Precise detection method and device for thermal conductivity of soldering lug

Abstract

The invention provides an accurate detection method and device for the thermal conductivity of a soldering lug. The method comprises the following steps: applying a thermal pulse to the surface of a to-be-measured material, detecting the propagation time difference of the thermal pulse from a starting point to each measurement point, and constructing a time distribution matrix based on all the propagation time differences; obtaining a target propagation time data set according to the time distribution matrix, and collecting a temperature corresponding to each propagation time in the target propagation time data set on the measurement point to construct a temperature distribution model; based on the temperature distribution model and the target propagation time data set, generating a simulation result of the thermal diffusion behavior of the to-be-tested material under the action of the thermal pulse, and judging whether the simulation result accords with a preset range or not; if the simulation result accords with a preset range, performing inversion analysis based on the simulation result to obtain a first thermal conductivity distribution characteristic graph; and determining the target thermal conductivity of the to-be-tested material based on the first thermal conductivity distribution characteristic pattern. According to the scheme, the thermal conductivity of the material can be accurately obtained.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of material performance processing, in particular, to a precise detection method and device for thermal conductivity of a soldering sheet. BACKGROUND

[0002] In the field of material performance research, thermal conductivity, as a key indicator of measuring the heat conduction capacity of materials, has important significance that cannot be ignored for industrial production, energy utilization, and new material development. Accurate determination of the thermal conductivity of materials not only optimizes product design, but also improves equipment operation efficiency and safety. However, many traditional measurement methods currently have significant limitations in practical application, such as insufficient measurement accuracy, complex operation, or high requirements for sample shape. These defects make it difficult to accurately evaluate the thermal conduction performance of some materials in specific environments, limiting the development of related technologies.

[0003] Under this background, the field of material performance research faces some challenges that need to be solved. Because the dynamic characteristics of heat propagation are often disturbed by the non-uniformity of the internal structure of the material, and this problem further extends to the acquisition and processing of temperature distribution data, due to the technical bottlenecks in the synchronization of multi-point temperature collection and the accuracy of data fitting, the finally calculated thermal conductivity value may deviate from the true situation. These two core factors are interrelated, the former directly affects the latter, and thus the calculation result of thermal conductivity lacks credibility.

[0004] Therefore, the present application provides a precise detection method and device for thermal conductivity of a soldering sheet to solve one of the above technical problems. SUMMARY

[0005] The purpose of the present application is to provide a precise detection method and device for thermal conductivity of a soldering sheet, which can solve at least one of the above technical problems. The specific scheme is as follows: According to the specific embodiment of the present application, in a first aspect, the present application provides a precise detection method for thermal conductivity of a soldering sheet, comprising: applying a heat pulse to the surface of the material to be measured, detecting the propagation time difference of the heat pulse from the starting point to each measurement point, and constructing a time distribution matrix based on all the propagation time differences; According to the time distribution matrix, a target propagation time data set is obtained, and the temperature corresponding to each propagation time in the target propagation time data set is collected at the measurement point to construct a temperature distribution model; Based on the temperature distribution model and the target propagation time data set, a simulation result of the thermal diffusion behavior of the material to be measured under the action of the heat pulse is generated, and it is judged whether the simulation result conforms to the preset range; if the simulation result meets a preset range, performing inversion analysis based on the simulation result to obtain a first thermal conductivity distribution characteristic map; determining a target thermal conductivity of the material to be measured based on the first thermal conductivity distribution characteristic map.

[0006] According to the specific embodiment of the present application, in a second aspect, the present application provides a precise detection device for thermal conductivity of a soldering sheet, comprising: a detection unit configured to apply a heat pulse to a surface of a material to be measured, detect a propagation time difference of the heat pulse from a starting point to each measurement point, and construct a time distribution matrix based on all the propagation time differences; a collection unit configured to obtain a target propagation time data set according to the time distribution matrix, collect a temperature corresponding to each propagation time in the target propagation time data set at the measurement point, and construct a temperature distribution model; a simulation unit configured to generate a simulation result of thermal diffusion behavior of the material to be measured under the action of the heat pulse based on the temperature distribution model and the target propagation time data set, and determine whether the simulation result meets a preset range; if the simulation result meets the preset range, performing inversion analysis based on the simulation result to obtain a first thermal conductivity distribution characteristic map; a processing unit configured to obtain a target thermal conductivity of the material to be measured based on the first thermal conductivity distribution characteristic map.

[0007] Compared with the prior art, the above-mentioned scheme of the present application has at least the following beneficial effects: 1. The present application can accurately capture the dynamic propagation process of heat in the material by applying a heat pulse to the surface of the material to be measured and detecting the propagation time difference of the heat pulse from the starting point to each measurement point, and then constructing a time distribution matrix, which quantifies the heat transfer characteristics of different regions and provides dynamic data support for subsequent analysis. According to the time distribution matrix, the target propagation time data set is obtained and the corresponding temperature is collected at the measurement point to construct a temperature distribution model, which effectively integrates the space-time dimensional parameters of time and temperature, overcomes the one-sidedness of temperature distribution analysis in traditional methods, and improves the restoration degree of the model to the real thermal state of the material; 2, The application generates thermal diffusion behavior simulation results based on a temperature distribution model and a target propagation time data set and judges whether the simulation results meet a preset range, ensures the reliability of the simulation data through a model verification mechanism, and avoids error accumulation caused by parameter deviation; if the simulation results meet the preset range, inverse analysis is performed to obtain a first thermal conductivity distribution feature map, which can intuitively present the spatial distribution difference of the thermal conductivity performance of the material, and provides a visual basis for the thermal performance evaluation of the heterogeneous material; finally, the target thermal conductivity is determined based on the first thermal conductivity distribution feature map, which can accurately reflect the actual thermal conductivity performance of the material. The scheme effectively solves the problems of insufficient precision, difficulty in adapting to complex working conditions and the like in the measurement of the thermal conductivity of the heterogeneous material by the traditional method, significantly improves the measurement precision and reliability, provides a comprehensive, efficient and accurate effective method for the material thermal performance evaluation, and has important application value in the fields of industrial production, energy utilization and new material research and development. BRIEF DESCRIPTION OF DRAWINGS

[0008] Figure 1 A flowchart of a precise detection method of solder joint thermal conductivity is shown; Figure 2 A flowchart of a method of constructing a time distribution matrix is shown; Figure 3 A flowchart of a method of constructing a temperature distribution model is shown; Figure 4 A flowchart of a method of constructing a target propagation time data set is shown; Figure 5 A flowchart of a method of generating simulation results of thermal diffusion behavior of a material to be measured under the action of a thermal pulse is shown; Figure 6 A flowchart of a method of generating a first thermal conductivity distribution feature map is shown; Figure 7 A flowchart of a method of generating a target thermal conductivity of a material to be measured is shown; Figure 8 A flowchart of a method of correcting a first thermal conductivity distribution feature map is shown; Figure 9 A unit block diagram of a precise detection device of solder joint thermal conductivity according to an embodiment of the application is shown. DETAILED DESCRIPTION

[0009] In order to make the objects, technical solutions and advantages of the application clearer, the application will be further described in detail below with reference to the drawings. Obviously, the described embodiments are only some of the embodiments of the application, but not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0010] The terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used in the description of the embodiments of the present application and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0011] It should be understood that the term "and / or" as used herein merely describes associated objects, and can exist in three forms: for example, A and / or B can mean: A alone, both A and B, and B alone. In addition, the character " / " in this document generally represents an "or" relationship between the front and rear associated objects.

[0012] It should be understood that although the terms first, second, third, etc. can be used in the embodiments of the present application to describe, these descriptions are not limited to these terms. These terms are only used to distinguish the description. For example, without departing from the scope of the embodiments of the present application, the first can also be called the second, and similarly, the second can also be called the first.

[0013] Depending on the context, the word "if" as used herein can be interpreted to mean "when" or "upon" or "in response to determining" or "in response to detecting." Similarly, the phrase "if it is determined" or "if [a stated condition or event] is detected" can be interpreted to mean "upon determining" or "in response to determining" or "upon detecting [the stated condition or event]" or "in response to detecting [the stated condition or event]."

[0014] It should also be noted that the terms "comprise", "comprising", or any other variant thereof, are intended to cover a non-exclusive inclusion, such that a product or process that comprises a list of elements does not only include those elements, but can also include other elements not expressly listed or inherent to such product or process. Without more limitations, an element defined by the phrase "comprising a" does not exclude the existence of additional identical elements in the product or process that includes the stated element.

[0015] It should be particularly noted that the symbols and / or numbers present in the specification, if not marked in the description of the drawings, are not drawing reference numbers.

[0016] The optional embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0017] The embodiments provided by the present application are a kind of precision detection method of soldering piece thermal conductivity.

[0018] The optional embodiments of the present application will be described in detail below with reference to the accompanying drawings.Figure 1 The embodiments of the present application are described in detail.

[0019] Figure 1 A flowchart of a precise detection method of soldering pad thermal conductivity is shown, as shown in the following steps. Figure 1

[0020] Step S101, a heat pulse is applied to the surface of the material to be measured, the propagation time difference of the heat pulse from the starting point to each measurement point is detected, and a time distribution matrix is constructed based on all the propagation time differences; Step S102, according to the time distribution matrix, a target propagation time data set is obtained, and the temperature corresponding to each propagation time in the target propagation time data set is collected on the measurement point to construct a temperature distribution model; Step S103, based on the temperature distribution model and the target propagation time data set, a simulation result of the thermal diffusion behavior of the material to be measured under the action of the heat pulse is generated, and it is judged whether the simulation result conforms to the preset range; Step S104, if the simulation result conforms to the preset range, inverse analysis is carried out based on the simulation result to obtain a first thermal conductivity distribution characteristic map; Step S105, based on the first thermal conductivity distribution characteristic map, the target thermal conductivity of the material to be measured is determined.

[0021] Step S106, if the simulation result does not conform to the preset range, the boundary conditions for generating the simulation result are adjusted, and the step of generating the simulation result of the thermal diffusion behavior of the material to be measured under the action of the heat pulse based on the temperature distribution model and the target propagation time data set is returned until the simulation result conforms to the preset range.

[0022] In some embodiments, in the test environment built under the condition of pulse heat source, a high-precision time recording module is deployed to apply a transient heat pulse to the surface of the material to be measured. A sensor array with a time resolution of microsecond level is used to accurately record the propagation time difference of the heat pulse from the starting point to each measurement point, and a heat propagation time distribution matrix is obtained through data processing to lay a foundation for subsequent analysis. Then, a high-sensitivity temperature sensor array is reasonably arranged at different positions of the material to be measured. The multi-point temperature corresponding to the propagation time is synchronously acquired to construct a temperature distribution model, and the acquisition time is strictly synchronized with the propagation time. The temperature distribution model and the target propagation time data set are combined to construct a temperature distribution dynamic simulation model based on the heat diffusion equation. The model is used to simulate the heat propagation behavior of the material under the condition of pulse heat source to obtain a simulation result. The overall thermal conductivity of the material to be measured is calculated according to the simulation result.

[0023] ​As a specific embodiment, if the spatiotemporal evolution characteristics of the thermal diffusion behavior do not conform to the preset theoretical range of thermal conductivity (i.e., the preset range), the optimization process is entered. The deviation between the thermal diffusion characteristics and the spatiotemporal evolution trajectory is analyzed, and the theoretical comparison results are compared point by point with the preset thermal conductivity range. For example, in a thermal conductivity test of a certain material, the preset thermal conductivity range is 200 to 250 W / m·K, but the simulation results show that the thermal conductivity of some areas reaches 280 W / m·K. These areas with large deviations are marked, and the boundary condition range that needs to be adjusted and the direction of initial parameter correction are determined. For example, it may be necessary to tighten the boundary conditions or correct the initial temperature distribution.

[0024] As a specific embodiment, based on the first thermal conductivity distribution characteristic map, temperature change data for the corresponding region is extracted from a pre-established temperature distribution model. For example, when the corrected thermal conductivity value for a region is 320 W / m·K, temperature data for that region at different time points can be obtained, such as initially 25°C and rising to 30°C after one hour. By comparing the regional temperature distribution characteristics (such as temperature gradient and change amplitude), temperature parameters associated with the corrected thermal conductivity value are obtained, and a regional temperature distribution feature set is constructed. Next, a weighted average algorithm is used to comprehensively process the regional thermal conductivity and temperature parameters, combined with time-related parameters extracted from the refined heat propagation time dataset, such as heat propagation delay time. For example, an initial temperature parameter weight of 0.6 and a thermal conductivity weight of 0.4 are set, and an associated weight value is calculated. If this value deviates from a preset threshold range (e.g., 0.8-1.2), the weight allocation scheme is dynamically adjusted for each region. For example, the thermal conductivity weight can be increased to 0.5 and the temperature weight can be decreased to 0.5, resulting in a recalculated associated weight value of 0.9, which falls within the threshold range. The thermal conductivity data is then recalculated using the adjusted weight distribution scheme to obtain an adjusted regional thermal conductivity distribution dataset. For example, the adjusted thermal conductivity value of a particular region changes from its initial value to 330 W / m·K. Based on this dataset, the overall thermal conductivity calculation parameters of the material are integrated and the data for each region is summarized. For example, the thermal conductivity of the material center is 340 W / m·K, and the edge is 180 W / m·K. The weighted summary yields an overall value of 260 W / m·K. Finally, the overall thermal conductivity value is compared with a preset standard (e.g., 250-300 W / m·K). If the calculated result meets the expectations, it is determined as the target thermal conductivity.

[0025] If the simulation results do not meet the preset range, the boundary conditions used to generate the simulation results, such as material surface temperature and heat flux, are adjusted. The thermal diffusion behavior simulation results are regenerated based on the temperature distribution model and the target propagation time dataset until the simulation results meet the preset range. For example, by increasing the boundary heat flux parameter, the simulation results can be made closer to the actual heat conduction behavior, ensuring the accuracy of the final thermal conductivity calculation.

[0026] Figure 2 A method flow chart for constructing a time distribution matrix is shown as follows, including the following steps. Figure 2

[0027] Step S201, obtaining original propagation time data from the starting point to each measurement point based on the sensor array; Step S202, comparing the expected propagation time corresponding to the measurement point with the original propagation time data to obtain the propagation time difference of each measurement point; Step S203, judging whether the propagation time difference is greater than the time threshold value; Step S204, if the propagation time difference is greater than the time threshold value, correcting the original propagation time data, and normalizing all the original propagation time data after the correction to obtain target propagation time data; Step S205, constructing a time distribution matrix based on all the target propagation time data after the normalization.

[0028] In some embodiments, under the condition of a pulsed heat source, a high-precision time recording module is deployed to apply a transient heat pulse to the surface of the material to be measured, and original propagation time data from the starting point to each measurement point is obtained based on a sensor array to obtain a preliminary data set of heat propagation time. The time resolution of the sensor array reaches the microsecond level. For example, when studying the heat pulse propagation characteristics of the material surface, an array composed of multiple micro sensors is arranged on the surface of the material to be measured, and the position of each sensor is the measurement point. After the heat pulse is emitted, each sensor records the heat arrival time. Assuming that the distance from the starting point to a certain sensor is 10 centimeters, the expected propagation time is 0.5 seconds, and the actual record is 0.7 seconds, which indicates that the data may be abnormal.

[0029] ​Then, the expected propagation time corresponding to the measurement point is compared with the original propagation time data to obtain the propagation time difference of each measurement point. Specifically, assuming that there are 5 measurement points, the expected time difference is 0.1, 0.2, 0.3, 0.4, and 0.5 seconds, and the actual data is 0.1, 0.25, 0.3, 0.6, and 0.5 seconds. It can be seen that the 4th point data deviates greatly. At this time, it is judged whether each propagation time difference is greater than a preset time threshold (such as 0.15 seconds). If the propagation time difference is greater than the time threshold, the original propagation time data is corrected, the abnormal 4th point data is corrected to a reasonable value close to 0.4 seconds, and the accurate distribution of heat propagation time is determined. Then, all the original propagation time data after the correction processing is standardized, all the original propagation time data after the correction processing is constructed into a two-dimensional matrix, such as a 5x5 matrix, and each point data in the matrix is normalized to the range of 0 to 1 to obtain target propagation time data. The matrix containing the target propagation time data is used as a time distribution matrix. For example, in the construction of the distribution matrix, the standardization processing facilitates the intuitive comparison of the differences between the regions, and it is found that the heat pulse propagates faster in the center region of the material and slower in the edge region, which may be caused by the uneven distribution of material density. Further, key time points are extracted from the overall time characteristics corresponding to all target propagation time data, and visual processing is performed to generate a dynamic distribution diagram of heat pulse propagation. For example, the generated dynamic distribution diagram shows that there is an obvious lag in a certain region at the 3rd second, and combined with the time distribution matrix data, it can be inferred that there may be a defect with high thermal resistance in the region. By judging whether there is a local time deviation in the dynamic distribution diagram, the final analysis result is completed. This multi-dimensional analysis method not only improves the detection accuracy, but also provides a scientific basis for material performance optimization.

[0030] Figure 3 A method flow chart for constructing a temperature distribution model is shown as Figure 3 shown, comprising the following steps.

[0031] Step S301, based on a preset correction model, correcting each target propagation time data in the time distribution matrix to obtain a target propagation time data set; Step S302, based on the sensor array, acquiring the temperature corresponding to each target propagation time data on the measurement point to obtain an original temperature distribution data set; Step S303, based on the original temperature distribution data set and the target propagation time data set, determining a correlation matrix of temperature distribution and propagation time; Step S304, determining a target function corresponding to the temperature distribution and the propagation time based on the correlation matrix, and constructing a temperature distribution model based on the target function.

[0032] In some embodiments, an operation of correcting the time distribution matrix data based on a preset correction model is performed. For the target propagation time data set, a high-sensitivity temperature sensor array is arranged at different positions of the material to be tested. For example, on the surface of a piece of material to be tested, 10 sensors are arranged at the center, edge and middle transition area to collect a multi-point original temperature distribution data set corresponding to the propagation time. During the acquisition process, ensure that the acquisition time is strictly synchronized with the propagation time, match the collected temperature data with the propagation time data point by point, and obtain an initial temperature distribution record set and a preliminary time-temperature association table. For example, in a certain test, the temperature recorded at a certain sensor point is 48.0 degrees, and the corresponding propagation time is 0.5 seconds. They are associated to form a corresponding record in the time-temperature association table.

[0033] The preset calibration model is calibrated using a material structure compensation coefficient and a sensor delay correction coefficient. These coefficients can be determined through a distance-response calibration experiment. The steps are as follows: A homogeneous metal plate made of the same material as the weld to be tested is used to create different density regions on the sample surface (by drilling holes to create 0%, 3%, and 6% density deviation zones). Under the same heat pulse conditions, the heat propagation time for each density region is measured. The time for the complete region and the time for the density deviation region are recorded. The coefficient for each region is calculated, and the average value is taken as the final coefficient.

[0034] It should be noted that the acquisition of the sensor delay correction coefficient requires a fixed heat source position. The sensor can be moved precisely and the collected delay data is fitted and solved.

[0035] As a feasible embodiment, if the preliminary time-temperature correlation table has data missing or abnormal values, it is modified. For example, when the temperature data of a certain sensing point is missing, the missing value is estimated to be 48.0 degrees by interpolation according to the temperature values of the adjacent points 47.0 degrees and 49.0 degrees. For the abnormal temperature value of 60.0 degrees, it is adjusted to 48.5 degrees by smoothing processing. After modification, the temperature distribution and the propagation time are matrix processed to form a complete temperature distribution and time correlation matrix. Assuming that the data in a certain row of the matrix is the temperature 47.5 degrees corresponding to the propagation time 0.6 seconds, a 10-row 2-column matrix is finally constructed, and the data missing rate is judged by setting the preset integrity standard such as a data missing rate lower than 5.0%. If the final matrix data missing rate is 0.0%, the required final business data result is obtained. At the same time, starting from the temperature distribution information collected from the sensor array, the influence of the internal structure of the material on the temperature distribution is further analyzed. For example, it is found through multiple tests that the temperature in the central region is always about 5.0 degrees higher than the edge, which is presumably related to the strong heat conduction performance of the material center, which helps to understand the relationship between material properties and temperature distribution. In addition, data interpolation and smoothing processing are also optimized in combination with historical test data. If there are many missing values in a certain test, the temperature distribution trend of the previous test is referred to for interpolation estimation to ensure that the correction result is closer to the actual situation, further improving the data accuracy.

[0036] As a specific embodiment, after obtaining the temperature distribution and time correlation matrix, the temperature distribution model is constructed. The original temperature distribution data set is preliminarily processed to eliminate abnormal data caused by environmental interference and other factors. For example, when the temperature of a certain sensing point suddenly jumps from about 46.5 degrees to 55.0 degrees, it is marked as noise and replaced with 46.8 degrees estimated based on the surrounding data. Then, the preliminarily sorted temperature distribution record set and time synchronization information are matched point by point to construct a time-correlated distribution matrix. The least squares method is used to perform curve fitting processing on the temperature distribution data. For example, when the test data shows that the temperature rises from 45.0 degrees to 50.0 degrees from 0.1 seconds to 1.0 seconds, a continuous function is constructed to describe the change trend. If the fitting result deviates from the actual data by less than the preset accuracy threshold, such as an error rate lower than 2.0%, it is considered as a qualified fitting result. Finally, the temperature distribution data is converted into a continuous distribution model, and the parameters are calibrated according to the deviation between the model and the actual data. For example, if it is found that the predicted value of the model based on the fitting function is 1.5 degrees lower in the edge area, the final accurate temperature distribution continuous function model is obtained as the temperature distribution model by adjusting the model parameters.

[0037] Figure 4 A method flow chart for constructing a target propagation time data set is shown as Figure 4 shown, comprising the following steps.

[0038] Step S401, judge whether the expected propagation time corresponding to each measurement point is same as the corrected target propagation time data or not; Step S402, if not, carry out deviation value compensation processing on the corrected target propagation time data corresponding to the measurement point, to obtain compensated propagation time data; Step S403, judge whether the deviation value between the expected propagation time corresponding to the measurement point and the compensated propagation time data is greater than the time threshold value or not; Step S404, if the deviation value is greater than the time threshold value, based on the preset correction model, carry out secondary correction on the target propagation time data, and obtain the target propagation time data set based on the secondary corrected target propagation time data.

[0039] In some embodiments, after obtaining the time distribution matrix of heat propagation, combined with the internal structure inhomogeneity characteristics of the material, a pre-established structure interference correction model is used to carry out first round correction processing on the propagation time data. For example, when testing the heat conduction of a certain composite material, there is a density difference in the interior of the material, so that the heat propagation time of part of the area in the preliminary time matrix is abnormal. Assuming that in the surface test of the material, the propagation time of a certain area is 0.8 seconds, and the average of the surrounding area is 0.5 seconds, by preliminarily adjusting the data of the area and other areas with deviation, the adjusted time distribution data set is obtained, and the preliminary correction of the propagation deviation caused by the structure inhomogeneity is completed. For the adjusted time distribution data set, combined with the influence factors of environmental temperature, material local heat capacity and other dynamic characteristics, the deviation value of each measurement point in the data set is compensated point by point. For example, the propagation time of a certain measurement point is still 0.7 seconds after preliminary adjustment, which is higher than the expected 0.6 seconds, and it is speculated that it may be due to the higher local heat capacity of the area. At this time, according to the preset parameters, the data of the point is fine-tuned to generate the compensated time distribution record, and the data accuracy is further refined.

[0040] As a specific embodiment, the time correction parameters of each point are obtained from the compensated time distribution record, and it is judged whether the expected propagation time corresponding to each measurement point is the same as the corrected target propagation time data. If there is a point whose time value deviates from the preset threshold (such as 0.1 seconds), the actual deviation reaches 0.2 seconds, that is, the expected propagation time of the point is not the same as the corrected target propagation time data, and the secondary correction is performed on the point parameters based on the preset correction model. For example, the time value of a certain point is 0.65 seconds after the first correction, which still does not meet the requirements, and a more reasonable correction value is calculated based on the data of the surrounding points, and finally the time of the point is adjusted to 0.62 seconds, and the secondary corrected target propagation time data is obtained. Repeat the above operation until the target propagation time data of all measurement points is verified, and the target propagation time data set is obtained based on the secondary corrected target propagation time data. Finally, according to the obtained target propagation time data set, the refined heat propagation time distribution characteristics are constructed in combination with the comprehensive influence of material properties and internal structure. For example, the corrected time distribution set is converted into an intuitive feature map. If the final feature map shows that the propagation time in the center region of the material is shorter, about 0.3 seconds, and the propagation time in the edge region is longer, about 0.6 seconds, and the deviation of all data is controlled within 0.05 seconds, it is considered that the result is refined successfully, and the refined time distribution result is obtained. In addition, it can also be verified in combination with the material microstructure data. If it is found through microscope observation that there are more micropore structures in the edge region, which is consistent with the phenomenon that the edge propagation time is longer in the time distribution map, it supports the rationality of data processing and mapping from the side, and ensures the reliability of the final data set.

[0041] Figure 5 A method flow chart for generating simulation results of thermal diffusion behavior of a material to be tested under the action of a thermal pulse is shown as Figure 5 The method comprises the following steps.

[0042] In step S501, the temperature distribution model and the target propagation time data set are parameter mapped to obtain heat propagation data. In step S502, based on the heat diffusion equation, the preset time step and the preset space grid, simulation operation is performed on all heat propagation data to obtain simulation data. In step S503, the simulation data is corrected based on the preset temperature threshold. In step S504, the simulation results are obtained according to the calibrated simulation data.

[0043] In some embodiments, the temperature distribution model is parameter-mapped with the target transit time dataset. The fitted temperature distribution model is point-by-point matched with the target transit time dataset. For example, in a thermal conduction test of a certain material, the target transit time dataset records temperature change data from 0.1 to 2.0 seconds, and the measured temperature at each time point is compared with the predicted value of the temperature distribution model to establish a corresponding relationship and perform parameter mapping, thereby obtaining integrated heat propagation basic data records and determining an initial distribution set matching the conditions of the pulse heat source to obtain heat propagation data.

[0044] As a specific embodiment, based on the heat diffusion equation, a preset time step, and a preset spatial grid, a numerical calculation tool is used to simulate and operate the heat propagation data. For example, the preset time step is 0.01 seconds, and the spatial grid is divided into 0.1 centimeter unit grids. According to the thermal physical properties of the material, the time step and the spatial grid parameters are dynamically adjusted during the operation process. By simulating the heat conduction process in the material, preliminary temperature distribution dynamic simulation data, i.e., simulation data, is obtained. If the simulation data exceeds the preset temperature threshold, it indicates that the simulation result has a deviation. At this time, the key variables in the simulation process, such as thermal conductivity and boundary conditions, are iteratively corrected. For example, when the simulation data shows that the temperature in the center region of the material is 2.5 degrees higher than the actual value, the related parameters are gradually adjusted, and the time-space evolution characteristics of the heat propagation behavior are checked layer by layer until the calibrated simulation data meets the expected distribution characteristics. Finally, according to the calibrated simulation data, the time-space evolution trajectory of the heat propagation behavior is presented in multiple dimensions. For example, the calibrated simulation data is converted into a three-dimensional dynamic image to generate a visual atlas that intuitively presents the diffusion process of heat from the center of the material to the surrounding area, clearly presenting the temperature change trend at different times and different positions, thereby obtaining the final dynamic simulation output result and intuitively presenting the heat diffusion behavior of the material under the action of the heat pulse.

[0045] Further, step-by-step adjustment is performed according to the determined boundary condition range and initial parameter correction direction. For example, if it is found that the temperature of the edge region of the material is too low, the boundary temperature of the temperature distribution model will be adjusted from 20 degrees to 22 degrees, and the center value of the initial temperature distribution will be fine-tuned from 50 degrees to 48 degrees, generating new heat propagation data, and then forming updated simulation data. If the characteristic value in the updated simulation data still deviates from the preset thermal conductivity range, the thermal diffusion characteristics are checked layer by layer. For example, when the heat propagation speed in the simulation data is too fast, the material density or specific heat capacity and other parameters are adjusted layer by layer, and the space-time evolution trajectory is recalculated after each iteration, until the characteristic value approaches the theoretical range, such as reducing the heat propagation speed deviation from 0.2 seconds to 0.05 seconds or less. Finally, according to the optimized simulation data, the optimized simulation results are mapped in multiple dimensions. For example, a dynamic heat map is generated to show the temperature gradient change over time during the heat diffusion from the center to the edge. Researchers can visually observe whether the final space-time evolution trajectory meets the preset theoretical comparison results to ensure that the simulation results can truly reflect the thermal diffusion characteristics of the material.

[0046] Figure 6 A method flow chart for generating a first thermal conductivity distribution feature map is shown as Figure 6 The method comprises the following steps.

[0047] Step S601, obtain the temperature field distribution data in the simulation results, divide the material region of the material to be tested according to the grid density, and obtain a plurality of grid cells; Step S602, obtain the temperature value of each grid cell according to the temperature field distribution data, and obtain the temperature field discrete data set and the temperature gradient corresponding to each grid cell based on all temperature values; Step S603, construct a heat conduction inversion solving matrix, and obtain the heat flux density distribution corresponding to each grid cell based on the heat conduction inversion solving matrix and the temperature field discrete data set; Step S604, obtain the first thermal conductivity of each grid cell based on the temperature gradient and the heat flux density distribution corresponding to each grid cell; Step S605, obtain the first thermal conductivity distribution feature map according to the spatial coordinates and the first thermal conductivity of all grid cells.

[0048] In some embodiments, the temperature field distribution data in the optimized heat diffusion simulation result is obtained, and the material region of the material to be tested is divided into grids according to a preset grid density. For example, when conducting heat conduction analysis on a piece of uniform material, the material region is divided into 100x100 grid units, and each grid unit represents a small region of the material. Boundary constraint conditions of the heat conduction equation are set for each grid unit, such as setting a constant temperature of 25°C at the edge region of the material, extracting temperature values from each grid node to form a temperature field discrete data set containing thousands of temperature points, and recording the temperature gradient change trend between the grid units.

[0049] As a feasible embodiment, a finite element analysis method is used to construct a heat conduction inverse solution matrix. Mathematical modeling is performed based on the relationship between the temperature gradient in the temperature field discrete data set and the heat flux density, and numerical iterative calculation is performed through the coefficient matrix of the heat conduction equation. For example, when the simulation result shows that the temperature change rate of some grid units is as high as 5°C / cm, and the temperature change rate of adjacent units is only 1°C / cm, the finite element method discretizes the entire region into small units, iteratively calculates the heat flux density distribution of each unit by constructing a coefficient matrix, and converts the complex heat conduction problem into a solvable small-scale problem, thereby obtaining the heat flux density distribution corresponding to each grid unit.

[0050] As a feasible embodiment, based on the temperature gradient and heat flux density distribution corresponding to each grid unit, the first thermal conductivity of each grid unit is obtained by reverse derivation calculation according to the physical relationship between thermal conductivity and heat flux density and temperature gradient. For example, in the experimental simulation, the heat flux density of the central region of the material is high, and the calculated thermal conductivity is 320 W / m·K, while the thermal conductivity of the edge region is 180 W / m·K. This calculation result quantitatively represents the spatial heterogeneity of the thermal conduction characteristics of the material. By establishing a functional relationship between the thermal conductivity and the spatial coordinates, a detailed thermal conductivity distribution data table is generated.

[0051] As a feasible embodiment, the thermal conductivity distribution data table is subjected to grid interpolation processing according to the spatial coordinates and the first thermal conductivity of all grid units. For example, when the thermal conductivity values of adjacent units in the data table suddenly decrease from 300 W / m·K to 200 W / m·K, the interpolation algorithm is used to adjust the values in the transition region to about 250 W / m·K, so that the thermal conductivity change trend between the grid units is smoothly transitioned. On this basis, a first thermal conductivity distribution characteristic map of the material is drawn, and the spatial boundaries of the high value region (such as the region with thermal conductivity higher than 280 W / m·K) and the low value region (such as the region with thermal conductivity lower than 200 W / m·K) in the characteristic map are marked with different colors or patterns, so as to intuitively display the spatial distribution law of the thermal conduction characteristics of the material, and provide a visual basis for material performance analysis and optimization design.

[0052] Figure 7 A method flowchart for generating a target thermal conductivity of a material to be tested is shown as Figure 7 The method includes the following steps.

[0053] Step S701, according to the preset material thermal database, determine the thermal parameter range of the material to be tested; Step S702, according to the thermal parameter range, correct the first thermal conductivity distribution characteristic map to obtain the second thermal conductivity distribution characteristic map; Step S703, according to the temperature distribution model, extract the temperature change data of each grid cell, and according to the second thermal conductivity distribution characteristic map, extract the initial thermal conductivity of each grid cell; Step S704, for each grid cell, compare the temperature change data and the initial thermal conductivity to form a temperature distribution feature set, and according to the target propagation time data set and the temperature distribution feature set, calculate the corresponding correlation weight of each grid cell; Step S705, according to the first preset threshold range and the correlation weight, adjust the initial thermal conductivity of each grid cell; Step S706, according to the adjusted initial thermal conductivity of all grid cells, obtain the target thermal conductivity of the material to be tested.

[0054] In some embodiments, the temperature change data of each grid cell is extracted from the temperature distribution model, and the initial thermal conductivity of each grid cell is extracted from the second thermal conductivity distribution characteristic map. Assuming that in a certain grid cell, the temperature distribution model shows that the initial temperature is 20°C, and after 1 hour it rises to 28°C, and the second thermal conductivity distribution characteristic map indicates that the initial thermal conductivity of the grid cell is 220 W / m·K. For each grid cell, compare the temperature change data and the initial thermal conductivity, analyze the temperature gradient, the trend of thermal conductivity with temperature change, etc. to form a temperature distribution feature set. Then, combined with the target propagation time data set, a weighted average algorithm is used to give the temperature parameter and the thermal conductivity parameter an initial weight of 0.6 and 0.4 respectively, and the correlation weight of each grid cell is calculated.

[0055] As a feasible embodiment, according to the first preset threshold range and the calculated correlation weight, the first preset threshold range is set to a reasonable range of correlation weight of 0.8-1.2, and the initial thermal conductivity of each grid cell is adjusted. If the calculated correlation weight of a certain grid cell is 0.7, which is lower than the lower limit of the threshold, the distribution weight of the cell is adjusted, the thermal conductivity weight is increased to 0.5, and the temperature weight is decreased to 0.5, and the thermal conductivity is recalculated. Finally, the initial thermal conductivities of all grid cells after adjustment are summarized, different weights are given according to the position, area, etc. of the grid cells in the material, and the target thermal conductivity of the material to be tested is calculated by weighted summation.

[0056] Figure 8 A method flow chart for revising the first thermal conductivity distribution characteristic map is shown as follows, Figure 8 comprising the following steps.

[0057] Step S801, comparing each first thermal conductivity in the first thermal conductivity distribution characteristic map with the thermal parameter range to obtain deviation data; Step S802, judging whether the deviation data exceeds the second preset threshold range; Step S803, if the second preset threshold range is exceeded, adjusting the grid density, and returning to the step of grid dividing the material region of the material to be tested according to the grid density until the deviation data does not exceed the second preset threshold range, and taking the first thermal conductivity distribution characteristic map as the second thermal conductivity distribution characteristic map.

[0058] In some embodiments, the operation of comparing each first thermal conductivity in the first thermal conductivity distribution characteristic map with the thermal parameter range. The thermal parameter range of the target material is obtained from the pre-established material thermal database, for example, when the target material is a certain type of composite material, the thermal conductivity range is 150 to 350 W / m·K, and the thermal conductivity estimation value of each region in the first thermal conductivity distribution characteristic map is compared with it. If the thermal conductivity estimation value of a certain region in the first thermal conductivity distribution characteristic map is 400 W / m·K, which exceeds the above range, the deviation data of 50 W / m·K is calculated, and the deviation data of all regions is obtained in the same way. Then, it is judged whether the deviation data exceeds the second preset threshold range. Assuming that the second preset threshold range is set to 30 W / m·K, if the calculated deviation data exceeds the range, such as the 50 W / m·K deviation described above, the grid density adjustment link is entered. For example, when the original grid is divided into 50×50 units, the grid density in the region with large deviation is increased to 80×80 units, the space distribution estimation region is refined and divided, and the adjusted grid division scheme is obtained.

[0059] As a feasible embodiment, based on the adjusted grid density scheme, the step of dividing the material region of the material to be tested into grids according to the grid density is returned. The boundary conditions of each grid unit are redefined, and the thermal parameters of each grid unit are processed one by one, and the thermal conductivity data corresponding to the spatial distribution estimation value are recalculated to obtain the recalculated thermal conductivity distribution data set. As in the refined grid, the thermal conductivity of a certain unit is corrected from 400 W / m·K to 330 W / m·K. Then, the recalculated thermal conductivity distribution data set is compared with the parameter range in the material thermal database again, and if there is still a deviation area, for example, the thermal conductivity value of the central area part of the unit is 360 W / m·K, which exceeds the upper limit, then the local grid density of the area is further optimized, such as increasing the grid density of the area to 100x100 units, and reacquiring the temperature field data and calculating. Repeat the above operation until the deviation data does not exceed the second preset threshold range. At this time, the first thermal conductivity distribution feature map is taken as the second thermal conductivity distribution feature map to obtain the final corrected thermal conductivity distribution feature map, for example, the final correction value shows that the thermal conductivity of the central area is 340 W / m·K, and the edge area is 170 W / m·K, which meets the requirements of the material thermal parameter range.

[0060] In a feasible embodiment, if the simulation result does not meet the preset range, the boundary conditions for generating the simulation result are adjusted according to the priority order of the heat flux boundary, the environmental temperature boundary and the contact thermal resistance boundary, and the step of generating the simulation result of the thermal diffusion behavior of the material to be tested under the action of the thermal pulse based on the temperature distribution model and the target propagation time data set is returned until the simulation result meets the temperature field average deviation of not more than 2%, the heat flux peak deviation of not more than 5%, and the thermal conductivity deviation value of not more than 0.5 W / (m·K).

[0061] The application also provides a device embodiment for implementing the method steps of the above embodiments, based on the same explanation of the same name and the same technical effect as the above embodiments, which will not be repeated here.

[0062] As shown in Figure 9 The application provides a precise detection device for thermal conductivity of a soldering sheet, which comprises: A detection unit 1001 is configured to apply a thermal pulse to the surface of the material to be tested, detect the propagation time difference of the thermal pulse from the starting point to each measurement point, and obtain a time distribution matrix based on all the propagation time differences; An acquisition unit 1002 is configured to obtain a target propagation time data set from the time distribution matrix, and acquire the temperature corresponding to each propagation time in the target propagation time data set at the measurement point to construct a temperature distribution model; The simulation unit 1003 is configured to generate a simulation result of thermal diffusion behavior of the material to be measured under the action of the thermal pulse based on the temperature distribution model and the target propagation time data set, and determine whether the simulation result meets a preset range; if the simulation result meets the preset range, performing inversion analysis based on the simulation result to obtain a first thermal conductivity distribution feature map; The processing unit 1004 is configured to obtain a target thermal conductivity of the material to be measured based on the first thermal conductivity distribution feature map.

[0063] As to the apparatus in the above-mentioned embodiments, the specific manners in which various modules perform operations have been described in detail in the embodiments of the method, and thus will not be described in detail here.

[0064] Although the operations are described in a particular, sequential order, this should not be understood as requiring that the operations be performed in that order, or in sequential order, or that all operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing can be advantageous.

[0065] The methods and apparatus of the present application can be implemented using standard programming techniques, and the various steps, operations, or procedures described herein can be implemented using one or more hardware or software modules. It should be noted that the words "module" and "block" as used herein are intended to comprise a hardware implementation and / or a software implementation of a device, and / or a device that is configured to receive input.

[0066] Any of the steps, operations, or procedures described herein can be performed or implemented using one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented using a computer program product comprising a computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or procedures described herein.

[0067] The foregoing description of the implementation of the present application has been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the application to the precise form disclosed, and various modifications and variations are possible in light of the above teachings or can be acquired from practice of the application. One skilled in the art will appreciate that the choice and arrangement of these embodiments are designed to illustrate the principles of the application and its practical application, so that those skilled in the art can utilize the application in various embodiments and various modifications suitable for the specific use contemplated.

[0068] As to the apparatus in the above-mentioned embodiments, the specific manners in which various modules perform operations have been described in detail in the embodiments of the method, and thus will not be described in detail here.

[0069] It will be further understood that "connected" can include direct connection between two elements and "indirect connection" between two elements through other elements.

[0070] It will be further understood that, unless otherwise specified, "or" as used herein is inclusive or, even if the context should imply that indications to the contrary are intended, wherein "consisting essentially of will be used where appropriate to preserve the recited function, even if additional steps are

[0071] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the specification and examples be considered as exemplary only, with the true scope and spirit of the application being indicated by the following claims.

[0072] It is to be understood that the application is not limited to the precise details of construction and the above-described embodiments and that various modifications and equivalent structures can be substituted for elements thereof without departing from the scope and spirit of the application. The scope of the application is to be measured only by the claims appended hereto.

[0073] The above embodiments are only used to illustrate the technical solutions of the present application, not limit the present application; even if the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand: they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for accurately detecting the thermal conductivity of a soldering piece, characterized in that: include: Applying a heat pulse to the surface of the material to be tested, detecting the propagation time difference of the heat pulse from the starting point to each measuring point, and constructing a time distribution matrix based on all the propagation time differences; obtaining a target propagation time dataset according to the time distribution matrix, and collecting a temperature corresponding to each propagation time in the target propagation time dataset at the measurement point to construct a temperature distribution model; generating a simulation result of the thermal diffusion behavior of the material to be tested under the action of the heat pulse based on the temperature distribution model and the target propagation time dataset, and determining whether the simulation result meets a preset range; If the simulation result meets the preset range, performing an inversion analysis based on the simulation result to obtain a first thermal conductivity distribution characteristic map; Based on the first thermal conductivity distribution characteristic diagram, a target thermal conductivity of the material to be tested is determined.

2. The method according to claim 1, characterized in that Detecting the propagation time difference of the heat pulse from the starting point to each measurement point and constructing a time distribution matrix based on all the propagation time differences includes: Acquiring original propagation time data of the heat pulse from the starting point to each of the measurement points based on a sensor array; Comparing the expected propagation time corresponding to the measurement point with the original propagation time data to obtain the propagation time difference of each measurement point; Determining whether each of the propagation time differences is greater than a time threshold; If the propagation time difference is greater than a time threshold, correcting the original propagation time data, and normalizing all the corrected original propagation time data to obtain target propagation time data; The time distribution matrix is ​​constructed based on all the target propagation time data.

3. The method according to claim 2, characterized in that Obtaining a target propagation time dataset according to the time distribution matrix, and collecting a temperature corresponding to each propagation time in the target propagation time dataset at the measurement point to construct a temperature distribution model, includes: Correcting each target propagation time data in the time distribution matrix based on a preset correction model to obtain a target propagation time data set; Based on the sensor array, the temperature corresponding to each target propagation time data is collected at the measurement point to obtain an original temperature distribution data set; determining a correlation matrix between temperature distribution and propagation time based on the original temperature distribution dataset and the target propagation time dataset; An objective function corresponding to temperature distribution and propagation time is determined based on the correlation matrix, and a temperature distribution model is constructed based on the objective function.

4. The method according to claim 3, characterized in that The step of correcting each target propagation time data in the time distribution matrix based on a preset correction model to obtain a target propagation time data set includes: Determining whether the expected propagation time corresponding to each of the measurement points is the same as the corrected target propagation time data; If they are not the same, performing deviation compensation processing on the corrected target propagation time data corresponding to the measurement point to obtain compensated propagation time data; Determining whether a deviation between the expected propagation time corresponding to the measurement point and the compensated propagation time data is greater than the time threshold; If the deviation value is greater than the time threshold, the target propagation time data is corrected twice based on the preset correction model, and the target propagation time dataset is obtained based on the target propagation time data after the second correction.

5. The method according to claim 1, wherein Generating a simulation result of the thermal diffusion behavior of the material to be tested under the action of the heat pulse based on the temperature distribution model and the target propagation time data set includes: performing parameter mapping processing on the temperature distribution model and the target propagation time data set to obtain heat propagation data; Based on a heat diffusion equation, a preset time step, and a preset spatial grid, a simulation operation is performed on all the heat propagation data to obtain simulation data; Correcting the simulation data based on a preset temperature threshold; The simulation result is obtained according to the calibrated simulation data.

6. The method according to claim 1, characterized in that The inversion analysis based on the simulation results is performed to obtain a first thermal conductivity distribution characteristic diagram, including: Obtaining temperature field distribution data from the simulation results, and performing grid division on the material region of the material to be tested according to a grid density to obtain a plurality of grid units; Acquire a temperature value of each of the grid cells according to the temperature field distribution data, and obtain a temperature field discrete data set and a temperature gradient corresponding to each of the grid cells based on all the temperature values; Constructing a heat conduction inversion solution matrix, and obtaining a heat flux density distribution corresponding to each of the grid cells based on the heat conduction inversion solution matrix and the temperature field discrete data set; Obtaining a first thermal conductivity of each of the grid cells based on the temperature gradient and the heat flux density distribution corresponding to each of the grid cells; The first thermal conductivity distribution characteristic map is obtained according to the spatial coordinates of all the grid cells and the first thermal conductivity.

7. The method according to claim 6, characterized in that The step of obtaining a target thermal conductivity of the material to be tested based on the first thermal conductivity distribution characteristic diagram includes: Determining the thermal parameter range of the material to be tested according to a preset material thermal database; Correcting the first thermal conductivity distribution characteristic map according to the thermal parameter range to obtain a second thermal conductivity distribution characteristic map; extracting temperature change data of each of the grid cells according to the temperature distribution model, and extracting initial thermal conductivity of each of the grid cells according to the second thermal conductivity distribution characteristic map; For each of the grid cells, comparing the temperature change data with the initial thermal conductivity to form a temperature distribution feature set, and calculating an association weight corresponding to each of the grid cells based on the target propagation time dataset and the temperature distribution feature set; Adjusting the initial thermal conductivity of each of the grid cells according to a first preset threshold range and an associated weight; The target thermal conductivity of the material to be tested is obtained according to the adjusted initial thermal conductivity of all the grid cells.

8. The method according to claim 7, characterized in that The step of correcting the first thermal conductivity distribution characteristic diagram according to the thermal parameter range to obtain a second thermal conductivity distribution characteristic diagram includes: comparing each of the first thermal conductivities in the first thermal conductivity distribution characteristic diagram with the thermal parameter range to obtain deviation data; Determining whether the deviation data exceeds a second preset threshold range; If it exceeds the second preset threshold range, the grid density is adjusted, and the process returns to the step of gridding the material area of ​​the material to be tested according to the grid density until the deviation data does not exceed the second preset threshold range, and the first thermal conductivity distribution characteristic map is used as the second thermal conductivity distribution characteristic map.

9. The method according to claim 1, characterized in that Also includes: If the simulation result does not meet the preset range, the boundary conditions used to generate the simulation result are adjusted, and the process returns to the step of generating the simulation result of the thermal diffusion behavior of the material to be tested under the action of the heat pulse based on the temperature distribution model and the target propagation time dataset, until the simulation result meets the preset range.

10. A precise detection device for thermal conductivity of soldering pads, characterized in that: include: a detection unit, configured to apply a heat pulse to the surface of the material to be tested, detect a propagation time difference of the heat pulse from a starting point to each measuring point, and construct a time distribution matrix based on all the propagation time differences; an acquisition unit, configured to obtain a target propagation time dataset according to the time distribution matrix, and to acquire a temperature corresponding to each propagation time in the target propagation time dataset at the measurement point to construct a temperature distribution model; a simulation unit, configured to generate a simulation result of the thermal diffusion behavior of the material to be tested under the action of the heat pulse based on the temperature distribution model and the target propagation time data set, and determine whether the simulation result meets a preset range; If the simulation result meets the preset range, performing an inversion analysis based on the simulation result to obtain a first thermal conductivity distribution characteristic map; A processing unit is configured to obtain a target thermal conductivity of the material to be tested based on the first thermal conductivity distribution characteristic map.

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