Method for measuring heat conductivity coefficient of nano adsorption material in moisture absorption state

Abstract

The invention provides a method for measuring the heat conductivity coefficient of a nano adsorption material in a moisture absorption state, and relates to the technical field of measurement of the heat conductivity coefficient of an adsorption material. The method creatively realizes synchronous dynamic measurement and analysis of the heat conductivity coefficient of the nano adsorption material in a variable environment, improves the humidity control precision and real-time performance, accurately captures an adjustment mechanism of a light-humidity coupling effect on heat conduction, and also realizes accurate measurement and analysis of the heat conductivity coefficient of the nano adsorption material by constructing a light effect increment model and a two-dimensional damage map. And intelligent early warning and management of environmental risks are realized.

Description

Technical Field

[0001] The invention relates to the technical field of measuring thermal conductivity of adsorption materials, in particular to a method for measuring the thermal conductivity of a nano-adsorption material in a moisture-absorbing state. Background Art

[0002] Nano-adsorbent materials are widely used in building energy conservation, environmental regulation, and thermal management due to their excellent porous structure and hygroscopic properties. However, their thermal conductivity is affected by the complex coupling of ambient humidity and light conditions, resulting in dynamic changes in thermal conductivity with hygroscopicity and light intensity, making it difficult for traditional thermal conductivity measurement methods to accurately reflect the thermal conductivity characteristics of the material under actual working conditions. In existing technologies, most measurement methods target steady-state or single environmental factors, lacking dynamic monitoring and separate analysis of humidity change paths and lighting effects. This is especially difficult to reveal when simulating typical application scenarios such as the interactive changes between air humidity and natural light in buildings, as the specific impact mechanism of light-humidity coupling on thermal conductivity is difficult to reveal.

[0003] Prior art publication CN110361416A discloses a method for measuring the thermal conductivity of a hygroscopic fiber fabric material in a hygroscopic state. The method comprises: S1, pre-humidifying the dried hygroscopic fiber fabric material until it reaches a hygroscopic equilibrium state, and measuring the moisture content α1; S2, continuing to measure the porosity φ and effective thermal conductivity λ, and calculating the thermal conductivity λsh of the fiber skeleton of the hygroscopic fiber fabric material; S3, continuing the hygroscopic fiber fabric material to absorb moisture until it reaches a state above the hygroscopic equilibrium state and below the saturated state, measuring the moisture content α2, and calculating the volume ratio φw of non-hygroscopic water; S4, calculating the thermal conductivity λeff at the moisture content α2 using a parallel conduction model. Although it can measure thermal conductivity, it focuses on calculating thermal conductivity through porosity and moisture content in a static hygroscopic equilibrium state. The measurement process is relatively cumbersome and relies on traditional thermal measurement methods and physical model corrections. It lacks real-time monitoring and separation of dynamic humidity changes and light coupling effects, and cannot reflect the coupling effects caused by light conditions.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0005] The object of the present invention is to provide a method for measuring the thermal conductivity of a nano-adsorbent material in a hygroscopic state, so as to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A method for measuring the thermal conductivity of a nano-adsorbent material in a hygroscopic state, comprising the following steps: S1: Place the adsorption material under constant temperature conditions for a moisture absorption test, collect the humidity of the test environment and the moisture content of the adsorption material during the moisture absorption process in real time, and collect the thermal conductivity of the adsorption material at the same time; S2: Construct a humidity change path, control the humidity of the test environment to run according to the humidity change path, and execute the light source state switching operation when the moisture content of the adsorption material reaches the preset moisture content node; S3: Measure the bright-state thermal conductivity and the dark-state thermal conductivity before and after the light source state switching operation, and calculate the thermal conductivity deviation caused by the light source state; S4: Based on the dark state thermal conductivity, a curve of the relationship between thermal conductivity and humidity under lightless conditions is constructed. Then, the light effect incremental surface is generated by combining the light intensity of the light source and the thermal conductivity deviation. S5: Generate the thermal conductivity of the adsorption material under different environments based on the relationship curve and the light effect incremental surface, and issue an early warning when there is a risk in the environment.

[0007] Preferably, the logic for controlling the humidity of the test environment according to the humidity change path is: The humidity of the test environment is adjusted in a linear growth manner to generate a relationship function between the humidity of the test environment under light conditions and the moisture content of the adsorption material. Then, the time-moisture content objective function is constructed based on the relationship function and the required moisture absorption rate. The target moisture content of the adsorption material is obtained according to the objective function, and the deviation between the real-time moisture content and the target moisture content is calculated. The humidity of the test environment is adjusted by the PID algorithm.

[0008] Preferably, when the humidity of the test environment is adjusted by the PID algorithm, a single-point retest process is also provided, and its specific logic is as follows: When the deviation between the real-time moisture content and the target moisture content does not exceed the deviation threshold, the PID algorithm is used to adjust the humidity of the test environment; When the deviation between the real-time moisture content and the target moisture content exceeds the deviation threshold, the humidity change path process is suspended and the control parameters in the PID algorithm are corrected. The correction formula is: ; In the formula 、 Respectively represent the proportional coefficient and integral time constant in the PID algorithm, 、 Represent the corrected proportional coefficient and integral time constant respectively, express The deviation between the real-time moisture content and the target moisture content at the moment, represents the deviation threshold, , represents the empirical coefficient, ; After the correction is completed, the humidity of the test environment is returned to the humidity node 10 seconds before the limit is exceeded, and the humidity change path process is restarted based on the corrected PID control parameters to achieve single-point retesting.

[0009] Preferably, when the humidity of the test environment is adjusted by the PID algorithm, a path accuracy verification is also provided, and its specific logic is as follows: Define the path reproducibility index: ; In the formula represents the reproducibility index, 、 Respectively The real-time moisture content and target moisture content at the moment, superscript Indicates the index of the moisture content node; When satisfied ,or When the humidity change path is considered to be insufficiently accurate, the control parameters in the PID algorithm are corrected and the humidity change path process is re-executed to achieve full path retesting.

[0010] Preferably, the logic for calculating the thermal conductivity deviation caused by the light source state is: When the real-time moisture content of the adsorption material reaches any preset moisture content node, the humidity change path process is suspended to maintain the test environment at a constant temperature and humidity state; Collect the thermal conductivity of the adsorption material under light conditions and calibrate it as the bright-state thermal conductivity; Turn off the light source, wait until the temperature in the test environment is stable, collect the thermal conductivity of the adsorption material under lightless conditions, and calibrate it as the dark thermal conductivity; The difference between the bright state thermal conductivity and the dark state thermal conductivity is calibrated as the thermal conductivity deviation.

[0011] Preferably, the relationship curve between thermal conductivity and humidity under the dark condition is fitted using a cubic spline interpolation method, and the fitting function expression is: ; In the formula express The dark thermal conductivity at time , express Always test the humidity of the environment. represents the fitting function.

[0012] Preferably, the light effect incremental surface is a binary surface, and is generated using radial basis function interpolation method, and the surface expression is: ; In the formula express Thermal conductivity deviation at time, represents the radial basis function, express Light intensity at the moment.

[0013] Preferably, when issuing an early warning when there is a risk in the environment, the rate of change of the moisture content under light conditions and the rate of change of the moisture content under no light conditions are first calculated; The ratio between the moisture content change rate under light conditions and the moisture content change rate under lightless conditions is defined as the light enhancement coefficient. The larger the light enhancement coefficient, the stronger the impact of light intensity on the moisture absorption rate and the higher the environmental risk.

[0014] Preferably, an early warning is issued by constructing a two-dimensional damage map, wherein the two-dimensional damage map includes a curve showing the relationship between thermal conductivity and humidity under dark conditions and a surface showing an incremental light effect under dark conditions; The horizontal axis of the relationship curve is the humidity of the test environment, and the vertical axis is the dark thermal conductivity; The input axis of the light effect incremental surface is the humidity and light intensity of the test environment, and the output axis is the thermal conductivity deviation; The area where the light enhancement coefficient exceeds 1.2 in the light effect increment surface is marked as the medium influence area. It is believed that the light intensity in this range has a certain influence on the thermal conductivity of the adsorption material. Will satisfy: ; The area is marked as a strong influence area. It is believed that the light intensity within this range has a strong influence on the thermal conductivity of the adsorption material, and an early warning is issued when the light intensity of the environment where the adsorption material is located reaches this range. represents the thermal conductivity deviation, Represents the illumination enhancement coefficient.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention establishes a dynamic closed-loop control system for humidity and moisture content, combined with the quantitative influence of light intensity on the material's thermal conductivity, to innovatively achieve simultaneous dynamic measurement and analysis of the thermal conductivity of nano-adsorbent materials under variable environments. This method not only improves the precision and real-time performance of humidity control, accurately capturing the regulatory mechanism of the light-humidity coupling effect on thermal conduction, but also enables intelligent early warning and management of environmental risks by constructing an incremental light effect model and a two-dimensional damage map. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of the overall method flow of the present invention; Figure 2 This is the incremental surface diagram of the light effect of the present invention. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to specific embodiments.

[0018] It should be noted that, unless otherwise defined, the technical or scientific terms used in the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0019] Example: See also Figures 1 to 2 , the present invention provides a technical solution: A method for measuring the thermal conductivity of a nano-adsorbent material in a hygroscopic state, comprising the following steps: S1: Place the adsorption material under constant temperature conditions for a moisture absorption test, collect the humidity of the test environment and the moisture content of the adsorption material during the moisture absorption process in real time, and collect the thermal conductivity of the adsorption material at the same time.

[0020] Specifically, moisture absorption tests can be conducted in a constant temperature and humidity sample chamber. A coiled tube can be installed inside the chamber, connected to an external constant temperature water bath, to maintain a constant temperature during the test. Changes in the thermal conductivity of the adsorbent material during the moisture absorption test can also be detected by placing the sensor probe of a thermal conductivity meter (such as a transient heat source device) next to the adsorbent material. The humidity within the chamber can be controlled using nitrogen: nitrogen flows through a humidity generator and into the sample chamber to maintain the required humidity within the chamber. Varying the nitrogen flow rate can achieve varying humidity levels. Furthermore, to simulate the effects of sunlight on the water vapor adsorption and desorption process and the coupled effects on the thermal conductivity of the material, a light source with variable intensity and wavelength is placed above the sample. By varying the light intensity and humidity within the chamber, different operating conditions can be created for the adsorbent material.

[0021] Here, a constant temperature and humidity sample test chamber is used to precisely and dynamically control temperature and humidity. Nitrogen flow regulation allows for flexible humidity adjustment. A transient heat source method is used to measure changes in the adsorbent material's thermal conductivity in real time. Furthermore, an adjustable light source is added to simulate sunlight, enabling simultaneous dynamic measurement of material properties under the coupled effects of humidity and light. Compared to traditional methods that measure thermal conductivity only in a steady state or single environment, this procedure offers high timeliness and multi-physics coupling control capabilities, significantly improving measurement accuracy and authenticity.

[0022] S2: Construct a humidity change path, control the humidity of the test environment to run according to the humidity change path, and execute the light source state switching operation when the moisture content of the adsorption material reaches the preset moisture content node.

[0023] The logic for controlling the humidity of the test environment according to the humidity change path is: The humidity of the test environment is adjusted in a linear growth manner to generate a relationship function between the humidity of the test environment under light conditions and the moisture content of the adsorption material. Then, the time-moisture content objective function is constructed based on the relationship function and the required moisture absorption rate.

[0024] In this step, the humidity changes linearly with time at the beginning. The functional relationship between humidity and time is expressed as: ; In the formula Represents the functional relationship between humidity and time; After detecting the moisture content and humidity at different times, the relationship function between humidity and moisture content can be fitted. Assume that the fitting function expression is as follows: ; In the formula 、 Respectively Moisture content and humidity at the moment, Represents the fitting function between humidity and moisture content; In other words, the final objective function of time-moisture content can be expressed as: ; It is understandable that the functional relationship between humidity and time is initially linearly increasing. This is to obtain the data of humidity and moisture content at different times in order to fit the relationship function between humidity and moisture content. After obtaining the relationship function between humidity and moisture content, when constructing the objective function of time-moisture content, the internal It is no longer limited to linear changes, but can be determined according to the needs of the user, thereby adjusting the relationship between time and moisture content, that is, adjusting the moisture absorption rate of the adsorption material.

[0025] The target moisture content of the adsorption material is obtained according to the objective function, and the deviation between the real-time moisture content and the target moisture content is calculated. The humidity of the test environment is adjusted by the PID algorithm. The PID algorithm is a mature existing technology, and its specific calculation formula will not be described in detail here.

[0026] It's understandable that the moisture content of an adsorbent material (i.e., the amount of water absorbed within the material) isn't a simple function of ambient humidity; rather, it responds to humidity over a specific timescale, exhibiting a certain degree of hysteresis and dynamic variation. To precisely control and dynamically measure the material's moisture absorption state, the system dynamically regulates ambient humidity through a "humidity change path" and implements closed-loop control through real-time feedback of moisture content.

[0027] When the humidity of the test environment is adjusted using the PID algorithm, a single-point retest process is also set up. The specific logic is as follows: When the deviation between the real-time moisture content and the target moisture content does not exceed the deviation threshold, the PID algorithm is used to adjust the humidity of the test environment; When the deviation between the real-time moisture content and the target moisture content exceeds the deviation threshold, the humidity change path process is suspended and the control parameters in the PID algorithm are corrected. The correction formula is: ; In the formula 、 Respectively represent the proportional coefficient and integral time constant in the PID algorithm, 、 Represent the corrected proportional coefficient and integral time constant respectively, express The deviation between the real-time moisture content and the target moisture content at the moment, represents the deviation threshold, , represents the empirical coefficient, .

[0028] As can be seen from the correction formula, when the deviation between the real-time moisture content and the target moisture content exceeds the deviation threshold, it indicates that the current control error is large and the system response is insufficient. At this point, the proportional coefficient is amplified to enhance the controller's response and improve its ability to immediately correct the error. The amplification factor increases linearly with the deviation, and the increase is controlled by empirical parameters to prevent excessive amplification of the proportional coefficient, which can lead to control oscillation. The integral action helps eliminate steady-state errors, but too small an integral time can cause system oscillation, while too large an integral time can slow the system response. Therefore, the integral time constant is multiplied by a square root factor inversely proportional to the deviation. This reduces the integral time constant when the deviation is large, accelerating the integral response; when the deviation is small, the integral time is increased to avoid oscillation caused by excessive integration.

[0029] After the correction is completed, the humidity of the test environment is returned to the humidity node 10 seconds before the limit is exceeded, and the humidity change path process is restarted based on the corrected PID control parameters to achieve single-point retesting.

[0030] When the humidity of the test environment is adjusted using the PID algorithm, path accuracy verification is also provided. The specific logic is as follows: Define the path reproducibility index: ; In the formula represents the reproducibility index, 、 Respectively The real-time moisture content and target moisture content at the moment, superscript Indicates the index of the moisture content node; When satisfied ,or When the humidity change path is considered to be insufficiently accurate, the control parameters in the PID algorithm are corrected and the humidity change path process is re-executed to achieve full path retesting.

[0031] In this step, by introducing the single-point remeasurement mechanism and the full-path remeasurement mechanism, the error accumulation caused by path deviation is effectively avoided, making the collected data more in line with the preset dynamic path, and avoiding PID control failure or performance degradation due to environmental disturbances, equipment nonlinearity or model errors, thereby ensuring the long-term stable and precise operation of the humidity control system and improving the scientific nature and repeatability of the data.

[0032] S3: Measure the bright-state thermal conductivity and the dark-state thermal conductivity before and after the light source state switching operation, and calculate the thermal conductivity deviation caused by the light source state.

[0033] The logic for calculating the thermal conductivity deviation caused by the light source state is: When the real-time moisture content of the adsorption material reaches any preset moisture content node, the humidity change path process is suspended to maintain the test environment at a constant temperature and humidity state; Collect the thermal conductivity of the adsorption material under light conditions and calibrate it as the bright-state thermal conductivity; Turn off the light source, wait until the temperature in the test environment is stable, collect the thermal conductivity of the adsorption material under lightless conditions, and calibrate it as the dark thermal conductivity. Use a constant temperature and humidity environment and stable temperature to effectively avoid short-term temperature fluctuations caused by heat input or loss during light source switching that affect the measurement results, thereby improving data repeatability and reliability. The difference between the bright state thermal conductivity and the dark state thermal conductivity is calibrated as the thermal conductivity deviation.

[0034] Measuring thermal conductivity in both the bright and dark states effectively isolates the direct effects of light on thermal conductivity. By pausing humidity changes when the moisture content reaches a preset threshold, we ensure stable ambient humidity and temperature, eliminating the effects of humidity and temperature fluctuations on thermal conductivity. Measuring thermal conductivity in both the bright (bright) and dark (dark) states accurately captures changes in thermal conductivity caused by light, improving the signal-to-noise ratio and data accuracy.

[0035] S4: Based on the dark state thermal conductivity, a curve of the relationship between thermal conductivity and humidity under lightless conditions is constructed, and then the light effect incremental surface is generated by combining the light intensity of the light source and the thermal conductivity deviation.

[0036] The thermal conductivity-humidity curve under dark conditions was fitted using cubic spline interpolation. This curve reflects the fundamental pattern of how the thermal conductivity of nano-adsorbent materials varies with ambient humidity (or moisture content) in the dark. Cubic spline interpolation ensures first- and second-order continuity at the data nodes, making it suitable for describing the nonlinear and smooth variation of a material's thermal conductivity with humidity. This curve reveals how the hygroscopic state of a material independently influences its thermal conductivity and serves as a core data point for evaluating a material's fundamental thermophysical properties.

[0037] The fitting function expression of the relationship curve is: ; In the formula express The dark thermal conductivity at time , express Always test the humidity of the environment. represents the fitting function.

[0038] The light effect incremental surface is a binary surface and is generated using the radial basis function interpolation method. The surface expression is: ; In the formula express Thermal conductivity deviation at time, represents the radial basis function, express Light intensity at the moment.

[0039] The radial basis function interpolation method efficiently handles multidimensional nonlinear relationships and is suitable for capturing the complex variations in thermal conductivity under the coupled effects of humidity and light intensity. The specific function type employed (such as Gaussian, multiquadratic, or polyharmonic spline) can be determined based on expert experience. This surface describes the spatial distribution of the additive effect of light intensity and humidity on thermal conductivity, revealing the mechanism of light-humidity coupling and its regulatory effect on thermal conductivity.

[0040] In this step, by fitting the dark-state basic curve and the illumination increment surface separately, we fully utilize multidimensional data and accurately capture the coupling effects of the two key environmental factors, humidity and illumination. This avoids the limitations of single-variable or linear models, separates the basic thermal conductivity behavior from the illumination effect, and clearly models them, making it easier to analyze their contributions to material properties separately and achieve scientific coupling mechanism analysis and visual expression.

[0041] S5: Generate the thermal conductivity of the adsorption material under different environments based on the relationship curve and the light effect incremental surface. That is, first obtain the dark thermal conductivity under lightless conditions based on the humidity and the relationship curve, and then obtain the thermal conductivity deviation based on the humidity, light intensity and the light effect incremental surface. The sum of the dark thermal conductivity and the thermal conductivity deviation is the bright thermal conductivity. At the same time, an early warning is issued when there is a risk in the environment.

[0042] When issuing an early warning when there is a risk in the environment, the rate of change of moisture content under light conditions and the rate of change of moisture content under no light conditions are first calculated; The ratio of the moisture content change rate under light conditions to the moisture content change rate under no light conditions is defined as the light enhancement coefficient. The larger the light enhancement coefficient, the stronger the effect of light intensity on moisture absorption rate and the higher the environmental risk. When , it means that light promotes the moisture absorption process. The larger the value, the more significant the effect of light on the moisture absorption rate.

[0043] An early warning is issued by constructing a two-dimensional damage map, which includes the relationship curve between thermal conductivity and humidity under dark conditions and the light effect incremental surface under light conditions.

[0044] The dual-dimensional damage map constructed here combines the basic thermal conductivity curve with the light-effect incremental surface to form a two-dimensional or three-dimensional visualization. It is used to intuitively display the thermal conductivity of nano-adsorbent materials under different combinations of humidity and light conditions. It not only shows the material's thermal performance state, but also reflects the "functional damage" or performance fluctuation trends caused by environmental changes.

[0045] The horizontal axis of the relationship curve is the humidity of the test environment, and the vertical axis is the dark thermal conductivity; The input axis of the light effect incremental surface is the humidity and light intensity of the test environment, and the output axis is the thermal conductivity deviation; The area where the light enhancement coefficient exceeds 1.2 in the light effect increment surface is marked as the medium influence area. It is believed that the light intensity in this range has a certain influence on the thermal conductivity of the adsorption material. Will satisfy: ; The area is marked as a strong influence area. It is believed that the light intensity within this range has a strong influence on the thermal conductivity of the adsorption material, and an early warning is issued when the light intensity of the environment where the adsorption material is located reaches this range. represents the thermal conductivity deviation, Represents the illumination enhancement coefficient.

[0046] The moderately affected and strongly affected zones refer to areas in the damage map where the light effect increment or light enhancement coefficient exceeds a preset threshold, indicating that light has an impact on the material's thermal conductivity at this combination of humidity and light intensity. In practical applications, strongly affected zones are key areas of focus during design and monitoring, as they may indicate significant material performance fluctuations, potential thermal management risks, or performance degradation hazards, necessitating early warning.

[0047] In this step, by incorporating two key environmental variables, humidity and light intensity, into the analysis at the same time, and using the basic thermal conductivity relationship curve under lightless conditions and the incremental surface caused by light, an intuitive and scientific two-dimensional damage map is constructed, effectively capturing the comprehensive impact of light-humidity coupling on the thermal conductivity of materials, avoiding blind spots and misjudgments under single factor analysis, and realizing dynamic quantification and risk warning of thermal conductivity of materials under the coupling of multiple environmental factors.

[0048] In this embodiment, the radial basis function for constructing the light effect incremental surface is constructed in the form of Gaussian radial basis function + exponential function, which can be specifically expressed as: ; In the formula 、 、 、 are all model parameters.

[0049] The Gaussian radial basis function is used in the humidity part here because the thermal conductivity of nano-adsorbent materials usually shows the most sensitive changes to humidity changes within a certain range (near the critical humidity), and the Gaussian function can well simulate this peak response. In addition, there is usually a "most sensitive area" for the influence of humidity on the thermal conductivity of materials. The "bell-shaped" curve of the Gaussian function just reflects this limitation and gradualness; the exponential response function is used in the light intensity part because the effect of light intensity on the thermal conductivity deviation usually shows a trend of rapid increase with increasing intensity, and then gradually tending to saturation. The exponential decay function in the second half can accurately simulate this nonlinear increasing and eventually stabilizing physical process, which is consistent with the dynamic characteristics of light-induced moisture absorption and thermal conductivity changes.

[0050] On this basis, 20 sets of measurement data with different humidity and light intensity are used to fit the light effect incremental surface. The measurement data are shown in the following table: Table 1: Measurement data

[0051] It can be seen from the fitting image that the overall surface presents a typical "peak-valley" structure, reflecting the coupled regulatory effect of light and humidity. At certain humidity levels (about 65%-75%), the influence of light is most significant, indicating that the material in this humidity range has the strongest photosensitive thermal conductivity. This will help explore how to regulate the thermal properties of materials through humidity and light, and guide the design of functional materials.

[0052] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0053] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0054] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment as needed.

[0055] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A method for measuring the thermal conductivity of a nano-adsorbent material in a hygroscopic state, characterized in that: The specific steps include: S1: Place the adsorption material under constant temperature conditions for a moisture absorption test, collect the humidity of the test environment and the moisture content of the adsorption material during the moisture absorption process in real time, and collect the thermal conductivity of the adsorption material at the same time; S2: Construct a humidity change path, control the humidity of the test environment to run according to the humidity change path, and execute the light source state switching operation when the moisture content of the adsorption material reaches the preset moisture content node; S3: Measure the bright-state thermal conductivity and the dark-state thermal conductivity before and after the light source state switching operation, and calculate the thermal conductivity deviation caused by the light source state; S4: Based on the dark state thermal conductivity, a curve of the relationship between thermal conductivity and humidity under lightless conditions is constructed. Then, the light effect incremental surface is generated by combining the light intensity of the light source and the thermal conductivity deviation. S5: Generate the thermal conductivity of the adsorption material under different environments based on the relationship curve and the light effect incremental surface, and issue an early warning when there is a risk in the environment.

2. The method for measuring the thermal conductivity of a nano-adsorbent material in a hygroscopic state according to claim 1, characterized in that: The logic for controlling the humidity of the test environment according to the humidity change path is: The humidity of the test environment is adjusted in a linear growth manner to generate a relationship function between the humidity of the test environment under light conditions and the moisture content of the adsorption material. Then, the time-moisture content objective function is constructed based on the relationship function and the required moisture absorption rate. The target moisture content of the adsorption material is obtained according to the objective function, and the deviation between the real-time moisture content and the target moisture content is calculated. The humidity of the test environment is adjusted by the PID algorithm.

3. The method for measuring the thermal conductivity of a nano-adsorbent material in a hygroscopic state according to claim 2, characterized in that: When the humidity of the test environment is adjusted using the PID algorithm, a single-point retest process is also set up. The specific logic is as follows: When the deviation between the real-time moisture content and the target moisture content does not exceed the deviation threshold, the PID algorithm is used to adjust the humidity of the test environment; When the deviation between the real-time moisture content and the target moisture content exceeds the deviation threshold, the humidity change path process is suspended and the control parameters in the PID algorithm are corrected. The correction formula is: ； In the formula 、 Respectively represent the proportional coefficient and integral time constant in the PID algorithm, 、 Represent the corrected proportional coefficient and integral time constant respectively, express The deviation between the real-time moisture content and the target moisture content at the moment, represents the deviation threshold, , represents the empirical coefficient, ; After the correction is completed, the humidity of the test environment is returned to the humidity node 10 seconds before the limit is exceeded, and the humidity change path process is restarted based on the corrected PID control parameters to achieve single-point retesting.

4. The method for measuring the thermal conductivity of a nano-adsorbent material in a hygroscopic state according to claim 3, characterized in that: When the humidity of the test environment is adjusted using the PID algorithm, path accuracy verification is also provided. The specific logic is as follows: Define the path reproducibility index: ； In the formula represents the reproducibility index, 、 Respectively The real-time moisture content and target moisture content at the moment, superscript Indicates the index of the moisture content node; When satisfied ,or When the humidity change path is considered to be insufficiently accurate, the control parameters in the PID algorithm are corrected and the humidity change path process is re-executed to achieve full path retesting.

5. The method for measuring the thermal conductivity of a nano-adsorbent material in a hygroscopic state according to claim 1, characterized in that: The logic for calculating the thermal conductivity deviation caused by the light source state is: When the real-time moisture content of the adsorption material reaches any preset moisture content node, the humidity change path process is suspended to maintain the test environment at a constant temperature and humidity state; Collect the thermal conductivity of the adsorption material under light conditions and calibrate it as the bright-state thermal conductivity; Turn off the light source, wait until the temperature in the test environment is stable, collect the thermal conductivity of the adsorption material under lightless conditions, and calibrate it as the dark thermal conductivity; The difference between the bright state thermal conductivity and the dark state thermal conductivity is calibrated as the thermal conductivity deviation.

6. The method for measuring the thermal conductivity of a nano-adsorbent material in a hygroscopic state according to claim 5, characterized in that: The relationship curve between thermal conductivity and humidity under the dark condition is fitted using cubic spline interpolation method, and the fitting function expression is: ； In the formula express The dark thermal conductivity at time , express Always test the humidity of the environment. represents the fitting function.

7. The method for measuring the thermal conductivity of a nano-adsorbent material in a hygroscopic state according to claim 6, characterized in that: The light effect incremental surface is a binary surface and is generated using radial basis function interpolation. The surface expression is: ； In the formula express Thermal conductivity deviation at time, represents the radial basis function, express Light intensity at the moment.

8. The method for measuring the thermal conductivity of a nano-adsorbent material in a hygroscopic state according to claim 7, characterized in that: When issuing an early warning when there is a risk in the environment, the rate of change of moisture content under light conditions and the rate of change of moisture content under no light conditions are first calculated; The ratio between the moisture content change rate under light conditions and the moisture content change rate under lightless conditions is defined as the light enhancement coefficient. The larger the light enhancement coefficient, the stronger the impact of light intensity on the moisture absorption rate and the higher the environmental risk.

9. The method for measuring the thermal conductivity of a nano-adsorbent material in a hygroscopic state according to claim 8, characterized in that: An early warning is issued by constructing a two-dimensional damage map, which includes a curve showing the relationship between thermal conductivity and humidity under dark conditions and a surface showing the incremental effect of light under dark conditions. The horizontal axis of the relationship curve is the humidity of the test environment, and the vertical axis is the dark thermal conductivity; The input axis of the light effect incremental surface is the humidity and light intensity of the test environment, and the output axis is the thermal conductivity deviation; The area where the light enhancement coefficient exceeds 1.2 in the light effect increment surface is marked as the medium influence area. It is believed that the light intensity in this range has a certain influence on the thermal conductivity of the adsorption material. Will satisfy: ； The area is marked as a strong influence area. It is believed that the light intensity within this range has a strong influence on the thermal conductivity of the adsorption material, and an early warning is issued when the light intensity of the environment where the adsorption material is located reaches this range. represents the thermal conductivity deviation, Represents the illumination enhancement coefficient.

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