A dynamic tracking performance calibration system and method for an adiabatic accelerating calorimeter

Abstract

The present application relates to the technical field of metrological test, in particular to a kind of dynamic tracking performance calibration system and method of adiabatic accelerating calorimeter, comprising: the surface temperature variation of the sample to be detected is simulated using programmable multifunctional signal source generator, and a kind of temperature rise curve type of the sample to be detected is set in advance, then when the temperature rise rate of adiabatic calorimeter is detected to reach the set threshold, it automatically enters adiabatic tracking mode;Real-time acquisition of sample temperature data and cavity temperature data, respectively generate target temperature curve and actual temperature curve, and the temperature rise rate of two curves at each time is calculated using filtering and central difference method;Based on target temperature curve and actual temperature curve, calculate tracking temperature difference and temperature rise rate error, and use tracking temperature difference and temperature rise rate error to analyze the dynamic tracking performance of adiabatic calorimeter.The present application is suitable for calibrating and evaluating the temperature tracking accuracy and temperature rise rate response capability of adiabatic accelerating calorimeter in adiabatic tracking mode.

Description

Technical Field

[0001] This invention relates to the field of metrology and testing technology, specifically to a dynamic tracking performance calibration system and method for an adiabatic accelerated calorimeter. Background Technology

[0002] An adiabatic accelerated calorimeter is an important instrument used to study the thermal properties of substances during thermal decomposition. Its main components include a heating furnace, a temperature control system, a measurement system, a sample cell, and a safety protection system. During testing, the sample is placed inside the sample cell. Electric heating blocks are evenly distributed around the outer cavity. These blocks promptly replenish the heat lost due to the temperature difference between the sample and its surrounding environment, maintaining a uniform and balanced temperature within the adiabatic furnace, thus ensuring an ideal adiabatic testing environment within the sample cell.

[0003] Regarding the calibration of adiabatic accelerated calorimeters, the only publicly available information online is the draft "Calibration Specification for Accelerated Adiabatic Calorimeters" released in November 2022 by the National Technical Committee on Metrology of Nanomaterials and New Materials (MTC29). This draft primarily addresses calibration requirements for temperature and pressure indication errors and adiabatic temperature consistency in adiabatic calorimeters. However, the following key issues exist: (1) Limitations of the quasi-static assumption: The calibration process regards the working mode of the adiabatic calorimeter as a "quasi-static" mode. The temperature indication error at a specific temperature point and the adiabatic temperature consistency error at a specific temperature rise rate are actually based on the calibration mode of the traditional environmental chamber. (2) Inability to reflect core performance: The core operating stage of the adiabatic calorimeter is the adiabatic tracking stage, not the step heating stage of the HWS. During the adiabatic tracking stage, due to the exponential growth of the sample's self-exothermic reaction, the equipment needs to have rapid and dynamic temperature tracking capabilities. This rapid dynamic heating rate adjustment process poses a high challenge to the equipment's algorithm, sensor accuracy, and heater control accuracy. Too high a temperature rate will lead to overheating of the sample cell; too low a temperature rate will lead to underheating. (3) Incomplete evaluation results: Quasi-static calibration evaluation results are difficult to reflect the accuracy and performance of the adiabatic tracking of the adiabatic thermometer, which is the core stage, and cannot fully evaluate the dynamic tracking capability of the equipment.

[0004] Therefore, there is an urgent need for a system and method specifically designed for calibrating and evaluating the dynamic performance under adiabatic tracking mode, in order to make up for the deficiencies of existing calibration specifications and comprehensively evaluate the core performance indicators of adiabatic thermal analyzers. Summary of the Invention

[0005] The purpose of this invention is to solve the technical problem that existing adiabatic calorimeter calibration techniques cannot evaluate dynamic performance under adiabatic tracking mode, and to provide a dynamic tracking performance calibration system and method for adiabatic accelerated calorimeters, which can directly evaluate the dynamic tracking capability of adiabatic calorimeters to target temperatures under adiabatic tracking mode.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: A dynamic tracking performance calibration system for an adiabatic accelerated calorimeter includes: A programmable multi-functional signal generator is used to simulate the surface temperature change curve of a sample during a self-exothermic process. A high-precision temperature data logger is used to record the actual temperature changes of the cavity of the adiabatic calorimeter. A high-precision temperature sensor is positioned in the center of the sample chamber to collect temperature data. The host computer and software system are used to synchronously control various devices and complete data acquisition and analysis. The high-precision temperature sensor is positioned at the center of the sample chamber, and its wires are laid with an armored shielding structure. The output of the programmable multi-functional signal generator is connected to the sample surface temperature acquisition channel of the adiabatic calorimeter via a sensor extension line. The probe of the high-precision temperature data logger is connected to the temperature measurement point inside the sample chamber. Communication lines between the devices are connected via a host computer.

[0007] Preferably, the programmable multi-functional signal generator is a Beamex MC6-T model, which supports the IEEE 1588 Precision Clock Protocol (PTP) and has a synchronization accuracy better than 500 ns.

[0008] Preferably, the high-precision temperature data logger is a Yokogawa GM10 model, which maintains time synchronization with the signal source via the PTP protocol.

[0009] Preferably, the high-precision temperature sensor uses a 0.05mm wire diameter S-type ultrafine thermocouple (response time 15-20ms).

[0010] Preferably, the host computer synchronizes the signal source and the recorder via the IEEE 1588 Precision Clock Protocol (PTP), with the time deviation controlled within ≤1ms.

[0011] Preferably, a method for calibrating the dynamic tracking performance of an adiabatic accelerated calorimeter includes the following steps: S100. Use a programmable multi-functional signal generator to simulate the surface temperature change of the sample during the self-exothermic process, and edit an adiabatic tracking temperature rise curve that needs to be calibrated in the programmable multi-functional signal generator; start the adiabatic calorimeter and set the temperature rise rate threshold. When the adiabatic calorimeter detects that the temperature rise rate has reached the set threshold, it automatically enters the adiabatic tracking mode. During the adiabatic tracking mode, the furnace temperature is adjusted in real time according to changes in the sample temperature to ensure that the sample is always in a dynamically thermally sealed state. Since the self-heating of the sample generally increases exponentially, in order to ensure the adiabatic state, the adiabatic calorimeter needs to rapidly and dynamically adjust the heating rate to ensure that the temperature of the chamber is consistent with the surface temperature of the sample.

[0012] S200: In the adiabatic tracking mode, the temperature data generated by the signal source is collected in real time, and the temperature data of the adiabatic calorimeter cavity is collected in real time using a high-precision temperature sensor. The target temperature curve and the actual temperature curve are generated respectively. Then, the temperature rise rate of the two curves at each moment is calculated using Savitzky-Golay filtering and the central difference method. S300 calculates the tracking temperature difference and temperature rise rate error based on the target temperature curve and the actual temperature curve, and uses the tracking temperature difference and temperature rise rate error to analyze the dynamic tracking performance of the adiabatic calorimeter.

[0013] Preferably, the temperature rise curve type preset in step S100 specifically includes: The exponential temperature rise curve simulates the exponential self-exothermic behavior of chemical substances; Step-shaped temperature rise curve to simulate step response characteristics; Piecewise linear temperature rise curves simulate the linear heating process at different stages; A composite temperature rise curve is used to simulate complex actual working conditions.

[0014] Preferably, step S200 specifically includes: S201. Real-time acquisition of temperature data of the sample to be tested and temperature data of the cavity. Simultaneously start the programmable multi-functional signal generator and high-precision temperature data recorder through the host computer. Use the IEEE 1588 PTP protocol to ensure that the time synchronization accuracy between the signal source and the recorder is ≤1ms. Start recording data and generate the target temperature curve and the actual temperature curve respectively. Anomaly detection is performed on the raw temperature data in the two temperature curves. Anomalies are detected if any of the following conditions are met: the temperature value exceeds the reasonable range, the temperature change slope suddenly exceeds the threshold, or the data point is missing or obviously erroneous. S202. A sliding window Savitzky-Golay filter is used to smooth the data. For data points within a window of length 2M+1, an nth-order polynomial is used for fitting. The polynomial coefficients are solved by the least squares method, and the value of the fitted polynomial at the center point is used as the filter output. S203, for the filtered output of the first... There are 10 data points, of which The temperature rise rate is calculated using the formula: ; in Indicates time The rate of temperature rise, , They represent , Temperature value at the time of sampling.

[0015] Preferably, the specific process of step S202 includes: Let the data in the window be The fitted polynomial is Then the coefficient vector is solved by the least squares method. This makes the sum of squared errors Minimum; at this point, the center point value after filtering is... .

[0016] Preferably, step S300 specifically includes: S301. Calculate the tracking temperature difference. The tracking temperature difference directly reflects the accuracy of the adiabatic calorimeter in following the target temperature in the adiabatic tracking mode. It is a comprehensive indicator for evaluating the PID control performance, heater response speed and sensor accuracy of the equipment. S302. Calculate the temperature rise rate error, which is used to measure the dynamic response capability of the adiabatic calorimeter to a rapid heating process.

[0017] Preferably, the specific process of step S301 includes: Obtain the temperature values ​​at each moment in the target temperature curve and the actual temperature curve, then calculate the difference between the target temperature and the actual temperature at each same moment, and take the maximum value of all differences as the tracking temperature difference: ; in, This represents the temperature value in the target temperature curve. This represents the temperature value in the actual temperature curve; where the tracking temperature difference is... The static tracking accuracy of the adiabatic calorimeter is negatively correlated with the tracking temperature difference. The smaller the value, the higher the static tracking accuracy of the adiabatic calorimeter; Preferably, the specific process of step S302 includes: Obtain the temperature rise rate at each moment in the target temperature curve and the actual temperature curve, then calculate the maximum absolute value of the difference between the target temperature rise rate and the actual temperature rise rate at the same moment: ; in, This represents the theoretical rate of temperature rise of the target temperature curve. The calculated temperature rise rate represents the actual temperature curve. Next, calculate the relative temperature rise rate error: ; When the relative temperature rise rate error When the error is less than the error threshold, it indicates that the adiabatic calorimeter has good dynamic response capability to the rapid heating process and meets the actual calibration evaluation requirements.

[0018] Compared with the prior art, the beneficial effects achieved by the present invention are: This invention proposes for the first time an evaluation method for dynamic performance under adiabatic tracking mode, filling a technical gap in calibration standards. By simulating the target temperature signal input, this invention directly evaluates the dynamic tracking capability of an adiabatic calorimeter to the target temperature, without relying on physical sample load. It offers advantages such as safety and reliability without the use of hazardous chemicals, repeatable test results unaffected by sample consistency, and high testing efficiency for rapid evaluation. This invention innovatively employs a data processing method combining Savitzky-Golay filtering and the central difference method, effectively suppressing noise while preserving the high-order characteristics of the signal, ensuring the accuracy of temperature rise rate error assessment. This invention supports the simulation of various temperature rise curves, including exponential, stepwise, and piecewise linear curves, enabling a comprehensive evaluation of the adiabatic calorimeter's tracking capability for different types of self-exothermic behavior. The IEEE 1588 Precision Clock Protocol (PTP) is used to achieve time synchronization between the signal source and the recorder, with a time deviation controlled within ≤1ms, ensuring the synchronization accuracy of temperature curve and temperature rise rate calculations. Attached Figure Description

[0019] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a framework diagram of a dynamic tracking performance calibration system for an adiabatic accelerated calorimeter according to the present invention; in the diagram, 1-adiabatic calorimeter body, 2-sample chamber, 3-temperature sensor, 4-high-precision temperature data logger, 5-programmable multi-functional signal generator, 6-sensor extension line, 7-host computer and software system.

[0020] Figure 2 This is a flowchart of a dynamic tracking performance calibration method for an adiabatic accelerated calorimeter according to the present invention.

[0021] Figure 3 This is a data processing flowchart of a dynamic tracking performance calibration method for an adiabatic accelerated calorimeter according to the present invention.

[0022] Figure 4 This is a schematic diagram of the error calculation for tracking temperature difference and temperature rise rate in this invention; in the diagram, the red curve represents the temperature difference change curve. Detailed Implementation

[0023] 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.

[0024] Please see Figures 1-4 The present invention provides the following technical solution: Example 1: As Figure 1 As shown, a dynamic tracking performance calibration system for an adiabatic accelerated calorimeter includes: Programmable multi-functional signal generator 5 is used to simulate the surface temperature change curve of a sample during the self-exothermic process; High-precision temperature data logger 4 is used to record the actual temperature changes of the cavity of the adiabatic calorimeter. A high-precision temperature sensor 3 is positioned at the center of the sample chamber to collect temperature data. The host computer and software system 7 is used to synchronously control various devices and complete data acquisition and analysis.

[0025] The high-precision temperature sensor is positioned at the center of the sample chamber, and its wires are laid with an armored shielding structure. The output of the programmable multi-functional signal generator is connected to the sample surface temperature acquisition channel of the adiabatic calorimeter via a sensor extension line. The probe of the high-precision temperature data logger is connected to the temperature measurement point inside the sample chamber. Communication lines between the devices are connected via a host computer.

[0026] Preferably, the programmable multi-functional signal generator is a Beamex MC6-T type programmable multi-functional signal generator, which has the following technical features: Supports IEEE 1588 Precision Clock Protocol (PTP) with synchronization accuracy better than 500 ns; The output signal has high accuracy, with an output temperature signal error of <0.1℃; Supports multiple signal types: DC voltage, DC current, thermocouple simulation, etc. Programmable control, supporting the editing of custom temperature profiles; Communication interfaces: Ethernet, USB, RS-232, etc.; The function of the programmable multi-functional signal generator is to connect the thermocouples that acquire the sample surface temperature in the adiabatic calorimeter to the programmable multi-functional signal generator. By editing the temperature curve in the multi-functional signal generator, the surface temperature change of the sample during the self-exothermic process can be simulated. Ideally, the calorimeter should use this temperature curve as the target tracking curve and adjust the temperature inside the chamber in real time to be completely consistent with this temperature curve.

[0027] Preferably, the high-precision temperature data logger is a Yokogawa GM10 model, which has the following technical features: Supports PTP protocol to maintain time synchronization with the signal source; Multi-channel temperature acquisition, supporting various sensors such as thermocouples and RTDs; The sampling rate can reach 1ms; Large storage capacity, supporting long-term continuous recording; Communication interfaces: Ethernet, USB, etc.; The function of a high-precision temperature data logger is to record the actual temperature change curve of the cavity in the tracking mode of the adiabatic calorimeter.

[0028] Preferably, the high-precision temperature sensor is arranged at the center of the sample chamber. In this embodiment, a 0.05mm diameter S-type ultrafine thermocouple is selected. As shown in Table 1, the S-type ultrafine thermocouple has the following technical characteristics: Table 1 Technical characteristics of S-type ultrafine thermocouples Preferably, the host computer and software system synchronize the signal source and recorder via the IEEE 1588 Precision Clock Protocol (PTP), with the time deviation controlled within ≤1ms. The host computer software implements the following functions: test curve editing and distribution; equipment synchronous start control; real-time data acquisition and display; data post-processing and analysis; and report generation and output.

[0029] Example 2: Figure 2 As shown, a method for calibrating the dynamic tracking performance of an adiabatic accelerated calorimeter includes the following steps: S100. Use a programmable multi-functional signal generator to simulate the surface temperature change of the sample during the self-exothermic process, and edit an adiabatic tracking temperature rise curve that needs to be calibrated in the programmable multi-functional signal generator; start the adiabatic calorimeter and set the temperature rise rate threshold. When the adiabatic calorimeter detects that the temperature rise rate has reached the set threshold, it automatically enters the adiabatic tracking mode. During the adiabatic tracking mode, the furnace temperature is adjusted in real time according to changes in the sample temperature to ensure that the sample is always in a dynamically thermally sealed state. Since the self-heating of the sample generally increases exponentially, in order to ensure the adiabatic state, the adiabatic calorimeter needs to rapidly and dynamically adjust the heating rate to ensure that the temperature of the chamber is consistent with the surface temperature of the sample.

[0030] S200: In the adiabatic tracking mode, the temperature data generated by the signal source is collected in real time, and the temperature data of the adiabatic calorimeter cavity is collected in real time using a high-precision temperature sensor. The target temperature curve and the actual temperature curve are generated respectively. Then, the temperature rise rate of the two curves at each moment is calculated using Savitzky-Golay filtering and the central difference method. S300 calculates the tracking temperature difference and temperature rise rate error based on the target temperature curve and the actual temperature curve, and uses the tracking temperature difference and temperature rise rate error to analyze the dynamic tracking performance of the adiabatic calorimeter.

[0031] Preferably, step S100 includes: In the host computer software, launch the configuration interface of the programmable multi-functional signal generator and edit the adiabatic tracking temperature rise curve that needs to be calibrated. To better reflect the curve characteristics of actual self-exothermic processes, the programmable signal generator supports generating the following types of temperature rise curves: (1) Exponential temperature rise curve: Simulating the exponential self-exothermic behavior of chemical substances such as lithium batteries, the formula is expressed as: ; in: Let A be the initial temperature, A be the temperature rise rate, and k be the exponential coefficient.

[0032] (2) Step-type temperature rise curve: simulates the step response characteristics and is used to evaluate the step response capability of the system. (3) Piecewise linear temperature rise curve: Simulates the linear temperature rise process at different stages. (4) Composite temperature rise curve: simulates complex actual working conditions and can include a combination of multiple stages.

[0033] In this embodiment, an exponential temperature rise curve is selected as the test curve, and the parameters are set as follows: initial temperature T0 = 50℃, temperature rise amplitude A = 100℃, and exponential coefficient k = 0.05.

[0034] Preferably, the adiabatic calorimeter is started and enters normal working state; the temperature rise rate threshold is set (in this embodiment, it is set to 0.02℃ / min); other experimental parameters are set, such as starting temperature and ending temperature; the experiment is started, and the adiabatic calorimeter will automatically enter adiabatic tracking mode after reaching the threshold.

[0035] Preferably, step S200 specifically includes: S201: The host computer simultaneously sends start commands to the programmable multi-functional signal generator and the high-precision temperature data logger; the IEEE 1588 PTP protocol is used to ensure that the time synchronization accuracy between the signal generator and the logger is ≤1ms; the target temperature curve and the actual temperature curve are recorded simultaneously; data acquisition continues until the test is completed.

[0036] After the test is completed, data is exported from the programmable multi-function signal generator and the high-precision temperature data logger respectively; the data format is uniformly CSV, which includes information such as timestamp and temperature value; the two sets of data are imported into the host computer software for further processing.

[0037] S202. Real-time acquisition of temperature data of the sample to be tested, and real-time acquisition of cavity temperature data using a high-precision temperature data recorder, generating target temperature curves and actual temperature curves respectively; outlier detection is performed on the original temperature data in the target temperature curve and actual temperature curve, then if: If the temperature value exceeds the upper limit threshold (e.g., 500℃) or the lower limit threshold (e.g., 0℃), it is marked as an outlier. If the temperature difference between adjacent temperatures exceeds the abrupt change threshold (e.g., 10℃), it is marked as an outlier. Interpolate or remove outliers.

[0038] S203. A sliding window Savitzky-Golay filter is used to smooth the data. The window center alignment characteristic of the Savitzky-Golay filter matches the symmetrical sampling strategy of the center difference method, which can minimize phase error. At the same time, the filtering process is equivalent to a weighted average of the signal, which can effectively suppress the influence of high-frequency noise on derivative calculation. The specific steps are as follows: (1) Savitzky-Golay Filter Principle: The Savitzky-Golay filter is a digital filter based on local polynomial regression. For data points within a window of length 2M+1, an nth-order polynomial is used for fitting. The polynomial coefficients are solved using the least squares method, and the value of the fitted polynomial at the center point is used as the filter output. Let the data within the window be... The fitted polynomial is Solving for the coefficient vector using the least squares method This makes the sum of squared errors Minimum. The filtered center point value is .

[0039] (2) Selection of polynomial order and window width: The polynomial order determines the smoothing characteristics of the filter. The higher the order, the stronger the ability to retain signal details, but the lower the noise suppression capability. The window width determines the frequency response of the filter. The wider the window, the stronger the low-pass filtering characteristics, but it will introduce a larger phase lag. This invention uses a second-order polynomial and a 7-point window (corresponding to 7 seconds of data at a 1Hz sampling rate), which can effectively suppress high-frequency noise while retaining the inflection point characteristics of the exponential temperature rise curve.

[0040] S204, for the filtered output of the first... There are 10 data points, of which The temperature rise rate is calculated using the formula: ; in Indicates time The rate of temperature rise, , They represent , Temperature value at the time of sampling.

[0041] Traditional methods that filter first and then differentiate introduce additional phase errors. This invention combines Savitzky-Golay filtering with the central difference method, directly applying the central difference formula to the filtered temperature sequence to calculate the temperature rise rate. Because Savitzky-Golay filtering has a symmetrical window structure, it is equivalent to weighted smoothing of the signal, without introducing significant phase shifts; while the central difference method itself has symmetrical sampling characteristics. The combination of these two methods minimizes phase errors and ensures the accuracy of the temperature rise rate calculation.

[0042] Preferably, step S300 specifically includes: S301. Calculate the tracking temperature difference. The tracking temperature difference directly reflects the accuracy of the adiabatic calorimeter in following the target temperature in adiabatic tracking mode. It is a comprehensive indicator for evaluating the PID control performance, heater response speed, and sensor accuracy of the equipment. Obtain the temperature values ​​at each moment in the target temperature curve and the actual temperature curve, then calculate the difference between the target temperature and the actual temperature at each same moment, and take the maximum value of all differences as the tracking temperature difference: ; in, This represents the temperature value in the target temperature curve. This represents the temperature value in the actual temperature curve; where the tracking temperature difference is... The static tracking accuracy of the adiabatic calorimeter is negatively correlated with the tracking temperature difference. The smaller the value, the higher the static tracking accuracy of the adiabatic calorimeter; S302. Calculate the temperature rise rate error, used to measure the dynamic response capability of the adiabatic calorimeter to a rapid heating process: Obtain the temperature rise rate at each moment in the target temperature curve and the actual temperature curve, then calculate the maximum absolute value of the difference between the target temperature rise rate and the actual temperature rise rate at the same moment: ; in, This represents the theoretical rate of temperature rise of the target temperature curve. The calculated temperature rise rate represents the actual temperature curve. Next, calculate the relative temperature rise rate error: ; When the relative temperature rise rate error When the error is less than the error threshold, it indicates that the adiabatic calorimeter has good dynamic response capability to the rapid heating process and meets the actual calibration evaluation requirements.

[0043] To verify the dynamic tracking performance of this invention, experimental verification was conducted, as follows: Test object: Adiabatic accelerated calorimeter; Test curves: Typical curves simulating thermal runaway of lithium batteries (heating rate 0.02~10℃ / min). Sampling frequency: 1Hz; Filtering parameters: window width 7 points, polynomial order 2.

[0044] Experimental Results: The experimental data comparison is as follows: Table 2 Comparison of Verification Experiment Data Experimental conclusions: As shown in Table 2, the verification experimental data demonstrate that the calibration system and method provided by this invention can accurately measure the dynamic tracking performance of the adiabatic calorimeter at different temperature rise rates, with relative errors all within ±1.5%, meeting the requirements for actual calibration evaluation.

[0045] Figure 3 The document demonstrates the specific data processing workflow, including steps such as data reading, outlier removal, Savitzky-Golay filtering, central difference method for calculating temperature rise rate, and error assessment.

[0046] Figure 4 The method for calculating the tracking temperature difference and temperature rise rate error is intuitively demonstrated. The solid line represents the target temperature curve, the dashed line represents the actual temperature curve, and the shaded area represents the tracking temperature difference.

[0047] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A calibration method for implementing a dynamic tracking performance calibration system for an adiabatic accelerated calorimeter, characterized in that, The dynamic tracking performance calibration system includes: a programmable multi-functional signal generator for simulating the surface temperature change curve of a sample during self-exothermic processes; a high-precision temperature data logger for recording the actual temperature changes within the cavity of the adiabatic calorimeter; a high-precision temperature sensor positioned at the center of the sample chamber for acquiring temperature data; and a host computer and software system for synchronously controlling the various devices and completing data acquisition and analysis. The high-precision temperature sensor is positioned at the center of the sample chamber, and its wires are shielded with an armored structure. The output of the programmable multi-functional signal generator is connected to the sample surface temperature acquisition channel of the adiabatic calorimeter via a sensor extension cable. The probe of the high-precision temperature data logger is connected to a temperature measurement point within the sample chamber. A communication line connects the various devices via the host computer. The specific calibration method includes the following steps: S100. Use a programmable multi-functional signal generator to simulate the surface temperature change of the sample during the self-exothermic process, and edit an adiabatic tracking temperature rise curve that needs to be calibrated in the programmable multi-functional signal generator; start the adiabatic calorimeter and set the temperature rise rate threshold. When the adiabatic calorimeter detects that the temperature rise rate has reached the set threshold, it automatically enters the adiabatic tracking mode. S200: In the adiabatic tracking mode, the temperature data generated by the signal source is collected in real time, and the temperature data of the adiabatic calorimeter cavity is collected in real time using a high-precision temperature sensor. The target temperature curve and the actual temperature curve are generated respectively. Then, the temperature rise rate of the two curves at each moment is calculated using Savitzky-Golay filtering and the central difference method. S300: Calculates the tracking temperature difference and temperature rise rate error based on the target temperature curve and the actual temperature curve, and uses the tracking temperature difference and temperature rise rate error to analyze the dynamic tracking performance of the adiabatic calorimeter. Specifically, step S200 includes: S201. Real-time acquisition of temperature data of the sample to be tested and temperature data of the cavity, generating target temperature curve and actual temperature curve respectively, and performing anomaly detection on the original temperature data in the two temperature curves. If any of the following conditions are met, such as the temperature value exceeding the reasonable range, the temperature change slope abruptly exceeding the threshold, or the data point being missing or obviously erroneous, it is considered an anomaly detected. S202. A sliding window Savitzky-Golay filter is used to smooth the data. For data points within a window of length 2M+1, an nth-order polynomial is used for fitting. The polynomial coefficients are solved by the least squares method, and the value of the fitted polynomial at the center point is used as the filter output. S203, for the filtered output of the first... There are 10 data points, of which The temperature rise rate is calculated using the formula: ; in Indicates time The rate of temperature rise, , They represent , Temperature value at the time of sampling.

2. The calibration method as described in claim 1, characterized in that, The temperature rise curve types preset in step S100 specifically include: The exponential temperature rise curve simulates the exponential self-exothermic behavior of chemical substances; Step-shaped temperature rise curve to simulate step response characteristics; Piecewise linear temperature rise curves simulate the linear heating process at different stages; A composite temperature rise curve is used to simulate complex actual working conditions.

3. The calibration method as described in claim 1, characterized in that, The specific process of step S202 includes: assuming the data in the window is... The fitted polynomial is Then the coefficient vector is solved by the least squares method. This makes the sum of squared errors Minimum; at this point, the center point value after filtering is... .

4. The calibration method as described in claim 1, characterized in that, Step S300 specifically includes: S301. Calculate the tracking temperature difference, which is used to measure the accuracy of the adiabatic thermal analyzer in tracking the target temperature in adiabatic tracking mode. S302. Calculate the temperature rise rate error, which is used to measure the dynamic response capability of the adiabatic calorimeter to a rapid heating process.

5. The calibration method as described in claim 4, characterized in that, The specific process of step S301 includes: Obtain the temperature values ​​at various times in the target temperature curve and the actual temperature curve, calculate the difference between the target temperature and the actual temperature at each same time, and take the maximum value of all differences as the tracking temperature difference: ； in, This represents the temperature value in the target temperature curve. This represents the temperature value in the actual temperature curve; where the tracking temperature difference is... It is negatively correlated with the static tracking accuracy of the adiabatic calorimeter.

6. The calibration method as described in claim 4, characterized in that, The specific process of step S302 includes: Obtain the temperature rise rate at each moment in the target temperature curve and the actual temperature curve, then calculate the maximum absolute value of the difference between the target temperature rise rate and the actual temperature rise rate at the same moment: ； in, This represents the theoretical rate of temperature rise of the target temperature curve. The calculated temperature rise rate represents the actual temperature curve. Next, calculate the relative temperature rise rate error: ; When the relative temperature rise rate error When the error is less than the error threshold, it indicates that the adiabatic calorimeter has good dynamic response capability to the rapid heating process and meets the actual calibration evaluation requirements.

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