A glass processing quality prediction system based on data analysis

Abstract

The present invention relates to the field of glass processing technology, and in particular to a glass processing quality prediction system based on data analysis, which is used to solve the problem that traditional glass processing quality detection relies on manual inspection and experience judgment, and the manual detection method has the problems of long detection cycle, low efficiency and large error; the system comprises an equipment monitoring module, an environment monitoring module, a data analysis module, an information comparison module and a quality prediction module; the system reduces the dependence on manual detection and improves the accuracy and reliability of data monitoring by performing all-round data collection on factors affecting the glass processing quality, and then uses data analysis technology to realize real-time, continuous and accurate monitoring of the entire glass processing process, and realizes quality prediction based on historical data, so as to facilitate timely emergency response when quality problems occur, reduce rework and waste losses caused by quality problems, and reduce the operating costs of enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of glass processing, and in particular to a glass processing quality prediction system based on data analysis. Background Art

[0002] With the rapid development of the glass manufacturing industry, the requirements for product quality and production efficiency are increasing. Glass processing is a highly automated and refined process involving a variety of physical, chemical and mechanical control parameters. Traditional glass processing quality inspection relies on manual inspection and empirical judgment. The manual inspection method has the problems of long inspection cycle, low efficiency, subjectivity and error, and it is difficult to meet the high requirements of modern industrial production for quality stability and production efficiency. Therefore, it is of great significance to develop a glass processing quality prediction system based on data analysis for the prediction and optimization of glass processing quality. Summary of the invention

[0003] In order to overcome the above-mentioned technical problems, the purpose of the present invention is to provide a glass processing quality prediction system based on data analysis, which solves the problem that traditional glass processing quality detection relies on manual inspection and experience judgment. The manual detection method has the disadvantages of long detection cycle, low efficiency and large error, and it is difficult to meet the high requirements of modern industrial production for quality stability and production efficiency.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A glass processing quality prediction system based on data analysis, comprising:

[0006] The data analysis module is used to obtain the equipment monitoring coefficient SBi according to the equipment monitoring parameters, obtain the environment monitoring coefficient HJi according to the environment monitoring parameters, and send the equipment monitoring coefficient SBi and the environment monitoring coefficient HJi to the information comparison module;

[0007] The specific process of the data analysis module obtaining the equipment monitoring coefficient SBi is as follows:

[0008] Quantize the temperature setting information ST, the power setting information SD and the stability setting information SW, extract the values ​​of the temperature setting information ST, the power setting information SD and the stability setting information SW, perform quantization operation according to the preset equipment monitoring function, and obtain the equipment monitoring coefficient SBi;

[0009] Among them, the device monitoring function is as follows: Where:

[0010] γ is the preset error adjustment factor, and γ=0.896 is taken;

[0011] Both π and e are mathematical constants;

[0012] α1, α2 and α3 are respectively the preset weight factors corresponding to the set temperature information ST, the set power information SD and the set stability information SW, α1, α2 and α3 satisfy α3>α2>α1>1.256, and α1=1.41, α2=1.79, α3=2.06;

[0013] The specific process of the data analysis module obtaining the environmental monitoring coefficient HJi is as follows:

[0014] The temperature and humidity information WS, the surrounding pressure information HY and the surrounding dust information HC are quantified, the values ​​of the temperature and humidity information WS, the surrounding pressure information HY and the surrounding dust information HC are extracted, and the quantization operation is performed according to the preset environmental monitoring function to obtain the environmental monitoring coefficient HJi;

[0015] Among them, the environment monitoring function is as follows:

[0016] Where:

[0017] is the preset error adjustment factor, taking δ=1.158;

[0018] Both π and e are mathematical constants;

[0019] β1, β2 and β3 are respectively the preset weight factors corresponding to the set temperature and humidity information WS, the ring pressure information HY and the ring dust information HC, β1, β2 and β3 satisfy β1>β2>β3>0.821, and β1=1.85, β2=1.44, β3=0.97;

[0020] An information comparison module is used to obtain an equipment deviation coefficient SP according to an equipment monitoring coefficient SBi, obtain an environmental deviation coefficient HP according to an environmental monitoring coefficient HJi, obtain a quality coefficient ZX according to the equipment deviation coefficient SP and the environmental deviation coefficient HP, and send the quality coefficient ZX to a quality prediction module;

[0021] The quality prediction module is used to obtain a quality prediction curve diagram according to the quality coefficient ZX, and update and display the quality prediction curve diagram on the display screen in real time.

[0022] As a further solution of the present invention: the specific process of the information comparison module obtaining the device deviation coefficient SP is as follows:

[0023] Obtain the preset device monitoring threshold SBiy and environment monitoring threshold HJiy;

[0024] The equipment monitoring coefficient SBi and the equipment monitoring threshold SBiy are quantified, the values ​​of the equipment monitoring coefficient SBi and the equipment monitoring threshold SBiy are extracted, and they are substituted into the formula for calculation. According to the formula Get the equipment deviation coefficient SP.

[0025] As a further solution of the present invention: the specific process of the information comparison module obtaining the environmental deviation coefficient HP is as follows:

[0026] The environmental monitoring coefficient HJi and the environmental monitoring threshold HJiy are quantified, the values ​​of the environmental monitoring coefficient HJi and the environmental monitoring threshold HJiy are extracted, and substituted into the formula for calculation. Obtain the environmental deviation coefficient HP.

[0027] As a further solution of the present invention: the specific process of the information comparison module obtaining the quality coefficient ZX is as follows:

[0028] Obtain the equipment deviation coefficient SP and the environmental deviation coefficient HP during all glass processing production processes of the monitoring device i from the historical data, and mark them as the historical equipment deviation coefficient SPk and the historical environmental deviation coefficient HPk respectively;

[0029] Obtain the qualified rate of glass products in the glass processing production process that simultaneously satisfies SP×(1-ζ)≤SPk≤SP×(1+ζ) and HP×(1-ζ)≤HPk≤HP×(1+ζ), and mark it as the qualified value HG; obtain the average value of all qualified values ​​HG, and mark it as the quality coefficient ZX; wherein ζ is a preset screening adjustment factor, and ζ=0.029 is taken;

[0030] The quality coefficient ZX is sent to the quality prediction module.

[0031] As a further solution of the present invention: the specific process of the quality prediction module obtaining the quality prediction curve is as follows:

[0032] A quality prediction curve is drawn in a rectangular coordinate system with the quality coefficient ZX as the ordinate and the time as the abscissa, and the quality prediction curve is updated and displayed in real time on the display screen.

[0033] As a further solution of the present invention: the glass processing quality prediction system based on data analysis also includes:

[0034] The equipment monitoring module is used to obtain the equipment monitoring parameters of the monitoring device i and send the equipment monitoring parameters to the data analysis module; wherein the equipment monitoring parameters include temperature setting information ST, power setting information SD and stability setting information SW.

[0035] As a further solution of the present invention: the specific process of the device monitoring module obtaining the device monitoring parameters is as follows:

[0036] All processing equipment in the glass processing production line are marked as monitoring equipment i in sequence, where i=1, ..., n, n is a positive integer, i is the number of any monitoring equipment, and n is the total number of monitoring equipment;

[0037] Obtain the average device temperature of the first operation process of the monitoring device i, and mark it as the initial temperature value CW; obtain the average device temperature of the current operation process of the monitoring device i, and mark it as the actual temperature value SW; obtain the difference between the initial temperature value CW and the actual temperature value SW, and mark it as the setting temperature information ST;

[0038] Get the average voltage and rated voltage of the monitoring device i during the current operation, get the difference between the two, and mark it as the voltage value DY; get the average current and rated current of the monitoring device i during the current operation, get the difference between the two, and mark it as the current value DL; quantify the voltage value DY and the current value DL, extract the values ​​of the voltage value DY and the current value DL, and substitute them into the formula for calculation; according to the formula The set power information SD is obtained; wherein e is a mathematical constant, s1 and s2 are preset proportional coefficients corresponding to the set voltage value DY and current value DL, respectively, s1 and s2 satisfy s1+s2=1, 0<s2<s1<1, and s1=0.57 and s2=0.43;

[0039] Obtain the vibration frequency, maximum vibration amplitude and maximum sound intensity per unit time of the current operation process of the monitoring device i, and mark them as frequency value PL, amplitude value FD and sound intensity value SQ respectively, quantify the frequency value PL, amplitude value FD and sound intensity value SQ, extract the values ​​of frequency value PL, amplitude value FD and sound intensity value SQ, and substitute them into the formula for calculation. The stabilization information SW is obtained; wherein w1, w2 and w3 are preset proportional coefficients corresponding to the set frequency value PL, amplitude value FD and sound intensity value SQ respectively, w1, w2 and w3 satisfy w1+w2+w3=1, 0<w3<w2<w1<1, and w1=0.42, w2=0.31, w3=0.27;

[0040] The setting temperature information ST, setting power information SD and setting stability information SW are sent to the data analysis module.

[0041] As a further solution of the present invention: the glass processing quality prediction system based on data analysis also includes:

[0042] The environmental monitoring module is used to obtain the environmental monitoring parameters of the environment in which the monitoring device i is located, and send the environmental monitoring parameters to the data analysis module; wherein the environmental monitoring parameters include temperature and humidity information WS, ambient pressure information HY and ambient dust information HC.

[0043] As a further solution of the present invention: the specific process of the environmental monitoring module obtaining environmental monitoring parameters is as follows:

[0044] Get the ambient temperature of the environment where the monitoring device i is located and the preset standard ambient temperature, get the difference between the two, and mark it as the ambient temperature value HW, get the ambient humidity of the environment where the monitoring device i is located and the preset standard ambient humidity, get the difference between the two, and mark it as the ambient humidity value HS, quantify the ambient temperature value HW and the ambient humidity value HS, extract the values ​​of the ambient temperature value HW and the ambient humidity value HS, and substitute them into the formula for calculation. According to the formula The temperature and humidity information WS is obtained; wherein h1 and h2 are the preset proportional coefficients corresponding to the set ambient temperature value HW and ambient humidity value HS, respectively, and h1 and h2 satisfy h1+h2=1, 0<h2

[0045] Obtain the ambient pressure of the environment in which the monitoring device i is located and the preset standard ambient pressure, obtain the difference between the two, and mark it as ambient pressure information HY;

[0046] Obtain the dust content of the environment where the monitoring device i is located, and mark it as environmental dust information HC;

[0047] The temperature and humidity information WS, the ring pressure information HY and the ring dust information HC are sent to the data analysis module.

[0048] Beneficial effects of the present invention:

[0049] A glass processing quality prediction system based on data analysis of the present invention first collects and analyzes data on a monitoring device to obtain device monitoring parameters. The device monitoring coefficient obtained according to the device monitoring parameters can comprehensively measure the abnormality of the monitoring device, and the larger the device monitoring coefficient is, the higher the abnormality is, and the greater the adverse effect on the glass processing quality is. Then, data is collected and analyzed on the environment where the monitoring device is located to obtain environmental monitoring parameters. The environmental monitoring coefficient obtained according to the environmental monitoring parameters can comprehensively measure the abnormality of the environment where the monitoring device is located, and the larger the environmental monitoring coefficient is, the higher the abnormality is, and the greater the adverse effect on the glass processing quality is. Finally, the device deviation coefficient and the environmental deviation coefficient are obtained according to the device monitoring coefficient and the environmental monitoring coefficient, and the abnormal deviation degree of the entire glass processing production line is comprehensively measured. Finally, the quality coefficient obtained according to the device deviation coefficient and the environmental deviation coefficient is used to predict the glass processing quality at the current moment through the glass processing quality of historical data, and then displayed.

[0050] ​The glass processing quality prediction system based on data analysis of the present invention reduces the reliance on manual detection and improves the accuracy and reliability of data monitoring by performing all-round data collection on the factors affecting the glass processing quality. Then, the data analysis technology is used to realize real-time, continuous and precise monitoring of the entire glass processing process, and quality prediction is realized based on historical data, so that emergency response can be made in time when quality problems occur, and rework and waste losses caused by quality problems can be reduced, thereby reducing the operating costs of the enterprise. Therefore, the system not only improves the production efficiency and product quality of glass processing, improves the stability and consistency of products, but also brings significant economic and social benefits to the enterprise, promotes the intelligent and automated development of the glass processing industry, and enhances the competitiveness of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The present invention will be further described below in conjunction with the accompanying drawings.

[0052] Figure 1 It is a principle block diagram of a glass processing quality prediction system based on data analysis in the present invention;

[0053] Figure 2 It is a flow chart of a working method of a glass processing quality prediction system based on data analysis in the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with 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.

[0055] Example 1

[0056] See also Figure 1 As shown, this embodiment is a glass processing quality prediction system based on data analysis, including the following modules: equipment monitoring module, environment monitoring module, data analysis module, information comparison module and quality prediction module;

[0057] The device monitoring module is used to obtain the device monitoring parameters of the monitoring device i and send the device monitoring parameters to the data analysis module; wherein the device monitoring parameters include temperature setting information ST, power setting information SD and stability setting information SW;

[0058] The environmental monitoring module is used to obtain the environmental monitoring parameters of the environment in which the monitoring device i is located, and send the environmental monitoring parameters to the data analysis module; wherein the environmental monitoring parameters include temperature and humidity information WS, ambient pressure information HY and ambient dust information HC;

[0059] The data analysis module is used to obtain the equipment monitoring coefficient SBi according to the equipment monitoring parameters, obtain the environment monitoring coefficient HJi according to the environment monitoring parameters, and send the equipment monitoring coefficient SBi and the environment monitoring coefficient HJi to the information comparison module;

[0060] The information comparison module is used to obtain the equipment deviation coefficient SP according to the equipment monitoring coefficient SBi, obtain the environment deviation coefficient HP according to the environment monitoring coefficient HJi, and obtain the quality coefficient ZX according to the equipment deviation coefficient SP and the environment deviation coefficient HP, and send the quality coefficient ZX to the quality prediction module;

[0061] The quality prediction module is used to obtain a quality prediction curve diagram according to the quality coefficient ZX, and update and display the quality prediction curve diagram on the display screen in real time.

[0062] Example 2

[0063] See also Figure 2 As shown, this embodiment is a working method of a glass processing quality prediction system based on data analysis, comprising the following steps:

[0064] Step 1: The device monitoring module obtains the device monitoring parameters of the monitoring device i, wherein the device monitoring parameters include the setting temperature information ST, the setting power information SD and the setting stability information SW, and sends the device monitoring parameters to the data analysis module;

[0065] Step 2: The environmental monitoring module obtains the environmental monitoring parameters of the environment in which the monitoring device i is located, wherein the environmental monitoring parameters include temperature and humidity information WS, ambient pressure information HY, and ambient dust information HC, and sends the environmental monitoring parameters to the data analysis module;

[0066] Step 3: The data analysis module obtains the equipment monitoring coefficient SBi according to the equipment monitoring parameters, obtains the environmental monitoring coefficient HJi according to the environmental monitoring parameters, and sends the equipment monitoring coefficient SBi and the environmental monitoring coefficient HJi to the information comparison module;

[0067] Step 4: The information comparison module obtains the equipment deviation coefficient SP according to the equipment monitoring coefficient SBi, obtains the environment deviation coefficient HP according to the environment monitoring coefficient HJi, obtains the quality coefficient ZX according to the equipment deviation coefficient SP and the environment deviation coefficient HP, and sends the quality coefficient ZX to the quality prediction module;

[0068] Step 5: The quality prediction module obtains a quality prediction curve according to the quality coefficient ZX, and updates and displays the quality prediction curve on the display screen in real time.

[0069] Example 3

[0070] Based on any of the above embodiments, Embodiment 3 of the present invention is a device monitoring module. The function of the device monitoring module is to obtain device monitoring parameters, wherein the device monitoring parameters include temperature setting information ST, power setting information SD, and stability setting information SW. The specific process is as follows:

[0071] The equipment monitoring module marks all processing equipment in the glass processing production line as monitoring equipment i, i=1, ..., n, n is a positive integer, i is the number of any monitoring equipment, and n is the total number of monitoring equipment;

[0072] The device monitoring module obtains the average device temperature of the monitoring device i during the first operation and marks it as the initial temperature value CW, obtains the average device temperature of the monitoring device i during the current operation and marks it as the actual temperature value SW, obtains the difference between the initial temperature value CW and the actual temperature value SW, and marks it as the setting temperature information ST;

[0073] The equipment monitoring module obtains the average voltage and rated voltage of the monitoring device i during the current operation, obtains the difference between the two, and marks it as the voltage value DY, obtains the average current and rated current of the monitoring device i during the current operation, obtains the difference between the two, and marks it as the current value DL, quantifies the voltage value DY and the current value DL, extracts the values ​​of the voltage value DY and the current value DL, and substitutes them into the formula for calculation. The set power information SD is obtained; wherein e is a mathematical constant, s1 and s2 are preset proportional coefficients corresponding to the set voltage value DY and current value DL, respectively, s1 and s2 satisfy s1+s2=1, 0<s2<s1<1, and s1=0.57 and s2=0.43;

[0074] The equipment monitoring module obtains the vibration frequency, maximum vibration amplitude and maximum sound intensity of the monitoring equipment i in the current operation process per unit time, and marks them as frequency value PL, amplitude value FD and sound intensity value SQ respectively, quantifies the frequency value PL, amplitude value FD and sound intensity value SQ, extracts the values ​​of frequency value PL, amplitude value FD and sound intensity value SQ, and substitutes them into the formula for calculation. The stabilization information SW is obtained; wherein w1, w2 and w3 are preset proportional coefficients corresponding to the set frequency value PL, amplitude value FD and sound intensity value SQ respectively, w1, w2 and w3 satisfy w1+w2+w3=1, 0<w3<w2<w1<1, and w1=0.42, w2=0.31, w3=0.27;

[0075] The equipment monitoring module sends the setting temperature information ST, setting power information SD and setting stability information SW to the data analysis module.

[0076] Example 4

[0077] Based on any of the above embodiments, Embodiment 4 of the present invention is an environment monitoring module. The function of the environment monitoring module is to obtain environment monitoring parameters, wherein the environment monitoring parameters include temperature and humidity information WS, ambient pressure information HY, and ambient dust information HC. The specific process is as follows:

[0078] The environmental monitoring module obtains the ambient temperature of the environment where the monitoring device i is located and the preset standard ambient temperature, obtains the difference between the two, and marks it as the ambient temperature value HW, obtains the ambient humidity of the environment where the monitoring device i is located and the preset standard ambient humidity, obtains the difference between the two, and marks it as the ambient humidity value HS, quantifies the ambient temperature value HW and the ambient humidity value HS, extracts the values ​​of the ambient temperature value HW and the ambient humidity value HS, and substitutes them into the formula for calculation. According to the formula The temperature and humidity information WS is obtained; wherein h1 and h2 are the preset proportional coefficients corresponding to the set ambient temperature value HW and ambient humidity value HS, respectively, and h1 and h2 satisfy h1+h2=1, 0<h2

[0079] The environmental monitoring module obtains the environmental pressure of the environment where the monitoring device i is located and the preset standard environmental pressure, obtains the difference between the two, and marks it as environmental pressure information HY;

[0080] The environment monitoring module obtains the dust content of the environment where the monitoring device i is located and marks it as environmental dust information HC;

[0081] The environmental monitoring module sends the temperature and humidity information WS, the ambient pressure information HY and the ambient dust information HC to the data analysis module.

[0082] Example 5

[0083] Based on any of the above embodiments, Embodiment 5 of the present invention is a data analysis module. The function of the data analysis module is to obtain the equipment monitoring coefficient SBi and the environment monitoring coefficient HJi. The specific process is as follows:

[0084] The data analysis module quantifies the temperature setting information ST, the power setting information SD and the stability setting information SW, extracts the values ​​of the temperature setting information ST, the power setting information SD and the stability setting information SW, performs quantization calculation according to the preset equipment monitoring function, and obtains the equipment monitoring coefficient SBi;

[0085] Among them, the device monitoring function is as follows:

[0086] Where:

[0087] γ is the preset error adjustment factor, and γ=0.896 is taken;

[0088] Both π and e are mathematical constants;​

[0089] α1, α2 and α3 are respectively the preset weight factors corresponding to the set temperature information ST, the set power information SD and the set stability information SW, α1, α2 and α3 satisfy α3>α2>α1>1.256, and α1=1.41, α2=1.79, α3=2.06;

[0090] The data analysis module quantifies the temperature and humidity information WS, the surrounding pressure information HY and the surrounding dust information HC, extracts the values ​​of the temperature and humidity information WS, the surrounding pressure information HY and the surrounding dust information HC, performs quantization calculations according to the preset environmental monitoring function, and obtains the environmental monitoring coefficient HJi;

[0091] Among them, the environment monitoring function is as follows:

[0092] Where:

[0093] is the preset error adjustment factor, taking δ=1.158;

[0094] Both π and e are mathematical constants;

[0095] β1, β2 and β3 are respectively the preset weight factors corresponding to the set temperature and humidity information WS, the ring pressure information HY and the ring dust information HC, β1, β2 and β3 satisfy β1>β2>β3>0.821, and β1=1.85, β2=1.44, β3=0.97;

[0096] The data analysis module sends the equipment monitoring coefficient SBi and the environmental monitoring coefficient HJi to the information comparison module.

[0097] Example 6

[0098] Based on any of the above embodiments, Embodiment 6 of the present invention is an information comparison module. The function of the information comparison module is to obtain the quality coefficient ZX. The specific process is as follows:

[0099] The information comparison module obtains the preset equipment monitoring threshold SBiy and environment monitoring threshold HJiy;

[0100] The information comparison module quantifies the equipment monitoring coefficient SBi and the equipment monitoring threshold SBiy, extracts the values ​​of the equipment monitoring coefficient SBi and the equipment monitoring threshold SBiy, and substitutes them into the formula for calculation. Get the equipment deviation coefficient SP;

[0101] The information comparison module quantifies the environmental monitoring coefficient HJi and the environmental monitoring threshold HJiy, extracts the values ​​of the environmental monitoring coefficient HJi and the environmental monitoring threshold HJiy, and substitutes them into the formula for calculation. Get the environmental deviation coefficient HP;

[0102] The information comparison module obtains the equipment deviation coefficient SP and the environmental deviation coefficient HP during all glass processing production processes of the monitoring device i from the historical data, and marks them as the historical equipment deviation coefficient SPk and the historical environmental deviation coefficient HPk respectively;

[0103] The information comparison module obtains the qualified rate of glass products in the glass processing production process that simultaneously satisfies SP×(1-ζ)≤SPk≤SP×(1+ζ) and HP×(1-ζ)≤HPk≤HP×(1+ζ), and marks it as the qualified value HG, obtains the average value of all qualified values ​​HG, and marks it as the quality coefficient ZX; wherein ζ is a preset screening adjustment factor, and ζ=0.029 is taken;

[0104] The information comparison module sends the quality coefficient ZX to the quality prediction module.

[0105] Example 7

[0106] Based on any of the above embodiments, Embodiment 7 of the present invention is a quality prediction module. The function of the quality prediction module is to obtain a quality prediction curve. The specific process is as follows:

[0107] The quality prediction module draws a quality prediction curve in a rectangular coordinate system with the quality coefficient ZX as the ordinate and time as the abscissa, and updates and displays the quality prediction curve in real time on the display screen.

[0108] Based on the above embodiments 1-7, the working principle of the present invention is as follows:

[0109] The present invention discloses a glass processing quality prediction system based on data analysis, which obtains equipment monitoring parameters of a monitoring device through an equipment monitoring module, wherein the equipment monitoring parameters include temperature setting information, power setting information and stability setting information, obtains environmental monitoring parameters of the environment in which the monitoring device is located through an environmental monitoring module, wherein the environmental monitoring parameters include temperature and humidity information, ring pressure information and ring dust information, obtains equipment monitoring coefficients according to equipment monitoring parameters through a data analysis module, obtains environmental monitoring coefficients according to environmental monitoring parameters, obtains equipment deviation coefficients according to equipment monitoring coefficients through an information comparison module, obtains environmental deviation coefficients according to environmental monitoring coefficients, obtains quality coefficients according to equipment deviation coefficients and environmental deviation coefficients, obtains quality prediction curves according to quality coefficients through a quality prediction module, and updates and displays the quality prediction curves on a display screen in real time; the system first collects and analyzes data on the monitoring device to obtain equipment monitoring parameters, and the equipment monitoring coefficients obtained according to the equipment monitoring parameters can comprehensively measure the abnormality of the monitoring device, and the larger the equipment monitoring coefficient is, the higher the abnormality is, and the greater the adverse effect on the glass processing quality is, and then collects and analyzes data on the environment in which the monitoring device is located to obtain environmental monitoring parameters, and obtains the environmental deviation coefficients according to the environmental monitoring parameters. The environmental monitoring coefficient can comprehensively measure the degree of abnormality of the environment in which the monitoring equipment is located, and the larger the environmental monitoring coefficient is, the higher the degree of abnormality is, and the greater the adverse effect on the glass processing quality is. Finally, the equipment deviation coefficient and the environmental deviation coefficient are obtained according to the equipment monitoring coefficient and the environmental monitoring coefficient, which can comprehensively measure the degree of abnormal deviation of the entire glass processing production line. Finally, the quality coefficient is obtained according to the equipment deviation coefficient and the environmental deviation coefficient. The quality of the glass processing quality at the current moment is predicted by the glass processing quality of historical data, and then displayed. The system collects all-round data on the factors affecting the glass processing quality, reduces the dependence on manual detection, and improves the accuracy and reliability of data monitoring. Then, data analysis technology is used to realize real-time, continuous and accurate monitoring of the entire glass processing process, and quality prediction is realized based on historical data, so that emergency response can be made in time when quality problems occur, reducing rework and waste losses caused by quality problems, and reducing the operating costs of enterprises. Therefore, the system not only improves the production efficiency and product quality of glass processing, improves the stability and consistency of products, but also brings significant economic and social benefits to enterprises, promotes the intelligent and automated development of the glass processing industry, and enhances the competitiveness of enterprises.

[0110] It should be further explained that the above formulas are obtained by technicians in this field based on their own work experience, and by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technicians in this field based on actual conditions.

[0111] 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.

[0112] 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 by the claims, they shall all fall within the protection scope of the present invention.

Claims

1. A glass processing quality prediction system based on data analysis, characterized in that: include: The equipment monitoring module is used to obtain the equipment monitoring parameters of the monitoring equipment i and send the equipment monitoring parameters to the data analysis module; wherein the equipment monitoring parameters include the setting temperature information ST, the setting power information SD and the setting stability information SW; The specific process of the device monitoring module obtaining device monitoring parameters is as follows: All processing equipment in the glass processing production line are marked as monitoring equipment i in sequence, where i=1, ..., n, n is a positive integer, i is the number of any monitoring equipment, and n is the total number of monitoring equipment; Obtain the average device temperature of the first operation process of the monitoring device i, and record it as the initial temperature value; obtain the average device temperature of the current operation process of the monitoring device i, and record it as the actual temperature value; obtain the difference between the initial temperature value and the actual temperature value, and mark it as the temperature setting information ST; Get the average voltage and rated voltage of the monitoring device i during the current operation, get the difference between the two, and mark it as the voltage value DY; get the average current and rated current of the monitoring device i during the current operation, get the difference between the two, and mark it as the current value DL; quantify the voltage value DY and the current value DL according to the formula The set voltage information SD is obtained; wherein e is a mathematical constant, s1 and s2 are preset proportional coefficients corresponding to the set voltage value DY and current value DL respectively; Obtain the vibration frequency, maximum vibration amplitude and maximum sound intensity per unit time of the current operation process of the monitoring device i, and mark them as frequency value PL, amplitude value FD and sound intensity value SQ respectively. Quantify the frequency value PL, amplitude value FD and sound intensity value SQ according to the formula The stabilization information SW is obtained; wherein w1, w2 and w3 are preset proportional coefficients corresponding to the set frequency value PL, amplitude value FD and sound intensity value SQ respectively; Send the setting temperature information ST, setting power information SD and setting stability information SW to the data analysis module; The environmental monitoring module is used to obtain the environmental monitoring parameters of the environment in which the monitoring device i is located, and send the environmental monitoring parameters to the data analysis module; wherein the environmental monitoring parameters include temperature and humidity information WS, ambient pressure information HY, and ambient dust information HC; The specific process of the environment monitoring module obtaining the environment monitoring parameters is as follows: Get the ambient temperature of the environment where the monitoring device i is located and the preset standard ambient temperature, get the difference between the two, and mark it as the ambient temperature value HW, get the ambient humidity of the environment where the monitoring device i is located and the preset standard ambient humidity, get the difference between the two, and mark it as the ambient humidity value HS, quantify the ambient temperature value HW and the ambient humidity value HS, and use the formula The temperature and humidity information WS is obtained; wherein h1 and h2 are respectively the preset proportional coefficients corresponding to the set ambient temperature value HW and the ambient humidity value HS; Obtain the ambient pressure of the environment in which the monitoring device i is located and the preset standard ambient pressure, obtain the difference between the two, and mark it as ambient pressure information HY; Obtain the dust content of the environment where the monitoring device i is located, and mark it as environmental dust information HC; Send the temperature and humidity information WS, the ring pressure information HY and the ring dust information HC to the data analysis module; The data analysis module is used to obtain the equipment monitoring coefficient SBi according to the equipment monitoring parameters, obtain the environment monitoring coefficient HJi according to the environment monitoring parameters, and send the equipment monitoring coefficient SBi and the environment monitoring coefficient HJi to the information comparison module; The specific process of the data analysis module obtaining the equipment monitoring coefficient SBi is as follows: The temperature setting information ST, the power setting information SD and the stability setting information SW are quantized according to the preset equipment monitoring function to obtain the equipment monitoring coefficient SBi; Among them, the device monitoring function is as follows: Where: γ is the preset error adjustment factor; Both π and e are mathematical constants; α1, α2 and α3 are preset weight factors corresponding to the set temperature information ST, the set power information SD and the set stability information SW respectively; The specific process of the data analysis module obtaining the environmental monitoring coefficient HJi is as follows: The temperature and humidity information WS, the ambient pressure information HY and the ambient dust information HC are quantified according to the preset environmental monitoring function to obtain the environmental monitoring coefficient HJi; Among them, the environment monitoring function is as follows: Where: δ is the preset error adjustment factor; Both π and e are mathematical constants; β1, β2 and β3 are preset weight factors corresponding to the set temperature and humidity information WS, ambient pressure information HY and ambient dust information HC respectively; An information comparison module is used to obtain an equipment deviation coefficient SP according to an equipment monitoring coefficient SBi, obtain an environmental deviation coefficient HP according to an environmental monitoring coefficient HJi, obtain a quality coefficient ZX according to the equipment deviation coefficient SP and the environmental deviation coefficient HP, and send the quality coefficient ZX to a quality prediction module; The specific process of the information comparison module obtaining the device deviation coefficient SP is as follows: Obtain the preset device monitoring threshold SBiy and environment monitoring threshold HJiy; The equipment monitoring coefficient SBi and the equipment monitoring threshold SBiy are quantified according to the formula Get the equipment deviation coefficient SP; The specific process of the information comparison module obtaining the environmental deviation coefficient HP is as follows: The environmental monitoring coefficient HJi and the environmental monitoring threshold HJiy are quantified according to the formula Get the environmental deviation coefficient HP; The specific process of the information comparison module obtaining the quality coefficient ZX is as follows: Obtain the equipment deviation coefficient SP and the environmental deviation coefficient HP during all glass processing production processes of the monitoring device i from the historical data, and mark them as the historical equipment deviation coefficient SPk and the historical environmental deviation coefficient HPk respectively; Obtain the qualified rate of glass products in the glass processing production process that simultaneously satisfies SP×(1-ζ)≤SPk≤SP×(1+ζ) and HP×(1-ζ)≤HPk≤HP×(1+ζ), and mark it as a qualified value HG; obtain the average value of all qualified values ​​HG, and mark it as a quality coefficient ZX; wherein ζ is a preset screening adjustment factor; Send the quality coefficient ZX to the quality prediction module; The quality prediction module is used to obtain a quality prediction curve according to the quality coefficient ZX, and update and display the quality prediction curve on the display screen in real time; The specific process of the quality prediction module obtaining the quality prediction curve is as follows: A quality prediction curve is drawn in a rectangular coordinate system with the quality coefficient ZX as the ordinate and the time as the abscissa, and the quality prediction curve is updated and displayed in real time on the display screen.

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