Online compensation method and device for thermal mass flow controller, controller and measuring system

Abstract

The application provides an online compensation method and device of a thermal mass flow controller, a mass flow controller and a mass flow measurement system, and belongs to the technical field of fluid measurement and control. The method comprises the following steps: collecting operating parameters of a to-be-measured gas; calculating a characteristic vector in a current state according to the operating parameters and a preset thermodynamic model, wherein the characteristic vector comprises an equivalent zero-point power and an apparent heat conductivity; matching the characteristic vector with a preset gas characteristic database; if a target gas is matched, calling a calibration curve parameter corresponding to the target gas; if the target gas is not matched, obtaining a compensation parameter set, wherein the compensation parameter set at least comprises a first compensation coefficient for compensating a sensor aging deviation and a second compensation coefficient for compensating a fluid attribute difference; and compensating a current flow according to the compensation parameter set and outputting a target flow value. The method is used for solving the problem of low measurement accuracy of the mass flow controller in the related art.

Description

Technical Field

[0001] This invention belongs to the field of fluid measurement and control technology, specifically relating to an online compensation method and device for a thermal mass flow controller, a mass flow controller, and a mass flow measurement system. Background Technology

[0002] Thermal mass flow controllers indirectly measure mass flow rate by measuring the heat loss of fluid as it flows through a heated sensor. Their core sensor typically includes a heating element and one or more temperature sensing elements (such as platinum resistance thermometers). However, in practical applications, mass flow controllers (MFCs) face three main challenges that lead to decreased measurement accuracy, preventing them from achieving precise measurements:

[0003] 1. Sensor zero drift: When operating in high humidity, corrosive, or particulate environments for extended periods, the physical properties of platinum resistance thermometers (such as resistance and thermal conductivity) will slowly change over time, leading to zero drift and causing measurement results to deviate from the true value. Traditional methods require taking the equipment offline for calibration using standard gases, which is time-consuming, labor-intensive, and disrupts production continuity.

[0004] 2. Fluid Property Dependence: The principle of thermal measurement inherently relies on the fluid's thermal conductivity, dynamic viscosity, and other physical properties. When the type of gas introduced differs from the calibration gas, or when the temperature and pressure of the same gas change significantly, it can lead to substantial measurement errors.

[0005] 3. Limitations of a single parameter: Most existing mass flow controllers rely solely on the temperature difference or heating power of a pair of platinum resistance thermometers to calculate flow rate. This limited information dimension makes it impossible to distinguish between signal anomalies caused by "changes in sensor performance" and "changes in fluid properties," thus hindering effective correction and leading to decreased measurement accuracy. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to address the above-mentioned shortcomings of the prior art by providing an online compensation method and device for a thermal mass flow controller, a mass flow controller, and a mass flow measurement system, thereby improving measurement accuracy and achieving accurate measurement of gas mass flow.

[0007] In a first aspect, the present invention provides an online compensation method for a thermal mass flow controller, comprising: acquiring operating parameters of the gas to be measured, the operating parameters including at least the current flow rate and the current heating power; calculating a feature vector under the current state based on the current flow rate, the current heating power, and a preset thermodynamic model, the feature vector including the equivalent zero-point power and the apparent thermal conductivity; matching the feature vector with a preset gas feature database; if a target gas is matched, calling the calibration curve corresponding to the target gas to output the target flow rate value; if no target gas is matched, obtaining a compensation parameter set, the compensation parameter set including at least a first compensation coefficient for compensating for sensor aging deviation and a second compensation coefficient for compensating for fluid property differences; compensating the current flow rate according to the compensation parameter set, and outputting the target flow rate value.

[0008] In some embodiments, before calculating the feature vector of the current state based on the current flow rate, current heating power, and a preset thermodynamic model, the online compensation method of the thermal mass flow controller further includes: when a zero flow state is detected, acquiring the heating resistance value and the temperature measuring resistance value; calculating the real-time ratio between the heating resistance value and the temperature measuring resistance value under the zero flow state, and determining the deviation between the real-time ratio and the reference parameter; if the deviation is greater than a preset threshold; generating a first compensation coefficient based on the real-time ratio and the reference parameter; if the deviation is less than or equal to the preset threshold; setting the first compensation coefficient to 1.

[0009] In some embodiments, before acquiring the operating parameters of the gas to be measured, the online compensation method for the thermal mass flow controller further includes: constructing a reference parameter, which is used to characterize the ratio between the heating resistance value and the temperature measuring resistance value under zero-point calibration conditions.

[0010] In some embodiments, the gas feature database is pre-constructed by: obtaining the feature vectors and flow rates of several preset gases at standard operating temperatures and pressures; associating and storing the gas identifier of each preset gas with its corresponding calibration curve to obtain the gas feature database, wherein the calibration curve is used to characterize the mapping relationship between the feature vectors and flow rates at standard operating temperatures and pressures.

[0011] In some embodiments, the second compensation coefficient is the ratio between the equivalent zero-point power under the current state and the zero-point power reference value under the standard gas.

[0012] In some embodiments, the current flow rate is compensated according to the compensation parameter set, and a target flow rate value is output. Specifically, this includes: multiplying the current flow rate by a first compensation coefficient to obtain a first compensated flow rate, wherein the first compensation coefficient is the ratio of the reference parameter divided by the real-time ratio under zero flow conditions; multiplying the first compensated flow rate by a second compensation coefficient to obtain and output the target flow rate value, wherein the second compensation coefficient is the zero-point power reference value under standard gas divided by the equivalent zero-point power under the current conditions.

[0013] Secondly, the present invention also provides an online compensation device for a thermal mass flow controller, comprising: a signal acquisition module for acquiring operating parameters of the gas to be measured, the operating parameters including at least the current flow rate and the current heating power; a multi-parameter fusion and processing module connected to the signal acquisition module for calculating a feature vector under the current state based on the current flow rate, the current heating power, and a preset thermodynamic model, the feature vector including the equivalent zero-point power and the apparent thermal conductivity; a compensation module connected to the multi-parameter fusion and processing module for matching the feature vector with a preset gas feature database; if a target gas is matched, calling the calibration curve corresponding to the target gas to output the target flow rate value; if no target gas is matched, obtaining a compensation parameter set, the compensation parameter set including at least a first compensation coefficient for compensating for sensor aging deviation and a second compensation coefficient for compensating for fluid property differences; and compensating the current flow rate according to the compensation parameter set to output the target flow rate value.

[0014] In some embodiments, the signal acquisition module is further configured to acquire the heating resistance value and the temperature measuring resistance value when a zero flow state is detected; the compensation module, connected to the signal acquisition module, is configured to calculate the real-time ratio between the heating resistance value and the temperature measuring resistance value under the zero flow state, and determine the deviation between the real-time ratio and the reference parameter; if the deviation is greater than a preset threshold, a first compensation coefficient is generated based on the real-time ratio and the reference parameter; if the deviation is less than or equal to the preset threshold, the first compensation coefficient is set to 1.

[0015] Thirdly, the present invention also provides a mass flow controller, comprising: a heating resistor, a temperature sensing resistor, and a solenoid valve, wherein the heating resistor and the temperature sensing resistor are connected to the solenoid valve through a flow channel, and a microprocessor electrically connected to the heating resistor, the temperature sensing resistor, and the solenoid valve, the microprocessor being configured to execute the online compensation method of the thermal mass flow controller of the first aspect.

[0016] Fourthly, the present invention also provides a mass flow measurement system, including a mass flow controller as described in the third aspect, and a control unit or display unit connected to the mass flow controller.

[0017] The online compensation method and device for a thermal mass flow controller, the mass flow controller itself, and the mass flow measurement system provided by this invention improve measurement accuracy through multi-parameter fusion of feature vectors and dual-path compensation logic. Specifically, in the multi-parameter fusion feature vector, the equivalent zero-point power reflects the sensor state, and the apparent thermal conductivity reflects the fluid properties. The dual-path compensation logic calls the calibration curve corresponding to the preset gas when matching is successful, avoiding deviations caused by sensor zero-point drift and changes in fluid properties; when matching fails, it compensates for deviations caused by sensor zero-point drift through a first compensation coefficient and for errors caused by fluid properties through a second compensation coefficient. Therefore, based on the multi-parameter fusion of feature vectors and dual-path compensation logic, targeted compensation can be achieved, ultimately outputting an accurate target flow rate, thereby realizing online, real-time, and adaptive high-precision measurement.

[0018] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0019] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. The above and other features and advantages will become more apparent to those skilled in the art from the detailed description of exemplary embodiments with reference to the accompanying drawings, in which:

[0020] Figure 1 A schematic flowchart illustrating an online compensation method for a thermal mass flow controller provided in an embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of another sensor zero-point drift diagnosis process provided in an embodiment of the present invention;

[0022] Figure 3 A flowchart illustrating a multi-parameter fusion and processing and fluid adaptive recognition method provided in an embodiment of the present invention;

[0023] Figure 4 This is a schematic diagram of a mass flow controller provided in an embodiment of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0025] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0026] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0027] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0028] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0029] Firstly, such as Figure 1 As shown, this embodiment provides an online compensation method for a thermal mass flow controller, particularly suitable for MFC based on the thermal strategy principle. The method includes:

[0030] Step 101: Collect the operating parameters of the gas to be tested. The operating parameters include at least the current flow rate and the current heating power.

[0031] Step 102: Calculate the feature vector under the current state based on the current flow rate, current heating power, and preset thermodynamic model. The feature vector includes the equivalent zero-point power and apparent thermal conductivity.

[0032] Step 103: Match the feature vector with the preset gas feature database.

[0033] Step 104: If the target gas is matched, call the calibration curve corresponding to the target gas to output the target flow rate value.

[0034] Step 105: If no target gas is matched, obtain the compensation parameter set. The compensation parameter set includes at least a first compensation coefficient for compensating for sensor aging deviation and a second compensation coefficient for compensating for fluid property differences.

[0035] Step 106: Compensate the current flow based on the compensation parameter set and output the target flow value.

[0036] The system collects real-time operating parameters of the gas under test, including current flow rate, current heating power, temperature, pressure, resistance values ​​(heating resistance and temperature sensing resistance), heating resistance temperature rise rate, and thermal time constant. The thermodynamic model can be a simplified model based on energy balance or a polynomial fitting model. Matching to the target gas occurs in two ways: one is a complete match, where the equivalent zero-point power and apparent thermal conductivity in the current state are identical to the eigenvector values ​​of the target gas in the gas feature database; the other is a match where the matching degree exceeds a confidence threshold. This involves calculating the similarity between the eigenvector in the current state and each eigenvector in the gas feature database; if the similarity exceeds the confidence threshold, a match is found. The calibration curve characterizes the mapping relationship between eigenvectors and flow rates under standard operating conditions at temperature and pressure. Sensor aging deviation corresponds to sensor zero-point drift, reflecting changes in sensor performance. Fluid property differences correspond to differences in thermophysical properties caused by different fluid types or changing operating conditions, reflecting changes in fluid properties. The first compensation coefficient is the ratio between the real-time ratio and the reference parameter. The real-time ratio refers to the ratio between the heating resistance value and the temperature measuring resistance value under zero flow conditions, while the reference parameter refers to the ratio between the heating resistance value and the temperature measuring resistance value under zero-point calibration conditions.

[0037] In this embodiment, measurement accuracy is improved through multi-parameter fusion feature vectors and dual-path compensation logic. Multi-parameter fusion refers to the process of extracting feature vectors from at least two operating parameters. Specifically, in the multi-parameter fusion feature vectors, the equivalent zero-point power reflects the sensor state, and the apparent thermal conductivity reflects the fluid properties. The dual-path compensation logic calls the calibration curve corresponding to the preset gas when matching is successful, avoiding deviations caused by sensor zero-point drift and changes in fluid properties. When matching fails, it compensates for deviations caused by sensor zero-point drift using a first compensation coefficient and for errors caused by fluid properties using a second compensation coefficient. Therefore, based on the multi-parameter fusion feature vectors combined with dual-path compensation logic, targeted and effective compensation can be achieved, ultimately outputting an accurate target flow rate, thus realizing online, real-time, and adaptive high-precision measurement. Example: If the sensor zero point drifts but the gas remains unchanged, the apparent thermal conductivity will be normal, but the equivalent zero-point power will deviate from its reference value, indicating that there is sensor zero-point drift. If the target gas is matched, the calibration curve of the target gas is called to directly compensate for the deviation caused by the sensor zero-point drift. If the gas changes but the sensor is normal, the apparent thermal conductivity will deviate from its reference value, and the equivalent zero-point power will also change and deviate from its reference value. If the target gas is matched and its calibration curve is called, the deviation caused by the change in fluid properties is directly compensated. If the target gas is not matched under both assumptions, the target flow rate is output after targeted compensation based on the first compensation coefficient and the second compensation coefficient.

[0038] In some embodiments, before calculating the eigenvector of the current state based on the current flow rate, current heating power, and a preset thermodynamic model, the online compensation method for the thermal mass flow controller further includes:

[0039] Step 10: When zero flow is detected, collect the heating resistance value and the temperature measuring resistance value.

[0040] Step 11: Calculate the real-time ratio between the heating resistance value and the temperature measuring resistance value under zero flow conditions, and determine the deviation between the real-time ratio and the reference parameter.

[0041] Step 12: If the deviation is greater than the preset threshold, generate the first compensation coefficient based on the real-time ratio relationship and the benchmark parameters.

[0042] Step 13: If the deviation is less than or equal to the preset threshold, the first compensation coefficient is set to 1.

[0043] Zero flow state refers to equipment standby or valve closure. This state can be detected by the mass flow controller. If detected, the heating resistance value and the temperature measuring resistance value are automatically collected, and their real-time ratio is calculated. The reference parameter characterizes the ratio between the heating resistance value and the temperature measuring resistance value under zero-point calibration conditions. Zero-point calibration refers to the factory calibration or recalibration phase of the MFC, under zero flow, standard operating temperature, and pressure conditions. The ratio between the heating platinum resistance and the temperature measuring platinum resistance is measured and stored as the reference parameter under zero-point calibration conditions. A first compensation coefficient is generated based on the real-time ratio and the reference parameter, specifically through two methods: one is dividing the reference parameter by the real-time ratio, and the other is dividing the real-time ratio by the reference parameter. If the deviation is less than or equal to a preset threshold, the sensor is considered normal, and the first compensation coefficient is set to 1.

[0044] In this embodiment, when a zero-flow state is detected, sensor zero-point drift diagnosis is triggered, and a first compensation coefficient is generated accordingly. This trigger-based generation of the first compensation coefficient improves measurement accuracy and avoids requiring equipment shutdown for calibration with standard gas, thus maintaining production continuity. Based on the physical stability characteristics of the sensor resistance ratio under stable operating conditions, a reference resistance ratio is established as a baseline parameter. During equipment operation, the real-time ratio between the heating resistance value and the temperature measuring resistance value is collected in real-time or triggered. By comparing the deviation between the real-time ratio under zero-flow conditions and the baseline parameter, sensor zero-point drift fault diagnosis and source location are performed. Upon confirmation of sensor zero-point drift, an early warning signal is triggered, and dynamic compensation is applied to the flow measurement value to output the target flow rate.

[0045] In some embodiments, after step 12, the online compensation method for the thermal mass flow controller further includes: issuing a warning signal for sensor zero-point drift.

[0046] In some embodiments, after step 12, the online compensation method for the thermal mass flow controller further includes: comparing the changing trends of the heating resistor value and the temperature measuring resistor value relative to their respective reference values, and determining whether the sensor zero-point drift originates from the heating resistor or the temperature measuring resistor.

[0047] In some embodiments, before acquiring the operating parameters of the gas to be measured, the online compensation method for the thermal mass flow controller further includes: constructing a reference parameter, which is used to characterize the ratio between the heating resistance value and the temperature measuring resistance value under zero-point calibration conditions.

[0048] In some embodiments, the gas feature database is pre-constructed by: obtaining the feature vectors and flow rates of several preset gases at standard operating temperatures and pressures; associating and storing the gas identifier of each preset gas with its corresponding calibration curve to obtain the gas feature database, wherein the calibration curve is used to characterize the mapping relationship between the feature vectors and flow rates at standard operating temperatures and pressures.

[0049] The purpose of using standard operating temperature and pressure is to obtain a unified benchmark and ensure calibration consistency. Calibration curves can also be used to characterize the mapping relationship between equivalent zero-point power and flow rate under standard operating temperature and pressure. The gas identifier for each preset gas is associated with and stored along with its corresponding calibration curve and apparent thermal conductivity to obtain a gas characteristic database. Alternatively, the gas identifier, calibration curve, and apparent thermal conductivity for each preset gas can be stored as a scale in the gas characteristic database.

[0050] In some embodiments, the second compensation coefficient is the ratio between the equivalent zero-point power under the current state and the zero-point power reference value under the standard gas.

[0051] The second compensation coefficient can be either the zero-point power reference value under standard gas divided by the equivalent zero-point power under the current state, or the equivalent zero-point power under the current state divided by the zero-point power reference value under standard gas.

[0052] In some embodiments, the current traffic is compensated according to a set of compensation parameters, and a target traffic value is output, specifically including:

[0053] Multiply the current flow rate by the first compensation coefficient to obtain the first compensation flow rate. The first compensation coefficient is the base parameter divided by the real-time ratio under zero flow conditions.

[0054] The target flow rate is obtained and output by multiplying the first compensation flow rate by the second compensation coefficient. The second compensation coefficient is the zero-point power reference value under standard gas divided by the equivalent zero-point power under the current state.

[0055] In some embodiments, the current traffic is compensated according to a set of compensation parameters, and a target traffic value is output, specifically including:

[0056] Divide the current flow rate by the first compensation coefficient to obtain the first compensation flow rate. The first compensation coefficient is the real-time ratio relationship under the zero flow rate state divided by the benchmark parameter.

[0057] Divide the first compensation flow rate by the second compensation coefficient to obtain and output the target flow rate value. The second compensation coefficient is the equivalent zero-point power under the current state divided by the zero-point power reference value under the standard gas.

[0058] In this embodiment, the compensation process can be broken down into two sub-processes. The first sub-process compensates for sensor zero-point drift to obtain the first compensated flow rate. The second sub-process compensates for the impact of fluid property changes on the measurement signal. The final output of the two sub-processes is the target flow rate value. Alternatively, the compensation process can be considered as a whole, where the current flow rate is multiplied by the first and second compensation coefficients to output the target flow rate value. In this embodiment, multi-parameter fusion processing is performed in real time to obtain the feature vector of the gas to be measured. By matching the feature vector with a preset gas feature database, the feature vector matching analysis is performed to identify the fluid type and fluid properties. Based on the identification results, the calibration curve is adaptively switched or signal compensation is performed based on the first and second compensation coefficients, ultimately achieving online, real-time, adaptive, and high-precision measurement.

[0059] Example:

[0060] The following is an example of an online compensation method for a thermal mass flow controller, where a platinum resistance resistor is used. This online compensation method is applied to a microprocessor and includes the following steps:

[0061] Step 1: Construct a database of baseline parameters and gas characteristics.

[0062] Establishing reference parameters: During the factory calibration or recalibration phase of MFC, under zero-point calibration conditions, i.e., zero flow, standard operating temperature and pressure conditions, the ratio of the heating platinum resistance to the temperature measuring platinum resistance is measured and stored as a reference parameter, denoted as Kref= (Rh / Rc)_ref.

[0063] Construct a gas characteristic database: Under standard operating temperature and pressure conditions, measure and store the equivalent zero-point power-flow curves of thermal resistance for various preset gases, as well as the apparent thermal conductivity λ as a scale.

[0064] Step 2: Perform sensor zero-point drift diagnosis.

[0065] like Figure 2 As shown, during the operation of MFC, when a zero flow state is detected (such as valve closure or equipment standby), the microprocessor automatically triggers the diagnostic mode, collects the heating platinum resistance value Rh_now and the temperature measuring platinum resistance value Rc_now in real time under the zero flow state, and calculates the real-time ratio Know= (Rh / Rc)_now.

[0066] Step 3: Implement sensor zero-point drift compensation.

[0067] The real-time ratio (Rh / Rc)_now is compared with the reference parameter (Rh / Rc)_ref stored in the memory. If the absolute deviation between the two exceeds the preset threshold δ, a first compensation coefficient K_comp = (Rh / Rc)_ref / (Rh / Rc)_now is generated, and the real-time collected current flow value q_meas is multiplied by the first compensation coefficient to obtain the first compensation flow q_cal1 = q_meas × K_comp.

[0068] Step 4: Collect fluid characteristic parameters.

[0069] like Figure 3 As shown, during the normal flow measurement process of MFC, the microprocessor collects the current flow rate and the current heating power of the thermal resistor in real time, and calculates the equivalent zero-point power P_zero and apparent thermal conductivity λ_app corresponding to the current state through multi-parameter fusion processing, and constructs the feature vector (P_zero_now, λ_app).

[0070] Step 5: Perform fluid type identification and compensation.

[0071] The feature vector is matched with the gas feature database preset in the microprocessor. If the feature value of the target gas is matched, the calibration curve corresponding to the target gas is automatically called. If the target gas is not matched precisely, but abnormal feature parameters are detected, general compensation is implemented according to the zero-point power change ratio, that is, the general gas property compensation algorithm is started. The target flow value q_cal2=q_cal1×(P_zero_ref / P_zero) is the zero-point power reference value under the standard gas, P_zero is the equivalent zero-point power under the current state, and (P_zero_ref / P_zero) is the second compensation coefficient.

[0072] For example, if a high-confidence match is found for another gas B (rather than calibration gas A), the microprocessor automatically calls the pre-stored calibration curve for gas B, enabling adaptive identification of the fluid type and switching of the measurement model. If an exact match cannot be found, but parameter changes indicate significant changes in gas temperature or pressure, a general gas property compensation algorithm is activated to compensate for the current flow rate in real time.

[0073] Step 6: Output calibration results.

[0074] The microprocessor processes the dual-path calibration logic in parallel, automatically completing data acquisition, calculation, and compensation within the device's workflow. It outputs the calibrated target flow value in real time, ensuring high measurement accuracy without manual intervention. The microprocessor employs a parallel architecture to process multi-parameter fusion in real time, seamlessly integrating automated calibration operations into the normal workflow of the Mass Flow Controller (MFC). The calibrated, precise flow value is output to the control system or display unit with a millisecond-level response speed, ensuring stable measurement accuracy within preset tolerances. The entire process is executed autonomously by the firmware, requiring no operator intervention or external commands. A dynamic closed-loop feedback mechanism continuously optimizes measurement reliability, significantly enhancing the MFC's environmental adaptability and data confidence under long-term operating conditions. Furthermore, the order of steps two through four is not unique; for example, step four may be located between steps two and three.

[0075] This example uses sensors to detect and collect multiple operating parameters during the operation of a mass flow controller (MFC) in real time, constructing a multivariate feature vector of the current "sensor-fluid" state based on the collected parameters. The microprocessor, through hardware interrupt mechanisms or timed task scheduling, employs a multi-parameter fusion processing algorithm to analyze and process the multivariate feature vector, extracting characteristic parameters representing the system's operating state. This enables dynamic acquisition and accurate diagnosis of state information during MFC operation. This method ensures the real-time and reliable extraction of state information, effectively supporting subsequent adaptive flow measurement and zero-point drift compensation. The microprocessor, through hardware interrupt mechanisms or timed task scheduling, triggers a diagnostic subroutine while the device executes the main flow measurement process: when a zero-flow state is detected (such as device standby or valve closure), it automatically collects the real-time ratio data of the heating platinum resistance thermometer and the temperature measuring platinum resistance thermometer; during dynamic flow measurement, it continuously acquires characteristic parameters such as equivalent zero-point power and apparent thermal conductivity. Based on a preset compensation module, the microprocessor instantly calculates the sensor zero-point drift deviation or fluid property changes, generates corresponding compensation coefficients or calls the appropriate calibration curve, and directly embeds the compensation results into the flow calculation link.

[0076] Secondly, this embodiment provides an online compensation device for a thermal mass flow controller, comprising:

[0077] The signal acquisition module is used to acquire the operating parameters of the gas to be measured. The operating parameters include at least the current flow rate and the current heating power.

[0078] The multi-parameter fusion and processing module is connected to the signal acquisition module. It is used to calculate the feature vector of the current state based on the current flow rate, current heating power and preset thermodynamic model. The feature vector includes the equivalent zero-point power and apparent thermal conductivity.

[0079] The compensation module, connected to the multi-parameter fusion and processing module, is used to match the feature vector with a preset gas feature database.

[0080] If the target gas is matched, the corresponding calibration curve for the target gas is called to output the target flow rate value.

[0081] If no target gas is matched, obtain a set of compensation parameters. The set of compensation parameters includes at least a first compensation coefficient for compensating for sensor aging deviation and a second compensation coefficient for compensating for fluid property differences.

[0082] And, it is used to compensate the current traffic based on the compensation parameter set and output the target traffic value.

[0083] In some embodiments, the signal acquisition module is also used to acquire the heating resistance value and the temperature measuring resistance value when a zero flow state is detected.

[0084] The compensation module, connected to the signal acquisition module, is used to calculate the real-time ratio between the heating resistance value and the temperature measuring resistance value under zero flow conditions, and to determine the deviation between the real-time ratio and the reference parameter. If the deviation is greater than a preset threshold, a first compensation coefficient is generated based on the real-time ratio and the reference parameter. If the deviation is less than or equal to the preset threshold, the first compensation coefficient is set to 1.

[0085] In some embodiments, the online compensation device of the thermal mass flow controller further includes a construction module connected to the compensation module. The construction module is used to construct reference parameters, which characterize the ratio between the heating resistance value and the temperature measuring resistance value under zero-point calibration conditions.

[0086] In some embodiments, the gas feature database is pre-constructed by: obtaining the feature vectors and flow rates of several preset gases at standard operating temperatures and pressures; associating and storing the gas identifier of each preset gas with its corresponding calibration curve to obtain the gas feature database, wherein the calibration curve is used to characterize the mapping relationship between the feature vectors and flow rates at standard operating temperatures and pressures.

[0087] In some embodiments, the second compensation coefficient is the ratio between the equivalent zero-point power under the current state and the zero-point power reference value under the standard gas.

[0088] In some embodiments, the compensation module is specifically used to multiply the current flow rate by a first compensation coefficient to obtain a first compensated flow rate, wherein the first compensation coefficient is a reference parameter divided by the real-time ratio under zero flow conditions. It is also used to multiply the first compensated flow rate by a second compensation coefficient to obtain and output a target flow rate value, wherein the second compensation coefficient is the zero-point power reference value under standard gas divided by the equivalent zero-point power under the current state.

[0089] It should be noted that the functions of each module in the online compensation device of the thermal mass flow controller in this embodiment correspond to the contents of the first aspect, and will not be described in detail here.

[0090] Thirdly, such as Figure 4 As shown, this embodiment provides a mass flow controller, including: a heating resistor and a temperature sensing resistor (i.e., Figure 4 The system comprises two platinum resistance thermometers (PTTs) and a solenoid valve, wherein the heating resistor and the temperature sensing resistor are connected to the solenoid valve via a flow channel, and a microprocessor electrically connected to the heating resistor, the temperature sensing resistor, and the solenoid valve. The microprocessor is configured as part of an online compensation method for a thermal mass flow controller. The microprocessor is responsible for signal acquisition, calculation, diagnosis, control, and communication.

[0091] The mass flow rate measurement controller (MFC) aims to address the issue of decreased measurement accuracy caused by sensor aging or changes in fluid properties during long-term operation. It achieves real-time detection and compensation for sensor zero-point drift, as well as intelligent identification and dynamic adjustment to changes in fluid type, thereby significantly improving the reliability and adaptability of MFC in industrial applications. Regarding sensor zero-point drift diagnosis, it is based on the physical stability principle of the ratio (R_h / R_c) of the heating platinum resistance thermometer and the temperature-measuring platinum resistance thermometer under stable operating conditions. Specifically, in zero-flow state, the microprocessor automatically collects the current resistance ratio and performs deviation analysis with the factory-set reference parameters. If the deviation exceeds a preset threshold, it is determined to be a zero-point drift fault, and a first compensation coefficient is generated to correct the flow measurement value. In terms of multi-parameter fusion and processing, after excluding sensor zero-point drift, the focus is on identifying changes in fluid properties. It continuously monitors characteristic vectors such as equivalent zero-point power and apparent thermal conductivity and matches them with a preset gas characteristic database. If the fluid type is successfully identified, it switches to the corresponding calibration curve; if only parameter anomalies are detected, a general compensation algorithm is activated for real-time correction. In terms of implementation, the microprocessor employs triggered parallel processing logic: it automatically performs zero-point drift diagnosis during device standby and synchronously monitors fluid characteristic vectors during normal measurements. The microprocessor autonomously completes data acquisition, calculation, and compensation throughout the entire process without manual intervention, ensuring seamless integration of the calibration process with the MFC workflow. This solution effectively achieves dual protection against sensor aging and fluid changes through step-by-step automated operations—including benchmark parameter construction, online monitoring, deviation determination, fluid identification, and dynamic compensation. Ultimately, it not only reduces maintenance costs but also maintains long-term accuracy in flow measurement, making it particularly suitable for high-precision flow control scenarios under varying operating conditions, providing reliable technical support for the intelligent upgrade of MFC.

[0092] Fourthly, this embodiment provides a mass flow measurement system, including a mass flow controller as described in the third aspect, and a control unit or display unit connected to the mass flow controller. The control unit can control multiple mass flow measurement systems. The display unit can display the measurement results of the mass flow measurement systems in real time.

[0093] The online compensation device, mass flow controller, and mass flow measurement system provided in the second to fourth aspects of the thermal mass flow controller enable online and proactive diagnostics: sensor zero-point drift can be monitored online without downtime for disassembly and reassembly, enabling predictive maintenance and significantly reducing unplanned downtime. They feature high precision and high reliability: through multi-parameter fusion and processing, they can effectively distinguish between different situations such as sensor failure and fluid property changes, avoiding the possibility of misjudgment by a single-parameter system, resulting in more reliable measurement results. They possess adaptive and multi-functional capabilities: they can automatically identify or adapt to different working gases and operating conditions, achieving high-precision measurement of multiple fluids with a single MFC, broadening the application range of the equipment. They offer a low-cost, high-value advantage: primarily based on algorithmic innovation, requiring minimal hardware modifications (only increasing the microprocessor's computing and storage capabilities), yet significantly enhancing the product's added value and market competitiveness.

[0094] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as integrated circuits, such as application-specific integrated circuits (ASICs).

[0095] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. An online compensation method for a thermal mass flow controller, characterized in that, include: Collect the operating parameters of the gas to be tested, including at least the current flow rate and the current heating power; Based on the current flow rate, the current heating power, and the preset thermodynamic model, calculate the feature vector of the current state, which includes the equivalent zero-point power and the apparent thermal conductivity. The feature vector is matched with a preset gas feature database; If a target gas is matched, the calibration curve corresponding to the target gas is invoked to output the target flow rate value; If no target gas is matched, obtain a set of compensation parameters. The set of compensation parameters includes at least a first compensation coefficient for compensating for sensor aging deviation and a second compensation coefficient for compensating for fluid property differences. The current flow is compensated according to the compensation parameter set, and the target flow value is output.

2. The online compensation method for the thermal mass flow controller according to claim 1, characterized in that, Before calculating the feature vector of the current state based on the current flow rate, the current heating power, and the preset thermodynamic model, the process further includes: When zero flow is detected, the heating resistance value and the temperature measuring resistance value are collected; Calculate the real-time ratio between the heating resistance value and the temperature measuring resistance value under zero flow conditions, and determine the deviation between the real-time ratio and the reference parameter; If the deviation is greater than a preset threshold; The first compensation coefficient is generated based on the real-time ratio relationship and the benchmark parameters; If the deviation is less than or equal to a preset threshold; The first compensation coefficient is set to 1.

3. The online compensation method for the thermal mass flow controller according to claim 2, characterized in that, Before collecting the operating parameters of the gas to be measured, the following is also included: The reference parameters are constructed to characterize the ratio between the heating resistance value and the temperature measuring resistance value under zero-point calibration conditions.

4. The online compensation method for the thermal mass flow controller according to claim 1, characterized in that, The gas feature database is pre-built in the following manner: Obtain the characteristic vectors and flow rates of several preset gases under standard operating conditions at temperature and pressure; The gas identifier of each preset gas is associated with and stored with its corresponding calibration curve to obtain the gas feature database. The calibration curve is used to characterize the mapping relationship between the feature vector and the flow rate value under standard operating conditions of temperature and pressure.

5. The online compensation method for the thermal mass flow controller according to claim 1, characterized in that, The second compensation coefficient is the ratio between the equivalent zero-point power under the current state and the reference value of the zero-point power under the standard gas.

6. The online compensation method for the thermal mass flow controller according to claim 2, characterized in that, The current traffic is compensated according to the compensation parameter set, and a target traffic value is output, specifically including: Multiply the current flow rate by the first compensation coefficient to obtain the first compensation flow rate. The first compensation coefficient is the base parameter divided by the real-time ratio under zero flow conditions. The first compensation flow rate is multiplied by the second compensation coefficient to obtain and output the target flow rate value. The second compensation coefficient is the zero-point power reference value under standard gas divided by the equivalent zero-point power under the current state.

7. An online compensation device for a thermal mass flow controller, characterized in that, include: The signal acquisition module is used to acquire the operating parameters of the gas to be tested, including at least the current flow rate and the current heating power. A multi-parameter fusion and processing module, connected to a signal acquisition module, is used to calculate a feature vector under the current state based on the current flow rate, the current heating power, and a preset thermodynamic model. The feature vector includes the equivalent zero-point power and the apparent thermal conductivity. The compensation module, connected to the multi-parameter fusion and processing module, is used to match the feature vector with a preset gas feature database. If a target gas is matched, the calibration curve corresponding to the target gas is invoked to output the target flow rate value; If no target gas is matched, obtain a set of compensation parameters. The set of compensation parameters includes at least a first compensation coefficient for compensating for sensor aging deviation and a second compensation coefficient for compensating for fluid property differences. And, used to compensate the current flow based on the compensation parameter set, and output a target flow value.

8. The online compensation device for the thermal mass flow controller according to claim 7, characterized in that, The signal acquisition module is also used to acquire the heating resistance value and the temperature measuring resistance value when a zero flow state is detected; The compensation module, connected to the signal acquisition module, is used to calculate the real-time ratio between the heating resistance value and the temperature measuring resistance value under zero flow conditions, and to determine the deviation between the real-time ratio and the reference parameter. If the deviation is greater than a preset threshold; The first compensation coefficient is generated based on the real-time ratio relationship and the benchmark parameters; If the deviation is less than or equal to a preset threshold; The first compensation coefficient is set to 1.

9. A mass flow controller, characterized in that, include: The system includes a heating resistor, a temperature sensing resistor, and a solenoid valve. The heating resistor and the temperature sensing resistor are connected to the solenoid valve via a flow channel. And a microprocessor electrically connected to a heating resistor, a temperature sensing resistor, and a solenoid valve, the microprocessor being configured to perform the online compensation method of the thermal mass flow controller according to any one of claims 1-6.

10. A mass flow rate measurement system, characterized in that, It includes the quality flow controller as described in claim 9, and a control unit or display unit connected to the quality flow controller.

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