Graphene cloth analysis method and system based on observation of temperature fluctuation

Abstract

The invention relates to the technical field of cloth detection, and discloses a graphene cloth analysis method and system based on temperature fluctuation observation, and the method comprises the steps: obtaining a plurality of pieces of reference graphene cloth with different graphene contents, and taking the reference graphene cloth as sample cloth; performing standardized pretreatment on the sample cloth; carrying out thermal imaging temperature difference change recording on all the sample cloth, and storing the obtained color value change process of each sample cloth into a database; performing standardized pretreatment on the target detection graphene cloth; performing thermal imaging temperature difference change recording on the target detection graphene cloth to obtain a color value change process of the target detection graphene cloth; and analyzing the graphene content of the target detection graphene cloth by identifying the color value change process of the target detection graphene cloth in the database. Therefore, by comparing the temperature change process of the thermal imaging color, the graphene content of the target detection graphene cloth can be quickly identified.

Description

Technical Field

[0001] This application belongs to the technical field of fabric detection, and relates to a graphene fabric analysis method and system based on observing temperature fluctuations. Background Art

[0002] As a new type of nanomaterial, graphene has been widely used in the textile industry due to its excellent electrical conductivity, thermal conductivity, and mechanical strength, forming graphene fabrics. However, the graphene content in the fabric directly affects its performance and application effects. Therefore, accurately detecting the graphene content in graphene fabrics is crucial for ensuring product quality and meeting specific application requirements.

[0003] Traditional graphene fabric detection methods mostly rely on chemical analysis or physical tests. Although these methods are accurate, they are complex to operate, time-consuming, and may damage the samples.

[0004] In the prior art, the Chinese invention patent with the patent application number CN201710724810.5 discloses a quantitative detection method for the graphene content in textiles, which successively includes the following steps: mixing a substance containing graphene with an excessive amount of acid for acidolysis until the substance containing graphene is completely degraded; then adding an alkaline substance solution to neutralize to a neutral pH of the system; obtaining a mixture of degraded fiber small molecules, salts generated by acid-base neutralization, and graphene; separating and quantitatively detecting graphene in the textile by solution dialysis; calculating the graphene content by weight of graphene according to the formula WG = mG / m0×100%. This technology separates the doped graphene in the textile and conducts quantitative analysis on it, providing necessary technical reserves for the future detection of graphene textiles and promoting the healthy development of the graphene functional textile market.

[0005] Although the prior art can accurately identify the graphene content in fabrics, it is slow and complex to operate.

[0006] Therefore, it is of great significance to develop a fast and simple graphene fabric detection method. Summary of the Invention

[0007] To have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. The summary is not a general review, nor is it intended to identify key / important constituent elements or delineate the protection scope of these embodiments, but rather serves as a preface to the subsequent detailed description.

[0008] To solve the problems existing in the related art, the embodiments of the present disclosure provide a graphene fabric analysis method and system based on observing temperature fluctuations to solve the problems of complex operation and long time consumption in detecting the graphene content in fabrics in the prior art.

[0009] In some embodiments, a graphene fabric analysis method based on observed temperature fluctuations is provided. The detection method includes: obtaining a plurality of reference graphene fabrics with different graphene contents as sample fabrics; performing standardized preprocessing on the sample fabrics, and the standardized preprocessing at least includes uniformly standardizing the fabric shape and partitioning the fabric; recording the thermal imaging temperature difference changes of all the sample fabrics. Among them, the process of recording the thermal imaging temperature difference changes includes: S-a, adjusting the temperature of the graphene fabric to temperature A; S-b, setting the ambient temperature to temperature B; S-c, placing the graphene fabric at temperature A in the ambient temperature at temperature B; S-d, recording the change process of the color value of each partition of the graphene fabric in real time; storing the change process of the color value of each obtained sample fabric in a database; performing standardized preprocessing on the target detection graphene fabric; recording the thermal imaging temperature difference changes of the target detection graphene fabric to obtain the change process of the color value of the target detection graphene fabric; analyzing the graphene content of the target detection graphene fabric by identifying the change process of the color value of the target detection graphene fabric in the database.

[0010] Preferably, the recording of the thermal imaging temperature difference changes includes: recording the infrared heating temperature difference and recording the infrared cooling temperature difference.

[0011] Preferably, in the recording of the infrared heating temperature difference, the temperature of the graphene fabric is adjusted to temperature A1, the ambient temperature is set to temperature B1, and B1 is higher than A1 by a preset temperature difference C; in the recording of the infrared cooling temperature difference, the temperature of the graphene fabric is adjusted to temperature A2, the ambient temperature is set to temperature B2, and A2 is higher than B2 by a preset temperature difference C.

[0012] Preferably, uniformly standardizing the fabric shape includes: making the graphene fabric into a cuboid with the same shape.

[0013] Preferably, partitioning the fabric includes: performing a grid processing on the graphene fabric and marking the positions in the grid.

[0014] Preferably, recording the change process of the color value includes: recording the change process of the color of each grid in the graphene fabric.

[0015] Preferably, the change process of the color includes: the change process of each color value.

[0016] In some embodiments, a graphene fabric analysis system based on observed temperature fluctuations is disclosed, including: a first chamber with adjustable temperature, an infrared thermal imager, a second chamber with adjustable temperature, a conveying mechanism, and a control module. Among them, the infrared thermal imager is arranged in the first chamber for capturing the thermal image of the first chamber, and the conveying mechanism is arranged between the first chamber and the second chamber for conveying the graphene fabric in the second chamber into the first chamber; the first chamber, the infrared thermal imager, the adjustable temperature, and the conveying mechanism are respectively connected to the control module, and the control module executes the above-mentioned graphene fabric analysis method based on observed temperature fluctuations.

[0017] Preferably, the infrared thermal imager is an infrared thermal imager.

[0018] Preferably, a segmentation mesh is used to set on the graphene fabric to perform grid-based position division.

[0019] The graphene fabric analysis method and system based on observed temperature fluctuations provided by the embodiments of the present disclosure can achieve the following technical effects: In the embodiments of the present disclosure, by recording the temperature change process of the thermal imaging color in each fabric partition of each sample fabric in a standardized temperature difference environment, a database is established, and the temperature change process identical to that of the target detection graphene fabric is identified in the database to analyze the graphene content of the target detection graphene fabric. First, the thermal imaging color change is used to record the temperature difference change, replacing the traditional method of using multiple temperature measurement devices to detect the temperatures of different fabric partitions; second, the identified database can be used as a comparison standard and reused multiple times; third, by comparing the temperature change process of the thermal imaging color, the graphene content of the target detection graphene fabric can be quickly identified. Therefore, it is possible to quickly identify the graphene content of the graphene fabric and the established database can be reused, reducing the complexity of subsequent operations.

[0020] The above general description and the following description are only exemplary and explanatory and are not used to limit this application. Description of the Drawings

[0021] One or more embodiments are exemplarily illustrated by corresponding drawings. These exemplary illustrations and the drawings do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements. The drawings do not constitute a proportional limitation, and among them: Figure 1 is a schematic diagram of a graphene fabric analysis system based on observed temperature fluctuations provided by the embodiments of the present disclosure; Figure 2 is a schematic flowchart of a graphene fabric analysis method based on observed temperature fluctuations provided by the embodiments of the present disclosure; Figure 3It is a schematic diagram of the standard grid processing of a graphene fabric provided by an embodiment of the present disclosure; Figure 4 It is a schematic diagram of the unbalanced grid processing of a graphene fabric provided by an embodiment of the present disclosure; The reference numerals in the figure are named as follows: 1 - first compartment, 2 - infrared thermal imager, 3 - second compartment, 4 - conveying mechanism. Detailed implementation manners

[0022] In order to understand the features and technical content of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are only for reference and illustration purposes and are not used to limit the embodiments of the present disclosure. In the following technical description, for the sake of explanation, multiple details are provided to provide a full understanding of the disclosed embodiments. However, one or more embodiments can still be implemented without these details. In other cases, well-known structures and systems can be shown in a simplified manner to simplify the drawings.

[0023] The following description and the accompanying drawings fully disclose specific embodiments of the present invention, enabling those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, process, and other changes. Examples merely represent possible variations. Unless explicitly required, individual components and functions are optional, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of the present invention includes the entire scope of the claims and all available equivalents of the claims. In this document, each embodiment may be individually or collectively referred to by the term "invention" for convenience only, and if more than one invention is actually disclosed, it is not intended to automatically limit the scope of the corresponding application to any single invention or inventive concept. In this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, or vehicle including a series of elements not only includes those elements but also includes other elements not expressly listed, or also includes elements inherent to such process, method, or vehicle. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, or vehicle including the said element. Each embodiment in this document is described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the methods, products, etc. disclosed in the embodiments, since they correspond to the method part disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0024] Traditional graphene fabric detection methods mostly rely on chemical analysis or physical tests. Although these methods are accurate, they are complex to operate, time-consuming, and may damage the samples.

[0025] Although the prior art can accurately identify the graphene content in the fabric, the speed is slow and the operation is complex.

[0026] Therefore, it is of great significance to develop a fast and simple graphene fabric detection method.

[0027] To solve the problems existing in the related art, the embodiments of the present disclosure provide a graphene fabric analysis method and system based on observing temperature fluctuations.

[0028] See Figure 1 , the embodiments of the present disclosure provide a graphene fabric analysis system based on observing temperature fluctuations, including: A first chamber 1 with adjustable temperature, an infrared thermal imager 2, a second chamber 3 with adjustable temperature, a conveying mechanism 4, and a control module (not shown in the figure). The infrared thermal imager 2 is arranged in the first chamber 1 for capturing the thermal image of the first chamber 1. The conveying mechanism 4 is arranged between the first chamber 1 and the second chamber 3 for conveying the graphene fabric in the second chamber 3 into the first chamber 1. The first chamber 1, the infrared thermal imager 2, the adjustable temperature, and the conveying mechanism 4 are respectively connected to the control module, the control module.

[0029] The first chamber 1 with adjustable temperature is used to place the fabric to provide a stable ambient temperature. The second chamber 3 with adjustable temperature is used to place the fabric and adjust the temperature of the chamber. The infrared thermal imager 2 can be the infrared thermal imager 2. The conveying mechanism 4 can be a conveyor belt with a heat-insulating outer shell for conveying the graphene fabric in the second chamber 3 to the first chamber 1.

[0030] Correspondingly, refer to Figure 2 , which is a method for analyzing graphene fabric based on observing temperature fluctuations in an embodiment of the present disclosure. The method includes: S10, obtaining a plurality of reference graphene fabrics with different graphene contents as sample fabrics.

[0031] Graphene is the main component of the graphene fabric, and the amount of its graphene content directly affects the electrical conductivity and mechanical properties of the graphene fabric. In order to obtain the change process of the color value of each partition of the graphene fabric in the subsequent data. For example, it can be 10 groups of graphene contents, which are 0%, 0.5%......5% respectively. It should be noted that in practical applications, the interval of the graphene content should not be too small. In this way, the subsequent change process of the color value will be more differentiated.

[0032] S20, performing standardized preprocessing on the sample fabric, and the standardized preprocessing at least includes uniformly standardizing the fabric shape and partitioning the fabric.

[0033] In order to unify the reference variables, all records of the thermal imaging temperature difference changes need to be subjected to standardized preprocessing.

[0034] Specifically, uniformly standardizing the fabric shape includes: making the graphene fabric into a cuboid with the same shape. It can be set according to the actual situation. For example, the cuboid is 5 cm thick, 100 cm long, and 100 cm wide.

[0035] Partitioning the fabric, dividing the graphene fabric.

[0036] Specifically, partitioning the fabric includes: performing a grid processing on the graphene fabric and marking the positions in the grid. Refer to Figure 3 , which is a schematic diagram of the standard grid processing of a graphene fabric.

[0037] In practical applications, partitions of different areas can also be used. See Figure 4 , a schematic diagram of the unbalanced grid processing of a graphene fabric.

[0038] It should be noted that the main significance of the partition is that in a temperature difference environment, the heat dissipation in the outer region and the inner region of the graphene fabric is different. Since the contact temperature difference environments of the outer region and the inner region of the fabric are different, the heat transfer rates in the outer region and the inner region are also different.

[0039] S30, record the thermal imaging temperature difference changes of all sample fabrics. Among them, the process of recording the thermal imaging temperature difference changes includes: S-a, adjust the temperature of the graphene fabric to temperature A; S-b, set the ambient temperature to temperature B; S-c, place the graphene fabric at temperature A in the ambient temperature of temperature B; S-d, record the change process of the color value of each partition of the graphene fabric in real time.

[0040] The specific process of S30 can be to put the sample fabric into the second chamber 3 with adjustable temperature and adjust the temperature of the sample fabric to temperature A. Then adjust the first chamber 1 with adjustable temperature to temperature B. Place the sample fabric at temperature A in the first chamber 1 with a constant temperature environment, and use the infrared thermal imager 2 to collect the change process of the color value of each partition of the graphene fabric in real time.

[0041] The colors in an infrared thermal image usually represent the temperature distribution of an object, and different colors correspond to different temperature ranges. In thermal imaging, each pixel represents a specific temperature data point. The corresponding data points will be assigned a unique color or gray level according to their values, which means that when the thermal sensor detects a change in thermal energy, it will express the change by adjusting the color or gray level of the pixel. There are various color palettes for thermal images. The color palette of a thermal image, also called a pseudocolor mapping table, is a set of rules or algorithms that convert the temperature data detected by a thermal imaging device into different color visual displays, aiming to help users more intuitively interpret temperature information from thermal images. Common types include iron red palette, rainbow palette, and gray scale palette.

[0042] Taking the rainbow color palette in the infrared thermal image as an example, graphene fabrics with temperatures ranging from 20°C to 25°C are usually dominated by blue, those with temperatures ranging from 30°C to 35°C are usually dominated by green, those with temperatures ranging from 35°C to 40°C are usually dominated by yellow, and when the graphene fabric reaches 40°C, it is usually dominated by red. During the use of the graphene fabric, the common temperature range of the graphene fabric is from 20°C to 40°C. Correspondingly, the overall color change trend is from blue to green, from green to yellow, and from yellow to red.

[0043] The color value can be an RGB color value. Record the changing numerical values in the control module.

[0044] It should be noted that the purpose of S30 is to record the temperature difference change, mainly the speed of temperature change. Since in practical applications, each partition of the graphene fabric is in a block shape, and the number is large, and contact is required. It is difficult to use a large number of temperature measurement devices to make contact in each partition. Therefore, this application uses the thermal image to directly identify the color value change to replace the direct measurement of temperature change.

[0045] In practical applications, the change of the color value at the center point of each partition with time can be recorded, or it can be the change speed of the color value.

[0046] S40, store the color value change process of each sample fabric obtained into the database.

[0047] Store the change records of the color value at the center point of each partition of each sample fabric with time, that is, the color value change process, into the database.

[0048] S50, perform standardized preprocessing on the target detection graphene fabric.

[0049] The target detection graphene fabric is made into a cuboid with the same shape and subjected to grid processing, and the positions in the grid are marked. The same standardized preprocessing steps as the sample fabric.

[0050] S60, record the thermal imaging temperature difference change of the target detection graphene fabric to obtain the color value change process of the target detection graphene fabric.

[0051] Perform the same thermal imaging temperature difference change record on the target detection graphene fabric as on the sample fabric.

[0052] S70, analyze the graphene content of the target detection graphene fabric by identifying the color value change process of the target detection graphene fabric in the database.

[0053] Compare the color value change process of the target detection graphene fabric in the database to find the same color value change process, and obtain the graphene content of the corresponding fabric.

[0054] It should be noted that the comparison is made on the color value change process of each group of partitions.

[0055] In a preferred embodiment, the recording of the thermal imaging temperature difference change is divided into two groups, including: the infrared heating temperature difference recording and the infrared cooling temperature difference recording. In the infrared heating temperature difference recording, the temperature of the graphene fabric is adjusted to temperature A1, and the ambient temperature is set to temperature B1, with B1 higher than A1 by a preset temperature difference C. In the infrared cooling temperature difference recording, the temperature of the graphene fabric is adjusted to temperature A2, and the ambient temperature is set to temperature B2, with A2 higher than B2 by a preset temperature difference C.

[0056] In this way, a common temperature for the graphene fabric to heat up and cool down is set. For example, let the graphene fabric at 20°C be in an ambient temperature of 30°C, and record the process of heating up by 10°C. Then let the graphene fabric at 20°C be in an ambient temperature of 100°C, and record the process of cooling down by 10°C. The color value change process of heating up by 10°C is not the same as the color value change process of cooling down by 10°C.

[0057] It should be understood that in the same background, the first ambient temperature is 10°C higher than the X object, and the second ambient temperature is 10°C lower than the X object. The heating and cooling speeds of the X object are not the same. Therefore, two temperature change processes will be generated. Correspondingly, the present application collects the infrared heating temperature difference recording and the infrared cooling temperature difference recording as two different reference data and stores them in the database.

[0058] The target detection graphene fabric also collects two groups of data, namely the infrared heating temperature difference recording and the infrared cooling temperature difference recording, and conducts a comparison in the database.

[0059] In this way, through the comparison of the two groups of data, it is helpful to accurately identify the graphene content of the target detection graphene fabric in the database.

[0060] In a preferred embodiment, the color value change process of each sample fabric obtained can also be used as a model sample to train a neural network model for identifying the graphene content.

[0061] The color value change process of the target detection graphene fabric is input into the neural network model to identify the graphene content.

[0062] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure, enabling those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, process, and other changes. Embodiments merely represent possible variations. Unless explicitly required, individual components and functions are optional, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terms used in this application are only for describing embodiments and do not limit the claims. As used in the description of embodiments and claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to also include the plural forms. Similarly, as used in this application, the term "and / or" refers to any and all possible combinations of one or more of the associated listed items. Additionally, when used in this application, the term "comprise" and its variants "comprises" and / or "comprising" etc. mean the presence of the stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or groupings of these. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, or vehicle including the element. Herein, what each embodiment focuses on can be the differences from other embodiments, and the same or similar parts among the embodiments can be referred to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method parts disclosed in the embodiments, the relevant parts can refer to the description of the method parts.

[0063] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner can depend on the specific application and design constraints of the technical solution. The technical personnel can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the embodiments of the present disclosure. The technical personnel can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, systems, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0064] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions noted in the blocks may occur in a different order than noted in the accompanying drawings. For example, two consecutive blocks may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. Each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A graphene fabric analysis method based on observed temperature fluctuations, characterized in that, The detection method includes: Obtain a plurality of reference graphene fabrics with different graphene contents as sample fabrics; Perform standardized preprocessing on the sample fabrics, and the standardized preprocessing includes at least unifying the standardized fabric shape and partitioning the fabric; Record the thermal imaging temperature difference changes of all sample fabrics. Among them, the process of recording the thermal imaging temperature difference changes includes: S-a, adjust the temperature of the graphene fabric to temperature A; S-b, set the ambient temperature to temperature B; S-c, place the graphene fabric at temperature A in the ambient temperature at temperature B; S-d, record the change process of the color value of each partition of the graphene fabric in real time; Store the change process of the color value of each obtained sample fabric in the database; Perform standardized preprocessing on the target detection graphene fabric; Record the thermal imaging temperature difference changes of the target detection graphene fabric to obtain the change process of the color value of the target detection graphene fabric; Analyze the graphene content of the target detection graphene fabric by identifying the change process of the color value of the target detection graphene fabric in the database.

2. The graphene fabric analysis method based on observed temperature fluctuations according to claim 1, characterized in that The thermal imaging temperature difference change record includes: infrared heating temperature difference record and infrared cooling temperature difference record.

3. The graphene fabric analysis method based on observed temperature fluctuations according to claim 2, wherein In the infrared heating temperature difference record, the temperature of the graphene fabric is adjusted to temperature A1, the ambient temperature is set to temperature B1, and B1 is higher than A1 by a preset temperature difference C; In the infrared cooling temperature difference record, the temperature of the graphene fabric is adjusted to temperature A2, the ambient temperature is set to temperature B2, and A2 is higher than B2 by a preset temperature difference C.

4. The graphene fabric analysis method based on observed temperature fluctuations according to claim 1, wherein Unifying the standardized fabric shape includes: Manufacture the graphene fabric into a cuboid with the same shape.

5. The graphene fabric analysis method based on observed temperature fluctuations according to claim 4, wherein Partitioning the fabric includes: Perform grid processing on the graphene fabric and mark the positions in the grid.

6. The graphene fabric analysis method based on observed temperature fluctuations according to claim 5, characterized in that, Recording the change process of the color value includes: Record the color change process of each grid in the graphene fabric.

7. The graphene fabric analysis method based on observed temperature fluctuations according to claim 1, characterized in that, The color change process includes: The change process of each color value.

8. A graphene fabric analysis system based on observed temperature fluctuations, characterized in that, It includes: A first chamber with adjustable temperature, an infrared thermal imager, a second chamber with adjustable temperature, a conveying mechanism, and a control module. Among them, the infrared thermal imager is arranged in the first chamber and is used to capture the thermal imaging of the first chamber. The conveying mechanism is arranged between the first chamber and the second chamber and is used to convey the graphene fabric in the second chamber into the first chamber; The first chamber, the infrared thermal imager, the adjustable temperature, and the conveying mechanism are respectively connected to the control module. The control module is used to execute the graphene fabric analysis method based on observing temperature fluctuations as described in any one of claims 1 to 7.

9. The graphene fabric analysis system based on observed temperature fluctuations according to claim 8, characterized in that The infrared thermal imager is an infrared thermal imager.

10. The graphene fabric analysis system based on observed temperature fluctuations according to claim 8, characterized in that, It includes: A dividing net, which is used to be arranged on the graphene fabric to perform grid position division.

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