Internet-based intelligent earphone quality analysis and test system

Abstract

The invention, which relates to the technical field of earphone quality, discloses an Internet-based intelligent earphone quality analysis and test system comprising a data acquisition module, an electric quantity interval setting module, a data analysis module, a correction coefficient acquisition module and a test value correction module. The change conditions of test values of various test parameters of the earphone under different electric quantities of the earphone are analyzed, and when the various test parameters of the earphone are detected, the real-time test values of the various test parameters of the earphone are corrected and calculated according to the correction coefficients corresponding to the various test parameters respectively; and then actual test values corresponding to the test parameters are obtained, so that the situation that the test results of the test parameters of the earphone are inaccurate due to inconsistent electric quantity is avoided, the probability of misjudgment is reduced, and the accuracy of earphone quality analysis and test is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of earphone quality, and in particular to an intelligent earphone quality analysis and testing system based on the Internet. Background Art

[0002] With the continuous development of science and technology, the demand for smart headphones, as a device that integrates audio playback, voice recognition, smart interconnection and other functions, is growing in the market. Consumers' requirements for the sound quality, comfort, and battery life of smart headphones are also constantly increasing. In order to meet the needs of consumers, smart headphone manufacturers need to strictly control the quality of their products. With the continuous expansion of the smart headphone market and the continuous advancement of technology, smart headphone quality analysis and testing based on the Internet and cloud computing have been more widely used. Smart headphone quality analysis and testing are combined with artificial intelligence, big data and other technologies to achieve more intelligent and precise quality control and exception handling. Cloud infrastructure is used to store and process a large amount of test data, and the test data is processed and stored at the same time, so that the test data can be accessed and called anytime, anywhere. Patent publication number CN115175081A discloses a headphone detection method, device, equipment and computer-readable storage medium, wherein the headphone detection method includes: playing preset test data through the sound-generating device of the headphone to output an acoustic signal; collecting acoustic signals through the microphone of the headphone to obtain the audio data to be detected of the headphone; comparing and analyzing the audio data to be detected with the preset test data to generate the detection result of the headphone. That is, the headphone aging system uses the built-in microphone of the headphone to collect the audio data generated by the specific signal played by the headphone sound-generating device, and uses the collected audio data for the analysis and detection of the headphone playback quality. Compared with the current use of professional audio analysis equipment to detect the headphone, this embodiment does not require additional analysis equipment, reduces production costs, and does not need to wait for the professional audio analysis equipment to be idle, so as to achieve synchronous detection of multiple or multiple pairs of headphones; However, when performing quality analysis tests on headphones, it is impossible to ensure that the battery level of the headphones remains consistent at all times. Fluctuations in the battery level of the headphones may cause instability in the headphone parameters. For example, low battery level may cause the headphone volume to be too low to work properly, or some functions may be restricted. This instability may be regarded as a headphone quality problem in the quality analysis test, which in turn affects the accuracy of the headphone quality analysis and leads to inaccurate quality analysis test results for smart headphones. Based on this, an Internet-based smart headphone quality analysis test system is proposed. Summary of the invention

[0003] The purpose of the present invention is to provide an Internet-based intelligent headphone quality analysis and testing system, which solves the technical problem that when performing quality analysis and testing on the headphone, the power level of the headphone cannot be guaranteed to remain consistent, and the fluctuation of the headphone power may cause instability of the headphone parameters and affect the accuracy of the headphone quality analysis.

[0004] The purpose of the present invention can be achieved through the following technical solutions: An Internet-based intelligent headset quality analysis and testing system, comprising: Data acquisition module: used to obtain the test values ​​of various test parameters of the headset; Power interval setting module: according to the preset calibrated power, set and generate multiple power intervals; Data analysis module: analyzes the test values ​​of various test parameters in each power range; Correction coefficient acquisition module: comprehensively analyzes the normal test values ​​of various test parameters in various power ranges and calculates the correction coefficients of various test parameters; Test value correction module: It corrects and calculates various test parameters of the headset according to the correction coefficient, obtains the actual test value and outputs it.

[0005] As a further solution of the present invention: a specific method of generating multiple power intervals is: By pre-setting n calibrated electrical quantities, the calibrated electrical quantities are sorted in ascending order according to the size of each calibrated electrical quantity, and then every two adjacent calibrated electrical quantities are combined into an electrical quantity interval, thereby generating multiple electrical quantity intervals, n≥2.

[0006] As a further solution of the present invention: the specific method of obtaining the normal test value of each test parameter in each power interval is: S1: randomly select one interval from each power interval as the target power interval; S2: Randomly select one parameter from various test parameters as the analysis parameter; Obtain a value Ca that satisfies a preset condition G among multiple test values ​​Ci of the analysis parameter within the target power range, wherein a refers to the number of values ​​corresponding to the preset condition G in Ci, i is the number corresponding to the test value, i≥a≥1, and compare and analyze the number a of values ​​that meet the preset condition in Ci with the preset limit value Y1, and obtain a normal test value D1 of the analysis parameter within the target power range according to the analysis result; S4: Repeat the above steps S2-S3 to obtain the normal test value Dj of each test parameter within the target power range, where j refers to the number corresponding to the test parameter, j≥1; S5: Repeat the above steps S1-S4 to obtain the normal test value Eej of each test parameter in each power interval, where e refers to the number corresponding to the power interval, e=n-1, e≥1.

[0007] As a further solution of the present invention: the specific method of obtaining the normal test value of the analysis parameter within the target power range is: When the quantity a is greater than the preset limit value Y1, the mean Cp of Ci is defined as the normal test value D1 of the analysis parameter within the target power range; when the quantity a is less than or equal to the preset limit value Y1, the mean of the maximum and minimum values ​​in Ci is defined as the normal test value D1 of the analysis parameter within the target power range.

[0008] As a further solution of the present invention: suppose the preset condition G is specifically: |Ci-Cp|≥Y2, where Y2 is a preset value.

[0009] As a further solution of the present invention: the specific method of obtaining the correction coefficient corresponding to each test parameter is: S01: Select the same parameters as those in step S2 from various test parameters as analysis parameters, obtain the coordinates of the reference points corresponding to the analysis parameters in each power interval, and mark them as Fe (Fxe, Fye), where Fxe is the median of each power interval, and Fye is the normal test value of the analysis parameter in each power interval. The correlation coefficient XG1 between the normal test value of the analysis parameter and the power is calculated by the Pearson correlation formula. When XG1 does not belong to the preset interval, the formula PC1=(FB1-FB0) / (Fx max -Fx min ) Calculate the correction coefficient PC1 corresponding to the analysis parameters, where Fx max and Fx min are the maximum and minimum values ​​in Fxe, respectively, and FB1 and FB0 are the horizontal coordinates of Fx max and Fx min The ordinate value corresponding to the coordinates of the parameter point; When XG1 is within the preset range, the value of the correction coefficient PC1 corresponding to the analysis parameter is calibrated to 0; S02: Repeat step S01 to obtain the correction coefficient PCj corresponding to each test parameter.

[0010] As a further solution of the present invention: the preset interval is [XY1, XY2], wherein XY1 and XY2 are both preset values, XY1 is the lower limit value of the preset interval, and XY2 is the upper limit value of the preset interval, satisfying XY2>XY1.

[0011] As a further solution of the present invention: the Pearson correlation formula is specifically: ; Among them, Fxp and Fyp are the means of Fxe and Fye respectively, e≥r≥1.

[0012] As a further solution of the present invention: the specific method of obtaining the actual test values ​​corresponding to each test parameter is: The actual test value Vj corresponding to each test parameter is obtained by modifying the calculation formula: Vj=Kj×[1+(|YL-SY| / SY)×PCj] and output, where Kj is the real-time test value of each test parameter, SY is the preset calibrated power value, where SY is 55% of the full power of the headset, and YL is the actual power value of the headset when it is tested.

[0013] Beneficial effects of the present invention: (1) The present invention obtains normal test values ​​of each test parameter in each power interval by analyzing the test values ​​of each test parameter of the headset in each power interval, and then comprehensively analyzes the normal test values ​​in each power interval and the normal test values ​​of each test parameter in each power interval to obtain the correction coefficients corresponding to each test parameter. The correction coefficients are used to represent the influence of the power level of the headset on each test parameter during the headset test. When the correction coefficient is a positive value, it means that the normal test value of the test parameter increases with the increase of the headset power level. When the correction coefficient is a negative value, it means that the normal test value of the test parameter decreases with the increase of the headset power level. When the correction coefficient is 0, it means that the normal test value of the test parameter is not affected by the headset power level. (2) The present invention analyzes the changes in the test values ​​of various test parameters of the earphones under different power levels, and when detecting various test parameters of the earphones, corrects and calculates the real-time test values ​​of various test parameters of the earphones according to the correction coefficients corresponding to each test parameter, and then obtains the actual test values ​​corresponding to each test parameter, thereby avoiding the inaccurate test results of various test parameters of the earphones caused by inconsistent power levels, reducing the probability of misjudgment, and further improving the accuracy of the earphone quality analysis test. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention will be further described below in conjunction with the accompanying drawings.

[0015] Figure 1 It is a schematic diagram of the system framework structure of an Internet-based intelligent headset quality analysis and testing system of the present invention; Figure 2 It is a schematic diagram of the method framework structure of an Internet-based intelligent headset quality analysis and testing system of the present invention. DETAILED DESCRIPTION

[0016] 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.

[0017] Embodiment 1 See also Figure 1-Figure 2 As shown, the present invention is an Internet-based intelligent headset quality analysis and testing system, including a data acquisition module, a power interval setting module, a data analysis module and a correction coefficient acquisition module; The data acquisition module acquires the test values ​​of various test parameters of the headset; The power interval setting module is used to set and generate multiple power intervals according to multiple pre-set calibrated power. The specific steps are as follows: n calibrated electric quantities are preset, and the n calibrated electric quantities are randomly selected, and are evenly set or unevenly set. According to the value of each calibrated electric quantity, each calibrated electric quantity is sorted in order from small to large, and then multiple electric quantity intervals are generated according to the values ​​of each two adjacent calibrated electric quantities. Here, n is a preset value, and the specific value is formulated by relevant personnel according to actual needs and usage scenarios, and n≥2; The data analysis module analyzes the test values ​​of various test parameters of the headset in each power interval, and then obtains the normal test values ​​of various test parameters in each power interval. The specific method is as follows: S1: randomly select one interval from each power interval as the target power interval; S2: Select any one parameter from the test parameters as the analysis parameter; S3: obtaining multiple test values ​​of the analysis parameter within the target power range, and marking them as Ci, where i is the number corresponding to the test value, i≥1; The values ​​that meet the preset condition G in the test value Ci are obtained and marked as Ca, where a refers to the number corresponding to the values ​​that meet the preset condition G in Ci, i≥a≥1, and the number a of the values ​​that meet the preset condition in Ci is analyzed, and the number a is compared with the preset limit value Y1. When the number a is greater than the preset limit value Y1, it means that the number of values ​​that meet the preset condition G in Ci is large, and the mean value of Ci is representative, and then the mean value Cp of Ci is defined as the normal test value D1 of the analysis parameter in the target power range. When the number a is less than or equal to the preset limit value Y1, it means that the number of values ​​that meet the preset condition in Ci is small, and the mean value of Ci is not representative, and then the mean of the maximum and minimum values ​​in Ci is defined as the normal test value D1 of the analysis parameter in the target power range, that is, through the formula D1=(C min +C max ) / 2, calculate and obtain the normal test value D1 of the analysis parameter within the target power range, where C max and C min are the maximum and minimum values ​​in Ci respectively. The preset condition G is: |Ci-Cp|≥Y2, where Y2 is the preset value. The specific values ​​of Y1 and Y2 are formulated by relevant personnel according to actual needs; S4: Repeat the above steps S2-S3 to obtain the normal test value Dj of each test parameter within the target power range, where j refers to the number corresponding to the test parameter, j≥1; S5: Repeat the above steps S1-S4 to obtain the normal test value Eej of each test parameter in each power interval, where e refers to the number corresponding to the power interval, e=n-1, e≥1: The correction coefficient acquisition module conducts a comprehensive analysis with the normal test values ​​of each test parameter in each power range, and obtains the correction coefficient corresponding to each test parameter according to the analysis results. The specific method is as follows: S01: Select the same parameters as those in step S2 from various test parameters as analysis parameters; S02: Obtain the coordinates of the reference points corresponding to the analysis parameters in each power interval, and mark them as Fe (Fxe, Fye), where Fxe is the median of each power interval, and Fye is the normal test value of the analysis parameter in each power interval; The correlation coefficient between the normal test value of the analysis parameter and the electric quantity is calculated by the Pearson correlation formula and marked as XG1; The Pearson correlation formula is as follows: ; Wherein, Fxp and Fyp are the means of Fxe and Fye respectively, e≥r≥1; When XG1 is not within the preset range, it means that there is a correlation between the normal test value of the analysis parameter and the power. The normal test value of the analysis parameter will change with the change of power. Then, the formula PC1=(FB1-FB0) / (Fx max -Fx min ) Calculate the correction coefficient PC1 corresponding to the analysis parameters, where Fx max and Fx min are the maximum and minimum values ​​in Fxe, respectively, and FB1 and FB0 are the horizontal coordinates of Fx max and Fx min The ordinate value corresponding to the coordinates of the parameter point; It should be noted that when PC1 is a positive value, it means that the normal test value of the analysis parameter increases with the increase of power, and when PC1 is a negative value, it means that the normal test value of the analysis parameter decreases with the increase of power; When XG1 is within the preset range, it means that there is no correlation between the normal test value of the analysis parameter and the power quantity, and the normal test value of the analysis parameter will not change with the change of power quantity. The correction coefficient PC1 corresponding to the analysis parameter is calibrated to 0; The preset interval is [XY1, XY2], where XY1 and XY2 are both preset values. The specific values ​​are determined by relevant staff according to actual needs. XY1 is the lower limit of the preset interval, and XY2 is the upper limit of the preset interval. XY2>XY1; S03: Repeat steps S01-S02 to obtain the correction coefficient PCj corresponding to each test parameter; By analyzing the test values ​​of various test parameters of the headset in various power intervals, the normal test values ​​of various test parameters in various power intervals are obtained, and then the normal test values ​​in each power interval and the normal test values ​​of various test parameters in each power interval are comprehensively analyzed to obtain the correction coefficients corresponding to various test parameters. The correction coefficients are used to represent the influence of the power of each test parameter during the headset test. When the correction coefficient is a positive value, it means that the normal test value of the test parameter increases with the increase of the headset power. When the correction coefficient is a negative value, it means that the normal test value of the test parameter decreases with the increase of the headset power. When the correction coefficient is 0, it means that the normal test value of the test parameter is not affected by the headset power. Embodiment 2 As the second embodiment of the present invention, when the present application is implemented, compared with the first embodiment, the technical solution of the present embodiment is different from the first embodiment only in that the present embodiment further includes a test value correction module; The test value correction module, when detecting the various test parameters of the headset, corrects and calculates the real-time test values ​​of the various test parameters of the headset according to the correction coefficients corresponding to the various test parameters, and then obtains the actual test values ​​corresponding to the various test parameters and outputs them. The specific method is as follows: The actual test value Vj corresponding to each test parameter is calculated by modifying the calculation formula: Vj=Kj×[1+(|YL-SY| / SY)×PCj] and outputted, where Kj is the real-time test value of each test parameter, obtained by real-time detection by the test equipment, SY is the preset calibrated power value, where SY is 55% of the full power of the headset, and YL is the actual power value of the headset when it is detected; By analyzing the changes in the test values ​​of various test parameters of the earphones under different power levels, and when detecting the various test parameters of the earphones, the real-time test values ​​of various test parameters of the earphones are corrected and calculated according to the correction coefficients corresponding to each test parameter, and then the actual test values ​​corresponding to each test parameter are obtained, so as to avoid inaccurate test results of various test parameters of the earphones due to inconsistent power levels, reduce the probability of misjudgment, and further improve the accuracy of the earphone quality analysis test.

[0018] Embodiment 3 As the third embodiment of the present invention, when the present application is specifically implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments mentioned above for implementation.

[0019] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0020] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An Internet-based intelligent headset quality analysis and testing system, characterized in that: include: Data acquisition module: used to obtain the test values ​​of various test parameters of the headset; Power interval setting module: according to the preset calibrated power, set and generate multiple power intervals; Data analysis module: analyzes the test values ​​of various test parameters in various power intervals to obtain the normal test values ​​of various test parameters in various power intervals; Correction coefficient acquisition module: comprehensively analyzes the normal test values ​​of various test parameters in various power ranges and calculates the correction coefficients of various test parameters; Test value correction module: It corrects and calculates various test parameters of the headset according to the correction coefficient, obtains the actual test value and outputs it.

2. The Internet-based intelligent headset quality analysis and testing system according to claim 1, characterized in that: The specific method of generating multiple power intervals is as follows: By pre-setting n calibrated electrical quantities, the calibrated electrical quantities are sorted in ascending order according to the size of each calibrated electrical quantity, and then every two adjacent calibrated electrical quantities are combined into an electrical quantity interval, thereby generating multiple electrical quantity intervals, n≥2.

3. The Internet-based intelligent headset quality analysis and testing system according to claim 1, characterized in that: The specific method of obtaining the normal test values ​​of each test parameter in each power range is: S1: randomly select one interval from each power interval as the target power interval; S2: Randomly select one parameter from various test parameters as the analysis parameter; Obtain a value Ca that satisfies a preset condition G among multiple test values ​​Ci of the analysis parameter within the target power range, wherein a refers to the number of values ​​corresponding to the preset condition G in Ci, i is the number corresponding to the test value, i≥a≥1, and compare and analyze the number a of values ​​that meet the preset condition in Ci with the preset limit value Y1, and obtain a normal test value D1 of the analysis parameter within the target power range according to the analysis result; S4: Repeat the above steps S2-S3 to obtain the normal test value Dj of each test parameter within the target power range, where j refers to the number corresponding to the test parameter, j≥1; S5: Repeat the above steps S1-S4 to obtain the normal test value Eej of each test parameter in each power interval, where e refers to the number corresponding to the power interval, e=n-1, e≥1.

4. The Internet-based intelligent headset quality analysis and testing system according to claim 3, characterized in that: The specific method of obtaining the normal test value of the analysis parameter within the target power range is: When the quantity a is greater than the preset limit value Y1, the mean Cp of Ci is defined as the normal test value D1 of the analysis parameter within the target power range; when the quantity a is less than or equal to the preset limit value Y1, the mean of the maximum and minimum values ​​in Ci is defined as the normal test value D1 of the analysis parameter within the target power range.

5. The Internet-based intelligent headset quality analysis and testing system according to claim 3, characterized in that: Assume that the preset condition G is specifically: |Ci-Cp|≥Y2, where Y2 is a preset value.

6. The Internet-based intelligent headset quality analysis and testing system according to claim 3, characterized in that: The specific method of obtaining the correction coefficient corresponding to each test parameter is: S01: Select the same parameters as those in step S2 from various test parameters as analysis parameters, obtain the coordinates of the reference points corresponding to the analysis parameters in each power interval, and mark them as Fe (Fxe, Fye), where Fxe is the median of each power interval, and Fye is the normal test value of the analysis parameter in each power interval. The correlation coefficient XG1 between the normal test value of the analysis parameter and the power is calculated by the Pearson correlation formula. When XG1 does not belong to the preset interval, the formula PC1=(FB1-FB0) / (Fx max -Fx min ) Calculate the correction coefficient PC1 corresponding to the analysis parameters, where Fx max and Fx min are the maximum and minimum values ​​in Fxe, respectively, and FB1 and FB0 are the horizontal coordinates of Fx max and Fx min The ordinate value corresponding to the coordinates of the parameter point; When XG1 is within the preset range, the value of the correction coefficient PC1 corresponding to the analysis parameter is calibrated to 0; S02: Repeat step S01 to obtain the correction coefficient PCj corresponding to each test parameter.

7. The Internet-based intelligent headset quality analysis and testing system according to claim 6, characterized in that: The preset interval is [XY1, XY2], where XY1 and XY2 are both preset values, XY1 is the lower limit value of the preset interval, and XY2 is the upper limit value of the preset interval, satisfying XY2>XY1.

8. The Internet-based intelligent headset quality analysis and testing system according to claim 6, characterized in that: The Pearson correlation formula is as follows: ; Among them, Fxp and Fyp are the means of Fxe and Fye respectively, e≥r≥1.

9. The Internet-based intelligent headset quality analysis and testing system according to claim 6, characterized in that: The specific method for obtaining the actual test values ​​corresponding to each test parameter is: The actual test value Vj corresponding to each test parameter is calculated by modifying the calculation formula: Vj=Kj×[1+(|YL-SY| / SY)×PCj], where Kj is the real-time test value of each test parameter, SY is the preset calibrated power value, where SY is 55% of the full power of the headset, and YL is the actual power value of the headset when it is tested.

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