A method and system for managing guqin production quality inspection data based on big data

Abstract

The present invention discloses a data management method and system for guqin production quality inspection based on big data, which specifically relates to the field of data analysis, including component characteristic collection, characteristic data modeling, comprehensive quality assessment and quality grading determination. The present invention realizes all-round monitoring of guqin quality. Secondly, by analyzing data through mathematical models, the essential laws of guqin quality characteristics are revealed, and the accuracy and reliability of quality assessment are improved. Furthermore, the comprehensive quality assessment module avoids the one-sidedness of single indicator evaluation, making the evaluation more comprehensive and objective. In addition, the quality grades are divided according to the quality trade-off index, which provides convenience for the grading management of guqin. Finally, the implementation of the present invention improves the efficiency and accuracy of guqin quality inspection, provides strong support for the inheritance of guqin production skills, and also provides reference for the quality inspection of other traditional musical instruments.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and more specifically, to a guqin production quality inspection data management method and system based on big data. Background Art

[0002] The existing guqin production quality inspection process mainly relies on some basic inspection equipment and standards. Starting from the selection of raw materials, a series of physical and chemical tests are carried out to ensure the quality of the materials. During the component processing, the size and shape of each component are strictly controlled using precision measuring tools. After the finished product is assembled, a series of structural and functional tests will be carried out to ensure the stability and durability of the guqin. The sound quality test is mainly completed by professional audio testing equipment, which can capture the spectrum, timbre and other parameters of the guqin's sound, providing certain data support for quality assessment.

[0003] However, the existing technology still has many shortcomings in practical application. On the one hand, although there are professional testing equipment, the test items are relatively basic and fail to fully cover all the key indicators of guqin quality. On the other hand, although the sound quality evaluation has data support, it still lacks a unified standard and quantitative evaluation system, which leads to the instability of quality evaluation results and makes it difficult to ensure that every guqin can reach the ideal quality level. Therefore, how to achieve accurate detection and scientific management of guqin production quality has become a major bottleneck restricting the development of the guqin industry. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a guqin production quality inspection data management method and system based on big data, and solves the problems raised in the above-mentioned background technology through the following scheme.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for managing guqin production quality inspection data based on big data, comprising: Step 1: Component characteristic collection: used to collect the contact and structural characteristic data of the guqin components, the structural geometry and acoustic correlation data, and the material texture and mechanical response data; Step 2: Characteristic data modeling: used to establish a mathematical model to analyze the data collected in step 1, including a multi-dimensional coupling model of vibration conduction characteristics, a cross-mapping model of structural geometry and acoustic association, and a dynamic balance model of material and mechanical response; Step 3: Comprehensive quality assessment: This is used to conduct a comprehensive assessment of the quality of the guqin. By establishing a panoramic fusion model, the calculation results of step 2 are imported into the panoramic fusion model to calculate the comprehensive trade-off index of the guqin quality. Step 4: Quality grading: Guqin is divided into quality grades based on the comprehensive quality index of Guqin.

[0006] Preferably, the component contact and structural characteristic data include the micro-roughness of the contact point between the nut and the string, the stress distribution at the glue point between the bottom surface of the dragon gum and the bottom of the guitar, the fractal dimension of the air holes in the resonance cavity on the side of the guitar body, and the difference between the embedding depth of the guitar badge and the flatness of the guitar surface, which are marked as R ys , S lj , D qk and ΔH qh The structural geometry and acoustic correlation data include the rate of change of the height of the yueshan along the string length, the angle deviation between the guillotine and the edge of the tail, the Helmholtz resonance frequency offset of the resonance cavity inside the body, and the correlation coefficient between the torsion angle and tension of the string at the goose foot, which are marked as K yg , Δα lj , Δf hm and ρ yz The material texture and mechanical response data include the local unevenness of the width of the Yueshan string groove, the directional consistency parameter of the dragon gum surface texture, the fractal complexity of the wood texture on the back of the piano body, and the deformation recovery time of the piano surface under the pressure of the finger when pressing the note, which are marked as U and xc , C lw 、F mb and T xf .

[0007] Preferably, the method for collecting the micro-roughness of the contact point between the Yueshan nut and the strings is as follows: an atomic force microscope is used to scan the contact point between the Yueshan nut and the strings to obtain a microscopic surface morphology image, roughness parameters are calculated through Gwyddion analysis, 3-5 scanning areas are selected at each contact point, each area is 10μm×10μm, and the average value is taken as the micro-roughness data of the contact point; the method for collecting the stress distribution at the bonding point between the bottom surface of the dragon gum and the bottom of the qin is as follows: a photoelastic patch method is used to paste a photoelastic material at the bonding point between the bottom surface of the dragon gum and the bottom of the qin, a dynamic load simulating playing is applied to the guqin, a polarized light device is used to observe and photograph the interference fringes generated by the photoelastic patch, and the fringes are analyzed by image processing software to obtain stress distribution data. Data; The method for collecting the fractal dimension of the air holes in the resonance cavity on the side of the piano body is specifically as follows: the resonance cavity on the side of the piano body is equally divided into three areas of upper, middle and lower along the length direction, 5 air holes are randomly selected in each area, and each selected air hole is individually scanned and modeled with the help of a three-dimensional scanner, and the three-dimensional model of each air hole is imported into the fractal analysis software, and the box dimension method is used to calculate its fractal dimension, and the calculation result of the fractal dimension of each air hole is recorded, and finally the average value of the fractal dimension of the 15 air holes is taken as the fractal dimension of the air holes in the resonance cavity of the piano body; The method for collecting the difference between the inlay depth of the guitar badges and the flatness of the guitar surface is specifically as follows: using a laser displacement sensor, first measuring the distance from the surface of the guitar badges to the reference plane, then measuring the distance from the guitar surface around the guitar badges to the reference plane, and then calculating the difference between the two.

[0008] Preferably, the method for collecting the rate of change of the height of the bridge along the chord length direction is as follows: use an electronic caliper to measure the height of the bridge every 10 mm along the chord length direction, measure at least 10 points, and calculate the height change rate through linear regression analysis; the method for collecting the angle deviation between the ridge and the edge of the guitar tail is as follows: use a three-coordinate measuring instrument to measure the angle between the ridge and the edge of the guitar tail, compare it with the design standard angle to obtain the deviation, measure 5-8 times at different positions of the ridge and the edge of the guitar tail, and take the average value; the method for collecting the Helmholtz resonance frequency offset of the resonance cavity inside the guitar body is as follows: within the guitar body A micro-speaker is placed as a sound source, a microphone is used to receive sound signals, the Helmholtz resonance frequency of the resonance cavity of the piano body is measured by a frequency sweep method, and the offset is obtained by comparing it with the theoretical design frequency. The measurement is repeated 3-5 times and an average value is taken. The method for collecting the correlation coefficient between the torsion angle and tension of the strings at the goose foot is specifically as follows: a micro-angle sensor is installed at the goose foot to measure the torsion angle of the strings, and a tension sensor is used to measure the tension of the strings. During the tuning process, the torsion angle and tension data under different tuning states are synchronously collected, and the correlation coefficient between the two is calculated by SPSS.

[0009] Preferably, the method for collecting the local unevenness of the width of the Yueshan string groove is specifically as follows: taking a picture of the Yueshan string groove with an optical microscope, measuring the width of the string groove at different positions through image analysis software, calculating the standard deviation of the width as local unevenness data, and selecting 8-10 measurement points in each string groove; the method for collecting the directional consistency parameter of the dragon gum surface texture is specifically as follows: observing the dragon gum surface texture with a scanning electron microscope, analyzing the texture direction through an image processing algorithm, and calculating the consistency parameter of the texture direction; the method for collecting the fractal complexity of the wood texture on the back of the piano body is specifically as follows: taking a picture of the wood texture on the back of the piano body with a high-resolution camera, importing the fractal analysis software, calculating the fractal complexity of the wood texture through a differential box dimension algorithm, taking pictures and calculating multiple areas on the back of the piano body, and taking the average value; the method for collecting the deformation recovery time of the piano surface under finger pressure when pressing a note is specifically as follows: pasting a micro strain gauge on the piano surface to simulate the pressure of the finger when pressing a note, recording the piano surface deformation data through a strain acquisition system, analyzing the deformation recovery time, performing 5-8 tests at different positions on the piano surface, and taking the average value.

[0010] Preferably, the multi-dimensional coupling model of vibration conduction characteristics is used to analyze component contact and structural characteristic data, which is specifically expressed as: , where X 1 =R ys , X 2 =S lj , X 3 =D qk , X 4 =ΔH qh , , , erf is the error function, sgn is the sign function, V 1 Indicates the comprehensive value of vibration conduction characteristics.

[0011] Preferably, the cross-mapping model of structural geometry and acoustic association is used to analyze structural geometry and acoustic association data, which is specifically expressed as: , where Y 11 =K yg , Y 12 =Δα lj , Y 13 =Δf hm , Y 14 =ρ yz ; Y 21 =Δα lj , Y 22 =Δf hm , Y 23 =ρ yz , Y 24 =K yg ; Y 31 =Δfhm , Y 32 =ρ yz , Y 33 =K yg , Y 34 =Δα lj ; B jk represents the mapping coefficient set according to the interaction relationship between data, atanh is the inverse hyperbolic tangent function, V 2 represents the comprehensive value of structural geometry and acoustic correlation, j represents the cross-combination round of structural geometry and acoustic correlation data, k is used to distinguish different data elements in each round of cross-combination, and Y k Represents the set of k-th data elements under different rounds of cross-combination.

[0012] Preferably, the dynamic equilibrium model of material and mechanical response is used to analyze material texture and mechanical response data, which is specifically expressed as: , where Z 1 =U xc , Z 2 =C lw , Z 3 =F mb , Z 4 =T xf , rank (Z l ) indicates Z l In Z 1 , Z 2 , Z 3 , Z 4 rank in, count(Z l ) = 4, V 3 Represents the comprehensive value of material and mechanical response.

[0013] Preferably, the panoramic fusion model is specifically expressed as: ,in , , QTI represents the comprehensive trade-off index of guqin quality.

[0014] Preferably, when QTI≥0.8, it means that the guqin performs excellently and has collection value; when 0.4≤QTI<0.8, it means that the guqin performs well and can meet high-level performance requirements; when QTI<0.4, it means that many characteristic indicators of the guqin deviate from the ideal values, and the production process needs to be comprehensively improved to improve the overall quality.

[0015] Preferably, a guqin production quality inspection data management system based on big data includes: Component characteristic acquisition module: used to collect the contact and structural characteristic data of the guqin components, the structural geometry and acoustic correlation data, and the material texture and mechanical response data; Characteristic data modeling module: used to establish mathematical models to analyze the data collected by the component characteristic acquisition module, including the multi-dimensional coupling model of vibration conduction characteristics, the cross-mapping model of structural geometry and acoustic association, and the dynamic balance model of material and mechanical response; Comprehensive quality assessment module: used to conduct a comprehensive assessment of the quality of the guqin. By establishing a panoramic fusion model, the calculation results of the characteristic data modeling module are imported into the panoramic fusion model to calculate the comprehensive trade-off index of the guqin quality. Quality grading module: categorizes the quality of guqin according to its comprehensive quality index.

[0016] Technical effects and advantages of the present invention: The present invention realizes all-round monitoring of the production quality of guqin by collecting the data of contact and structural characteristics of guqin components, data of structural geometry and acoustic correlation, and data of material texture and mechanical response. These data cover the key elements of guqin production, can accurately reflect the physical characteristics and acoustic performance of guqin, and provide a scientific basis for the quality assessment of guqin. Secondly, the present invention analyzes the collected data by establishing mathematical models, including a multi-dimensional coupling model of vibration conduction characteristics, a cross-mapping model of structural geometry and acoustic association, and a dynamic balance model of material and mechanical response. These models can deeply explore the intrinsic relationship between data and reveal the essential laws of the quality characteristics of guqin, thereby improving the accuracy and reliability of quality assessment. Furthermore, the present invention adopts a comprehensive quality evaluation module to comprehensively evaluate the quality of the guqin. By establishing a panoramic fusion model, the data of multiple dimensions are integrated into a comprehensive trade-off index, which can comprehensively reflect the overall quality level of the guqin. This method avoids the one-sidedness of a single indicator evaluation and improves the comprehensiveness and objectivity of the evaluation. In addition, the present invention also divides the quality grades of guqin according to the comprehensive weight index of guqin quality, which provides convenience for the graded management of guqin. This graded management method helps consumers and producers better understand the quality status of guqin and promotes the healthy development of the guqin market. Finally, the implementation of this invention not only improves the efficiency and accuracy of guqin production quality inspection, but also provides strong support for the inheritance and development of guqin production skills. Through the application of big data technology, the quality control in the guqin production process is more scientific and standardized, which helps to promote the inheritance and innovation of guqin culture. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the structure of the method of the present invention.

[0018] Figure 2 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0019] 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.

[0020] refer to Figure 1 A method for managing guqin production quality inspection data based on big data is shown, and the specific steps include: Step 1: Component characteristic collection: used to collect the contact and structural characteristic data of the guqin components, the structural geometry and acoustic correlation data, and the material texture and mechanical response data.

[0021] The component contact and structural characteristic data include the micro-roughness of the contact point between the nut and the string, the stress distribution at the glue point between the bottom of the dragon gum and the bottom of the guitar, the fractal dimension of the air holes in the resonance cavity on the side of the guitar body, and the difference between the depth of the guitar badge inlay and the flatness of the guitar surface, which are marked as R ys , S lj , D qk and ΔH qh The structural geometry and acoustic correlation data include the rate of change of the height of the yueshan along the string length, the angle deviation between the guillotine and the edge of the tail, the Helmholtz resonance frequency offset of the resonance cavity inside the body, and the correlation coefficient between the torsion angle and tension of the string at the goose foot, which are marked as K yg , Δα lj , Δf hm and ρ yz The material texture and mechanical response data include the local unevenness of the width of the Yueshan string groove, the directional consistency parameter of the dragon gum surface texture, the fractal complexity of the wood texture on the back of the piano body, and the deformation recovery time of the piano surface under the pressure of the finger when pressing the note, which are marked as U and xc , C lw 、F mb and T xf .

[0022] The method for collecting the micro-roughness of the contact point between the Yueshan string nut and the string is specifically as follows: an atomic force microscope is used to scan the contact point between the Yueshan string nut and the string to obtain a microscopic surface morphology image, roughness parameters are calculated through Gwyddion analysis, 3-5 scanning areas are selected at each contact point, each area is 10μm×10μm, and the average value is taken as the micro-roughness data of the contact point; the method for collecting the stress distribution at the bonding point between the bottom surface of the dragon gum and the bottom of the qin is specifically as follows: a photoelastic patch method is used to paste a photoelastic material at the bonding point between the bottom surface of the dragon gum and the bottom of the qin, a dynamic load simulating playing is applied to the guqin, a polarized light device is used to observe and photograph the interference fringes generated by the photoelastic patch, and the fringes are analyzed by image processing software to obtain stress distribution data; The method for collecting the fractal dimension of the pores in the resonance cavity on the side of the piano body is specifically as follows: the resonance cavity on the side of the piano body is equally divided into three areas of upper, middle and lower along the length direction, 5 pores are randomly selected from each area, and each selected pore is individually scanned and modeled with the help of a three-dimensional scanner, and the three-dimensional model of each pore is imported into the fractal analysis software, and its fractal dimension is calculated using the box dimension method, and the calculation results of the fractal dimension of each pore are recorded, and finally the average value of the fractal dimension of the 15 pores is taken as the fractal dimension of the pores in the resonance cavity of the piano body; The method for collecting the difference between the inlay depth of the guitar badge and the flatness of the guitar surface is specifically as follows: using a laser displacement sensor, first measuring the distance from the surface of the guitar badge to the reference plane, then measuring the distance from the guitar surface around the guitar badge to the reference plane, and then calculating the difference between the two.

[0023] The method for collecting the rate of change of the height of the bridge along the chord length direction is as follows: use an electronic caliper to measure the height of the bridge every 10 mm along the chord length direction, measure at least 10 points, and calculate the height change rate through linear regression analysis; the method for collecting the angle deviation between the dragon gum and the edge of the guitar tail is as follows: use a three-coordinate measuring instrument to measure the angle between the dragon gum and the edge of the guitar tail, compare it with the design standard angle to obtain the deviation, measure 5-8 times at different positions of the dragon gum and the edge of the guitar tail, and take the average value; the method for collecting the Helmholtz resonance frequency offset of the resonance cavity inside the guitar body is as follows: place a A micro speaker is set as a sound source, a microphone is used to receive sound signals, the Helmholtz resonance frequency of the resonance cavity of the piano body is measured by a frequency sweep method, and the offset is obtained by comparing it with the theoretical design frequency. The measurement is repeated 3-5 times and the average value is taken. The method for collecting the correlation coefficient between the torsion angle and tension of the strings at the goose foot is specifically as follows: a micro angle sensor is installed at the goose foot to measure the torsion angle of the strings, and a tension sensor is used to measure the tension of the strings. During the tuning process, the torsion angle and tension data under different tuning states are synchronously collected, and the correlation coefficient between the two is calculated by SPSS.

[0024] The method for collecting the local unevenness of the width of the Yueshan string groove is specifically as follows: taking a picture of the Yueshan string groove with an optical microscope, measuring the width of the string groove at different positions through image analysis software, calculating the standard deviation of the width as the local unevenness data, and selecting 8-10 measurement points in each string groove; the method for collecting the directional consistency parameter of the dragon gum surface texture is specifically as follows: observing the dragon gum surface texture with a scanning electron microscope, analyzing the texture direction through an image processing algorithm, and calculating the consistency parameter of the texture direction; the method for collecting the fractal complexity of the wood texture on the back of the piano body is specifically as follows: taking a picture of the wood texture on the back of the piano body with a high-resolution camera, importing the fractal analysis software, calculating the fractal complexity of the wood texture through a differential box dimension algorithm, taking pictures and calculating multiple areas on the back of the piano body, and taking the average value; the method for collecting the deformation recovery time of the piano surface under finger pressure when pressing a note is specifically as follows: pasting a micro strain gauge on the piano surface to simulate the pressure of the finger when pressing a note, recording the piano surface deformation data through a strain acquisition system, analyzing and obtaining the deformation recovery time, performing 5-8 tests at different positions on the piano surface, and taking the average value.

[0025] Step 2: Characteristic data modeling: used to establish a mathematical model to analyze the data collected in step 1, including a multi-dimensional coupling model of vibration conduction characteristics, a cross-mapping model of structural geometry and acoustic association, and a dynamic balance model of material and mechanical response.

[0026] The multi-dimensional coupling model of vibration transmission characteristics is used to analyze the contact and structural characteristics data of components, which is specifically expressed as follows: , where X 1 =R ys , X 2 =S lj , X 3 =D qk , X 4 =ΔH qh , , , erf is the error function, sgn is the sign function, V 1 Indicates the comprehensive value of vibration conduction characteristics.

[0027] The cross-mapping model of structural geometry and acoustic association is used to analyze the structural geometry and acoustic association data, which is specifically expressed as: , where Y 11 =K yg , Y 12 =Δα lj , Y 13 =Δf hm , Y 14 =ρ yz ; Y 21 =Δα lj , Y 22 =Δfhm , Y 23 =ρ yz , Y 24 =K yg ; Y 31 =Δf hm , Y 32 =ρ yz , Y 33 =K yg , Y 34 =Δα lj ; B jk represents the mapping coefficient set according to the interaction relationship between data, atanh is the inverse hyperbolic tangent function, V 2 represents the comprehensive value of structural geometry and acoustic correlation, j represents the cross-combination round of structural geometry and acoustic correlation data, k is used to distinguish different data elements in each round of cross-combination, and Y k Represents the set of k-th data elements under different rounds of cross-combination.

[0028] The dynamic equilibrium model of material and mechanical response is used to analyze material texture and mechanical response data, which is specifically expressed as: , where Z 1 =U xc , Z 2 =C lw , Z 3 =F mb , Z 4 =T xf , rank (Z l ) indicates Z l In Z 1 , Z 2 , Z 3 , Z 4 rank in, count(Z l ) = 4, V 3 Represents the comprehensive value of material and mechanical response.

[0029] Step 3: Comprehensive quality assessment: This is used to conduct a comprehensive assessment of the quality of the guqin. By establishing a panoramic fusion model, the calculation results of step 2 are imported into the panoramic fusion model to calculate the comprehensive trade-off index of the guqin's quality.

[0030] The panoramic fusion model is specifically expressed as: ,in , , QTI represents the comprehensive trade-off index of guqin quality.

[0031] Step 4: Quality grading: Guqin is divided into quality grades based on the comprehensive quality index of Guqin.

[0032] When QTI≥0.8, it means that the guqin performs excellently and has collection value; when 0.4≤QTI<0.8, it means that the guqin performs well and can meet high-level performance requirements; when QTI<0.4, it means that many characteristic indicators of the guqin deviate from the ideal values, and the production process needs to be comprehensively improved to improve the overall quality.

[0033] refer to Figure 2 , a guqin production quality inspection data management system based on big data, including: Component characteristic acquisition module: used to collect the contact and structural characteristic data of the guqin components, the structural geometry and acoustic correlation data, and the material texture and mechanical response data; Characteristic data modeling module: used to establish mathematical models to analyze the data collected by the component characteristic acquisition module, including the multi-dimensional coupling model of vibration conduction characteristics, the cross-mapping model of structural geometry and acoustic association, and the dynamic balance model of material and mechanical response; Comprehensive quality assessment module: used to conduct a comprehensive assessment of the quality of the guqin. By establishing a panoramic fusion model, the calculation results of the characteristic data modeling module are imported into the panoramic fusion model to calculate the comprehensive trade-off index of the guqin quality. Quality grading module: categorizes the quality of guqin according to its comprehensive quality index.

[0034] The present invention realizes all-round monitoring of the production quality of guqin by collecting the data of contact and structural characteristics of guqin components, data of structural geometry and acoustic correlation, and data of material texture and mechanical response. These data cover the key elements of guqin production, can accurately reflect the physical characteristics and acoustic performance of guqin, and provide a scientific basis for the quality assessment of guqin. Secondly, the present invention analyzes the collected data by establishing mathematical models, including a multi-dimensional coupling model of vibration conduction characteristics, a cross-mapping model of structural geometry and acoustic association, and a dynamic balance model of material and mechanical response. These models can deeply explore the intrinsic relationship between data and reveal the essential laws of the quality characteristics of guqin, thereby improving the accuracy and reliability of quality assessment. Furthermore, the present invention adopts a comprehensive quality evaluation module to comprehensively evaluate the quality of the guqin. By establishing a panoramic fusion model, the data of multiple dimensions are integrated into a comprehensive trade-off index, which can comprehensively reflect the overall quality level of the guqin. This method avoids the one-sidedness of a single indicator evaluation and improves the comprehensiveness and objectivity of the evaluation. In addition, the present invention also divides the quality grades of guqin according to the comprehensive weight index of guqin quality, which provides convenience for the graded management of guqin. This graded management method helps consumers and producers better understand the quality status of guqin and promotes the healthy development of the guqin market. Finally, the implementation of this invention not only improves the efficiency and accuracy of guqin production quality inspection, but also provides strong support for the inheritance and development of guqin production skills. Through the application of big data technology, the quality control in the guqin production process is more scientific and standardized, which helps to promote the inheritance and innovation of guqin culture.

[0035] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other; Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for managing guqin production quality inspection data based on big data, characterized in that: include: Step 1: Component characteristic collection: used to collect the contact and structural characteristic data of the guqin components, the structural geometry and acoustic correlation data, and the material texture and mechanical response data; Step 2: Characteristic data modeling: used to establish a mathematical model to analyze the data collected in step 1, including a multi-dimensional coupling model of vibration conduction characteristics, a cross-mapping model of structural geometry and acoustic association, and a dynamic balance model of material and mechanical response; Step 3: Comprehensive quality assessment: This is used to conduct a comprehensive assessment of the quality of the guqin. By establishing a panoramic fusion model, the calculation results of step 2 are imported into the panoramic fusion model to calculate the comprehensive trade-off index of the guqin quality. Step 4: Quality grading: Guqin is divided into quality grades based on the comprehensive quality index of Guqin.

2. According to the big data-based guqin production quality inspection data management method of claim 1, it is characterized by: The component contact and structural characteristic data include the micro-roughness of the contact point between the nut and the string, the stress distribution at the glue point between the bottom of the dragon gum and the bottom of the guitar, the fractal dimension of the air holes in the resonance cavity on the side of the guitar body, and the difference between the depth of the guitar badge inlay and the flatness of the guitar surface, which are marked as R ys , S lj , D qk and ΔH qh The structural geometry and acoustic correlation data include the rate of change of the height of the yueshan along the string length, the angle deviation between the guillotine and the edge of the tail, the Helmholtz resonance frequency offset of the resonance cavity inside the body, and the correlation coefficient between the torsion angle and tension of the string at the goose foot, which are marked as K yg , Δα lj , Δf hm and ρ yz The material texture and mechanical response data include the local unevenness of the width of the Yueshan string groove, the directional consistency parameter of the dragon gum surface texture, the fractal complexity of the wood texture on the back of the piano body, and the deformation recovery time of the piano surface under the pressure of the finger when pressing the note, which are marked as U and xc , C lw 、F mb and T xf .

3. The method for managing guqin production quality inspection data based on big data according to claim 2 is characterized in that: The method for collecting the micro-roughness of the contact point between the Yueshan string nut and the string is specifically as follows: an atomic force microscope is used to scan the contact point between the Yueshan string nut and the string to obtain a microscopic surface morphology image, roughness parameters are calculated through Gwyddion analysis, 3-5 scanning areas are selected at each contact point, each area is 10μm×10μm, and the average value is taken as the micro-roughness data of the contact point; the method for collecting the stress distribution at the bonding point between the bottom surface of the dragon gum and the bottom of the qin is specifically as follows: a photoelastic patch method is used to paste a photoelastic material at the bonding point between the bottom surface of the dragon gum and the bottom of the qin, a dynamic load simulating playing is applied to the guqin, a polarized light device is used to observe and photograph the interference fringes generated by the photoelastic patch, and the fringes are analyzed by image processing software to obtain stress distribution data; The method for collecting the fractal dimension of the pores in the resonance cavity on the side of the piano body is specifically as follows: the resonance cavity on the side of the piano body is equally divided into three areas of upper, middle and lower along the length direction, 5 pores are randomly selected from each area, and each selected pore is individually scanned and modeled with the help of a three-dimensional scanner, and the three-dimensional model of each pore is imported into the fractal analysis software, and its fractal dimension is calculated using the box dimension method, and the calculation results of the fractal dimension of each pore are recorded, and finally the average value of the fractal dimension of the 15 pores is taken as the fractal dimension of the pores in the resonance cavity of the piano body; The method for collecting the difference between the inlay depth of the guitar badge and the flatness of the guitar surface is specifically as follows: using a laser displacement sensor, first measuring the distance from the surface of the guitar badge to the reference plane, then measuring the distance from the guitar surface around the guitar badge to the reference plane, and then calculating the difference between the two.

4. The method for managing guqin production quality inspection data based on big data according to claim 2 is characterized in that: The method for collecting the rate of change of the height of the bridge along the chord length direction is as follows: use an electronic caliper to measure the height of the bridge every 10 mm along the chord length direction, measure at least 10 points, and calculate the height change rate through linear regression analysis; the method for collecting the angle deviation between the dragon gum and the edge of the guitar tail is as follows: use a three-coordinate measuring instrument to measure the angle between the dragon gum and the edge of the guitar tail, compare it with the design standard angle to obtain the deviation, measure 5-8 times at different positions of the dragon gum and the edge of the guitar tail, and take the average value; the method for collecting the Helmholtz resonance frequency offset of the resonance cavity inside the guitar body is as follows: place a A micro speaker is set as a sound source, a microphone is used to receive sound signals, the Helmholtz resonance frequency of the resonance cavity of the piano body is measured by a frequency sweep method, and the offset is obtained by comparing it with the theoretical design frequency. The measurement is repeated 3-5 times and the average value is taken. The method for collecting the correlation coefficient between the torsion angle and tension of the strings at the goose foot is specifically as follows: a micro angle sensor is installed at the goose foot to measure the torsion angle of the strings, and a tension sensor is used to measure the tension of the strings. During the tuning process, the torsion angle and tension data under different tuning states are synchronously collected, and the correlation coefficient between the two is calculated by SPSS.

5. The method for managing guqin production quality inspection data based on big data according to claim 2 is characterized in that: The method for collecting the local unevenness of the width of the Yueshan string groove is specifically as follows: taking a picture of the Yueshan string groove with an optical microscope, measuring the width of the string groove at different positions through image analysis software, calculating the standard deviation of the width as the local unevenness data, and selecting 8-10 measurement points in each string groove; the method for collecting the directional consistency parameter of the dragon gum surface texture is specifically as follows: observing the dragon gum surface texture with a scanning electron microscope, analyzing the texture direction through an image processing algorithm, and calculating the consistency parameter of the texture direction; the method for collecting the fractal complexity of the wood texture on the back of the piano body is specifically as follows: taking a picture of the wood texture on the back of the piano body with a high-resolution camera, importing the fractal analysis software, calculating the fractal complexity of the wood texture through a differential box dimension algorithm, taking pictures and calculating multiple areas on the back of the piano body, and taking the average value; the method for collecting the deformation recovery time of the piano surface under finger pressure when pressing a note is specifically as follows: pasting a micro strain gauge on the piano surface to simulate the pressure of the finger when pressing a note, recording the piano surface deformation data through a strain acquisition system, analyzing and obtaining the deformation recovery time, performing 5-8 tests at different positions on the piano surface, and taking the average value.

6. The method for managing guqin production quality inspection data based on big data according to claim 1 is characterized by: The multi-dimensional coupling model of vibration transmission characteristics is used to analyze the contact and structural characteristics data of components, which is specifically expressed as follows: , where X1=R ys , X2=S lj , X3=D qk , X4=ΔH qh , , , erf is the error function, sgn is the sign function, and V1 represents the comprehensive value of the vibration conduction characteristics.

7. The method for managing guqin production quality inspection data based on big data according to claim 1 is characterized by: The cross-mapping model of structural geometry and acoustic association is used to analyze the structural geometry and acoustic association data, which is specifically expressed as follows: , where Y 11 =K yg , Y 12 =Δα lj , Y 13 =Δf hm , Y 14 =ρ yz ; Y 21 =Δα lj , Y 22 =Δf hm , Y 23 =ρ yz , Y 24 =K yg ; Y 31 =Δf hm , Y 32 =ρ yz , Y 33 =K yg , Y 34 =Δα lj ; B jk represents the mapping coefficient set according to the interaction relationship between data, atanh is the inverse hyperbolic tangent function, V2 represents the comprehensive value of structural geometry and acoustic correlation, j represents the cross-combination round of structural geometry and acoustic correlation data, k is used to distinguish different data elements in each round of cross-combination, and Y k Represents the set of k-th data elements under different rounds of cross-combination.

8. The method for managing guqin production quality inspection data based on big data according to claim 1 is characterized by: The dynamic equilibrium model of material and mechanical response is used to analyze material texture and mechanical response data, which is specifically expressed as: , where Z1=U xc , Z2=C lw , Z3=F mb , Z4=T xf , rank (Z l ) indicates Z l Rank in Z1, Z2, Z3, Z4, count (Z l ) = 4, V3 represents the comprehensive value of material and mechanical response.

9. The method for managing guqin production quality inspection data based on big data according to claim 1 is characterized by: The panoramic fusion model is specifically expressed as: ,in , , QTI represents the comprehensive quality trade-off index of guqin.

10. A Guqin production quality inspection data management system based on big data, used to implement a Guqin production quality inspection data management method based on big data as described in any one of claims 1 to 9, characterized in that: include: Component characteristic acquisition module: used to collect the contact and structural characteristic data of the guqin components, the structural geometry and acoustic correlation data, and the material texture and mechanical response data; Characteristic data modeling module: used to establish mathematical models to analyze the data collected by the component characteristic acquisition module, including the multi-dimensional coupling model of vibration conduction characteristics, the cross-mapping model of structural geometry and acoustic association, and the dynamic balance model of material and mechanical response; Comprehensive quality assessment module: used to conduct a comprehensive assessment of the quality of the guqin. By establishing a panoramic fusion model, the calculation results of the characteristic data modeling module are imported into the panoramic fusion model to calculate the comprehensive trade-off index of the guqin quality. Quality grading module: categorizes the quality of guqin according to its comprehensive quality index.

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