Automatic quality detection method and system for stacked graphene radiators based on the Internet of Things

Abstract

The present application discloses an automatic quality detection method and system for stacked graphene heat sinks based on the Internet of Things. The thickness of the heat sink in the graphene heat sink is detected according to a detection device to obtain the thickness of the heat sink; if the thickness of the heat sink belongs to a preset first detection range, the heat dissipation holes on the heat sink are detected according to the detection device to obtain the heat dissipation hole data; if the heat dissipation hole data belongs to a second detection range, the temperature of the heat sink is detected according to the detection device to obtain the temperature data; according to the thickness of the heat sink, the heat dissipation hole data and the temperature data, the abnormal area of ​​the heat sink is determined, and the abnormal area is replaced. The present application determines whether there is an abnormality in the heat sink according to the thickness of the heat sink, the heat dissipation holes and the temperature of the heat sink, and replaces the abnormal area. There is no need for personnel to manually check the heat sink, which greatly improves the convenience of using the graphene heat sink.

Description

Technical Field

[0001] The present invention relates to the technical field of graphene radiators, and in particular to an automatic quality detection method and system for stacked graphene radiators based on the Internet of Things. Background Art

[0002] With the popularity and wide application of electronic devices, miniaturization and high speed of electronic components have become a general trend. With the development of this trend, thermal management of electronic devices has become increasingly important, because excessive heat generation may damage the device or slow down its performance. As an important thermal management component in electronic devices, the heat sink can effectively transfer heat from the electronic device to the surrounding environment. Therefore, the performance and reliability of the heat sink are very important for the long-term use of electronic devices.

[0003] In recent years, graphene has become a highly regarded heat dissipation material due to its high thermal conductivity, good mechanical strength and chemical stability. By stacking multiple layers of graphene together, a graphene heat sink can be formed, which has excellent heat dissipation performance and reliability and has become a highly regarded thermal management solution for electronic devices.

[0004] At present, the difficulty in ensuring the quality stability between graphene layers has become one of the main issues affecting the performance and reliability of graphene heat sinks. Existing heat sink quality monitoring methods are mostly done through manual inspection, which is inefficient and susceptible to errors. Therefore, a new heat sink quality inspection method is needed to ensure the performance and reliability of graphene heat sinks. Summary of the invention

[0005] The present invention is used to improve the existing radiator quality monitoring methods which are mostly completed through manual inspection. This method is inefficient and susceptible to errors. Based on this, the present application provides an automatic detection method and system for the quality of stacked graphene radiators based on the Internet of Things.

[0006] In a first aspect, the present application provides a method for automatically detecting the quality of a stacked graphene heat sink based on the Internet of Things, which adopts the following technical solutions, including:

[0007] Detecting the thickness of the heat sink in the graphene heat sink using a detection device to obtain the thickness of the heat sink;

[0008] If the thickness of the heat sink falls within a preset first detection range, detecting the heat dissipation holes on the heat sink according to the detection device to obtain heat dissipation hole data;

[0009] If the heat dissipation hole data belongs to the second detection range, the temperature of the heat sink is detected by the detection device to obtain temperature data;

[0010] According to the heat sink thickness, the heat dissipation hole data and the temperature data, an abnormal area of ​​the heat sink is determined, and the abnormal area is replaced.

[0011] By adopting the above technical solution, the system detects the thickness, heat dissipation holes and temperature of the heat sink through the detection device. When the thickness of the heat sink belongs to the first detection range, the system detects the heat dissipation hole data again. When the heat dissipation hole data belongs to the second detection range, the system detects the temperature of the heat sink again. The system determines the abnormal area of ​​the heat sink based on the heat sink data, the heat dissipation hole data and the temperature data, and replaces the abnormal area. At the same time, when the thickness of the heat sink does not belong to the first detection range or the heat dissipation hole data does not belong to the second detection range, it indicates that the heat sink is abnormally too high, that is, there is a high abnormal area. The system replaces the high abnormal area of ​​the heat sink, thereby judging whether the heat sink is abnormal based on the thickness of the heat sink, the heat dissipation holes and the temperature of the heat sink, and replacing the abnormal area, thereby eliminating the need for personnel to manually check the heat sink, thereby facilitating rapid positioning of the abnormal area of ​​the heat sink, so as to facilitate replacement of the heat sink, thereby greatly improving the convenience of using the graphene radiator.

[0012] Optionally, detecting the thickness of the heat sink in the graphene heat sink according to the detection device to obtain the thickness of the heat sink includes:

[0013] Control the detection device to emit ultrasonic waves, receive ultrasonic waves reflected by the heat sink, and record the arrival time and amplitude of the reflected waves;

[0014] Calculating the thickness of the heat sink according to the arrival time and amplitude of the reflected wave;

[0015] Find the normal thickness data of the heat sink in the preset cloud database;

[0016] Divide the normal thickness data by the heat sink thickness to calculate a thickness ratio;

[0017] According to the preset thickness anomaly standard and the thickness ratio, a thickness anomaly mode is determined, and the thickness anomaly mode at least includes: low thickness anomaly, medium thickness anomaly and high thickness anomaly.

[0018] By adopting the above technical solution, the system emits ultrasonic waves through the detection device, and the ultrasonic waves are reflected by the heat sink to form reflected waves. The system calculates the thickness of the heat sink according to the arrival time and amplitude of the reflected wave, and calculates the thickness ratio based on the normal thickness data in the cloud database. Finally, the thickness abnormality mode is determined according to the thickness ratio, so as to facilitate the judgment of the abnormal area of ​​the radiator according to the thickness abnormality mode, and then facilitate the judgment of whether to perform the heat dissipation hole detection, so as to quickly determine the abnormal area of ​​the heat sink.

[0019] Optionally, if the heat sink thickness falls within a preset first detection range, detecting the heat dissipation holes on the heat sink according to a detection device to obtain heat dissipation hole data includes:

[0020] If the thickness abnormality mode is the low thickness abnormality or the medium thickness abnormality, it is determined that the heat sink thickness belongs to the first detection range;

[0021] The heat dissipation holes on the heat sink are detected by the detection device to obtain the heat dissipation hole data, which includes: the number of heat dissipation holes and the diameter of the heat dissipation holes.

[0022] By adopting the above technical solution, when the thickness abnormality mode is in the low abnormality mode or the medium abnormality mode, the system detects the heat dissipation holes on the heat sink to obtain information on the number of heat dissipation holes and the diameter of the heat dissipation holes, so as to determine whether the quality of the heat sink is normal based on the number of heat dissipation holes.

[0023] Optionally, when the heat sink thickness falls within a preset first detection range, after detecting the heat dissipation holes on the heat sink according to the detection device and obtaining the heat dissipation hole data, the method further includes:

[0024] Searching the cloud database for the normal number and normal diameter of the heat dissipation holes of the heat sink;

[0025] If the number of the heat dissipation holes is the same as the normal number of the heat dissipation holes, the diameter of the heat dissipation holes is divided by the normal diameter to obtain a diameter ratio;

[0026] According to the preset diameter abnormality standard and the diameter ratio, determining the weight abnormality mode: low weight abnormality, medium weight abnormality and high weight abnormality;

[0027] If the number of the heat dissipation holes is different from the normal number of the heat dissipation holes, it is determined that the high weight is abnormal.

[0028] By adopting the above technical solution, the system searches for the normal number and normal diameter of the heat dissipation holes of the heat sink according to the cloud database. If the number of heat dissipation holes is the same as the normal number of heat dissipation holes, the diameter of the heat dissipation holes is divided by the normal diameter to obtain the diameter ratio, and then the weight abnormality mode is determined according to the diameter abnormality standard and the diameter ratio. If the number of heat dissipation holes is different from the normal number of heat dissipation holes, it is judged as the high weight abnormality, and the weight of the heat sink is evaluated according to the heat dissipation hole data, which makes it easier to judge whether the weight of the heat sink is abnormal, so as to replace the heat sink with abnormal weight.

[0029] Optionally, if the heat dissipation hole data belongs to the second detection range, detecting the temperature of the heat sink according to the detection device to obtain the temperature data includes:

[0030] If the weight abnormality mode is the low weight abnormality or the medium weight abnormality, it is determined that the heat dissipation hole data belongs to the second detection range;

[0031] The temperature indicator light can be used to display the heat sink temperature in real time;

[0032] Adjust the heat sink temperature according to the detection device and the preset control temperature;

[0033] When the temperature of the heat sink is at the control temperature, the brightness of the temperature indicator light is detected by the photosensor to obtain a brightness value;

[0034] Searching the cloud database for a normal brightness value corresponding to the regulated temperature;

[0035] Divide the brightness value by the normal brightness value to calculate a brightness ratio;

[0036] According to the preset brightness abnormality standard and the brightness ratio, a temperature abnormality mode is determined, and the temperature abnormality mode includes: low temperature abnormality, medium temperature abnormality and high temperature abnormality.

[0037] By adopting the above technical solution, the system controls the temperature of the heat sink and detects the brightness of the temperature indicator light when the temperature is controlled to obtain a brightness value, and compares the brightness value with the normal brightness value in the cloud database to calculate the brightness ratio. Finally, the temperature abnormality mode is determined according to the brightness ratio, thereby facilitating the search for heat sink areas with abnormal temperatures so as to facilitate timely replacement of the temperature abnormal areas.

[0038] Optionally, determining an abnormal area of ​​the heat sink according to the heat sink thickness, the heat dissipation hole data, and the temperature data, and replacing the abnormal area includes:

[0039] If the abnormal mode has the high thickness abnormality, the high weight abnormality, or the high temperature abnormality, the abnormal area of ​​the heat sink corresponding to the high thickness abnormality, the high weight abnormality, or the high temperature abnormality is replaced;

[0040] Re-testing the heat sink according to the detection device until it is detected that the high thickness abnormality, the high weight abnormality, or the high temperature abnormality does not exist, and judging that the heat sink is qualified;

[0041] If the abnormal pattern is the medium thickness abnormality, the medium weight abnormality and the medium temperature abnormality, the abnormal area corresponding to the medium thickness abnormality, the medium weight abnormality and the medium temperature abnormality is replaced;

[0042] Re-testing the heat sink according to the detection device until the abnormal mode is detected to be the low thickness abnormality, the low weight abnormality and the low temperature abnormality, and then judging that the heat sink is qualified;

[0043] If the abnormal mode includes the low thickness abnormality, the low weight abnormality, or the low temperature abnormality and does not include the high thickness abnormality, the high weight abnormality, or the high temperature abnormality, then the heat sink is determined to be qualified.

[0044] By adopting the above technical solution, when the system detects a high abnormality, the system replaces the high abnormality area and re-detects the heat sink until there is no high abnormality area on the heat sink; if there is only a medium abnormality on the heat sink, the system replaces the medium abnormality area and re-detects the heat sink until there is only a low abnormality area on the heat sink. If there is only a low abnormality or a low abnormality and a medium abnormality on the heat sink, the system determines that the quality of the heat sink is qualified, and thus determines whether the quality of the heat sink is qualified based on the thickness, weight and temperature of the heat sink, so as to facilitate timely replacement of unqualified heat sinks.

[0045] Optionally, after determining the abnormal area of ​​the radiator according to the heat sink thickness, the heat dissipation hole data and the temperature data, and replacing the abnormal area, the method further includes:

[0046] marking the heat sink whose abnormality mode is the low thickness abnormality, the low weight abnormality and the low temperature abnormality as excellent quality;

[0047] Record the number of excellent radiators corresponding to the excellent quality and the testing time;

[0048] The number of excellent products and the inspection time are displayed.

[0049] By adopting the above technical solution, the system marks and displays the heat sinks of excellent quality, so that users can check the quality of the graphene heat sink.

[0050] In a second aspect, the present application provides an automatic quality detection device for stacked graphene heat sinks based on the Internet of Things, which adopts the following technical solutions, including:

[0051] A thickness module, used to detect the thickness of the heat sink in the graphene heat sink according to the detection device to obtain the thickness of the heat sink;

[0052] A quality module, configured to detect the heat dissipation holes on the heat dissipation fin according to a detection device to obtain heat dissipation hole data if the heat dissipation fin thickness falls within a preset first detection range;

[0053] A temperature module, used to detect the temperature of the heat sink according to the detection device to obtain temperature data if the heat dissipation hole data belongs to the second detection range;

[0054] A replacement module is used to determine an abnormal area of ​​the radiator according to the heat sink thickness, the heat dissipation hole data and the temperature data, and replace the abnormal area.

[0055] By adopting the above technical solution, the system detects the thickness, heat dissipation holes and temperature of the heat sink through the detection device. When the thickness of the heat sink belongs to the first detection range, the system detects the heat dissipation hole data again. When the heat dissipation hole data belongs to the second detection range, the system detects the temperature of the heat sink again. The system determines the abnormal area of ​​the heat sink based on the heat sink data, the heat dissipation hole data and the temperature data, and replaces the abnormal area. At the same time, when the thickness of the heat sink does not belong to the first detection range or the heat dissipation hole data does not belong to the second detection range, it indicates that the heat sink is abnormally too high, that is, there is a high abnormal area. The system replaces the high abnormal area of ​​the heat sink, thereby judging whether the heat sink is abnormal based on the thickness of the heat sink, the heat dissipation holes and the temperature of the heat sink, and replacing the abnormal area, thereby eliminating the need for personnel to manually check the heat sink, thereby facilitating rapid positioning of the abnormal area of ​​the heat sink, so as to facilitate replacement of the heat sink, thereby greatly improving the convenience of using the graphene radiator.

[0056] In a third aspect, the present application further provides a control device, the device comprising:

[0057] It includes a memory and a processor, wherein the memory stores a computer program that can be loaded and executed by the processor, such as the above-mentioned automatic quality detection method of stacked graphene radiators based on the Internet of Things.

[0058] In a fourth aspect, the present application also provides a computer-readable storage medium storing a computer program that can be loaded and executed by a processor as described above, which is a method for automatically detecting the quality of a stacked graphene heat sink based on the Internet of Things.

[0059] In summary, the present application includes at least one of the following beneficial technical effects:

[0060] 1. Judge whether there is any abnormality in the heat sink based on the thickness, heat dissipation holes and heat sink temperature, and replace the abnormal area, so that there is no need for personnel to manually check the heat sink, and it is convenient to quickly locate the abnormal area of ​​the heat sink, so as to replace the heat sink, which greatly improves the convenience of using the graphene heat sink.

[0061] 2. Determine the temperature abnormality mode according to the brightness ratio of the temperature indicator light, so as to find the heat sink area with abnormal temperature and replace the abnormal temperature area in time.

[0062] 3. Evaluate the weight of the heat sink based on the heat dissipation hole data to determine whether the weight of the heat sink is abnormal, so as to replace the heat sink with abnormal weight. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a flow chart of an automatic quality detection method for stacked graphene radiators based on the Internet of Things in this application.

[0064] Figure 2 This is a structural block diagram of an automatic quality detection device for stacked graphene radiators based on the Internet of Things in this application. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] In view of the limitations of existing technologies, researchers have begun to explore a new method for automatic quality detection of stacked graphene heat sinks based on the Internet of Things. This method applies Internet of Things technology, sensor technology and computer technology to the heat sink to achieve intelligent monitoring and maintenance of the heat sink and improve the performance and reliability of the heat sink. The emergence of this invention can effectively solve the problems of inefficiency and susceptibility to errors in the existing technology, and has broad market prospects and application value.

[0067] In the embodiment of the present application, the executing entity is a control system, which controls ultrasonic detection equipment, laser displacement sensors, temperature sensors, and replacement devices, and at the same time obtains data such as the thickness, heat dissipation holes, and heat sink temperature according to various sensors, determines whether there is an abnormality in the heat sink, and replaces the abnormal area, thereby eliminating the need for personnel to manually check the heat sink, making it easier to quickly locate the abnormal area of ​​the heat sink, so as to replace the heat sink, greatly improving the convenience of using the graphene radiator.

[0068] Reference Figure 1 , a method for automatic quality detection of stacked graphene heat sinks based on the Internet of Things, at least including steps S10 to S40.

[0069] S10, detecting the thickness of the heat sink in the graphene heat sink using a detection device to obtain the thickness of the heat sink.

[0070] S20: If the thickness of the heat sink is within a preset first detection range, the heat dissipation holes on the heat sink are detected by a detection device to obtain heat dissipation hole data.

[0071] S30: If the heat dissipation hole data belongs to the second detection range, the temperature of the heat sink is detected by a detection device to obtain temperature data.

[0072] S40, determining an abnormal area of ​​the radiator according to the heat sink thickness, the heat dissipation hole data and the temperature data, and replacing the abnormal area.

[0073] Among them, the detection device includes ultrasonic detection equipment, laser displacement sensor, temperature sensor and other equipment. The first detection range and the second detection range are set in advance by the manufacturer to determine whether the heat sink is in a high abnormality.

[0074] Specifically, the system detects the thickness, heat dissipation holes and temperature of the heat sink through a detection device. When the thickness of the heat sink belongs to the first detection range, the system detects the heat dissipation hole data again. When the heat dissipation hole data belongs to the second detection range, the system detects the temperature of the heat sink again. The system determines the abnormal area of ​​the heat sink based on the heat sink data, the heat dissipation hole data and the temperature data, and replaces the abnormal area. At the same time, when the thickness of the heat sink does not belong to the first detection range or the heat dissipation hole data does not belong to the second detection range, it indicates that the heat sink is abnormally too high, that is, there is a high abnormal area. The system replaces the high abnormal area of ​​the heat sink, thereby judging whether the heat sink is abnormal based on the thickness, the heat dissipation holes and the temperature of the heat sink, and replacing the abnormal area. There is no need for personnel to manually check the heat sink, which makes it easy to quickly locate the abnormal area of ​​the heat sink, so as to replace the heat sink, greatly improving the convenience of using the graphene radiator.

[0075] In some embodiments, step S10 specifically includes the following steps: controlling the detection device to emit ultrasonic waves, and receiving ultrasonic waves reflected by the heat sink, recording the arrival time and amplitude of the reflected wave; calculating the thickness of the heat sink based on the arrival time and amplitude of the reflected wave; searching for normal thickness data of the heat sink in a preset cloud database; dividing the normal thickness data by the thickness of the heat sink to calculate the thickness ratio; determining the thickness abnormality mode based on the preset thickness abnormality standard and thickness ratio, the thickness abnormality mode at least including: low thickness abnormality, medium thickness abnormality and high thickness abnormality.

[0076] Among them, in the embodiment of the present application, a thickness ratio greater than or equal to 95% is identified as a low thickness abnormality, a thickness ratio greater than or equal to 86% and less than 95% is identified as a medium thickness abnormality, and a thickness ratio less than 86% is identified as a high thickness abnormality. Therefore, the first detection range is greater than or equal to 86%.

[0077] Specifically, the system emits ultrasonic waves through the detection device, and the ultrasonic waves are reflected by the heat sink to form reflected waves. The system calculates the thickness of the heat sink based on the arrival time and amplitude of the reflected wave, and calculates the thickness ratio based on the normal thickness data in the cloud database. Finally, the thickness abnormality pattern is determined based on the thickness ratio, which makes it easy to judge the abnormal area of ​​the radiator based on the thickness abnormality pattern, and then to judge whether to perform heat dissipation hole detection, so as to quickly determine the abnormal area of ​​the heat sink.

[0078] In some embodiments, step S20 specifically includes the following steps: if the thickness abnormality mode is a low thickness abnormality or a medium thickness abnormality, it is determined that the thickness of the heat sink belongs to the first detection range; the heat dissipation holes on the heat sink are detected according to the detection device to obtain the heat dissipation hole data, and the heat dissipation hole data includes: the number of heat dissipation holes and the diameter of the heat dissipation holes.

[0079] Specifically, when the thickness abnormality mode is in the low abnormality mode or the medium abnormality mode, the system detects the heat dissipation holes on the heat sink to obtain information on the number of heat dissipation holes and the diameter of the heat dissipation holes, so as to determine whether the quality of the heat sink is normal based on the number of heat dissipation holes.

[0080] In some embodiments, considering evaluating the weight abnormality of the heat sink based on the heat dissipation holes, the corresponding processing steps are as follows: search the normal number and normal diameter of the heat dissipation holes of the heat sink in the cloud database; if the number of heat dissipation holes is the same as the normal number of heat dissipation holes, divide the diameter of the heat dissipation holes by the normal diameter to obtain the diameter ratio; according to the preset diameter abnormality standard and the diameter ratio, determine the weight abnormality mode: low weight abnormality, medium weight abnormality and high weight abnormality; if the number of heat dissipation holes is different from the normal number of heat dissipation holes, it is judged as a high weight abnormality.

[0081] Specifically, the system searches the cloud database for the normal number and diameter of the heat sink's cooling holes. If the number of cooling holes is the same as the normal number of cooling holes, the diameter of the cooling holes is divided by the normal diameter to obtain the diameter ratio. The weight abnormality pattern is then determined based on the diameter abnormality standard and the diameter ratio. If the number of cooling holes is different from the normal number of cooling holes, it is judged to be a high weight abnormality. The weight of the heat sink is then evaluated based on the cooling hole data, which makes it easier to determine whether the weight of the heat sink is abnormal, so that the heat sink with abnormal weight can be replaced.

[0082] In some embodiments, step S30 specifically includes the following steps: if the weight abnormality mode is a low weight abnormality or a medium weight abnormality, it is determined that the heat dissipation hole data belongs to the second detection range; the heat sink temperature is displayed in real time by the temperature indicator light; the heat sink temperature is adjusted according to the detection device and the preset control temperature; when the heat sink temperature is at the control temperature, the brightness of the temperature indicator light is detected according to the photosensitive sensor to obtain the brightness value; the normal brightness value corresponding to the control temperature is searched in the cloud database; the brightness value is divided by the normal brightness value to calculate the brightness ratio; according to the preset brightness abnormality standard and the brightness ratio, the temperature abnormality mode is determined, and the temperature abnormality modes include: low temperature abnormality, medium temperature abnormality and high temperature abnormality.

[0083] There may be multiple control temperatures.

[0084] Specifically, the system regulates the temperature of the heat sink and detects the brightness of the temperature indicator light when the temperature is adjusted to obtain a brightness value. The brightness value is compared with the normal brightness value in the cloud database, and the brightness ratio is calculated. Finally, the temperature abnormality mode is determined based on the brightness ratio, thereby facilitating the search for heat sink areas with abnormal temperatures so that the abnormal temperature areas can be replaced in a timely manner.

[0085] In some embodiments, step S40 specifically includes the following steps: if the abnormal mode has a high thickness abnormality, a high weight abnormality, or a high temperature abnormality, then the abnormal area of ​​the heat sink corresponding to the high thickness abnormality, the high weight abnormality, or the high temperature abnormality is replaced; the heat sink is re-detected according to the detection device until it is detected that there is no high thickness abnormality, the high weight abnormality, or the high temperature abnormality, and the heat sink is judged to be qualified; if the abnormal mode is a medium thickness abnormality, a medium weight abnormality, and a medium temperature abnormality, then the abnormal area corresponding to the medium thickness abnormality, the medium weight abnormality, and the medium temperature abnormality is replaced; the heat sink is re-detected according to the detection device until it is detected that the abnormal mode is a low thickness abnormality, a low weight abnormality, and a low temperature abnormality, and the heat sink is judged to be qualified; if the abnormal mode has a low thickness abnormality, a low weight abnormality, or a low temperature abnormality and there is no high thickness abnormality, a high weight abnormality, or a high temperature abnormality, then the heat sink is judged to be qualified.

[0086] Specifically, when the system detects a high abnormality, the system replaces the high abnormality area and re-tests the heat sink until there is no high abnormality area on the heat sink; if the heat sink only has a medium abnormality, the system replaces the medium abnormality area and re-tests the heat sink until there is only a low abnormality area on the heat sink; if the heat sink only has a low abnormality or has both a low abnormality and a medium abnormality, the system determines that the quality of the heat sink is qualified, and thus determines whether the quality of the heat sink is qualified based on the thickness, quality and temperature of the heat sink, so as to facilitate timely replacement of unqualified heat sinks.

[0087] In some embodiments, considering the problem of selecting heat sinks with excellent quality, the corresponding processing steps are as follows: mark the heat sinks with abnormal modes of low thickness abnormality, low weight abnormality and low temperature abnormality as excellent quality; record the number of excellent products and the detection time of the heat sinks corresponding to the excellent quality; display the number of excellent products and the detection time.

[0088] Specifically, the system marks and displays heat sinks of excellent quality, making it easier for users to check the quality of graphene heat sinks.

[0089] In summary, the implementation principle of the automatic quality detection method of the stacked graphene heat sink based on the Internet of Things in the embodiment of the present application is as follows: the system detects the thickness, heat dissipation holes and temperature of the heat sink through a detection device. When the thickness of the heat sink belongs to the first detection range, the system detects the heat dissipation hole data again. When the heat dissipation hole data belongs to the second detection range, the system detects the temperature of the heat sink again. The system determines the abnormal area of ​​the heat sink based on the heat sink data, the heat dissipation hole data and the temperature data, and replaces the abnormal area. At the same time, when the thickness of the heat sink does not belong to the first detection range or the heat dissipation hole data does not belong to the second detection range, it indicates that the heat sink is abnormally too high, that is, there is a high abnormal area. The system replaces the high abnormal area of ​​the heat sink, thereby judging whether the heat sink is abnormal based on the thickness of the heat sink, the heat dissipation holes and the temperature of the heat sink, and replacing the abnormal area, so that there is no need for personnel to manually check the heat sink, and it is convenient to quickly locate the abnormal area of ​​the heat sink, so as to replace the heat sink, which greatly improves the convenience of using the graphene heat sink.

[0090] Figure 1 FIG. 1 is a flow chart of an automatic quality detection method for stacked graphene heat sinks based on the Internet of Things in one embodiment. It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but the steps are not necessarily executed in the order indicated by the arrows; unless otherwise specified in this document, there is no strict order restriction for the execution of the steps, and the steps may be executed in other orders; and Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0091] Based on the same technical concept, Figure 2 The embodiment of the present application also provides an automatic quality detection device for stacked graphene heat sinks based on the Internet of Things, which adopts the following technical solution. The device includes:

[0092] The thickness module 210 is used to detect the thickness of the heat sink in the graphene heat sink according to the detection device to obtain the thickness of the heat sink;

[0093] The quality module 220 is used to detect the heat dissipation holes on the heat dissipation fin according to the detection device to obtain the heat dissipation hole data if the thickness of the heat dissipation fin falls within a preset first detection range;

[0094] The temperature module 230 is used to detect the temperature of the heat sink according to the detection device to obtain temperature data if the heat dissipation hole data belongs to the second detection range;

[0095] The replacement module 240 is used to determine the abnormal area of ​​the heat sink according to the heat sink thickness, heat dissipation hole data and temperature data, and replace the abnormal area.

[0096] In some embodiments, the thickness module 210 is specifically used to control the detection device to transmit ultrasonic waves, receive ultrasonic waves reflected by the heat sink, and record the arrival time and amplitude of the reflected waves;

[0097] Calculate the thickness of the heat sink based on the arrival time and amplitude of the reflected wave;

[0098] Find the normal thickness data of the heat sink in the preset cloud database;

[0099] Divide the normal thickness data by the heat sink thickness to calculate the thickness ratio;

[0100] According to the preset thickness anomaly standard and thickness ratio, the thickness anomaly mode is determined, and the thickness anomaly mode at least includes: low thickness anomaly, medium thickness anomaly and high thickness anomaly.

[0101] In some embodiments, the quality module 220 is specifically configured to determine that the thickness of the heat sink belongs to the first detection range if the thickness abnormality mode is a low thickness abnormality or a medium thickness abnormality;

[0102] The heat dissipation holes on the heat sink are detected by the detection device to obtain the heat dissipation hole data, which includes: the number of heat dissipation holes and the diameter of the heat dissipation holes.

[0103] In some embodiments, the quality module 220 is further used to search the cloud database for the normal number and normal diameter of the heat dissipation holes of the heat sink;

[0104] If the number of heat dissipation holes is the same as the normal number of heat dissipation holes, divide the heat dissipation hole diameter by the normal diameter to obtain the diameter ratio;

[0105] According to the preset diameter abnormality standard and diameter ratio, the weight abnormality mode is determined: low weight abnormality, medium weight abnormality and high weight abnormality;

[0106] If the number of heat dissipation holes is different from the normal number of heat dissipation holes, it is judged as a high weight abnormality.

[0107] In some embodiments, the temperature module 230 is specifically used to determine that the heat dissipation hole data belongs to the second detection range if the weight abnormality mode is low weight abnormality or medium weight abnormality;

[0108] The temperature indicator light can be used to display the heat sink temperature in real time;

[0109] Adjust the heat sink temperature according to the detection device and the preset control temperature;

[0110] When the temperature of the heat sink is at the control temperature, the brightness of the temperature indicator light is detected by the photosensitive sensor to obtain the brightness value;

[0111] Search the cloud database for the normal brightness value corresponding to the controlled temperature;

[0112] Divide the brightness value by the normal brightness value to calculate the brightness ratio;

[0113] According to the preset brightness abnormality standard and brightness ratio, the temperature abnormality mode is determined, and the temperature abnormality mode includes: low temperature abnormality, medium temperature abnormality and high temperature abnormality.

[0114] In some embodiments, the replacement module 240 is specifically used to replace the abnormal area of ​​the heat sink corresponding to the high thickness abnormality, high weight abnormality or high temperature abnormality if the abnormal mode has a high thickness abnormality, a high weight abnormality or a high temperature abnormality;

[0115] The heat sink is re-tested according to the detection device until it is detected that there is no abnormal high thickness, abnormal high weight or abnormal high temperature, and the heat sink is judged to be qualified;

[0116] If the abnormal pattern is medium thickness abnormality, medium weight abnormality and medium temperature abnormality, the abnormal area corresponding to the medium thickness abnormality, medium weight abnormality and medium temperature abnormality is replaced;

[0117] Re-test the heat sink using the detection device until the abnormal mode is detected as low thickness abnormality, low weight abnormality and low temperature abnormality, and the heat sink is judged to be qualified;

[0118] If the abnormal mode has low thickness abnormality, low weight abnormality, or low temperature abnormality and does not have high thickness abnormality, high weight abnormality, or high temperature abnormality, the heat sink is judged to be qualified.

[0119] In some embodiments, the replacement module 240 is further used to mark the heat sink with abnormal modes of low thickness abnormality, low weight abnormality and low temperature abnormality as excellent quality;

[0120] Record the number of excellent radiators corresponding to excellent quality and the testing time;

[0121] Displays the number of excellent products and the inspection time.

[0122] The embodiment of the present application also discloses a control device.

[0123] Specifically, the control device includes a memory and a processor, and the memory stores a computer program that can be loaded by the processor and execute the above-mentioned method for automatically detecting the quality of stacked graphene radiators based on the Internet of Things.

[0124] The embodiment of the present application also discloses a computer-readable storage medium.

[0125] Specifically, the computer-readable storage medium stores a computer program that can be loaded by a processor and executed such as the above-mentioned method for automatic quality detection of stacked graphene radiators based on the Internet of Things. The computer-readable storage medium includes, for example: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0126] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be considered exemplary and non-restrictive in all respects, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present invention.

Claims

1. An automatic quality detection method for stacked graphene radiators based on the Internet of Things, characterized in that: include: Detecting the thickness of the heat sink in the graphene heat sink using a detection device to obtain the thickness of the heat sink; If the thickness of the heat sink falls within a preset first detection range, detecting the heat dissipation holes on the heat sink according to the detection device to obtain heat dissipation hole data; If the heat dissipation hole data belongs to the second detection range, the temperature of the heat sink is detected by the detection device to obtain temperature data; According to the heat sink thickness, the heat dissipation hole data and the temperature data, an abnormal area of ​​the heat sink is determined, and the abnormal area is replaced.

2. The method according to claim 1, characterized in that The method of detecting the thickness of the heat sink in the graphene heat sink according to the detection device to obtain the thickness of the heat sink includes: Control the detection device to emit ultrasonic waves, receive ultrasonic waves reflected by the heat sink, and record the arrival time and amplitude of the reflected waves; Calculating the thickness of the heat sink according to the arrival time and amplitude of the reflected wave; Find the normal thickness data of the heat sink in the preset cloud database; Divide the normal thickness data by the heat sink thickness to calculate the thickness ratio; According to the preset thickness anomaly standard and the thickness ratio, a thickness anomaly mode is determined, and the thickness anomaly mode at least includes: low thickness anomaly, medium thickness anomaly and high thickness anomaly.

3. The method according to claim 2, characterized in that If the thickness of the heat sink is within a preset first detection range, detecting the heat dissipation holes on the heat sink according to the detection device to obtain heat dissipation hole data includes: If the thickness abnormality mode is the low thickness abnormality or the medium thickness abnormality, it is determined that the heat sink thickness belongs to the first detection range; The heat dissipation holes on the heat sink are detected by the detection device to obtain the heat dissipation hole data, which includes: the number of heat dissipation holes and the diameter of the heat dissipation holes.

4. The method according to claim 3, characterized in that If the thickness of the heat sink falls within a preset first detection range, after detecting the heat dissipation holes on the heat sink according to the detection device and obtaining the heat dissipation hole data, the method further includes: Searching the cloud database for the normal number and normal diameter of the heat dissipation holes of the heat sink; If the number of the heat dissipation holes is the same as the normal number of the heat dissipation holes, the diameter of the heat dissipation holes is divided by the normal diameter to obtain a diameter ratio; According to the preset diameter abnormality standard and the diameter ratio, determining the weight abnormality mode: low weight abnormality, medium weight abnormality and high weight abnormality; If the number of the heat dissipation holes is different from the normal number of the heat dissipation holes, it is determined that the high weight is abnormal.

5. The method according to claim 4, characterized in that If the heat dissipation hole data belongs to the second detection range, detecting the temperature of the heat sink according to the detection device to obtain the temperature data includes: If the weight abnormality mode is the low weight abnormality or the medium weight abnormality, it is determined that the heat dissipation hole data belongs to the second detection range; The temperature indicator light can be used to display the heat sink temperature in real time; Adjust the heat sink temperature according to the detection device and the preset control temperature; When the temperature of the heat sink is at the control temperature, the brightness of the temperature indicator light is detected by the photosensor to obtain a brightness value; Searching the cloud database for a normal brightness value corresponding to the regulated temperature; Divide the brightness value by the normal brightness value to calculate a brightness ratio; According to the preset brightness abnormality standard and the brightness ratio, a temperature abnormality mode is determined, and the temperature abnormality mode includes: low temperature abnormality, medium temperature abnormality and high temperature abnormality.

6. The method according to claim 5, characterized in that The determining the abnormal area of ​​the radiator according to the heat sink thickness, the heat dissipation hole data and the temperature data, and replacing the abnormal area includes: If the abnormal mode has the high thickness abnormality, the high weight abnormality, or the high temperature abnormality, the abnormal area of ​​the heat sink corresponding to the high thickness abnormality, the high weight abnormality, or the high temperature abnormality is replaced; Re-testing the heat sink according to the detection device until it is detected that the high thickness abnormality, the high weight abnormality, or the high temperature abnormality does not exist, and judging that the heat sink is qualified; If the abnormal pattern is the medium thickness abnormality, the medium weight abnormality and the medium temperature abnormality, the abnormal area corresponding to the medium thickness abnormality, the medium weight abnormality and the medium temperature abnormality is replaced; Re-testing the heat sink according to the detection device until the abnormal mode is detected to be the low thickness abnormality, the low weight abnormality and the low temperature abnormality, and then judging that the heat sink is qualified; If the abnormal mode includes the low thickness abnormality, the low weight abnormality, or the low temperature abnormality and does not include the high thickness abnormality, the high weight abnormality, or the high temperature abnormality, then the heat sink is determined to be qualified.

7. The method according to claim 6, characterized in that After determining the abnormal area of ​​the radiator according to the heat sink thickness, the heat dissipation hole data and the temperature data, and replacing the abnormal area, the method further includes: marking the heat sink whose abnormality mode is the low thickness abnormality, the low weight abnormality and the low temperature abnormality as excellent quality; Record the number of excellent radiators corresponding to the excellent quality and the testing time; The number of excellent products and the inspection time are displayed.

8. An automatic quality detection device for stacked graphene radiators based on the Internet of Things, characterized in that: The device comprises: A thickness module, used to detect the thickness of the heat sink in the graphene heat sink according to the detection device to obtain the thickness of the heat sink; A quality module, configured to detect the heat dissipation holes on the heat dissipation fin according to a detection device to obtain heat dissipation hole data if the heat dissipation fin thickness falls within a preset first detection range; A temperature module, used to detect the temperature of the heat sink according to the detection device to obtain temperature data if the heat dissipation hole data belongs to the second detection range; A replacement module is used to determine an abnormal area of ​​the radiator according to the heat sink thickness, the heat dissipation hole data and the temperature data, and replace the abnormal area.

9. A control device, characterized in that: The device comprises: The method comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 7.

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