A system and method for evaluating the quality of inductor components based on big data analysis

Abstract

The invention discloses an inductor component quality assessment system and method based on big data analysis, belonging to the field of inductor components. The invention introduces an AC signal of an acquired input signal and an AC signal in an output signal into an AC signal removal effect judgment model to evaluate the AC signal removal effect, introduces a DC signal of an acquired input signal and a DC signal in an output signal into a DC signal retention effect judgment model to evaluate the DC signal retention effect, introduces the AC signal removal effect evaluation result and the DC signal retention effect evaluation result into an inductor quality analysis strategy to perform inductor quality analysis, performs inductor quality early warning according to the inductor quality analysis result, comprehensively evaluates the inductor operation by performing inductor operation data, and comprehensively analyzes the production quality of the inductor by performing operation characteristic data of the inductor, thereby improving the accuracy of inductor production detection and evaluation efficiency.

Description

Technical Field

[0001] The present invention belongs to the field of inductor components, and specifically relates to an inductor component quality assessment system and method based on big data analysis. Background Art

[0002] Inductor, also called inductor or coil, is an electronic component used to store and release magnetic energy. It consists of a coil made of copper wire or other conductive materials. When power is applied, a magnetic field is generated. When the current changes, the magnetic field will also change. This changing magnetic field will generate electromagnetic induction, thereby generating electromotive force and current in the inductor. In the circuit, the inductor plays the role of "passing direct current and blocking alternating current". When evaluating the quality of inductors, the existing technology mostly analyzes and evaluates the appearance of the inductor, but the existing technology cannot comprehensively evaluate the operation of the inductor through the inductor operation data, and cannot comprehensively analyze its production quality through the operation characteristic data of the inductor, resulting in low accuracy and evaluation efficiency of inductor production detection. The above problems exist in all the existing technologies;

[0003] For example, a Chinese patent with the authorization publication number CN115564767B discloses a method for monitoring the quality of inductor winding based on machine vision. The method includes: obtaining multiple grayscale images of the inductor coil; obtaining the coil reflective area and the skeleton area of ​​the coil reflective area in the grayscale image, obtaining the corresponding goodness of fit based on the skeleton area and then obtaining the skeleton straightness; dividing the coil reflective area into a first part and a second part based on the skeleton area, obtaining a first sequence and a second sequence based on the Euclidean distance from each pixel point on the perpendicular line of the first part and the second part to the edge of the coil reflective area to obtain the shape variation, combining the correlation between the first sequence and the second sequence and all the mutation values ​​in the two sequences to obtain the shape significance; obtaining the winding regularity based on the skeleton straightness and the shape significance, and evaluating the winding quality of the inductor coil based on the winding regularity; improving the accuracy of winding quality monitoring;

[0004] At the same time, the Chinese patent with application publication number CN114267530A discloses the technical field of integrated inductor production, and specifically discloses a production line for producing integrated inductors, including: an inductor forming device for producing a first inductor semi-finished product; an inductor surface treatment device for treating the surface of the first inductor semi-finished product to produce a second inductor semi-finished product; a laser paint stripping device for laser paint stripping the second inductor semi-finished product to expose the pins of the second inductor semi-finished product to produce a stripped inductor semi-finished product; an electroplating device for electroplating the pins of the stripped inductor semi-finished product to produce a plated inductor finished product. The present invention provides an inductor surface treatment device and an electroplating device to process the inductor as a whole, the inductor surface and the pins of the inductor respectively, so that the quality of the produced inductor finished product is good. At the same time, the detection and packaging device can identify the inductors that do not meet the requirements, thereby effectively ensuring the quality of the final inductor finished product.

[0005] The above patents all have the problems raised by this background technology: the existing technology is unable to conduct a comprehensive evaluation of the inductor operation through the inductor operation data, and is unable to conduct a comprehensive analysis of the production quality of the inductor through the inductor operation characteristic data, resulting in low accuracy and evaluation efficiency of inductor production detection. In order to solve these problems, this application designs an inductor component quality evaluation system and method based on big data analysis. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention proposes a system and method for evaluating the quality of an inductor component based on big data analysis. The present invention obtains an inductor component to be evaluated, installs the inductor component at a specified position on a test circuit, obtains design parameters of the inductor component, a signal output module inputs a DC and AC signal of a set voltage into the inductor component, a signal receiving module receives the output signal passing through the inductor component, separates the received DC signal and AC signal, imports the obtained AC signal of the input signal and the AC signal in the output signal into an AC signal removal effect judgment model to evaluate the AC signal removal effect, imports the obtained DC signal of the input signal and the DC signal in the output signal into a DC signal retention effect judgment model to evaluate the DC signal retention effect, imports the AC signal removal effect evaluation results and the DC signal retention effect evaluation results into an inductor quality analysis strategy to perform inductor quality analysis, performs inductor quality warning according to the inductor quality analysis results, comprehensively evaluates the inductor operation by performing a comprehensive evaluation on the inductor operation data, and comprehensively analyzes the production quality of the inductor by performing a comprehensive analysis on the inductor operation characteristic data, thereby improving the accuracy and evaluation efficiency of inductor production detection.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for evaluating the quality of an inductor component based on big data analysis includes the following specific steps:

[0009] Obtain an inductor component to be evaluated, install the inductor component at a specified position on a test circuit, and obtain design parameters of the inductor component;

[0010] The signal output module inputs a DC and AC signal of a set voltage into the inductor element, wherein the frequency of the AC is 100%-110% of the set threshold value of the inductor element for eliminating the AC frequency, and the signal receiving module receives the output signal after passing through the inductor element and separates the received DC signal and AC signal;

[0011] The AC signal of the input signal and the AC signal in the output signal are introduced into the AC signal removal effect judgment model to evaluate the AC signal removal effect;

[0012] The obtained DC signal of the input signal and the DC signal in the output signal are introduced into the DC signal retention effect judgment model to evaluate the DC signal retention effect;

[0013] The AC signal removal effect evaluation results and the DC signal retention effect evaluation results are imported into the inductor quality analysis strategy to perform inductor quality analysis, and an inductor quality warning is performed based on the inductor quality analysis results.

[0014] It should be noted that the specific steps of obtaining the inductor element to be evaluated, installing the inductor element at a specified position on the test circuit, and obtaining the design parameters of the inductor element are as follows:

[0015] S11, obtaining an inductor element to be evaluated, and designing a detection circuit, wherein the detection circuit includes at least a signal output module for releasing a DC and AC signal of a set voltage to the inductor element, a mounting component for mounting the inductor element, and a signal receiving module for the output signal passing through the inductor element, and mounting the inductor element to be detected on the mounting component of the detection circuit;

[0016] S12, obtaining a safe operating temperature of the inductor element and a threshold value for eliminating AC frequency, wherein the threshold value for eliminating AC frequency is a minimum value of a set AC frequency range that is blocked from passing;

[0017] It should be specifically explained here that the step of importing the acquired AC signal of the input signal and the AC signal in the output signal into the AC signal removal effect judgment model to evaluate the AC signal removal effect includes the following specific steps:

[0018] S31, obtaining a waveform diagram of the AC signal in the input signal and a waveform diagram of the AC signal in the output signal, placing the waveform diagram of the AC signal in the input signal and the waveform diagram of the AC signal in the output signal on the same scale, making a correspondence between the time length of the output AC signal and the input AC signal, and obtaining an image of the change of the input AC signal over time and an image of the change of the output AC signal over time;

[0019] S32, importing the acquired image of the change of the input AC signal over time and the image of the change of the output AC signal over time into the AC signal removal coefficient calculation formula to calculate the AC signal removal coefficient, wherein the AC signal removal coefficient calculation formula is: Wherein, T is the test time, u2t is the voltage value of the output AC signal of the inductor element at the test time t, u1t is the voltage value of the input AC signal of the inductor element at the test time t, dt is the time integral, and || is the absolute value symbol.

[0020] It should be noted that the specific steps of importing the acquired DC signal of the input signal and the DC signal in the output signal into the DC signal retention effect judgment model to evaluate the DC signal retention effect are as follows:

[0021] S41, obtaining a waveform diagram of a DC signal in an input signal and a waveform diagram of a DC signal in an output signal, placing the waveform diagram of the DC signal in the input signal and the waveform diagram of the DC signal in the output signal on the same scale, making a correspondence between the time length of the output DC signal and the input DC signal, and obtaining an image of the change of the input DC signal over time and an image of the change of the output DC signal over time;

[0022] S42, importing the acquired image of the change of the input DC signal over time and the acquired image of the change of the output DC signal over time into a DC signal retention coefficient calculation formula to calculate the DC signal retention coefficient, wherein the DC signal retention coefficient calculation formula is: Among them, c t is the difficulty coefficient of retaining the DC signal at time t, I t1 is the voltage value of the output DC signal, I t2 is the voltage value of the input DC signal, where the calculation formula for the retention difficulty coefficient of the DC signal at time t is:

[0023] It should be noted that the step of importing the AC signal removal effect evaluation result and the DC signal retention effect evaluation result into the inductor quality analysis strategy for inductor quality analysis includes the following specific steps:

[0024] S51, obtaining the calculated AC signal removal coefficient and DC signal retention coefficient;

[0025] S52, importing the obtained AC signal removal coefficient and DC signal retention coefficient into the inductance quality analysis value calculation formula to calculate the inductance quality analysis value, wherein the inductance quality analysis value calculation formula is: Where N is the total number of inductors in the test batch, Pz is the set evaluation score, preferably 10, r is the proportion of AC signal removal, A 1i is the AC signal removal coefficient of the i-th inductor, A 2i is the DC signal retention coefficient of the i-th inductor;

[0026] S53: Divide the obtained inductance quality analysis value by the set evaluation score to obtain an inductance score value.

[0027] It should be noted that the AC signal removal ratio, temperature ratio coefficient, AC ratio coefficient and set evaluation score are obtained as follows: at least 5000 groups of inductors to be tested are obtained, and the inductors are manually judged for quality. At the same time, the inductor data to be tested are imported into the inductor quality analysis value calculation formula to calculate the inductor quality analysis value, and the calculated inductor quality analysis value and the manual quality judgment result are imported into the fitting software, and the AC signal removal ratio, temperature ratio coefficient, AC ratio coefficient and set evaluation score values ​​that meet the highest manual quality judgment result accuracy are output;

[0028] It should be noted that the specific steps of performing inductor quality warning according to the inductor quality analysis result are:

[0029] S44. If the calculated inductance score value of the inductor batch to be tested is greater than or equal to 0.95, the quality of the corresponding batch of inductors is judged to be qualified;

[0030] If the calculated inductance score of the inductor batch to be tested is greater than or equal to 0.8 and less than 0.95, the quality of the corresponding batch of inductors is judged as a level 3 abnormal warning;

[0031] If the calculated average score of the power amplifier batch to be tested is greater than or equal to 0.7 and less than 0.8, the quality of the corresponding batch of inductors is judged as a level 2 abnormal warning;

[0032] If the calculated average score of the power amplifier batch to be tested is less than 0.7, the quality of the corresponding batch of inductors is judged as a first-level abnormal warning.

[0033] A system for evaluating the quality of inductance components based on big data analysis is implemented based on the above-mentioned method for evaluating the quality of inductance components based on big data analysis. The system specifically comprises a testing unit, an experimental data acquisition unit, an AC signal removal effect evaluation unit, a DC signal retention effect evaluation unit, an inductance quality early warning unit and a control unit. The testing unit is used to obtain the inductance component to be evaluated, install the inductance component at a specified position on the test circuit, and obtain the design parameters of the inductance component. The experimental data acquisition unit is used for the signal output module to input DC and AC signals of set voltage into the inductance component, and the signal receiving module receives the output signal passing through the inductance component and separates the received DC signal and AC signal.

[0034] It should be specifically explained here that the AC signal removal effect evaluation unit is used to import the acquired AC signal of the input signal and the AC signal in the output signal into the AC signal removal effect judgment model to perform AC signal removal effect evaluation, the DC signal retention effect evaluation unit is used to import the acquired DC signal of the input signal and the DC signal in the output signal into the DC signal retention effect judgment model to perform DC signal retention effect evaluation, and the inductor quality warning unit is used to import the AC signal removal effect evaluation result and the DC signal retention effect evaluation result into the inductor quality analysis strategy to perform inductor quality analysis, and perform inductor quality warning according to the inductor quality analysis result.

[0035] It should be specifically explained here that the control unit is used to control the operation of the test unit, the experimental data acquisition unit, the AC signal removal effect evaluation unit, the DC signal retention effect evaluation unit and the inductance quality early warning unit.

[0036] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0037] The processor executes the above-mentioned method for evaluating the quality of inductor components based on big data analysis by calling the computer program stored in the memory.

[0038] A computer-readable storage medium stores instructions. When the instructions are executed on a computer, the computer executes the above-mentioned method for evaluating the quality of an inductor component based on big data analysis.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The present invention obtains an inductor element to be evaluated, installs the inductor element at a designated position on a test circuit, obtains design parameters of the inductor element, a signal output module inputs a DC and AC signal of a set voltage into the inductor element, a signal receiving module receives the output signal passing through the inductor element, separates the received DC signal and AC signal, imports the acquired AC signal of the input signal and the AC signal in the output signal into an AC signal removal effect judgment model to evaluate the AC signal removal effect, imports the acquired DC signal of the input signal and the DC signal in the output signal into a DC signal retention effect judgment model to evaluate the DC signal retention effect, imports the AC signal removal effect evaluation result and the DC signal retention effect evaluation result into an inductor quality analysis strategy to perform inductor quality analysis, performs inductor quality warning according to the inductor quality analysis result, comprehensively evaluates the inductor operation by performing a comprehensive evaluation on the inductor operation data, and comprehensively analyzes the production quality of the inductor by performing a comprehensive analysis on the operation characteristic data of the inductor, thereby improving the accuracy and evaluation efficiency of inductor production detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the overall process of an inductor component quality assessment method based on big data analysis of the present invention;

[0042] Figure 2 This is a schematic diagram of the overall framework of an inductor component quality assessment system based on big data analysis of the present invention. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0044] Example 1

[0045] The technical problem solved by this embodiment is that the prior art cannot comprehensively evaluate the operation of the inductor by using the operation data of the inductor, and cannot comprehensively analyze the production quality of the inductor by using the operation characteristic data of the inductor, resulting in low accuracy and evaluation efficiency of inductor production detection;

[0046] To solve the above problems, please refer to Figure 1 The present invention provides a preferred embodiment: Figure 1 As shown, a method for evaluating the quality of an inductor component based on big data analysis includes the following specific steps:

[0047] Obtain an inductor component to be evaluated, install the inductor component at a specified position on a test circuit, and obtain design parameters of the inductor component;

[0048] The signal output module inputs a DC and AC signal of a set voltage into the inductor element, wherein the frequency of the AC is 100%-110% of the set threshold value of the inductor element for eliminating the AC frequency, and the signal receiving module receives the output signal after passing through the inductor element and separates the received DC signal and AC signal;

[0049] Inputting DC and AC signals of set voltage into the inductor for testing has the following main benefits:

[0050] Comprehensive testing: By inputting different types of signals (DC and AC), the performance of the inductor components under different working conditions can be comprehensively evaluated to ensure its reliability in practical applications;

[0051] Temperature test: Inputting signals at different voltages can simulate the working state of the inductor under different temperature conditions, which helps to understand the thermal stability of the inductor.

[0052] Voltage stability test: The performance of the inductor under different voltages can be tested by inputting a set voltage to ensure its stability within the designed voltage range;

[0053] In short, by inputting DC and AC signals of set voltage into the inductor for testing, it can be ensured that the performance of the inductor meets the design requirements and improve the reliability of the product;

[0054] The AC signal of the input signal and the AC signal in the output signal are introduced into the AC signal removal effect judgment model to evaluate the AC signal removal effect;

[0055] The obtained DC signal of the input signal and the DC signal in the output signal are introduced into the DC signal retention effect judgment model to evaluate the DC signal retention effect;

[0056] Import the AC signal removal effect evaluation results and the DC signal retention effect evaluation results into the inductor quality analysis strategy to perform inductor quality analysis, and perform inductor quality warning based on the inductor quality analysis results;

[0057] It should be noted that in this embodiment, the specific steps of obtaining the inductor element to be evaluated, installing the inductor element at a specified position on the test circuit, and obtaining the design parameters of the inductor element are as follows:

[0058] S11, obtaining an inductor element to be evaluated, and designing a detection circuit, wherein the detection circuit includes at least a signal output module for releasing a DC and AC signal of a set voltage to the inductor element, a mounting component for mounting the inductor element, and a signal receiving module for the output signal passing through the inductor element, and mounting the inductor element to be detected on the mounting component of the detection circuit;

[0059] S12, obtaining a safe operating temperature of the inductor element and a threshold value for eliminating AC frequency, wherein the threshold value for eliminating AC frequency is a minimum value of a set AC frequency range that is blocked from passing;

[0060] It should be noted that in this embodiment, the AC signal of the input signal and the AC signal in the output signal are introduced into the AC signal removal effect judgment model to evaluate the AC signal removal effect, which includes the following specific steps:

[0061] S31, obtaining a waveform diagram of the AC signal in the input signal and a waveform diagram of the AC signal in the output signal, placing the waveform diagram of the AC signal in the input signal and the waveform diagram of the AC signal in the output signal on the same scale, making a correspondence between the time length of the output AC signal and the input AC signal, and obtaining an image of the change of the input AC signal over time and an image of the change of the output AC signal over time;

[0062] S32, importing the acquired image of the change of the input AC signal over time and the image of the change of the output AC signal over time into the AC signal removal coefficient calculation formula to calculate the AC signal removal coefficient, wherein the AC signal removal coefficient calculation formula is: Wherein, T is the test time, u2t is the voltage value of the output AC signal of the inductor element at the test time t, u1t is the voltage value of the input AC signal of the inductor element at the test time t, dt is the time integral, and || is the absolute value symbol;

[0063] It should be noted that in this embodiment, the specific steps of importing the acquired DC signal of the input signal and the DC signal in the output signal into the DC signal retention effect judgment model to evaluate the DC signal retention effect are:

[0064] S41, obtaining a waveform diagram of a DC signal in an input signal and a waveform diagram of a DC signal in an output signal, placing the waveform diagram of the DC signal in the input signal and the waveform diagram of the DC signal in the output signal on the same scale, making a correspondence between the time length of the output DC signal and the input DC signal, and obtaining an image of the change of the input DC signal over time and an image of the change of the output DC signal over time;

[0065] S42, importing the acquired image of the change of the input DC signal over time and the acquired image of the change of the output DC signal over time into a DC signal retention coefficient calculation formula to calculate the DC signal retention coefficient, wherein the DC signal retention coefficient calculation formula is: Among them, c t is the difficulty coefficient of retaining the DC signal at time t, I t1 is the voltage value of the output DC signal, It2 is the voltage value of the input DC signal, where the calculation formula for the retention difficulty coefficient of the DC signal at time t is:

[0066] It should be noted that the steps of importing the AC signal removal effect evaluation results and the DC signal retention effect evaluation results into the inductor quality analysis strategy for inductor quality analysis include the following:

[0067] S51, obtaining the calculated AC signal removal coefficient and DC signal retention coefficient;

[0068] S52, importing the obtained AC signal removal coefficient and DC signal retention coefficient into the inductance quality analysis value calculation formula to calculate the inductance quality analysis value, wherein the inductance quality analysis value calculation formula is: Where N is the total number of inductors in the test batch, Pz is the set evaluation score, preferably 10, r is the proportion of AC signal removal, A 1i is the AC signal removal coefficient of the i-th inductor, A 2i is the DC signal retention coefficient of the i-th inductor;

[0069] S53: Divide the obtained inductance quality analysis value by the set evaluation score to obtain an inductance score value.

[0070] It should be noted that the AC signal removal ratio, temperature ratio coefficient, AC ratio coefficient and set evaluation score are obtained as follows: at least 5000 groups of inductors to be tested are obtained, and the inductors are manually judged for quality. At the same time, the inductor data to be tested are imported into the inductor quality analysis value calculation formula to calculate the inductor quality analysis value, and the calculated inductor quality analysis value and the manual quality judgment result are imported into the fitting software, and the AC signal removal ratio, temperature ratio coefficient, AC ratio coefficient and set evaluation score values ​​that meet the highest manual quality judgment result accuracy are output;

[0071] It should be noted that in this embodiment, the specific steps of performing an inductor quality warning according to the inductor quality analysis result are:

[0072] S44. If the calculated inductance score value of the inductor batch to be tested is greater than or equal to 0.95, the quality of the corresponding batch of inductors is judged to be qualified;

[0073] If the calculated inductance score of the inductor batch to be tested is greater than or equal to 0.8 and less than 0.95, the quality of the corresponding batch of inductors is judged as a level 3 abnormal warning;

[0074] If the calculated average score of the power amplifier batch to be tested is greater than or equal to 0.7 and less than 0.8, the quality of the corresponding batch of inductors is judged as a level 2 abnormal warning;

[0075] If the calculated average score of the power amplifier batch to be tested is less than 0.7, the quality of the corresponding batch of inductors is judged as a first-level abnormal warning;

[0076] Different levels of measures corresponding to abnormal quality judgment of batch inductors can be considered according to specific circumstances. The following are some possible measures:

[0077] Level 1 anomaly:

[0078] Suspension of production and investigation: Immediately stop the production of this batch of inductors and conduct a careful investigation of the production process to determine the root cause of the problem;

[0079] Rework or scrap: Rework or scrap the products that have been produced to ensure that defective products do not enter the market;

[0080] Strengthen monitoring: Carry out stricter monitoring and inspection of subsequent production processes to prevent similar problems from happening again;

[0081] 2. Secondary abnormality:

[0082] In-depth investigation: Conduct a more in-depth investigation of production processes, raw materials, equipment, etc. to find out the cause of the problem;

[0083] Process improvement: Based on the survey results, formulate and implement targeted production process and process improvement measures to eliminate the root causes of the problems;

[0084] Strengthen training: Provide training and guidance to production operators to improve their awareness and skills of quality control;

[0085] 3. Level 3 abnormality:

[0086] Strengthen quality control: Increase quality control at every stage of the inductor production process to ensure that the quality meets the requirements;

[0087] Improve the process: Improve the deficiencies in the production process, and supervise and inspect the production process to ensure stable quality;

[0088] It should be noted in this embodiment that the advantages of this embodiment over the prior art are as follows: obtaining an inductor element to be evaluated, installing the inductor element at a specified position on the test circuit, obtaining design parameters of the inductor element, the signal output module inputting a DC and AC signal of a set voltage into the inductor element, the signal receiving module receiving the output signal passing through the inductor element, separating the received DC signal and AC signal, importing the obtained AC signal of the input signal and the AC signal in the output signal into an AC signal removal effect judgment model to evaluate the AC signal removal effect, importing the obtained DC signal of the input signal and the DC signal in the output signal into a DC signal retention effect judgment model to evaluate the DC signal retention effect, importing the AC signal removal effect evaluation results and the DC signal retention effect evaluation results into the inductor quality analysis strategy to perform inductor quality analysis, performing inductor quality warning according to the inductor quality analysis results, performing a comprehensive evaluation of the inductor operation by performing a comprehensive evaluation of the inductor operation data, and performing a comprehensive analysis of the production quality of the inductor by performing a comprehensive analysis of the inductor operation characteristic data, thereby improving the accuracy and evaluation efficiency of inductor production detection.

[0089] Example 2

[0090] like Figure 2 As shown, a system for evaluating the quality of an inductor component based on big data analysis is implemented based on the above-mentioned method for evaluating the quality of an inductor component based on big data analysis, and specifically includes a test unit, an experimental data acquisition unit, an AC signal removal effect evaluation unit, a DC signal retention effect evaluation unit, an inductor quality early warning unit and a control unit. The test unit is used to obtain the inductor component to be evaluated, install the inductor component at a specified position on the test circuit, and obtain the design parameters of the inductor component. The experimental data acquisition unit is used for the signal output module to input DC and AC signals of set voltages into the inductor component, and the signal receiving module receives the output signal passing through the inductor component and separates the received DC signal and AC signal; the AC signal removal effect evaluation unit is used for The acquired AC signal of the input signal and the AC signal in the output signal are introduced into the AC signal removal effect judgment model to evaluate the AC signal removal effect. The DC signal retention effect evaluation unit is used to introduce the acquired DC signal of the input signal and the DC signal in the output signal into the DC signal retention effect judgment model to evaluate the DC signal retention effect. The inductor quality early warning unit is used to import the AC signal removal effect evaluation result and the DC signal retention effect evaluation result into the inductor quality analysis strategy to perform inductor quality analysis, and perform inductor quality early warning according to the inductor quality analysis result. The control unit is used to control the operation of the test unit, the experimental data acquisition unit, the AC signal removal effect evaluation unit, the DC signal retention effect evaluation unit and the inductor quality early warning unit.

[0091] Example 3

[0092] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0093] The processor executes the above-mentioned method for evaluating the quality of inductor components based on big data analysis by calling a computer program stored in the memory.

[0094] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement a method for evaluating the quality of an inductor component based on big data analysis provided by the above method embodiment. The electronic device may also include other components for implementing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input / output interface, so as to input and output data. This embodiment will not be described in detail here.

[0095] Example 4

[0096] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0097] When the computer program runs on a computer device, the computer device executes the above-mentioned inductor component quality assessment method based on big data analysis.

[0098] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0099] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0100] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0101] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

Claims

1. A method for evaluating the quality of inductor components based on big data analysis, characterized in that: It includes the following specific steps: Obtain an inductor component to be evaluated, install the inductor component at a specified position on a test circuit, and obtain design parameters of the inductor component; The signal output module inputs a DC and AC signal of a set voltage into the inductor element, wherein the frequency of the AC is 100%-110% of the set threshold value of the inductor element for eliminating the AC frequency, and the signal receiving module receives the output signal after passing through the inductor element and separates the received DC signal and AC signal; The AC signal of the input signal and the AC signal in the output signal are introduced into the AC signal removal effect judgment model to evaluate the AC signal removal effect; The step of importing the acquired AC signal of the input signal and the AC signal in the output signal into the AC signal removal effect judgment model to evaluate the AC signal removal effect includes the following specific steps: S31, obtaining a waveform diagram of the AC signal in the input signal and a waveform diagram of the AC signal in the output signal, placing the waveform diagram of the AC signal in the input signal and the waveform diagram of the AC signal in the output signal on the same scale, making a correspondence between the time length of the output AC signal and the input AC signal, and obtaining an image of the change of the input AC signal over time and an image of the change of the output AC signal over time; S32, importing the acquired image of the change of the input AC signal over time and the image of the change of the output AC signal over time into the AC signal removal coefficient calculation formula to calculate the AC signal removal coefficient, wherein the AC signal removal coefficient calculation formula is: Wherein, T is the test time, u2t is the voltage value of the output AC signal of the inductor element at the test time t, u1t is the voltage value of the input AC signal of the inductor element at the test time t, dt is the time integral, and || is the absolute value symbol; The obtained DC signal of the input signal and the DC signal in the output signal are introduced into the DC signal retention effect judgment model to evaluate the DC signal retention effect; The specific steps of importing the acquired DC signal of the input signal and the DC signal in the output signal into the DC signal retention effect judgment model to evaluate the DC signal retention effect are as follows: S41, obtaining a waveform diagram of a DC signal in an input signal and a waveform diagram of a DC signal in an output signal, placing the waveform diagram of the DC signal in the input signal and the waveform diagram of the DC signal in the output signal on the same scale, making a correspondence between the time length of the output DC signal and the input DC signal, and obtaining an image of the change of the input DC signal over time and an image of the change of the output DC signal over time; S42, importing the acquired image of the change of the input DC signal over time and the acquired image of the change of the output DC signal over time into a DC signal retention coefficient calculation formula to calculate the DC signal retention coefficient, wherein the DC signal retention coefficient calculation formula is: Among them, c t is the difficulty coefficient of retaining the DC signal at time t, I t1 is the voltage value of the output DC signal, I t2 is the voltage value of the input DC signal, where the calculation formula for the retention difficulty coefficient of the DC signal at time t is: The AC signal removal effect evaluation results and the DC signal retention effect evaluation results are imported into the inductor quality analysis strategy to perform inductor quality analysis, and an inductor quality warning is performed based on the inductor quality analysis results.

2. The method for evaluating the quality of an inductor component based on big data analysis according to claim 1, characterized in that: The specific steps of obtaining the inductor element to be evaluated, installing the inductor element at a specified position on the test circuit, and obtaining the design parameters of the inductor element are as follows: S11, obtaining an inductor element to be evaluated, and designing a detection circuit, wherein the detection circuit includes at least a signal output module for releasing a DC and AC signal of a set voltage to the inductor element, a mounting component for mounting the inductor element, and a signal receiving module for the output signal passing through the inductor element, and mounting the inductor element to be detected on the mounting component of the detection circuit; S12. Obtaining a safe operating temperature of the inductor element and an AC frequency elimination threshold, wherein the AC frequency elimination threshold is a minimum value of a set AC frequency range that is blocked from passing.

3. The method for evaluating the quality of an inductor component based on big data analysis according to claim 2, characterized in that: The step of importing the AC signal removal effect evaluation result and the DC signal retention effect evaluation result into the inductor quality analysis strategy to perform inductor quality analysis includes the following specific steps: S51, obtaining the calculated AC signal removal coefficient and DC signal retention coefficient; S52, importing the obtained AC signal removal coefficient and DC signal retention coefficient into the inductance quality analysis value calculation formula to calculate the inductance quality analysis value, wherein the inductance quality analysis value calculation formula is: Where N is the total number of inductors in the test batch, Pz is the set evaluation score, r is the proportion of AC signal removal, A 1i is the AC signal removal coefficient of the i-th inductor, A 2i is the DC signal retention coefficient of the i-th inductor; S53: Divide the obtained inductance quality analysis value by the set evaluation score to obtain an inductance score value.

4. The method for evaluating the quality of an inductor component based on big data analysis according to claim 3, characterized in that: The specific steps of performing inductor quality warning according to the inductor quality analysis result are as follows: If the calculated inductance score of the inductor batch to be tested is greater than or equal to 0.95, the quality of the corresponding batch of inductors is judged to be qualified; If the calculated inductance score of the inductor batch to be tested is greater than or equal to 0.8 and less than 0.95, the quality of the corresponding batch of inductors is judged as a level 3 abnormal warning; If the calculated average score of the power amplifier batch to be tested is greater than or equal to 0.7 and less than 0.8, the quality of the corresponding batch of inductors is judged as a level 2 abnormal warning; If the calculated average score of the power amplifier batch to be tested is less than 0.7, the quality of the corresponding batch of inductors is judged as a first-level abnormal warning.

5. An inductor component quality assessment system based on big data analysis, which is implemented based on the inductor component quality assessment method based on big data analysis as claimed in any one of claims 1 to 4, characterized in that: It specifically includes a test unit, an experimental data acquisition unit, an AC signal removal effect evaluation unit, a DC signal retention effect evaluation unit, an inductance quality warning unit and a control unit. The test unit is used to obtain the inductance element that needs to be evaluated, install the inductance element at a specified position on the test circuit, and obtain the design parameters of the inductance element. The experimental data acquisition unit is used for the signal output module to input DC and AC signals of set voltage into the inductance element, and the signal receiving module receives the output signal passing through the inductance element and separates the received DC signal and AC signal.

6. The inductor component quality assessment system based on big data analysis as claimed in claim 5, characterized in that: The AC signal removal effect evaluation unit is used to import the acquired AC signal of the input signal and the AC signal in the output signal into the AC signal removal effect judgment model to perform AC signal removal effect evaluation; the DC signal retention effect evaluation unit is used to import the acquired DC signal of the input signal and the DC signal in the output signal into the DC signal retention effect judgment model to perform DC signal retention effect evaluation; the inductor quality early warning unit is used to import the AC signal removal effect evaluation result and the DC signal retention effect evaluation result into the inductor quality analysis strategy to perform inductor quality analysis, and perform inductor quality early warning according to the inductor quality analysis result; the control unit is used to control the operation of the test unit, the experimental data acquisition unit, the AC signal removal effect evaluation unit, the DC signal retention effect evaluation unit and the inductor quality early warning unit.

7. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the inductor component quality assessment method based on big data analysis as described in any one of claims 1 to 4 by calling the computer program stored in the memory.

8. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes the inductor component quality assessment method based on big data analysis as described in any one of claims 1 to 4.

Images