CN117146743A - Automatic detection method and system for quality of stacked graphene radiator based on Internet of things - Google Patents
Automatic detection method and system for quality of stacked graphene radiator based on Internet of things Download PDFInfo
- Publication number
- CN117146743A CN117146743A CN202311135553.3A CN202311135553A CN117146743A CN 117146743 A CN117146743 A CN 117146743A CN 202311135553 A CN202311135553 A CN 202311135553A CN 117146743 A CN117146743 A CN 117146743A
- Authority
- CN
- China
- Prior art keywords
- thickness
- abnormality
- temperature
- radiating
- fin
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 122
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 title claims abstract description 42
- 229910021389 graphene Inorganic materials 0.000 title claims abstract description 42
- 230000002159 abnormal effect Effects 0.000 claims abstract description 96
- 238000001816 cooling Methods 0.000 claims abstract description 60
- 230000017525 heat dissipation Effects 0.000 claims abstract description 28
- 238000000034 method Methods 0.000 claims abstract description 25
- 230000005856 abnormality Effects 0.000 claims description 169
- 230000033228 biological regulation Effects 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 6
- 238000007689 inspection Methods 0.000 claims description 3
- 230000006855 networking Effects 0.000 claims 1
- 230000005855 radiation Effects 0.000 description 6
- 230000001276 controlling effect Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000006073 displacement reaction Methods 0.000 description 2
- 229910002804 graphite Inorganic materials 0.000 description 2
- 239000010439 graphite Substances 0.000 description 2
- -1 graphite alkene Chemical class 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000020169 heat generation Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B17/00—Measuring arrangements characterised by the use of infrasonic, sonic or ultrasonic vibrations
- G01B17/02—Measuring arrangements characterised by the use of infrasonic, sonic or ultrasonic vibrations for measuring thickness
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
- G01B11/06—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness for measuring thickness ; e.g. of sheet material
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
- G01B21/10—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring diameters
- G01B21/14—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring diameters internal diameters
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K1/00—Details of thermometers not specially adapted for particular types of thermometer
- G01K1/02—Means for indicating or recording specially adapted for thermometers
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
- G01K13/00—Thermometers specially adapted for specific purposes
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Length Measuring Devices With Unspecified Measuring Means (AREA)
Abstract
The application discloses a method and a system for automatically detecting the quality of a stacked graphene radiator based on the Internet of things, wherein the thickness of a radiating fin in the graphene radiator is detected according to a detection device, and the thickness of the radiating fin is obtained; if the thickness of the radiating fin belongs to a preset first detection range, detecting radiating holes on the radiating fin according to a detection device to obtain radiating hole data; if the heat dissipation hole data belong to the second detection range, detecting the temperature of the heat dissipation plate according to the detection device to obtain temperature data; and determining an abnormal region of the radiator according to the thickness of the radiating fin, the radiating hole data and the temperature data, and replacing the abnormal region. According to the application, whether the cooling fin is abnormal or not is judged according to the thickness of the cooling fin, the cooling holes and the temperature of the cooling fin, and the abnormal area is replaced without manually checking the cooling fin by a person, so that the convenience in use of the graphene radiator is greatly improved.
Description
Technical Field
The application relates to the technical field of graphene radiators, in particular to a method and a system for automatically detecting the quality of a stacked graphene radiator based on the Internet of things.
Background
With the popularization and widespread use of electronic devices, miniaturization and high-speed of electronic components have become a general trend. With this trend, thermal management of electronic devices has become increasingly important, as excessive heat generation may damage the device or slow its performance. The heat sink, as an important thermal management component in the electronic device, may effectively transfer heat from the electronic device to the surrounding environment. Therefore, the performance and reliability of the heat sink are very important for long-term use of the electronic device.
In recent years, graphene has become a heat dissipation material of great interest because of its high thermal conductivity, good mechanical strength and chemical stability. A graphene heat spreader can be formed by stacking multiple layers of graphene together, and has excellent heat dissipation performance and reliability, and has become a thermal management solution for electronic devices of great interest.
Currently, difficulty in ensuring quality stability between graphene layers has become one of the major problems affecting graphene heat spreader performance and reliability. The existing radiator quality monitoring method is mostly completed through manual inspection, and is low in efficiency and susceptible to errors. Therefore, a new heat sink quality detection method is needed to ensure graphene heat sink performance and reliability.
Disclosure of Invention
The application is used for improving the problems that the existing radiator quality monitoring method is mostly completed through manual inspection, and the method is low in efficiency and is easily affected by errors.
In a first aspect, the application provides a method for automatically detecting the quality of a stacked graphene radiator based on the internet of things, which adopts the following technical scheme that:
detecting the thickness of the radiating fin in the graphene radiator according to the detection device, and obtaining the thickness of the radiating fin;
if the thickness of the radiating fin belongs to a preset first detection range, detecting radiating holes on the radiating fin according to a detection device to obtain radiating hole data;
if the heat dissipation hole data belong to the second detection range, detecting the temperature of the heat dissipation plate according to the detection device to obtain temperature data;
and determining an abnormal region of the radiator according to the thickness of the radiating fin, the radiating hole data and the temperature data, and replacing the abnormal region.
Through adopting above-mentioned technical scheme, the system detects thickness, louvre and temperature of fin through detection device, when fin thickness belongs to first detection range, the system detects the louvre data again, when louvre data belongs to the second detection range, the system detects the temperature of fin again, the system is according to fin data and louvre data and temperature data, confirm the unusual region of fin, and replace unusual region, simultaneously when fin thickness does not belong to first detection range or louvre data does not belong to the second detection range, indicate that the fin is unusual too high, there is high unusual region promptly, the system replaces high unusual region of fin, thereby judge whether the fin exists unusual according to thickness of fin, louvre and fin temperature, and replace unusual region, thereby need not personnel to manually inspect the fin, and then be convenient for fix the unusual region of fin fast, so as to replace the fin, the convenience of using of graphene radiator has been improved greatly.
Optionally, detect the fin thickness in the graphite alkene radiator according to detection device, acquire fin thickness, include:
controlling the detection device to emit ultrasonic waves, receiving the ultrasonic waves reflected by the radiating fins, and recording the arrival time and amplitude of the reflected waves;
calculating the thickness of the cooling fin according to the arrival time and the amplitude of the reflected wave;
searching normal thickness data of the cooling fin in a preset cloud database;
dividing the normal thickness data by the fin thickness to calculate a thickness ratio;
determining a thickness abnormality mode according to a preset thickness abnormality standard and the thickness ratio, wherein the thickness abnormality mode at least comprises: low thickness anomaly, medium thickness anomaly, and high thickness anomaly.
Through adopting above-mentioned technical scheme, the system sends the ultrasonic wave through detection device, and the ultrasonic wave forms the reflected wave after the fin reflection, and the system calculates fin thickness according to arrival time and range of reflected wave to normal thickness data in cloud database calculates thickness ratio, and finally confirms thickness abnormal mode according to thickness ratio, thereby is convenient for judge the unusual region of radiator according to thickness abnormal mode, and then is convenient for judge whether carry out the detection of louvre, so that confirm the unusual region of fin fast.
Optionally, if the thickness of the cooling fin belongs to a preset first detection range, detecting cooling holes on the cooling fin according to the detection device to obtain cooling hole data, including:
if the thickness abnormality mode is the low thickness abnormality or the medium thickness abnormality, judging that the thickness of the cooling fin belongs to the first detection range;
according to detection device detects the louvre on the fin, acquires louvre data, and louvre data includes: the number of the radiating holes and the diameter of the radiating holes.
Through adopting above-mentioned technical scheme, when thickness abnormal mode is in low abnormal mode or well abnormal mode, the system detects the louvre on the fin to obtain the information of louvre quantity and louvre diameter, so that whether the quality of fin is normal according to louvre quantity determination.
Optionally, when the thickness of the fin is within the preset first detection range, detecting the heat dissipation hole on the fin according to the detection device, and after obtaining the heat dissipation hole data, further including:
searching the normal number and the normal diameter of radiating holes of the radiating fins in the cloud database;
if the number of the radiating holes is the same as the normal number of the radiating holes, dividing the diameter of the radiating holes by the normal diameter to obtain a diameter ratio;
determining a weight anomaly mode according to a preset diameter anomaly standard and the diameter ratio: low weight anomaly, medium weight anomaly, and high weight anomaly;
and if the number of the radiating holes is different from the normal number of the radiating holes, judging that the weight is abnormal.
Through adopting above-mentioned technical scheme, the system looks for the louvre normal quantity and the normal diameter of fin according to cloud database, if louvre quantity is the same with louvre normal quantity, then divides the louvre diameter normal diameter, obtains the diameter ratio, and then according to diameter abnormal standard and diameter ratio, confirm weight abnormal mode, if louvre quantity is different with louvre normal quantity, then judge to high weight is unusual to evaluate the weight of fin according to louvre data, and then be convenient for judge whether the weight of fin appears unusual, so that change to the unusual fin of weight.
Optionally, if the heat dissipation hole data belongs to the second detection range, detecting the temperature of the heat dissipation plate according to the detection device to obtain temperature data, including:
if the quality abnormality mode is the low quality abnormality or the medium quality abnormality, judging that the heat dissipation hole data belongs to the second detection range;
displaying the temperature of the radiating fin in real time through a temperature indicator lamp;
adjusting the temperature of the radiating fin according to the detection device and a preset adjusting temperature;
when the temperature of the radiating fin is at the regulation temperature, detecting the brightness of the temperature indicator lamp according to the photosensitive sensor to obtain a brightness value;
searching a normal brightness value corresponding to the regulation temperature in the cloud database;
dividing the brightness value by the normal brightness value to calculate a brightness ratio;
determining a temperature anomaly mode according to a preset brightness anomaly standard and the brightness ratio, wherein the temperature anomaly mode comprises the following steps: low temperature anomalies, medium temperature anomalies, and high temperature anomalies.
Through adopting above-mentioned technical scheme, the system regulates and control the temperature of fin to detect the luminance of temperature indicator when regulating and controlling the temperature, obtain the luminance value, and more the luminance value compares with the normal luminance value in the cloud database, calculates the luminance ratio, and finally confirms the temperature anomaly mode according to the luminance ratio, thereby is convenient for seek the fin region of temperature anomaly, in order to replace the temperature anomaly region in time.
Optionally, the determining an abnormal area of the heat sink according to the thickness of the heat sink, the heat dissipation hole data and the temperature data, and replacing the abnormal area includes:
if the abnormal mode exists the high thickness abnormality or the high quality abnormality or the high temperature abnormality, replacing the abnormal area of the radiating fin corresponding to the high thickness abnormality or the high quality abnormality or the high temperature abnormality;
detecting the cooling fin again according to the detection device until the cooling fin is judged to be qualified when the high-thickness abnormality or the high-quality abnormality or the high-temperature abnormality is detected to be absent;
if the abnormal mode is the middle-thickness abnormality, the middle-quality abnormality and the middle-temperature abnormality, replacing the abnormal region corresponding to the middle-thickness abnormality, the middle-quality abnormality and the middle-temperature abnormality;
detecting the cooling fin again according to the detection device until the abnormal mode is detected to be the low thickness abnormality, the low quality abnormality and the low temperature abnormality, and judging that the cooling fin is qualified;
and if the abnormal mode has the low thickness abnormality or the low quality abnormality or the low temperature abnormality and does not have the high thickness abnormality or the high quality abnormality or the high temperature abnormality, judging that the cooling fin is qualified.
By adopting the technical scheme, when the system detects high abnormality, the system replaces the high abnormality area and detects the radiating fin again until the radiating fin does not have the high abnormality area; if the cooling fin only has middle abnormality, the system replaces the middle abnormality area, then the cooling fin is re-detected until the cooling fin only has low abnormality area, if the cooling fin only has low abnormality or has low abnormality and middle abnormality, the system judges that the quality of the cooling fin is qualified, thereby judging whether the quality of the cooling fin is qualified according to the thickness, the quality and the temperature of the cooling fin, and facilitating the timely replacement of the unqualified cooling fin.
Optionally, after determining an abnormal area of the heat sink according to the thickness of the heat sink, the heat dissipation hole data, and the temperature data, and replacing the abnormal area, the method further includes:
marking the heat sink having the abnormality pattern of the low thickness abnormality, the low quality abnormality, and the low temperature abnormality as excellent quality;
recording the quantity of excellent products of the heat radiator corresponding to the excellent quality and the detection time;
displaying the number of excellent products and the detection time.
Through adopting above-mentioned technical scheme, the system marks and shows the fin of outstanding quality to the user of being convenient for looks over the quality condition of graphite alkene radiator.
In a second aspect, the application provides an automatic quality detection device for a stacked graphene radiator based on the internet of things, which adopts the following technical scheme that:
the thickness module is used for detecting the thickness of the radiating fin in the graphene radiator according to the detection device to obtain the thickness of the radiating fin;
the quality module is used for detecting the radiating holes on the radiating fins according to the detection device to obtain radiating hole data if the thickness of the radiating fins is in a preset first detection range;
the temperature module is used for detecting the temperature of the radiating fin according to the detection device to obtain temperature data if the radiating hole data belong to a second detection range;
and the replacement module is used for determining an abnormal area of the radiator according to the thickness of the radiating fin, the radiating hole data and the temperature data and replacing the abnormal area.
Through adopting above-mentioned technical scheme, the system detects thickness, louvre and temperature of fin through detection device, when fin thickness belongs to first detection range, the system detects the louvre data again, when louvre data belongs to the second detection range, the system detects the temperature of fin again, the system is according to fin data and louvre data and temperature data, confirm the unusual region of fin, and replace unusual region, simultaneously when fin thickness does not belong to first detection range or louvre data does not belong to the second detection range, indicate that the fin is unusual too high, there is high unusual region promptly, the system replaces high unusual region of fin, thereby judge whether the fin exists unusual according to thickness of fin, louvre and fin temperature, and replace unusual region, thereby need not personnel to manually inspect the fin, and then be convenient for fix the unusual region of fin fast, so as to replace the fin, the convenience of using of graphene radiator has been improved greatly.
In a third aspect, the present application also provides a control apparatus, the apparatus comprising:
the method comprises a memory and a processor, wherein the memory is stored with a computer program which can be loaded by the processor and execute the method for automatically detecting the quality of the stacked graphene radiator based on the Internet of things.
In a fourth aspect, the present application also provides a computer readable storage medium storing a computer program capable of being loaded by a processor and executing the method for automatically detecting the quality of a stacked graphene radiator based on the internet of things as described above
In summary, the present application includes at least one of the following beneficial technical effects:
1. judging whether the radiating fin is abnormal according to the thickness of the radiating fin, the radiating holes and the temperature of the radiating fin, and replacing an abnormal area, so that a person is not required to manually check the radiating fin, and the abnormal area of the radiating fin is conveniently and rapidly positioned, so that the radiating fin is conveniently replaced, and the use convenience of the graphene radiator is greatly improved.
2. And determining a temperature abnormality mode according to the brightness ratio of the temperature indicator lamp, so that a radiating fin area with abnormal temperature can be conveniently searched, and the temperature abnormality area can be conveniently replaced in time.
3. And the weight of the radiating fin is evaluated according to the radiating hole data, so that whether the weight of the radiating fin is abnormal or not is judged, and the radiating fin with abnormal weight is replaced conveniently.
Drawings
Fig. 1 is a flow diagram of an automatic detection method for quality of a stacked graphene radiator based on the internet of things.
Fig. 2 is a structural block diagram of an automatic quality detection device for a stacked graphene radiator based on the internet of things.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Aiming at the limitation of the prior art, researchers begin to explore a new method for automatically detecting the quality of the stacked graphene radiator based on the Internet of things. The method applies the Internet of things technology, the sensor technology and the computer technology to the radiator so as to realize intelligent monitoring and maintenance of the radiator and improve the performance and reliability of the radiator. The application can effectively solve the problems of low efficiency and easy error influence in the prior art, and has wide market prospect and application value.
In the embodiment of the application, the execution main body is a control system, the ultrasonic detection equipment, the laser displacement sensor, the temperature sensor, the replacement device and the like are controlled by the control system, meanwhile, the thickness, the radiating holes, the temperature and other data of the radiating fins are obtained according to various sensors, whether the radiating fins are abnormal or not is judged, and the abnormal area is replaced, so that personnel are not required to manually inspect the radiating fins, the abnormal area of the radiating fins is conveniently and rapidly positioned, the radiating fins are conveniently replaced, and the use convenience of the graphene radiator is greatly improved.
Referring to fig. 1, a method for automatically detecting quality of a stacked graphene radiator based on the internet of things at least includes steps S10 to S40.
S10, detecting the thickness of the radiating fin in the graphene radiator according to the detection device, and obtaining the thickness of the radiating fin.
S20, if the thickness of the radiating fin belongs to a preset first detection range, detecting radiating holes on the radiating fin according to the detection device, and acquiring radiating hole data.
And S30, if the heat dissipation hole data belong to the second detection range, detecting the temperature of the heat dissipation plate according to the detection device, and acquiring temperature data.
S40, determining an abnormal area of the radiator according to the thickness of the radiating fin, the radiating hole data and the temperature data, and replacing the abnormal area.
The detection device comprises ultrasonic detection equipment, a laser displacement sensor, a temperature sensor and the like, and the first detection range and the second detection range are set in advance by a manufacturer and are used for judging whether the radiating fin is in high abnormality or not.
Specifically, the system detects thickness, the radiating holes and temperature of the radiating fins through the detection device, when the thickness of the radiating fins belongs to a first detection range, the system detects radiating hole data, when the radiating hole data belongs to a second detection range, the system detects the temperature of the radiating fins again, the system determines abnormal areas of the radiating fins according to the radiating fin data, the radiating hole data and the temperature data and replaces the abnormal areas, meanwhile, when the thickness of the radiating fins does not belong to the first detection range or the radiating hole data does not belong to the second detection range, the abnormal high areas of the radiating fins are indicated, namely, the system replaces the high abnormal areas of the radiating fins, so that whether the radiating fins are abnormal or not is judged according to the thickness of the radiating fins, the radiating holes and the radiating fin temperature, and the abnormal areas are replaced, so that people do not need to manually check the radiating fins, the abnormal areas of the radiating fins are conveniently and rapidly positioned, and the radiating fins are conveniently replaced, and the convenience in use of the graphene radiator is greatly improved.
In some embodiments, step S10 specifically includes the steps of: controlling the detection device to emit ultrasonic waves, receiving the ultrasonic waves reflected by the radiating fins, and recording the arrival time and amplitude of the reflected waves; calculating the thickness of the radiating fin according to the arrival time and the amplitude of the reflected wave; searching normal thickness data of the cooling fin in a preset cloud database; dividing the normal thickness data by the thickness of the radiating fin to calculate a thickness ratio; determining a thickness abnormality mode according to a preset thickness abnormality standard and a thickness ratio, wherein the thickness abnormality mode at least comprises: low thickness anomaly, medium thickness anomaly, and high thickness anomaly.
In the embodiment of the application, the thickness ratio is greater than or equal to 95% and is determined as low thickness abnormality, the thickness ratio is greater than or equal to 86% and is less than 95% and is determined as medium thickness abnormality, and the thickness ratio is less than 86% and is determined as high thickness abnormality, so that the first detection range is greater than or equal to 86%.
Specifically, the system sends out ultrasonic waves through the detection device, the ultrasonic waves form reflected waves after being reflected by the radiating fins, the system calculates the thickness of the radiating fins according to the arrival time and the amplitude of the reflected waves, normal thickness data in the cloud database are calculated, thickness ratio is calculated, and finally thickness abnormal modes are determined according to the thickness ratio, so that the abnormal areas of the radiator can be judged according to the thickness abnormal modes, whether the detection of the radiating holes is carried out or not can be judged conveniently, and the abnormal areas of the radiating fins can be determined quickly.
In some embodiments, step S20 specifically includes the steps of: if the thickness abnormality mode is low thickness abnormality or medium thickness abnormality, judging that the thickness of the cooling fin belongs to a first detection range; according to detection device detects the louvre on the fin, acquires louvre data, and louvre data includes: the number of the radiating holes and the diameter of the radiating holes.
Specifically, when the thickness abnormality mode is in the low abnormality mode or the medium abnormality mode, the system detects the heat radiation holes on the heat radiation fins, thereby obtaining information of the number of the heat radiation holes and the diameter of the heat radiation holes, so as to determine whether the quality of the heat radiation fins is normal or not according to the number of the heat radiation holes.
In some embodiments, considering the evaluation of weight anomalies of the heat sink according to the heat sink holes, the corresponding processing steps are as follows: searching the normal number and the normal diameter of radiating holes of the radiating fins in the cloud database; if the number of the radiating holes is the same as the normal number of the radiating holes, dividing the diameter of the radiating holes by the normal diameter to obtain a diameter ratio; determining a weight anomaly mode according to a preset diameter anomaly standard and a diameter ratio: low weight anomaly, medium weight anomaly, and high weight anomaly; if the number of the radiating holes is different from the normal number of the radiating holes, judging that the weight is abnormal.
Specifically, the system searches the normal number and the normal diameter of the radiating holes of the radiating fin according to the cloud database, if the number of the radiating holes is the same as the normal number of the radiating holes, the diameter of the radiating holes is divided by the normal diameter to obtain a diameter ratio, and then a weight abnormality mode is determined according to the diameter abnormality standard and the diameter ratio, if the number of the radiating holes is different from the normal number of the radiating holes, the system judges that the weight is abnormal, so that the weight of the radiating fin is evaluated according to the radiating hole data, and further, whether the weight of the radiating fin is abnormal or not is judged, so that the radiating fin with the weight abnormality is replaced conveniently.
In some embodiments, step S30 specifically includes the steps of: if the quality abnormal mode is low quality abnormal or medium quality abnormal, judging that the heat dissipation hole data belongs to a second detection range; displaying the temperature of the radiating fin in real time through a temperature indicator lamp; adjusting the temperature of the radiating fin according to the detection device and a preset adjusting temperature; when the temperature of the radiating fin is at the regulation temperature, detecting the brightness of the temperature indicator lamp according to the photosensitive sensor to obtain a brightness value; searching a normal brightness value corresponding to the regulation temperature in a cloud database; dividing the brightness value by the normal brightness value to calculate a brightness ratio; according to a preset brightness abnormality standard and a preset brightness ratio, determining a temperature abnormality mode, wherein the temperature abnormality mode comprises: low temperature anomalies, medium temperature anomalies, and high temperature anomalies.
Wherein, there can be a plurality of regulation temperatures.
Specifically, the system regulates and controls the temperature of the radiating fins, detects the brightness of the temperature indicator lamp when regulating and controlling the temperature to obtain a brightness value, compares the brightness value with a normal brightness value in the cloud database, calculates a brightness ratio, and finally determines a temperature abnormality mode according to the brightness ratio, so that the radiating fin area with abnormal temperature is conveniently searched, and the temperature abnormality area is conveniently replaced in time.
In some embodiments, step S40 specifically includes the steps of: if the abnormal mode has high thickness abnormality or high quality abnormality or high temperature abnormality, replacing an abnormal region of the radiating fin corresponding to the high thickness abnormality or the high quality abnormality or the high temperature abnormality; detecting the cooling fin again according to the detection device until no high-thickness abnormality or high-quality abnormality or high-temperature abnormality is detected, and judging that the cooling fin is qualified; if the abnormal mode is middle-thickness abnormality, middle-quality abnormality and middle-temperature abnormality, replacing an abnormal region corresponding to the middle-thickness abnormality, the middle-quality abnormality and the middle-temperature abnormality; detecting the cooling fin again according to the detection device until the abnormal mode is detected to be low thickness abnormality, low quality abnormality and low temperature abnormality, and judging that the cooling fin is qualified; and if the abnormal mode has low thickness abnormality or low quality abnormality or low temperature abnormality and does not have high thickness abnormality or high quality abnormality or high temperature abnormality, judging that the cooling fin is qualified.
Specifically, when the system detects a high abnormality, the system replaces the high abnormality area and detects the cooling fin again until the cooling fin does not have the high abnormality area; if the cooling fin is only abnormal, the system replaces the central abnormal region, and then re-detects the cooling fin until only the low abnormal region exists on the cooling fin; if the cooling fin has low abnormality or low abnormality and medium abnormality, the system judges the quality of the cooling fin to be qualified, and judges whether the quality of the cooling fin is qualified according to the thickness, the quality and the temperature of the cooling fin so as to replace the unqualified cooling fin in time.
In some embodiments, considering the problem of good quality selection of the heat sink, the corresponding processing steps are as follows: marking the heat sink with the abnormal mode of low thickness abnormality, low quality abnormality and low temperature abnormality as excellent quality; recording the quantity of excellent products of the radiator corresponding to the excellent quality and the detection time; displaying the number of excellent products and the detection time.
Specifically, the system marks and displays the heat sink with excellent quality, so that a user can conveniently check the quality condition of the graphene heat sink.
In summary, the implementation principle of the method for automatically detecting the quality of the stacked graphene radiator based on the Internet of things provided by the embodiment of the application is as follows: the system detects thickness, radiating holes and temperature of the radiating fins through the detection device, when the thickness of the radiating fins belongs to a first detection range, the system detects radiating hole data, when the radiating hole data belongs to a second detection range, the system detects the temperature of the radiating fins again, the system determines abnormal areas of the radiating fins according to the radiating fin data, the radiating hole data and the temperature data and replaces the abnormal areas, meanwhile, when the thickness of the radiating fins does not belong to the first detection range or the radiating hole data does not belong to the second detection range, the abnormal areas of the radiating fins are indicated to be too high, namely, the system replaces the high abnormal areas of the radiating fins, so that whether the radiating fins are abnormal or not is judged according to the thickness of the radiating fins, the radiating holes and the temperature of the radiating fins, and the abnormal areas are replaced, so that people do not need to manually check the radiating fins, the abnormal areas of the radiating fins are conveniently and rapidly positioned, the radiating fins are conveniently replaced, and the convenience of using the graphene radiator is greatly improved.
Fig. 1 is a schematic flow chart of a method for automatically detecting quality of a stacked graphene radiator based on the internet of things in an embodiment. It should be understood that, although the steps in the flowchart of fig. 1 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows; the steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders; and at least some of the steps in fig. 1 may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the sub-steps or stages are performed necessarily occur in sequence, but may be performed alternately or alternately with at least some of the other steps or sub-steps of other steps.
Based on the same technical concept, referring to fig. 2, the embodiment of the application also provides an automatic quality detection device for a stacked graphene radiator based on the internet of things, which adopts the following technical scheme that:
the thickness module 210 is configured to detect a thickness of a cooling fin in the graphene radiator according to the detection device, and obtain the thickness of the cooling fin;
the quality module 220 is configured to detect the heat dissipation hole on the heat dissipation plate according to the detection device to obtain heat dissipation hole data if the thickness of the heat dissipation plate belongs to a preset first detection range;
the temperature module 230 is configured to detect a temperature of the cooling fin according to the detection device to obtain temperature data if the cooling hole data belongs to the second detection range;
and a replacement module 240, configured to determine an abnormal area of the radiator according to the fin thickness, the heat sink hole data, and the temperature data, and replace the abnormal area.
In some embodiments, the thickness module 210 is specifically configured to 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 radiating fin according to the arrival time and the amplitude of the reflected wave;
searching normal thickness data of the cooling fin in a preset cloud database;
dividing the normal thickness data by the thickness of the radiating fin to calculate a thickness ratio;
determining a thickness abnormality mode according to a preset thickness abnormality standard and a thickness ratio, wherein the thickness abnormality mode at least comprises: low thickness anomaly, medium thickness anomaly, and high thickness anomaly.
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 anomaly mode is a low thickness anomaly or a medium thickness anomaly;
according to detection device detects the louvre on the fin, acquires louvre data, and louvre data includes: the number of the radiating holes and the diameter of the radiating holes.
In some embodiments, the quality module 220 is further configured to look up the normal number and normal diameter of the heat sink holes of the heat sink in the cloud database;
if the number of the radiating holes is the same as the normal number of the radiating holes, dividing the diameter of the radiating holes by the normal diameter to obtain a diameter ratio;
determining a weight anomaly mode according to a preset diameter anomaly standard and a diameter ratio: low weight anomaly, medium weight anomaly, and high weight anomaly;
if the number of the radiating holes is different from the normal number of the radiating holes, judging that the weight is abnormal.
In some embodiments, the temperature module 230 is specifically configured to determine that the heat dissipation hole data belongs to the second detection range if the quality anomaly mode is a low quality anomaly or a medium quality anomaly;
displaying the temperature of the radiating fin in real time through a temperature indicator lamp;
adjusting the temperature of the radiating fin according to the detection device and a preset adjusting temperature;
when the temperature of the radiating fin is at the regulation temperature, detecting the brightness of the temperature indicator lamp according to the photosensitive sensor to obtain a brightness value;
searching a normal brightness value corresponding to the regulation temperature in a cloud database;
dividing the brightness value by the normal brightness value to calculate a brightness ratio;
according to a preset brightness abnormality standard and a preset brightness ratio, determining a temperature abnormality mode, wherein the temperature abnormality mode comprises: low temperature anomalies, medium temperature anomalies, and high temperature anomalies.
In some embodiments, the replacing module 240 is specifically configured to replace an abnormal area of the heat sink corresponding to the high thickness abnormality or the high quality abnormality or the high temperature abnormality if the abnormal mode has the high thickness abnormality or the high quality abnormality or the high temperature abnormality;
detecting the cooling fin again according to the detection device until no high-thickness abnormality or high-quality abnormality or high-temperature abnormality is detected, and judging that the cooling fin is qualified;
if the abnormal mode is middle-thickness abnormality, middle-quality abnormality and middle-temperature abnormality, replacing an abnormal region corresponding to the middle-thickness abnormality, the middle-quality abnormality and the middle-temperature abnormality;
detecting the cooling fin again according to the detection device until the abnormal mode is detected to be low thickness abnormality, low quality abnormality and low temperature abnormality, and judging that the cooling fin is qualified;
and if the abnormal mode has low thickness abnormality or low quality abnormality or low temperature abnormality and does not have high thickness abnormality or high quality abnormality or high temperature abnormality, judging that the cooling fin is qualified.
In some embodiments, the replacement module 240 is also used to mark heat sinks with anomaly patterns of low thickness anomalies, low quality anomalies, and low temperature anomalies as excellent quality;
recording the quantity of excellent products of the radiator corresponding to the excellent quality and the detection time;
displaying the number of excellent products and the detection time.
The embodiment of the application also discloses a control device.
Specifically, the control device comprises a memory and a processor, wherein the memory is stored with a computer program which can be loaded by the processor and execute the method for automatically detecting the quality of the stacked graphene radiator based on the Internet of things.
The embodiment of the application also discloses a computer readable storage medium.
Specifically, the computer readable storage medium stores a computer program capable of being loaded by a processor and executing the method for automatically detecting the quality of the stacked graphene radiator based on the internet of things, where the computer readable storage medium includes: a U-disk, a removable hard disk, a Read-only memory (R0M), a random access memory (RandomAccessMemory, RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (10)
1. The automatic quality detection method for the stacked graphene radiator based on the Internet of things is characterized by comprising the following steps of:
detecting the thickness of the radiating fin in the graphene radiator according to the detection device, and obtaining the thickness of the radiating fin;
if the thickness of the radiating fin belongs to a preset first detection range, detecting radiating holes on the radiating fin according to a detection device to obtain radiating hole data;
if the heat dissipation hole data belong to the second detection range, detecting the temperature of the heat dissipation plate according to the detection device to obtain temperature data;
and determining an abnormal region of the radiator according to the thickness of the radiating fin, the radiating hole data and the temperature data, and replacing the abnormal region.
2. The method of claim 1, wherein the detecting the thickness of the heat sink in the graphene heat sink according to the detecting device, to obtain the thickness of the heat sink, includes:
controlling the detection device to emit ultrasonic waves, receiving the ultrasonic waves reflected by the radiating fins, and recording the arrival time and amplitude of the reflected waves;
calculating the thickness of the cooling fin according to the arrival time and the amplitude of the reflected wave;
searching normal thickness data of the cooling fin in a preset cloud database;
dividing the normal thickness data by the fin thickness to calculate a thickness ratio;
determining a thickness abnormality mode according to a preset thickness abnormality standard and the thickness ratio, wherein the thickness abnormality mode at least comprises: low thickness anomaly, medium thickness anomaly, and high thickness anomaly.
3. The method of claim 2, wherein if the thickness of the heat sink falls within a preset first detection range, detecting the heat sink hole on the heat sink according to the detection device, and obtaining heat sink hole data includes:
if the thickness abnormality mode is the low thickness abnormality or the medium thickness abnormality, judging that the thickness of the cooling fin belongs to the first detection range;
according to detection device detects the louvre on the fin, acquires louvre data, and louvre data includes: the number of the radiating holes and the diameter of the radiating holes.
4. The method of claim 3, wherein when the thickness of the fin falls within the preset first detection range, detecting the heat dissipation hole on the fin according to the detection device, and obtaining the heat dissipation hole data, further comprises:
searching the normal number and the normal diameter of radiating holes of the radiating fins in the cloud database;
if the number of the radiating holes is the same as the normal number of the radiating holes, dividing the diameter of the radiating holes by the normal diameter to obtain a diameter ratio;
determining a weight anomaly mode according to a preset diameter anomaly standard and the diameter ratio: low weight anomaly, medium weight anomaly, and high weight anomaly;
and if the number of the radiating holes is different from the normal number of the radiating holes, judging that the weight is abnormal.
5. The method of claim 4, wherein if the heat dissipation hole data belongs to the second detection range, detecting the temperature of the heat dissipation plate according to the detection device, and obtaining temperature data includes:
if the quality abnormality mode is the low quality abnormality or the medium quality abnormality, judging that the heat dissipation hole data belongs to the second detection range;
displaying the temperature of the radiating fin in real time through a temperature indicator lamp;
adjusting the temperature of the radiating fin according to the detection device and a preset adjusting temperature;
when the temperature of the radiating fin is at the regulation temperature, detecting the brightness of the temperature indicator lamp according to the photosensitive sensor to obtain a brightness value;
searching a normal brightness value corresponding to the regulation temperature in the cloud database;
dividing the brightness value by the normal brightness value to calculate a brightness ratio;
determining a temperature anomaly mode according to a preset brightness anomaly standard and the brightness ratio, wherein the temperature anomaly mode comprises the following steps: low temperature anomalies, medium temperature anomalies, and high temperature anomalies.
6. The method of claim 5, wherein determining an abnormal region of the heat sink based on the fin thickness, the heat sink data, and the temperature data, and replacing the abnormal region, comprises:
if the abnormal mode exists the high thickness abnormality or the high quality abnormality or the high temperature abnormality, replacing the abnormal area of the radiating fin corresponding to the high thickness abnormality or the high quality abnormality or the high temperature abnormality;
detecting the cooling fin again according to the detection device until the cooling fin is judged to be qualified when the high-thickness abnormality or the high-quality abnormality or the high-temperature abnormality is detected to be absent;
if the abnormal mode is the middle-thickness abnormality, the middle-quality abnormality and the middle-temperature abnormality, replacing the abnormal region corresponding to the middle-thickness abnormality, the middle-quality abnormality and the middle-temperature abnormality;
detecting the cooling fin again according to the detection device until the abnormal mode is detected to be the low thickness abnormality, the low quality abnormality and the low temperature abnormality, and judging that the cooling fin is qualified;
and if the abnormal mode has the low thickness abnormality or the low quality abnormality or the low temperature abnormality and does not have the high thickness abnormality or the high quality abnormality or the high temperature abnormality, judging that the cooling fin is qualified.
7. The method of claim 6, further comprising, after said determining an abnormal region of the heat sink based on said fin thickness, said heat sink hole data, and said temperature data, and replacing said abnormal region:
marking the heat sink having the abnormality pattern of the low thickness abnormality, the low quality abnormality, and the low temperature abnormality as excellent quality;
recording the quantity of excellent products of the heat radiator corresponding to the excellent quality and the detection time;
displaying the number of excellent products and the detection time.
8. Based on thing networking stacked graphene radiator quality automated inspection device, its characterized in that, the device includes:
the thickness module is used for detecting the thickness of the radiating fin in the graphene radiator according to the detection device to obtain the thickness of the radiating fin;
the quality module is used for detecting the radiating holes on the radiating fins according to the detection device to obtain radiating hole data if the thickness of the radiating fins is in a preset first detection range;
the temperature module is used for detecting the temperature of the radiating fin according to the detection device to obtain temperature data if the radiating hole data belong to a second detection range;
and the replacement module is used for determining an abnormal area of the radiator according to the thickness of the radiating fin, the radiating hole data and the temperature data and replacing the abnormal area.
9. A control apparatus, characterized in that the apparatus comprises:
comprising a memory and a processor, said memory having stored thereon a computer program capable of being loaded by the processor and performing the method according to any 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 which performs the method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311135553.3A CN117146743A (en) | 2023-08-31 | 2023-08-31 | Automatic detection method and system for quality of stacked graphene radiator based on Internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311135553.3A CN117146743A (en) | 2023-08-31 | 2023-08-31 | Automatic detection method and system for quality of stacked graphene radiator based on Internet of things |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117146743A true CN117146743A (en) | 2023-12-01 |
Family
ID=88909609
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311135553.3A Pending CN117146743A (en) | 2023-08-31 | 2023-08-31 | Automatic detection method and system for quality of stacked graphene radiator based on Internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117146743A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117347772A (en) * | 2023-12-04 | 2024-01-05 | 深圳市铭瑞达五金制品有限公司 | Fault monitoring system and method for graphene radiator |
-
2023
- 2023-08-31 CN CN202311135553.3A patent/CN117146743A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117347772A (en) * | 2023-12-04 | 2024-01-05 | 深圳市铭瑞达五金制品有限公司 | Fault monitoring system and method for graphene radiator |
| CN117347772B (en) * | 2023-12-04 | 2024-03-26 | 深圳市铭瑞达五金制品有限公司 | Fault monitoring system and method for graphene radiator |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11119458B2 (en) | Building management system with virtual points and optimized data integration | |
| CN117146743A (en) | Automatic detection method and system for quality of stacked graphene radiator based on Internet of things | |
| CN110494811B (en) | Building management system with declarative view of time series data | |
| US10317261B2 (en) | Systems and methods for controlling flow rate using differential pressure measurements | |
| US20110213735A1 (en) | Selecting An Installation Rack For A Device In A Data Center | |
| US8641271B2 (en) | Method for testing heat pipes | |
| JP2020040119A (en) | Parameter proposal method of soldering process | |
| CN107328810B (en) | Method and device for measuring thermal conductivity of anisotropic material based on hot wire method | |
| US20180316517A1 (en) | Building management system with user interactivity analytics | |
| CN108169679A (en) | Battery specific heat capacity detection method and system | |
| CN108375426A (en) | A kind of temperature checking method and system | |
| CN109710482A (en) | Electronic element performance detection method and device, readable medium and electronic equipment | |
| US7545630B2 (en) | Method and apparatus for thermal dissipation | |
| CN113029234B (en) | Method and system for detecting temperature and humidity environment of computer room for data center | |
| CN109060874A (en) | The installation quality detection method and system of semiconductor thermoelectric refrigeration device | |
| CN106053527A (en) | Method for testing anisotropic heat diffusion coefficient of power battery at same time | |
| US20210224435A1 (en) | Method for designing new purley cpu heat sink | |
| US11480932B2 (en) | Systems and methods for applying semantic information to data in a building management system | |
| Hao et al. | Application of visual analytics for thermal state management in large data centres | |
| CN114579375A (en) | Circuit board quality detection method and device, computer equipment and storage medium | |
| JP5562192B2 (en) | Information processing apparatus, abnormal temperature detection method, and program | |
| CN117078684B (en) | LED lamp visual detection method, system and storage medium | |
| Jiang et al. | Fault direction space method for on-line fault detection | |
| CN114441598B (en) | 3D stacked and packaged integrated circuit chip and failure positioning method and device thereof | |
| CN116437649B (en) | Machine room safety operation and maintenance method and device based on blockchain and readable storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |