Sun-shading glass air tightness detection system based on data analysis

Abstract

The invention relates to the technical field of glass detection, in particular to a sun-shading glass air tightness detection system based on data analysis, which is used for solving the problems that the traditional sun-shading glass air tightness detection method is low in detection precision and low in detection efficiency, the quality of sun-shading glass and the stability in the production process cannot be comprehensively and accurately evaluated, and the detection efficiency is low. And the air tightness performance of the sunshade glass is difficult to accurately evaluate. The system can accurately judge whether the sun-shading glass has the air tightness defect or not by analyzing the air tightness in real time, the detection efficiency and accuracy of the air tightness of the sun-shading glass are remarkably improved, personal errors are reduced, potential quality problems are early warned in time, sealant coating equipment is analyzed in real time, and the sealing performance of the sun-shading glass is improved. And the consistency and the stability of the gluing quality can be ensured, so that the quality control level in the sunshade glass manufacturing process is improved, the production cost is effectively reduced, the air tightness and the service life of a final product are ensured, and the market competitiveness of an enterprise is improved.

Description

Technical Field

[0001] The invention relates to the technical field of glass detection, and in particular to a sunshade glass air tightness detection system based on data analysis. Background Art

[0002] With the development of building technology and the automobile industry, the application of sunshade glass is becoming more and more extensive. Sunshade glass not only needs to have good sunshade performance, but its airtightness also plays a vital role in ensuring the stability of the internal environment, energy saving and the service life of the product. For example, in the field of construction, sunshade glass with poor airtightness will cause excessive heat exchange between indoor and outdoor, increase the energy consumption of air conditioning and other cooling and heating equipment; in the automotive field, it will affect the comfort of the car and the normal operation of electronic equipment.

[0003] Traditional sunshade glass air tightness testing methods mostly rely on manual operation, with low testing efficiency and easy to be affected by human factors, making it difficult to ensure the stability and reliability of the test results. In addition, the air tightness of sunshade glass depends largely on the quality of the glue coating process, while the existing glue coating equipment has limited monitoring means and lacks real-time and intelligence, resulting in large fluctuations in glue coating quality, which in turn affects the air tightness of sunshade glass.

[0004] How to improve the traditional sunshade glass air tightness detection method often has low detection accuracy and low detection efficiency, and cannot comprehensively and accurately evaluate the quality of sunshade glass and the stability in the production process. It is difficult to accurately evaluate the air tightness performance of sunshade glass, which is not conducive to the optimization of production technology and the continuous improvement of product quality. It is difficult to meet the needs of modern industrial production for high-quality and high-efficiency detection.

[0005] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0006] In order to overcome the above-mentioned technical problems, the purpose of the present invention is to provide a sunshade glass air tightness detection system based on data analysis, which solves the problems that traditional sunshade glass air tightness detection methods often have low detection accuracy and low detection efficiency, and are unable to comprehensively and accurately evaluate the quality of the sunshade glass and the stability of the production process, and it is difficult to accurately evaluate the air tightness performance of the sunshade glass.

[0007] The purpose of the present invention can be achieved through the following technical solutions: Sunshade glass air tightness detection system based on data analysis, including: A glass detection module, used to obtain glass detection parameters of the detection object i and send the glass detection parameters to the information processing module; wherein the glass detection parameters include pressure information YL and concentration information ND; An information processing module, used to obtain a glass detection coefficient BJi according to glass detection parameters, and send the glass detection coefficient BJi to an analysis and judgment module; The specific process of the information processing module obtaining the glass detection coefficient BJi is as follows: The pressure information YL and the concentration information ND are normalized, and the values ​​of the pressure information YL and the concentration information ND are quantified according to a preset glass detection model to obtain a glass detection coefficient BJi; Among them, the glass detection model is as follows: Where: λ is the preset error adjustment factor, which is set to 0.968; π is a mathematical constant; κ1 and κ2 are preset weight factors corresponding to the set pressure information YL and concentration information ND, respectively. κ1 and κ2 satisfy κ2>κ1>2.188, and κ1=2.63 and κ2=3.25 are taken; An analysis and judgment module is used to generate a glass abnormality warning instruction according to the glass detection coefficient BJi, and send the glass abnormality warning instruction to the abnormality warning module; The abnormality warning module is used to sound the glass abnormality warning alarm after receiving the glass abnormality warning instruction.

[0008] As a further solution of the present invention: the specific process of the glass detection module obtaining glass detection parameters is as follows: All the sunshade glass products to be tested for air tightness are marked as test objects i in sequence, where i=1, ..., n, where n is a positive integer, i is the number of any test object, and n is the total number of test objects. The test object i is placed in a closed box, and the interior of the closed box is equally divided into two closed chambers using the test object i, and they are marked as the first closed chamber and the second closed chamber respectively; The indoor pressures of the first closed chamber and the second closed chamber are obtained, and are marked as the first initial pressure value CY1 and the second initial pressure value CY2, respectively. Nitrogen is introduced into the first closed chamber to make the indoor pressure reach the preset pressure. After the preset time, the indoor pressures of the first closed chamber and the second closed chamber are obtained, and are marked as the first actual pressure value SY1 and the second actual pressure value SY2, respectively. The difference between the first initial pressure value CY1 and the first actual pressure value SY1 is obtained, and is marked as the first pressure change value YB1. The difference between the second initial pressure value CY2 and the second actual pressure value SY2 is obtained, and is marked as the second pressure change value YB2. The first pressure change value YB1 and the second pressure change value YB2 are quantified, the values ​​of the first pressure change value YB1 and the second pressure change value YB2 are extracted, and are substituted into the formula for calculation. According to the formula Obtain pressure information YL; wherein y1 and y2 are preset proportional coefficients corresponding to the set first pressure change value YB1 and the second pressure change value YB2, respectively, y1 and y2 satisfy y1+y2=1, 0<y1<y2<1, y1=0.31, y2=0.69; The indoor hydrogen concentrations of the first closed chamber and the second closed chamber are obtained, and they are marked as the first initial concentration value CN1 and the second initial concentration value CN2, respectively. Hydrogen is introduced into the first closed chamber to make its indoor concentration reach a preset concentration. After a preset time, the indoor hydrogen concentrations of the first closed chamber and the second closed chamber are obtained, and they are marked as the first actual concentration value SN1 and the second actual concentration value SN2, respectively. The difference between the first initial concentration value CN1 and the first actual concentration value SN1 is obtained, and it is marked as the first concentration change value NB1. The difference between the second initial concentration value CN2 and the second actual concentration value SN2 is obtained, and it is marked as the second concentration change value NB2. The first concentration change value NB1 and the second concentration change value NB2 are quantified, and the values ​​of the first concentration change value NB1 and the second concentration change value NB2 are extracted, and they are substituted into the formula for calculation. According to the formula Concentration information ND is obtained; wherein e is a mathematical constant, d1 and d2 are preset proportional coefficients corresponding to the first concentration change value NB1 and the second concentration change value NB2, respectively, d1 and d2 satisfy d1+d2=1, 0<d1<d2<1, and d1=0.22 and d2=0.78; The pressure information YL and concentration information ND are sent to the information processing module.

[0009] As a further solution of the present invention: the specific process of the analysis and judgment module generating the glass abnormality warning instruction is as follows: Compare the glass detection coefficient BJi with the preset glass detection threshold BJz, and the comparison results are as follows: If the glass detection coefficient BJi ≥ the glass detection threshold BJz, the detection object i corresponding to the glass detection coefficient BJi is marked as unqualified sunshade glass, and a glass abnormality warning instruction is generated and sent to the abnormality warning module; If the glass detection coefficient BJi is less than the glass detection threshold BJz, the detection object i corresponding to the glass detection coefficient BJi is marked as a qualified sunshade glass; Obtain the number of unqualified sunshade glass and qualified sunshade glass in the preset total number of tests, and mark them as unqualified value BL and qualified value HL respectively, quantify the unqualified value BL and qualified value HL, extract the values ​​of the unqualified value BL and qualified value HL, and substitute them into the formula for calculation. Get the glass anomaly coefficient BY; The glass anomaly coefficient BY is compared with the preset glass anomaly threshold BYz, and the comparison results are as follows: If the glass abnormality coefficient BY≥glass abnormality threshold BYz, a device monitoring instruction is generated and sent to the device monitoring module.

[0010] As a further solution of the present invention: the sunshade glass air tightness detection system based on data analysis also includes: The equipment monitoring module is used to obtain the equipment monitoring parameters of the sealant coating equipment after receiving the equipment monitoring instruction, and send the equipment monitoring parameters to the information processing module; wherein the equipment monitoring parameters include the coating information TJ, the vibration information ZD and the temperature and pressure information WY.

[0011] As a further solution of the present invention: the specific process of the equipment monitoring module obtaining equipment monitoring parameters is as follows: After receiving the equipment monitoring instruction, the sealant extrusion amount per unit time during the process of the sealant coating equipment coating the unqualified sunshade glass and the preset standard sealant extrusion amount are obtained, and the difference between the two is obtained, and it is marked as the glue amount value JL. The maximum thickness and minimum thickness of the sealant in the preset coating area after the sealant coating equipment coats the unqualified sunshade glass are obtained, and the difference between the two is obtained, and it is marked as the glue thickness value JH. The glue amount value JL and the glue thickness value JH are quantified, and the values ​​of the glue amount value JL and the glue thickness value JH are extracted, and they are substituted into the formula for calculation. According to the formula The glue coating information TJ is obtained; wherein t1 and t2 are preset proportional coefficients corresponding to the set glue quantity value JL and glue thickness value JH respectively, t1 and t2 satisfy t1+t2=1, 0<t1<t2<1, and t1=0.37 and t2=0.63; Obtain the vibration frequency and maximum vibration displacement per unit time during the process of applying sealant to unqualified sunshade glass by the sealant coating equipment, and mark them as the vibration frequency value ZC and the vibration displacement value ZY respectively, draw a diamond figure by using the values ​​of the vibration frequency value ZC and the vibration displacement value ZY as the diagonal length of the diamond, obtain the area of ​​the diamond figure, and mark it as vibration information ZD; Obtain the internal temperature of the sealant coating equipment and the preset standard temperature, obtain the difference between the two, and mark it as the internal temperature value NW; obtain the internal pressure of the sealant coating equipment and the preset standard pressure, obtain the difference between the two, and mark it as the internal pressure value NY; quantify the internal temperature value NW and the internal pressure value NY, extract the values ​​of the internal temperature value NW and the internal pressure value NY, and substitute them into the formula for calculation; according to the formula The temperature and pressure information WY is obtained; wherein, e is a mathematical constant, w1 and w2 are respectively the preset proportional coefficients corresponding to the set internal temperature value NW and the internal pressure value NY, w1 and w2 satisfy w1+w2=1, 0<w2<w1<1, and w1=0.58 and w2=0.42; The glue coating information TJ, vibration information ZD and temperature and pressure information WY are sent to the information processing module.

[0012] As a further solution of the present invention: the information processing module is also used to obtain the equipment monitoring coefficient SJi according to the equipment monitoring parameters, and send the equipment monitoring coefficient SJi to the analysis and judgment module.

[0013] As a further solution of the present invention: the specific process of the information processing module obtaining the equipment monitoring coefficient SJi is as follows: The glue coating information TJ, vibration information ZD and temperature and pressure information WY are normalized, and the values ​​of the glue coating information TJ, vibration information ZD and temperature and pressure information WY are quantified according to the preset equipment monitoring model to obtain the equipment monitoring coefficient SJi; Among them, the equipment monitoring model is as follows: Where: η is the preset error adjustment factor, and η=1.109; e and π are both mathematical constants; δ1, δ2 and δ3 are preset weight factors corresponding to the set glue coating information TJ, vibration information ZD and temperature and pressure information WY respectively. δ1, δ2 and δ3 satisfy δ1>δ2>δ3>1.429, and δ1=2.86, δ2=2.18, δ3=1.71; The equipment monitoring coefficient SJi is sent to the analysis and judgment module.

[0014] As a further solution of the present invention: the analysis and judgment module is also used to generate an equipment abnormality warning instruction according to the equipment monitoring coefficient SJi, and send the equipment abnormality warning instruction to the abnormal warning module.

[0015] As a further solution of the present invention: the specific process of the analysis and judgment module generating the device abnormality warning instruction is as follows: The equipment monitoring coefficient SJi is compared with the preset equipment monitoring threshold SJz. The comparison results are as follows: If the equipment monitoring coefficient SJi≥equipment monitoring threshold SJz, an equipment abnormality warning instruction is generated and sent to the abnormality warning module.

[0016] As a further solution of the present invention: the abnormal warning module is also used to sound an equipment abnormal warning alarm after receiving an equipment abnormal warning instruction.

[0017] Beneficial effects of the present invention: The sunshade glass air tightness detection system based on data analysis of the present invention first detects the sunshade glass product to obtain glass detection parameters, and then uses data analysis technology to obtain a glass detection coefficient based on the glass detection parameters to comprehensively measure the abnormality of the air tightness of the sunshade glass, and the larger the glass detection parameter, the higher the abnormality. When there are too many sunshade glasses with high abnormality, the sealant coating equipment that affects the air tightness of the sunshade glass is monitored to obtain equipment monitoring parameters, and then uses data analysis technology to obtain an equipment monitoring coefficient based on the equipment monitoring parameters to comprehensively measure the abnormality of the sealant coating equipment state, and the larger the equipment monitoring coefficient, the higher the abnormality, and finally an abnormal alarm is issued; The sunshade glass airtightness detection system based on data analysis of the present invention can accurately determine whether the sunshade glass has airtightness defects by performing real-time analysis of airtightness, significantly improving the detection efficiency and accuracy of the sunshade glass airtightness, reducing human errors, and timely warning of potential quality problems, thereby improving the production efficiency and quality stability of the sunshade glass. It also performs real-time analysis of the sealant coating equipment to ensure the consistency and stability of the coating quality, thereby improving the quality control level of the sunshade glass manufacturing process, effectively reducing production costs, ensuring the airtightness and service life of the final product, and improving the market competitiveness of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0019] Figure 1 The figure is a principle block diagram of the sunshade glass air tightness detection system based on data analysis of the present invention.

[0020] Figure 2 The present invention is a flow chart of the working method of the sunshade glass air tightness detection system based on data analysis. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described 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, 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. Example

[0022] This embodiment is a sunshade glass air tightness detection system based on data analysis, which includes the following modules: glass detection module, information processing module, analysis and judgment module, abnormal warning module and equipment monitoring module; The glass detection module is used to obtain glass detection parameters of the detection object i and send the glass detection parameters to the information processing module; wherein the glass detection parameters include pressure information YL and concentration information ND; The information processing module is used to obtain the glass detection coefficient BJi according to the glass detection parameters, and send the glass detection coefficient BJi to the analysis and judgment module; it is also used to obtain the equipment monitoring coefficient SJi according to the equipment monitoring parameters, and send the equipment monitoring coefficient SJi to the analysis and judgment module; The analysis and judgment module is used to generate a glass abnormality warning instruction and an equipment monitoring instruction according to the glass detection coefficient BJi, and send the glass abnormality warning instruction to the abnormal warning module, and send the equipment monitoring instruction to the equipment monitoring module; it is also used to generate an equipment abnormality warning instruction according to the equipment monitoring coefficient SJi, and send the equipment abnormality warning instruction to the abnormal warning module; The abnormality warning module is used to sound a glass abnormality warning alarm after receiving a glass abnormality warning instruction; and is also used to sound a device abnormality warning alarm after receiving a device abnormality warning instruction; Among them, the equipment monitoring module is used to obtain the equipment monitoring parameters of the sealant coating equipment after receiving the equipment monitoring instruction, and send the equipment monitoring parameters to the information processing module; wherein the equipment monitoring parameters include glue coating information TJ, vibration information ZD and temperature and pressure information WY. Example

[0023] This embodiment is a working method of a sunshade glass air tightness detection system based on data analysis, comprising the following steps: Step 1: The glass detection module obtains glass detection parameters of the detection object i, wherein the glass detection parameters include pressure information YL and concentration information ND, and sends the glass detection parameters to the information processing module; Step 2: The information processing module obtains the glass detection coefficient BJi according to the glass detection parameters, and sends the glass detection coefficient BJi to the analysis and judgment module; Step 3: The analysis and judgment module generates a glass abnormality warning instruction and an equipment monitoring instruction according to the glass detection coefficient BJi, and sends the glass abnormality warning instruction to the abnormality warning module and sends the equipment monitoring instruction to the equipment monitoring module; Step 4: After the abnormality warning module receives the glass abnormality warning instruction, the glass abnormality warning alarm sounds; Step 5: The equipment monitoring module obtains the equipment monitoring parameters of the sealant coating equipment after receiving the equipment monitoring instruction, wherein the equipment monitoring parameters include coating information TJ, vibration information ZD and temperature and pressure information WY, and sends the equipment monitoring parameters to the information processing module; Step 6: The information processing module obtains the equipment monitoring coefficient SJi according to the equipment monitoring parameters, and sends the equipment monitoring coefficient SJi to the analysis and judgment module; Step 7: The analysis and judgment module generates an equipment abnormality warning instruction according to the equipment monitoring coefficient SJi, and sends the equipment abnormality warning instruction to the abnormality warning module; Step 8: After receiving the device abnormality warning instruction, the abnormality warning module sounds the device abnormality warning alarm. Example

[0024] Based on any of the above embodiments, Embodiment 3 of the present invention is a glass detection module. The function of the glass detection module is to obtain glass detection parameters, wherein the glass detection parameters include pressure information YL and concentration information ND. The specific process is as follows: The glass detection module marks all the sunshade glass products to be tested for air tightness as detection objects i in sequence, where i=1, ..., n, where n is a positive integer, i is the number of any detection object, and n is the total number of detection objects. The detection object i is placed in a closed box, and the interior of the closed box is equally divided into two closed chambers using the detection object i, and the two chambers are marked as the first closed chamber and the second closed chamber respectively; The glass detection module obtains the indoor pressure of the first closed chamber and the second closed chamber, and marks them as the first initial pressure value CY1 and the second initial pressure value CY2 respectively, introduces nitrogen into the first closed chamber to make its indoor pressure reach the preset pressure, obtains the indoor pressure of the first closed chamber and the second closed chamber after the preset time, and marks them as the first actual pressure value SY1 and the second actual pressure value SY2 respectively, obtains the difference between the first initial pressure value CY1 and the first actual pressure value SY1, and marks it as the first pressure change value YB1, obtains the difference between the second initial pressure value CY2 and the second actual pressure value SY2, and marks it as the second pressure change value YB2, quantifies the first pressure change value YB1 and the second pressure change value YB2, extracts the values ​​of the first pressure change value YB1 and the second pressure change value YB2, and substitutes them into the formula for calculation, according to the formula Obtain pressure information YL; wherein y1 and y2 are preset proportional coefficients corresponding to the set first pressure change value YB1 and the second pressure change value YB2, respectively, y1 and y2 satisfy y1+y2=1, 0<y1<y2<1, y1=0.31, y2=0.69; The glass detection module obtains the indoor hydrogen concentrations of the first closed chamber and the second closed chamber, and marks them as the first initial concentration value CN1 and the second initial concentration value CN2 respectively, introduces hydrogen into the first closed chamber and makes its indoor concentration reach a preset concentration, obtains the indoor hydrogen concentrations of the first closed chamber and the second closed chamber after a preset time, and marks them as the first actual concentration value SN1 and the second actual concentration value SN2 respectively, obtains the difference between the first initial concentration value CN1 and the first actual concentration value SN1, and marks it as the first concentration change value NB1, obtains the difference between the second initial concentration value CN2 and the second actual concentration value SN2, and marks it as the second concentration change value NB2, quantifies the first concentration change value NB1 and the second concentration change value NB2, extracts the values ​​of the first concentration change value NB1 and the second concentration change value NB2, and substitutes them into the formula for calculation, according to the formula Concentration information ND is obtained; wherein e is a mathematical constant, d1 and d2 are preset proportional coefficients corresponding to the first concentration change value NB1 and the second concentration change value NB2, respectively, d1 and d2 satisfy d1+d2=1, 0<d1<d2<1, and d1=0.22 and d2=0.78; The glass detection module sends the pressure information YL and the concentration information ND to the information processing module. Example

[0025] Based on any of the above embodiments, Embodiment 4 of the present invention is an information processing module, and the information processing module has two functions: One of the functions is to obtain the glass detection coefficient BJi. The specific process is as follows: The information processing module normalizes the pressure information YL and the concentration information ND, and quantifies the values ​​of the pressure information YL and the concentration information ND according to a preset glass detection model to obtain a glass detection coefficient BJi; Among them, the glass detection model is as follows: Where: λ is the preset error adjustment factor, which is set to 0.968; π is a mathematical constant; κ1 and κ2 are preset weight factors corresponding to the set pressure information YL and concentration information ND, respectively. κ1 and κ2 satisfy κ2>κ1>2.188, and κ1=2.63 and κ2=3.25 are taken; The information processing module sends the glass detection coefficient BJi to the analysis and judgment module; The second function is to obtain the equipment monitoring coefficient SJi. The specific process is as follows: The information processing module normalizes the glue coating information TJ, vibration information ZD and temperature and pressure information WY, and quantifies the values ​​of the glue coating information TJ, vibration information ZD and temperature and pressure information WY according to the preset equipment monitoring model to obtain the equipment monitoring coefficient SJi; Among them, the equipment monitoring model is as follows: Where: η is the preset error adjustment factor, and η=1.109; e and π are both mathematical constants; δ1, δ2 and δ3 are preset weight factors corresponding to the set glue coating information TJ, vibration information ZD and temperature and pressure information WY respectively. δ1, δ2 and δ3 satisfy δ1>δ2>δ3>1.429, and δ1=2.86, δ2=2.18, δ3=1.71; The information processing module sends the equipment monitoring coefficient SJi to the analysis and judgment module. Example

[0026] Based on any of the above embodiments, Embodiment 5 of the present invention is an analysis and judgment module, which has two functions: One of the functions is to generate glass abnormality warning instructions and equipment monitoring instructions. The specific process is as follows: The analysis and judgment module compares the glass detection coefficient BJi with the preset glass detection threshold BJz. The comparison results are as follows: If the glass detection coefficient BJi ≥ the glass detection threshold BJz, the detection object i corresponding to the glass detection coefficient BJi is marked as unqualified sunshade glass, and a glass abnormality warning instruction is generated and sent to the abnormality warning module; If the glass detection coefficient BJi is less than the glass detection threshold BJz, the detection object i corresponding to the glass detection coefficient BJi is marked as a qualified sunshade glass; The analysis and judgment module obtains the number of unqualified sunshade glass and qualified sunshade glass in the preset total number of tests, and marks them as unqualified value BL and qualified value HL respectively, quantifies the unqualified value BL and qualified value HL, extracts the values ​​of the unqualified value BL and qualified value HL, and substitutes them into the formula for calculation. Get the glass anomaly coefficient BY; The glass anomaly coefficient BY is compared with the preset glass anomaly threshold BYz, and the comparison results are as follows: If the glass abnormality coefficient BY ≥ the glass abnormality threshold BYz, a device monitoring instruction is generated and sent to the device monitoring module; The second function is to generate equipment abnormality warning instructions. The specific process is as follows: The analysis and judgment module compares the equipment monitoring coefficient SJi with the preset equipment monitoring threshold SJz. The comparison results are as follows: If the equipment monitoring coefficient SJi≥equipment monitoring threshold SJz, an equipment abnormality warning instruction is generated and sent to the abnormality warning module. Example

[0027] Based on any of the above embodiments, Embodiment 6 of the present invention is an abnormal warning module, and the abnormal warning module has two functions: One function is to sound a glass abnormality warning alarm after receiving a glass abnormality warning command; The second function is to sound the equipment abnormality warning alarm after receiving the equipment abnormality warning instruction. Example

[0028] Based on any of the above embodiments, Embodiment 7 of the present invention is an equipment monitoring module. The function of the equipment monitoring module is to obtain equipment monitoring parameters, wherein the equipment monitoring parameters include glue coating information TJ, vibration information ZD and temperature and pressure information WY. The specific process is as follows: After receiving the equipment monitoring instruction, the equipment monitoring module obtains the sealant extrusion amount per unit time and the preset standard sealant extrusion amount during the sealant coating process of the sealant coating equipment on the unqualified sunshade glass, obtains the difference between the two, and marks it as the glue amount value JL, obtains the maximum thickness and minimum thickness of the sealant in the preset glue coating area after the sealant coating equipment coats the unqualified sunshade glass, obtains the difference between the two, and marks it as the glue thickness value JH, quantifies the glue amount value JL and the glue thickness value JH, extracts the values ​​of the glue amount value JL and the glue thickness value JH, and substitutes them into the formula for calculation. According to the formula The glue coating information TJ is obtained; wherein t1 and t2 are preset proportional coefficients corresponding to the set glue quantity value JL and glue thickness value JH respectively, t1 and t2 satisfy t1+t2=1, 0<t1<t2<1, and t1=0.37 and t2=0.63; The equipment monitoring module obtains the vibration frequency and maximum vibration displacement per unit time during the process of the sealant coating equipment coating the unqualified sunshade glass, and marks them as the vibration frequency value ZC and the vibration displacement value ZY respectively, draws a diamond figure by using the values ​​of the vibration frequency value ZC and the vibration displacement value ZY as the diagonal length of the diamond, obtains the area of ​​the diamond figure, and marks it as vibration information ZD; The equipment monitoring module obtains the internal temperature of the sealant coating equipment and the preset standard temperature, obtains the difference between the two, and marks it as the internal temperature value NW, obtains the internal pressure of the sealant coating equipment and the preset standard pressure, obtains the difference between the two, and marks it as the internal pressure value NY, quantifies the internal temperature value NW and the internal pressure value NY, extracts the values ​​of the internal temperature value NW and the internal pressure value NY, and substitutes them into the formula for calculation. The temperature and pressure information WY is obtained; wherein, e is a mathematical constant, w1 and w2 are respectively the preset proportional coefficients corresponding to the set internal temperature value NW and the internal pressure value NY, w1 and w2 satisfy w1+w2=1, 0<w2<w1<1, and w1=0.58 and w2=0.42; The equipment monitoring module sends the glue coating information TJ, vibration information ZD and temperature and pressure information WY to the information processing module.

[0029] Based on the above embodiments 1-7, the working principle of the present invention is as follows: The sunshade glass airtightness detection system based on data analysis of the present invention obtains glass detection parameters of the detection object through a glass detection module, wherein the glass detection parameters include pressure information and concentration information, obtains a glass detection coefficient according to the glass detection parameters through an information processing module, generates a glass abnormality warning instruction and an equipment monitoring instruction according to the glass detection coefficient through an analysis and judgment module, sounds a glass abnormality warning alarm after receiving the glass abnormality warning instruction through the abnormality warning module, obtains equipment monitoring parameters of the sealant coating equipment through the equipment monitoring module after receiving the equipment monitoring instruction, wherein the equipment monitoring parameters include glue coating information, vibration information and temperature and pressure information, obtains an equipment monitoring coefficient according to the equipment monitoring parameters through an information processing module, generates an equipment abnormality warning instruction according to the equipment monitoring coefficient through an analysis and judgment module, sounds an equipment abnormality warning alarm after receiving the equipment abnormality warning instruction through the abnormality warning module; the system first detects the sunshade glass product to obtain the glass detection parameters, and then uses data analysis technology to obtain the glass according to the glass detection parameters The detection coefficient can comprehensively measure the abnormal degree of airtightness of sunshade glass, and the larger the glass detection parameter, the higher the abnormal degree. When there are too many sunshade glasses with high abnormality, the sealant coating equipment that affects the airtightness of the sunshade glass is monitored to obtain the equipment monitoring parameters. Then, the equipment monitoring coefficient obtained according to the equipment monitoring parameters using data analysis technology can comprehensively measure the abnormality of the sealant coating equipment status, and the larger the equipment monitoring coefficient, the higher the abnormality, and finally an abnormal alarm is issued; the system can accurately determine whether the sunshade glass has airtightness defects by performing real-time analysis of airtightness, significantly improving the detection efficiency and accuracy of airtightness of sunshade glass, reducing human errors, and timely warning of potential quality problems, thereby improving the production efficiency and quality stability of sunshade glass, and also performing real-time analysis of sealant coating equipment to ensure the consistency and stability of glue coating quality, thereby improving the quality control level of the sunshade glass manufacturing process, effectively reducing production costs, ensuring the airtightness and service life of the final product, and improving the market competitiveness of the enterprise.

[0030] The above formulas are all obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by technicians in this field according to actual conditions.

[0031] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0032] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the invention or exceed the scope defined in this application, they shall all fall within the protection scope of the present invention.

Claims

1. Sunshade glass air tightness detection system based on data analysis, characterized in that: include: A glass detection module, used to obtain glass detection parameters of the detection object i and send the glass detection parameters to the information processing module; wherein the glass detection parameters include pressure information YL and concentration information ND; An information processing module, used to obtain a glass detection coefficient BJi according to glass detection parameters, and send the glass detection coefficient BJi to an analysis and judgment module; The specific process of the information processing module obtaining the glass detection coefficient BJi is as follows: The values ​​of the pressure information YL and the concentration information ND are quantified according to the preset glass detection model to obtain the glass detection coefficient BJi; Among them, the glass detection model is as follows: Where: λ is the preset error adjustment factor; π is a mathematical constant; κ1 and κ2 are preset weight factors corresponding to the set pressure information YL and concentration information ND respectively; An analysis and judgment module is used to generate a glass abnormality warning instruction according to the glass detection coefficient BJi, and send the glass abnormality warning instruction to the abnormality warning module; The abnormality warning module is used to sound the glass abnormality warning alarm after receiving the glass abnormality warning instruction.

2. The sunshade glass air tightness detection system based on data analysis according to claim 1 is characterized in that: The specific process of the glass detection module obtaining glass detection parameters is as follows: All the sunshade glass products to be tested for air tightness are marked as test objects i in sequence, where i=1, ..., n, where n is a positive integer, i is the number of any test object, and n is the total number of test objects. The test object i is placed in a closed box, and the interior of the closed box is equally divided into two closed chambers using the test object i, and they are marked as the first closed chamber and the second closed chamber respectively; The indoor pressures of the first closed chamber and the second closed chamber are obtained, and they are marked as the first initial pressure value CY1 and the second initial pressure value CY2, respectively. Nitrogen is introduced into the first closed chamber to make the indoor pressure reach the preset pressure. After the preset time, the indoor pressures of the first closed chamber and the second closed chamber are obtained, and they are marked as the first actual pressure value SY1 and the second actual pressure value SY2, respectively. The difference between the first initial pressure value CY1 and the first actual pressure value SY1 is obtained, and it is marked as the first pressure change value YB1. The difference between the second initial pressure value CY2 and the second actual pressure value SY2 is obtained, and it is marked as the second pressure change value YB2. The first pressure change value YB1 and the second pressure change value YB2 are quantified. According to the formula Obtain pressure information YL; wherein y1 and y2 are preset proportional coefficients corresponding to the set first pressure change value YB1 and the second pressure change value YB2 respectively; The indoor hydrogen concentrations of the first closed chamber and the second closed chamber are obtained, and they are marked as the first initial concentration value CN1 and the second initial concentration value CN2, respectively. Hydrogen is introduced into the first closed chamber to make its indoor concentration reach a preset concentration. After a preset time, the indoor hydrogen concentrations of the first closed chamber and the second closed chamber are obtained, and they are marked as the first actual concentration value SN1 and the second actual concentration value SN2, respectively. The difference between the first initial concentration value CN1 and the first actual concentration value SN1 is obtained, and it is marked as the first concentration change value NB1. The difference between the second initial concentration value CN2 and the second actual concentration value SN2 is obtained, and it is marked as the second concentration change value NB2. The first concentration change value NB1 and the second concentration change value NB2 are quantified. According to the formula Concentration information ND is obtained; wherein e is a mathematical constant, d1 and d2 are preset proportional coefficients corresponding to the set first concentration change value NB1 and the second concentration change value NB2 respectively; The pressure information YL and concentration information ND are sent to the information processing module.

3. The sunshade glass air tightness detection system based on data analysis according to claim 1 is characterized in that: The specific process of the analysis and judgment module generating the glass abnormality warning instruction is as follows: Compare the glass detection coefficient BJi with the preset glass detection threshold BJz, and the comparison results are as follows: If the glass detection coefficient BJi≥glass detection threshold BJz, the detection object i corresponding to the glass detection coefficient BJi is marked as unqualified sunshade glass, and a glass abnormality warning instruction is generated and sent to the abnormal warning module.

4. The sunshade glass air tightness detection system based on data analysis according to claim 1 is characterized in that: Also includes: The equipment monitoring module is used to obtain the equipment monitoring parameters of the sealant coating equipment after receiving the equipment monitoring instruction, and send the equipment monitoring parameters to the information processing module; wherein the equipment monitoring parameters include the coating information TJ, the vibration information ZD and the temperature and pressure information WY.

5. The sunshade glass air tightness detection system based on data analysis according to claim 4 is characterized in that: The specific process of the device monitoring module obtaining device monitoring parameters is as follows: After receiving the equipment monitoring instruction, the sealant extrusion amount per unit time during the process of the sealant coating equipment coating the unqualified sunshade glass and the preset standard sealant extrusion amount are obtained, and the difference between the two is obtained and marked as the glue amount value JL. The maximum and minimum thickness of the sealant in the preset coating area after the sealant coating equipment coats the unqualified sunshade glass is obtained, and the difference between the two is obtained and marked as the glue thickness value JH. The glue amount value JL and the glue thickness value JH are quantified and processed according to the formula Obtain glue coating information TJ; wherein t1 and t2 are preset proportional coefficients corresponding to the set glue quantity value JL and glue thickness value JH respectively; Obtain the vibration frequency and maximum vibration displacement per unit time during the process of applying sealant to unqualified sunshade glass by the sealant coating equipment, and mark them as the vibration frequency value ZC and the vibration displacement value ZY respectively, draw a diamond figure by using the values ​​of the vibration frequency value ZC and the vibration displacement value ZY as the diagonal length of the diamond, obtain the area of ​​the diamond figure, and mark it as vibration information ZD; Obtain the internal temperature of the sealant coating equipment and the preset standard temperature, obtain the difference between the two, and mark it as the internal temperature value NW; obtain the internal pressure of the sealant coating equipment and the preset standard pressure, obtain the difference between the two, and mark it as the internal pressure value NY; quantify the internal temperature value NW and the internal pressure value NY, and use the formula The temperature and pressure information WY is obtained; wherein e is a mathematical constant, and w1 and w2 are preset proportional coefficients corresponding to the set internal temperature value NW and internal pressure value NY respectively; The glue coating information TJ, vibration information ZD and temperature and pressure information WY are sent to the information processing module.

6. The sunshade glass air tightness detection system based on data analysis according to claim 1 is characterized in that: The information processing module is also used to obtain the equipment monitoring coefficient SJi according to the equipment monitoring parameters, and send the equipment monitoring coefficient SJi to the analysis and judgment module.

7. The sunshade glass air tightness detection system based on data analysis according to claim 6 is characterized in that: The specific process of the information processing module obtaining the equipment monitoring coefficient SJi is as follows: The values ​​of the glue coating information TJ, the vibration information ZD and the temperature and pressure information WY are quantified according to the preset equipment monitoring model to obtain the equipment monitoring coefficient SJi; Among them, the equipment monitoring model is as follows: Where: η is the preset error adjustment factor; e and π are both mathematical constants; δ1, δ2 and δ3 are preset weight factors corresponding to the set glue coating information TJ, vibration information ZD and temperature and pressure information WY respectively; The equipment monitoring coefficient SJi is sent to the analysis and judgment module.

8. The sunshade glass air tightness detection system based on data analysis according to claim 1, characterized in that: The analysis and judgment module is also used to generate an equipment abnormality warning instruction according to the equipment monitoring coefficient SJi, and send the equipment abnormality warning instruction to the abnormal warning module.

9. The sunshade glass air tightness detection system based on data analysis according to claim 8, characterized in that: The specific process of the analysis and judgment module generating the device abnormality warning instruction is as follows: The equipment monitoring coefficient SJi is compared with the preset equipment monitoring threshold SJz. The comparison results are as follows: If the equipment monitoring coefficient SJi≥equipment monitoring threshold SJz, an equipment abnormality warning instruction is generated and sent to the abnormality warning module.

10. The sunshade glass air tightness detection system based on data analysis according to claim 1, characterized in that: The abnormality warning module is also used to sound an equipment abnormality warning alarm after receiving an equipment abnormality warning instruction.

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