Foaming ceramic plate heat conductivity coefficient detection device and detection method

Abstract

The invention relates to the technical field of heat conduction detection, and discloses a foamed ceramic plate heat conductivity coefficient detection device and method.The detection device is composed of a main body frame, a limiting mechanism, a sound wave measurement module, a heat source module and a detection module, and the limiting mechanism is used for positioning a to-be-detected foamed ceramic plate at a test position; the sound wave measuring module measures the pore structure so as to guide the heat source module to control the relative position of the heat conduction unit through the end displacement unit to provide a heat source for the foaming ceramic plate, the detection module is composed of a same-direction detection set and a different-direction detection set, temperature data are collected in different directions, and the data are analyzed through the data analysis unit. According to the device, by accurately controlling the supply position of the heat source, the heat conduction performance of a detected object is most accurately fed back through data collected by the detection module, and the problems that in the prior art, steady-state heat conduction detection is low in efficiency and high in cost are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal conductivity detection, and particularly relates to a device and method for detecting the thermal conductivity of a foamed ceramic plate. Background Art

[0002] A foamed ceramic plate is a porous material with good heat insulation performance, usually manufactured by combining a ceramic matrix material with a foaming agent. Its core feature is the large number of tiny pores in its structure, which makes the foamed ceramic have a low density and excellent thermal insulation performance.

[0003] In the conventional method for detecting the thermal conductivity of a foamed ceramic plate, a steady-state thermal conductivity detection method is adopted, that is, the foamed ceramic plate is continuously heated, and the overall temperature change of the foamed ceramic plate is continuously monitored to calculate the thermal conductivity of the foamed ceramic plate.

[0004] However, in this method, there are problems such as long detection time and the need to set up an adiabatic structure, resulting in low detection efficiency and high cost. Summary of the Invention

[0005] The purpose of the present invention is to provide a device and method for detecting the thermal conductivity of a foamed ceramic plate, aiming to solve the problems of low efficiency and high cost in the steady-state heat conduction detection in the prior art.

[0006] The present invention is implemented as follows. In the first aspect, the present invention provides a device for detecting the thermal conductivity of a foamed ceramic plate, including: A main body frame, a limiting mechanism, an acoustic wave measurement module, a heat source module, and a detection module; The limiting mechanism is arranged on the main body frame, and the limiting mechanism is used to limit and clamp the foamed ceramic plate to be detected, so that the foamed ceramic plate to be detected is in the test position; Both the heat source module and the detection module are arranged inside the main body frame, and the heat source module includes a heat conduction unit and a head displacement unit; The acoustic wave measurement module is used to measure the pore structure of the foamed ceramic plate to be detected based on acoustic wave signals in the test position, so as to obtain the pore structure information of the foamed ceramic plate to be detected, and perform a simulation analysis on the heat conduction performance of the foamed ceramic plate to be detected according to the pore structure information, so as to obtain the predicted thermal conductivity of the foamed ceramic plate to be detected. Through the analysis of the predicted thermal conductivity, the working position of the heat conduction unit for supplying heat to the foamed ceramic plate to be detected and the specific processing form of transient heat conduction processing are obtained; The heat conduction unit is arranged inside the main body frame through the end displacement unit, and the end displacement unit is used to control the relative position relationship between the heat conduction unit and the foamed ceramic plate to be detected at the test position, so that the heat conduction unit is in the working position for supplying heat source to the foamed ceramic plate to be detected; The heat conduction unit is used to perform transient heat conduction treatment on the foamed ceramic plate to be detected; The detection module includes a same-direction detection group, an opposite-direction detection group and a data analysis unit; The same-direction detection group includes several detection units arranged in the same direction as the heat source module. The same-direction detection group is used to collect temperature data of the foamed ceramic plate to be detected at the test position in the same direction to obtain first detection data; The opposite-direction detection group includes several detection units arranged in the opposite direction to the heat source module. The opposite-direction detection group is used to collect temperature data of the foamed ceramic plate to be detected at the test position in the opposite direction to obtain second detection data; The data analysis unit is electrically connected to the same-direction detection group and the opposite-direction detection group respectively. The data analysis unit is used to receive the first detection data and the second detection data, and perform data analysis on the first detection data and the second detection data according to a pre-trained thermal conductivity analysis algorithm to obtain the detected thermal conductivity of the foamed ceramic plate to be detected.

[0007] In a second aspect, the present invention provides a method for detecting the thermal conductivity of a foamed ceramic plate, which is used to implement the device for detecting the thermal conductivity of a foamed ceramic plate according to any one of the first aspects, and includes: Mark the foamed ceramic plate to be detected as the detection object, and obtain the specification information of the detection object; wherein, the specification information includes the size data, thickness data and preset thermal conductivity standard of the detection object; Analyze the execution plan for detecting the thermal conductivity of the limiting mechanism and the heat source module according to the specification information of the detection object, so as to obtain the detection execution plan of the limiting mechanism and the heat source module corresponding to the detection object; Drive the limiting mechanism to limit and clamp the detection object according to the detection execution plan, so that the detection object is in the test position; Drive the end displacement unit in the heat source module according to the detection execution plan to control the heat conduction unit to move to each specified working position in sequence, and drive the heat conduction unit to supply heat source to the detection object; Continuously collect detection data of the detection object through the same-direction detection group and the opposite-direction detection group in the detection module to obtain first detection data and second detection data; The data analysis unit in the detection module analyzes the first detection data and the second detection data according to a pre-trained thermal conductivity analysis algorithm to obtain the detected thermal conductivity of the foamed ceramic board to be detected.

[0008] The present invention provides a device for detecting the thermal conductivity of a foamed ceramic board, which has the following beneficial effects: The detection device of the present invention is composed of a main frame, a limiting mechanism, an acoustic wave measurement module, a heat source module and a detection module. The limiting mechanism is used to position the foamed ceramic board to be detected at the test position. The acoustic wave measurement module measures the pore structure to guide the heat source module to control the relative position of the heat conduction unit through the end displacement unit to provide heat for the foamed ceramic board. The detection module is composed of a co-directional detection group and an opposite-direction detection group, which collect temperature data in different directions and analyze these data through the data analysis unit to calculate the thermal conductivity of the foamed ceramic board. By precisely controlling the supply position of the heat source, the data collected by the detection module can most accurately reflect the thermal conductivity of the detection object, solving the problems of low efficiency and high cost in steady-state heat conduction detection in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 is a schematic structural diagram of a device for detecting the thermal conductivity of a foamed ceramic board provided by an embodiment of the present invention; Figure 2 is a schematic step diagram of a method for detecting the thermal conductivity of a foamed ceramic board provided by an embodiment of the present invention.

[0010] Reference numerals: 1 - main frame, 2 - limiting mechanism, 3 - acoustic wave measurement module, 4 - heat source module, 5 - detection module, 41 - heat conduction unit, 42 - end displacement unit, 51 - co-directional detection group, 52 - opposite-direction detection group, 53 - data analysis unit. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0011] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0012] The implementation of the present invention will be described in detail below with reference to specific embodiments.

[0013] Referring to Figure 1 、 Figure 2 shown, a preferred embodiment is provided by the present invention.

[0014] In a first aspect, the present invention provides a device for detecting the thermal conductivity of a foamed ceramic board, including: The main body frame 1, the limiting mechanism 2, the acoustic wave measurement module 3, the heat source module 4, and the detection module 5.

[0015] Specifically, the main body frame 1 is the support structure of the entire device, providing a stable physical foundation for installing and fixing all other modules (such as the limiting mechanism 2, the heat source module 4, the detection module 5, etc.). The main body frame 1 provides support for other components, ensuring the mechanical stability and accuracy of the device.

[0016] More specifically, the limiting mechanism 2 is a part installed on the main body frame 1, mainly used for accurately positioning and clamping the foamed ceramic plate to be detected. It ensures that the foamed ceramic plate to be detected remains stationary during the test, maintaining its position in the test position for temperature and heat conduction characteristic analysis. The function of the limiting mechanism 2 is to ensure that the foamed ceramic plate does not displace or shift during the test, thereby ensuring the accuracy of the test data.

[0017] More specifically, the heat source module 4 is a key part of the system for heating the material to be detected and controlling heat conduction. It includes two sub-modules: the heat conduction unit 41 and the end displacement unit 42.

[0018] More specifically, the heat conduction unit 41 is the core component for applying the heat source and controlling the heat conduction process. It transfers heat to the plate by contacting or approaching the foamed ceramic plate, simulating the heat source supply process. By heating the foamed ceramic plate to be detected, transient heat conduction treatment is applied, enabling heat to be transferred from the heat source module 4 to the ceramic plate and then diffusing to different regions through the thermal conductivity characteristics of the plate, thereby generating a temperature gradient for subsequent detection.

[0019] More specifically, the function of the acoustic wave measurement module 3 is to emit high-frequency acoustic wave signals into the ceramic plate using piezoelectric sensors or ultrasonic probes. Since the propagation speed of acoustic waves in solid materials is affected by factors such as material density, elastic modulus, and pore structure, the propagation speed of acoustic waves will change with the change in porosity. When acoustic waves pass through the inside of the foamed ceramic plate, some signals will be scattered or attenuated due to factors such as pores, cracks, and non-uniformities. The receiver measures the propagation time (i.e., propagation delay) of the acoustic waves and the change in signal intensity.

[0020] More specifically, based on the received signal propagation time, the sound speed is calculated, and the porosity of the foamed ceramic plate is inferred using the relationship between the sound speed and the pore structure. Generally, the higher the porosity, the slower the sound speed. Combining the attenuation degree of the signal and the signals reflected multiple times, the pore morphology of the ceramic plate (such as the size and distribution of pores) can be further inferred. By processing the original signal through signal processing algorithms (such as Fourier transform, time-domain analysis, etc.), high-precision pore structure information is obtained, and the pore structure of the foamed ceramic plate can be visualized through 3D modeling.

[0021] More specifically, based on the obtained pore structure information (including porosity, pore size and distribution, etc.), the influence of porosity on thermal conductivity is derived using thermal conduction models (such as effective medium theory, porosity model, etc.). Generally speaking, the higher the porosity, the lower the thermal conductivity usually is, because the thermal conductivity of air is much lower than that of the ceramic itself. According to the pore structure information, finite element software (such as ANSYS, COMSOL, etc.) is used for thermal conduction simulation. By simulating the heat flux distribution of the foamed ceramic plate under different pore structures, data such as temperature field, heat flux density, and thermal conductivity during the thermal conduction process are obtained. If the nonlinear behavior of the material needs to be considered, a multi-physics coupling model can be introduced to consider the comprehensive influence of temperature gradient and porosity on the thermal conduction performance. Through this method, the thermal conduction performance of the ceramic plate can be simulated more accurately.

[0022] More specifically, using the simulation results and pore structure information, the predicted value of the thermal conductivity of the foamed ceramic plate to be detected is obtained. This value reflects the influence of different pore structures on thermal conduction and helps engineers better understand the thermal conduction characteristics of the material in actual use.

[0023] More specifically, according to the simulation results, the optimal position of the heat source supply is determined. In the thermal conduction experiment, the selection and position of the heat source directly affect the efficiency and uniformity of heat transfer. Through simulation analysis, the optimal point of the heat source supply can be found to ensure that the thermal conduction process of the foamed ceramic plate can be fully activated to achieve the required heat treatment effect. In addition to steady-state thermal conduction, the foamed ceramic plate may experience transient thermal conduction processes, especially in the case of rapid heating or cooling. Using the transient analysis part in the thermal conduction simulation (such as the time-dependent change of the temperature field), the characteristics of transient thermal conduction can be predicted, and it can provide a basis for how to adjust parameters such as heating time and heating power in actual operation.

[0024] More specifically, the end displacement unit 42 is responsible for adjusting the relative position of the thermal conduction unit 41 to ensure that it accurately aligns with the foamed ceramic plate to be detected. It places the heat source module 4 in the appropriate working position by controlling the movement of the thermal conduction unit 41, ensuring that the heat source can conduct heat with the foamed ceramic plate to be detected in an accurate position and manner. This precise position control is crucial for obtaining correct thermal conduction data.

[0025] More specifically, the detection module 5 includes a co-directional detection group 51, a counter-directional detection group 52, and a data analysis unit 53, which are mainly used to collect and analyze the temperature data of the material to be detected, and then calculate the thermal conductivity.

[0026] More specifically, the co-directional detection group 51 includes a number of detection units in the same direction as the heat source module 4, which are installed on the main body frame 1. This group of detection units is used to collect the temperature data of the foamed ceramic board at the test position in real time. They monitor the co-directional propagation of the heat applied by the heat source module 4 and obtain the temperature change data (the first detection data) in the heat source direction. The feature that the first detection data can reflect is the speed at which a certain position of the foamed ceramic board to be detected receives the heat supplied by the heat source and transfers it to the surrounding area. The speed can reflect the thermal conductivity of the foamed ceramic board.

[0027] More specifically, the counter-directional detection group 52 is similar to the co-directional detection group 51, but their detection units are arranged in the direction opposite to the heat source module 4. These detection units are used to collect the temperature data (the second detection data) of the foamed ceramic board at the test position with the opposite heat propagation direction. By monitoring the temperature change in the opposite direction, the conduction information about the heat conduction from the heat source to the back of the foamed ceramic board or in the opposite direction is obtained. The feature that the second detection data can reflect is the speed at which a certain position of the foamed ceramic board to be detected receives the heat supplied by the heat source and transfers it to the back area. The speed can reflect the thermal conductivity of the foamed ceramic board.

[0028] More specifically, the data analysis unit 53 is electrically connected to the co-directional and counter-directional detection groups 52 and is responsible for receiving and processing the temperature data collected from the detection groups. This unit performs data analysis on the first detection data and the second detection data according to the pre-trained thermal conductivity analysis algorithm. By analyzing these data, the thermal conductivity of the foamed ceramic board to be tested can be calculated. The thermal conductivity is a measure of the thermal conductivity of the material and reflects the ability of the material to conduct heat.

[0029] It can be understood that the limiting mechanism 2 ensures that the foamed ceramic board always remains in the correct position to ensure the accuracy of the test. The heat source module 4 provides a heat source and controls the position of the heat source through the end displacement unit 42 to precisely adjust the way of applying heat to the ceramic board. The co-directional and counter-directional detection groups 52 respectively collect the heat propagation situation on the ceramic board to form temperature data. The data analysis unit 53 analyzes the thermal conductivity of the material to be tested by comparing the co-directional and counter-directional temperature data and combining the heat conduction theory and the pre-trained thermal conductivity analysis algorithm.

[0030] The present invention provides a device for detecting the thermal conductivity of a foamed ceramic board, which has the following beneficial effects: The detection device of the present invention is composed of a main body frame 1, a limiting mechanism 2, an acoustic wave measurement module 3, a heat source module 4, and a detection module 5. The limiting mechanism 2 is used to position the foamed ceramic plate to be detected at the test position. The acoustic wave measurement module 3 measures the pore structure to guide the heat source module 4 to control the relative position of the heat conduction unit 41 through the end displacement unit 42 to provide a heat source for the foamed ceramic plate. The detection module 5 is composed of a co-directional and a counter-directional detection group, collects temperature data in different directions, and analyzes these data through a data analysis unit to calculate the thermal conductivity of the foamed ceramic plate. By precisely controlling the supply position of the heat source, the data collected by the detection module 5 can most accurately reflect the thermal conductivity of the detection object, solving the problems of low efficiency and high cost in steady-state heat conduction detection in the prior art.

[0031] Preferably, the heat conduction unit 41 is a laser pulse heater, and the laser pulse heater is used to perform transient heat conduction treatment by emitting laser pulses to the foamed ceramic plate to be detected.

[0032] Specifically, the basic principle of the laser pulse heater is to release high-energy laser pulses in a short time to quickly heat the surface of the material to be detected. The laser pulse heater will apply an instantaneous heat source to the material in a very short time, generate heat, and trigger the heat conduction process inside the material. For porous and low-thermal-conductivity materials such as foamed ceramic plates, laser pulse heating can form a temperature gradient on its surface and quickly spread to the inside of the material, thereby simulating how heat diffuses in the material. Data is collected through this transient heating process and the thermal conductivity of the material is analyzed.

[0033] More specifically, the laser pulse heater emits laser pulses for a short time (millisecond or microsecond level), concentrating energy on the surface of the foamed ceramic plate. The laser pulse instantaneously inputs a large amount of thermal energy to the material surface, causing the heat to quickly spread along the thickness direction of the material. Due to the low thermal conductivity of the ceramic plate, an obvious temperature gradient will be generated between its surface and the inside. Since laser heating is instantaneous, within an extremely short time, the material will undergo a heat conduction process, thus forming an expansion of a temperature wave. By monitoring this process, data on the thermal conductivity of the material can be obtained. The laser pulse heater simulates the real heat conduction process by instantaneously heating the material surface, quickly generating temperature changes that can be used to calculate the thermal conductivity. By precisely controlling the pulse width, frequency, and power of the laser, the laser pulse heater can adjust the rate and intensity of heat input, thereby affecting the formation of the temperature gradient.

[0034] More specifically, a laser pulse heater usually needs to adjust its relative position as required to ensure that it can correctly emit laser pulses to the target position on the ceramic plate. The end displacement unit 42 is responsible for precisely controlling the position and angle of the laser heater to ensure the correct irradiation of the laser beam. The end displacement unit 42 can adjust the irradiation angle or distance of the laser according to different test requirements to ensure the uniformity of the heating process.

[0035] More specifically, the instantaneous heat applied by the laser pulse heater will generate a temperature gradient within the foamed ceramic plate. The heat diffuses from the heating source direction to other parts of the ceramic plate. The temperature sensors in the co-direction detection group 51 and the cross-direction detection group 52 will respectively monitor the temperature changes in the co-direction and the reverse direction of the heat source. The co-direction detection group 51 will record the temperature data of the heat propagating from the laser source direction, while the cross-direction detection group 52 will record the temperature changes of the heat propagating in the opposite direction. These temperature data provide the basis for subsequent thermal conductivity calculations.

[0036] More specifically, the temperature data collected by the detection module 5 will be transmitted to the data analysis unit 53. The data analysis unit 53 calculates the thermal conductivity of the material to be detected by analyzing the co-direction and cross-direction temperature data according to the transient heat conduction theory. The data analysis unit 53 processes the data based on a heat conduction model (such as the transient heat conduction equation) and compares it with the experimental results to obtain the thermal conductivity index of the foamed ceramic plate.

[0037] Preferably, the detection units in the co-direction detection group 51 and the cross-direction detection group 52 are infrared detectors.

[0038] Specifically, an infrared detector is a sensor that can detect infrared radiation (usually radiation related to temperature). Since a temperature gradient will appear in the foamed ceramic plate after laser pulse heating, the infrared detector monitors the temperature distribution of the material by detecting the changes in infrared radiation on the surface or inside.

[0039] More specifically, the temperature change of an object will affect the infrared spectrum it emits. When the temperature rises, the object will release more infrared radiation. The infrared detector can receive and convert these radiation signals into electrical signals, and then calculate the temperature change.

[0040] More specifically, after the laser pulse heater heats the material surface, heat diffusion will occur inside the material. Through the co-direction and cross-direction infrared detectors, the heat propagating outward from the laser heating point (source point) (co-direction) and the process of heat diffusion in the opposite direction (cross-direction) can be measured respectively. The measurement data of these temperature changes are crucial for calculating the thermal conductivity coefficient.

[0041] More specifically, the infrared detectors of the co-directional detection group 51 are located on the same side of the laser pulse irradiation area or on the side relatively close to the laser source, and they record the temperature changes when the heat diffuses towards the laser source. Since the laser pulse directly heats the material surface, these detectors can monitor the initial temperature response on the material surface and inside, and capture the process of heat conduction in the material in the same direction.

[0042] More specifically, the infrared detectors of the counter-directional detection group 52 are located on the other side of the laser irradiation area (usually the reverse side of the material), that is, opposite to the laser source. These infrared detectors are responsible for monitoring the temperature changes when the heat propagates from the laser source direction to the reverse direction. Due to the different thermal conductivities of the materials, the speed and amplitude of the heat propagation to the reverse direction are different from those in the co-directional case. By recording the counter-directional temperature changes, the diffusion characteristics of the heat can be further analyzed.

[0043] Referring to Figure 2 As shown, in a second aspect, the present invention provides a method for detecting the thermal conductivity of a foamed ceramic board, which is applied to a device for detecting the thermal conductivity of a foamed ceramic board according to any one of the first aspect, and includes: S1: Mark the foamed ceramic board to be detected as the detection object, and obtain the specification information of the detection object; wherein, the specification information includes the size data, thickness data, and preset thermal conductivity standard of the detection object; S2: Perform acoustic wave measurement on the detection object to obtain the pore structure information of the detection object; S3: Analyze the execution plan for detecting the thermal conductivity of the limiting mechanism 2 and the heat source module 4 according to the specification information and pore structure information of the detection object, so as to obtain the detection execution plan of the limiting mechanism 2 and the heat source module 4 corresponding to the detection object; S4: Drive the limiting mechanism 2 to limit and clamp the detection object according to the detection execution plan, so that the detection object is in the test position; S5: Drive the end displacement unit 42 in the heat source module 4 to control the heat conduction unit 41 to move to each specified working position in sequence according to the detection execution plan, and drive the heat conduction unit 41 to supply heat to the detection object; S6: Continuously collect detection data of the detection object through the co-directional detection group 51 and the counter-directional detection group 52 in the detection module 5 to obtain the first detection data and the second detection data; S7: Analyze the first detection data and the second detection data through the data analysis unit 53 in the detection module 5 according to the pre-trained thermal conductivity analysis algorithm to obtain the detected thermal conductivity of the foamed ceramic board to be detected.

[0044] Specifically, in steps S1, S2, and S3 of the embodiments provided by the present invention, the foamed ceramic plate to be detected is marked as the detection object, and the specification information of the detection object is obtained. The specification information includes the size data, thickness data, and preset thermal conductivity standard of the detection object. An analysis of the execution plan for detecting the thermal conductivity of the limiting mechanism 2 and the heat source module 4 is performed according to the specification information of the detection object, so as to obtain the detection execution plan of the limiting mechanism 2 and the heat source module 4 corresponding to the detection object.

[0045] More specifically, the function of the limiting mechanism 2 is to limit and clamp the detection object. In order to ensure the stability of the limiting and clamping operation, it is necessary to make the center of gravity of the detection object stable when the limiting mechanism 2 limits and clamps the detection object.

[0046] More specifically, the implementation of the above steps requires first obtaining the size data and thickness data of the detection object, and analyzing the center of gravity based on this to confirm how the limiting mechanism 2 performs the limiting and clamping operation on the detection object, that is, which parts of the detection object the limiting mechanism 2 specifically clamps.

[0047] More specifically, acoustic measurement is performed on the detection object to obtain the pore structure information of the detection object, and the pore structure information is used to analyze the subsequent steps to obtain the working position of the heat conduction unit and the specific processing form of transient heat conduction processing.

[0048] More specifically, the function of the heat source module 4 is to heat the specified working position of the detection object, so as to supply the detection module 5 to collect temperature data, and calculate the final thermal conductivity of the detection object.

[0049] It should be noted that in order to ensure the accuracy of the detection, it is necessary to control the specified working position of the heat source module 4, that is, to ensure the accuracy and effectiveness of the test by controlling the distance between the specified working position of the heat source module 4 and the detection position of the detection module 5.

[0050] Specifically, transient heat conduction processing is performed on the detection object through the heat source module 4, and then the temperature change on the surface of the detection object is detected, so that the thermal conductivity of the detection object can be calculated. This method does not require a stable and long-term heat source supply to the detection object, thus improving the detection efficiency and reducing the cost of the adiabatic structure at the same time.

[0051] More specifically, in order to achieve the best detection effect of transient heat conduction, it is necessary to control the distance between the designated working position of the heat source module 4 and the detection position of the detection module 5, so that the detection position of the detection module 5 is at a position that is most sensitive to the effect brought about by the thermal conductivity of the detection object. Therefore, it is necessary to obtain the preset thermal conductivity standard in the specification information of the detection object in advance. The preset thermal conductivity standard includes the most likely thermal conductivity of several detection objects. The size data and thickness data of the detection object are calculated based on the preset thermal conductivity standard to obtain the designated working position of the heat source module 4.

[0052] Specifically, in step S4 of the embodiment provided by the present invention, the limiting mechanism 2 is driven to limit and clamp the detection object according to the detection execution scheme so that the detection object is in the test position. The function of the limiting mechanism 2 is to fix the detection object to ensure that the detection object receives subsequent detection. The function of the detection execution scheme is to limit the limiting and clamping method of the limiting mechanism 2 on the detection object. When the detection object is in the test position, the detection object can receive subsequent tests of the heat source module 4 and the detection module 5 corresponding to the detection execution scheme.

[0053] Specifically, in step S5 of the embodiment provided by the present invention, the end displacement unit 42 in the heat source module 4 is driven according to the detection execution scheme to control the heat conduction unit 41 to move to each designated working position in sequence, and drive the heat conduction unit 41 to supply heat source to the detection object.

[0054] More specifically, the heat source module 4 includes an end displacement unit 42 and a heat conduction unit 41 . The end displacement unit 42 is used to control the end orientation of the heat conduction unit 41 so as to direct the heat conduction unit 41 to a designated working position on the detection object.

[0055] It is understandable that there is more than one designated working position on the detection object, so it is necessary to drive the end displacement unit 42 to control the heat conduction unit 41 to move to each working position in turn to supply heat source. After the heat conduction unit 41 supplies heat source to the detection object, the temperature at the receiving point of the detection object will rise, and according to the different thermal conductivity of the detection object itself, the detection data collected by the detection module 5 on the detection object will also be different. A comprehensive analysis is performed based on the detection data collected by each detection module 5, and then the thermal conductivity of the detection object can be obtained.

[0056] Specifically, in steps S6 and S7 of the embodiment provided by the present invention, the same-direction detection group 51 and the opposite-direction detection group 52 in the detection module 5 continuously collect detection data of the detection object to obtain first detection data and second detection data; the data analysis unit 53 in the detection module 5 performs data analysis on the first detection data and the second detection data according to a pre-trained thermal conductivity analysis algorithm to obtain the detection thermal conductivity of the foamed ceramic board to be detected.

[0057] More specifically, the first detection data is based on the detection data collected by the detection unit in the same direction, and the second detection data is based on the detection data collected by the detection unit in the opposite direction. The distances between each detection unit and the working position are different, and this distance difference brings about different thermal conductivity of the detection object. Based on the different thermal conductivity, the correlation calculation of each detection data is performed to obtain the detection thermal conductivity coefficient of the foamed ceramic plate to be detected.

[0058] It should be noted that in order to ensure the accuracy of the detection, the most important key point is to select the designated working position of the heat source module 4. By controlling the distance between the working position of the heat source module 4 and each detection unit, the data collected by the detection unit can efficiently and accurately feedback the thermal conductivity of the detection object.

[0059] Specifically, there are multiple detection units, and they are divided into front and back sides. This means that after the working position receives the heat source supply, the heat needs to travel different distances to be transmitted to the detection position corresponding to each detection unit. Therefore, the temperature data collected at each detection position and the heat data received at the working position can feedback the thermal conductivity of the detection object. By performing a comprehensive analysis of the performance calculated and fed back by multiple detection units, the most accurate detection result of the thermal conductivity coefficient of the detection object can be obtained.

[0060] More specifically, in order to ensure that the distance between the detection unit and the working position can effectively feedback the thermal conductivity, it is necessary to obtain several conventional thermal conductivities of the detection object in advance, and perform reverse analysis based on these conventional thermal conductivities to obtain the feedback effects of each specific working position corresponding to these conventional thermal conductivities, and further obtain the optimal working position that can simultaneously correspond to each conventional thermal conductivity, so as to supply a heat source to the detection object.

[0061] Preferably, the steps of analyzing the execution scheme of thermal conductivity detection of the limiting mechanism 2, the heat source module 4 and the detection module 5 according to the specification information and the pore structure information of the detection object to obtain the detection execution scheme of the limiting mechanism 2, the heat source module 4 and the detection module 5 corresponding to the detection object include: S31: Perform digital simulation processing on the detection object according to the size data, thickness data, and pore structure information of the detection object to obtain a digital simulation model of the detection object; S32: Analyze the center-of-gravity position of the detection object based on the digital simulation model to obtain the object center-of-gravity characteristics of the detection object; S33: Perform simulation analysis of the theoretical heat conduction condition of the detection object based on the digital simulation model to obtain the heat conduction simulation characteristics of the detection object; S34: Perform limit simulation processing on the limit mechanism 2 according to the object center-of-gravity characteristics of the detection object to obtain the limit simulation scheme and the corresponding limit simulation effect of the limit mechanism 2 corresponding to the detection object; S35: Feedback and optimize the limit simulation scheme according to the limit simulation effect to obtain a limit execution scheme for driving the limit mechanism 2 to perform limit clamping on the detection object; wherein, the limit execution scheme is the detection execution scheme of the limit mechanism 2 corresponding to the detection object; S36: Perform simulation analysis of the heat source supply scheme for the digital simulation model with the heat conduction simulation characteristics according to the preset heat conduction standard to obtain the heat source supply execution scheme of the heat source module 4 corresponding to the detection object; wherein, the heat source supply execution scheme is the detection execution scheme of the heat source module 4 corresponding to the detection object.

[0062] Specifically, according to the size data and thickness data of the object to be detected, digital simulation processing is performed to generate a digital model of the detection object. This process involves converting the physical characteristics (such as size and shape) of the detection object into a virtual model that can be processed by a computer. Through digital simulation, the system can generate an accurate virtual model to ensure that subsequent simulation and testing steps can be based on real physical characteristics. This helps to improve the accuracy of the system in subsequent analysis.

[0063] More specifically, based on the digital simulation model, the center-of-gravity position of the detection object is analyzed to obtain the object center-of-gravity characteristics of the detection object. The center-of-gravity position is crucial for the subsequent limit clamping process. The object center-of-gravity characteristics of the detection object determine its force balance and stability. By accurately analyzing the center-of-gravity position, the system can provide optimized clamping points for the limit mechanism 2 to avoid problems such as tilting or moving during the testing process.

[0064] More specifically, according to the object center-of-gravity characteristics of the detection object, limit simulation processing of the limit mechanism 2 is performed. Through simulation, the limit scheme and limit effect of the limit mechanism 2 corresponding to the detection object are obtained. The limit simulation processing can evaluate the clamping effect in advance to ensure that the detection object is stably and accurately fixed at the test position during the testing process.

[0065] More specifically, based on the digital simulation model, a simulation analysis of the theoretical heat conduction condition of the detection object is carried out to obtain the heat conduction simulation characteristics of the detection object. The heat conduction simulation characteristics are used to reflect the theoretical heat conduction performance of the detection object. According to the heat conduction simulation characteristics, it can be deduced at what position and what kind of detection of the detection object can bring the best test effect.

[0066] More specifically, the limit scheme is feedback-optimized according to the limit simulation effect. The optimized scheme will be used as the limit execution scheme to drive the limit mechanism 2 to limit and clamp the detection object during actual operation. Through the feedback of the limit simulation effect, the clamping scheme can be continuously optimized, and the accuracy and efficiency of the limit mechanism 2 can be improved. The optimization process ensures that the limit mechanism can adapt to detection objects of different specifications during actual operation.

[0067] More specifically, according to the preset heat conduction standard, a simulation analysis of the heat source supply scheme for the digital simulation model is carried out. Through this analysis, the heat source supply execution scheme of the heat source module 4 corresponding to the detection object is obtained. Through the simulation of the heat source supply scheme, the distribution of the heat source on the detection object can be evaluated to ensure uniform heat conduction and meet the predetermined standard. This process avoids problems such as uneven heating or overheating during the actual heat supply process. The preset heat conduction standard can be used as a benchmark to help the system better simulate the heat source supply method that meets the requirements of the heat conduction test and ensure the reliability and scientific nature of the test results.

[0068] More specifically, according to the simulation analysis results of the heat source supply scheme, the heat source supply execution scheme of the heat source module 4 is determined. This scheme will be used as the control basis for the heat source module 4 during actual operation. Through the simulation of the execution scheme, the system can accurately control the working mode of the heat source module 4 to ensure that heat is transferred to the detection object in the best way, guarantee the measurement accuracy of the thermal conductivity. The optimization of the heat source supply execution scheme can not only improve the stability of the heating process, but also improve the test efficiency, reduce the errors caused by uneven heating or overheating, and ensure the scientific nature of the results.

[0069] Preferably, the steps of obtaining the heat source supply execution scheme of the heat source module 4 corresponding to the detection object by performing a simulation analysis of the heat source supply on the digital simulation model with the heat conduction simulation characteristics according to the preset heat conduction standard include: S351: Analyze the heat conduction simulation characteristics according to the preset heat conduction standard to obtain several conventional heat conduction standards reflected by the preset heat conduction standard; S352: Analyze the data acquisition positions of the digital simulation model according to the limit execution scheme and the setting information of the detection module 5 in the main frame 1 to obtain the distribution of temperature data acquisition nodes; wherein, the distribution of temperature data acquisition nodes includes a number of temperature data acquisition nodes, and the temperature data acquisition nodes are the specific positions where each detection unit in the detection module 5 collects detection data for the detection object. S353: Combine and process the distribution of temperature data acquisition nodes with the digital simulation model to obtain a detection simulation model. S354: Perform reverse calculation and analysis on the detection effect of the heat supply position of the detection simulation model according to the conventional heat conduction standard to obtain the distribution of heat supply effect position characteristics of the detection simulation model under the conventional heat conduction standard; wherein, the distribution of heat supply effect position characteristics is used to describe the detection effect of the detection module 5 after heat source supply work is carried out at specific positions of the detection object under the conventional heat conduction standard. S355: Perform weighted evaluation on the heat supply position of the detection simulation model according to the distribution of supply effect position characteristics corresponding to each conventional heat conduction standard to obtain a number of heat source supply working positions and the heat source supply temperature characteristics corresponding to each heat source supply working position, which are jointly used as the heat source supply execution scheme.

[0070] Specifically, conduct a detailed analysis of the heat conduction simulation characteristics according to the preset heat conduction standard, extract a number of conventional heat conduction standards, and each standard represents different heat conduction requirements and boundary conditions for subsequent simulation analysis. By analyzing the preset standards, it can be ensured that consistent heat conduction rules are followed in all simulation and experimental processes, which guarantees the consistency between the heat source supply process and the actual application scenario and improves the scientific nature of the test results.

[0071] More specifically, according to the limit execution scheme and the setting information of the detection module 5 in the main frame 1, analyze the data acquisition positions of the digital simulation model, and determine the distribution of temperature data acquisition nodes. These nodes represent the positions where the detection module 5 actually collects data. By reasonably arranging and analyzing the data acquisition nodes, the accuracy and representativeness of temperature data can be ensured, and key heat source distribution areas can be avoided from being missed. By reasonably arranging the nodes, it is ensured that the entire surface of the detection object and key positions are covered, thereby ensuring the comprehensiveness of the heat source distribution analysis.

[0072] More specifically, combine the distribution of temperature data acquisition nodes with the digital simulation model to generate a detection simulation model. This model can simulate the actual situation of temperature distribution and heat source supply. By combining the distribution of temperature acquisition nodes with the digital model, a simulation model closer to the actual test scenario is obtained, which can accurately simulate heat source conduction and temperature changes and ensure the reliability of experimental data.

[0073] More specifically, according to the conventional heat conduction standard, the detection simulation model is inversely calculated to analyze the characteristic distribution of the heat source supply effect at different positions. This process helps to evaluate the heat source transfer effect at different positions. The inverse calculation can accurately reflect the heat source supply effect at each position, help to identify the areas with uneven heat source supply, and optimize the heat supply positions. This kind of analysis can discover potential problems of uneven heat source distribution and provide an optimization basis for the subsequent heat source supply plan.

[0074] More specifically, based on the conventional heat conduction standard and the characteristic distribution of the heat supply effect position, the detection simulation model is weighted and evaluated, and finally multiple heat source supply positions and the corresponding heat source supply temperature characteristics are obtained. These characteristics will be used as the basis for the heat source supply implementation plan. Through weighted evaluation, the optimal heat source supply position can be selected according to actual needs, which not only improves the uniformity of heat source transfer but also ensures that heat can be effectively transferred to the key parts of the detection object. The supply temperature characteristics obtained after weighted evaluation can make the heat source supply more accurate, avoid overheating or insufficient heating, and improve the accuracy of the test.

[0075] Preferably, the step of weighted evaluation of the heat supply position of the detection simulation model according to the characteristic distribution of the supply effect position corresponding to each of the conventional heat conduction standards to obtain several heat source supply working positions and the heat source supply temperature characteristics corresponding to each of the heat source supply working positions includes: S3551: Conduct a weighted comprehensive analysis of the detection effect after heat supply at each specific position in the detection simulation model according to the characteristic distribution of the supply effect position corresponding to each of the conventional heat conduction standards to obtain the basic detection effect at each specific position in the detection simulation model; S3552: Based on the basic detection effect at each specific position in the detection simulation model, add the heat source supply method to each specific position in the detection simulation model, and correct the effect of the basic detection effect according to the added heat source supply method to obtain the final detection effect corresponding to various types of heat source supply methods at each specific position; S3553: Select each specific position and various types of heat source supply methods in the detection simulation model according to the final detection effect corresponding to various types of heat source supply methods at each specific position to determine several heat source supply working positions and the heat source supply temperature characteristics corresponding to each of the heat source supply working positions.

[0076] Specifically, according to the distribution of the supply effect position characteristics corresponding to each conventional heat conduction standard, a weighted comprehensive analysis of the effects after heat source supply is carried out on each specific position in the detection simulation model. Through the weighted method, the detection effects of each position under different heat source supply conditions are integrated, so as to obtain the basic detection effect of this position. The weighted analysis comprehensively considers the heating effects reflected by each conventional heat conduction standard, thus avoiding the deviation that may be brought by a single standard and ensuring that the basic detection effect has wide applicability and accuracy. By analyzing each position in the detection model, it is ensured that the heat source supply effect of each position is reasonably evaluated, and the reliability and comprehensiveness of the test data are improved.

[0077] More specifically, on the basis of the basic detection effect, different heat source supply methods are added to each specific position in the detection simulation model. Then, the basic detection effect is corrected according to the added heat source supply method to obtain the final detection effect. This step will consider the influence of different heat source types (such as constant temperature, pulse, gradient, etc.) on each position and adjust its effect. By adding different types of heat source supply methods, various heat source supply modes that may appear in actual applications can be simulated, thus improving the adaptability of the detection scheme. By correcting the basic effect, the specific influence of heat source supply on different positions can be more precisely reflected, and the accuracy and scientific nature of the detection result are improved.

[0078] More specifically, based on the final detection effect of each position, the selection of each position and different heat source supply methods in the detection simulation model is carried out. By analyzing the effects of different positions and heat source supply methods, several heat source supply working positions are finally determined, and corresponding heat source supply temperature characteristics are assigned to each position.

[0079] Preferably, the step of obtaining the detected thermal conductivity of the foamed ceramic plate to be detected by analyzing the first detection data and the second detection data by the data analysis unit 53 in the detection module 5 according to the pre-trained thermal conductivity analysis algorithm includes: S71: Obtain the heat source supply working data of the heat conduction unit 41 corresponding to the first detection data and the second detection data; wherein, the heat source supply working data includes the working position and working mode of the heat conduction unit 41 for heat source supply to the detection object. S72: Take the first detection data and the second detection data as effect characteristics, take the heat source supply working data as cause characteristics, and perform an analysis of mapping association between the effect characteristics and the cause characteristics according to the pre-trained thermal conductivity analysis algorithm to obtain the mapping association characteristics between the effect characteristics and the cause characteristics. S73: Perform a conversion process on the thermal conductivity of the foamed ceramic plate to be detected according to the mapping correlation feature, so as to obtain the detected thermal conductivity of the foamed ceramic plate to be detected.

[0080] Specifically, obtain the heat source supply working data of the heat conduction unit 41 corresponding to the first detection data and the second detection data. The heat source supply working data includes: working position: the position where the heat source is supplied, that is, the specific position on the test object where the heat applied by the heat source is located; working mode: the heat supplied by the heat source. The obtained heat source supply working data covers the application position and method of the heat source, providing complete input data for subsequent heat conduction analysis. By clarifying the working position and mode of the heat source supply, the action process of the heat source on the foamed ceramic plate can be accurately described, providing accurate background information for data analysis and ensuring that the analysis results are more scientific.

[0081] More specifically, regard the first detection data and the second detection data as effect features, which reflect the response (such as temperature change, etc.) of the material to be tested (foamed ceramic plate) under the heat source supply. Regard the heat source supply working data as cause features, which reflect the heat transfer effect of the heat source supply method and position on the material. Based on the pre-trained thermal conductivity analysis algorithm, perform mapping correlation analysis on the effect features (detection data) and cause features (heat source supply data), that is, through the rules learned by the algorithm, map the features of the heat source supply to the test results, revealing the relationship between the heat source supply and the test results.

[0082] It can be understood that by establishing a mapping relationship between the cause features and the effect features through the algorithm, the data analysis is not limited to empirical values, but accurately infers the relationship between the heat source supply and the heat conduction effect through a mathematical model and training data. Through automatic learning and mapping of the algorithm, human intervention can be reduced, making the analysis results more scientific and efficient. The thermal conductivity analysis algorithm is based on training data and can flexibly handle different heat source supply modes and material characteristics, providing accurate prediction for the thermal conductivity of the foamed ceramic plate.

[0083] More specifically, according to the above mapping correlation feature, use the relationship between the heat source supply and the detection data to perform a conversion process on the thermal conductivity of the foamed ceramic plate to be detected, thereby calculating its detected thermal conductivity. This processing process mainly includes: applying the obtained mapping correlation feature to the heat source supply conditions of the material to be tested, and according to the correlation feature, converting the heat transfer effect of the heat source supply mode and position on the material to be tested, so as to obtain the thermal conductivity of the material.

[0084] It can be understood that by using the mapping correlation features for conversion processing, the thermal conductivity of the foamed ceramic plate to be tested can be accurately calculated according to the differences in the heat source supply mode and position. This process not only relies on the results of direct measurement but also combines the heat conduction characteristics of the material, providing more accurate measurement data. The algorithm used in the conversion processing is pre-trained and can complete the calculation in a short time, reducing the complex calculation process that may occur in traditional methods and improving the test efficiency. Through the application of mapping correlation features, the system can adapt to different types of heat source supply modes and make adjustments according to different material characteristics, making the thermal conductivity measurement more in line with the actual application requirements.

[0085] Preferably, the pre-training steps of the thermal conductivity analysis algorithm include: S701: Construct a machine learning model and collect several groups of training data corresponding to the machine learning model; wherein, the training data includes thermal conductivity performance data and thermal conductivity test data, the thermal conductivity test data includes thermal conductivity effect data and thermal conductivity cause data, the data content of the thermal conductivity performance data corresponds to the mapping correlation features, and the data content of the thermal conductivity effect data and the thermal conductivity cause data respectively corresponds to the effect features and the cause features; S702: Substitute each group of the training data into the machine learning model, and let the machine learning model perform model training according to the training data to obtain model parameters corresponding to the training data; wherein, the model parameters are used to reflect the mapping correlation features between the thermal conductivity performance data and the thermal conductivity test data in the training data; S703: Perform feature extraction and algorithm conversion on the model parameters to obtain the thermal conductivity analysis algorithm. Specifically, select a suitable machine learning algorithm (such as regression analysis, support vector machine, deep learning, etc.) to construct the model, and the goal of this model is to learn and capture the mapping relationship between the thermal conductivity performance and the thermal conductivity test data through the input training data.

[0086] More specifically, collect training data: The training data includes: Thermal conductivity data: representing the target output, i.e., the thermal conductivity of the material to be tested (such as a foamed ceramic board). Thermal conductivity test data: including thermal conductivity effect data and thermal conductivity cause data, corresponding to the input features of the model respectively. Thermal conductivity effect data: corresponding to the test result data, such as temperature change, heat conduction efficiency, etc., which are the effect features of the model. Thermal conductivity cause data: including heat source supply data, working position, working mode, etc., which are the cause features of the model. These data are obtained through historical experiments or simulations, covering a variety of materials, heat source supply modes, and test conditions. Collecting various forms of training data helps to build a comprehensive and accurate machine learning model, ensuring that the model can handle different experimental conditions and material types. By taking the thermal conductivity effect and cause as different input features respectively, it helps the machine learning model better understand the complex relationship between heat source supply and material thermal conductivity performance.

[0087] More specifically, substitute each group of collected training data into the constructed machine learning model. Each group of data includes the corresponding thermal conductivity effect data and thermal conductivity cause data. The goal of the model is to learn the relationship between the input (cause features) and the output (effect features) from these data. By inputting the training data into the model, supervised learning methods are used for training. During the training process, the machine learning model continuously adjusts its internal parameters (such as weights, biases, etc.) to minimize the prediction error, that is, the difference between the thermal conductivity data output by the model and the actually observed thermal conductivity data. Through training, the machine learning model can continuously adjust its internal parameters, enabling the model to accurately reflect the mapping relationship between the thermal conductivity data and the thermal conductivity test data. The model can automatically adjust according to the characteristics of the training data, learn the heat conduction behavior of materials under different heat source supply conditions, and enhance the adaptability and accuracy of the model.

[0088] More specifically, after training, the model can obtain a set of optimized model parameters. These parameters reflect the relationship between the thermal conductivity data (output) and the thermal conductivity test data (input). The model parameters are used to generate the mapping association features between the thermal conductivity and the thermal conductivity test data. Through these features, the model can explain how the given thermal conductivity test data (effect features and cause features) affect the final thermal conductivity result. The model parameters reveal the association between the thermal conductivity effect and the thermal conductivity cause in a feedback form, enhancing the understanding of the heat conduction process. This mapping relationship can help with rapid calculation and prediction under new materials or new test conditions. Through the optimized model parameters, the model can efficiently generate accurate thermal conductivity prediction results from new input data without the need for re - experimental testing.

[0089] More specifically, by further analyzing and extracting the trained model parameters, the most important features affecting the thermal conductivity are identified. These features may include the type of heat source, the heating position, the thermal conductivity of the material, etc. The extracted features are then converted into a thermal conductivity analysis algorithm applicable to practical applications. The output of the algorithm is the thermal conductivity data (such as thermal conductivity) of the material to be tested, and it can predict the heat conduction performance of the material under different test conditions. Through feature extraction, it is possible to clarify which factors have important effects on the thermal conductivity, reduce redundant data, and improve the accuracy of the model. By converting and optimizing the model parameters, an efficient thermal conductivity analysis algorithm can be obtained, which not only improves the prediction accuracy but also can be quickly applied under various material and heat source conditions.

[0090] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A thermal conductivity detection device for a foamed ceramic plate, characterized in that: include: Main frame, limiting mechanism, acoustic wave measurement module, heat source module and detection module; The limiting mechanism is arranged on the main frame, and is used to limit and clamp the foamed ceramic board to be tested, so that the foamed ceramic board to be tested is in a testing position; The heat source module and the detection module are both arranged inside the main frame, and the heat source module includes a heat conduction unit and an end displacement unit; The acoustic wave measurement module is used to measure the pore structure of the foamed ceramic plate to be tested at the test position based on the acoustic wave signal, so as to obtain the pore structure information of the foamed ceramic plate to be tested, and simulate the thermal conductivity performance of the foamed ceramic plate to be tested according to the pore structure information, so as to obtain the predicted thermal conductivity of the foamed ceramic plate to be tested, and obtain the working position of the heat conduction unit supplying the heat source to the foamed ceramic plate to be tested and the specific processing form of transient heat conduction processing through the analysis of the predicted thermal conductivity; The heat conduction unit is arranged inside the main frame through the end displacement unit, and the end displacement unit is used to control the relative position relationship between the heat conduction unit and the foamed ceramic board to be tested in the test position, so that the heat conduction unit is in a working position for supplying a heat source to the foamed ceramic board to be tested; The heat conduction unit is used to perform transient heat conduction treatment on the foamed ceramic plate to be tested; The detection module includes a same-direction detection group, a different-direction detection group and a data analysis unit; The same-direction detection group includes a plurality of detection units arranged in the same direction as the heat source module, and the same-direction detection group is used to collect temperature data in the same direction of the foamed ceramic plate to be detected at the test position to obtain first detection data; The anisotropic detection group includes a plurality of detection units arranged in the opposite direction to the heat source module, and the anisotropic detection group is used to collect temperature data in the opposite direction of the foamed ceramic plate to be detected at the test position to obtain second detection data; The data analysis unit is electrically connected to the same-direction detection group and the opposite-direction detection group respectively, and is used to receive the first detection data and the second detection data, and perform data analysis on the first detection data and the second detection data using a pre-trained thermal conductivity analysis algorithm to obtain the detection thermal conductivity of the foamed ceramic board to be detected.

2. A foamed ceramic board thermal conductivity detection device as claimed in claim 1, characterized in that: The heat conduction unit is a laser pulse heater, and the laser pulse heater is used to realize transient heat conduction processing by emitting laser pulses to the foamed ceramic plate to be detected.

3. A foamed ceramic board thermal conductivity detection device as claimed in claim 1, characterized in that: The detection units in the same-direction detection group and the different-direction detection group are infrared detectors.

4. A method for detecting thermal conductivity of a foamed ceramic plate, applied to a device for detecting thermal conductivity of a foamed ceramic plate as claimed in any one of claims 1 to 3, characterized in that: include: Marking the foamed ceramic plate to be inspected as an inspection object, and obtaining specification information of the inspection object; wherein the specification information includes size data, thickness data and a preset thermal conductivity standard of the inspection object; Performing acoustic wave measurement on the detection object to obtain pore structure information of the detection object; Analyzing the execution scheme of thermal conductivity detection of the limit mechanism and the heat source module according to the specification information and the pore structure information of the detection object, so as to obtain the detection execution scheme of the limit mechanism and the heat source module corresponding to the detection object; According to the detection execution scheme, the limiting mechanism is driven to limit and clamp the detection object so that the detection object is in a test position; According to the detection execution scheme, the end displacement unit in the heat source module is driven to control the heat conduction unit to move to each designated working position in sequence, and the heat conduction unit is driven to supply heat source to the detection object; Continuously collecting detection data of the detection object through the same-direction detection group and the different-direction detection group in the detection module to obtain first detection data and second detection data; The first detection data and the second detection data are analyzed by the data analysis unit in the detection module according to a pre-trained thermal conductivity analysis algorithm to obtain the detection thermal conductivity of the foamed ceramic board to be detected.

5. A method for detecting thermal conductivity of a foamed ceramic plate as claimed in claim 4, characterized in that: The steps of analyzing the execution scheme of thermal conductivity detection of the limit mechanism, the heat source module and the detection module according to the specification information and the pore structure information of the detection object to obtain the detection execution scheme of the limit mechanism, the heat source module and the detection module corresponding to the detection object include: Performing digital simulation processing on the detection object according to the size data, thickness data and pore structure information of the detection object to obtain a digital simulation model of the detection object; Analyzing the center of gravity position of the detection object based on the digital simulation model to obtain the object center of gravity characteristics of the detection object; Performing simulation analysis on the theoretical heat conduction condition of the detection object based on the digital simulation model to obtain the heat conduction simulation characteristics of the detection object; Performing a limit simulation process on the limit mechanism according to the center of gravity characteristics of the detection object, so as to obtain a limit simulation scheme and a corresponding limit simulation effect of the limit mechanism corresponding to the detection object; Feedback optimization is performed on the limit simulation scheme according to the limit simulation effect to obtain a limit execution scheme for driving the limit mechanism to limit and clamp the detection object; wherein the limit execution scheme is a detection execution scheme of the limit mechanism corresponding to the detection object; According to the preset thermal conductivity standard, a heat source supply scheme simulation analysis is performed on the digital simulation model having the thermal conductivity simulation characteristics to obtain a heat source supply execution scheme of the heat source module corresponding to the detection object; wherein the heat source supply execution scheme is a detection execution scheme of the heat source module corresponding to the detection object.

6. A method for detecting thermal conductivity of a foamed ceramic plate as claimed in claim 5, characterized in that: The steps of performing a heat source supply scheme simulation analysis on the digital simulation model having the heat conduction simulation characteristics according to the preset heat conduction standard to obtain a heat source supply execution scheme of the heat source module corresponding to the detection object include: Analyzing the thermal conductivity simulation characteristics according to the preset thermal conductivity standard to obtain several conventional thermal conductivity standards fed back by the preset thermal conductivity standard; According to the limit execution scheme and the setting information of the detection module in the main frame, the data collection position of the digital simulation model is analyzed to obtain the temperature data collection node distribution; wherein the temperature data collection node distribution includes a plurality of temperature data collection nodes, and the temperature data collection nodes are specific positions where each detection unit in the detection module collects detection data for the detection object; Combining the temperature data acquisition node distribution with the digital simulation model to obtain a detection simulation model; According to the conventional thermal conductivity standard, the detection effect of the heating position of the detection simulation model is reversely calculated and analyzed to obtain the heating effect position characteristic distribution of the detection simulation model under the conventional thermal conductivity standard; wherein the heating effect position characteristic distribution is used to describe the detection effect of the detection module after the heat source is supplied to each specific position of the detection object under the conventional thermal conductivity standard; The detection simulation model is subjected to a weighted evaluation of the heating position according to the characteristic distribution of the heating effect position corresponding to each of the conventional thermal conductivity standards, so as to obtain a plurality of heat source supply working positions and the heat source supply temperature characteristics corresponding to each of the heat source supply working positions, which are used together as a heat source supply implementation plan.

7. A method for detecting thermal conductivity of a foamed ceramic plate as claimed in claim 6, characterized in that: The step of performing a weighted evaluation of the heating position on the detection simulation model according to the heating effect position characteristic distribution corresponding to each of the conventional thermal conductivity standards to obtain a plurality of heat source supply working positions and heat source supply temperature characteristics corresponding to each of the heat source supply working positions comprises: According to the heating effect position characteristic distribution corresponding to each conventional thermal conductivity standard, a weighted comprehensive analysis of the post-heating detection effect of each specific position in the detection simulation model is performed to obtain the basic detection effect of each specific position in the detection simulation model; Based on the basic detection effect of each specific position in the detection simulation model, a heat source supply method is added to each specific position in the detection simulation model, and the basic detection effect is corrected according to the added heat source supply method to obtain the final detection effect of each specific position corresponding to various types of heat source supply methods; According to the final detection results of various types of heat source supply methods corresponding to each specific position, the specific positions of the detection simulation model and various types of heat source supply methods are selected to determine several heat source supply working positions and the heat source supply temperature characteristics corresponding to each of the heat source supply working positions.

8. A method for detecting thermal conductivity of a foamed ceramic plate as claimed in claim 4, characterized in that: The step of performing data analysis on the first detection data and the second detection data according to a pre-trained thermal conductivity analysis algorithm by a data analysis unit in the detection module to obtain the detection thermal conductivity of the foamed ceramic board to be detected includes: Acquire heat source supply working data of the heat conduction unit corresponding to the first detection data and the second detection data; wherein the heat source supply working data includes a working position and a working mode of the heat conduction unit performing heat source supply work on the detection object; Taking the first detection data and the second detection data as effect features, taking the heat source supply working data as cause features, and performing mapping association analysis on the effect features and the cause features according to a pre-trained thermal conductivity analysis algorithm to obtain mapping association features between the effect features and the cause features; The thermal conductivity of the foamed ceramic board to be detected is converted according to the mapping association feature to obtain the detected thermal conductivity coefficient of the foamed ceramic board to be detected.

9. A method for detecting thermal conductivity of a foamed ceramic plate as claimed in claim 8, characterized in that: The pre-training steps of the thermal conductivity analysis algorithm include: Constructing a machine learning model, and collecting several sets of training data corresponding to the machine learning model; wherein the training data includes thermal conductivity performance data and thermal conductivity test data, the thermal conductivity test data includes thermal conductivity effect data and thermal conductivity cause data, the data content of the thermal conductivity performance data corresponds to the mapping association feature, and the data content of the thermal conductivity effect data and the thermal conductivity cause data respectively corresponds to the effect feature and the cause feature; Substituting each group of the training data into the machine learning model, allowing the machine learning model to perform model training according to the training data to obtain model parameters corresponding to the training data; wherein the model parameters are used to feedback mapping association features between the thermal conductivity performance data and the thermal conductivity test data in the training data; The model parameters are subjected to feature extraction and algorithm conversion to obtain a thermal conductivity analysis algorithm.

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