CN117687385A - Automatic test method and system applied to flat PCBA control module - Google Patents
Automatic test method and system applied to flat PCBA control module Download PDFInfo
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Abstract
The invention relates to the technical field of test of a mounted circuit board, and discloses an automatic test method and an automatic test system applied to a flat PCBA control module, wherein the automatic test method comprises the following steps of: optimizing an initial image to obtain an optimized image, obtaining a gray gradient and a judging parameter based on the optimized image, obtaining an optimized circuit board set by using the gray gradient and the judging parameter, obtaining a fitting parameter set, fitting a temperature gray conversion relation by using the fitting parameter set, performing mounting operation on the optimized circuit board to obtain a mounted circuit board, performing power supply operation on the mounted circuit board, obtaining a general gray value set and a semiconductor gray value set, calculating a first surface temperature by using the temperature gray conversion relation, calculating an internal temperature of the formula by using the temperature gray conversion relation, a second gray value and an internal temperature, and judging the state of components based on the first surface temperature and the internal temperature, thereby realizing the test on the mounted circuit board. The invention can solve the problems of inaccurate test result and resource waste of the mounting circuit board.
Description
Technical Field
The invention relates to the technical field of testing of a mounted circuit board, in particular to an automatic testing method, an automatic testing system, electronic equipment and a computer readable storage medium applied to a flat PCBA control module.
Background
Along with the development of technology, the application of the mounted circuit board is particularly prominent, and correspondingly, the production and manufacturing process of the mounted circuit board is more and more complex, and the mounted circuit board is influenced by factors such as equipment, environment and human errors, so that a plurality of defects which can influence the performance of the product are generated, and further, the mounted circuit board can be tested particularly important.
At present, the method for testing the mounted circuit board comprises the following steps: and measuring the potential of each point in the mounted circuit board, and testing the mounted circuit board by comparing the measured potential with a theoretical estimated value.
Although the method can realize the test of the mounted circuit board, the fault condition of the circuit board is not considered before the test of the mounted circuit board, and the problems of inaccurate test result and resource waste of the mounted circuit board are caused.
Disclosure of Invention
The invention provides an automatic test method, an automatic test system and a computer readable storage medium applied to a flat PCBA control module, and mainly aims to solve the problems of inaccurate test results and resource waste of a mounted circuit board.
In order to achieve the above object, the present invention provides an automatic test method applied to a flat panel PCBA control module, comprising:
Receiving a test instruction, and confirming an initial circuit board set to be tested based on the test instruction, wherein the initial circuit board set comprises one or more circuit boards;
sequentially extracting circuit boards from the one or more circuit boards, and performing the following operations on the extracted circuit boards:
obtaining an initial image of a circuit board, optimizing the initial image to obtain an optimized image, obtaining a gray gradient based on the optimized image, calculating a judgment parameter by using the optimized image, and obtaining an optimized circuit board set by using the gray gradient and the judgment parameter;
obtaining a fitting parameter set for fitting a temperature gray scale conversion relation, wherein the fitting parameter set comprises: s temperature values and g gray values, wherein s=g, each temperature value corresponds to one gray value, and a fitting parameter set is used for fitting a temperature gray conversion relation;
sequentially extracting the optimized circuit boards from the optimized circuit board set, and executing the following operations on the extracted optimized circuit boards:
performing mounting operation on the optimized circuit board to obtain a mounted circuit board, wherein the mounted circuit board comprises a plurality of components, the components comprise common components and semiconductor components, the mounted circuit board is subjected to power supply operation, and an initial gray image set of the mounted circuit board after power supply is obtained;
Acquiring a maximum gray level image based on the initial gray level image set, acquiring a reference gray level value set by utilizing the maximum gray level image, and acquiring a general gray level value set and a semiconductor gray level value set according to the reference gray level value set;
sequentially extracting first gray values from the general gray value set, and calculating a first surface temperature by using a temperature gray conversion relation and the first gray values;
sequentially extracting second gray values from the semiconductor gray value set, calculating a second surface temperature by using a temperature gray conversion relation and the second gray values, and calculating an internal temperature based on a pre-constructed internal temperature calculation formula and the second surface temperature;
and judging the state of the component based on the first surface temperature and the internal temperature, and realizing the test of the mounted circuit board.
Optionally, the optimizing the initial image to obtain an optimized image includes:
and executing bilateral filtering operation on the initial image to obtain a noise reduction image, wherein constraint conditions of bilateral filtering are preset, and the constraint conditions are as follows:
wherein sigma represents standard deviation of pixel value domain in bilateral filtering, Z and H represent length and width of bilateral filtering window respectively, X represents convolution operation, L is Laplacian, M (i, j) represents pixel value of bilateral filtering window in space adjacent domain (i, j), q is preset proportionality coefficient;
And performing contrast enhancement operation on the noise reduction image to obtain an optimized image.
Optionally, the calculating the judging parameter by using the optimized image is as follows:
P(x,y)＝k 1 αln(α)+k 2 βln(β)+k 3 γln(γ)
wherein P (x, y) represents the judgment parameter, k at the center point (x, y) of the bilateral filter window 1 、k 2 K 3 Are all preset parameters, and k 1 、k 2 K 3 The values of (a) are respectively related to alpha, beta and gamma, and alpha, beta and gamma respectively represent the proportional coefficients of which the slope values are more than, equal to and less than 0 in the bilateral filter window.
Optionally, the obtaining the optimized circuit board set by using the gray gradient and the judgment parameter includes:
inputting gray gradient and judgment parameters to a pre-trained circuit board fault recognition model, and judging the state of the circuit board by using the circuit board fault recognition model, wherein the state of the circuit board comprises: normal circuit board, scratch circuit board, circuit board and short circuit board;
after confirming that the state of the circuit board is a normal circuit board, summarizing the confirmed normal circuit board to obtain an optimized circuit board set.
Optionally, the acquiring a fitting parameter set for fitting the temperature gray scale conversion relation includes:
a preset temperature gradient set is called, wherein the temperature gradient set comprises a plurality of temperature values, the temperature values are sequentially extracted from the temperature gradient set, and the following operations are performed on the extracted temperature values:
Inputting the extracted temperature value to a pre-built temperature generator;
acquiring an infrared image of a temperature generator after inputting a temperature value to obtain a reference infrared image, and acquiring a gray value based on the reference infrared image;
and summarizing the gray value and the corresponding temperature value to obtain a fitting parameter set.
Optionally, the temperature gray scale conversion relation is as follows:
T＝w(d 5 h 5 +d 4 h 4 +d 3 h 3 +d 2 h 2 +d 1 h+d 0 )
wherein T represents the surface temperature of the component after conversion by the gray value, w represents the conversion temperature correction coefficient, and h is the grayValue d 5 、d 4 、d 3 、d 2 、d 1 D 0 All coefficients are found using the fitting parameter set.
Optionally, the internal temperature calculation formula is as follows:
wherein T is 1 Is the internal temperature of the semiconductor component, T 2 Is the surface temperature of the semiconductor component, T 0 Represents the ambient temperature, e is the internal temperature correction coefficient, R 1 R is the thermal resistance from the inside of the semiconductor component to the environment 2 Is the thermal resistance from the inside of the semiconductor component to the surface of the semiconductor component.
Optionally, the acquiring the initial gray scale image set of the powered mounting circuit board includes:
inputting a preset image acquisition frequency and a preset image acquisition period to a preset thermal infrared imager, and acquiring an infrared thermal imaging image of the powered real circuit board by using the thermal infrared imager with the input image acquisition frequency and the image acquisition period;
And carrying out graying operation on the infrared thermal imaging image to obtain an initial gray image, and summarizing the initial gray image to obtain an initial gray image set.
Optionally, the determining the state of the component based on the first surface temperature and the internal temperature includes:
and acquiring a preset surface temperature range and a preset internal temperature range, and confirming that the first surface temperature is in the surface temperature range, and prompting that the mounted circuit board works normally after the internal temperature is in the internal temperature range, or prompting that components in the mounted circuit board are damaged.
In order to solve the above problems, the present invention further provides an automatic test system applied to a flat panel PCBA control module, the system comprising:
the testing instruction receiving module is used for receiving a testing instruction and confirming an initial circuit board set to be tested based on the testing instruction, wherein the initial circuit board set comprises one or more circuit boards;
the circuit board screening module is used for sequentially extracting the circuit boards from the one or more circuit boards and executing the following operations on the extracted circuit boards:
obtaining an initial image of a circuit board, optimizing the initial image to obtain an optimized image, obtaining a gray gradient based on the optimized image, calculating a judgment parameter by using the optimized image, and obtaining an optimized circuit board set by using the gray gradient and the judgment parameter;
The component temperature calculation formula construction module is used for acquiring a fitting parameter set for fitting a temperature gray scale conversion relation, wherein the fitting parameter set comprises: s temperature values and g gray values, wherein s=g, each temperature value corresponds to one gray value, and a fitting parameter set is used for fitting a temperature gray conversion relation;
the device testing module of the mounted circuit board is used for intensively and sequentially extracting the optimized circuit boards from the optimized circuit boards and executing the following operations on the extracted optimized circuit boards:
performing mounting operation on the optimized circuit board to obtain a mounted circuit board, wherein the mounted circuit board comprises a plurality of components, the components comprise common components and semiconductor components, the mounted circuit board is subjected to power supply operation, and an initial gray image set of the mounted circuit board after power supply is obtained;
acquiring a maximum gray level image based on the initial gray level image set, acquiring a reference gray level value set by utilizing the maximum gray level image, and acquiring a general gray level value set and a semiconductor gray level value set according to the reference gray level value set;
sequentially extracting first gray values from the general gray value set, and calculating a first surface temperature by using a temperature gray conversion relation and the first gray values;
Sequentially extracting second gray values from the semiconductor gray value set, calculating a second surface temperature by using a temperature gray conversion relation and the second gray values, and calculating an internal temperature based on a pre-constructed internal temperature calculation formula and the second surface temperature;
and judging the state of the component based on the first surface temperature and the internal temperature, and realizing the test of the mounted circuit board.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
a memory storing at least one instruction; and
And the processor executes the instructions stored in the memory to realize the automatic test method applied to the tablet PCBA control module.
In order to solve the above-mentioned problems, the present invention further provides a computer readable storage medium, where at least one instruction is stored, where the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned automatic test method applied to a tablet PCBA control module.
In order to solve the problems described in the background art, the embodiment of the invention acquires the initial image of the circuit board, optimizes the initial image to obtain an optimized image, acquires gray gradient based on the optimized image, calculates the judgment parameter by using the optimized image, and acquires the optimized circuit board set by using the gray gradient and the judgment parameter. Further, the reliability of the judgment parameters and the gray gradient calculated by optimizing the image is improved, the unqualified circuit boards in the initial circuit board set are removed by the judgment parameters and the gray gradient, the optimized circuit board set is obtained, the resources for testing the mounted circuit board are saved, and the reliability of the subsequent analysis result of whether the components in the mounted circuit board are faulty is improved. The method comprises the steps of fitting a temperature gray level conversion relation by using a fitting parameter set, and executing installation operation on an optimized circuit board to obtain a mounted circuit board, wherein the mounted circuit board comprises a plurality of components, the components comprise common components and semiconductor components, and an initial gray level image set of the mounted circuit board after power supply is obtained. The first surface temperature is calculated by using the temperature gray level conversion relation and the first gray level value, the second surface temperature is calculated by using the temperature gray level conversion relation and the second gray level value, and the internal temperature is calculated based on the pre-built internal temperature calculation formula and the second surface temperature. Therefore, the automatic test method, the automatic test system, the electronic equipment and the computer readable storage medium applied to the flat PCBA control module can solve the problems of inaccurate test results and resource waste of the mounted circuit board.
Drawings
FIG. 1 is a flow chart of an automatic test method applied to a flat PCBA control module according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of an automatic test system for a flat panel PCBA control module according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device for implementing the automatic testing method applied to a flat PCBA control module according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides an automatic test method applied to a flat PCBA control module. The execution main body of the automatic test method applied to the tablet PCBA control module comprises at least one of a server, a terminal and the like which can be configured to execute the method provided by the embodiment of the application. In other words, the automatic test method applied to the tablet PCBA control module may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Referring to fig. 1, a flow chart of an automatic test method applied to a flat panel PCBA control module according to an embodiment of the present invention is shown. In this embodiment, the automatic test method applied to the flat panel PCBA control module includes:
s1, receiving a test instruction, and confirming an initial circuit board set to be tested based on the test instruction, wherein the initial circuit board set comprises one or more circuit boards.
It should be noted that PCBA refers to a mounted circuit board, i.e., a circuit board mounted with components. The test instruction is an instruction sent by a circuit board tester to test one or more circuit boards to be tested and used for detecting whether the circuit boards can work normally. An initial set of circuit boards refers to a set of circuit boards to be tested that have not yet been tested. The embodiment of the invention realizes the test of the circuit board through multiple screening and dividing of the initial circuit board set.
For example, the king acts as a circuit board tester in a factory, and a batch of mounted circuit boards is now tested, which is tested by issuing test instructions from the king in order to ensure that the mounted circuit boards are functioning properly.
S2, obtaining an initial image of the circuit board, optimizing the initial image to obtain an optimized image, obtaining a gray gradient based on the optimized image, calculating a judgment parameter by using the optimized image, and obtaining an optimized circuit board set by using the gray gradient and the judgment parameter.
It should be understood that the initial image is an image of the circuit board including the soldering region, and the defect detection on the physical layer of the circuit board can be realized by analyzing the initial image, so that the time for testing the mounted circuit board is saved.
Further, the optimizing the initial image to obtain an optimized image includes:
and executing bilateral filtering operation on the initial image to obtain a noise reduction image, wherein constraint conditions of bilateral filtering are preset, and the constraint conditions are as follows:
wherein sigma represents standard deviation of pixel value domain in bilateral filtering, Z and H represent length and width of bilateral filtering window respectively, X represents convolution operation, L is Laplacian, M (i, j) represents pixel value of bilateral filtering window in space adjacent domain (i, j), q is preset proportionality coefficient;
and performing contrast enhancement operation on the noise reduction image to obtain an optimized image.
It is understood that bilateral filtering is a nonlinear filtering method, combines spatial proximity of images and pixel value similarity to perform compromise processing, and simultaneously considers airspace information and gray level similarity to achieve the purpose of edge protection and denoising. The constraint condition is the constraint required when solving the pixel threshold weight in bilateral filtering, and by drawing the constraint condition in the pixel domain, the instruction of bilateral filtering can be realized, and better noise reduction effect and the highlighting of the boundary of possible circuit board faults can be realized in the image through the constraint condition. The pixel domain value weight of bilateral filtering is calculated by using constraint conditions in the prior art, and is not described in detail herein.
It should be explained that the contrast enhancement operation highlights the difference between the welded part and the non-welded part in the circuit board, which is favorable for analyzing the optimized image, and realizes the judgment of whether the circuit board has faults, namely whether the circuit board has abnormal conditions such as short circuit, circuit break, lack welding and the like. The value of the proportionality coefficient is related to the size of the bilateral filtering window and the color of the circuit board, and is 1. The purpose of the preset scaling factor is to make the difference between the pixel value corresponding to the fault position of the circuit board in the initial image and the pixel value corresponding to other positions more obvious. In addition, the contrast enhancement operation increases the difference between possible fault areas by transforming the gray values, and optionally, optimizes the noise reduction image by using a linear enhancement method of contrast, resulting in an optimized image. The contrast linear enhancement method is the prior art, and other technologies can achieve the same action and effects, and are not described herein.
Further, the calculation of the judgment parameters by using the optimized image is as follows:
P(x,y)＝k 1 αln(α)+k 2 βln(β)+k 3 γln(γ)
wherein P (x, y) represents the judgment parameter, k at the center point (x, y) of the bilateral filter window 1 、k 2 K 3 Are all preset parameters, and k 1 、k 2 K 3 The values of (a) are respectively related to alpha, beta and gamma, and alpha, beta and gamma respectively represent the proportional coefficients of which the slope values are more than, equal to and less than 0 in the bilateral filter windows;
it can be appreciated that the preset parameter k 1 、k 2 K 3 The purpose of (a) is to enhance the degree of discrepancy between the fault location and the normal location.
It should be explained that the calculation formula of the slope value in the bilateral filtering window is as follows:
where t (i, j) is a slope value of (i, j) in the bilateral filter window, M (i, j+1) represents a pixel value of the bilateral filter window in the spatial neighborhood (i, j+1), and M (i+1, j) represents a pixel value of the bilateral filter window in the spatial neighborhood (i+1, j).
It can be understood that the scaling factor is the ratio of the number of pixels meeting the condition in the bilateral filtering window to the total number of pixels in the bilateral filtering window. When the proportionality coefficient is 0, the term corresponding to the proportionality coefficient in the judgment parameter calculation formula is 0. Alternatively, a Sobel gradient is selected as the gray gradient, and other techniques are adopted to achieve the same effect, which is not described herein.
Further, the obtaining the optimized circuit board set by using the gray gradient and the judgment parameter includes:
inputting gray gradient and judgment parameters to a pre-trained circuit board fault recognition model, and judging the state of the circuit board by using the circuit board fault recognition model, wherein the state of the circuit board comprises: normal circuit board, scratch circuit board, circuit board and short circuit board;
After confirming that the state of the circuit board is a normal circuit board, summarizing the confirmed normal circuit board to obtain an optimized circuit board set.
It should be understood that the circuit board fault recognition model is a model for recognizing whether scratches, short circuits and open circuits exist on the circuit board, and optionally, the circuit board fault recognition model is a support vector machine. The scratch circuit board is a circuit board with scratches identified by using a circuit board fault identification model, the short circuit board is a circuit board with short circuit of the circuit board, the broken circuit board is a circuit board with broken circuit connection, and the normal circuit board is a circuit board without faults identified by using the circuit board fault identification model. In addition, when scratches, short circuits and circuit breaks occur on the circuit board, normal use of the circuit board cannot be guaranteed, so that the initial circuit board set can be divided for the first time through the circuit board fault identification model, and the optimized circuit board set is obtained.
S3, acquiring a fitting parameter set for fitting a temperature gray scale conversion relation, wherein the fitting parameter set comprises: s temperature values and g gray values, wherein s=g, each temperature value corresponds to one gray value, and a fitting parameter set is used for fitting a temperature gray conversion relation.
It can be understood that the temperature gray scale conversion relation is a formula for calculating the temperature value of the component by using the gray scale value of the component in the mounted circuit board.
Further, the obtaining a fitting parameter set for fitting the temperature gray scale conversion relation includes:
a preset temperature gradient set is called, wherein the temperature gradient set comprises a plurality of temperature values, the temperature values are sequentially extracted from the temperature gradient set, and the following operations are performed on the extracted temperature values:
inputting the extracted temperature value to a pre-built temperature generator;
acquiring an infrared image of a temperature generator after inputting a temperature value to obtain a reference infrared image, and acquiring a gray value based on the reference infrared image;
and summarizing the gray value and the corresponding temperature value to obtain a fitting parameter set.
It should be understood that the more temperature gradients in the temperature gradient set, the more accurate the fitted temperature gray scale conversion relation, and optionally, the preset temperature ranges from 0 ℃ to 140 ℃ and the values are equally taken, so as to obtain eight temperature gradients, which are respectively 0, 20, 40, 60, 80, 100, 120 and 140 ℃. The temperature generator generates an instrument of a specified temperature, and optionally, a blackbody furnace is selected as the temperature generator. The technology for acquiring the gray value based on the reference infrared image is the prior art, and is not described herein.
It should be explained that the temperature gray scale conversion relation is as follows:
T＝w(d 5 h 5 +d 4 h 4 +d 3 h 3 +d 2 h 2 +d 1 h+d 0 )
Wherein T represents the surface temperature of the component after the conversion of the gray value, w represents the conversion temperature correction coefficient, h represents the gray value, and d 5 、d 4 、d 3 、d 2 、d 1 D 0 All coefficients are found using the fitting parameter set.
It can be understood that the surface temperature of the component is the temperature of the surface of the component, and the temperature correction coefficient is a correction coefficient affected by the test environment when the gray value of the component is converted into the surface temperature of the component, for example, the temperature of the test environment in the test environment, the distance between the component and the component when the component is collected, and other influencing factors. Optionally, after the temperature correction coefficient is obtained through an experimental mode, an infrared thermal imaging image of the component is acquired under the environment and the condition of obtaining the temperature correction coefficient. Optionally, solving d by Gaussian elimination and fitting parameter set 5 、d 4 、d 3 、d 2 、d 1 D 0 。
S4, performing installation operation on the optimized circuit board to obtain a mounted circuit board, wherein the mounted circuit board comprises a plurality of components, the components comprise common components and semiconductor components, the power supply operation is performed on the mounted circuit board, and an initial gray level image set of the mounted circuit board after power supply is obtained.
It should be explained that the mounting operation is performed on the optimized circuit board, that is, the components are soldered to the optimized circuit board according to the standard mounting manner, so as to obtain the mounted circuit board. At this time, if the mounted circuit board fails, the failure occurs because of the damage of the components.
Further, semiconductor devices are electronic devices that are electrically conductive between good conductors and insulators, utilizing the specific electrical characteristics of semiconductor materials to perform specific functions, and can be used to generate, control, receive, transform, amplify signals, and perform energy conversion. The general components are components other than semiconductor components in the mounted circuit board, for example: resistor, capacitor, etc.
It can be appreciated that the acquiring the initial gray scale image set of the powered mounted circuit board includes:
inputting a preset image acquisition frequency and a preset image acquisition period to a preset thermal infrared imager, and acquiring an infrared thermal imaging image of the powered real circuit board by using the thermal infrared imager with the input image acquisition frequency and the image acquisition period;
and carrying out graying operation on the infrared thermal imaging image to obtain an initial gray image, and summarizing the initial gray image to obtain an initial gray image set.
It can be understood that the image acquisition frequency is the frequency of acquiring the infrared thermal imaging image of the mounting circuit board after power supply by using the infrared thermal imaging instrument, the image acquisition period is the time period of acquiring the infrared thermal imaging image, and whether the components in the mounting circuit board work normally can be judged by acquiring the infrared thermal imaging image in the image acquisition period. Thermal infrared imagers are instruments that image the entire target in a non-contact manner in the form of a "face" and that generate images by capturing infrared radiation from objects in the scene. Optionally, the technology for performing the graying operation on the infrared thermal imaging image is a python algorithm, and other technologies are selected to achieve the same effect, which is not described herein.
It should be explained that the failure of the mounted circuit board is a variable process, for example: when the mounting circuit board is short-circuited, the temperature of one component in the mounting circuit board is continuously increased, so that an initial image set needs to be acquired, and when the mounting circuit board is broken, the maximum temperature value in the mounting circuit board is not changed.
By way of example, the image acquisition frequency is set to 1 second for acquisition once, and five initial gray-scale images are acquired in total within 5 seconds, and the initial image set is composed of the five initial gray-scale images.
S5, acquiring a maximum gray level image based on the initial gray level image set, acquiring a reference gray level value set by using the maximum gray level image, and acquiring a general gray level value set and a semiconductor gray level value set according to the reference gray level value set.
Further, the maximum gray level image is an initial gray level image corresponding to the maximum gray level value in the maximum value of the gray level values corresponding to each initial gray level image in the initial gray level image set.
The initial gray image set includes 5 initial gray images, wherein the maximum value of gray values of each initial gray image in the 5 initial gray images is respectively: 11. 12, 13, 14 and 15, the maximum gray image is the initial gray image with a gray value of 15.
It should be explained that the acquiring the reference gray value set by using the maximum gray image includes:
acquiring a maximum gray value point in the maximum gray image, acquiring a maximum gray image area by using a region growing algorithm and the maximum gray image with the maximum gray value point as a starting point, removing the maximum gray image area in the maximum gray image to obtain a second gray image, and returning to the step of acquiring the maximum gray value point in the maximum gray image by taking the second gray image as the maximum gray image until the maximum gray value point is smaller than a preset gray threshold;
and summarizing the maximum gray value points to obtain a reference gray value set.
It should be understood that the gray value point is a pixel point corresponding to the gray value in the image, and optionally, the technology for obtaining the maximum gray value point is a python algorithm, and other technologies are selected to achieve the same technical effects, which is not described herein. The region growing algorithm is a prior art and will not be described in detail herein. Optionally, the method of eliminating the maximum gray image area in the maximum gray image is a pre-trained neural network model, and other technologies can achieve the same effect, which is not described herein. The gray threshold is a threshold for judging abnormal factors such as component overheat possibly existing in the maximum gray image through the gray value. The maximum gray value point in the maximum gray image is provided with and corresponds to only one component. Therefore, the reference gray value set can be divided by the position where the maximum gray value point is located.
It should be appreciated that the acquiring the general gray value set and the semiconductor gray value set according to the reference gray value set includes:
and sequentially extracting gray values from the reference gray value sets, and dividing the reference gray value sets by using positions corresponding to the gray values to obtain a general gray value set and a semiconductor gray value set.
Further, the general gray value set is a set of gray values corresponding to the general component, and the semiconductor gray value set is a set of gray values corresponding to the semiconductor component. The maximum gray value point of each position in the mounting circuit board comprises corresponding components, so that the confirmation of the type of the components can be realized through the maximum gray value point, and optionally, the technology for judging the components corresponding to the maximum gray value is a pre-trained deep learning network, and the technology for realizing the position judgment in the image through the deep learning network is the prior art and is not repeated here.
S6, sequentially extracting first gray values from the general gray value set, calculating a first surface temperature by using a temperature gray conversion relation and the first gray values, sequentially extracting second gray values from the semiconductor gray value set, calculating a second surface temperature by using the temperature gray conversion relation and the second gray values, and calculating an internal temperature based on a pre-built internal temperature calculation formula and the second surface temperature.
It should be understood that the first gray value is a gray value corresponding to a general component, and the second gray value is a gray value corresponding to a semiconductor component.
It should be explained that the internal temperature calculation formula is as follows:
wherein T is 1 Is the internal temperature of the semiconductor component, T 2 Is the surface temperature of the semiconductor component, T 0 Represents the ambient temperature, e is the internal temperature correction coefficient, R 1 R is the thermal resistance from the inside of the semiconductor component to the environment 2 Is the thermal resistance from the inside of the semiconductor component to the surface of the semiconductor component.
It should be explained that the surface temperature is the temperature of the surface of the component, the maximum gray value is calculated by the temperature gray conversion relation, the internal temperature is the maximum temperature inside the semiconductor component, generally the temperature at the pn junction of the semiconductor component, and optionally, the thermal resistance from the inside of the semiconductor component to the environment and the thermal resistance from the inside of the semiconductor component to the surface of the semiconductor component can be obtained by a product manual or a technical manual, and the two thermal resistance values are related to the heat dissipation mode and the packaging mode of the semiconductor component, and the same action and effect can be achieved by adopting other methods, which are not described herein.
S7, judging the state of the component based on the first surface temperature and the internal temperature, and testing the mounted circuit board.
It should be appreciated that the determining the state of the component based on the first surface temperature and the internal temperature includes:
and acquiring a preset surface temperature range and a preset internal temperature range, and confirming that the first surface temperature is in the surface temperature range, and prompting that the mounted circuit board works normally after the internal temperature is in the internal temperature range, or prompting that components in the mounted circuit board are damaged.
It should be understood that the surface temperature range and the internal temperature range are both temperature sections for judging whether the components in the mounted circuit board can normally work, and the damage of the components in the mounted circuit board includes: the component breaking damage is damage to the mounted circuit board caused by the component breaking, namely the mounted circuit board cannot work normally, and the component overheating damage is damage to the mounted circuit board caused by overheating of one or a plurality of components. For example: the components are overheated and damaged due to factors such as short circuit of the components, abnormal heat dissipation of the components and the like.
Illustratively, the internal temperature ranges are: and the internal temperature of the semiconductor component is 130 ℃ from 40 ℃ to 120 ℃, so that the damage of the component in the packaged circuit board is prompted. If the internal temperature is smaller than the internal temperature range and the first surface temperature is smaller than the surface temperature range, at least one component in the packaged circuit board is disconnected.
In order to solve the problems described in the background art, the embodiment of the invention acquires the initial image of the circuit board, optimizes the initial image to obtain an optimized image, acquires gray gradient based on the optimized image, calculates the judgment parameter by using the optimized image, and acquires the optimized circuit board set by using the gray gradient and the judgment parameter. Further, the reliability of the judgment parameters and the gray gradient calculated by optimizing the image is improved, the unqualified circuit boards in the initial circuit board set are removed by the judgment parameters and the gray gradient, the optimized circuit board set is obtained, the resources for testing the mounted circuit board are saved, and the reliability of the subsequent analysis result of whether the components in the mounted circuit board are faulty is improved. The method comprises the steps of fitting a temperature gray level conversion relation by using a fitting parameter set, and executing installation operation on an optimized circuit board to obtain a mounted circuit board, wherein the mounted circuit board comprises a plurality of components, the components comprise common components and semiconductor components, and an initial gray level image set of the mounted circuit board after power supply is obtained. The first surface temperature is calculated by using the temperature gray level conversion relation and the first gray level value, the second surface temperature is calculated by using the temperature gray level conversion relation and the second gray level value, and the internal temperature is calculated based on the pre-built internal temperature calculation formula and the second surface temperature. Therefore, the automatic test method, the automatic test system, the electronic equipment and the computer readable storage medium applied to the flat PCBA control module can solve the problems of inaccurate test results and resource waste of the mounted circuit board.
FIG. 2 is a functional block diagram of an automatic test system for a flat panel PCBA control module according to one embodiment of the present invention.
The automatic test system 100 of the present invention applied to a flat panel PCBA control module may be installed in an electronic device. Depending on the functions implemented, the automatic test system 100 applied to the flat PCBA control module may include a test instruction receiving module 101, a circuit board screening module 102, a component temperature calculation formula construction module 103, and a packaged circuit board component test module 104. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
The test instruction receiving module 101 is configured to receive a test instruction, and confirm an initial circuit board set to be tested based on the test instruction, where the initial circuit board set includes one or more circuit boards;
the circuit board screening module 102 is configured to sequentially extract circuit boards from the one or more circuit boards, and perform the following operations on the extracted circuit boards:
obtaining an initial image of a circuit board, optimizing the initial image to obtain an optimized image, obtaining a gray gradient based on the optimized image, calculating a judgment parameter by using the optimized image, and obtaining an optimized circuit board set by using the gray gradient and the judgment parameter;
The component temperature calculation formula construction module 103 is configured to obtain a fitting parameter set for fitting a temperature gray scale conversion relation, where the fitting parameter set includes: s temperature values and g gray values, wherein s=g, each temperature value corresponds to one gray value, and a fitting parameter set is used for fitting a temperature gray conversion relation;
the component testing module 104 for a mounted circuit board is configured to extract the optimized circuit boards from the optimized circuit board set in sequence, and perform the following operations on the extracted optimized circuit boards:
performing mounting operation on the optimized circuit board to obtain a mounted circuit board, wherein the mounted circuit board comprises a plurality of components, the components comprise common components and semiconductor components, the mounted circuit board is subjected to power supply operation, and an initial gray image set of the mounted circuit board after power supply is obtained;
acquiring a maximum gray level image based on the initial gray level image set, acquiring a reference gray level value set by utilizing the maximum gray level image, and acquiring a general gray level value set and a semiconductor gray level value set according to the reference gray level value set;
sequentially extracting first gray values from the general gray value set, and calculating a first surface temperature by using a temperature gray conversion relation and the first gray values;
Sequentially extracting second gray values from the semiconductor gray value set, calculating a second surface temperature by using a temperature gray conversion relation and the second gray values, and calculating an internal temperature based on a pre-constructed internal temperature calculation formula and the second surface temperature;
and judging the state of the component based on the first surface temperature and the internal temperature, and realizing the test of the mounted circuit board.
Fig. 3 is a schematic structural diagram of an electronic device for implementing an automatic test method applied to a flat PCBA control module according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as an automatic test program applied to a tablet PCBA control module.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as code of an automatic test program applied to a tablet PCBA control module, etc., but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects the respective components of the entire electronic device using various interfaces and lines, executes programs or modules stored in the memory 11 (e.g., an automatic test program applied to a tablet PCBA Control module, etc.), and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process data.
The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 3 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 10 through a power management system, so as to perform functions of charge management, discharge management, and power consumption management through the power management system. The power supply may also include one or more of any of a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
Further, the electronic device 1 may also comprise a network interface, optionally the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the electronic device 1 and other electronic devices.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The automatic test program stored in the memory 11 of the electronic device 1 and applied to the tablet PCBA control module is a combination of instructions that, when executed in the processor 10, can implement:
receiving a test instruction, and confirming an initial circuit board set to be tested based on the test instruction, wherein the initial circuit board set comprises one or more circuit boards;
sequentially extracting circuit boards from the one or more circuit boards, and performing the following operations on the extracted circuit boards:
Obtaining an initial image of a circuit board, optimizing the initial image to obtain an optimized image, obtaining a gray gradient based on the optimized image, calculating a judgment parameter by using the optimized image, and obtaining an optimized circuit board set by using the gray gradient and the judgment parameter;
obtaining a fitting parameter set for fitting a temperature gray scale conversion relation, wherein the fitting parameter set comprises: s temperature values and g gray values, wherein s=g, each temperature value corresponds to one gray value, and a fitting parameter set is used for fitting a temperature gray conversion relation;
sequentially extracting the optimized circuit boards from the optimized circuit board set, and executing the following operations on the extracted optimized circuit boards:
performing mounting operation on the optimized circuit board to obtain a mounted circuit board, wherein the mounted circuit board comprises a plurality of components, the components comprise common components and semiconductor components, the mounted circuit board is subjected to power supply operation, and an initial gray image set of the mounted circuit board after power supply is obtained;
acquiring a maximum gray level image based on the initial gray level image set, acquiring a reference gray level value set by utilizing the maximum gray level image, and acquiring a general gray level value set and a semiconductor gray level value set according to the reference gray level value set;
sequentially extracting first gray values from the general gray value set, and calculating a first surface temperature by using a temperature gray conversion relation and the first gray values;
Sequentially extracting second gray values from the semiconductor gray value set, calculating a second surface temperature by using a temperature gray conversion relation and the second gray values, and calculating an internal temperature based on a pre-constructed internal temperature calculation formula and the second surface temperature;
and judging the state of the component based on the first surface temperature and the internal temperature, and realizing the test of the mounted circuit board.
Specifically, the specific implementation method of the above instructions by the processor 10 may refer to descriptions of related steps in the corresponding embodiments of fig. 1 to 3, which are not repeated herein.
Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-only memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
Receiving a test instruction, and confirming an initial circuit board set to be tested based on the test instruction, wherein the initial circuit board set comprises one or more circuit boards;
sequentially extracting circuit boards from the one or more circuit boards, and performing the following operations on the extracted circuit boards:
obtaining an initial image of a circuit board, optimizing the initial image to obtain an optimized image, obtaining a gray gradient based on the optimized image, calculating a judgment parameter by using the optimized image, and obtaining an optimized circuit board set by using the gray gradient and the judgment parameter;
obtaining a fitting parameter set for fitting a temperature gray scale conversion relation, wherein the fitting parameter set comprises: s temperature values and g gray values, wherein s=g, each temperature value corresponds to one gray value, and a fitting parameter set is used for fitting a temperature gray conversion relation;
sequentially extracting the optimized circuit boards from the optimized circuit board set, and executing the following operations on the extracted optimized circuit boards:
performing mounting operation on the optimized circuit board to obtain a mounted circuit board, wherein the mounted circuit board comprises a plurality of components, the components comprise common components and semiconductor components, the mounted circuit board is subjected to power supply operation, and an initial gray image set of the mounted circuit board after power supply is obtained;
Acquiring a maximum gray level image based on the initial gray level image set, acquiring a reference gray level value set by utilizing the maximum gray level image, and acquiring a general gray level value set and a semiconductor gray level value set according to the reference gray level value set;
sequentially extracting first gray values from the general gray value set, and calculating a first surface temperature by using a temperature gray conversion relation and the first gray values;
sequentially extracting second gray values from the semiconductor gray value set, calculating a second surface temperature by using a temperature gray conversion relation and the second gray values, and calculating an internal temperature based on a pre-constructed internal temperature calculation formula and the second surface temperature;
and judging the state of the component based on the first surface temperature and the internal temperature, and realizing the test of the mounted circuit board.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, system and method may be implemented in other manners. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention 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 invention 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. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Multiple units or systems as set forth in the system claims may also be implemented by means of one unit or system in software or hardware. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. An automatic test method applied to a flat panel PCBA control module, the method comprising:
receiving a test instruction, and confirming an initial circuit board set to be tested based on the test instruction, wherein the initial circuit board set comprises one or more circuit boards;
sequentially extracting circuit boards from the one or more circuit boards, and performing the following operations on the extracted circuit boards:
obtaining an initial image of a circuit board, optimizing the initial image to obtain an optimized image, obtaining a gray gradient based on the optimized image, calculating a judgment parameter by using the optimized image, and obtaining an optimized circuit board set by using the gray gradient and the judgment parameter;
Obtaining a fitting parameter set for fitting a temperature gray scale conversion relation, wherein the fitting parameter set comprises: s temperature values and g gray values, wherein s=g, each temperature value corresponds to one gray value, and a fitting parameter set is used for fitting a temperature gray conversion relation;
sequentially extracting the optimized circuit boards from the optimized circuit board set, and executing the following operations on the extracted optimized circuit boards:
performing mounting operation on the optimized circuit board to obtain a mounted circuit board, wherein the mounted circuit board comprises a plurality of components, the components comprise common components and semiconductor components, the mounted circuit board is subjected to power supply operation, and an initial gray image set of the mounted circuit board after power supply is obtained;
acquiring a maximum gray level image based on the initial gray level image set, acquiring a reference gray level value set by utilizing the maximum gray level image, and acquiring a general gray level value set and a semiconductor gray level value set according to the reference gray level value set;
sequentially extracting first gray values from the general gray value set, and calculating a first surface temperature by using a temperature gray conversion relation and the first gray values;
sequentially extracting second gray values from the semiconductor gray value set, calculating a second surface temperature by using a temperature gray conversion relation and the second gray values, and calculating an internal temperature based on a pre-constructed internal temperature calculation formula and the second surface temperature;
And judging the state of the component based on the first surface temperature and the internal temperature, and realizing the test of the mounted circuit board.
2. The automatic test method applied to a flat panel PCBA control module according to claim 1, wherein said optimizing said initial image to obtain an optimized image comprises:
and executing bilateral filtering operation on the initial image to obtain a noise reduction image, wherein constraint conditions of bilateral filtering are preset, and the constraint conditions are as follows:
wherein sigma represents standard deviation of pixel value domain in bilateral filtering, Z and H represent length and width of bilateral filtering window respectively, X represents convolution operation, L is Laplacian, M (i, j) represents pixel value of bilateral filtering window in space adjacent domain (i, j), q is preset proportionality coefficient;
and performing contrast enhancement operation on the noise reduction image to obtain an optimized image.
3. The automatic test method for a flat panel PCBA control module according to claim 1, wherein the calculation of the judgment parameters using the optimized image is as follows:
P(x,y)＝k 1 αln(α)+k 2 βln(β)+k 3 γln(γ)
wherein P (x, y) represents the judgment parameter, k at the center point (x, y) of the bilateral filter window 1 、k 2 K 3 Are all preset parameters, and k 1 、k 2 K 3 The values of (a) are respectively related to alpha, beta and gamma, and alpha, beta and gamma respectively represent the proportional coefficients of which the slope values are more than, equal to and less than 0 in the bilateral filter window 。
4. The automatic test method for a flat panel PCBA control module according to claim 1, wherein the obtaining an optimized circuit board set using gray scale gradients and judgment parameters comprises:
inputting gray gradient and judgment parameters to a pre-trained circuit board fault recognition model, and judging the state of the circuit board by using the circuit board fault recognition model, wherein the state of the circuit board comprises: normal circuit board, scratch circuit board, circuit board and short circuit board;
after confirming that the state of the circuit board is a normal circuit board, summarizing the confirmed normal circuit board to obtain an optimized circuit board set.
5. The automatic test method for a flat panel PCBA control module according to claim 1, wherein said obtaining a fitting parameter set for fitting a temperature gray scale conversion relation comprises:
a preset temperature gradient set is called, wherein the temperature gradient set comprises a plurality of temperature values, the temperature values are sequentially extracted from the temperature gradient set, and the following operations are performed on the extracted temperature values:
inputting the extracted temperature value to a pre-built temperature generator;
acquiring an infrared image of a temperature generator after inputting a temperature value to obtain a reference infrared image, and acquiring a gray value based on the reference infrared image;
And summarizing the gray value and the corresponding temperature value to obtain a fitting parameter set.
6. The automatic test method applied to a flat panel PCBA control module according to claim 1, wherein the temperature gray scale conversion relation is as follows:
T＝w(d 5 h 5 +d 4 h 4 +d 3 h 3 +d 2 h 2 +d 1 h+d 0 )
wherein T represents the surface temperature of the component after conversion by gray values, and w representsIndicating the conversion temperature correction coefficient, h is the gray value, d 5 、d 4 、d 3 、d 2 、d 1 D 0 All coefficients are found using the fitting parameter set.
7. The automatic test method applied to a flat panel PCBA control module according to claim 1, wherein the internal temperature calculation formula is as follows:
wherein T is 1 Is the internal temperature of the semiconductor component, T 2 Is the surface temperature of the semiconductor component, T 0 Represents the ambient temperature, e is the internal temperature correction coefficient, R 1 R is the thermal resistance from the inside of the semiconductor component to the environment 2 Is the thermal resistance from the inside of the semiconductor component to the surface of the semiconductor component.
8. The automatic test method for a flat panel PCBA control module according to claim 1, wherein said acquiring an initial gray scale image set of a powered mounted circuit board comprises:
inputting a preset image acquisition frequency and a preset image acquisition period to a preset thermal infrared imager, and acquiring an infrared thermal imaging image of the powered real circuit board by using the thermal infrared imager with the input image acquisition frequency and the image acquisition period;
And carrying out graying operation on the infrared thermal imaging image to obtain an initial gray image, and summarizing the initial gray image to obtain an initial gray image set.
9. The automatic test method for a flat panel PCBA control module according to claim 1, wherein the determining the status of the component based on the first surface temperature and the internal temperature comprises:
and acquiring a preset surface temperature range and a preset internal temperature range, and confirming that the first surface temperature is in the surface temperature range, and prompting that the mounted circuit board works normally after the internal temperature is in the internal temperature range, or prompting that components in the mounted circuit board are damaged.
10. An automatic test system for a flat panel PCBA control module, the system comprising:
the testing instruction receiving module is used for receiving a testing instruction and confirming an initial circuit board set to be tested based on the testing instruction, wherein the initial circuit board set comprises one or more circuit boards;
the circuit board screening module is used for sequentially extracting the circuit boards from the one or more circuit boards and executing the following operations on the extracted circuit boards:
obtaining an initial image of a circuit board, optimizing the initial image to obtain an optimized image, obtaining a gray gradient based on the optimized image, calculating a judgment parameter by using the optimized image, and obtaining an optimized circuit board set by using the gray gradient and the judgment parameter;
The component temperature calculation formula construction module is used for acquiring a fitting parameter set for fitting a temperature gray scale conversion relation, wherein the fitting parameter set comprises: s temperature values and g gray values, wherein s=g, each temperature value corresponds to one gray value, and a fitting parameter set is used for fitting a temperature gray conversion relation;
the device testing module of the mounted circuit board is used for intensively and sequentially extracting the optimized circuit boards from the optimized circuit boards and executing the following operations on the extracted optimized circuit boards:
performing mounting operation on the optimized circuit board to obtain a mounted circuit board, wherein the mounted circuit board comprises a plurality of components, the components comprise common components and semiconductor components, the mounted circuit board is subjected to power supply operation, and an initial gray image set of the mounted circuit board after power supply is obtained;
acquiring a maximum gray level image based on the initial gray level image set, acquiring a reference gray level value set by utilizing the maximum gray level image, and acquiring a general gray level value set and a semiconductor gray level value set according to the reference gray level value set;
sequentially extracting first gray values from the general gray value set, and calculating a first surface temperature by using a temperature gray conversion relation and the first gray values;
Sequentially extracting second gray values from the semiconductor gray value set, calculating a second surface temperature by using a temperature gray conversion relation and the second gray values, and calculating an internal temperature based on a pre-constructed internal temperature calculation formula and the second surface temperature;
and judging the state of the component based on the first surface temperature and the internal temperature, and realizing the test of the mounted circuit board.
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