Production data processing method and system, electronic equipment and medium
By collecting and analyzing the production line data of the flat glass, the process point parameters are accurately obtained and the correlation relationship is constructed, and the target process point parameters that have the greatest impact on the yield rate are selected, which solves the problems of low efficiency and high manual participation in traditional flat glass production technology, and a more efficient and safer production process is achieved.
Patent Information
- Application Number
- CN202510002039.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-02
- Publication Date
- 2025-05-06
AI Technical Summary
Traditional flat glass production technology is inefficient and requires a lot of manual participation, especially in high temperature environments, which increases production costs and safety risks.
By collecting production line data, the delay time between the process point position and the defect detector or stacking position is obtained, the detection time is compensated, the process point parameters are accurately obtained, and the correlation relationship between the process point parameters and the yield data is constructed to screen out the target process point parameters that have the greatest impact on the yield rate.
It improves the automation level of flat glass production, reduces manual intervention, improves production efficiency, and reduces production costs and safety risks.
Smart Images
Figure CN119941020A_ABST
Abstract
Description
Technical Field
[0001] The present application belongs to the field of computer information technology, and relates to a production data processing method, and in particular to a production data processing method, system, electronic equipment and medium. Background Art
[0002] Flat glass production is an important basic material used in many fields such as construction, automobiles, and electronics. Its production process usually involves multiple steps such as melting, stretching, and annealing. Traditional flat glass production technology mostly uses float glass technology. After melting the glass raw materials at high temperature, the molten glass on the float pool is used to form a flat and uniform glass strip on the surface of the metal liquid, and then flat glass is made through cooling, molding, cutting and other processes. Although this process can meet the needs of large-scale production, its production efficiency is limited in many aspects, especially the low degree of automation in the production process. At present, the main problem of traditional flat glass production technology is that the production efficiency is low and a large amount of manual participation is required. Although some production links have been automated, such as glass melting, stretching and cutting, manual monitoring and adjustment are still required due to the complex temperature control, material mixing and quality inspection in the float glass production process. Especially in high temperature environments, manual operation not only increases production costs, but also increases safety hazards.
[0003] Therefore, improving the level of automation in flat glass production and reducing manual intervention have become urgent needs for the development of the industry. Summary of the invention
[0004] In view of the above-mentioned shortcomings of the prior art, the purpose of the present application is to provide a production data processing method, system, electronic device and medium to solve the problems in the prior art.
[0005] In a first aspect, the present application provides a production data processing method, the production data processing method comprising: collecting production line data to obtain a production database, the production line data comprising detection time, glass belt speed, production line length and process point parameters; obtaining a delay time from a process point position to a defect detector or a stacking position according to the glass belt speed and the process point parameters; compensating the detection time using the delay time to obtain an associated time; obtaining an associated relationship between the process point parameters and yield data using the associated time and the production line data; screening the similarity between the process point parameters and the yield according to different process point parameters and the associated relationship, and obtaining the target process point parameters that have the greatest impact on the yield.
[0006] In this application, the detection time is compensated according to the delay time caused by the distance between the process point and the defect detector, the process point parameters corresponding to the detection time are accurately obtained, and the correlation relationship is established using the process point parameters and the yield data. Then, the target process point parameters with the greatest impact on the yield are screened according to the similarity between different process point parameters and the correlation relationship. This production data processing method can comprehensively analyze and process glass production data, and improve the production process and production efficiency by using the influence of different process parameters on the yield.
[0007] In an implementation of the first aspect, obtaining the delay time from the process point position to the defect detector or the stacking position according to the glass ribbon speed and the process point parameters includes: obtaining the distance from the process point position to the defect detector or the stacking position according to the process point parameters; and obtaining the delay time from the process point position to the defect detector or the stacking position by using the glass ribbon speed and the distance from the process point position to the defect detector or the stacking position.
[0008] In an implementation of the first aspect, the process point parameters include multiple types of defect data, and using the associated time and the production line data to obtain the association between the process point parameters and the yield data includes: using the associated time to obtain the top temperature and the bottom temperature of the pool corresponding to any type of defect data; using the top temperature and the bottom temperature of the pool to establish a regression equation for the defect data; and obtaining the association between any type of the process point parameters and the yield data based on the regression equation and the defect data.
[0009] In an implementation of the first aspect, establishing a regression equation for the defect data using the top temperature of the whirlpool and the bottom temperature of the pool includes: cleaning the top data, the bottom temperature of the pool and the yield data to eliminate erroneous data.
[0010] In an implementation of the first aspect, establishing the regression equation of the defect data using the top temperature of the whirlpool and the bottom temperature of the pool also includes: normalizing the top temperature of the whirlpool, the bottom temperature of the pool and the yield data.
[0011] In an implementation of the first aspect, the similarities between the process point parameters and the yield are screened according to different process point parameters and the association relationship, and the target process point parameters with the greatest impact on the yield are obtained, including: using a data classification algorithm to analyze and process the association relationship between the process point parameters and the yield data to obtain the association relationship between the process point parameters and the defect distribution; using a similarity analysis algorithm to obtain the similarity between the process point parameters and the yield according to the association relationship between the process point parameters and the defect distribution; screening according to the similarities between different process point parameters and the yield to obtain the target process point parameters with the greatest impact on the yield.
[0012] In an implementation of the first aspect, screening is performed based on the similarity between different process point parameters and the yield rate to obtain the target process point parameters that have the greatest impact on the yield rate, including: screening is performed based on the Euclidean distance between different process point parameters and the yield rate to obtain the process point parameters that have the greatest impact on the yield rate as the target process point parameters.
[0013] In a second aspect, the present application provides a production data processing system, which includes: a data acquisition module, which is used to collect production line data to obtain a production database, wherein the production line data includes detection time, glass belt speed, production line length and process point parameters; an error acquisition module, which is used to obtain the delay time from the process point position to the defect detector or stacking position according to the glass belt speed and the process point parameters; a time compensation module, which is used to compensate the detection time using the delay time to obtain the associated time; a data processing module, which is used to obtain the association relationship between the process point parameters and the yield data using the associated time and the production line data; a data analysis module, which is used to screen the similarity between the process point parameters and the yield according to different process point parameters and the association relationship, and obtain the target process point parameters that have the greatest impact on the yield.
[0014] In a third aspect, the present application provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program stored in the memory so that the electronic device performs the production data processing method as described in any one of the first aspects.
[0015] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the production data processing method described in any one of the first aspects. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Figure 1A Shown is a schematic diagram of an application scenario of the production data processing method described in this application.
[0017] Figure 1B Shown are structural diagrams of the client-cloud interaction scenarios in these implementations.
[0018] Figure 2 Shown is a flow chart of the production data processing method described in an embodiment of the present application.
[0019] Figure 3 Shown is a flow chart of the production data processing method described in an embodiment of the present application.
[0020] Figure 4Shown is a flow chart of the production data processing method described in an embodiment of the present application.
[0021] Figure 5 Shown is a structural schematic diagram of the production data processing system described in an embodiment of the present application.
[0022] Figure 6 Shown is a schematic diagram of the structure of an electronic device described in an embodiment of the present application.
[0023] Component number description
[0024] 1 Flat glass production equipment
[0025] 11 Automatic production equipment
[0026] 12 Local Processor
[0027] 13 Display Terminal
[0028] 2-end-cloud interactive system
[0029] 20 Terminal
[0030] 21 Cloud Server
[0031] 100 Production data processing system
[0032] 110 Data acquisition module
[0033] 120 Error acquisition module
[0034] 130 Time compensation module
[0035] 140 Data processing module
[0036] 150 Data Analysis Module
[0037] 600 Electronic equipment
[0038] 610 Memory
[0039] 620 processor
[0040] 630 Display
[0041] Steps S11 to S15
[0042] Steps S141 to S143
[0043] Steps S151 to S153 DETAILED DESCRIPTION
[0044] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.
[0045] It should be noted that in the embodiments of the present application, the words "optionally" or "for example" represent examples, illustrations or descriptions. Any embodiment or design described as "optionally" or "for example" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "optionally" or "for example" is intended to present related concepts in a specific way.
[0046] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can represent: a, b, c, ab, ac, bc or abc, where a, b, c can be single or multiple.
[0047] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.
[0048] The flat glass industry has many types of products and a long manufacturing process. Operators in different sections perform their duties and lack unified management, resulting in data islands and data faults. The large amount of production data generated on the production line contains hidden problems in production. By analyzing and processing the data in combination with the production process, the production process can be effectively improved to help production personnel improve production efficiency.
[0049] At least in response to the above-mentioned problems, an embodiment of the present application provides a production data processing method, which includes: collecting production line data to obtain a production database, the production line data including detection time, glass belt speed, production line length and process point parameters; obtaining a delay time from the process point position to a defect detector or a stacking position according to the glass belt speed and the process point parameters; compensating the detection time using the delay time to obtain an associated time; obtaining an associated relationship between the process point parameters and yield data using the associated time and the production line data; screening the similarity between the process point parameters and the yield according to different process point parameters and the associated relationship, and obtaining the target process point parameters that have the greatest impact on the yield.
[0050] In the embodiment of the present application, the detection time is compensated according to the delay time caused by the distance between the process point and the defect detector, the process point parameters corresponding to the detection time are accurately obtained, and the correlation relationship is established using the process point parameters and the yield data, and then the target process point parameters with the greatest impact on the yield are obtained according to the similarity between different process point parameters and the correlation relationship. This production data processing method can comprehensively analyze and process glass production data, improve the production process and improve production efficiency by using the influence of different process parameters on the yield.
[0051] Figure 1A The diagram shows an application scenario of the production data processing method described in the present application. The flat glass production device 1 can be used to implement the production data processing method provided in the embodiment of the present application, but the application scenario of the production data processing method provided in the embodiment of the present application is not limited to Figure 1A The flat glass production device 1 shown. Figure 1A As shown, the flat glass production device 1 includes an automatic production device 11, a local processor 12 and a display terminal 13. The production data processing method provided in the embodiment of the present application can be applied to the local processor 12.
[0052] in, Figure 1A The local processor 12 in the embodiment may be a local processor or a local processor cluster composed of multiple local processors or a cloud computing center, etc., which is not limited to the specifics herein. Figure 1A Only one automatic production device 11, one local processor 12 and one display terminal 13 are shown, but it should be understood that Figure 1A The examples are only used to understand the present solution, and the specific numbers of local processors 12 and display terminals 13 should be flexibly determined based on actual conditions.
[0053] In some other implementations, the flat glass production device 1 may not include the display terminal 13, but only include a local processor 12 with a display function and an automatic production device 11. The production data processing method provided in the embodiment of the present application can be applied to the local processor 12. The local processor 12 with a display function may include a tablet computer, a laptop computer, a PDA, a mobile phone, a personal computer, a monitoring device, etc., which are not limited here.
[0054] In still other implementations, the production data processing method described in this application may be applied to end-cloud interaction scenarios. Figure 1B The following is a schematic diagram of the structure of the client-cloud interaction scenario in these implementations. Figure 1B As shown, the terminal-cloud interaction system 2 includes a terminal 20 and a cloud server 21. The terminal 20 and the cloud server 21 can communicate with each other, and the communication method is not limited to wired or wireless.
[0055] Among them, the terminal 20 can be mobile or fixed, for example, the terminal 20 can be a wireless terminal or a wired terminal. The wireless terminal can refer to a device with wireless transceiver function, which can be deployed indoors, outdoors and in industrial workshops. The terminal 20 can be a mobile phone, a tablet computer, a laptop computer, etc., which is not limited here. The cloud server 21 can include one or more servers, or one or more processing nodes, or one or more virtual machines running on the server. The cloud server 21 can also be called a server cluster, a management platform, a data processing center, etc., which is not limited in the embodiments of the present application.
[0056] The technical solutions in the embodiments of the present application will be described in detail below in conjunction with the drawings in the embodiments of the present application.
[0057] The following embodiments of the present application provide a method for processing production data, for example, Figure 1A The local processor 12 shown or Figure 1B It is implemented by the cloud server 21 shown. Figure 2 The flowchart of the production data processing method described in the embodiment of the present application is shown as follows: Figure 2 As shown, the production data processing method includes steps S11 to S15.
[0058] Step S11, collecting production line data to obtain a production database, wherein the production line data includes detection time, glass ribbon speed, production line length and process point parameters.
[0059] Optionally, the detection time is the defect detection time T1. The production line data includes the detection time T1, the glass ribbon speed V1, the production line length and the process point parameters. The process point parameters include the defect type Z1, the defect size D1, the horizontal axis coordinate X1 and the vertical axis coordinate Y1 of the defect position, the top temperature C1 collected in a cycle of 30 seconds, the pool bottom temperature C2 and the collection time T2. The production database Mysql database is constructed using the production line data.
[0060] Step S12, obtaining a delay time from the process point position to the defect detector or the stacking position according to the glass ribbon speed and the process point parameters.
[0061] Specifically, there is still a distance between the position where the defect detector detects the product and the position where the defect detector is located. If the detection time is directly used as the corresponding time for obtaining the process point parameters, errors will occur. Therefore, the delay time from the process point position to the defect detector or the stacking position is obtained using the glass ribbon speed and the process point parameters, so as to compensate for the detection time.
[0062] Step S13: Compensate the detection time with the delay time to obtain the associated time.
[0063] Step S14, using the associated time and the production line data to obtain the association relationship between the process point parameters and the yield rate data.
[0064] Optionally, the association relationship is the detection time of the process point parameter plus the delay time, and the same time as the defect detection or glass grabbing is found and matched one by one with the process point parameter to obtain the association relationship.
[0065] Step S15, screening the similarities between the process point parameters and the yield rate according to the different process point parameters and the association relationship, and obtaining the target process point parameters that have the greatest impact on the yield rate.
[0066] In some possible implementations, the production line data includes detection time T1, glass ribbon speed V1, production line length and process point parameters, and the production database is constructed using the production line data. According to the glass ribbon speed and the defect detector and process point position in the process point parameters, the delay time from the process point position to the defect detector or stacking position is obtained. The detection time is compensated by the delay time to obtain the associated time. The associated time and the production line data are used to obtain the association relationship between the process point parameters and the yield data. Among them, the process point parameters correspond to the yield. According to different process point parameters and the association relationship, the similarity between the process point parameters and the yield is calculated, and the similarity between the process point parameters and the yield is screened to obtain the target process point parameters that have the greatest impact on the yield.
[0067] In the embodiment of the present application, the detection time is compensated according to the delay time caused by the distance between the process point and the defect detector, the process point parameters corresponding to the detection time are accurately obtained, and the correlation relationship is established using the process point parameters and the yield data, and then the target process point parameters with the greatest impact on the yield are obtained according to the similarity between different process point parameters and the correlation relationship. This production data processing method can comprehensively analyze and process glass production data, improve the production process and improve production efficiency by using the influence of different process parameters on the yield.
[0068] In one embodiment of the present application, obtaining the delay time from the process point position to the defect detector or the stacking position according to the glass ribbon speed and the process point parameters includes: obtaining the distance from the process point position to the defect detector or the stacking position according to the process point parameters; and obtaining the delay time from the process point position to the defect detector or the stacking position using the glass ribbon speed and the distance from the process point position to the defect detector or the stacking position.
[0069] In some possible implementations, the distance L from the process point to the defect detector or stacking position is obtained according to the process point parameter. The delay time T3 = L / V1 is calculated using the glass ribbon speed V1 and the distance L from the process point to the defect detector or stacking position, but the present application is not limited thereto.
[0070] Figure 3 The flowchart of the production data processing method described in the embodiment of the present application is shown as follows: Figure 3 As shown, the step S14 includes steps S141 to S143.
[0071] Step S141, using the associated time to obtain the top temperature and bottom temperature corresponding to any type of defect data. The top temperature is the temperature above the glass liquid level inside the kiln during the glass production process, that is, the temperature of the top area. The bottom temperature is the temperature of the bottom of the kiln during the glass production process. The top temperature and the bottom temperature are important process point parameters for melting glass raw materials, forming a uniform glass ribbon and ensuring glass quality.
[0072] Step S142, using the top temperature of the steam generator and the bottom temperature of the steam generator to establish a regression equation for the defect data.
[0073] Step S143, obtaining a correlation between any type of the process point parameters and the yield data according to the regression equation and the defect data.
[0074] In some possible implementations, taking the bubble defect of flat glass as an example, the defect data of all bubble defects of type Z1 are obtained from the production database, and the detection time plus the delay time are used for time compensation to obtain the associated time. The associated time is used to obtain the corresponding process point parameters, which include the top temperature C1 and the bottom temperature C2. The regression equation of the defect data is established using the top temperature C1 and the bottom temperature C2, h(c) = θ 0 +θ 1 *C1+θ 2 *C2, where h(c) is the number of Z1 type bubble defects per unit time.
[0075] Use the samples in the database to perform regression fitting and calculate θ 0 ,θ 1 and θ 2 The value of the square of the deviation For θ 0 Find the partial derivative and make it equal to 0, For θ 1 Find the partial derivative and make it equal to 0, For θ 2 Find the partial derivative and make it equal to 0, Solving the above equations, we get θ 0 ,θ 1 and θ 2 The value of .
[0076]
[0077] The correlation between any type of process point parameter and yield data is obtained according to the regression equation and the defect data. The yield corresponds to whether the flat glass has defect data. A certain type of process point parameter is different in defect type.
[0078] In one embodiment of the present application, establishing a regression equation for the defect data using the top temperature of the whirlpool and the bottom temperature of the pool includes: cleaning the top data, the bottom temperature of the pool and the yield data to eliminate erroneous data.
[0079] In some possible implementations, the top temperature, the bottom temperature and the yield rate data in the database are cleaned to exclude erroneous data. For example, the yield rate is less than 0 or greater than 100%, and the top temperature and the bottom temperature are less than 0. The above is only a possible implementation of the embodiment of the present application, and the present application is not limited thereto.
[0080] In one embodiment of the present application, establishing a regression equation for the defect data using the top temperature of the whirlpool and the bottom temperature of the pool also includes: normalizing the top temperature of the whirlpool, the bottom temperature of the pool and the yield data.
[0081] In some possible implementations, the top temperature, the bottom temperature and the yield data are normalized, for example, Among them, C01 is the normalized top temperature, and C1 is the top temperature.
[0082] Figure 4 The flowchart of the production data processing method described in the embodiment of the present application is shown as follows: Figure 4 As shown, the step S15 includes steps S151 to S153.
[0083] Step S151, using a data classification algorithm to analyze and process the correlation between the process point parameters and the yield data, so as to obtain the correlation between the process point parameters and the defect distribution.
[0084] Step S152, using a similarity analysis algorithm, obtaining the similarity between the process point parameters and the yield rate according to the correlation between the process point parameters and the defect distribution.
[0085] Step S153, screening is performed based on the similarity between different process point parameters and the yield rate, to obtain the target process point parameters that have the greatest impact on the yield rate.
[0086] In some possible implementations, the correlation between the process point parameters and the yield rate data is analyzed and processed using a data classification algorithm to obtain the correlation between the process point parameters and the defect distribution. The similarity between the process point parameters and the yield rate is obtained based on the correlation between the process point parameters and the defect distribution using a similarity analysis algorithm. The process point parameter values and the yield rate values are collected in the same time period. A similarity curve is drawn with time as the horizontal axis and the normalized process point parameters and the yield rate values as the vertical axis. Among them, the yield rate value is the qualified products per unit time divided by the total production volume.
[0087] Calculate Euclidean distance
[0088] According to the calculated Euclidean distance, the total distance on the entire curve is calculated by integration. The similarity between different process point parameters and the yield is analyzed by the total distance, and the target process point parameter with the greatest impact on the yield is obtained. The target process point parameter with the greatest impact on the yield is the target process point parameter whose change has the highest correlation with the yield of the glass during the glass production process. For example, the target process point parameter is the top temperature.
[0089] In one embodiment of the present application, screening is performed based on the similarity between different process point parameters and the yield rate, and obtaining the target process point parameter with the greatest impact on the yield rate includes: screening based on the Euclidean distance between different process point parameters and the yield rate, and obtaining the process point parameter with the greatest impact on the yield rate as the target process point parameter. Optionally, a series of process temperature points are stored in the flat glass production furnace, and these temperature points have different degrees of impact on the glass quality. Screening is performed based on the Euclidean distance between different process point parameters and the yield rate, and the process point parameter with the greatest impact on the yield rate is obtained as the target process point parameter. The target process point parameter is used to automatically control the production of flat glass to improve production efficiency and production quality.
[0090] Figure 5 The structure diagram of the production data processing system described in the embodiment of the present application is shown as follows: Figure 5 As shown, the production data processing system 100 includes a data acquisition module 110 , an error acquisition module 120 , a time compensation module 130 , a data processing module 140 and a data analysis module 150 .
[0091] The data acquisition module 110 is used to collect production line data to obtain a production database. The production line data includes detection time, glass ribbon speed, production line length and process point parameters.
[0092] The error acquisition module 120 is used to acquire the delay time from the process point position to the defect detector or the stacking position according to the glass ribbon speed and the process point parameters.
[0093] The time compensation module 130 is used to compensate the detection time by using the delay time to obtain the associated time.
[0094] The data processing module 140 is used to obtain the correlation relationship between the process point parameters and the yield rate data by using the correlated time and the production line data.
[0095] The data analysis module 150 is used to screen the similarities between the process point parameters and the yield rate according to different process point parameters and the association relationship, and obtain the target process point parameters that have the greatest impact on the yield rate.
[0096] In the embodiment of the present application, the time compensation module 130 is used to compensate the detection time according to the delay time caused by the distance between the process point and the defect detector, and accurately obtain the process point parameters corresponding to the detection time. The data processing module 140 is used to use the process point parameters and the yield data to build an association relationship. The data analysis module 150 is used to screen according to the similarity between different process point parameters and the association relationship, and obtain the target process point parameters that have the greatest impact on the yield. This production data processing system 100 can perform comprehensive analysis and processing on glass production data, and improve the production process and production efficiency by using the influence of different process parameters on the yield.
[0097] In the several embodiments provided in the present application, it should be understood that the disclosed system, device or method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules or units, which can be electrical, mechanical or other forms.
[0098] The modules / units described as separate components may or may not be physically separated, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, the functional modules / units in the various embodiments of the present application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.
[0099] Those of ordinary skill in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.
[0100] An embodiment of the present application also provides an electronic device. Figure 6The structure diagram of the electronic device 600 according to the embodiment of the present application is shown. Figure 6 As shown, in this embodiment, the electronic device 600 includes a memory 610 and a processor 620 .
[0101] The memory 610 is used to store computer programs; preferably, the memory 610 includes: ROM, RAM, magnetic disk, USB flash drive, memory card or optical disk, etc., various media that can store program codes.
[0102] Specifically, the memory 610 may include a computer system readable medium in the form of a volatile memory, such as a random access memory (RAM) and / or a cache memory. The electronic device 600 may further include other removable / non-removable, volatile / non-volatile computer system storage media. The memory 610 may include at least one program product having a set (e.g., at least one) of program modules, which are configured to perform the functions of the various embodiments of the present application. It is understood that the memory 610 may be a volatile memory or a non-volatile memory, and may also include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), which is used as an external cache. By way of exemplary but not limiting description, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM). The memory described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable categories of memory.
[0103] The processor 620 is connected to the memory 610 and is used to execute the computer program stored in the memory 610 so that the electronic device 600 executes the production data processing method described in any embodiment of the present application.
[0104] Optionally, the processor 620 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
[0105] Optionally, the electronic device 600 in this embodiment may further include a display 630. The display 630 is communicatively connected to the memory 610 and the processor 620, and is used to display a graphical user interface (GUI) interaction interface related to the production data processing method described in the embodiment of the present application.
[0106] The present application also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the production data processing method described in any embodiment of the present application is implemented.
[0107] The terms "component", "module", "system", etc. used in this specification are used to represent computer-related entities, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program and / or a computer. By way of illustration, both applications running on a computing device and a computing device can be components. One or more components may reside in a process and / or an execution thread, and a component may be located on a computer and / or distributed between two or more computers. In addition, these components may be executed from various computer-readable media having various data structures stored thereon. Components may, for example, communicate through local and / or remote processes according to signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system and / or a network, such as the Internet interacting with other systems through signals).
[0108] The descriptions of the processes or structures corresponding to the above-mentioned figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.
[0109] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.
Claims
1. A production data processing method, characterized in that: include: Collecting production line data to obtain a production database, wherein the production line data includes detection time, glass ribbon speed, production line length and process point parameters; Obtaining a delay time from a process point position to a defect detector or a stacking position according to the glass ribbon speed and the process point parameters; Compensating the detection time using the delay time to obtain a time after association; Using the associated time and the production line data to obtain the association relationship between the process point parameter and the yield rate data; The similarity between the process point parameters and the yield rate is screened according to different process point parameters and the association relationship, so as to obtain the target process point parameters having the greatest impact on the yield rate.
2. The production data processing method according to claim 1, characterized in that: Obtaining the delay time from the process point position to the defect detector or the stacking position according to the glass ribbon speed and the process point parameters includes: Obtaining the distance from the process point position to the defect detector or stacking position according to the process point parameters; The delay time from the process point position to the defect detector or the stacking position is obtained by using the glass ribbon speed and the distance from the process point position to the defect detector or the stacking position.
3. The production data processing method according to claim 1, characterized in that: The process point parameters include multiple types of defect data, and the correlation relationship between the process point parameters and the yield rate data is obtained by using the associated time and the production line data, including: Using the associated time, obtain the top temperature and bottom temperature of the pool corresponding to any type of defect data; Establishing a regression equation for the defect data using the top temperature and the bottom temperature of the pool; The correlation between any type of the process point parameters and the yield data is obtained according to the regression equation and the defect data.
4. The production data processing method according to claim 3, characterized in that: The regression equation of the defect data is established by using the top temperature and the bottom temperature of the pool, including: The top temperature data, the bottom temperature of the pool and the yield rate data are cleaned to eliminate erroneous data.
5. The production data processing method according to claim 1, characterized in that: The regression equation of the defect data established by using the top temperature and the bottom temperature of the pool also includes: The top temperature, the bottom temperature and the yield rate data are normalized.
6. The production data processing method according to claim 1, characterized in that: The similarity between the process point parameters and the yield rate is screened according to different process point parameters and the correlation relationship, and the target process point parameters having the greatest impact on the yield rate are obtained, including: Analyzing and processing the correlation between the process point parameters and the yield rate data by using a data classification algorithm to obtain the correlation between the process point parameters and the defect distribution; Using a similarity analysis algorithm, the similarity between the process point parameters and the yield rate is obtained according to the correlation between the process point parameters and the defect distribution; Screening is performed based on the similarity between different process point parameters and yield rate to obtain the target process point parameters that have the greatest impact on the yield rate.
7. The production data processing method according to claim 1, characterized in that: According to the similarity between different process point parameters and yield rate, the target process point parameters with the greatest impact on yield rate are screened, including: Screening is performed according to the Euclidean distance between different process point parameters and the yield rate, and the process point parameters that have the greatest impact on the yield rate are obtained as the target process point parameters.
8. A production data processing system, characterized in that: include: A data acquisition module, used to collect production line data to obtain a production database, wherein the production line data includes detection time, glass ribbon speed, production line length and process point parameters; An error acquisition module, used for acquiring a delay time from a process point position to a defect detector or a stacking position according to the glass ribbon speed and the process point parameters; A time compensation module, used to compensate the detection time using the delay time to obtain a time after association; A data processing module, used for obtaining the correlation relationship between the process point parameter and the yield rate data by using the correlated time and the production line data; The data analysis module is used to screen the similarity between the process point parameters and the yield rate according to different process point parameters and the association relationship, and obtain the target process point parameters that have the greatest impact on the yield rate.
9. An electronic device, characterized in that: The electronic device comprises: Memory for storing computer programs; A processor, wherein the processor is used to execute the computer program stored in the memory so that the electronic device executes the production data processing method according to any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the production data processing method described in any one of claims 1 to 7 is implemented.