CN111274735A - Thermal diffusion coefficient testing method and device, computer equipment and storage medium - Google Patents
Thermal diffusion coefficient testing method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a thermal diffusion coefficient testing method, a thermal diffusion coefficient testing device, computer equipment and a storage medium, wherein the method comprises the following steps: collecting temperature measurement data of an object to be tested; and testing and analyzing the temperature measurement data according to a pre-trained neural network to obtain the thermal diffusion coefficient of the object to be tested. The technical scheme of the embodiment of the invention can complete the test of the thermal diffusion coefficient of the object to be tested and expand the test range of the thermal diffusion coefficient on the premise of not damaging the object to be tested and having no special sample preparation requirement.
Description
Technical Field
The embodiment of the invention relates to the technical field of material testing, in particular to a thermal diffusion coefficient testing method and device, computer equipment and a storage medium.
Background
The thermal diffusion coefficient is taken as an important parameter of the material, the heat transfer performance and the temperature balance performance of the material are represented, and the thermal diffusion coefficient is an important index for analyzing the heat conduction process in the material.
In the thermal diffusion coefficient test of materials, the existing test methods mainly comprise a laser method, a transient plane heat source method and a hot plate method.
In the process of implementing the invention, the inventor finds that the prior art has the following defects: the laser method needs to sample an object to be tested to prepare a cylindrical sample with a specific size, and requires that the internal components of the sample are uniform and consistent, the upper surface and the lower surface of the sample are parallel and smooth, and particularly the thickness of the sample is uniform and consistent. The transient planar heat source method requires that the test probe be deep inside the sample and that the sample surface be flat, with the sample in good contact with and surrounding the probe. The hot plate method requires a large amount of sample and has a very limited range of testing for thermal diffusivity. Therefore, the existing thermal diffusivity test method needs a large number of samples, or has strict requirements on the size and the precision of the samples, or needs to damage an object to be tested, and has certain limitation on the test range of the thermal diffusivity.
Disclosure of Invention
The embodiment of the invention provides a thermal diffusion coefficient testing method, a thermal diffusion coefficient testing device, computer equipment and a storage medium, which are used for testing the thermal diffusion coefficient of an object to be tested on the premise of not damaging the object to be tested and having no special sample preparation requirement and expanding the testing range of the thermal diffusion coefficient.
In a first aspect, an embodiment of the present invention provides a thermal diffusivity testing method, including:
collecting temperature measurement data of an object to be tested;
and testing and analyzing the temperature measurement data according to a pre-trained neural network to obtain the thermal diffusion coefficient of the object to be tested.
In a second aspect, an embodiment of the present invention further provides a thermal diffusivity testing apparatus, including:
the temperature measurement data acquisition module is used for acquiring temperature measurement data of an object to be tested;
and the thermal diffusion coefficient testing module is used for testing and analyzing the temperature measurement data according to a pre-trained neural network to obtain the thermal diffusion coefficient of the object to be tested.
In a third aspect, an embodiment of the present invention further provides a computer device, where the computer device includes:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a thermal diffusivity test method as provided by any embodiment of the present invention.
In a fourth aspect, an embodiment of the present invention further provides a computer storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the thermal diffusivity test method provided in any embodiment of the present invention.
According to the embodiment of the invention, the collected temperature measurement data of the object to be tested is tested and analyzed according to the pre-trained neural network to obtain the thermal diffusion coefficient of the object to be tested, so that the problems of the existing thermal diffusion coefficient test that the object to be tested needs to be damaged, the sample preparation requirement is strict, the test range is limited and the like are solved, the test of the thermal diffusion coefficient of the object to be tested is completed on the premise of not damaging the object to be tested and having no special sample preparation requirement, and the test range of the thermal diffusion coefficient is expanded.
Drawings
FIG. 1 is a flow chart of a thermal diffusivity test method according to one embodiment of the present invention;
FIG. 2a is a flow chart of a thermal diffusivity test method according to a second embodiment of the present invention;
FIG. 2b is a schematic diagram illustrating an effect of a temperature data simulation model according to a second embodiment of the present invention;
FIG. 2c is a schematic diagram illustrating an effect of a simulated temperature plane according to a second embodiment of the present invention;
FIG. 3 is a schematic diagram of a thermal diffusivity testing apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention.
It should be further noted that, for the convenience of description, only some but not all of the relevant aspects of the present invention are shown in the drawings. Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
The terms "first" and "second," and the like in the description and claims of embodiments of the invention and in the drawings, are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not set forth for a listed step or element but may include steps or elements not listed.
Example one
Fig. 1 is a flowchart of a thermal diffusivity testing method according to an embodiment of the present invention, where the embodiment is applicable to a situation where a thermal diffusivity of an object to be tested is tested without destroying the object to be tested and without special sampling requirements, and the method can be executed by a thermal diffusivity testing apparatus, which can be implemented by software and/or hardware, and can be generally integrated in a computer device. Accordingly, as shown in fig. 1, the method comprises the following operations:
and S110, collecting temperature measurement data of the object to be tested.
Wherein, the object to be tested can be a material or an article needing to test the thermal diffusivity. The temperature measurement data may be temperature data obtained by acquiring a temperature of the object to be tested. Optionally, the type of the temperature measurement data may be infrared temperature measurement data, and the embodiment of the present invention does not limit the specific type of the temperature measurement data.
In the embodiment of the invention, when the thermal diffusion coefficient of the object to be tested is tested, the temperature measurement data of the object to be tested can be collected. Optionally, the infrared temperature measurement may be performed on the surface of the object to be tested, so as to collect temperature measurement data without damaging the object to be tested. It should be noted that only one object to be tested is needed to be used as a sample for collecting temperature measurement data, and therefore, the collection mode of the temperature measurement data in the embodiment of the present invention does not need strict and special sample preparation requirements.
And S120, testing and analyzing the temperature measurement data according to a pre-trained neural network to obtain the thermal diffusion coefficient of the object to be tested.
After the temperature measurement data of the object to be tested is obtained, the collected temperature measurement data can be tested and analyzed according to the pre-trained neural network, and therefore the thermal diffusion coefficient of the object to be tested is obtained. Wherein the neural network may be trained on temperature data samples including thermal diffusivity.
Therefore, the thermal diffusion coefficient testing method provided by the embodiment of the invention only needs to acquire the temperature data of the surface of the object to be tested, does not need to damage the object to be tested, and does not have special sample preparation requirements. In addition, the testing range of the thermal diffusion coefficient depends on the size of the thermal diffusion coefficient in the temperature data sample, and theoretically, the range of the thermal diffusion coefficient in the temperature data sample can be infinite, so that the testing range of the thermal diffusion coefficient can be expanded by the thermal diffusion coefficient testing method provided by the embodiment of the invention.
In an optional embodiment of the present invention, before the performing the test analysis on the temperature measurement data according to the pre-trained neural network, the method may further include: collecting numerical simulation temperature data; and training the neural network according to the numerical simulation temperature data.
The numerical simulation temperature data may be temperature data acquired by using a temperature data simulation model.
Specifically, when the neural network is trained, the neural network can be trained according to the collected numerical simulation temperature data.
According to the embodiment of the invention, the collected temperature measurement data of the object to be tested is tested and analyzed according to the pre-trained neural network to obtain the thermal diffusion coefficient of the object to be tested, so that the problems of the existing thermal diffusion coefficient test that the object to be tested needs to be damaged, the sample preparation requirement is strict, the test range is limited and the like are solved, the test of the thermal diffusion coefficient of the object to be tested is completed on the premise of not damaging the object to be tested and having no special sample preparation requirement, and the test range of the thermal diffusion coefficient is expanded.
Example two
Fig. 2a is a flowchart of a thermal diffusivity testing method according to a second embodiment of the present invention, which is embodied based on the above embodiments, and in this embodiment, a specific implementation manner is given in which temperature measurement data of an object to be tested is collected, the temperature measurement data is subjected to test analysis according to a pre-trained neural network to obtain a thermal diffusivity of the object to be tested, and training of the neural network is performed according to collected numerical simulation temperature data. Accordingly, as shown in fig. 2a, the method of the present embodiment may include:
s210, collecting numerical simulation temperature data.
In an optional embodiment of the present invention, the collecting numerical simulation temperature data may include: forming a simulation temperature surface according to the temperature data simulation model and the time parameter; performing sliding sampling on the simulation temperature surface according to the area of a preset sample block to obtain a plurality of block temperature data; the area of the preset sample block is the product of the sliding distance and the sliding time; forming a training temperature block data sample according to the block temperature data and the first example parameters corresponding to the block temperature data; wherein, the first example parameter is the related parameter of the temperature data simulation example; and forming the numerical simulation temperature data according to the training temperature block data samples.
Wherein the temperature data simulation model may be a pre-constructed geometric model. The simulation temperature surface can be a two-dimensional temperature data distribution surface formed according to a temperature data simulation model and time parameters. The block temperature data may be block data intercepted at the simulated temperature plane, the block data including temperature data. The training temperature block data sample can be used as a training sample to train the neural network.
Specifically, the sliding sampling can be performed on a simulation temperature surface formed by the temperature data simulation model and the time parameter according to the area of the preset sample block, so that the temperature data of a plurality of blocks can be obtained. And then forming training temperature block data samples according to the block temperature data and the first example parameters corresponding to the block temperature data, and finally forming numerical simulation temperature data according to the training temperature block data samples.
In an alternative embodiment of the invention, the first algorithm parameter may satisfy the expression that α ═ n α max/m, where α represents the thermal diffusivity, α max represents the upper limit value of the thermal diffusivity α, n represents the first algorithm parameter, m represents the second algorithm parameter, m and n are positive integers, and m is greater than or equal to n.
Fig. 2b is a schematic diagram illustrating an effect of a temperature data simulation model according to the second embodiment of the present invention, and fig. 2c is a schematic diagram illustrating an effect of a simulation temperature plane according to the second embodiment of the present invention. In a specific example, as shown in fig. 2b, the temperature data simulation model may be a triangle formed by L1, L2, and L3, wherein the boundary conditions on both sides of L1 and L2 are set to be adiabatic, and the boundary conditions on L3 are set to be convective heat transfer with air. Under the above boundary conditions, temperature gradients of different magnitudes may be formed along the side L3. The initial temperature of the entire geometric region composed of L1, L2 and L3 was set to be a uniform temperature T0Denotes heating the object to be tested to T0Wherein, T0The thermal diffusion coefficient α and the thermal physical parameters have the following relationship that α is lambda/rho c, wherein the setting rule of the thermal conductivity lambda, the density rho and the specific heat c can satisfy 0<α is not more than α max and α is n α max/m, wherein α max is the upper test limit of thermal diffusivity, and m is the equal division of α maxN is a multiple of 1/m set by the simulation example of the current temperature data, m and n are integers and 0<N is less than or equal to m, the total number of the temperature data simulation examples can be set to be N, the thermal diffusivity values α of the N examples cover all N α max/m, the N values can be repeated for multiple times, and the temperature data simulation termination condition can be set according to actual requirements, such as geometrically reducing all the temperatures to 20 ℃ at room temperature
As shown in fig. 2c, the temperature values of each temperature data simulation example are extracted in time sequence at the boundary of L3, and the length dimension L3 and the time dimension t1 (i.e. time parameter) of the temperature data simulation model can form a simulation temperature plane. And performing slippage sampling on the simulation temperature surface, wherein the size of a slippage sampling block can be delta L multiplied by delta t, the distance of each slippage is delta L in the length dimension, and delta t in the time dimension. The result of each sampling, namely the current delta L multiplied by delta t block temperature data, and the corresponding first example parameter n value are stored together as a temperature block-n value sample as a training temperature block data sample, and all the acquired training temperature block data samples jointly form numerical simulation temperature data.
And S220, training the neural network according to the numerical simulation temperature data.
In an optional embodiment of the present invention, the training of the neural network according to the numerical simulation temperature data may include: calculating a training probability distribution vector of a thermal diffusion coefficient according to the numerical simulation temperature data; and taking the block temperature data in the numerical simulation temperature data as the input of the neural network, taking the training probability distribution vector of the thermal diffusion coefficient as the output of the neural network, and training the neural network.
Wherein, the training probability distribution vector can be used as an output vector to train the neural network.
Correspondingly, when the neural network is trained, the training probability distribution vector of the thermal diffusion coefficient can be calculated according to the numerical simulation temperature data. After the training probability distribution vector of the thermal diffusion coefficient is obtained through calculation, the block temperature data in the numerical simulation temperature data can be used as the input of the neural network, the training probability distribution vector of the thermal diffusion coefficient is used as the output of the neural network, and the neural network is trained.
As illustrated by the specific example above, the training probability distribution vector may alternatively be represented as (p)1，p2，…，pm) Wherein p isiIn particular, a training probability distribution vector for the thermal diffusivity can be calculated from the value of the first example parameter n in the numerical simulation temperature data>3, when n is 1, then p1100%, when n is 2, then p2100%. The input of the neural network is the block temperature data obtained in the above example, and the output of the neural network is the training probability distribution vector (p)1，p2，…，pm) A neural network for testing a probability distribution of the thermal diffusivity over (α max/m, 2 α max/m, …, α max) can be obtained.
And S230, collecting temperature measurement data of the object to be tested.
In an optional embodiment of the present invention, the acquiring temperature measurement data of the object to be tested may include: heating the object to be tested to a first target measurement temperature according to a thermal diffusion coefficient measurement condition; the first target measurement temperature is the sum of the target measurement temperature and a set temperature offset; before the temperature of the object to be tested is reduced to a second target measurement temperature, measuring and recording temperature data of a set area on the surface of the object to be tested; wherein the second target measured temperature is a difference between the target measured temperature and the set temperature offset; and processing the temperature data obtained by measurement to obtain the measured temperature block data with set quantity.
The thermal diffusivity measuring condition is a temperature condition of the thermal diffusivity of the object to be tested, and the object to be tested is heated to a target measuring temperature if needed. The target measured temperature may be a temperature matched to the object to be tested for testing a thermal diffusivity. It will be appreciated that the target object to be tested will be different, as will the corresponding target measured temperature. The set temperature offset may be a temperature offset value set according to the type of the object to be tested and the actual demand. The setting region may be a region set according to actual requirements, and optionally, the setting region may be a square region configured to calculate a sliding distance of the area of the preset sample block. The set number may be determined according to the performance of the temperature measurement device, and the embodiment of the present invention does not limit this.
Specifically, as described in the above specific example, when acquiring temperature measurement data of an object to be tested, the object to be tested may be heated to a first target measurement temperature according to a thermal diffusivity measurement condition, where the first target measurement temperature may be a sum of the target measurement temperature and a set temperature offset Δ T ℃. Then, temperature measurement and recording can be started on a set area of the surface Δ L x Δ L of the object to be tested by using the infrared temperature measurement device until the surface temperature of the object to be tested is reduced to a second target measurement temperature, wherein the second target measurement temperature can be the difference value between the target measurement temperature and the set temperature offset Δ T ℃, temperature measurement and recording are stopped at the moment, the temperature result measured by the infrared temperature measurement device is processed into Δ L x Δ T measurement temperature block data, and k measurement temperature block data can be acquired in total. The first target measured temperature and the second target measured temperature may also be set in other manners according to the target measured temperature, for example, different temperature offsets are used for calculation, which is not limited in the embodiment of the present invention.
S240, testing and analyzing the temperature measurement data according to a pre-trained neural network to obtain the thermal diffusion coefficient of the object to be tested.
In an optional embodiment of the present invention, the performing a test analysis on the temperature measurement data according to a pre-trained neural network to obtain a thermal diffusivity of the object to be tested may include: taking the measured temperature block data of the set number as the input of the neural network to obtain output probability distribution vectors corresponding to the thermal diffusion coefficients of the set number; calculating the thermal diffusion coefficients of the set number according to the output probability distribution vector; and calculating the average value of the thermal diffusion coefficients of the set numbers to be used as the thermal diffusion coefficient of the object to be tested.
The output probability distribution vector may be an output result derived by the neural network according to the input measured temperature block data.
Specifically, as explained in the above specific example, when performing test analysis on temperature measurement data according to a pre-trained neural network, k pieces of measured temperature block data can be used as input of the neural network, so as to obtain output probability distribution vectors (p) with k corresponding thermal diffusivity coefficients (α max/m, 2 α max/m, …, α max)1，p2，…，pd). According to the output probability distribution vector (p)1，p2，…，pd) The result of calculating the thermal diffusivity of the object to be tested may be α - α max/m p1+2αmax/m*p2+3αmax/m*p3+…+mαmax/m*pdFinally, k average values of α are calculated
The thermal diffusion coefficient of the object to be tested can be obtained.
By adopting the technical scheme, the neural network is trained according to the acquired numerical simulation temperature data to test and analyze the temperature measurement data of the object to be tested, so that the thermal diffusion coefficient of the object to be tested is obtained, the problems of the existing thermal diffusion coefficient test that the object to be tested needs to be damaged, the sample preparation requirement is strict, the test range is limited and the like are solved, the test of the thermal diffusion coefficient of the object to be tested is completed on the premise that the object to be tested is not damaged and no special sample preparation requirement exists, and the test range of the thermal diffusion coefficient is expanded.
It should be noted that any permutation and combination between the technical features in the above embodiments also belong to the scope of the present invention.
EXAMPLE III
Fig. 3 is a schematic diagram of a thermal diffusivity testing apparatus according to a third embodiment of the present invention, as shown in fig. 3, the apparatus includes: temperature measurement data acquisition module 310 and thermal diffusivity test module 320, wherein:
a temperature measurement data acquisition module 310 for acquiring temperature measurement data of an object to be tested;
the thermal diffusion coefficient testing module 320 is configured to perform testing analysis on the temperature measurement data according to a pre-trained neural network to obtain a thermal diffusion coefficient of the object to be tested.
According to the embodiment of the invention, the collected temperature measurement data of the object to be tested is tested and analyzed according to the pre-trained neural network to obtain the thermal diffusion coefficient of the object to be tested, so that the problems of the existing thermal diffusion coefficient test that the object to be tested needs to be damaged, the sample preparation requirement is strict, the test range is limited and the like are solved, the test of the thermal diffusion coefficient of the object to be tested is completed on the premise of not damaging the object to be tested and having no special sample preparation requirement, and the test range of the thermal diffusion coefficient is expanded.
Optionally, the apparatus further comprises: the numerical simulation temperature data acquisition module is used for acquiring numerical simulation temperature data; and the neural network training module is used for training the neural network according to the numerical simulation temperature data.
Optionally, the numerical simulation temperature data acquisition module includes: the simulation temperature surface forming unit is used for forming a simulation temperature surface according to the temperature data simulation model and the time parameter; the block temperature data acquisition unit is used for performing sliding sampling on the simulation temperature surface according to the area of a preset sample block to obtain a plurality of block temperature data; the area of the preset sample block is the product of the sliding distance and the sliding time; a training temperature block data sample composition unit, configured to compose a training temperature block data sample according to each of the block temperature data and the first example parameter corresponding to each of the block temperature data; wherein, the first example parameter is the related parameter of the temperature data simulation example; and the numerical simulation temperature data forming unit is used for forming the numerical simulation temperature data according to the data samples of the training temperature blocks.
Optionally, the first example parameter satisfies the following expression that α is n α max/m, where α represents a thermal diffusivity, α max represents an upper limit value of the thermal diffusivity α, n represents the first example parameter, m represents the second example parameter, m and n are positive integers, and m is greater than or equal to n.
Optionally, the neural network training module includes: the training probability distribution vector calculation unit is used for calculating a training probability distribution vector of the thermal diffusion coefficient according to the numerical simulation temperature data; and the neural network training unit is used for taking the block temperature data in the numerical simulation temperature data as the input of the neural network, taking the training probability distribution vector of the thermal diffusion coefficient as the output of the neural network and training the neural network.
Optionally, the temperature measurement data collecting module 310 includes: the heating unit of the object to be tested is used for heating the object to be tested to a first target measurement temperature according to the thermal diffusion coefficient measurement condition; the first target measurement temperature is the sum of the target measurement temperature and a set temperature offset; the temperature data measuring and recording unit is used for measuring and recording the temperature data of the set area on the surface of the object to be tested before the temperature of the object to be tested is reduced to a second target measuring temperature; wherein the second target measured temperature is a difference between the target measured temperature and the set temperature offset; and the measured temperature block data acquisition unit is used for processing the temperature data acquired by measurement to obtain the measured temperature block data with set quantity.
Optionally, the thermal diffusivity test module 320 includes: an output probability distribution vector obtaining unit, configured to use the set number of measured temperature block data as an input of the neural network, to obtain output probability distribution vectors corresponding to the set number of thermal diffusion coefficients; a first thermal diffusion coefficient calculation unit configured to calculate thermal diffusion coefficients of the respective set numbers according to the output probability distribution vector; and the second thermal diffusion coefficient calculating unit is used for calculating the average value of the thermal diffusion coefficients of the set numbers as the thermal diffusion coefficient of the object to be tested.
The thermal diffusion coefficient testing device can execute the thermal diffusion coefficient testing method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the thermal diffusivity test method provided in any embodiment of the present invention.
Since the thermal diffusivity testing apparatus described above is an apparatus capable of executing the thermal diffusivity testing method in the embodiment of the present invention, based on the thermal diffusivity testing method described in the embodiment of the present invention, a person skilled in the art can understand a specific implementation manner of the thermal diffusivity testing apparatus in the embodiment of the present invention and various variations thereof, and therefore, a detailed description of how the thermal diffusivity testing apparatus implements the thermal diffusivity testing method in the embodiment of the present invention is not given here. The apparatus used by those skilled in the art to implement the thermal diffusivity test method in the embodiments of the present invention is within the scope of the present application.
Example four
Fig. 4 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention. FIG. 4 illustrates a block diagram of a computer device 412 suitable for use in implementing embodiments of the present invention. The computer device 412 shown in FIG. 4 is only one example and should not impose any limitations on the functionality or scope of use of embodiments of the present invention.
As shown in FIG. 4, computer device 412 is in the form of a general purpose computing device. Components of computer device 412 may include, but are not limited to: one or more processors 416, a storage device 428, and a bus 418 that couples the various system components including the storage device 428 and the processors 416.
The computer device 412 may also communicate with one or more external devices 414 (e.g., keyboard, pointing device, camera, display 424, etc.), with one or more devices that enable a user to interact with the computer device 412, and/or with any devices (e.g., network card, modem, etc.) that enable the computer device 412 to communicate with one or more other computing devices. Such communication may be through an Input/Output (I/O) interface 422. Also, computer device 412 may communicate with one or more networks (e.g., a Local Area Network (LAN), Wide Area Network (WAN), and/or a public Network, such as the internet) through Network adapter 420. As shown, network adapter 420 communicates with the other modules of computer device 412 over bus 418. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the computer device 412, including but not limited to: microcode, device drivers, Redundant processing units, external disk drive arrays, disk array (RAID) systems, tape drives, and data backup storage systems, to name a few.
The processor 416 executes programs stored in the storage device 428 to perform various functional applications and data processing, such as implementing the thermal diffusivity test methods provided by the above-described embodiments of the present invention.
That is, the processing unit implements, when executing the program: collecting temperature measurement data of an object to be tested; and testing and analyzing the temperature measurement data according to a pre-trained neural network to obtain the thermal diffusion coefficient of the object to be tested.
EXAMPLE five
An embodiment five of the present invention further provides a computer storage medium storing a computer program, which when executed by a computer processor is configured to perform the thermal diffusivity test method according to any one of the above embodiments of the present invention: collecting temperature measurement data of an object to be tested; and testing and analyzing the temperature measurement data according to a pre-trained neural network to obtain the thermal diffusion coefficient of the object to be tested.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM) or flash Memory), an optical fiber, a portable compact disc Read Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, Radio Frequency (RF), etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A thermal diffusivity test method, comprising:
collecting temperature measurement data of an object to be tested;
and testing and analyzing the temperature measurement data according to a pre-trained neural network to obtain the thermal diffusion coefficient of the object to be tested.
2. The method of claim 1, further comprising, prior to the performing a test analysis of the temperature measurement data according to a pre-trained neural network:
collecting numerical simulation temperature data;
and training the neural network according to the numerical simulation temperature data.
3. The method of claim 2, wherein collecting numerical simulation temperature data comprises:
forming a simulation temperature surface according to the temperature data simulation model and the time parameter;
performing sliding sampling on the simulation temperature surface according to the area of a preset sample block to obtain a plurality of block temperature data; the area of the preset sample block is the product of the sliding distance and the sliding time;
forming a training temperature block data sample according to the block temperature data and the first example parameters corresponding to the block temperature data; wherein, the first example parameter is the related parameter of the temperature data simulation example;
and forming the numerical simulation temperature data according to the training temperature block data samples.
4. The method of claim 3, wherein the first example parameter satisfies the following expression:
α＝nαmax/m
wherein α represents a thermal diffusivity, α max represents an upper limit value of the thermal diffusivity α, n represents the first example parameter, m represents the second example parameter, m and n are positive integers, and m is greater than or equal to n.
5. The method of claim 4, wherein the training of the neural network based on the numerical simulation temperature data comprises:
calculating a training probability distribution vector of a thermal diffusion coefficient according to the numerical simulation temperature data;
and taking the block temperature data in the numerical simulation temperature data as the input of the neural network, taking the training probability distribution vector of the thermal diffusion coefficient as the output of the neural network, and training the neural network.
6. The method of claim 5, wherein said acquiring temperature measurement data of the object to be tested comprises:
heating the object to be tested to a first target measurement temperature according to a thermal diffusion coefficient measurement condition; the first target measurement temperature is the sum of the target measurement temperature and a set temperature offset;
before the temperature of the object to be tested is reduced to a second target measurement temperature, measuring and recording temperature data of a set area on the surface of the object to be tested; wherein the second target measured temperature is a difference between the target measured temperature and the set temperature offset;
and processing the temperature data obtained by measurement to obtain the measured temperature block data with set quantity.
7. The method of claim 6, wherein the performing a test analysis on the temperature measurement data according to a pre-trained neural network to obtain the thermal diffusivity of the subject to be tested comprises:
taking the measured temperature block data of the set number as the input of the neural network to obtain output probability distribution vectors corresponding to the thermal diffusion coefficients of the set number;
calculating the thermal diffusion coefficients of the set number according to the output probability distribution vector;
and calculating the average value of the thermal diffusion coefficients of the set numbers to be used as the thermal diffusion coefficient of the object to be tested.
8. A thermal diffusivity test apparatus, comprising:
the temperature measurement data acquisition module is used for acquiring temperature measurement data of an object to be tested;
and the thermal diffusion coefficient testing module is used for testing and analyzing the temperature measurement data according to a pre-trained neural network to obtain the thermal diffusion coefficient of the object to be tested.
9. A computer device, characterized in that the computer device comprises:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a thermal diffusivity test method as defined in any one of claims 1-7.
10. A computer storage medium on which a computer program is stored which, when being executed by a processor, carries out the thermal diffusivity test method as defined in any one of claims 1 to 7.
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