Intelligent detection system for electronic components

Abstract

The invention discloses an intelligent detection system for an electronic component, relates to the technical field of intelligent detection, and solves the technical problems that single appearance detection cannot realize defect analysis on the whole electronic component and is inconvenient to carry out risk analysis on the whole state of the electronic component. According to the method, analysis is carried out from the two aspects of the appearance and the working parameters of the electronic component, existing defects are positioned and specific defects are analyzed through machine vision aiming at the situation that the appearance is abnormal, subsequent direct observation is facilitated, and the working parameters are compared with standard parameters aiming at analysis of the working parameters, so that the detection accuracy is improved. Meanwhile, normal and abnormal conditions are independently analyzed, comprehensive analysis is carried out by combining historical data and abnormal characteristics of the electronic component, risk prediction analysis is carried out on the electronic component, detection is carried out from appearance and working parameters, detection on the electronic component is more comprehensive, and therefore the detection accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent detection technology, and in particular to an intelligent detection system for electronic components. Background Art

[0002] With the development of computer technology, computer motherboards play a vital role in electronic devices. Computer motherboards are composed of many electronic components, including chips, slots, capacitors, etc. These components are arranged and connected in a complex way on the motherboard, which brings challenges to quality inspection during the production process.

[0003] According to Chinese patent application number CN202311384617.3, a method and device for intelligent detection of computer motherboard components based on machine vision are disclosed. In the method and device for intelligent detection of computer motherboard components based on machine vision provided in the embodiment of the present application, the real-time image of the motherboard is collected, and template matching and defect recognition of the motherboard are performed on the real-time image based on the machine vision solution to determine whether the motherboard has surface defects. The motherboard performance test is performed on the complete motherboard with surface defects to determine whether the motherboard has performance defects.

[0004] The above patent identifies defects in motherboard components based on corresponding images. This method can only detect the appearance of electronic components, but cannot detect the internal parts of electronic components. This makes it inconvenient to determine whether there are potential risks in electronic components, which will further affect subsequent use. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an intelligent detection system for electronic components, which solves the problem that a single appearance detection cannot realize defect analysis of the entire electronic component and is not convenient for risk analysis of the overall status of the electronic component.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an electronic component intelligent detection system, comprising:

[0007] The electronic component information acquisition unit is used to acquire the appearance image information and the working parameter information of the electronic component, and transmit the appearance image information to the appearance detection and analysis unit, and transmit the working parameter information to the parameter detection and analysis unit;

[0008] An appearance inspection and analysis unit is used to analyze the appearance image information transmitted by the electronic component information acquisition unit, determine the specific location of the abnormality by matching the appearance image of the electronic component with the standard image, determine the specific appearance defect according to the corresponding abnormal feature, generate appearance inspection information, and transmit the appearance inspection information to the inspection information output unit;

[0009] A parameter detection and analysis unit is used to detect the state of the electronic components according to the working parameter information transmitted by the electronic component information acquisition unit, and compare it with the standard parameters to generate a normal working signal and an abnormal working signal, and analyze the abnormal working signal at the same time, analyze the abnormal working parameters and determine the specific cause according to the abnormal parameter characteristics, and generate parameter detection information at the same time, and transmit the parameter detection information to the detection information output unit, and then transmit the normal working signal to the secondary analysis and determination unit;

[0010] The secondary analysis and determination unit is used to analyze the acquired normal working signal, and through periodic monitoring of the parameters of the electronic components, analyze the fluctuation of the parameters of the electronic components within the periodic time to generate a stable fluctuation signal and an unstable fluctuation signal. At the same time, the stable fluctuation signal and the unstable fluctuation signal are analyzed to generate parameter detection information, and the parameter detection information is transmitted to the detection information output unit.

[0011] The detection information output unit is used to display the acquired parameter detection information to the corresponding operator.

[0012] As a further solution of the present invention: the specific manner in which the appearance detection analysis unit generates the appearance detection information is:

[0013] Acquire the appearance image of the electronic component, and label the acquired appearance image as i, where i=1, 2, ..., 6, then acquire the standard image corresponding to the appearance image i, and compare the appearance image i with the corresponding standard image, select the appearance image that is different from the standard image, acquire it and record it as an abnormal image, and do not process the appearance image that is the same as the standard image;

[0014] Then, the abnormal image is obtained, and the area with differences in the abnormal image is obtained and recorded as the difference area, and the difference area is scanned by machine vision. Then, the abnormal features of the difference area are obtained and matched with the abnormal features of appearance defects recorded in the database, and appearance detection information is generated.

[0015] As a further solution of the present invention: the specific manner in which the parameter detection and analysis unit generates a normal working signal and an abnormal working signal is:

[0016] Obtaining working parameter information of the electronic components, wherein the working parameter information includes voltage, current and resistance, and obtaining corresponding standard parameters of the electronic components under normal working conditions, and comparing the working parameters with the standard parameters;

[0017] If the working parameters are different from the standard parameters, an abnormal working signal is generated and analyzed. Otherwise, if the working parameters are the same as the standard parameters, a normal working signal is generated.

[0018] As a further solution of the present invention: the specific method in which the parameter detection and analysis unit analyzes the abnormal working signal is:

[0019] The corresponding working parameters of the abnormalities are obtained and recorded as abnormal parameters. At the same time, the values ​​of the abnormal parameters are compared with the values ​​of the standard parameters to generate numerical comparison signals, and historical data are obtained. At the same time, the abnormal records in the historical data are collected, and then the abnormal parameter features corresponding to the abnormal records are extracted. At the same time, the current abnormal features corresponding to the abnormal parameters of the current electronic components are obtained, and the current abnormal features are matched and filtered with the abnormal parameter features to obtain the filtered historical records, and the number of filtered historical records is analyzed and judged.

[0020] As a further solution of the present invention: the specific method in which the parameter detection and analysis unit analyzes and determines the number of screening history records is:

[0021] When the number of screening history records is equal to 1, it means that the corresponding screening history record is the specific reason for the current abnormal parameter, and parameter detection information is generated. Conversely, when the number of screening history records is greater than 1, the screening history records are further analyzed, and the specific analysis method is:

[0022] Obtain changes in abnormal parameters of electronic components within time period t, and generate corresponding abnormal parameter change information. At the same time, analyze changes in abnormal parameters in filtered historical records to generate historical abnormal parameter change information. Then match the abnormal parameter change information with the historical abnormal parameter change information. Use the specific cause corresponding to the matched filtered historical records as the specific cause of the current electronic component, and generate parameter detection information.

[0023] As a further solution of the present invention: the specific manner in which the secondary analysis determination unit generates the stable fluctuation signal and the unstable fluctuation signal is:

[0024] The parameters of electronic components are obtained, and the changes of the parameters of electronic components are monitored with a time period of T. The change difference of the electronic component parameters per unit time within the time T is calculated, and the mean of the change difference is calculated. Then, the mean of the change difference is compared with the preset value. If the mean of the change difference is greater than the preset value, an unstable fluctuation signal is generated. Otherwise, if the mean of the change difference is less than the preset value, a stable fluctuation signal is generated.

[0025] As a further solution of the present invention: the specific manner in which the secondary analysis and determination unit analyzes the stable fluctuation signal is:

[0026] Monitor and analyze the working temperature corresponding to the electronic components, obtain the temperature value within the time T, and establish the corresponding temperature-time relationship diagram, and at the same time obtain the number of times the temperature value exceeds the temperature threshold within the time T, and record the corresponding number as c, and obtain the temperature excess value Kc corresponding to the number c, then obtain the working parameters corresponding to the number c and record them as Uc, I c and Rc respectively, and calculate the difference between the working parameters and the standard parameters as Up, I p and Rp, and at the same time calculate the sum of the differences of all working parameters as (Up+I p+Rp), then substitute the sum of the differences (Up+I p+Rp) and the temperature excess value Kc into the formula Fc=(Up+I p+Rp)+Kc to calculate the comprehensive value Fc corresponding to the number c, and calculate the comprehensive values ​​corresponding to all the numbers in this way;

[0027] Then, the calculated comprehensive value Fc is compared and judged. If the comprehensive value Fc increases within the time T, a danger warning signal is generated. Conversely, if the comprehensive value Fc does not increase within the time T, a normal signal is generated. At the same time, the danger warning signal and the normal signal are integrated to generate parameter detection information.

[0028] As a further solution of the present invention: the specific method in which the secondary analysis determination unit analyzes the unstable fluctuation signal is:

[0029] The current detection environment of electronic components is changed, and the working parameters of electronic components under different detection environments are analyzed. If the working parameters are stably changing, a normal signal is generated. Conversely, if the working parameters are unstably changing, a danger warning signal is generated. At the same time, the two signals are integrated to generate parameter detection information.

[0030] The present invention provides an intelligent detection system for electronic components. Compared with the prior art, it has the following advantages:

[0031] Beneficial effects:

[0032] The present invention analyzes electronic components from two aspects, namely, appearance and working parameters. If there is an abnormality in the appearance, machine vision is used to locate the existing defects and analyze the specific defects, which is convenient for subsequent direct observation. For the analysis of working parameters, the working parameters are compared with standard parameters. At the same time, normal and abnormal situations are analyzed separately. By combining historical data and its own abnormal characteristics for comprehensive analysis, risk prediction analysis of electronic components is performed. Not only is the appearance tested, but also the working parameters can be tested, making the detection of electronic components more comprehensive, thereby improving the accuracy of detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is a system block diagram of the present invention;

[0034] Figure 2 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] For example, see Figure 1 and Figure 2 The present application provides an intelligent detection system for electronic components, including: an electronic component information acquisition unit, an appearance detection and analysis unit, a parameter detection and analysis unit, a secondary analysis and determination unit, and a detection information output unit.

[0037] The electronic component information acquisition unit is used to acquire the appearance image information and working parameter information of the electronic components, and transmit the appearance image information to the appearance detection and analysis unit, and transmit the working parameter information to the parameter detection and analysis unit.

[0038] The appearance inspection and analysis unit is used to analyze the acquired appearance image information, determine the specific location of the abnormality by matching the appearance image of the electronic component with the standard image, determine the specific appearance defect according to the corresponding abnormal feature, and generate appearance inspection information. At the same time, the appearance inspection information is transmitted to the inspection information output unit, and the specific method of generating the appearance inspection information is:

[0039] The appearance images of the electronic components to be inspected are acquired by a high-definition camera, and the appearance images acquired here are appearance images of six surfaces. The acquired appearance images are labeled i, and i=1, 2, ..., 6. Then, a standard image corresponding to the appearance image i is acquired, and the standard image here refers to an image without any defects on the surface of the electronic component. The appearance image i is compared with the corresponding standard image, and appearance images that are different from the standard image are screened out and acquired and recorded as abnormal images, while appearance images that are the same as the standard image are not processed. Specifically, through advanced image processing algorithms, the system accurately identifies appearance images that have any slight differences from the standard image, and marks these images as abnormal images.

[0040] Then, the abnormal image is acquired, and the area with differences in the abnormal image is acquired and recorded as the difference area, and the difference area is scanned by machine vision. Then, the abnormal features of the difference area are acquired and matched with the abnormal features of appearance defects recorded in the database, and appearance detection information is generated. Specifically, once the abnormal image is identified, the system further locates and extracts the abnormal area for in-depth analysis. These difference areas are scanned by high-resolution machine vision to reveal specific abnormal features, such as scratches, dents, cracks, etc.

[0041] For example, suppose we inspect a batch of capacitors. A high-definition camera captures the six sides of the capacitors to obtain comprehensive viewing angle coverage. Each image is automatically compared with the corresponding view in the standard image library. Suppose that during the comparison process, the system finds an inconspicuous spot in a corner of image No. 3 (i=3). This spot does not exist on the standard image. The system marks image No. 3 as an abnormal image and automatically extracts the difference area containing the spot. The spot is scanned at high resolution to determine its shape, size, color depth and other features. Based on the analysis results, the system determines that the spot is an impurity contamination in the manufacturing process. The abnormal features are recorded and matched with known defect features in the database for confirmation. The inspection information generated by the system will include this result, and indicate the specific abnormal image number and defect location, providing suggestions for the quality grading and subsequent processing of the capacitor.

[0042] The detection information output unit is used to display the acquired appearance detection information to the corresponding operator.

[0043] Embodiment 2, as the embodiment 2 of the present invention, this embodiment is implemented on the basis of embodiment 1, and is different from embodiment 1 in that.

[0044] The parameter detection and analysis unit is used to detect the state of the electronic components according to the obtained working parameter information, and compare with the standard parameters to generate a normal working signal and an abnormal working signal, and analyze the abnormal working signal at the same time, analyze the abnormal working parameters and determine the specific cause according to the abnormal parameter characteristics, and generate parameter detection information at the same time, and transmit the parameter detection information to the detection information output unit, and then transmit the normal working signal to the secondary analysis and determination unit. The specific method of generating the parameter detection information is:

[0045] Obtaining working parameter information of the electronic components, wherein the working parameter information includes voltage, current and resistance, and obtaining corresponding standard parameters of the electronic components under normal working conditions, wherein the standard parameters here include voltage, current and resistance under normal working conditions, and comparing the working parameters with the standard parameters. If the working parameters are different from the standard parameters, it means that the electronic components have corresponding working parameter abnormalities, resulting in abnormal overall working conditions, and generating a working abnormality signal. On the contrary, if the working parameters are the same as the standard parameters, it means that the electronic components are in normal working conditions, and generating a working normal signal.

[0046] For example, the corresponding resistance value of the electronic component under normal working conditions is 220Ω, so 220Ω is the standard parameter of the electronic component. However, when testing the electronic component, it is found that the resistance value of the electronic component is 215Ω. Therefore, the corresponding working parameters of the electronic component are different from the standard parameters, so the corresponding working state of the electronic component is abnormal, and an abnormal working signal is generated.

[0047] The generated work abnormality signal is analyzed, the corresponding work parameter with the abnormality is obtained and recorded as the abnormal parameter, and the value of the abnormal parameter is compared with the value of the standard parameter to generate a numerical comparison signal, and the numerical comparison signal here includes a large signal and a small signal, which are specifically represented as an abnormal parameter large signal and an abnormal parameter small signal, and historical data is obtained, and the abnormal records in the historical data are collected, and the abnormal record is represented by a record of abnormal results obtained by detecting electronic components. For example, when there is a resistance abnormality when detecting electronic components, the corresponding detection record is obtained, and then the abnormal parameter feature corresponding to the abnormal record is extracted, and the current abnormal feature corresponding to the abnormal parameter of the current electronic component is obtained, and the current abnormal feature is matched and filtered with the abnormal parameter feature to obtain a screening historical record, and the number of screening historical records is analyzed and judged;

[0048] When the number of screening history records is equal to 1, it means that the corresponding screening history record is the specific reason for the current abnormal parameter, and parameter detection information is generated. On the contrary, when the number of screening history records is greater than 1, it means that there is a screening history record corresponding to the abnormal parameter feature that is the same as the current abnormal feature. In this case, further judgment and analysis are required, and the screening history records are further analyzed, and the specific analysis method is:

[0049] Obtain changes in abnormal parameters of electronic components within time period t, and generate corresponding abnormal parameter change information. At the same time, analyze changes in abnormal parameters in filtered historical records to generate historical abnormal parameter change information. Then match the abnormal parameter change information with the historical abnormal parameter change information. Use the specific cause corresponding to the matched filtered historical records as the specific cause of the current electronic component, and generate parameter detection information.

[0050] For example, after preliminary screening of the remaining three groups of screening historical records, it is necessary to further analyze the three groups of screening historical records by obtaining changes in abnormal parameters (such as voltage) of electronic components within a time period t, and the corresponding changes include increasing changes, decreasing changes or stable changes. Then, the abnormal parameter changes (also voltage) corresponding to the three groups of screening historical records are obtained respectively, and at the same time, the situations in the screening historical records that are the same as the abnormal parameter changes of the current electronic components are obtained, and the specific reasons corresponding to the screening historical records are used as the specific reasons for the current abnormal parameters.

[0051] The secondary analysis and determination unit is used to analyze the acquired normal working signal, and to generate a stable fluctuation signal and an unstable fluctuation signal by periodically monitoring the parameters of the electronic components and analyzing the fluctuation of the parameters of the electronic components within the periodic time. At the same time, the stable fluctuation signal and the unstable fluctuation signal are analyzed to generate parameter detection information, and the parameter detection information is transmitted to the detection information output unit. The specific method of generating the detection information is:

[0052] Acquire the parameters of electronic components, and monitor the changes of the parameters of electronic components with a time period of T, calculate the change difference of the parameters of the electronic components per unit time within the time period T, and calculate the mean of the change difference, then compare the mean of the change difference with the preset value, and the preset value here is the comparison threshold, and the specific value is set by the operator. If the mean of the change difference is greater than the preset value, it means that the change of the parameters of the electronic components within the time period T is unstable, and an unstable fluctuation signal is generated. On the contrary, if the mean of the change difference is less than the preset value, it means that the change of the parameters of the electronic components is stable, and a stable fluctuation signal is generated;

[0053] Set a reasonable time period T (such as every 5 seconds), continuously record the values ​​of the above key parameters during this period, use data analysis software to calculate the difference in the change of each parameter in each period, and then find the average of the difference during the entire monitoring period. According to the working standards and historical performance data of the components, experienced operators set a suitable comparison threshold (preset value). This threshold should reflect the acceptable range of parameter changes. Exceeding this range may mean potential performance instability or failure risks. Suppose that in the stress test of a new microprocessor, the mean difference of the monitored CPU temperature data within each hour (period T) exceeds the set threshold (for example, from 70°C to 75°C), the system immediately sends out an unstable fluctuation signal, otherwise it generates a stable fluctuation signal.

[0054] The specific method of analyzing the unstable fluctuation signal is as follows: monitoring and analyzing the working temperature corresponding to the electronic components, obtaining the value of the temperature within the time T, and establishing the corresponding temperature-time relationship diagram, and obtaining the number of times the temperature value exceeds the temperature threshold within the time T, and the temperature threshold here is the temperature warning value under normal working conditions set by the operator, and the corresponding number is recorded as c, and the temperature excess value Kc corresponding to the number c is obtained, and the temperature excess value Kc here represents the difference between the working temperature and the temperature threshold at the corresponding time point, and then the working parameters corresponding to the number c are obtained and recorded as Uc, Ic and Rc respectively, and the difference between the working parameters and the standard parameters is calculated and recorded as Up, Ip and Rp, and the sum of the differences of all the working parameters is calculated and recorded as (Up+Ip+Rp), and then the sum of the differences (Up+Ip+Rp) and the temperature excess value Kc are substituted into the formula Fc=(Up+Ip+Rp)+Kc to calculate the comprehensive value Fc corresponding to the number c, and the comprehensive value Fc calculated here is the risk value corresponding to the number c, and the comprehensive values ​​corresponding to all the numbers are calculated by analogy;

[0055] Then, the calculated comprehensive value Fc is compared and judged. If the comprehensive value Fc increases within the time T, it means that the corresponding electronic component has potential danger, and a danger warning signal is generated. On the contrary, if the comprehensive value Fc does not increase within the time T, it means that the corresponding electronic component does not have potential danger, and a normal signal is generated. At the same time, the danger warning signal and the normal signal are integrated to generate parameter detection information;

[0056] Assume that we monitor a power module whose key performance indicators include output voltage, output current and module temperature. These parameters are combined into a comprehensive performance indicator Fc through a specific algorithm (such as weighted average method). For example, the following formula can be used:

[0057] Fc=w v ×V+w i ×I+wt ×T

[0058] Where V, I, and T are the standardized voltage, current, and temperature values, respectively, and w v 、w i 、w t is the corresponding weight coefficient, reflecting the influence of each parameter on the overall performance. Within the set time period T (for example, every 10 minutes), continuously monitor the change of Fc. Observe whether it shows an increasing trend, which may mean performance degradation or potential failure risk;

[0059] If F_c Fc shows an increasing trend, the system will automatically generate a dangerous warning signal. For example, if the output voltage of the power module continues to drop while the current and temperature rise, the Fc value may increase. At this time, the system will issue a warning to prompt the user to check whether there are problems such as circuit overload or poor heat dissipation. If Fc remains stable or shows a decreasing trend, a normal signal is generated. This means that the component is stable under the current operating conditions and no immediate intervention is required.

[0060] The specific method of analyzing the stable fluctuation signal is: changing the current detection environment of the electronic components, and analyzing the working parameters of the electronic components under different detection environments. If the working parameters are stable, a normal signal is generated. On the contrary, if the working parameters are unstable, a danger warning signal is generated. At the same time, the two signals are integrated to generate parameter detection information.

[0061] The generated parameter detection information is transmitted to the detection information output unit.

[0062] A detection information output unit is used to display the acquired parameter detection information to the corresponding operator.

[0063] Embodiment 3, as the embodiment 3 of the present invention, focuses on combining the implementation processes of embodiment 1 and embodiment 2 for implementation.

[0064] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0065] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An intelligent detection system for electronic components, characterized in that: include: An appearance inspection and analysis unit is used to analyze the appearance image information transmitted by the electronic component information acquisition unit, determine the specific location of the abnormality by matching the appearance image of the electronic component with the standard image, determine the specific appearance defect according to the corresponding abnormal feature, generate appearance inspection information, and transmit the appearance inspection information to the inspection information output unit; A parameter detection and analysis unit is used to detect the state of the electronic components according to the working parameter information transmitted by the electronic component information acquisition unit, and compare it with the standard parameters to generate a normal working signal and an abnormal working signal, and analyze the abnormal working signal at the same time, analyze the abnormal working parameters and determine the specific cause according to the abnormal parameter characteristics, and generate parameter detection information at the same time, and transmit the parameter detection information to the detection information output unit, and then transmit the normal working signal to the secondary analysis and determination unit; The secondary analysis and determination unit is used to analyze the acquired normal working signal, and through periodic monitoring of the parameters of the electronic components, analyze the fluctuation of the parameters of the electronic components within the periodic time to generate a stable fluctuation signal and an unstable fluctuation signal. At the same time, the stable fluctuation signal and the unstable fluctuation signal are analyzed to generate parameter detection information, and the parameter detection information is transmitted to the detection information output unit.

2. The electronic component intelligent detection system according to claim 1, characterized in that: It also includes an electronic component information acquisition unit and a detection information output unit; The electronic component information acquisition unit is used to acquire the appearance image information and the working parameter information of the electronic component, and transmit the appearance image information to the appearance detection and analysis unit, and transmit the working parameter information to the parameter detection and analysis unit; The detection information output unit is used to display the acquired parameter detection information to the corresponding operator.

3. The electronic component intelligent detection system according to claim 1, characterized in that: The specific method of the appearance detection analysis unit generating the appearance detection information is as follows: Acquire the appearance image of the electronic component, and label the acquired appearance image as i, where i=1, 2, ..., 6, then acquire the standard image corresponding to the appearance image i, and compare the appearance image i with the corresponding standard image, select the appearance image that is different from the standard image, acquire it and record it as an abnormal image, and do not process the appearance image that is the same as the standard image; Then, the abnormal image is obtained, and the area with differences in the abnormal image is obtained and recorded as the difference area, and the difference area is scanned by machine vision. Then, the abnormal features of the difference area are obtained and matched with the abnormal features of appearance defects recorded in the database, and appearance detection information is generated.

4. The electronic component intelligent detection system according to claim 1, characterized in that: The specific method for the parameter detection and analysis unit to generate a normal working signal and an abnormal working signal is: Obtaining working parameter information of the electronic components, wherein the working parameter information includes voltage, current and resistance, and obtaining corresponding standard parameters of the electronic components under normal working conditions, and comparing the working parameters with the standard parameters; If the working parameters are different from the standard parameters, an abnormal working signal is generated and analyzed. Otherwise, if the working parameters are the same as the standard parameters, a normal working signal is generated.

5. The electronic component intelligent detection system according to claim 4, characterized in that: The specific method for the parameter detection and analysis unit to analyze the abnormal working signal is as follows: The corresponding working parameters of the abnormalities are obtained and recorded as abnormal parameters. At the same time, the values ​​of the abnormal parameters are compared with the values ​​of the standard parameters to generate numerical comparison signals, and historical data are obtained. At the same time, the abnormal records in the historical data are collected, and then the abnormal parameter features corresponding to the abnormal records are extracted. At the same time, the current abnormal features corresponding to the abnormal parameters of the current electronic components are obtained, and the current abnormal features are matched and filtered with the abnormal parameter features to obtain the filtered historical records, and the number of filtered historical records is analyzed and judged.

6. The electronic component intelligent detection system according to claim 5, characterized in that: The specific method in which the parameter detection and analysis unit analyzes and determines the number of screening history records is as follows: When the number of screening history records is equal to 1, it means that the corresponding screening history record is the specific reason for the current abnormal parameter, and parameter detection information is generated. Conversely, when the number of screening history records is greater than 1, the screening history records are further analyzed, and the specific analysis method is: Obtain changes in abnormal parameters of electronic components within time period t, and generate corresponding abnormal parameter change information. At the same time, analyze changes in abnormal parameters in filtered historical records to generate historical abnormal parameter change information. Then match the abnormal parameter change information with the historical abnormal parameter change information. Use the specific cause corresponding to the matched filtered historical records as the specific cause of the current electronic component, and generate parameter detection information.

7. The electronic component intelligent detection system according to claim 1, characterized in that: The specific method in which the secondary analysis determination unit generates the stable fluctuation signal and the unstable fluctuation signal is: The parameters of electronic components are obtained, and the changes of the parameters of electronic components are monitored with a time period of T. The change difference of the electronic component parameters per unit time within the time T is calculated, and the mean of the change difference is calculated. Then, the mean of the change difference is compared with the preset value. If the mean of the change difference is greater than the preset value, an unstable fluctuation signal is generated. Otherwise, if the mean of the change difference is less than the preset value, a stable fluctuation signal is generated.

8. The electronic component intelligent detection system according to claim 1, characterized in that: The specific method in which the secondary analysis and determination unit analyzes the stable fluctuation signal is: Monitor and analyze the working temperature corresponding to the electronic components, obtain the temperature value within the time T, and establish the corresponding temperature-time relationship diagram, and at the same time obtain the number of times the temperature value exceeds the temperature threshold within the time T, and record the corresponding number as c, and obtain the temperature excess value Kc corresponding to the number c, then obtain the working parameters corresponding to the number c and record them as Uc, Ic and Rc respectively, and calculate the difference between the working parameters and the standard parameters as Up, Ip and Rp, and at the same time calculate the sum of the differences of all working parameters as (Up+Ip+Rp), then substitute the sum of the differences (Up+Ip+Rp) and the temperature excess value Kc into the formula Fc=(Up+Ip+Rp)+Kc to calculate the comprehensive value Fc corresponding to the number c, and calculate the comprehensive values ​​corresponding to all the numbers in this way; Then, the calculated comprehensive value Fc is compared and judged. If the comprehensive value Fc increases within the time T, a danger warning signal is generated. Conversely, if the comprehensive value Fc does not increase within the time T, a normal signal is generated. At the same time, the danger warning signal and the normal signal are integrated to generate parameter detection information.

9. The electronic component intelligent detection system according to claim 1, characterized in that: The specific method in which the secondary analysis unit analyzes the unstable fluctuation signal is as follows: The current detection environment of electronic components is changed, and the working parameters of electronic components under different detection environments are analyzed. If the working parameters are stably changing, a normal signal is generated. Conversely, if the working parameters are unstably changing, a danger warning signal is generated. At the same time, the two signals are integrated to generate parameter detection information.

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