Cigarette gram weight regulation and control system based on big data
By using a big data-based cigarette weight control system, combined with data processing and prediction modules, precise control of cigarette weight is achieved, solving the problem of low control accuracy in traditional methods and improving the combustion performance and taste of tobacco.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-12
- Publication Date
- 2026-03-13
AI Technical Summary
Traditional methods of controlling the weight of cigarettes rely on manual experience and mechanical adjustments, which result in low control precision and susceptibility to human factors, affecting the combustion performance, taste, and quality of tobacco.
A big data-based cigarette weight control system is adopted, including a data processing and judgment module, a data prediction and judgment module, a correlation analysis and optimization module, and an implementation prediction module. It combines big data to perform data analysis and prediction, thereby achieving precise control of cigarette weight data.
It improves the precision of cigarette weight control, enables real-time monitoring and precise control, solves the problem of low control precision in traditional methods, and enhances the combustion performance and taste of tobacco.
Smart Images
Figure CN121658799A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of cigarette manufacturing technology, and in particular to a cigarette weight control system based on big data. Background Technology
[0002] In the tobacco processing process, the weight of cigarettes is a very important indicator, as it directly affects the combustion performance, taste, and quality of tobacco. Therefore, how to accurately control the weight of cigarettes is an important research topic in the tobacco processing field.
[0003] Traditional methods for controlling the weight of cigarettes mainly rely on manual experience and mechanical adjustments. However, this method has the disadvantage of low control precision and is easily affected by human factors, resulting in low precision in controlling the weight of cigarettes and affecting the combustion performance, taste, and quality of tobacco.
[0004] Therefore, it is necessary to improve and design the method of controlling the weight of cigarettes in order to solve the problem that the traditional method of controlling the weight of cigarettes relies on manual experience and mechanical equipment adjustment, which has the disadvantage of low control precision and is easily affected by human factors, resulting in low precision in the control of the weight of cigarettes and affecting the combustion performance, taste and quality of tobacco. Summary of the Invention
[0005] The purpose of this invention is to propose a cigarette weight control system based on big data, in order to solve the problems of low control precision and susceptibility to human factors in traditional cigarette weight control methods that rely on manual experience and mechanical equipment adjustments, which affect the combustion performance, taste and quality of tobacco.
[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:
[0007] A big data-based cigarette weight control system, including
[0008] The data processing and judgment module is used to first collect cigarette weighing data, then preprocess the collected cigarette weighing data, and finally analyze and judge the preprocessed cigarette weighing data in conjunction with a large database to obtain qualified cigarette weighing data and unqualified cigarette weighing data.
[0009] The data prediction and judgment module is connected to the data processing and judgment module. The data prediction and judgment module is used to process the non-compliant cigarette weighing data returned by the data processing and judgment module, and then predict and judge the compliant cigarette weighing data to obtain suitable and unsuitable cigarette weighing data.
[0010] The implementation prediction module and the data prediction judgment module are connected to the implementation prediction module and the correlation analysis and optimization module, respectively. The correlation analysis and optimization module is connected to the implementation prediction module. The implementation prediction module is used to first perform implementation prediction on the suitable cigarette weighing data judged by the data prediction judgment module, and then perform implementation prediction on the unsuitable cigarette weighing data judged by the data prediction judgment module after the cigarette weighing optimization parameters output by the correlation analysis and optimization module, so as to obtain accurate cigarette weighing data.
[0011] Preferably, the data processing and judgment module includes a data acquisition module, which is used to collect the cigarette weighing data at the weighing unit to obtain the cigarette weighing data.
[0012] The data preprocessing module is connected to the data acquisition module. The data preprocessing module preprocesses the cigarette weight data acquired by the data acquisition module to obtain preprocessed cigarette weight data; and
[0013] The data pre-judgment module is connected to the data preprocessing module. The data pre-judgment module, in conjunction with a large database, analyzes and judges the cigarette weighing data after it has been preprocessed by the data preprocessing module, and obtains qualified cigarette weighing data and unqualified cigarette weighing data. The unqualified cigarette weighing data is returned to the data preprocessing module for further preprocessing.
[0014] Preferably, the weighing unit is an electronic scale.
[0015] Preferably, the big data database is connected to the data pre-judgment module.
[0016] Preferably, the data prediction and judgment module includes a data prediction module connected to a data pre-judgment module. The data prediction module is used to predict and analyze the qualified cigarette weight data judged by the data pre-judgment module to obtain the predicted cigarette weight data; and
[0017] The prediction and judgment module is connected to the data prediction module. The prediction and judgment module is used to predict and judge the cigarette weight data predicted by the data prediction module to obtain suitable and unsuitable cigarette weight data.
[0018] Preferably, the correlation analysis and optimization module includes an optical correlation analysis module, and the prediction and judgment module is connected to the optical correlation analysis module. The optical correlation analysis module is used to analyze and process the unsuitable cigarette weighing data after the prediction and judgment module has judged it, and obtain the analyzed cigarette weighing data.
[0019] The parameter recommendation module is connected to the optical analysis module. The parameter recommendation module is used to recommend cigarette weighing parameters based on the cigarette weighing data analyzed by the optical analysis module.
[0020] The parameter prediction module and the parameter recommendation module are connected. The parameter prediction module is used to predict the cigarette weighing parameters recommended by the parameter recommendation module and obtain the predicted cigarette weighing parameters.
[0021] The predictive analysis module is connected to the parameter prediction module. The predictive analysis module analyzes and judges the cigarette weighing parameters predicted by the predictive analysis module, obtaining the cigarette weighing parameters that meet the conditions and those that do not. The cigarette weighing parameters that do not meet the conditions are returned to the correlation analysis module for further analysis and processing.
[0022] The optimization parameter output module is connected to the prediction analysis module. The optimization parameter output module is also connected to the implementation prediction module. The optimization parameter output module is used to output the cigarette weighing parameters that meet the conditions after being judged by the prediction analysis module. Then, the output cigarette weighing parameters are input into the implementation prediction module for implementation and prediction to obtain accurate cigarette weighing data.
[0023] Preferably, the predictive analysis module is a CPU processor, and the outer wall of the CPU processor is provided with a plug interface. The plug interface is connected to the data transmission component through an anti-loosening connector. The data transmission component is an optimization parameter output module.
[0024] Preferably, the anti-loosening connector includes a connecting frame, a pressing rod, a conical push block, a spring, and an anti-loosening movable block. The connecting frame is located at the interface on the outer wall of the CPU processor. The connecting frame has a plug-in port that communicates with the interface. An assembly frame is provided on the side wall of the plug-in port inside the connecting frame. Connecting plates are respectively connected between the side wall of the connecting frame and the side wall of the assembly frame. A mounting groove for accommodating the spring is defined between the two connecting plates. One end of the spring is connected to the inner side wall of the connecting frame, and the other end of the spring is connected to the side wall of the movable plate that is slidably connected inside the mounting groove. The other side wall of the movable plate is connected to the anti-loosening movable block that is slidably connected to the side wall of the assembly frame. The top of the movable plate is connected to the top of the connecting plate via a driving bar. One end of the two driving bars located between the side wall of the connecting frame and the side wall of the assembly frame is located below the conical push block. The conical push block is driven and engaged with the two driving bars respectively. The top of the conical push block is connected to the pressing rod that is slidably connected to the top of the connecting frame. The end of the pressing rod away from the side wall of the connecting frame is connected to the bottom of the pressing block.
[0025] Preferably, the movable plate has a sliding groove, the drive bar is slidably connected to the sliding groove, one end of the drive bar is connected to the top of the movable plate in the mounting groove, and the bottom and top of the movable plate are slidably connected to another sliding groove on the inner sidewall of the connecting plate through a slide bar.
[0026] Preferably, the data transmission component includes a plug interface, a transmission connector, and a data transmission line; the plug interface is respectively plugged into the anti-loosening moving block, the plug interface is respectively opened on the side wall of the transmission connector, the transmission connector passes through the plug-in port on the connecting frame and plugs into the plug interface on the outer wall of the CPU processor, the other end of the transmission connector is connected to the data transmission line, and the other end of the data transmission line is connected to the prediction module.
[0027] Compared with the prior art, the present invention has at least one of the following beneficial effects:
[0028] 1. The cigarette weight control system based on big data in this invention not only uses comparison with a large database for analysis, effectively avoiding the situation where traditional control methods rely on manual experience to control cigarette weight, but also has the advantage of high control precision. It can realize real-time monitoring and precise control of cigarette weight, further improving the control accuracy. It solves the problem that traditional cigarette weight control methods rely on manual experience and mechanical equipment adjustments, which have the disadvantages of low control precision and are easily affected by human factors, resulting in low precision in cigarette weight control and affecting the combustion performance, taste and quality of tobacco.
[0029] 2. In this invention, the interface on the outer wall of the CPU processor is connected to the data transmission component via an anti-loosening connector. After the interface on the outer wall of the CPU processor is connected to the data transmission component, the anti-loosening connector can be used to prevent the data transmission component from becoming loose, making the connection between the data transmission component and the interface on the outer wall of the CPU processor more stable. Attached Figure Description
[0030] Figure 1 This is a schematic diagram of the overall module connection of a cigarette weight control system based on big data in this invention.
[0031] Figure 2 This is a schematic diagram showing the connection of each module in a big data-based cigarette weight control system of the present invention.
[0032] Figure 3 This is a schematic diagram showing the integration of the CPU processor, data transmission device, interface, and anti-loosening connector in the structure of this invention.
[0033] Figure 4 This is a schematic diagram of the data transmission component integration in the structure of the present invention.
[0034] Figure 5 This is a schematic diagram of the first position integration of the anti-loosening connector in the structure of the present invention.
[0035] Figure 6 This is a schematic diagram of the second-position integration of the anti-loosening connector in the structure of the present invention.
[0036] In the diagram, 1-CPU processor, 2-interface, 3-anti-loosening connector, 4-data transmission component, 5-data transmission line, 6-connecting frame, 7-pressing rod, 8-conical push block, 9-spring, 10-anti-loosening moving block, 11-interface, 12-assembly frame, 13-connecting plate, 14-mounting slot, 15-moving plate, 16-drive bar, 17-transmission connector, 18-pressing block, 19-slide groove, 20-slide bar, 21-interface socket. Detailed Implementation
[0037] like Figure 1-6 As shown, to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.
[0038] Example
[0039] In the tobacco processing process, the weight of cigarettes is a very important indicator, as it directly affects the combustion performance, taste, and quality of tobacco. Therefore, how to accurately control the weight of cigarettes is an important research topic in the tobacco processing field.
[0040] Traditional methods for controlling the weight of cigarettes mainly rely on manual experience and mechanical adjustments. However, this method has the disadvantage of low control precision and is easily affected by human factors, resulting in low precision in controlling the weight of cigarettes and affecting the combustion performance, taste, and quality of tobacco.
[0041] Therefore, it is necessary to improve and design the method of controlling the weight of cigarettes in order to solve the problem that the traditional method of controlling the weight of cigarettes relies on manual experience and mechanical equipment adjustment, which has the disadvantage of low control precision and is easily affected by human factors, resulting in low precision in the control of the weight of cigarettes and affecting the combustion performance, taste and quality of tobacco.
[0042] For details, please refer to Figure 1 A big data-based cigarette weight control system includes a data processing and judgment module, a data prediction and judgment module, a correlation analysis and optimization module, and an implementation prediction module.
[0043] The data processing and judgment module is used to first collect cigarette weighing data, then preprocess the collected cigarette weighing data, and then combine it with a large database to analyze and judge the preprocessed cigarette weighing data to obtain qualified cigarette weighing data and unqualified cigarette weighing data.
[0044] The data processing and judgment module is connected to the data prediction and judgment module. The data prediction and judgment module is used to return the unqualified cigarette weighing data judged by the data processing and judgment module to the data processing and judgment module for processing, and then predict and judge the qualified cigarette weighing data judged by the data processing and judgment module to obtain the appropriate cigarette weighing data and the unsuitable cigarette weighing data.
[0045] The data prediction and judgment module is connected to the implementation prediction module and the correlation analysis and optimization module, respectively. The correlation analysis and optimization module is connected to the implementation prediction module. The implementation prediction module is used to first perform implementation prediction on the suitable cigarette weighing data judged by the data prediction and judgment module, and then perform implementation prediction on the unsuitable cigarette weighing data judged by the data prediction and judgment module after the cigarette weighing optimization parameters output by the correlation analysis and optimization module, so as to obtain accurate cigarette weighing data.
[0046] In practical applications, the cigarette weighing data is input into the data processing and judgment module. The data processing and judgment module first preprocesses the input cigarette weighing data, and then combines it with a large database to analyze and judge the preprocessed cigarette weighing data to obtain qualified cigarette weighing data and unqualified cigarette weighing data.
[0047] Then, the weighing data of qualified cigarettes and the weighing data of unqualified cigarettes are input into the data prediction and judgment module. The data prediction and judgment module first returns the weighing data of unqualified cigarettes judged by the data processing and judgment module to the data processing and judgment module for processing, and then predicts and judges the weighing data of qualified cigarettes judged by the data processing and judgment module to obtain the weighing data of suitable cigarettes and unsuitable cigarettes.
[0048] Finally, the appropriate cigarette weight data is first implemented in the prediction module, which performs prediction on the appropriate cigarette weight data; then the inappropriate cigarette weight data is input into the correlation analysis and optimization module, which optimizes the parameters of the inappropriate cigarette weight data, and finally the optimized cigarette weight parameters are implemented for prediction to obtain accurate cigarette weight data.
[0049] Please see Figure 2 The data processing and judgment module includes a data acquisition module, a data preprocessing module, and a data prejudgment module; wherein, the data acquisition module is used to collect the cigarette weighing data at the weighing unit to obtain the cigarette weighing data;
[0050] The data acquisition module is connected to the data preprocessing module. The data preprocessing module is used to preprocess the cigarette weight data acquired by the data acquisition module to obtain preprocessed cigarette weight data; and
[0051] The data preprocessing module is connected to the data prejudgment module. The data prejudgment module, in conjunction with a large database, analyzes and judges the cigarette weighing data after it has been preprocessed by the data preprocessing module, and obtains qualified cigarette weighing data and unqualified cigarette weighing data. The unqualified cigarette weighing data is returned to the data preprocessing module for further preprocessing.
[0052] It should be noted that the weighing unit is an electronic scale.
[0053] It should be noted that the connection between the big data database and the data pre-judgment module...
[0054] Please see Figure 2 The data prediction and judgment module includes a data prediction module and a prediction and judgment module; wherein, the data prediction module is connected to the data prediction and judgment module, and the data prediction module is used to predict and analyze the qualified cigarette weight data after the data prediction and judgment module has judged it, and obtain the predicted cigarette weight data.
[0055] The data prediction module is connected to the prediction judgment module. The prediction judgment module is used to predict and judge the cigarette weight data predicted by the data prediction module to obtain suitable cigarette weight data and unsuitable cigarette weight data.
[0056] Please see Figure 2 The correlation analysis and optimization module includes an optical correlation analysis module, a parameter recommendation module, a parameter prediction module, a prediction analysis module, and an optimization parameter output module. Among them, the prediction judgment module is connected to the optical correlation analysis module. The optical correlation analysis module is used to analyze and process the unsuitable cigarette weighing data judged by the prediction judgment module to obtain the analyzed cigarette weighing data.
[0057] The optical connection analysis module is connected to the parameter recommendation module. The parameter recommendation module is used to recommend cigarette weighing parameters based on the cigarette weighing data analyzed by the optical connection analysis module.
[0058] The parameter recommendation module is connected to the parameter prediction module. The parameter prediction module is used to predict the cigarette weighing parameters recommended by the parameter recommendation module and obtain the predicted cigarette weighing parameters.
[0059] The parameter prediction module is connected to the prediction analysis module. The prediction analysis module is used to analyze and judge the cigarette weighing parameters predicted by the prediction analysis module, and obtain the cigarette weighing parameters that meet the conditions and the cigarette weighing parameters that do not meet the conditions. The cigarette weighing parameters that do not meet the conditions are returned to the correlation analysis module for analysis and processing.
[0060] The predictive analysis module is connected to the optimization parameter output module, which in turn is connected to the implementation prediction module. The optimization parameter output module outputs the cigarette weighing parameters that meet the conditions after being judged by the predictive analysis module. Then, the output cigarette weighing parameters are input into the implementation prediction module for implementation and prediction to obtain accurate cigarette weighing data.
[0061] It should be noted that you should refer to [link / reference]. Figure 3 , Figure 4 , Figure 5 and Figure 6 The predictive analysis module is a CPU processor 1. The outer wall of the CPU processor 1 is provided with an interface 2. The interface 2 is connected to the data transmission component 4 through a non-loosening connector 3. The data transmission component 4 is an optimization parameter output module.
[0062] The anti-loosening connector 3, used to prevent the data transmission component 4 from disengaging from the interface 2 on the outer wall of the CPU processor 1, includes a connecting frame 6, a pressing rod 7, a conical push block 8, a spring 9, and an anti-loosening moving block 10.
[0063] Specifically, the connecting frame 6 is located at the interface 2 on the outer wall of the CPU processor 1. The connecting frame 6 has a plug-in port 11 that communicates with the interface 2. An assembly frame 12 is provided on the side wall of the plug-in port 11 inside the connecting frame 6. A connecting plate 13 is connected between the side wall of the connecting frame 6 and the side wall of the assembly frame 12. A mounting groove 14 for accommodating the spring 9 is defined between the two connecting plates 13. One end of the spring 9 is connected to the inner side wall of the connecting frame 6, and the other end of the spring 9 is connected to the side wall of the movable plate 15 that is slidably connected inside the mounting groove 14. Next, the other side wall of the movable plate 15 is connected to the anti-loosening movable block 10, which is slidably connected to the side wall of the assembly frame 12. The top of the movable plate 15 is connected to the drive bar 16, which is slidably connected to the top of the connecting plate 13. One end of the two drive bars 16 located between the side wall of the connecting frame 6 and the side wall of the assembly frame 12 is located below the conical push block 8. The conical push block 8 is driven and cooperated with the two drive bars 16 respectively. The top of the conical push block 8 is connected to the pressing rod 17, which is slidably connected to the top of the connecting frame 6. The end of the pressing rod 17 away from the side wall of the connecting frame 6 is connected to the bottom of the pressing block 18.
[0064] It should be noted that the movable plate 15 has a sliding groove 19, the drive bar 16 is slidably connected to the sliding groove 19, one end of the drive bar 16 is connected to the top of the movable plate 15 in the mounting groove 14, and the bottom and top of the movable plate 15 are slidably connected to another sliding groove on the inner side wall of the connecting plate through the slide bar 20.
[0065] The data transmission component 4 includes a plug socket 21, a transmission connector 17, and a data transmission line 5. The plug socket 21 is respectively plugged into the anti-loosening moving block 10. The plug socket 21 is respectively opened on the side wall of the transmission connector 17. The transmission connector 17 passes through the plug-in port 11 on the connecting frame 6 and is plugged into the plug interface 2 on the outer wall of the CPU processor 1. The other end of the transmission connector 17 is connected to the data transmission line 5, and the other end of the data transmission line 5 is connected to the implementation prediction module.
[0066] In practical applications, when the data transmission device 4 is plugged into the interface 2 on the outer wall of the CPU processor 1, the pressing block 18 can be pressed down, causing the pressing block to drive the tapered push block 8 between the side wall of the connecting frame 6 and the side wall of the assembly frame 12 to move downwards towards the driving bar 16 via the pressing rod 7; when the tapered push block 8 moves down to contact the two ends of the two driving bars 16 between the side wall of the connecting frame 6 and the side wall of the assembly frame 12, the two driving bars 16 will move apart, and the moving plate 15 connected to the other end of the two driving bars 16 will drive the two anti-loosening moving blocks 10 to move apart. At this time, the data transmission device 4 is plugged into the interface 2 on the outer wall of the CPU processor 1.
[0067] When the data transmission device 4 is connected to the interface 2 on the outer wall of the CPU processor 1, pull the pressing block 18 upwards. This causes the pressing block to drive the tapered push block 8 between the side wall of the connecting frame 6 and the side wall of the assembly frame 12 to rise away from the drive bar 16 via the pressing rod 7. When the tapered push block 8 moves upwards and is not in contact with the two ends of the two drive bars 16 between the side wall of the connecting frame 6 and the side wall of the assembly frame 12, the two drive bars 16 move towards each other. The moving plate 15 connected to the other end of the two drive bars 16 then drives the two anti-loosening moving blocks 10 to move towards each other and enter the transmission connector 17. The transmission connector 17 is the insertion socket 21 on the side wall of the transmission connector 17 in the data transmission device 4. This anti-loosening treatment is applied to the data transmission device 4, making the connection between the data transmission device 4 and the interface 2 on the outer wall of the CPU processor 1 more stable.
[0068] This application not only employs a comparison method with a large database for analysis, effectively avoiding the situation where traditional control methods rely on manual experience to adjust the weight of cigarettes, but also has the advantage of high control precision. It can achieve real-time monitoring and precise control of the weight of cigarettes, further improving the accuracy of control. This solves the problem that traditional cigarette weight control methods rely on manual experience and mechanical equipment adjustments, which have the disadvantages of low control precision and are easily affected by human factors, resulting in low precision in cigarette weight control and affecting the combustion performance, taste, and quality of tobacco.
[0069] The CPU processor outer wall connector in this application is connected to the data transmission component via an anti-loosening connector. After the CPU processor outer wall connector and the data transmission component are connected, the anti-loosening connector can be used to prevent the data transmission component from becoming loose, making the connection between the data transmission component and the CPU processor outer wall connector more stable.
[0070] Although the invention has been described herein with reference to several illustrative embodiments, it should be understood that many other modifications and implementations can be devised by those skilled in the art, which will fall within the scope and spirit of the principles disclosed herein. More specifically, various variations and modifications can be made to the components and / or layout of the subject matter arrangement within the scope of the disclosure, drawings, and claims. Besides variations and modifications to the components and / or layout, other uses will be apparent to those skilled in the art.
Claims
1. A cigarette weight control system based on big data, characterized in that: include The data processing and judgment module is used to first collect cigarette weighing data, then preprocess the collected cigarette weighing data, and finally analyze and judge the preprocessed cigarette weighing data in conjunction with a large database to obtain qualified cigarette weighing data and unqualified cigarette weighing data. The data prediction and judgment module is connected to the data processing and judgment module. The data prediction and judgment module returns the unqualified cigarette weight data (determined by the data processing and judgment module) to the data processing and judgment module for further processing. It then predicts and judges the qualified cigarette weight data (determined by the data processing and judgment module) to obtain suitable and unsuitable cigarette weight data. The implementation prediction module and the data prediction judgment module are connected to the implementation prediction module and the correlation analysis and optimization module, respectively. The correlation analysis and optimization module is connected to the implementation prediction module. The implementation prediction module is used to first perform implementation prediction on the suitable cigarette weighing data judged by the data prediction judgment module, and then perform implementation prediction on the unsuitable cigarette weighing data judged by the data prediction judgment module after the cigarette weighing optimization parameters output by the correlation analysis and optimization module, so as to obtain accurate cigarette weighing data.
2. The cigarette weight control system based on big data according to claim 1, characterized in that: The data processing and judgment module includes: The data acquisition module is used to collect the cigarette weighing data at the weighing unit and obtain the cigarette weighing data. The data preprocessing module is connected to the data acquisition module. The data preprocessing module preprocesses the cigarette weight data acquired by the data acquisition module to obtain preprocessed cigarette weight data; and The data pre-judgment module is connected to the data preprocessing module. The data pre-judgment module, in conjunction with a large database, analyzes and judges the cigarette weighing data after it has been preprocessed by the data preprocessing module, and obtains qualified cigarette weighing data and unqualified cigarette weighing data. The unqualified cigarette weighing data is returned to the data preprocessing module for further preprocessing.
3. The cigarette weight control system based on big data according to claim 2, characterized in that: The weighing unit is an electronic scale.
4. The cigarette weight control system based on big data according to claim 2, characterized in that: The large database is connected to the data pre-judgment module.
5. The cigarette weight control system based on big data according to claim 2, characterized in that: The data prediction and judgment module includes: The data prediction module, connected to the data pre-judgment module, is used to predict and analyze the qualified cigarette weight data determined by the data pre-judgment module, obtaining the predicted cigarette weight data; and The prediction and judgment module is connected to the data prediction module. The prediction and judgment module is used to predict and judge the cigarette weight data predicted by the data prediction module to obtain suitable and unsuitable cigarette weight data.
6. The cigarette weight control system based on big data according to claim 5, characterized in that: The correlation analysis and optimization module includes The optical connection analysis module is connected to the prediction and judgment module. The optical connection analysis module is used to analyze and process the unsuitable cigarette weighing data after the prediction and judgment module has judged it, and obtain the analyzed cigarette weighing data. The parameter recommendation module is connected to the optical analysis module. The parameter recommendation module is used to recommend cigarette weighing parameters based on the cigarette weighing data analyzed by the optical analysis module. The parameter prediction module and the parameter recommendation module are connected. The parameter prediction module is used to predict the cigarette weighing parameters recommended by the parameter recommendation module and obtain the predicted cigarette weighing parameters. The predictive analysis module is connected to the parameter prediction module. The predictive analysis module is used to analyze and judge the cigarette weighing parameters predicted by the predictive analysis module, and obtain the cigarette weighing parameters that meet the conditions and the cigarette weighing parameters that do not meet the conditions. The cigarette weighing parameters that do not meet the conditions are returned to the correlation analysis module for analysis and processing. as well as The optimization parameter output module is connected to the prediction analysis module. The optimization parameter output module is also connected to the implementation prediction module. The optimization parameter output module is used to output the cigarette weighing parameters that meet the conditions after being judged by the prediction analysis module. Then, the output cigarette weighing parameters are input into the implementation prediction module for implementation and prediction to obtain accurate cigarette weighing data.
7. The cigarette weight control system based on big data according to claim 5, characterized in that: The predictive analysis module is a CPU processor (1). The CPU processor (1) has an interface (2) on its outer wall. The interface (2) is connected to the data transmission component (4) through a non-loose connector (3). The data transmission component (4) is an optimization parameter output module.
8. The cigarette weight control system based on big data according to claim 7, characterized in that: The anti-loosening connector (3) includes a connecting frame (6), a pressing rod (7), a conical push block (8), a spring (9), and an anti-loosening moving block (10). The connecting frame (6) is located at the plug-in interface (2) on the outer wall of the CPU processor (1). The plug-in port (11) of the connecting frame (6) is interconnected with the plug-in interface (2). An assembly frame (12) is provided on the side wall of the plug-in port (11) inside the connecting frame (6). A connecting plate (13) is connected between the side wall of the connecting frame (6) and the side wall of the assembly frame (12). A mounting groove (14) for accommodating the spring (9) is defined between the two connecting plates (13). One end of the spring (9) is connected to the inner side wall of the connecting frame (6), and the other end of the spring (9) is connected to the inner side wall of the connecting frame (6). The side wall of the movable plate (15) is slidably connected to the inside of the mounting groove (14). The other side wall of the movable plate (15) is slidably connected to the anti-loosening movable block (10) of the assembly frame (12). The top of the movable plate (15) is slidably connected to the top of the connecting plate (13). One end of the two driving bars (16) located between the side wall of the connecting frame (6) and the side wall of the assembly frame (12) is located below the conical push block (8). The conical push block (8) is driven and cooperated with the two driving bars (16). The top of the conical push block (8) is connected to the pressing rod (7) slidably connected to the top of the connecting frame (6). The end of the pressing rod (7) away from the side wall of the connecting frame (6) is connected to the bottom of the pressing block (18).
9. A cigarette weight control system based on big data according to claim 8, characterized in that: The movable plate (15) has a sliding groove (19), and the drive bar (16) is slidably connected to the sliding groove (19). One end of the drive bar (16) is connected to the top of the movable plate (15) in the mounting groove (14). The bottom and top of the movable plate (15) are slidably connected to another sliding groove on the inner sidewall of the connecting plate through a slide bar (20).
10. A cigarette weight control system based on big data according to claim 8, characterized in that: The data transmission component (4) includes a plug socket (21), a transmission connector (17), and a data transmission line (5); the plug socket (21) is respectively plugged into the anti-loosening moving block (10), the plug socket (21) is respectively opened on the side wall of the transmission connector (17), the transmission connector (17) passes through the plug-in port (11) on the connecting frame (6) and is plugged into the plug interface (2) on the outer wall of the CPU processor (1), the other end of the transmission connector (17) is connected to the data transmission line (5), and the other end of the data transmission line (5) is connected to the implementation prediction module.