A method, system and electronic device for calibrating thermal relaxation parameters and correcting dynamic temperature measurement of a stream-following micro-probe

Abstract

The present application relates to the technical field of dynamic temperature measurement and temperature sensor dynamic characteristic correction, and particularly relates to a method and system for calibrating thermal relaxation parameters of a flow-following micro-probe core and correcting dynamic temperature measurement, and electronic equipment, the method comprising: obtaining response data of a target flow-following micro-probe core; establishing a second-order thermal response model; determining thermal relaxation parameters according to the second-order thermal response model through temperature step response data; establishing a thermal relaxation parameter database; determining a fluid medium type corresponding to to-be-corrected temperature measurement data; calling corresponding thermal relaxation parameters from the thermal relaxation parameter database according to to-be-corrected flow-following micro-probe core information and the fluid medium type, and establishing a second-order forward response relationship; establishing a temperature inversion model based on the forward response relationship, correcting original measurement temperature of the to-be-corrected flow-following micro-probe core, and obtaining an external fluid temperature time sequence after correction. The present application can improve the accuracy and reliability of dynamic temperature measurement of the flow-following micro-probe core.

Description

Technical Field

[0001] This invention relates to the field of dynamic temperature measurement and dynamic characteristic correction technology of temperature sensors, and in particular to a method, system and electronic device for thermal relaxation parameter calibration and dynamic temperature measurement correction of a flow-fed microprobe. Background Technology

[0002] A flow-fed micro-probe is a miniature packaged measuring device that can be placed in a gaseous or liquid medium and collect parameters such as temperature and pressure as it moves with the fluid. Compared to fixed sensors, flow-fed micro-probes can acquire dynamic measurement information at different locations along the fluid's path, making them suitable for fluid spaces where it is difficult to deploy fixed sensors.

[0003] Existing flow-fed microprobes typically consist of a semi-enclosed package, a channel, a temperature sensor, a control unit, a data storage unit, and a power supply unit. Because the temperature sensor is located inside the package structure, and there is a packaging material and an internal heat transfer structure between the external fluid and the sensor, the external fluid temperature cannot be instantaneously transferred to the sensor. This results in a response lag and amplitude deviation between the measured temperature and the actual fluid temperature, i.e., thermal relaxation.

[0004] Existing first-order inertial models based on a single time constant are insufficient to accurately describe the rapid initial response and slow stabilization process of the temperature response of a flow-carrying microprobe core. Furthermore, direct temperature differential compensation can easily amplify measurement noise, causing oscillations in the correction results. Simultaneously, the thermal response characteristics of microprobe cores with different specifications and under different heat transfer conditions vary, making it difficult to meet dynamic temperature measurement requirements with fixed parameters.

[0005] Therefore, it is necessary to propose a method for calibrating thermal relaxation parameters and performing dynamic temperature measurement correction on a flow-dependent micro-probe core. This method can achieve accurate calibration of thermal relaxation parameters and stable correction of continuous dynamic temperature measurement data without changing the micro-probe core structure and acquisition method. Summary of the Invention

[0006] The purpose of this invention is to provide a method, system, and electronic device for calibrating thermal relaxation parameters and dynamically correcting temperature measurement of a flow-fed microprobe. This addresses the problem that when a flow-fed microprobe performs dynamic temperature measurement in a gas or liquid medium, the thermal resistance and thermal capacity effects generated by components such as the packaging structure, internal materials, and temperature sensor cause the measured temperature to exhibit response lag, amplitude attenuation, and characteristic change deviation relative to the true temperature of the external fluid.

[0007] To achieve the above objectives, the present invention provides the following solution: A method for calibrating thermal relaxation parameters and dynamically correcting temperature measurement of a flow-dependent microprobe core includes: Acquire the response data of the target flow-fed micro probe core, wherein the response data includes temperature step response data and dynamic temperature measurement data; Based on the unsteady heat transfer process inside the target flow-following micro probe core, the heat transfer process is equivalently divided into an external equivalent heat capacity region and an internal sensing equivalent heat capacity region, and a second-order thermal response model is established based on the dynamic temperature measurement data. Based on the second-order thermal response model, thermal relaxation parameters are determined using the temperature step response data. The thermal relaxation parameters are associated with the target flow-fed micro probe core specifications, batches, and application conditions to establish a thermal relaxation parameter database. Based on the information collected by the micro probe core to be calibrated and / or the preset application conditions, determine the type of fluid medium corresponding to the temperature measurement data to be calibrated; Based on the information of the micro probe core to be calibrated and / or the fluid medium type, the corresponding thermal relaxation parameter is retrieved from the thermal relaxation parameter database to establish a second-order positive response relationship, wherein the target micro probe core and the micro probe core to be calibrated are of the same specification and / or from the same batch. A temperature inversion model is established based on the second-order positive response relationship to correct the original measured temperature of the micro-probe core to be corrected, and the corrected external fluid temperature time series is obtained.

[0008] Optionally, the temperature step response data is obtained by changing at least one of the first stable temperature, the second stable temperature, the temperature step direction, and the temperature step amplitude.

[0009] Optionally, the dynamic response relationship corresponding to the second-order thermal response model is as follows: ; in, The first thermal relaxation time constant is... This is the second thermal relaxation time constant. Sampling time External fluid temperature, Sampling time Temperature sensors measure temperature.

[0010] Optionally, determining the thermal relaxation parameters using the temperature step response data includes: The temperature step response data is normalized, and a joint fitting objective function for the temperature step response data is established using the first thermal relaxation time constant and the second thermal relaxation time constant as parameters to be determined. ; Where M is the number of sets of temperature step response data. The number of sampling points. Let be the measured normalized hysteresis temperature difference at the i-th sampling time in the j-th group of step response temperature data. Let be the theoretical normalized hysteresis temperature difference at the i-th sampling time in the j-th group of step response temperature data; The objective function is solved using a nonlinear least squares method to determine the thermal relaxation parameters, wherein the thermal relaxation parameters include a first thermal relaxation time constant and a second thermal relaxation time constant.

[0011] Optionally, establishing the second-order positive response relationship includes: Based on the thermal relaxation parameters invoked, the original temperature sequence collected by the micro-probe core to be calibrated, and the corresponding sampling time information, the second-order thermal response model is numerically discretized to construct a second-order positive response discretization operator. The second-order positive response relation is constructed based on the second-order positive response discrete operator.

[0012] Optionally, the second-order positive response relationship is: ; in, The external fluid temperature For temperature sensors to measure temperature, It is a second-order positive response discrete operator. Sampling time, The initial thermal state of the flow-carrying microprobe core at the start of the temperature sequence to be calibrated. To measure noise, The first thermal relaxation time constant is... This is the second thermal relaxation time constant.

[0013] Optionally, the temperature inversion model includes: ; in, The corrected external fluid temperature obtained through inversion. For regularization constraint operators, The regularization coefficient is . It is the square of the L2 norm.

[0014] Optionally, obtaining the corrected external fluid temperature time series includes: The temperature inversion model was solved using a numerical optimization method to obtain the corrected external fluid temperature. The corrected external fluid temperature is converted into a corrected external fluid temperature time series based on the sampling time information.

[0015] This invention also provides a system for calibrating and dynamically correcting the thermal relaxation parameters of a flow-fed microprobe core, used to realize the method for calibrating and dynamically correcting the thermal relaxation parameters of a flow-fed microprobe core, including: The data acquisition module is used to acquire the response data of the target flow-following micro probe core, wherein the response data includes temperature step response data and dynamic temperature measurement data; The thermal relaxation parameter calibration module is used to divide the heat transfer process into an external equivalent heat capacity region and an internal sensing equivalent heat capacity region based on the unsteady heat transfer process inside the target flow-following micro probe core, and to establish a second-order thermal response model based on the dynamic temperature measurement data. Based on the second-order thermal response model, the thermal relaxation parameter is determined through the temperature step response data. A thermal relaxation parameter database is used to associate and store the thermal relaxation parameters with the target downstream micro probe core specifications, batches, and application conditions to establish a thermal relaxation parameter database. The fluid medium identification module is used to determine the type of fluid medium corresponding to the temperature measurement data to be calibrated based on the information collected by the micro probe core to be calibrated and / or the preset application conditions. The thermal relaxation parameter acquisition module is used to retrieve the corresponding thermal relaxation parameter from the thermal relaxation parameter database based on the information of the micro probe core to be calibrated and / or the fluid medium type. The second-order response model construction module is used to establish a second-order positive response relationship based on the called thermal relaxation parameters; The inversion objective function construction module is used to establish a temperature inversion model based on the second-order positive response relationship; The regularization inversion module is used to correct the original measured temperature of the micro probe core to be corrected and obtain the corrected external fluid temperature time series. The results output module is used to output the correction results.

[0016] The present invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements a method for calibrating thermal relaxation parameters and dynamically correcting temperature of a flow-fed microprobe.

[0017] The beneficial effects of this invention are as follows: By equating the unsteady heat transfer process inside the flow-carrying micro-probe core to a thermal resistance-thermal capacity network, this invention establishes a second-order thermal response model that includes both fast and slow response processes. This model can more accurately describe the temperature response hysteresis and amplitude decay characteristics caused by the packaging structure. Based on this, through normalization and joint fitting of multiple sets of temperature step experimental data, the first and second thermal relaxation time constants are determined, reducing the number of parameters and improving the stability and reliability of thermal response parameter identification.

[0018] This invention further constructs a regularized temperature inversion correction method based on a second-order positive response model. By combining data consistency constraints with regularization constraints, it achieves dynamic correction of the original measured temperature of the flow-fed micro-core, avoiding noise amplification and non-physical oscillation problems in traditional differential compensation methods. It can effectively recover the temperature change characteristics affected by thermal relaxation, improving the accuracy and reliability of dynamic temperature measurement results. Simultaneously, this invention does not require changes to the existing structure, acquisition method, or data storage method of the flow-fed micro-core. Thermal relaxation correction can be achieved through data post-processing, and corresponding thermal response parameters can be retrieved according to the micro-core specifications, batch, and application conditions, demonstrating good engineering applicability. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments 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.

[0020] Figure 1 This is a schematic diagram illustrating the working principle of the thermal relaxation parameter calibration and dynamic temperature measurement correction system for the micro-probe core in this embodiment of the invention. Figure 2 This is a roadmap of dynamic temperature measurement and calibration technology in an embodiment of the present invention; Figure 3 This is a simplified diagram of the flow-dependent microprobe core structure and temperature transfer path in an embodiment of the present invention; Figure 4 This is a schematic diagram of the equivalent thermal resistance-thermal capacity second-order response model of the two regions of the flow-following micro probe in an embodiment of the present invention; Figure 5 This is a flowchart of the thermal relaxation parameter calibration method for the micro-probe core in the embodiment of the present invention; Figure 6 This is a flowchart of the continuous dynamic temperature measurement regularization correction method for the micro-probe core in this embodiment of the invention; Figure 7 This is a schematic diagram of the functional modules of the dynamic temperature measurement and correction system for the micro-probe core in an embodiment of the present invention; Figure 8 This is a flowchart illustrating the overall process of calibrating thermal relaxation parameters and dynamically correcting temperature of a microprobe core in accordance with the present invention. Figure 9 This is a comparison chart of the step response temperature correction results in the thermal relaxation parameter calibration experiment of this invention. Figure 10 This is a comparison of the temperature difference before and after step response correction in the thermal relaxation parameter calibration experiment of this invention. Figure 11This is a comparison chart of the regularized inversion correction data from the continuous dynamic temperature measurement of the micro-probe core in this embodiment of the invention; Wherein, 1—external fluid convection heat transfer path; 2—capsule shell; 3—homogeneous encapsulation material inside the capsule; 4—channel; 5—internal heat conduction path; 6—temperature sensor chip; 7—internal support structure; 8—power supply module. Detailed Implementation

[0021] 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.

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] like Figure 2 and Figure 8 As shown in this embodiment, a method for calibrating thermal relaxation parameters and dynamically correcting temperature of a flow-dependent microprobe core includes: Acquire the response data of the target micro-probe core under flow, including temperature step response data and dynamic temperature measurement data; Based on the unsteady heat transfer process inside the target flow-following micro probe core, the heat transfer process is equivalently divided into an external equivalent heat capacity region and an internal sensing equivalent heat capacity region, and a second-order thermal response model is established based on dynamic temperature measurement data. Based on the second-order thermal response model, thermal relaxation parameters are determined using temperature step response data. A thermal relaxation parameter database is established by associating and storing thermal relaxation parameters with the target flow-following micro-core specifications, batches, and application conditions. Based on the information collected by the micro probe core to be calibrated and / or the preset application conditions, determine the type of fluid medium corresponding to the temperature measurement data to be calibrated; Based on the information of the micro-probe core to be calibrated and / or the fluid medium type, the corresponding thermal relaxation parameters are retrieved from the thermal relaxation parameter database to establish a second-order positive response relationship. The target micro-probe core and the micro-probe core to be calibrated are of the same specification and / or from the same batch. A temperature inversion model is established based on the second-order positive response relationship. The original measured temperature of the micro-probe core to be calibrated is corrected, and the time series of external fluid temperature after calibration is obtained.

[0024] Specifically, establishing a second-order thermal response model for a representative in-flow micro probe core (i.e., the target in-flow micro probe core) includes: S1-1, Select at least one representative flow-in-flow micro-core from flow-in-flow micro-cores of the same specifications and / or the same batch as the micro-core to be calibrated for thermal relaxation parameter calibration.

[0025] The same specifications include identical or similar external dimensions, packaging materials, temperature sensor type and location, and internal structure. When in-flow micro probes of the same specification and / or from the same batch are manufactured using the same process, the thermal relaxation parameters obtained from the calibration of a representative in-flow micro probe are used as the common thermal relaxation parameters for the corresponding in-flow micro probes.

[0026] In S1-2, after the external fluid temperature changes, heat is transferred to the temperature sensor region via the outer surface and internal structure of the flow-fed micro-probe core. Based on the unsteady-state heat transfer process inside the flow-fed micro-probe core, it is equivalently divided into an external equivalent heat capacity region and an internal sensing equivalent heat capacity region. The external equivalent heat capacity region is used to characterize the region that preferentially responds to changes in the external fluid temperature, while the internal sensing equivalent heat capacity region is used to characterize the equivalent thermal response region formed by the temperature sensor and its surrounding structure.

[0027] S1-3, as Figure 4 As shown, the external fluid temperature is assumed to be... The temperature of the external equivalent heat capacity region is The temperature sensor measures the temperature as follows: The equivalent heat transfer resistance between the external fluid and the external equivalent heat capacity region is defined as... The equivalent thermal resistance between the external equivalent heat capacity region and the internal sensing equivalent heat capacity region is: The equivalent heat capacity of the external equivalent heat capacity region is denoted as The equivalent heat capacity of the internal sensing equivalent heat capacity region is denoted as .

[0028] like Figure 3 As shown, the flow-fed micro probe adopts a semi-enclosed capsule encapsulation structure with partially connected channels 4. External fluid first exchanges heat with the capsule structure through the capsule shell 2 and channels 4, forming an external fluid convection heat transfer path 1. Heat is gradually transferred to the temperature sensor chip 6 via the homogeneous encapsulation material 3 inside the capsule and the internal heat conduction path 5, enabling dynamic response and measurement of external fluid temperature changes. An internal support structure 7 is used to fix the temperature sensor chip 6 and related electronic components and maintain internal structural stability; a power supply module 8 is used to provide operating power to the temperature sensor chip 6 and related electronic units.

[0029] S1-4, the energy balance relationship of the external equivalent heat capacity region is: (1); The energy balance relationship of the internal sensing equivalent heat capacity region is as follows: ; From equation (2), we get: ; Taking the time derivative of equation (3), we get: ; Substituting equations (3) and (4) into equation (1), we obtain the second-order thermal response equation of the flow-dependent micro probe core: ; in: ; ; In the formula, The first-order combined thermal hysteresis coefficient, It is the second-order combined thermal inertia coefficient.

[0030] The denominator of the transfer function corresponding to equation (5) can be expressed as the product of two first-order inertial elements: ; Therefore, we can conclude that: ; ; in, The first thermal relaxation time constant is... Let be the second thermal relaxation time constant, and satisfy: ; The first thermal relaxation time constant is used to characterize the slow thermal response of the in-flow micro probe core, and the second thermal relaxation time constant is used to characterize the fast thermal response of the in-flow micro probe core.

[0031] The dynamic response relationship corresponding to the second-order thermal response model of the flow-driven microprobe core is as follows: ; The first and second thermal relaxation time constants are comprehensive equivalent parameters determined by the structure, packaging material, temperature sensor, fluid thermal properties, and heat transfer conditions of the flow-fed micro probe core, rather than corresponding to a single region or component of the flow-fed micro probe core.

[0032] In this embodiment, the first isothermal water body is used to enable the flow-fed micro-core to reach an initial thermal equilibrium state before the temperature step occurs, and the second isothermal water body is used to provide the target stable temperature after the temperature step occurs. A reference thermometer is set in the second isothermal water body to measure the actual temperature of the second isothermal water body and to serve as the temperature reference for the flow-fed micro-core step response data.

[0033] The reference thermometer is positioned near a predetermined location after the in-flow micro probe enters the second constant-temperature water body. In this embodiment, the in-flow micro probe is located within 2 cm of the reference thermometer's measuring end to reduce the impact of uneven water temperature on the parameter calibration results.

[0034] To characterize the thermal relaxation response of the flow-fed micro probe under different temperature ranges, different step temperature differences, and heating and cooling conditions, this embodiment sets three temperature ranges: low temperature, medium temperature, and high temperature. In each temperature range, five step temperature differences of 2℃, 3℃, 5℃, 8℃, and 10℃ are set, as shown in Tables 1-3, which represent the measurement durations corresponding to heating step conditions, cooling step conditions, and different step temperature differences, respectively.

[0035] Table 1 Table 2 Table 3 The system consists of 30 sets of step temperature measurement conditions, which are formed by three temperature ranges, five types of step temperature differences, and two step directions: heating and cooling.

[0036] Furthermore, the thermal relaxation parameters are determined using temperature step response data, including: The temperature step response data are normalized, and the first and second thermal relaxation time constants are used as parameters to be determined. A joint fitting objective function for the temperature step response data is then established. The objective function is solved using a nonlinear least squares method to determine the thermal relaxation parameters, which include the first thermal relaxation time constant and the second thermal relaxation time constant.

[0037] Specifically, such as Figure 5 As shown, the data processing of multiple sets of step response temperature data, and the calibration of thermal relaxation parameters include: S2-1, Place the representative in-flow micro probe in a calibration fluid medium with a first stable temperature until the representative in-flow micro probe and the calibration fluid medium reach thermal equilibrium.

[0038] Let the start time of the temperature step be The first stable temperature is Then, at the start of the temperature step: ; in, The moment when the temperature step begins The first stable temperature, The moment when the temperature step begins The temperature sensor measures the temperature.

[0039] and: ; S2-2, the representative in-flow micro probe that has reached thermal equilibrium is transferred to a calibration fluid medium with a second stable temperature, so that the representative in-flow micro probe undergoes a temperature step from the first stable temperature to the second stable temperature, and the step response temperature data corresponding to the representative in-flow micro probe is obtained.

[0040] In this embodiment, the transfer time is controlled within 2 seconds, and the in-flow micro probe is placed within 2 cm of the measuring end of the reference thermometer. The moment when the in-flow micro probe enters the second constant-temperature water body is taken as the temperature step start time. During the predetermined measurement period, the in-flow micro probe continuously records the measured temperature of its internal temperature sensor, and the reference thermometer is used to record the actual temperature of the second constant-temperature water body.

[0041] Let the second stable temperature be Define a signed step temperature difference. for: ; when When, the temperature step is a heating step; when At that time, the temperature step is a cooling step.

[0042] Define the relative time after a temperature step. for: ; Define the thermal relaxation hysteresis temperature difference of the in-flow microprobe core relative to the second steady-state temperature. for: ; in, relative time The temperature sensor measures the temperature.

[0043] Substituting equation (17) into equation (12), we obtain the second-order homogeneous equation corresponding to the thermal relaxation hysteresis temperature difference: ; Under the condition that the representative flow-dependent micro-core and the calibration fluid medium are fully in thermal equilibrium before the temperature step begins, the following conditions must be met: ; ; This yields the second-order step response of the thermal relaxation hysteresis temperature difference: ; The step measurement temperature of the flow-driven micro probe core is expressed as: ; By using equations (21) and (22), a correspondence is established between the rate of change and the shape of the step response curve of the microprobe core with the first thermal relaxation time constant and the second thermal relaxation time constant.

[0044] S2-3, change at least one of the first stable temperature, the second stable temperature, the temperature step direction, and the temperature step amplitude to obtain multiple sets of step response temperature data.

[0045] Multiple sets of step response data, including response temperature sequences under different temperature ranges, different step temperature differences, and heating and cooling conditions, are used to jointly constrain thermal relaxation parameters and improve the accuracy and stability of parameter calibration.

[0046] S2-4, let there be a total of Group step response temperature data, number The initial settling temperature of the step response temperature data set is The final stable temperature is The step start time is The number of sampling points is .

[0047] No. Signed step temperature difference of the set of step response temperature data for: ; No. The first group of step response temperature data The relative time corresponding to each sampling moment for: ; in, For the first The first group of step response temperature data Each sampling time.

[0048] No. The first group of step response temperature data Measured normalized hysteresis temperature difference at each sampling time for: ; in, For the first The first group of step response temperature data Each sampling time The corresponding temperature sensor measures the temperature.

[0049] The theoretical normalized hysteresis temperature difference at the i-th sampling time in the corresponding j-th group of step response temperature data. for: ; By normalizing the step response temperature difference, the influence of different step temperature difference amplitudes on the fitting results of the first and second thermal relaxation time constants is reduced, so that the response data corresponding to different step temperature differences and different temperature change directions can share a set of thermal relaxation time constants for joint calibration.

[0050] The joint fitting of two thermal relaxation time constants and the establishment of a thermal relaxation parameter database include: S3-1, using the first and second thermal relaxation time constants as parameters to be determined, establishes a joint fitting objective function for multiple sets of step response temperature data: ; The first thermal relaxation time constant is obtained by solving equation (27) using the nonlinear least squares method. Second thermal relaxation time constant .

[0051] Under initial thermal equilibrium conditions, the amplitudes of the fast and slow responses in the second-order step response are jointly determined by the step response characteristics and the thermal relaxation time constant; therefore, they are not considered as independent parameters to be fitted. By reducing the number of parameters to be optimized, the influence of parameter correlation, time constant exchange, and initial value dependence on the calibration results can be reduced, thereby improving the stability of thermal relaxation parameter identification.

[0052] In this embodiment, water is used as the calibration fluid medium. Thirty sets of step temperature measurement data are used together to calibrate the first and second thermal relaxation time constants. The temperature sampling period of the flow-fed microprobe is 3 seconds. This is shared by all operating conditions. and The parameters to be determined are denoted as . The nonlinear least squares method is used to solve the above joint fitting objective function. During the solution process, the first and second thermal relaxation time constants are set to positive numbers, and a constraint condition is set that the first thermal relaxation time constant is greater than the second thermal relaxation time constant.

[0053] Since each step measurement in this embodiment begins after the micro-probe core and the initial water body have reached full thermal equilibrium, the fast response amplitude and slow response amplitude in the second-order step response are jointly determined by the step temperature difference, the first thermal relaxation time constant and the second thermal relaxation time constant, and the fast response amplitude and slow response amplitude are no longer used as independent parameters to be fitted.

[0054] Therefore, joint fitting only requires inverting the first and second thermal relaxation time constants, reducing the correlation between multiple fitting parameters and avoiding the interchange of the two time constants under different operating conditions.

[0055] The solution yields the first thermal relaxation time constant for this specification and batch of in-flow micro probes under aqueous conditions as follows: ; The second thermal relaxation time constant is: ; Using the actual temperature of the second constant-temperature water body measured by the reference thermometer as a benchmark, the root mean square error of the original flow-following micro-core temperature measurement was 1.8℃, and the average absolute error was 0.92℃.

[0056] After correction using the first and second thermal relaxation time constants obtained from calibration, the root mean square error of the correction temperature is 0.18℃, and the mean absolute error is 0.12℃.

[0057] The first and second thermal relaxation time constants obtained from calibration were used to back-substitute and verify the step response data of each group. The theoretical response curve and the experimental response curve showed consistent trends, which can well describe the rapid response stage and the slow stabilization stage in the temperature response process of the micro-core flowing with the current.

[0058] S3-2, the first and second thermal relaxation time constants obtained from the calibration are used as the thermal relaxation parameters of the corresponding specification or batch of in-flow micro probe cores under specific fluid medium and heat exchange conditions.

[0059] Based on the micro probe core specifications, batch, fluid medium type, and at least one of the following information: temperature, pressure, and flow conditions, thermal relaxation parameters are associated and stored to establish a thermal relaxation parameter database.

[0060] When micro probes of the same specification or batch are applied to the same or similar fluid media and heat exchange conditions, the corresponding first thermal relaxation time constant and second thermal relaxation time constant are used for dynamic temperature measurement correction.

[0061] The first thermal relaxation time constant of 131.41 s and the second thermal relaxation time constant of 33.67 s obtained in this embodiment are associated with the specification information, batch information and water medium information of the flow-fed micro probe core and stored in the thermal relaxation parameter database.

[0062] When the same specification and batch of in-flow micro probe cores are used under the same or similar water medium heat exchange conditions as in this embodiment, the first thermal relaxation time constant and the second thermal relaxation time constant are retrieved from the thermal relaxation parameter database for thermal relaxation correction of subsequent continuous dynamic temperature measurement data.

[0063] The first and second thermal relaxation time constants obtained in this embodiment only correspond to the current specification, current batch of in-flow micro probe cores, and the water medium conditions of this embodiment. When the specification, batch, fluid medium, or heat exchange conditions of the in-flow micro probe core change significantly, the corresponding first and second thermal relaxation time constants should be re-determined according to the calibration method of this embodiment.

[0064] Furthermore, establishing the second-order positive response relationship includes: Based on the thermal relaxation parameters, the original temperature sequence collected by the micro-probe core to be calibrated, and the corresponding sampling time information, the second-order thermal response model is numerically discretized to construct a second-order positive response discretization operator. Based on the second-order positive response discrete operator, a second-order positive response relation is constructed.

[0065] Specifically, such as Figure 6 As shown, establishing the second-order correction relationship for continuous temperature measurement includes: S4-1, the micro probe core to be calibrated is placed in the gas or liquid medium to be measured, so that it moves under the action of the fluid, and temperature data is collected according to the preset sampling period.

[0066] After the in-flow measurement is completed and the sample is recovered, the original temperature sequence and corresponding sampling time information stored in the in-flow micro probe core to be calibrated are read.

[0067] The original temperature time series is represented as follows: ; in, Indicates the first The core temperature measured at each sampling time is T, where T represents transpose.

[0068] Sampling time information is represented as follows: ; in, Indicates the first Each sampling time.

[0069] In this embodiment, the flow-fed micro probe is placed in the fluid and moves along the flow channel under the action of the fluid, continuously collecting and storing temperature data according to a preset sampling cycle.

[0070] After the micro probe completes its measurement and is recovered, the raw temperature data and corresponding sampling time information stored inside are read by the data reading device.

[0071] S4-2, based on the specification information and / or batch information of the micro probe core to be calibrated, and the information of the fluid medium, retrieve the corresponding first thermal relaxation time constant and second thermal relaxation time constant from the thermal relaxation parameter database.

[0072] In this embodiment, the first and second thermal relaxation time constants calibrated under water medium conditions in Example 1 are used: ; ; The parameters are used to demonstrate the process of constructing the second-order positive response operator, establishing the regularized objective function, and solving the external fluid temperature sequence.

[0073] S4-3, by reversing equation (12) to solve for the external fluid temperature, the dynamic temperature measurement correction relationship under continuous time conditions is obtained: ; In the formula, the first term The temperature is measured by the temperature sensor at a sampling time t. The second term is a first-order dynamic hysteresis compensation term, and the third term is a second-order thermal inertia compensation term.

[0074] Equation (30) shows that the external fluid temperature can be corrected based on the original measured temperature of the micro-core and its first and second order time variation characteristics.

[0075] Since directly calculating the first and second derivatives of the original measured temperature can easily amplify measurement noise, this embodiment preferably uses a combination of a second-order positive response model and a regularized inverse problem to solve the external fluid temperature time series.

[0076] S4-4, the first thermal relaxation time constant obtained from calibration. Second thermal relaxation time constant The original temperature sequence and corresponding sampling time information were used to numerically discretize the second-order thermal response model of the flow-fed microprobe core, and a second-order positive response discretization operator was constructed.

[0077] The temperature sensor of the flow-fed micro probe measures the temperature as follows: The external fluid temperature to be determined is The second-order positive response relation is then expressed as: ; In the formula, It is a second-order positive response discrete operator. Sampling time, The initial thermal state of the flow-carrying microprobe core at the start of the temperature sequence to be calibrated. For measuring noise.

[0078] The second-order positive response discrete operator is used to describe the dynamic response process of the external fluid temperature after heat transfer through the flow-following micro-probe core encapsulation structure to form the measured temperature.

[0079] When the temperature sequence starts in thermal equilibrium, the initial thermal state is determined based on the initial temperature. Alternatively, the measured temperature and the external fluid temperature can be converted into a temperature deviation relative to the initial temperature, so that the initial state is zeroed before response calculation.

[0080] For data sampled at equal time intervals, a discrete operator is constructed based on a fixed sampling interval; for data sampled at non-equal time intervals, the data is discretized based on the actual sampling time interval, or a discrete model with equal time intervals is constructed after resampling.

[0081] This embodiment does not limit the specific discrete form of the second-order positive response discrete operator, as long as it can characterize the second-order dynamic response relationship of the external fluid temperature forming the measured temperature through the heat transfer process of the flow-following micro probe core.

[0082] like Figure 6 As shown, a regularized temperature inversion objective function is established, and the output corrected temperature data includes: S5-1, based on external fluid temperature For the variable to be determined, establish a temperature inversion objective function that includes a data consistency term and a regularization constraint term: ; In the formula, The corrected external fluid temperature obtained through inversion. For regularization constraint operators, The regularization coefficient is . It is the square of the L2 norm.

[0083] The data consistency term is used to constrain the deviation between the predicted measured temperature sequence obtained after the external fluid temperature sequence to be determined is processed by the second-order positive response discretization operator and the original measured temperature sequence of the flow-following micro probe.

[0084] The regularization constraint term is used to suppress non-physical oscillations in the external fluid temperature time series obtained by inversion.

[0085] The regularization constraint operator is either a first-order or second-order difference constraint operator. The regularization coefficient is determined based on the data fitting error of the validation data and the inversion temperature stability.

[0086] S5-2, the external fluid temperature after correction is obtained by solving equation (32) using a numerical optimization method. .

[0087] The corrected external fluid temperature is converted into a corrected external fluid temperature time series based on the sampling time information: ; in, For the first External fluid temperature after correction at each sampling time The corrected external fluid temperature time series is stored and output in correspondence with the original temperature time series of the flow-fed micro probe.

[0088] The corrected external fluid temperature time series is used to compensate for the temperature response lag and temperature amplitude decay caused by thermal relaxation of the microprobe core, and to recover the weakened high temperature peak, low temperature valley, temperature gradient and temperature change characteristic location.

[0089] In this embodiment, by solving the regularized temperature inversion objective function, the corrected external fluid temperature time series corresponding to the original temperature time series of the flow-following micro-core is obtained.

[0090] The correction results show that in the section where the original measured temperature increases, the corrected external fluid temperature compensates for a higher temperature relative to the original measured temperature; conversely, in the section where the original measured temperature decreases, the corrected external fluid temperature compensates for a lower temperature relative to the original measured temperature. This correction direction is consistent with the response characteristics of the micro-core thermal relaxation caused by the underestimation of high-temperature peaks and the elevation of low-temperature valleys.

[0091] The sources and application scenarios of continuous dynamic temperature measurement data in this embodiment are only used to illustrate the usage process of the method in this embodiment and do not constitute a limitation on the application scope of this embodiment. This embodiment can also be used for the correction of in-flow dynamic temperature measurement data in other gaseous or liquid media. Figure 9 The figure shows a comparison of the correction results of a representative set of step response experimental data in the thermal relaxation parameter calibration experiment. After correction using the method of this embodiment, the corrected temperature can quickly approach the reference temperature, significantly reducing the response hysteresis of the original measured temperature, thus verifying the effectiveness of the established second-order thermal response model and thermal relaxation parameters. Figure 10 The graph shows a comparison of the errors before and after calibration in the thermal relaxation parameter calibration experiment. Compared with before calibration, the temperature error after calibration is significantly reduced and remains within a small range, indicating that this embodiment can effectively compensate for the dynamic temperature measurement error caused by thermal relaxation. Figure 11 The image shows a comparison of the regularized inversion correction results of continuous dynamic temperature measurement of the micro-probe core. The corrected temperature curve generally maintains the trend of the original measured temperature. In the region of rapid temperature change, it effectively reduces the response lag and amplitude attenuation caused by thermal inertia, and is closer to the real change characteristics of the external fluid temperature, thus verifying the effectiveness of the dynamic temperature measurement correction method in this embodiment.

[0092] like Figure 1 and Figure 7 As shown, this embodiment also provides a thermal relaxation parameter calibration and dynamic temperature measurement correction system for a flow-fed microprobe core, used to realize the thermal relaxation parameter calibration and dynamic temperature measurement correction method for a flow-fed microprobe core, including: The data acquisition module is used to acquire the response data of the target flow-fed micro probe core, including temperature step response data and dynamic temperature measurement data. The thermal relaxation parameter calibration module is used to divide the heat transfer process into an external equivalent heat capacity region and an internal sensing equivalent heat capacity region based on the unsteady heat transfer process inside the target flow-following micro probe core. It also establishes a second-order thermal response model based on dynamic temperature measurement data and determines the thermal relaxation parameters based on the temperature step response data. The thermal relaxation parameter database is used to associate and store thermal relaxation parameters with the target flow-fed micro probe core specifications, batches, and application conditions to establish the thermal relaxation parameter database. The fluid medium identification module is used to determine the type of fluid medium corresponding to the temperature measurement data to be calibrated based on the information collected by the micro probe core to be calibrated and / or the preset application conditions. The thermal relaxation parameter acquisition module is used to retrieve the corresponding thermal relaxation parameters from the thermal relaxation parameter database based on the information of the micro probe core to be calibrated and / or the fluid medium type. The thermal relaxation parameter database is used to store the correspondence between thermal relaxation parameters and at least one of the following information: in-flow micro probe specifications; in-flow micro probe batch; fluid medium type; fluid temperature range; fluid pressure range; and fluid flow conditions.

[0093] The second-order response model construction module is used to establish second-order positive response relationships; The inversion objective function construction module is used to establish a temperature inversion model based on the second-order positive response relationship; The regularization inversion module is used to correct the original measured temperature of the micro-probe core to be corrected and obtain the corrected external fluid temperature time series. The results output module is used to output the correction results.

[0094] The system also includes a recycling device for collecting and storing used micro probes for subsequent data reading.

[0095] This embodiment also provides an electronic device, including a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it realizes a method for calibrating the thermal relaxation parameters and dynamically measuring and correcting the temperature of the microprobe core.

[0096] Through the above embodiments, the present invention does not require changes to the existing mechanical structure, temperature acquisition method, and data storage method of the in-flow micro probe. Dynamic temperature correction can be achieved simply by calibrating the thermal relaxation parameters and post-processing the data, thereby improving the accuracy and engineering applicability of dynamic temperature measurement of the in-flow micro probe.

[0097] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for calibrating thermal relaxation parameters and dynamically correcting temperature of a flow-fed microprobe core, characterized in that, include: Acquire the response data of the target flow-fed micro probe core, wherein the response data includes temperature step response data and dynamic temperature measurement data; Based on the unsteady heat transfer process inside the target flow-following micro probe core, the heat transfer process is equivalently divided into an external equivalent heat capacity region and an internal sensing equivalent heat capacity region, and a second-order thermal response model is established based on the dynamic temperature measurement data. Based on the second-order thermal response model, thermal relaxation parameters are determined using the temperature step response data. The thermal relaxation parameters are associated with the target flow-fed micro probe core specifications, batches, and application conditions to establish a thermal relaxation parameter database. Based on the information collected by the micro probe core to be calibrated and / or the preset application conditions, determine the type of fluid medium corresponding to the temperature measurement data to be calibrated; Based on the information of the micro probe core to be calibrated and / or the fluid medium type, the corresponding thermal relaxation parameter is retrieved from the thermal relaxation parameter database to establish a second-order positive response relationship, wherein the target micro probe core and the micro probe core to be calibrated are of the same specification and / or from the same batch. A temperature inversion model is established based on the second-order positive response relationship to correct the original measured temperature of the micro-probe core to be corrected, and the corrected external fluid temperature time series is obtained.

2. The method for calibrating thermal relaxation parameters and dynamically correcting temperature of a flow-fed microprobe core according to claim 1, characterized in that, The temperature step response data is obtained by changing at least one of the first stable temperature, the second stable temperature, the temperature step direction, and the temperature step amplitude.

3. The method for calibrating thermal relaxation parameters and dynamically correcting temperature of a flow-fed microprobe core according to claim 1, characterized in that, The dynamic response relationship corresponding to the second-order thermal response model is as follows: ； in, The first thermal relaxation time constant is... This is the second thermal relaxation time constant. Sampling time External fluid temperature, Sampling time Temperature sensors measure temperature.

4. The method for calibrating thermal relaxation parameters and dynamically correcting temperature of a flow-fed microprobe core according to claim 3, characterized in that, Determining thermal relaxation parameters using the temperature step response data includes: The temperature step response data is normalized, and a joint fitting objective function for the temperature step response data is established using the first thermal relaxation time constant and the second thermal relaxation time constant as parameters to be determined. ； Where M is the number of sets of temperature step response data. The number of sampling points. Let be the measured normalized hysteresis temperature difference at the i-th sampling time in the j-th group of step response temperature data. Let be the theoretical normalized hysteresis temperature difference at the i-th sampling time in the j-th group of step response temperature data; The objective function is solved using a nonlinear least squares method to determine the thermal relaxation parameters, wherein the thermal relaxation parameters include a first thermal relaxation time constant and a second thermal relaxation time constant.

5. The method for calibrating thermal relaxation parameters and dynamically correcting temperature of a flow-fed microprobe core according to claim 1, characterized in that, Establishing the second-order positive response relationship includes: Based on the thermal relaxation parameters invoked, the original temperature sequence collected by the micro-probe core to be calibrated, and the corresponding sampling time information, the second-order thermal response model is numerically discretized to construct a second-order positive response discretization operator. The second-order positive response relation is constructed based on the second-order positive response discrete operator.

6. The method for calibrating thermal relaxation parameters and dynamically correcting temperature of a flow-fed microprobe core according to claim 1, characterized in that, The second-order positive response relationship is as follows: ； in, The external fluid temperature For temperature sensors to measure temperature, It is a second-order positive response discrete operator. Sampling time, The initial thermal state of the flow-carrying microprobe core at the start of the temperature sequence to be calibrated. To measure noise, The first thermal relaxation time constant is... This is the second thermal relaxation time constant.

7. The method for calibrating thermal relaxation parameters and dynamically correcting temperature of a flow-fed microprobe core according to claim 6, characterized in that, The temperature inversion model includes: ； in, The corrected external fluid temperature obtained through inversion. For regularization constraint operators, The regularization coefficient is . It is the square of the L2 norm.

8. The method for calibrating thermal relaxation parameters and dynamically correcting temperature of a flow-fed microprobe core according to claim 1, characterized in that, The corrected external fluid temperature time series was obtained as follows: The temperature inversion model was solved using a numerical optimization method to obtain the corrected external fluid temperature. The corrected external fluid temperature is converted into a corrected external fluid temperature time series based on the sampling time information.

9. A system for calibrating and dynamically correcting the thermal relaxation parameters of a flow-fed microprobe core, used to implement the method for calibrating and dynamically correcting the thermal relaxation parameters of a flow-fed microprobe core as described in any one of claims 1-8, characterized in that, include: The data acquisition module is used to acquire the response data of the target flow-following micro probe core, wherein the response data includes temperature step response data and dynamic temperature measurement data; The thermal relaxation parameter calibration module is used to divide the heat transfer process into an external equivalent heat capacity region and an internal sensing equivalent heat capacity region based on the unsteady heat transfer process inside the target flow-following micro probe core, and to establish a second-order thermal response model based on the dynamic temperature measurement data. Based on the second-order thermal response model, the thermal relaxation parameter is determined through the temperature step response data. A thermal relaxation parameter database is used to associate and store the thermal relaxation parameters with the target downstream micro probe core specifications, batches, and application conditions to establish a thermal relaxation parameter database. The fluid medium identification module is used to determine the type of fluid medium corresponding to the temperature measurement data to be calibrated based on the information collected by the micro probe core to be calibrated and / or the preset application conditions. The thermal relaxation parameter acquisition module is used to retrieve the corresponding thermal relaxation parameter from the thermal relaxation parameter database based on the information of the micro probe core to be calibrated and / or the fluid medium type. The second-order response model construction module is used to establish a second-order positive response relationship based on the called thermal relaxation parameters; The inversion objective function construction module is used to establish a temperature inversion model based on the second-order positive response relationship; The regularization inversion module is used to correct the original measured temperature of the micro probe core to be corrected and obtain the corrected external fluid temperature time series. The results output module is used to output the correction results.

10. An electronic device comprising a processor and a memory, wherein the memory stores a computer program, characterized in that, When the computer program is executed by the processor, it implements the thermal relaxation parameter calibration and dynamic temperature measurement correction method for the flow-fed micro probe as described in any one of claims 1-8.

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