CN113834852A - Method and system for detecting heat dissipation performance of product with graphene coating - Google Patents
Method and system for detecting heat dissipation performance of product with graphene coating Download PDFInfo
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Abstract
The invention discloses a method and a system for detecting heat dissipation performance of a product with a graphene coating. In addition, the data of unqualified products are analyzed to judge whether the products can be reused or not, so that the economic loss caused by unqualified products can be reduced. This application still through the data to unqualified product analysis, learns its heat dispersion's influence factor and generates the correction data and use for follow-up production, reduces economic loss, also can train in order to improve its precision efficiency detection model simultaneously.
Description
Technical Field
The application relates to the field of data processing and data transmission, in particular to a method and a system for detecting heat dissipation performance of a product with a graphene coating.
Background
The graphene coating can be applied to heat dissipation parts of household appliances, heating ventilation and industrial equipment, such as air conditioner radiators, household appliance radiators, heating system radiators, LED lamp radiators, heat exchange equipment radiators and the like, and can cover base materials such as carbon steel, stainless steel, aluminum materials, copper and the like.
The heat dispersion of graphite alkene coating receives the influence factor more, the heat dispersion of current product that has graphite alkene coating detects and is the manual work to a batch of products of shaping and carry out the sampling test more, efficiency is lower and has certain error, if the performance change appears, can't be quick concrete to a certain factor, and can't carry out the analysis to the unqualified product of performance and whether can transfer to other equipment in or other operational environment, and can't be quick formulate the revision data, and can't carry out convenient design and verification according to the heat dissipation demand.
Therefore, the prior art has defects and needs to be improved urgently.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a method and a system for detecting heat dissipation performance of a product with a graphene coating, which can detect the heat dissipation performance of the product more effectively and more quickly.
The invention provides a method for detecting heat dissipation performance of a product with a graphene coating, which is characterized by comprising the following steps:
acquiring parameter data of a plurality of groups of experimental products and parameter data of a graphene coating on the surface;
acquiring an initial temperature value of an experimental product, acquiring a real-time temperature value of the experimental product after a preset time interval, calculating a temperature difference value between the initial temperature value and the real-time temperature value of the experimental product, calculating a temperature change rate of the experimental product based on the temperature difference value and the preset time interval, and constructing an efficiency detection model based on the temperature difference value, the temperature change rate, parameter data of the experimental product and surface graphene coating parameter data;
and acquiring data of the product to be detected, inputting the data of the product to be detected into the efficiency detection model for processing and analysis, and outputting an analysis result.
In this scheme, the obtaining data of waiting to detect the product, the parameter data input efficiency detection model that will wait to detect the product is handled the analysis and is exported analysis result, includes:
the method comprises the steps of obtaining parameter data of a product to be detected, parameter data of a surface graphene coating and an initial temperature value of the product to be detected, obtaining a real-time temperature value of the product to be detected after a preset time interval, calculating a temperature difference value between the initial temperature value and the real-time temperature value of the product to be detected, calculating a temperature change rate of an experimental product based on the temperature difference value and the preset time interval, inputting the parameter data of the product to be detected, the parameter data of the surface graphene coating, the temperature difference value and the temperature change rate into an efficiency detection model, processing and analyzing, and outputting an analysis result.
In this scheme, the efficiency detection model specifically includes:
acquiring parameter data of a historical detection product, parameter data of a surface graphene coating, a temperature difference value and a temperature change rate;
preprocessing the parameter data of the historical detection product, the parameter data of the surface graphene coating, the temperature difference value and the temperature change rate to obtain a training sample set;
inputting the training sample set into the initialized efficiency detection model for training;
acquiring the accuracy of an output result;
and if the accuracy is greater than a preset accuracy threshold, stopping training to obtain the efficiency detection model.
In the scheme, the preset qualified tolerance range is obtained, whether the analysis result output by the efficiency detection model is within the preset result tolerance range is judged, if yes, the product to be detected is judged to be a qualified product, and if not, the product to be detected is judged to be an unqualified product.
In the scheme, the preset conversion condition is obtained, whether the analysis result of the unqualified product meets the preset conversion condition is judged, if yes, the unqualified product is marked as the conversion product, and if not, the conversion product is marked as the secondary processing product.
In the scheme, detection parameter data of a secondary processing product and detection parameter data of a graphene coating on the surface of the secondary processing product are obtained, the temperature change rate of the secondary processing product is preset, detection parameter data of an experimental product with the same temperature change rate and detection parameter data of a graphene coating on the surface of the experimental product are obtained, the detection parameter data of the secondary processing product and the detection parameter data of the graphene coating on the surface of the experimental product are input into an efficiency detection model for comparative analysis, and correction data of the secondary processing product are output.
In the scheme, parameter data of the product to be detected and a preset temperature change rate are obtained, the parameter data are input into an efficiency detection model for processing and analysis, and the parameter data of the graphene coating on the surface of the product to be detected are output.
The invention provides a heat dissipation performance detection system of a product with a graphene coating, which comprises a memory and a processor, wherein the memory comprises a heat dissipation performance detection program of the product with the graphene coating, and the heat dissipation performance detection program of the product with the graphene coating realizes the following steps when being executed by the processor:
acquiring parameter data of a plurality of groups of experimental products and parameter data of a graphene coating on the surface;
acquiring an initial temperature value of an experimental product, acquiring a real-time temperature value of the experimental product after a preset time interval, calculating a temperature difference value between the initial temperature value and the real-time temperature value of the experimental product, calculating a temperature change rate of the experimental product based on the temperature difference value and the preset time interval, and constructing an efficiency detection model based on the temperature difference value, the temperature change rate, parameter data of the experimental product and surface graphene coating parameter data;
and acquiring data of the product to be detected, inputting the data of the product to be detected into the efficiency detection model for processing and analysis, and outputting an analysis result.
In this scheme, the obtaining data of waiting to detect the product, the parameter data input efficiency detection model that will wait to detect the product is handled the analysis and is exported analysis result, includes:
the method comprises the steps of obtaining parameter data of a product to be detected, parameter data of a surface graphene coating and an initial temperature value of the product to be detected, obtaining a real-time temperature value of the product to be detected after a preset time interval, calculating a temperature difference value between the initial temperature value and the real-time temperature value of the product to be detected, calculating a temperature change rate of an experimental product based on the temperature difference value and the preset time interval, inputting the parameter data of the product to be detected, the parameter data of the surface graphene coating, the temperature difference value and the temperature change rate into an efficiency detection model, processing and analyzing, and outputting an analysis result.
In this scheme, the efficiency detection model specifically includes:
acquiring parameter data of a historical detection product, parameter data of a surface graphene coating, a temperature difference value and a temperature change rate;
preprocessing the parameter data of the historical detection product, the parameter data of the surface graphene coating, the temperature difference value and the temperature change rate to obtain a training sample set;
inputting the training sample set into the initialized efficiency detection model for training;
acquiring the accuracy of an output result;
and if the accuracy is greater than a preset accuracy threshold, stopping training to obtain the efficiency detection model.
In the scheme, the preset qualified tolerance range is obtained, whether the analysis result output by the efficiency detection model is within the preset result tolerance range is judged, if yes, the product to be detected is judged to be a qualified product, and if not, the product to be detected is judged to be an unqualified product.
In the scheme, the preset conversion condition is obtained, whether the analysis result of the unqualified product meets the preset conversion condition is judged, if yes, the unqualified product is marked as the conversion product, and if not, the conversion product is marked as the secondary processing product.
In the scheme, detection parameter data of a secondary processing product and detection parameter data of a graphene coating on the surface of the secondary processing product are obtained, the temperature change rate of the secondary processing product is preset, detection parameter data of an experimental product with the same temperature change rate and detection parameter data of a graphene coating on the surface of the experimental product are obtained, the detection parameter data of the secondary processing product and the detection parameter data of the graphene coating on the surface of the experimental product are input into an efficiency detection model for comparative analysis, and correction data of the secondary processing product are output.
In the scheme, parameter data of the product to be detected and a preset temperature change rate are obtained, the parameter data are input into an efficiency detection model for processing and analysis, and the parameter data of the graphene coating on the surface of the product to be detected are output.
The invention discloses a method and a system for detecting heat dissipation performance of a product with a graphene coating. In addition, the data of unqualified products are analyzed to judge whether the products can be reused or not, so that the economic loss caused by unqualified products can be reduced. This application still through the data to unqualified product analysis, learns its heat dispersion's influence factor and generates the correction data and use for follow-up production, reduces economic loss, also can train in order to improve its precision efficiency detection model simultaneously.
Drawings
Fig. 1 shows a flow chart of a method for detecting heat dissipation performance of a product with a graphene coating according to the invention;
fig. 2 shows a block diagram of a heat dissipation performance detection system of a product with a graphene coating according to the invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
Fig. 1 shows a flowchart of a method for detecting heat dissipation performance of a product with a graphene coating according to the present invention.
As shown in fig. 1, the invention discloses a method for detecting heat dissipation performance of a product with a graphene coating, which includes:
s102, acquiring parameter data of a plurality of groups of experimental products and parameter data of a graphene coating on the surface;
s104, acquiring an initial temperature value of an experimental product, acquiring a real-time temperature value of the experimental product after a preset time interval, calculating a temperature difference value between the initial temperature value and the real-time temperature value of the experimental product, calculating a temperature change rate of the experimental product based on the temperature difference value and the preset time interval, and constructing an efficiency detection model based on the temperature difference value, the temperature change rate, parameter data of the experimental product and surface graphene coating parameter data;
and S106, acquiring data of the product to be detected, inputting the data of the product to be detected into the efficiency detection model for processing and analysis, and outputting an analysis result.
According to the method, data of materials, shapes, volumes, outer surface areas and inner surface areas of experimental products, coverage areas of graphene coatings on the surfaces of the experimental products, thickness of the graphene coatings and the like are obtained, the experimental products are qualified products, the experimental data are acquired through data detection and experimental data acquisition, the experimental products are heated to a set temperature value, namely an initial temperature value, the heating mode can adopt a power-on heating mode, a heating source heating mode and the like, then the experimental products heated to the set temperature value are placed in a constant temperature environment, after a preset time interval, the temperature value of the experimental products, namely a real-time temperature value is detected, the temperature difference value of the initial temperature value and the real-time temperature value is calculated, and the temperature change rate of the experimental products in the preset time interval is calculated based on the temperature difference value and the preset time interval, the radiating efficiency who is the experiment product, this is standard data, the collection of above-mentioned experimental data is automatic process, need not manual operation, it is high-efficient and accurate, based on the temperature difference, temperature variation rate, an efficiency detection model is found to the parameter data and the surperficial graphite alkene coating parameter data of experiment product, this efficiency detection model possesses each model, the radiating efficiency data of the experiment product of specification, can regard as the radiating efficiency detection database who waits to detect the product, examine time measuring, wait to detect product and the experiment product of standard of same parameter data and carry out the data contrast, specifically be the numerical value contrast of temperature variation rate, can treat that the radiating efficiency of quick detection product detects whether qualified.
According to the method, the steps of acquiring the data of the product to be detected, inputting the parameter data of the product to be detected into the efficiency detection model for processing and analysis and outputting an analysis result comprise:
the method comprises the steps of obtaining parameter data of a product to be detected, parameter data of a surface graphene coating and an initial temperature value of the product to be detected, obtaining a real-time temperature value of the product to be detected after a preset time interval, calculating a temperature difference value between the initial temperature value and the real-time temperature value of the product to be detected, calculating a temperature change rate of an experimental product based on the temperature difference value and the preset time interval, inputting the parameter data of the product to be detected, the parameter data of the surface graphene coating, the temperature difference value and the temperature change rate into an efficiency detection model, processing and analyzing, and outputting an analysis result.
It should be noted that, when the heat dissipation efficiency of the product to be detected is detected by the efficiency detection model, the data of the material, the shape, the volume, the outer surface area and the inner surface area of the product to be detected, the data of the coverage area of the graphene coating on the surface of the product to be detected and the thickness of the graphene coating are obtained, the product to be detected is heated to a set temperature value, the set temperature value of the product to be detected is the same as the initial temperature value of the experimental product, the heating mode can be an electrifying heating mode, a heating source heating mode and the like, then the product to be detected heated to the set temperature value is placed in a constant temperature environment, as above, after a preset time interval, the temperature value of the product to be detected, namely the real-time temperature value of the product to be detected is obtained, the temperature difference value between the initial temperature value of the product to be detected and the real-time temperature value is calculated, and the temperature change rate of the product to be detected within the preset time interval is calculated based on the temperature difference value and the preset time interval, the heat dissipation efficiency of the product to be detected is determined, the obtained data are input into the efficiency detection model for processing and analysis, the difference value of the temperature change rate data of the experimental product with the same data and the temperature change rate data of the product to be detected is the output analysis result, if the difference value is zero, the heat dissipation efficiency of the product to be detected is up to standard, the product to be detected accords with the standard of qualified products and belongs to the qualified products, and if the heat dissipation efficiency of the product to be detected is smaller than the heat dissipation efficiency of the experimental product, the heat dissipation efficiency of the product to be detected is unqualified and needs to be processed on the next step.
According to the method of the present invention, the efficiency detection model specifically comprises:
the method comprises the steps of obtaining parameter data of a historical detection product, parameter data of a surface graphene coating, a temperature difference value and a temperature change rate, preprocessing the parameter data of the historical detection product, the parameter data of the surface graphene coating, the temperature difference value and the temperature change rate to obtain a training sample set, inputting the training sample set into an initialized efficiency detection model for training to obtain the accuracy of an output result, and stopping training to obtain the efficiency detection model if the accuracy is greater than a preset accuracy threshold.
It should be noted that the efficiency detection model needs a large amount of historical data to train, the larger the data size is, the more accurate the result is, the efficiency detection model in the application can train by taking the parameter data of the historical detection product, the parameter data of the surface graphene coating, the temperature difference value and the temperature change rate as input, and by comparing a large amount of test data with real data, the obtained result is more accurate, so that the output result of the efficiency detection model is more accurate. Preferably, the accuracy threshold is typically set at 85%.
According to the method, a preset qualified tolerance range is obtained, whether the analysis result output by the efficiency detection model is within the preset result tolerance range or not is judged, if yes, the product to be detected is judged to be a qualified product, and if not, the product to be detected is judged to be an unqualified product.
It should be noted that the preset acceptable tolerance range is specifically designed according to the field, specification and use requirements of the actual product, for example, the heat dissipation efficiency tolerance range of the heat dissipation fins is determined according to the specification, the lowest heat dissipation efficiency allowed by the use process of the heat dissipation fins is taken as the starting point of the range, the heat dissipation efficiency greater than the lowest heat dissipation efficiency is the acceptable range, if the heat dissipation efficiency is lower than the lowest heat dissipation efficiency, the heat dissipation fins are unqualified products, and when the temperature change rate of the product to be detected does not fall within the preset acceptable tolerance range, the product to be detected is determined to be unqualified products.
According to the method, the temperature change rate of the qualified product and the temperature change rate of the experimental product are obtained, the rate difference is calculated based on the temperature change rate of the qualified product and the temperature change rate of the experimental product, the detection parameter data of the qualified product and the detection parameter data of the experimental product are obtained, and if the temperature change rate of the qualified product is larger than the temperature change rate of the experimental product, the detection parameter data of the qualified product is input into the efficiency detection model to replace the data of the corresponding experimental product, otherwise, the data is not replaced.
It should be noted that, when the temperature change rate of the product to be detected is greater than the temperature change rate of the experimental product, further parameter acquisition is carried out on the product to be detected and the experimental product with the same parameters, such as substrate material surface roughness, flatness, the degree of adhesion of substrate and graphene coating, the flatness of graphene coating, the acquisition of data such as roughness, and in the same way, the acquisition of the same parameters is carried out on the experimental product, and comparison is carried out, the comparison difference value of each data is analyzed and then input into the efficiency detection model for training, so that the data of the efficiency detection model is more accurate, and the data of the product with better heat dissipation efficiency can be supplied to the production process of the subsequent product, the quality of the subsequent product can be effectively increased, and the economic benefit of the product is improved.
According to the method, the preset conversion condition is obtained, whether the analysis result of the unqualified product meets the preset conversion condition is judged, if yes, the unqualified product is marked as the conversion product, and if not, the conversion product is marked as the secondary processing product.
It should be noted that, because the heat dissipation efficiency of the product is affected by many factors, when the product to be detected is marked as an unqualified product, a preset diversion condition is obtained, which includes a diversion tolerance range, the lowest value of the tolerance range is the lowest value of the temperature change rate in the efficiency detection model, if the temperature change rate of the product to be detected is lower than the lowest temperature change rate in the experimental product, the product to be detected is marked as a secondary processing product, and needs to be processed again, if the temperature change rate of the product to be detected is within the preset diversion tolerance range, the parameter data of the experimental product with the same temperature change rate as that of the product to be detected is called out and compared with the parameter data of the product to be detected, for example, when the temperature change rates of the product to be detected and one of the experimental products are the same, the data of the material, the shape, the volume, the external surface area and the internal surface area of the product are different from each other, and analyzing according to the application place and equipment of the product, when the product is not influenced by different parameters of the data for replacement use, marking the unqualified product as a diversion product, otherwise marking the unqualified product as a secondary processing product, and effectively treating the unqualified product, reducing economic loss and reducing the waste of resources of the product through diversion analysis of the unqualified product.
According to the method, detection parameter data of a secondary processing product and detection parameter data of a graphene coating on the surface of the secondary processing product are obtained, the temperature change rate of the secondary processing product is preset, detection parameter data of an experimental product with the same temperature change rate and detection parameter data of the graphene coating on the surface of the experimental product are obtained, the detection parameter data of the secondary processing product and the detection parameter data of the graphene coating on the surface of the experimental product are input into an efficiency detection model for comparative analysis, and correction data of the secondary processing product are output.
It should be noted that, when the secondary processing product is processed, further data acquisition is performed, such as acquisition of data of substrate material surface roughness, flatness, degree of adhesion between the substrate and the graphene coating, flatness and roughness of the graphene coating, and obtaining a value of a preset temperature change rate of the secondary processing product, which is a design requirement when the product is produced, the data is input into a comparison efficiency detection model and each item of data of an experimental product with the same data as the secondary processing product is called out, if the experimental product has further data acquisition, namely an excellent data replacement process of the qualified product, each item of data of the experimental product and the secondary processing product is directly compared and analyzed, and rework is performed by using processing parameters of the experimental product during secondary processing, if no further data acquisition exists in the experimental product, the experimental product is further subjected to data acquisition and data comparison and analysis, so that the yield of secondary processing can be effectively increased, the heat dissipation efficiency of the secondary processing product is increased, the product quality is improved, and the economic benefit of the product is improved.
According to the method, parameter data of the product to be detected and a preset temperature change rate are obtained and input into an efficiency detection model for processing and analysis, and the parameter data of the graphene coating on the surface of the product to be detected is output.
It should be noted that the parameter data of the product to be detected herein specifically includes material, shape, volume, outer surface area, inner surface area, substrate material surface roughness, flatness, etc., and the preset temperature change rate is the design requirement, because the parameter number in the efficiency detection model is large, the functional relationship between each data can be formed according to each parameter and temperature change rate, at this time, the efficiency detection model can be applied to the design process of a new product, according to the input of the required data of the product and the required temperature change rate, the efficiency detection model can calculate and output the corresponding parameter data of the graphene coating, which is convenient for determining the design parameters of the new product, greatly improves the design precision of the product, shortens the design time and the design cost, and continuously optimizes the design parameters in the subsequent detection process of the heat dissipation efficiency of the product, so that the quality of the product is better.
According to the method, the method further comprises the following steps:
establishing a product database;
the product database comprises historical inventory product heat dissipation efficiency data of various models and specifications;
the product heat dissipation efficiency parameters comprise the temperature change rate of the product, product parameter data and surface graphene coating parameter data;
carrying out similarity comparison in the product database according to the obtained preset temperature change rate and product parameter data of the product to be detected, and obtaining historical stock products in the product database, wherein the similarity of the product database and the temperature change rate and product parameter data of the product to be detected meets the preset value requirement;
taking the obtained surface graphene coating parameter data of the historical inventory product as a surface graphene coating parameter data inspection standard of the product to be detected;
and if the surface graphene coating parameter data of the product to be detected does not meet the preset threshold range of the surface graphene coating parameter data of the historical inventory product, defining the product to be detected as an unqualified product.
It should be noted that, in order to increase an obtaining way of a product inspection standard, a product database is established, wherein the product database includes heat dissipation efficiency data of historical stock products of various models and specifications, similarity comparison is performed in the database according to a preset temperature change rate of a product to be inspected and product parameter data, the similarity comparison may be euclidean distance or cosine comparison, the historical stock product meeting a preset value requirement is searched in the product database, surface graphene coating parameter data of the historical stock product is used as a surface graphene coating parameter data inspection standard of the product to be inspected, and if the product to be inspected does not meet a preset threshold range requirement, the product to be inspected is defined as a non-conforming product.
According to the method, the method further comprises the following steps:
presetting a target temperature change rate according to a secondary product to be processed;
carrying out secondary processing on the product according to the correction data obtained by the secondary product to be processed in the efficiency detection model to obtain a corrected product;
detecting according to the detection parameter data of the corrected product and the detection parameter data of the graphene coating on the surface of the corrected product to obtain a temperature change rate;
comparing a threshold value according to the temperature change rate and the preset target temperature change rate;
if the threshold comparison result meets the preset threshold requirement, the secondary processing product is qualified;
and if the threshold comparison result does not meet the preset threshold requirement, the secondary processing product is unqualified.
The secondary processing product is processed through the correction data obtained by the efficiency detection model to obtain a corrected product, the temperature change rate is obtained through detection according to the detection parameter data of the corrected product and the detection parameter data of the graphene coating on the surface of the corrected product, and the qualification condition of the secondary processing product is judged through threshold value comparison between the temperature change rate and a preset target temperature change rate.
According to the method, the method further comprises the following steps:
correcting the efficiency detection model according to the product detection environment parameter data of each model and specification to obtain a corrected efficiency detection model;
the correction efficiency detection model comprises product detection heat dissipation efficiency data of products of various models and specifications in different environment parameter data;
the environmental parameter data comprises temperature, humidity, air pressure and illumination intensity parameters in a product detection environment;
performing secondary detection on unqualified products according to the correction efficiency detection model;
acquiring detection environment parameter data of unqualified products to be secondarily detected;
inputting parameter data of unqualified products to be secondarily detected, parameter data of the graphene coating on the surface, a temperature difference value, a temperature change rate and environmental parameter data into a correction efficiency detection model for processing and outputting a secondary processing result;
and analyzing the tolerance range of the unqualified product according to the obtained secondary treatment result.
In order to improve the accuracy of the product detection result, the products which are not qualified in the primary detection are secondarily detected through the correction efficiency detection model, the correction efficiency detection model is obtained by correcting the products of various models and specifications under different detection environment parameter data, wherein the environment parameter data comprises the temperature, humidity, air pressure and illumination intensity parameters in the product detection environment, and then the data of various parameters, the temperature change rate, the environment parameter data and the like of the unqualified products are input into the correction efficiency detection model to be secondarily detected to obtain the secondary detection result, and the tolerance range is analyzed to judge the secondary detection qualification condition of the products.
Fig. 2 shows a block diagram of a heat dissipation performance detection system of a product with a graphene coating according to the invention.
As shown in fig. 2, a second aspect of the present invention provides a heat dissipation performance detection system 2 for a product with a graphene coating, including a memory 21 and a processor 22, where the memory 21 includes a heat dissipation performance detection program for the product with the graphene coating, and when executed by the processor 22, the heat dissipation performance detection program for the product with the graphene coating implements the following steps:
acquiring parameter data of a plurality of groups of experimental products and parameter data of a graphene coating on the surface;
acquiring an initial temperature value of an experimental product, acquiring a real-time temperature value of the experimental product after a preset time interval, calculating a temperature difference value between the initial temperature value and the real-time temperature value of the experimental product, calculating a temperature change rate of the experimental product based on the temperature difference value and the preset time interval, and constructing an efficiency detection model based on the temperature difference value, the temperature change rate, parameter data of the experimental product and surface graphene coating parameter data;
and acquiring data of the product to be detected, inputting the data of the product to be detected into the efficiency detection model for processing and analysis, and outputting an analysis result.
According to the system, data of materials, shapes, volumes, outer surface areas and inner surface areas of experimental products, coverage areas of graphene coatings on the surfaces of the experimental products, thickness of the graphene coatings and the like are obtained, the experimental products are qualified products, the experimental data are acquired through data detection and experimental data acquisition, the experimental products are heated to a set temperature value, namely an initial temperature value, the heating mode can adopt a power-on heating mode, a heating source heating mode and the like, then the experimental products heated to the set temperature value are placed in a constant temperature environment, after a preset time interval, the temperature value of the experimental products, namely a real-time temperature value is detected, the temperature difference value of the initial temperature value and the real-time temperature value is calculated, and the temperature change rate of the experimental products in the preset time interval is calculated based on the temperature difference value and the preset time interval, the radiating efficiency who is the experiment product, this is standard data, the collection of above-mentioned experimental data is automatic process, need not manual operation, it is high-efficient and accurate, based on the temperature difference, temperature variation rate, an efficiency detection model is found to the parameter data and the surperficial graphite alkene coating parameter data of experiment product, this efficiency detection model possesses each model, the radiating efficiency data of the experiment product of specification, can regard as the radiating efficiency detection database who waits to detect the product, examine time measuring, wait to detect product and the experiment product of standard of same parameter data and carry out the data contrast, specifically be the numerical value contrast of temperature variation rate, can treat that the radiating efficiency of quick detection product detects whether qualified.
According to the system of the present invention, the acquiring data of the product to be detected, inputting the parameter data of the product to be detected into the efficiency detection model for processing and analysis, and outputting the analysis result includes:
the method comprises the steps of obtaining parameter data of a product to be detected, parameter data of a surface graphene coating and an initial temperature value of the product to be detected, obtaining a real-time temperature value of the product to be detected after a preset time interval, calculating a temperature difference value between the initial temperature value and the real-time temperature value of the product to be detected, calculating a temperature change rate of an experimental product based on the temperature difference value and the preset time interval, inputting the parameter data of the product to be detected, the parameter data of the surface graphene coating, the temperature difference value and the temperature change rate into an efficiency detection model, processing and analyzing, and outputting an analysis result.
It should be noted that, when the heat dissipation efficiency of the product to be detected is detected by the efficiency detection model, the data of the material, the shape, the volume, the outer surface area and the inner surface area of the product to be detected, the data of the coverage area of the graphene coating on the surface of the product to be detected and the thickness of the graphene coating are obtained, the product to be detected is heated to a set temperature value, the set temperature value of the product to be detected is the same as the initial temperature value of the experimental product, the heating mode can be an electrifying heating mode, a heating source heating mode and the like, then the product to be detected heated to the set temperature value is placed in a constant temperature environment, as above, after a preset time interval, the temperature value of the product to be detected, namely the real-time temperature value of the product to be detected is obtained, the temperature difference value between the initial temperature value of the product to be detected and the real-time temperature value is calculated, and the temperature change rate of the product to be detected within the preset time interval is calculated based on the temperature difference value and the preset time interval, the heat dissipation efficiency of the product to be detected is determined, the obtained data are input into the efficiency detection model for processing and analysis, the difference value of the temperature change rate data of the experimental product with the same data and the temperature change rate data of the product to be detected is the output analysis result, if the difference value is zero, the heat dissipation efficiency of the product to be detected is up to standard, the product to be detected accords with the standard of qualified products and belongs to the qualified products, and if the heat dissipation efficiency of the product to be detected is smaller than the heat dissipation efficiency of the experimental product, the heat dissipation efficiency of the product to be detected is unqualified and needs to be processed on the next step.
According to the system of the present invention, the efficiency detection model specifically comprises:
the method comprises the steps of obtaining parameter data of a historical detection product, parameter data of a surface graphene coating, a temperature difference value and a temperature change rate, preprocessing the parameter data of the historical detection product, the parameter data of the surface graphene coating, the temperature difference value and the temperature change rate to obtain a training sample set, inputting the training sample set into an initialized efficiency detection model for training to obtain the accuracy of an output result, and stopping training to obtain the efficiency detection model if the accuracy is greater than a preset accuracy threshold.
It should be noted that the efficiency detection model needs a large amount of historical data to train, the larger the data size is, the more accurate the result is, the efficiency detection model in the application can train by taking the parameter data of the historical detection product, the parameter data of the surface graphene coating, the temperature difference value and the temperature change rate as input, and by comparing a large amount of test data with real data, the obtained result is more accurate, so that the output result of the efficiency detection model is more accurate. Preferably, the accuracy threshold is typically set at 85%.
According to the system, the preset qualified tolerance range is obtained, whether the analysis result output by the efficiency detection model is within the preset result tolerance range or not is judged, if yes, the product to be detected is judged to be a qualified product, and if not, the product to be detected is judged to be an unqualified product.
It should be noted that the preset acceptable tolerance range is specifically designed according to the field, specification and use requirements of the actual product, for example, the heat dissipation efficiency tolerance range of the heat dissipation fins is determined according to the specification, the lowest heat dissipation efficiency allowed by the use process of the heat dissipation fins is taken as the starting point of the range, the heat dissipation efficiency greater than the lowest heat dissipation efficiency is the acceptable range, if the heat dissipation efficiency is lower than the lowest heat dissipation efficiency, the heat dissipation fins are unqualified products, and when the temperature change rate of the product to be detected does not fall within the preset acceptable tolerance range, the product to be detected is determined to be unqualified products.
According to the system, the temperature change rate of the qualified product and the temperature change rate of the experimental product are obtained, the rate difference is calculated based on the temperature change rate of the qualified product and the temperature change rate of the experimental product, the detection parameter data of the qualified product and the detection parameter data of the experimental product are obtained, and if the temperature change rate of the qualified product is larger than the temperature change rate of the experimental product, the detection parameter data of the qualified product is input into the efficiency detection model to replace the data of the corresponding experimental product, otherwise, the data is not replaced.
It should be noted that, when the temperature change rate of the product to be detected is greater than the temperature change rate of the experimental product, further parameter acquisition is carried out on the product to be detected and the experimental product with the same parameters, such as substrate material surface roughness, flatness, the degree of adhesion of substrate and graphene coating, the flatness of graphene coating, the acquisition of data such as roughness, and in the same way, the acquisition of the same parameters is carried out on the experimental product, and comparison is carried out, the comparison difference value of each data is analyzed and then input into the efficiency detection model for training, so that the data of the efficiency detection model is more accurate, and the data of the product with better heat dissipation efficiency can be supplied to the production process of the subsequent product, the quality of the subsequent product can be effectively increased, and the economic benefit of the product is improved.
According to the system, the preset conversion condition is obtained, whether the analysis result of the unqualified product meets the preset conversion condition is judged, if yes, the unqualified product is marked as the conversion product, and if not, the conversion product is marked as the secondary processing product.
It should be noted that, because the heat dissipation efficiency of the product is affected by many factors, when the product to be detected is marked as an unqualified product, a preset diversion condition is obtained, which includes a diversion tolerance range, the lowest value of the tolerance range is the lowest value of the temperature change rate in the efficiency detection model, if the temperature change rate of the product to be detected is lower than the lowest temperature change rate in the experimental product, the product to be detected is marked as a secondary processing product, and needs to be processed again, if the temperature change rate of the product to be detected is within the preset diversion tolerance range, the parameter data of the experimental product with the same temperature change rate as that of the product to be detected is called out and compared with the parameter data of the product to be detected, for example, when the temperature change rates of the product to be detected and one of the experimental products are the same, the data of the material, the shape, the volume, the external surface area and the internal surface area of the product are different from each other, and analyzing according to the application place and equipment of the product, when the product is not influenced by different parameters of the data for replacement use, marking the unqualified product as a diversion product, otherwise marking the unqualified product as a secondary processing product, and effectively treating the unqualified product, reducing economic loss and reducing the waste of resources of the product through diversion analysis of the unqualified product.
According to the system, detection parameter data of a secondary processing product and detection parameter data of a graphene coating on the surface of the secondary processing product are obtained, the temperature change rate of the secondary processing product is preset, detection parameter data of an experimental product with the same temperature change rate and detection parameter data of the graphene coating on the surface of the experimental product are obtained, the detection parameter data of the secondary processing product and the detection parameter data of the graphene coating on the surface of the experimental product are input into an efficiency detection model for comparative analysis, and correction data of the secondary processing product are output.
It should be noted that, when the secondary processing product is processed, further data acquisition is performed, such as acquisition of data of substrate material surface roughness, flatness, degree of adhesion between the substrate and the graphene coating, flatness and roughness of the graphene coating, and obtaining a value of a preset temperature change rate of the secondary processing product, which is a design requirement when the product is produced, the data is input into a comparison efficiency detection model and each item of data of an experimental product with the same data as the secondary processing product is called out, if the experimental product has further data acquisition, namely an excellent data replacement process of the qualified product, each item of data of the experimental product and the secondary processing product is directly compared and analyzed, and rework is performed by using processing parameters of the experimental product during secondary processing, if no further data acquisition exists in the experimental product, the experimental product is further subjected to data acquisition and data comparison and analysis, so that the yield of secondary processing can be effectively increased, the heat dissipation efficiency of the secondary processing product is increased, the product quality is improved, and the economic benefit of the product is improved.
According to the system, parameter data of a product to be detected and a preset temperature change rate are obtained and input into the efficiency detection model for processing and analysis, and the parameter data of the graphene coating on the surface of the product to be detected is output.
It should be noted that the parameter data of the product to be detected herein specifically includes material, shape, volume, outer surface area, inner surface area, substrate material surface roughness, flatness, etc., and the preset temperature change rate is the design requirement, because the parameter number in the efficiency detection model is large, the functional relationship between each data can be formed according to each parameter and temperature change rate, at this time, the efficiency detection model can be applied to the design process of a new product, according to the input of the required data of the product and the required temperature change rate, the efficiency detection model can calculate and output the corresponding parameter data of the graphene coating, which is convenient for determining the design parameters of the new product, greatly improves the design precision of the product, shortens the design time and the design cost, and continuously optimizes the design parameters in the subsequent detection process of the heat dissipation efficiency of the product, so that the quality of the product is better.
According to the system of the invention, it further comprises:
establishing a product database;
the product database comprises historical inventory product heat dissipation efficiency data of various models and specifications;
the product heat dissipation efficiency parameters comprise the temperature change rate of the product, product parameter data and surface graphene coating parameter data;
carrying out similarity comparison in the product database according to the obtained preset temperature change rate and product parameter data of the product to be detected, and obtaining historical stock products in the product database, wherein the similarity of the product database and the temperature change rate and product parameter data of the product to be detected meets the preset value requirement;
taking the obtained surface graphene coating parameter data of the historical inventory product as a surface graphene coating parameter data inspection standard of the product to be detected;
and if the surface graphene coating parameter data of the product to be detected does not meet the preset threshold range of the surface graphene coating parameter data of the historical inventory product, defining the product to be detected as an unqualified product.
It should be noted that, in order to increase an obtaining way of a product inspection standard, a product database is established, wherein the product database includes heat dissipation efficiency data of historical stock products of various models and specifications, similarity comparison is performed in the database according to a preset temperature change rate of a product to be inspected and product parameter data, the similarity comparison may be euclidean distance or cosine comparison, the historical stock product meeting a preset value requirement is searched in the product database, surface graphene coating parameter data of the historical stock product is used as a surface graphene coating parameter data inspection standard of the product to be inspected, and if the product to be inspected does not meet a preset threshold range requirement, the product to be inspected is defined as a non-conforming product.
According to the system of the invention, it further comprises:
presetting a target temperature change rate according to a secondary product to be processed;
carrying out secondary processing on the product according to the correction data obtained by the secondary product to be processed in the efficiency detection model to obtain a corrected product;
detecting according to the detection parameter data of the corrected product and the detection parameter data of the graphene coating on the surface of the corrected product to obtain a temperature change rate;
comparing a threshold value according to the temperature change rate and the preset target temperature change rate;
if the threshold comparison result meets the preset threshold requirement, the secondary processing product is qualified;
and if the threshold comparison result does not meet the preset threshold requirement, the secondary processing product is unqualified.
The secondary processing product is processed through the correction data obtained by the efficiency detection model to obtain a corrected product, the temperature change rate is obtained through detection according to the detection parameter data of the corrected product and the detection parameter data of the graphene coating on the surface of the corrected product, and the qualification condition of the secondary processing product is judged through threshold value comparison between the temperature change rate and a preset target temperature change rate.
According to the system of the invention, it further comprises:
correcting the efficiency detection model according to the product detection environment parameter data of each model and specification to obtain a corrected efficiency detection model;
the correction efficiency detection model comprises product detection heat dissipation efficiency data of products of various models and specifications in different environment parameter data;
the environmental parameter data comprises temperature, humidity, air pressure and illumination intensity parameters in a product detection environment;
performing secondary detection on unqualified products according to the correction efficiency detection model;
acquiring detection environment parameter data of unqualified products to be secondarily detected;
inputting parameter data of unqualified products to be secondarily detected, parameter data of the graphene coating on the surface, a temperature difference value, a temperature change rate and environmental parameter data into a correction efficiency detection model for processing and outputting a secondary processing result;
and analyzing the tolerance range of the unqualified product according to the obtained secondary treatment result.
In order to improve the accuracy of the product detection result, the products which are not qualified in the primary detection are secondarily detected through the correction efficiency detection model, the correction efficiency detection model is obtained by correcting the products of various models and specifications under different detection environment parameter data, wherein the environment parameter data comprises the temperature, humidity, air pressure and illumination intensity parameters in the product detection environment, and then the data of various parameters, the temperature change rate, the environment parameter data and the like of the unqualified products are input into the correction efficiency detection model to be secondarily detected to obtain the secondary detection result, and the tolerance range is analyzed to judge the secondary detection qualification condition of the products.
The invention discloses a method and a system for detecting heat dissipation performance of a product with a graphene coating. In addition, the data of unqualified products are analyzed to judge whether the products can be reused or not, so that the economic loss caused by unqualified products can be reduced. This application still through the data to unqualified product analysis, learns its heat dispersion's influence factor and generates the correction data and use for follow-up production, reduces economic loss, also can train in order to improve its precision efficiency detection model simultaneously.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Claims (10)
1. A method for detecting heat dissipation performance of a product with a graphene coating is characterized by comprising the following steps:
acquiring parameter data of a plurality of groups of experimental products and parameter data of a graphene coating on the surface;
acquiring an initial temperature value of an experimental product, acquiring a real-time temperature value of the experimental product after a preset time interval, calculating a temperature difference value between the initial temperature value and the real-time temperature value of the experimental product, calculating a temperature change rate of the experimental product based on the temperature difference value and the preset time interval, and constructing an efficiency detection model based on the temperature difference value, the temperature change rate, parameter data of the experimental product and surface graphene coating parameter data;
and acquiring data of the product to be detected, inputting the data of the product to be detected into the efficiency detection model for processing and analysis, and outputting an analysis result.
2. The method for detecting the heat dissipation performance of the product with the graphene coating according to claim 1, wherein the steps of obtaining data of the product to be detected, inputting parameter data of the product to be detected into an efficiency detection model for processing and analysis, and outputting an analysis result include:
the method comprises the steps of obtaining parameter data of a product to be detected, parameter data of a surface graphene coating and an initial temperature value of the product to be detected, obtaining a real-time temperature value of the product to be detected after a preset time interval, calculating a temperature difference value between the initial temperature value and the real-time temperature value of the product to be detected, calculating a temperature change rate of an experimental product based on the temperature difference value and the preset time interval, inputting the parameter data of the product to be detected, the parameter data of the surface graphene coating, the temperature difference value and the temperature change rate into an efficiency detection model, processing and analyzing, and outputting an analysis result.
3. The method for detecting the heat dissipation performance of the product with the graphene coating according to claim 1, wherein the efficiency detection model specifically comprises:
acquiring parameter data of a historical detection product, parameter data of a surface graphene coating, a temperature difference value and a temperature change rate;
preprocessing the parameter data of the historical detection product, the parameter data of the surface graphene coating, the temperature difference value and the temperature change rate to obtain a training sample set;
inputting the training sample set into the initialized efficiency detection model for training;
acquiring the accuracy of an output result;
and if the accuracy is greater than a preset accuracy threshold, stopping training to obtain the efficiency detection model.
4. The method for detecting the heat dissipation performance of the product with the graphene coating according to claim 2, wherein: and acquiring a preset qualified tolerance range, judging whether the analysis result output by the efficiency detection model is within the preset result tolerance range, if so, judging that the product to be detected is a qualified product, and otherwise, judging that the product to be detected is an unqualified product.
5. The method for detecting the heat dissipation performance of the product with the graphene coating according to claim 4, wherein: and acquiring a preset transfer condition, judging whether the analysis result of the unqualified product meets the preset transfer condition, if so, marking the unqualified product as a transfer product, and otherwise, marking the unqualified product as a secondary processing product.
6. The method for detecting the heat dissipation performance of the product with the graphene coating according to claim 5, wherein: the method comprises the steps of obtaining detection parameter data of a secondary processing product and detection parameter data of a graphene coating on the surface of the secondary processing product, presetting the temperature change rate of the secondary processing product, obtaining detection parameter data of an experimental product with the same temperature change rate and detection parameter data of the graphene coating on the surface of the experimental product, inputting the detection parameter data of the secondary processing product and the detection parameter data of the graphene coating on the surface of the experimental product into an efficiency detection model for comparative analysis, and outputting correction data of the secondary processing product.
7. The method for detecting the heat dissipation performance of the product with the graphene coating according to claim 1, wherein: acquiring parameter data of a product to be detected and a preset temperature change rate, inputting the parameter data and the preset temperature change rate into an efficiency detection model for processing and analysis, and outputting the parameter data of the graphene coating on the surface of the product to be detected.
8. The system for detecting the heat dissipation performance of the product with the graphene coating is characterized by comprising a memory and a processor, wherein the memory comprises a heat dissipation performance detection program of the product with the graphene coating, and when the processor executes the heat dissipation performance detection program of the product with the graphene coating, the following steps are realized:
acquiring parameter data of a plurality of groups of experimental products and parameter data of a graphene coating on the surface;
acquiring an initial temperature value of an experimental product, acquiring a real-time temperature value of the experimental product after a preset time interval, calculating a temperature difference value between the initial temperature value and the real-time temperature value of the experimental product, calculating a temperature change rate of the experimental product based on the temperature difference value and the preset time interval, and constructing an efficiency detection model based on the temperature difference value, the temperature change rate, parameter data of the experimental product and surface graphene coating parameter data;
and acquiring data of the product to be detected, inputting the data of the product to be detected into the efficiency detection model for processing and analysis, and outputting an analysis result.
9. The system for detecting the heat dissipation performance of a product with a graphene coating according to claim 8, wherein the acquiring data of the product to be detected, inputting parameter data of the product to be detected into the efficiency detection model for processing and analysis, and outputting an analysis result includes:
the method comprises the steps of obtaining parameter data of a product to be detected, parameter data of a surface graphene coating and an initial temperature value of the product to be detected, obtaining a real-time temperature value of the product to be detected after a preset time interval, calculating a temperature difference value between the initial temperature value and the real-time temperature value of the product to be detected, calculating a temperature change rate of an experimental product based on the temperature difference value and the preset time interval, inputting the parameter data of the product to be detected, the parameter data of the surface graphene coating, the temperature difference value and the temperature change rate into an efficiency detection model, processing and analyzing, and outputting an analysis result.
10. The system for detecting the heat dissipation performance of the product with the graphene coating according to claim 9, wherein: and acquiring a preset qualified tolerance range, judging whether the analysis result output by the efficiency detection model is within the preset result tolerance range, if so, judging that the product to be detected is a qualified product, and otherwise, judging that the product to be detected is an unqualified product.
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