A method and device for detecting the thermal conductivity of a homogeneous plate-shaped thermal insulation material

Abstract

The present application relates to the technical field of building material thermal performance detection, and particularly relates to a homogeneous plate-shaped heat insulation material thermal conductivity detection method and equipment. The method first collects continuous power signals, displacement signals and temperature difference signals under the establishment of a non-zero temperature difference and constant pressure test environment, and converts them into average heating power sequences, average displacement change rate sequences and time period temperature difference change amounts based on a fixed observation time length. Subsequently, the present application constructs a fluctuation characteristic matrix, applies a singular value decomposition algorithm to extract the maximum singular value and the second largest singular value, and further calculates a cooperative change proportion. When the cooperative change proportion converges to a preset interference threshold and the time period temperature difference change amount is lower than a preset temperature difference fluctuation threshold, it is determined that the steady state is reached and the steady state time period is recorded. Finally, the steady state time period is used to retrieve parameters to calculate the thermal conductivity. The present application objectively quantifies the attenuation process of the electromechanical coupling fluctuation, and eliminates the calculation deviation caused by the transition period drift parameters.

Description

Technical Field

[0001] This invention relates to the field of thermal performance testing technology for building materials, specifically to a method and equipment for testing the thermal conductivity of homogeneous plate-shaped thermal insulation materials. Background Technology

[0002] In the field of thermal performance testing of building materials, when testing the thermal conductivity of homogeneous plate-shaped thermal insulation materials, it is usually necessary to place the specimen in an environment where one side is heated and the surface is subjected to constant pressure. Because thermal insulation materials are poor conductors of heat, the process of establishing a stable one-dimensional temperature gradient within them is extremely slow. During the transition phase of slowly establishing a constant heat flow, the insulation material continuously absorbs sensible heat within the testing equipment, resulting in a very slight tendency for thermal expansion. The servo pressure mechanism, while maintaining a constant pressure, will produce a very small displacement, which is the displacement feedback in response to the material's thermal expansion tendency. This displacement feedback from the servo pressure mechanism and the heating power of the main heating plate form a low-frequency coupled correlation fluctuation.

[0003] Existing testing equipment typically relies solely on observing whether the displacement of the servo pressure mechanism stagnates or setting a fixed test delay to determine whether a homogeneous plate-shaped thermal insulation material has reached a steady state. However, this conventional approach is easily misled by the closed-loop hysteresis and low-frequency coupled fluctuations of the servo pressure mechanism. When the displacement surface shows a temporary stagnation, the material's interior often has not yet established a true one-dimensional constant heat flux state. If the detection is prematurely interrupted during this transition phase, the extracted heating power will be mixed with a non-constant endothermic component from the material, ultimately leading to a positive deviation in the thermal conductivity test results. Summary of the Invention

[0004] To address the technical problem that the determination of whether a homogeneous plate-shaped thermal insulation material has reached a steady state is easily misled by the closed-loop hysteresis and low-frequency coupling fluctuations of the servo pressure mechanism, leading to deviations in the thermal conductivity test results, the present invention aims to provide a method and equipment for testing the thermal conductivity of homogeneous plate-shaped thermal insulation materials. The specific technical solution adopted is as follows: A method for testing the thermal conductivity of a homogeneous plate-shaped thermal insulation material, the method comprising: The heating power signal of the main heating plate, the displacement signal of the servo pressure mechanism, and the temperature difference signal on both sides of the homogeneous plate-shaped thermal insulation material under test are collected simultaneously. The heating power signal, displacement signal, and temperature difference signal are divided into time periods to construct the average heating power sequence, the average displacement change rate sequence, and the temperature difference change over the time period. When the standard deviation of the average heating power sequence is greater than or equal to the preset power noise floor, and the standard deviation of the average displacement change rate sequence is greater than or equal to the preset displacement noise floor, the average heating power sequence and the average displacement change rate sequence are standardized and concatenated into a fluctuation feature matrix. Singular value decomposition is performed on the fluctuation feature matrix to obtain the maximum and second largest singular values; the cooperative change ratio is calculated based on the maximum and second largest singular values. When the coordinated change ratio is less than or equal to a preset interference threshold, and the temperature difference change over the time period is less than or equal to a preset temperature difference fluctuation threshold, a steady state is determined to be reached, and the current time period is taken as the steady state time period; the thermal conductivity is calculated based on the heating power signal, displacement signal, and temperature difference signal within the steady state time period.

[0005] Furthermore, the construction of the average heating power sequence, the average displacement change rate sequence, and the temperature difference change over a time period includes: The average value of the heating power signal within a time period is calculated as the average heating power of the time period; based on the preset sliding time window length, the average heating power of the time period corresponding to multiple consecutive sorted indices is extracted to form an average heating power sequence. Calculate the difference between the displacement signal at the end and beginning of the time period, divide the difference by the fixed observation duration to obtain the average displacement change rate of the time period; extract the average displacement change rate of the time period corresponding to multiple consecutive sorting indices to form an average displacement change rate sequence. Calculate the average value of the temperature difference signal within each time period, use the average value as the average test temperature difference of the current time period, and subtract the average test temperature difference of the previous time period from the average test temperature difference of the current time period. Take the absolute value of the difference as the temperature difference change of the current time period.

[0006] Furthermore, before standardizing the average heating power sequence and the average displacement change rate sequence, the following steps are also included: When it is determined that the standard deviation of the average heating power sequence is less than the preset power noise floor, or the standard deviation of the average displacement change rate sequence is less than the preset displacement noise floor, the cooperative change ratio of the current time period is assigned a preset constant.

[0007] Furthermore, the method for obtaining the fluctuation feature matrix includes: The average value of the average heating power sequence is subtracted from each discrete data point in the average heating power sequence, and the result of the subtraction is divided by the standard deviation of the average heating power sequence to obtain the standardized power sequence. Subtract the average value of the average displacement rate of change sequence from each discrete data point in the average displacement rate of change sequence, and divide the result of the subtraction by the standard deviation of the average displacement rate of change sequence to obtain the standardized displacement sequence. The standardized power sequence and the standardized displacement sequence are horizontally concatenated as column vectors to form the fluctuation feature matrix.

[0008] Furthermore, before calculating the synergistic change ratio, the method also includes: The system calculates the cumulative number of data points acquired over the current time period and obtains the average test temperature difference for that time period. When the cumulative number of data points is greater than or equal to a preset sliding time window length, and the average test temperature difference reaches a preset target temperature difference, the system calculates the collaborative change ratio. When the cumulative number of data points is less than the preset sliding time window length, or the average test temperature difference does not reach the preset target temperature difference, the system does not calculate the collaborative change ratio until the cumulative number of data points is greater than or equal to the preset sliding time window length, and the average test temperature difference reaches the preset target temperature difference.

[0009] Furthermore, the method for obtaining the coordinated change ratio includes: The fluctuation feature matrix is ​​subjected to singular value decomposition to obtain the largest singular value and the second largest singular value; the largest singular value is used as the numerator and the second largest singular value is used as the denominator to calculate the cooperative change ratio.

[0010] Further, the step of determining that a steady state has been reached when the coordinated change ratio is less than or equal to a preset interference threshold and the temperature difference change over the time period is less than or equal to a preset temperature difference fluctuation threshold, and taking the current time period as the steady-state time period, includes: The cumulative number of currently acquired time periods is counted; when the cumulative number reaches the preset maximum number of test time periods, and the coordinated change ratio still has not converged to within the preset interference threshold, timeout forced protection is triggered, and the current time period is forcibly determined as a steady-state time period.

[0011] Furthermore, the method for obtaining the thermal conductivity includes: The average value of the heating power signal during the steady-state time period is calculated as the steady-state heating power; the average value of the temperature difference signal during the steady-state time period is calculated as the steady-state test temperature difference; the average value of the displacement signal during the steady-state time period is calculated as the steady-state thickness. Obtain the constant measurement area of ​​the main heating plate; use the product of the steady-state heating power and the steady-state thickness as the numerator, and the product of the constant measurement area and the steady-state test temperature difference as the denominator to calculate the ratio and obtain the thermal conductivity.

[0012] Furthermore, before calculating the thermal conductivity, the following is also included: If the steady-state test temperature difference is less than the minimum test temperature difference threshold, an alarm for abnormal temperature difference establishment is issued and the thermal conductivity output is blocked; if the steady-state test temperature difference is greater than or equal to the minimum test temperature difference threshold, the thermal conductivity is calculated.

[0013] This invention also proposes a device for detecting the thermal conductivity of homogeneous plate-shaped thermal insulation materials, including a data acquisition module for simultaneously acquiring heating power signals, displacement signals, and temperature difference signals; a memory for storing preset sliding time window lengths, preset power noise levels, preset displacement noise levels, preset interference thresholds, preset temperature difference fluctuation thresholds, and historical data; and a processor configured to execute the steps of any of the methods described in the invention for detecting the thermal conductivity of homogeneous plate-shaped thermal insulation materials.

[0014] The present invention has the following beneficial effects: This invention first provides a data foundation for steady-state determination by simultaneously acquiring the heating power signal of the main heating plate, the displacement signal of the servo pressure mechanism, and the temperature difference signal on both sides of the homogeneous plate-shaped thermal insulation material under test. By introducing dual standard deviation determination of power noise floor and displacement noise floor, the absolutely stable state is identified in advance before constructing the fluctuation feature matrix, effectively intercepting the invalid amplification of sensor quantization noise. The principal components of the test fluctuation feature matrix are extracted by the singular value decomposition algorithm, obtaining the cooperative change ratio corresponding to the heat transfer transition behavior. Unlike the existing technology that relies on a single numerical stagnation phenomenon, this invention objectively quantifies the attenuation process of the coupled fluctuation between heat transfer tendency and mechanical constant pressure feedback by judging the convergence characteristics of the cooperative change ratio to the white noise floor level, effectively eliminating the deception and interference of low-frequency small fluctuations during the transition period. Finally, by retrieving non-aliased historical data during the steady-state time period, the reading path of drift parameters during the unbalanced transition period is blocked, eliminating the deviation in thermal conductivity calculation caused by it, and significantly improving the accuracy and robustness of equipment detection. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a method for detecting the thermal conductivity of a homogeneous plate-shaped thermal insulation material, as provided in one embodiment of the present invention. Detailed Implementation

[0016] The following description, in conjunction with the accompanying drawings, details the specific scheme of the method and equipment for detecting the thermal conductivity of homogeneous plate-shaped thermal insulation materials provided by the present invention.

[0017] The thermal conductivity testing device for homogeneous plate-shaped thermal insulation materials described in this invention is based on a dual-sample symmetrical testing architecture. Its hardware architecture includes at least: a main heating plate, two cold plates symmetrically distributed on both sides of the main heating plate, a servo pressure mechanism for maintaining constant contact pressure, and a high-precision displacement and temperature sensor group. The temperature sensor group is arranged on the main heating plate and the cold plates to collect temperature data. The displacement sensor is installed between the servo pressure mechanism and the stationary frame of the testing host to directly measure the real-time absolute distance between the movable cold plate and the main heating plate. The method described in this invention is essentially the core control logic embedded in the microprocessor of the thermal conductivity testing device. It should be noted that this invention does not simply substitute formulas, but rather determines when to truncate data and which moment's data to extract by performing orthogonal decomposition on the original fluctuation signals collected in real time by the sensors. The steady-state heating power, steady-state thickness, and steady-state test temperature difference used to calculate the thermal conductivity are all static parameters retrieved from the device's historical database based on the actual steady-state time period determined by this algorithm, thereby eliminating measurement deviations that may be caused by the device's original judgment logic.

[0018] Please see Figure 1 The diagram illustrates a flowchart of a method for detecting the thermal conductivity of a homogeneous plate-shaped thermal insulation material according to an embodiment of the present invention. The method includes: Step S101: Synchronously collect the heating power signal of the main heating plate, the displacement signal of the servo pressure mechanism, and the temperature difference signal on both sides of the homogeneous plate-shaped thermal insulation material to be tested; divide the heating power signal, displacement signal, and temperature difference signal into time periods to construct the average heating power sequence, the average displacement change rate sequence, and the temperature difference change over the time period.

[0019] This invention controls a servo pressure mechanism in a thermal conductivity testing device to move downwards and contact the insulation material specimen. Pressure data from the contact surface is acquired in real time using a pressure sensor. This pressure data is divided by the pressure area of ​​the specimen to obtain the contact surface pressure, establishing a constant pressure environment. Simultaneously, a preset target temperature difference is configured between the main heating plate and the cold plate to ensure a clear non-zero temperature gradient exists between them. Based on this, the invention activates a high-speed data acquisition card, extracting trigger cycles from the same reference hardware clock chip at a fixed sampling frequency. Based on these trigger cycles, the device synchronously reads feedback from three sensors, thereby acquiring half of the continuous real-time total electrical power signal of the main heating plate, which evolves over time and is recorded as the continuous heating power signal of a single-sided material specimen. The invention also acquires the continuous absolute position signal fed back by the servo pressure mechanism, recording it as a continuous displacement signal. This displacement signal represents the absolute position value fed back by the displacement sensor between the main heating plate and the cold plate. Finally, the invention acquires the real-time temperature difference signal between the surface of the main heating plate and the surface of the cold plate, recording it as a continuous temperature difference signal. It should be noted that the pressure-bearing area of ​​the specimen is the area of ​​the insulation material specimen subjected to the action of the pressure sensor, which can also be understood as the full-size area of ​​the pressure sensor and the insulation material specimen.

[0020] These three elements are tightly coupled through the thermophysical properties of the materials: Since thermal insulation materials are poor conductors of heat, the process of establishing a stable one-dimensional temperature gradient within them is extremely slow. During the transition phase of slowly establishing a constant heat flow, the continuous absorption of sensible heat within the material generates a very slight tendency for thermal expansion. This slight thermal expansion tendency causes a minor disturbance to the constant-pressure closed-loop control of the servo pressure mechanism of the testing equipment, resulting in low-frequency coupled fluctuations between the displacement feedback of the servo pressure mechanism and the heating power of the main heating plate. Specifically, the displacement signal of the absolute position of the continuous pressure relief of the servo pressure mechanism reflects the macroscopic comprehensive deformation rate of the insulation material specimen within the current time period, under the combined coupling effect of the internal thermal expansion tendency and the constant-pressure feedback of the servo pressure mechanism.

[0021] It should be noted that the displacement signal output by the servo pressure mechanism mentioned in the embodiments of the present invention is the absolute coordinate reading directly output by the displacement sensor configured on the servo pressure mechanism, which represents the real-time absolute distance between the main heating plate and the cold plate.

[0022] This invention first establishes a test environment with non-zero temperature difference and constant pressure, and relies on a unified system clock to collect synchronous sensor data from the same source, aiming to eliminate the time difference in the acquisition of multiple sensors.

[0023] In this embodiment of the invention, the system first establishes a test environment with constant pressure and non-zero temperature difference: the servo pressure mechanism is controlled to drive the pressure plate to contact the specimen, and a preset constant pressure of 2 kPa is maintained according to the feedback of the pressure sensor; at the same time, a preset target temperature difference between the main heating plate and the cold plate is configured to ensure that there is a clear non-zero temperature gradient between them, thereby avoiding the risk of the denominator being zero when calculating the subsequent heat conduction formula.

[0024] When the temperature difference between the main heating plate and the cold plate reaches the preset target temperature difference and the servo pressure tends to be constant, the system starts the data acquisition process, and this moment is recorded as the starting test origin. The specific acquisition method is as follows: a trigger cycle is issued based on a unified hardware reference clock, and feedback data from multiple sensor channels is read synchronously by a high-speed acquisition card; among them, the heating power signal characterizing the energy consumption of the main heating plate is acquired by the sensor, the compensation displacement signal generated by the servo pressure mechanism to maintain constant pressure is acquired by the displacement sensor, and the continuous temperature difference signal between the main heating plate and the cold plate is acquired by the thermocouple or thermistor sensor.

[0025] Subsequently, the system divides the aforementioned high-frequency sampled continuous signal into time segments according to a fixed observation duration, and performs arithmetic averaging or endpoint subtraction operations to construct the average heating power sequence, the average displacement change rate sequence, and the temperature difference change over the time segment. This operation compresses the original fluctuation data into a discrete scalar sequence that can reflect the macroscopic characteristics of each time segment, reducing the computational load on the processor while providing a standardized data foundation for subsequent feature matrix construction and steady-state identification.

[0026] As a specific example, the preset target temperature difference is set to 20℃; for example, in this device, the temperature of the main heating plate is 35℃ and the temperature of the cold plate is 15℃, so the preset target temperature difference is 20℃; setting the preset target temperature difference can effectively ensure that there is a clear non-zero temperature difference between the main heating plate and the cold plate.

[0027] Step S102: When the standard deviation of the average heating power sequence is greater than or equal to the preset power noise floor, and the standard deviation of the average displacement change rate sequence is greater than or equal to the preset displacement noise floor, the average heating power sequence and the average displacement change rate sequence are standardized and spliced ​​into a fluctuation feature matrix.

[0028] The standard deviation of the average heating power sequence is compared with a preset power noise floor, and the standard deviation of the average displacement change rate sequence is also compared with a preset displacement noise floor. When either of the two standard deviations is less than the corresponding preset noise floor, it indicates that the original physical signal in the current time period has basically stopped. If subsequent steps are forced at this time, it will lead to calculation failure or result distortion. When the standard deviation of the average heating power sequence is greater than or equal to the preset power noise floor, and the standard deviation of the average displacement change rate sequence is greater than or equal to the preset displacement noise floor, it indicates that there are still fluctuations with analytical value in the average heating power sequence and the average displacement change rate sequence. In this case, proceed to the next step.

[0029] Since power and displacement differ greatly in magnitude and unit, this invention standardizes the obtained average heating power sequence and average displacement change rate sequence, and finally assembles these two sets of one-dimensional sequences into a two-dimensional fluctuation feature matrix, which serves as the input data for subsequent orthogonal decomposition.

[0030] Step S103: Perform singular value decomposition on the fluctuation feature matrix to obtain the maximum singular value and the second largest singular value; calculate the cooperative change ratio based on the maximum singular value and the second largest singular value.

[0031] To objectively quantify the attenuation process of the coupling fluctuations between heat transfer tendency and mechanical constant pressure feedback, this invention utilizes a singular value decomposition (SVD) algorithm to extract the largest singular value from the aforementioned fluctuation feature matrix as a characteristic value of the coordinated change in power and displacement. This characteristic value quantifies the electromechanical coupling fluctuation energy caused by heat transfer drive. Simultaneously, the second largest singular value is extracted as an independent signal interference characteristic value, which quantifies the random white noise energy of the sensor. The final calculated coordinated change ratio objectively quantifies the attenuation process of the coupling fluctuations between heat transfer tendency and mechanical constant pressure feedback.

[0032] Step S104: When the coordinated change ratio is less than or equal to a preset interference threshold, and the temperature difference change over the time period is less than or equal to a preset temperature difference fluctuation threshold, a steady state is determined to be reached, and the current time period is taken as the steady state time period; the thermal conductivity is calculated based on the heating power signal, displacement signal, and temperature difference signal within the steady state time period.

[0033] Unlike existing technologies that rely on a single numerical stagnation phenomenon, this invention employs a dual-criteria mechanism. Only when the value of the coordinated change ratio is less than or equal to a preset interference threshold, and the value of the temperature difference change over a time period is less than or equal to a preset temperature difference fluctuation threshold, does this invention confirm that the current fluctuation has completely decayed to the underlying white noise level, confirming the establishment of a one-dimensional steady-state constant heat flow state within the material. At this point, a steady state is determined, and the current time period is taken as the steady-state time period. During the thermal conductivity calculation stage, the reading path of drift parameters during the test transition period can be cut off. This invention directly extracts the heating power signal, displacement signal, and temperature difference signal within the steady-state time period to calculate the final thermal conductivity. This judgment mechanism effectively eliminates the interference of low-frequency, minute fluctuations during the transition period, accurately locating the moment when the true one-dimensional constant heat flow state is achieved. By locking the steady-state time period to extract static parameters, the reading path of drift parameters during the unbalanced transition period is blocked, eliminating the resulting deviation in thermal conductivity calculation.

[0034] As a specific example, the preset interference threshold is set to 1.5. This preset interference threshold is used to measure the determination boundary of the intensity of power-displacement correlation fluctuation. Since the cooperative change ratio in a pure random white noise environment is theoretically close to 1.0, but considering the inherent ripple and quantization error of the sensor, the value range is generally set to 1.2 to 1.8.

[0035] As a specific example, the preset temperature difference fluctuation threshold is set to 0.05℃. This invention compares the temperature difference change in the current time period with the preset temperature difference fluctuation threshold to further determine whether the temperature gradient drift of the test system has converged to a stable range. The implementer can adaptively adjust the preset temperature difference fluctuation threshold according to the required accuracy of the specific equipment.

[0036] Following steps S101 to S104, this embodiment of the invention provides a method for detecting the thermal conductivity of homogeneous plate-shaped thermal insulation materials. Under a constant pressure and non-zero temperature difference environment, the method simultaneously collects the heating power of the main heating plate, the displacement of the servo pressure mechanism, and the temperature difference signals on both sides of the material. It then constructs an average heating power sequence, an average displacement change rate sequence, and a temperature difference change over each time period. Using preset power noise floor and preset displacement noise floor, the standard deviation of each sequence is intercepted and verified. A standardized fluctuation characteristic matrix is ​​constructed while meeting the analytical basis. The maximum and second-largest singular values ​​are extracted through singular value decomposition to calculate the cooperative change ratio. Combining the convergence characteristics of the cooperative change ratio and the temperature difference change over the time period, a dual determination is made to lock in the steady-state time period. Finally, the thermal conductivity is calculated based on the average parameters within the steady-state time period, substituted into the formula. This method solves the problem of steady-state misjudgment caused by existing technologies relying on the appearance of stagnation of a single value. By quantifying the coupling fluctuation decay process between heat transfer tendency and constant pressure feedback, it accurately identifies the true one-dimensional constant heat flow state, effectively avoids the interference of drift parameters during the unbalanced transition period on the calculation results, and significantly improves the accuracy and robustness of equipment detection.

[0037] In some possible implementations of this invention, the construction of the average heating power sequence, the average displacement change rate sequence, and the temperature difference change over a time period includes: calculating the average value of the heating power signal over a time period as the average heating power over the time period; extracting the average heating power over a time period corresponding to multiple consecutive sorting indices based on a preset sliding time window length to form an average heating power sequence; it should be noted that the sorting index is also the index number corresponding to the time period.

[0038] Calculate the difference between the displacement signal at the end and beginning of the time period, divide the difference by the fixed observation duration to obtain the average displacement change rate of the time period; extract the average displacement change rate of the time period corresponding to multiple consecutive sorting indices to form an average displacement change rate sequence. Calculate the average value of the temperature difference signal within each time period. Use this average value as the average test temperature difference for the current time period. Subtract the average test temperature difference of the previous time period from the average test temperature difference for the current time period, and take the absolute value of the difference as the temperature difference change for the current time period. It should be noted that the length of the time period is the fixed observation duration.

[0039] In this embodiment of the invention, the total electrical power of the main heating plate is collected in real time by a power sensor. Since this embodiment of the invention is based on a dual-sample symmetrical test architecture, with the main heating plate as the center and a cold plate placed symmetrically on each side, the total electrical power generated by the main heating plate will be conducted equally to the samples on both sides under this symmetrical structure. Therefore, half of the total electrical power signal of the main heating plate needs to be taken as the heating power signal of the material sample on one side.

[0040] The average heating power signal over a time period is calculated to obtain the average heating power over that time period. This average heating power represents the macroscopic level of total thermal energy injected into the specimen by the system during the current time period. The difference between the displacement signal at the end and beginning of the time period is calculated, and this difference is divided by a fixed observation duration to obtain the average displacement change rate over the time period. This average displacement change rate reflects the macroscopic comprehensive deformation rate of the specimen under the combined coupling effect of internal thermal expansion tendency and servo constant pressure feedback during the current time period. The average temperature difference signal in each time period is calculated, and the absolute value of the difference between this average and the average of the adjacent previous time period is obtained as the temperature difference change over the time period, reflecting the severity of temperature system fluctuations. By discretizing the initial signal, a high-quality data foundation is provided for subsequent feature matrix construction and steady-state identification.

[0041] As a specific example, the fixed observation duration is set to 60 seconds. In other embodiments, the implementer may adjust the fixed observation duration according to the actual situation.

[0042] As a specific example, the preset sliding time window length is set to 30; wherein the length of the sliding time window can be set according to the sampling stability and testing accuracy requirements of the detection equipment. In this embodiment of the invention, the length is preferably set to 20 to 50.

[0043] In some possible implementations of this invention, before standardizing the average heating power sequence and the average displacement change rate sequence, the method further includes: when it is determined that the standard deviation of the average heating power sequence is less than the preset power noise floor, or the standard deviation of the average displacement change rate sequence is less than the preset displacement noise floor, the cooperative change ratio of the current time period is assigned a preset constant, and the process directly proceeds to the steady-state determination step.

[0044] When the standard deviation of the average heating power sequence is determined to be less than the preset power noise floor, or the standard deviation of the average displacement change rate sequence is determined to be less than the preset displacement noise floor, it indicates that the original physical signal within the current time window has essentially stopped. If subsequent dimensionless division operations are forcibly performed at this point, extremely small quantization white noise will be incorrectly amplified. Therefore, this invention, through screening and determination, directly terminates subsequent steps, forcibly assigning the cooperative change ratio of the current time period to a preset constant, directly determining that it has entered an absolutely stable state. This avoids calculation failures caused by a zero denominator or amplification of minute noise, ensuring the robustness of the system.

[0045] As a specific example, the preset power noise floor is set to 10. -4W; where the specific value is obtained in advance by statistically calibrating the root mean square error of the no-load data based on the ADC quantization resolution of the system power control module and the inherent circuit thermal noise under the no-load closed-loop operation state of the equipment, and represents the background white noise limit of the equipment under the condition of no real physical deformation or heat transfer disturbance.

[0046] As a specific example, the preset displacement noise floor is set to 10. -4 mm / s; where the specific value is determined by the physical resolution of the displacement sensor and the mechanical ripple of the servo pressure control mechanism in the locked state.

[0047] As a specific example, the preset constant is set to 1.0; when the electromechanical coupling physical effects such as heat transfer perturbations inside the material are completely attenuated, the two-dimensional signal collected by the system is dominated only by mutually independent random thermal noise. At this time, the expected energy variance of the wave characteristic matrix in any orthogonal direction tends to be equal, and the theoretical limit of the ratio of the two corresponding eigenvalues ​​strictly approaches 1.0.

[0048] When the standard deviation of the average heating power sequence is greater than or equal to the preset power noise floor, and the standard deviation of the average displacement change rate sequence is greater than or equal to the preset displacement noise floor, the average heating power sequence and the average displacement change rate sequence are standardized and concatenated into a fluctuation feature matrix.

[0049] In some possible implementations of this invention, the method for obtaining the fluctuation feature matrix includes: subtracting the average value of the average heating power sequence from each discrete data point in the average heating power sequence, dividing the result of the subtraction by the standard deviation of the average heating power sequence to obtain the result value of each discrete data point in the average heating power sequence, and constructing a normalized power sequence from the result value; subtracting the average value of the average displacement change rate sequence from each discrete data point in the average displacement change rate sequence, dividing the result of the subtraction by the standard deviation of the average displacement change rate sequence to obtain the result value of each discrete data point in the average displacement change rate sequence, and constructing a normalized displacement sequence from the result value; and horizontally concatenating the normalized power sequence and the normalized displacement sequence as column vectors to form the fluctuation feature matrix. The elements in the first column of the constructed fluctuation feature matrix are the elements in the normalized power sequence, and the elements in the second column are the elements in the normalized displacement sequence. For example, for the normalized power sequence {p1,p2,p3,……,pn} and the normalized displacement sequence {v1,v2,v3,……,vn}, the constructed fluctuation feature matrix is: Where p1 is the first element value in the normalized power sequence; p2 is the second element value in the normalized power sequence; p3 is the third element value in the normalized power sequence; pn is the last element value in the normalized power sequence; v1 is the first element value in the normalized displacement sequence; v2 is the second element value in the normalized displacement sequence; v3 is the third element value in the normalized displacement sequence; and vn is the last element value in the normalized displacement sequence.

[0050] Specifically, the formula for calculating the average heating power within the sliding time window is as follows: The standard deviation formula for the average heating power sequence is expressed as: ;in, This represents the standard deviation of the average heating power sequence; This represents the average heating power within the sliding time window; The value represents the average heating power extracted in the j-th time period; i represents the sort index of the current time period; N represents the number of time periods extracted within the sliding time window. The standard deviation of the average heating power sequence. The larger the calculated value, the greater the amplitude and severity of the fluctuation in the heating power of the main heating plate within the current sliding time window.

[0051] Similarly, the formula for calculating the average rate of change of displacement within the sliding time window is as follows: The standard deviation formula for the mean displacement rate of change sequence is expressed as: ,in, The standard deviation of the average displacement rate of change sequence; This represents the average rate of change of the average displacement within the sliding time window; The value represents the average displacement change rate of the j-th time period being traversed and extracted; i represents the sort index of the current time period; and N represents the number of time periods extracted within the sliding time window.

[0052] This invention calculates the mean and standard deviation of the power set and displacement set to quantify the fluctuation amplitude of the data within a sliding time window. The standard deviation is then compared to a preset noise floor limit. If the standard deviation of the average heating power sequence is less than the preset power noise floor or the standard deviation of the average displacement change rate sequence is less than the preset displacement noise floor, it indicates that the signal has essentially stopped. This invention directly terminates subsequent calculations and forces the signal to enter an absolutely stable state, effectively avoiding computational crashes and noise amplification problems caused by a zero denominator.

[0053] Furthermore, if the present invention determines that the standard deviation of the average heating power sequence is greater than or equal to a preset power noise floor, and the standard deviation of the average displacement rate of change sequence is greater than or equal to a preset displacement noise floor, it indicates that the average heating power sequence and the average displacement rate of change sequence still exhibit fluctuations with analytical value. The present invention then performs dimensionless standardization processing on the average heating power sequence and the average displacement rate of change sequence. To eliminate the significant differences in numerical magnitude and units between power and displacement, the standardization process maps sensor feedback data with huge order-of-magnitude differences to the same scale benchmark, ensuring that power fluctuations and displacement fluctuations have equal weight in subsequent matrix decomposition, thereby constructing a two-dimensional fluctuation feature matrix that accurately reflects the cooperative fluctuation characteristics.

[0054] As a concrete example, the formula for standardizing the average heating power sequence is expressed as: .in, This represents the standardized power fluctuation value for the j-th time period; This represents the standard deviation of the average heating power sequence; This represents the average heating power within the sliding time window; This represents the average heating power extracted during the j-th time period. Represents a very small positive number; Term ( () represents the fluctuation range of each discrete data point in the current sequence relative to the mean; (term) This indicates the range of overall fluctuations in the current average heating power sequence.

[0055] Similarly, the formula for standardizing the average displacement rate of change sequence is expressed as: .in, This represents the standardized displacement fluctuation value for the j-th time period; The standard deviation of the average displacement rate of change sequence; This represents the average rate of change of the average displacement within the sliding time window; This represents the average rate of change of displacement during the j-th time period that was extracted during the traversal; Represents a very small positive number; Term ( () represents the fluctuation range of each discrete data point in the current sequence relative to the mean; (term) This indicates the range of overall fluctuations in the current average displacement change rate sequence.

[0056] As a concrete example, the smallest positive number To prevent the denominator from being zero, it is specifically set to 10. -8 The value of the extremely small positive number should be higher than the minimum precision of the computer's floating-point calculation. For double-precision floating-point calculations, a range of 10 is recommended. -8 Up to 10 -12When the standard deviation of the average heating power sequence or the standard deviation of the average displacement change rate sequence approaches the preset noise floor due to entering a steady state, the maximum amplitude of the standardized components can be limited by the regularization effect of the minimum positive number, thereby preventing mathematical overflow of the diagonal matrix elements in the subsequent singular value decomposition process and ensuring the numerical stability of the algorithm under extremely stable conditions.

[0057] After generating the standardized power fluctuation values ​​and standardized displacement fluctuation values, the continuous standardized power fluctuation values ​​are arranged vertically and set as the first column vector of the matrix; the continuous standardized displacement fluctuation values ​​are arranged vertically and set as the second column vector of the matrix. The first and second column vectors are then concatenated horizontally side-by-side to obtain a fluctuation feature matrix with the same number of rows as the sliding time window length and two columns, providing a data foundation for subsequent singular value decomposition.

[0058] In some possible implementations of this invention, before calculating the coordinated change ratio, the method further includes: counting the cumulative number of currently acquired time periods and obtaining the average test temperature difference for the current time period; when the cumulative number is greater than or equal to a preset sliding time window length, and the average test temperature difference reaches a preset target temperature difference, the coordinated change ratio is calculated; when the cumulative number is less than the preset sliding time window length, or the average test temperature difference does not reach the preset target temperature difference, the coordinated change ratio is not calculated until the cumulative number is greater than or equal to the preset sliding time window length, and the average test temperature difference reaches the preset target temperature difference. It should be noted that the cumulative number of time periods is the total number of time periods up to the current moment.

[0059] In this embodiment of the invention, to ensure the effectiveness of the steady-state determination logic, a threshold is set for enabling the calculation of the cooperative change ratio and the steady-state determination. Specifically, the cumulative number of currently acquired time periods is counted in real time, and the average test temperature difference for the current time period is obtained.

[0060] First, regarding the limitation on the cumulative quantity, the invention presets a sliding time window length of 30. In the initial testing phase, if the invention determines that the cumulative quantity of the currently acquired time period is less than the sliding time window length, it indicates that the collected historical data is insufficient to fill a complete sliding time window. At this point, only the currently received discrete feature data is sequentially stored in the historical database, and the current calculation loop is terminated; that is, the calculation of the co-variance ratio is no longer performed until the cumulative quantity is greater than or equal to the preset sliding time window length, and the average test temperature difference reaches the preset target temperature difference. Then, the calculation of the co-variance ratio continues. This operation effectively avoids program crashes caused by array out-of-bounds errors in the initial testing phase. Therefore, the invention only initiates subsequent calculations when the cumulative quantity of the time period is greater than or equal to the preset sliding time window length, providing a sufficient data foundation for subsequent standard deviation calculations and matrix construction.

[0061] Secondly, regarding the limitation of the average test temperature difference, this invention configures a preset target temperature difference between the main heating plate and the cold plate to ensure a clear non-zero temperature gradient between them. For a period after the device is first started, the collected heating power and displacement signals are almost static. Without the preset target temperature difference, the results of subsequent singular value decomposition would show extremely small fluctuations and extreme stability, leading to a huge calculated thermal conductivity. Therefore, this invention requires the average test temperature difference to reach the preset target temperature difference to ensure that the heat transfer process has truly started and the temperature difference signal has been established within the effective range. This avoids distortion in the calculation of the proportional change due to an excessively small or unstable temperature difference in the early stages of heating, preventing meaningless misjudgments.

[0062] In some possible implementations of this invention, the method for obtaining the cooperative change ratio includes: performing singular value decomposition on the fluctuation feature matrix to obtain the maximum singular value and the second largest singular value; using the maximum singular value as the numerator and the second largest singular value as the denominator to calculate the cooperative change ratio. The second largest singular value is the maximum value among the singular values ​​other than the maximum singular value.

[0063] During the singular value decomposition (SVD) of the fluctuation feature matrix, the SVD algorithm is invoked to decompose the fluctuation feature matrix into a left singular matrix, a right singular matrix, and a diagonal matrix containing singular values. After the SVD is completed, the real numbers on the main diagonal of the diagonal matrix are extracted and arranged in descending order. The largest singular value is selected as the largest singular value, and the second largest singular value is set as the second largest singular value.

[0064] In the context of engineering data representation, singular values ​​can extract the principal components of a data matrix. The maximum singular value corresponds to the direction with the largest data variance, i.e., the linear trend in which data points are most concentrated in a two-dimensional space. In a physical scenario, when a homogeneous plate-shaped thermal insulation material is in a heat absorption transition period, the continuous minute heat absorption within the material will generate a tendency for thermal expansion. This will simultaneously drive the servo displacement feedback and the heating power of the main heating plate. This physical mechanism makes the power fluctuation and displacement fluctuation highly linearly correlated, and the two are in sync, forming the main component of data fluctuation. Therefore, in this invention, the maximum singular value quantifies the energy magnitude of the strongly coupled and coordinated fluctuation between the power supplementary heating and the servo maintaining constant pressure caused by the minute heat absorption within the homogeneous plate-shaped thermal insulation material.

[0065] In mathematics, the second largest singular value corresponds to a minor direction orthogonal to the principal component direction, representing residual fluctuations that cannot be explained by the principal trend. In a physical scenario, the inherent random white noise of the sensors in the testing equipment is independent and uncorrelated; the noise of the power sensor does not synchronize with the noise of the displacement sensor. This independent, irregular random fluctuation is distributed in a direction perpendicular to the principal trend. Therefore, in this invention, the second largest singular value quantifies the energy of the purely independent random white noise of the sensors in the testing equipment that is not generated by interaction with the heat transfer system. It should be noted that, in this embodiment of the invention, to prevent calculation crashes due to the second largest singular value being zero during the calculation of the cooperative change ratio, when the singular value decomposition process is performed, if the output value of the second largest singular value is zero, a very small positive compensation amount is assigned to the second largest singular value; in this embodiment of the invention, the very small positive compensation amount is set to 10. -8 Implementers can make adaptive adjustments based on specific circumstances.

[0066] During the transition period of continuous heat preservation of the material, the principal component coupling fluctuations are strong, and the maximum singular value is significantly larger than the second largest singular value. However, once the system enters a steady state, the material no longer absorbs heat, the aforementioned electromechanical coupling phenomenon disappears, and only isotropic random white noise remains in the matrix. Under pure white noise conditions, the fluctuation variances in different directions within the two-dimensional fluctuation characteristic matrix tend to be consistent, causing the values ​​of the maximum and second largest singular values ​​to gradually converge. It is precisely by utilizing this convergence characteristic that this invention can accurately determine the moment when a constant heat flux state is achieved by calculating the cooperative change ratio.

[0067] As a specific example, the formula for the proportion of coordinated change is expressed as: .in, This represents the proportion of coordinated change calculated in the i-th time period; Indicates the maximum singular value extracted; This represents the extracted second largest singular value.

[0068] In some possible implementations of this invention, the step of determining that a steady state has been reached when the coordinated change ratio is less than or equal to a preset interference threshold and the temperature difference change over the time period is less than or equal to a preset temperature difference fluctuation threshold, and using the current time period as the steady-state time period, further includes: The cumulative number of currently acquired time periods is counted; when the cumulative number reaches the preset maximum number of test time periods, and the coordinated change ratio still has not converged to within the preset interference threshold, timeout forced protection is triggered, and the current time period is forcibly determined as a steady-state time period.

[0069] In conventional testing, equipment relies on the magnitude of the coordinated change ratio to determine steady state. However, in actual working conditions, if the equipment malfunctions, sensors drift, or materials experience non-steady-state disturbances, the calculated coordinated change ratio may fail to meet steady-state requirements. To prevent the equipment from entering an infinite waiting loop, this invention sets a preset maximum number of test time periods as a time constraint. When the index reaches the upper limit and the steady-state index still fails to meet the standard, the equipment determines that the test has timed out and forcibly terminates the test process by specifying a fixed steady-state time period.

[0070] As a specific example, the maximum preset number of test time periods is set to 1440. Since this invention sets a fixed observation duration of 60 seconds, if the calculated cooperative change ratio fails to meet the steady-state requirements for an extended period, and the number of test time periods reaches 1440 (a total duration of 24 hours), if the steady-state index is still not reached, this invention will determine that the test has timed out, stop data collection, and forcibly proceed to the thermal conductivity calculation step. Implementers can adaptively adjust the maximum number of test time periods based on the theoretical time required for different types of insulation materials to reach steady state.

[0071] In some implementations of this invention, before calculating the thermal conductivity, the method further includes: comparing the steady-state test temperature difference with a preset minimum test temperature difference threshold; if the steady-state test temperature difference is less than the minimum test temperature difference threshold, issuing a temperature difference establishment abnormality alarm and blocking the thermal conductivity output; if the steady-state test temperature difference is greater than or equal to the minimum test temperature difference threshold, performing the calculation of the thermal conductivity.

[0072] In calculating thermal conductivity, the parameter signals from the steady-state time period need to be substituted into the one-dimensional steady-state heat conduction formula. The calculation result directly depends on the magnitude of the steady-state test temperature difference, which is the denominator. From the perspective of algorithm stability, if the steady-state test temperature difference is extremely small or even zero, it will directly cause the formula to crash due to division by zero or result data overflow. From the perspective of physical heat transfer, if the steady-state test temperature difference is extremely small, the heat flow will become extremely small, or even be drowned out by the noise of the sensor itself and the fluctuations in the ambient temperature. At this time, the acquired power signal and temperature difference signal are no longer in a stable proportional relationship, and the calculated thermal conductivity will have a huge random deviation, losing its physical reference value.

[0073] In some possible implementations of this invention, the method for obtaining the thermal conductivity includes: calculating the average value of the heating power signal within a steady-state time period as the steady-state heating power; calculating the average value of the temperature difference signal within a steady-state time period as the steady-state test temperature difference; calculating the average value of the displacement signal within a steady-state time period as the steady-state thickness; obtaining the constant measurement area of ​​the main heating plate; and calculating the ratio using the product of the steady-state heating power and the steady-state thickness as the numerator and the product of the constant measurement area and the steady-state test temperature difference as the denominator to obtain the thermal conductivity.

[0074] It should be noted that the displacement signal of the servo pressure mechanism represents the real-time absolute distance between the main heating plate and the cold plate. The arithmetic mean of the displacement signal can filter out mechanical micro-vibrations and measurement noise during the sampling period. The obtained mean reflects the true physical size of the material specimen under the current constant heat flow and constant pressure dual equilibrium state. Therefore, it is used as the steady-state thickness for calculating the thermal conductivity.

[0075] As a specific example, the constant metering area refers to the area of ​​the middle metering region of the main heating plate, which is directly used for one-dimensional conduction perpendicular to the surface of the specimen, in the thermal coefficient testing equipment. This area is an inherent mechanical calibration constant of the testing equipment at the time of manufacture, and the system can directly call it from the memory of the control motherboard.

[0076] This invention determines that a one-dimensional constant heat flux state has been reached and then locks in the corresponding steady-state time period. Extracting various test parameters based on this steady-state time period aims to obtain a set of pure static test characteristics that do not contain material-derived endothermic components during the transition period. This mechanism completely blocks the reading path of drift parameters during the unbalanced transition period, fundamentally eliminating the resulting deviation in thermal conductivity calculation.

[0077] As a concrete example, the formula for thermal conductivity is expressed as: .in, Indicates thermal conductivity; L represents steady-state heating power; A represents steady-state thickness; and A represents constant metering area. This represents the steady-state test temperature difference.

[0078] Steady-state heating power characterizes the macroscopic level of total heat energy injected into the specimen within the current observation interval. Multiplying it by the steady-state thickness reflects the total heat transfer scale required to penetrate the target thickness of the material, quantifying the total energy input required to maintain heat transfer at a specific thickness. Steady-state test temperature difference reflects the driving potential of the temperature gradient between the hot and cold plates. Multiplying it by a constant metering area represents the heat transfer driving intensity per unit area of ​​applied temperature gradient, quantifying the strength of the boundary driving conditions that promote heat flow. Thermal conductivity characterizes the physical law that, under the same constant metering area and temperature difference driving conditions, the greater the steady-state heating power required to penetrate the same thickness, the greater the calculated thermal conductivity value.

[0079] As a specific example, the preset minimum test temperature difference threshold is recommended to be in the range of 5℃ to 10℃. In a preferred embodiment, this minimum test temperature difference threshold is set to 5℃. It should be noted that the minimum test temperature difference threshold is a safety minimum value preset based on the temperature sensor's range accuracy and industry-standard testing specifications; its setting ensures that the calculation formula has a clear physical meaning and prevents abnormal calculation results caused by sensor malfunction or drastic environmental changes. Implementers can adaptively adjust the minimum test temperature difference threshold according to the accuracy of the specific device's temperature sensor.

[0080] In summary, this invention constructs a fluctuation characteristic analysis mechanism based on singular value decomposition, collects continuous power, displacement, and temperature difference signals, and constructs a fluctuation characteristic matrix using the average heating power sequence and the average displacement change rate sequence. By orthogonally decomposing the fluctuation characteristic matrix, it identifies steady-state time periods, solving the problems of existing technologies that rely on single numerical stagnation and are easily misled by the closed-loop lag of the servo pressure mechanism and low-frequency coupled fluctuations, leading to premature trigger detection cutoff and positive deviations in results. This achieves accurate identification of the establishment moment of the true one-dimensional constant heat flow state. By determining the convergence characteristics of the cooperative change ratio towards the white noise level, the attenuation process of coupled fluctuations between heat transfer tendency and mechanical constant pressure feedback is objectively quantified. By retrieving non-aliased historical data during the steady-state time period, the reading path of drift parameters during the unbalanced transition period is completely blocked, effectively eliminating the deception and interference of small low-frequency fluctuations during the transition period and eliminating the resulting deviations in thermal conductivity calculation.

Claims

1. A method for testing the thermal conductivity of a homogeneous plate-shaped thermal insulation material, characterized in that, The method includes: The heating power signal of the main heating plate, the displacement signal of the servo pressure mechanism, and the temperature difference signal on both sides of the homogeneous plate-shaped thermal insulation material under test are collected simultaneously. The heating power signal, displacement signal, and temperature difference signal are divided into time periods to construct the average heating power sequence, the average displacement change rate sequence, and the temperature difference change over the time period. When the standard deviation of the average heating power sequence is greater than or equal to the preset power noise floor, and the standard deviation of the average displacement change rate sequence is greater than or equal to the preset displacement noise floor, the average heating power sequence and the average displacement change rate sequence are standardized and concatenated into a fluctuation feature matrix. Singular value decomposition is performed on the fluctuation feature matrix to obtain the maximum and second largest singular values; the cooperative change ratio is calculated based on the maximum and second largest singular values. When the coordinated change ratio is less than or equal to a preset interference threshold, and the temperature difference change over the time period is less than or equal to a preset temperature difference fluctuation threshold, a steady state is determined to be reached, and the current time period is taken as the steady state time period; the thermal conductivity is calculated based on the heating power signal, displacement signal, and temperature difference signal within the steady state time period.

2. The method for detecting the thermal conductivity of a homogeneous plate-shaped thermal insulation material according to claim 1, characterized in that, The construction of the average heating power sequence, the average displacement change rate sequence, and the temperature difference change over time includes: The average value of the heating power signal within a time period is calculated as the average heating power of the time period; based on the preset sliding time window length, the average heating power of the time period corresponding to multiple consecutive sorted indices is extracted to form an average heating power sequence. Calculate the difference between the displacement signal at the end and beginning of the time period, divide the difference by the fixed observation duration to obtain the average displacement change rate of the time period; extract the average displacement change rate of the time period corresponding to multiple consecutive sorting indices to form an average displacement change rate sequence. Calculate the average value of the temperature difference signal within each time period, use the average value as the average test temperature difference of the current time period, and subtract the average test temperature difference of the previous time period from the average test temperature difference of the current time period. Take the absolute value of the difference as the temperature difference change of the current time period.

3. The method for testing the thermal conductivity of a homogeneous plate-shaped thermal insulation material according to claim 1, characterized in that, Before standardizing the average heating power sequence and the average displacement change rate sequence, the following steps are also included: When it is determined that the standard deviation of the average heating power sequence is less than the preset power noise floor, or the standard deviation of the average displacement change rate sequence is less than the preset displacement noise floor, the cooperative change ratio of the current time period is assigned a preset constant.

4. The method for testing the thermal conductivity of a homogeneous plate-shaped thermal insulation material according to claim 1, characterized in that, The method for obtaining the fluctuation feature matrix includes: The average value of the average heating power sequence is subtracted from each discrete data point in the average heating power sequence, and the result of the subtraction is divided by the standard deviation of the average heating power sequence to obtain the standardized power sequence. Subtract the average value of the average displacement rate of change sequence from each discrete data point in the average displacement rate of change sequence, and divide the result of the subtraction by the standard deviation of the average displacement rate of change sequence to obtain the standardized displacement sequence. The standardized power sequence and the standardized displacement sequence are horizontally concatenated as column vectors to form the fluctuation feature matrix.

5. The method for testing the thermal conductivity of a homogeneous plate-shaped thermal insulation material according to claim 1, characterized in that, Before calculating the synergistic change ratio, the following is also included: The system calculates the cumulative number of data points acquired over the current time period and obtains the average test temperature difference for that time period. When the cumulative number of data points is greater than or equal to a preset sliding time window length, and the average test temperature difference reaches a preset target temperature difference, the system calculates the collaborative change ratio. When the cumulative number of data points is less than the preset sliding time window length, or the average test temperature difference does not reach the preset target temperature difference, the system does not calculate the collaborative change ratio until the cumulative number of data points is greater than or equal to the preset sliding time window length, and the average test temperature difference reaches the preset target temperature difference.

6. The method for detecting the thermal conductivity of a homogeneous plate-shaped thermal insulation material according to claim 1, characterized in that, The method for obtaining the coordinated change ratio includes: The fluctuation feature matrix is ​​subjected to singular value decomposition to obtain the largest singular value and the second largest singular value; the largest singular value is used as the numerator and the second largest singular value is used as the denominator to calculate the cooperative change ratio.

7. The method for testing the thermal conductivity of a homogeneous plate-shaped thermal insulation material according to claim 1, characterized in that, When the coordinated change ratio is less than or equal to a preset interference threshold, and the temperature difference change over the time period is less than or equal to a preset temperature fluctuation threshold, a steady state is determined to be reached, and the current time period is taken as the steady state time period, including: The cumulative number of currently acquired time periods is counted; when the cumulative number reaches the preset maximum number of test time periods, and the coordinated change ratio still has not converged to within the preset interference threshold, timeout forced protection is triggered, and the current time period is forcibly determined as a steady-state time period.

8. The method for testing the thermal conductivity of a homogeneous plate-shaped thermal insulation material according to claim 1, characterized in that, The method for obtaining the thermal conductivity includes: The average value of the heating power signal during the steady-state time period is calculated as the steady-state heating power; the average value of the temperature difference signal during the steady-state time period is calculated as the steady-state test temperature difference; the average value of the displacement signal during the steady-state time period is calculated as the steady-state thickness. Obtain the constant measurement area of ​​the main heating plate; use the product of the steady-state heating power and the steady-state thickness as the numerator, and the product of the constant measurement area and the steady-state test temperature difference as the denominator to calculate the ratio and obtain the thermal conductivity.

9. The method for testing the thermal conductivity of a homogeneous plate-shaped thermal insulation material according to claim 8, characterized in that, Before calculating thermal conductivity, the following is also included: If the steady-state test temperature difference is less than the minimum test temperature difference threshold, an alarm for abnormal temperature difference establishment is issued and the thermal conductivity output is blocked; if the steady-state test temperature difference is greater than or equal to the minimum test temperature difference threshold, the thermal conductivity is calculated.

10. A device for testing the thermal conductivity of homogeneous plate-shaped thermal insulation materials, characterized in that, The device includes: The data acquisition module is used to synchronously acquire heating power signals, displacement signals, and temperature difference signals; the memory is used to store preset sliding time window length, preset power noise floor, preset displacement noise floor, preset interference threshold, preset temperature difference fluctuation threshold, and historical data; the processor is configured to execute the steps of the method for detecting the thermal conductivity of a homogeneous plate-shaped thermal insulation material according to any one of claims 1 to 9.

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