm1 and the standard flow rate Qm2. S4: The trained neural network is obtained using standard flow rate Qm1, standard flow rate Qm2 and Cp of gas in actual application; The method for calculating the standard flow rate Qm1 and standard flow rate Qm2 in step S3 is as follows: A set of data models were converted to standard conditions using the formulas: Qm=K*△T / Cp and Qv=v*s=(t2*c-t1*c) / (t1*cos45°+ t2*cos45°)*s, to obtain standard flow rates Qm1 and Qm2. Where Cp is the isobaric specific heat of the measured gas, K is the instrument coefficient generated during calibration, △T is the temperature difference measured by the PT platinum resistance wire, v is the ultrasonic wave propagation speed in air, s is the cross-sectional area of ​​the pipe at the transducer location, c is the sound wave propagation speed in air, t1 is the downstream propagation time, and t2 is the upstream propagation time. Q v The operating flow rate is obtained using the ultrasonic principle; The specific method for obtaining the trained neural network in step S4 is as follows: When Cp changes, Qm1 / Qm2=f. Since Qm2 is not affected by the change in Cp, when f≠1 within 1 minute, the output Qm=Qm2; when f=1 within 1 minute, Qm=Qm1, thus obtaining the trained neural network.

2. The gas flow sensor with automatic calibration function according to claim 1, characterized in that, The flow measurement unit includes a resistance wire I, a heating wire, and a resistance wire II arranged sequentially along the airflow direction. Resistance wire I and resistance wire II are arranged symmetrically about the heating wire. All three resistance wires are connected to a MEMS chip.

3. The gas flow sensor with automatic calibration function according to claim 2, characterized in that, The ultrasonic transducers I and II are respectively staggered on both sides of the pipe. The output end of the ultrasonic transducer I and the input end of the ultrasonic transducer II are arranged on the same straight line, and the straight line is set at an angle with the pipe axis.

4. The gas flow sensor with automatic calibration function according to claim 3, characterized in that, The angle between the straight line and the pipeline axis is 45°.

5. The gas flow sensor with automatic calibration function according to any one of claims 1-4, characterized in that, The circuit module includes a power supply circuit, a drive circuit, a signal detection circuit, a buffer, a DAC module, an EEPROM module, and a microcontroller. The signal detection circuit and the drive circuit are both connected to the power supply circuit. The drive circuit is connected to the MEMS chip. The power supply circuit, ultrasonic transducer I, ultrasonic transducer II, and the MEMS chip are all connected to the signal detection circuit. The signal detection circuit, the buffer, the DAC module, and the EEPROM module are all connected to the microcontroller. The microcontroller contains a neural network.

6. The gas flow sensor with automatic calibration function according to claim 5, characterized in that, The method of use is as follows: First, the airflow passes through the inside of the pipe. The sensor chip module and the ultrasonic transducer group collect the gas flow rate, pressure and temperature, and transmit the gas flow rate, pressure and temperature to the circuit module. The neural network trained in the MCU controller of the circuit module analyzes the collected flow rate, temperature and pressure information, and corrects the flow rate information in real time based on the flow rate, temperature and pressure information to realize the measurement of gas volume.

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