A tobacco silk production control and early warning platform based on big data analysis

Abstract

The invention discloses a tobacco silk production control and early warning platform based on big data analysis, including an operation terminal, a control system, an early warning platform and a cleaning system, wherein the operation terminal realizes bidirectional connection with the control system, the early warning platform and the cleaning system respectively, and the control system and the early warning platform realize bidirectional connection; the control system is used to control and collect all data generated in the tobacco silk production process, and perform preliminary processing on the data; the operation terminal is used to receive the data after preliminary processing, and display the cleaned data after classification and statistics; the cleaning system is used to obtain the data after preliminary processing from the operation terminal for node calculation, screening and cleaning of the data; the early warning platform is used to monitor according to the collected data, and generate and output fault warnings at the same time. The platform solves the problems that the existing tobacco silk production cannot know the specific situation of tobacco in the production process, and the qualified rate of the manufactured products is low.

Description

Technical Field

[0001] The present invention belongs to the technical field of tobacco silk making, and specifically relates to a tobacco silk making control and early warning platform based on big data analysis. Background Art

[0002] Big data is the hottest term in the IT industry nowadays. The subsequent data warehouses, data security, data analysis, data mining, etc., which are used to realize the commercial value of big data, have gradually become the focus of profits that industry professionals are vying for. With the advent of the big data era, big data analysis has also emerged. The encounter of big data analysis with the World Cup is an inevitable occurrence in the big data era, and big data analysis will also change every aspect of our lives in the future.

[0003] The existing tobacco silk refining process is unable to know the specific situation of the tobacco during the production process, whether there are other external factors affecting the tobacco silk refining process, and the qualified rate of the products produced is low. Therefore, there is an urgent need for a tobacco silk refining control and early warning platform based on big data analysis.

[0004] The patent application with authorization announcement number CN 208002088U discloses a tobacco silk-making control system based on big data analysis, including a main PCServer and a silk-making PLC. The main PCServer is connected to a database and a first network switch, and the database is connected to a backup PCServer. The first network switch is respectively connected to an Internet terminal, a monitoring computer, a management computer and an equipment status monitoring server, and the monitoring computer and the management computer are both connected to a second network switch. A bus network is provided on the first network switch, and the first network switch is connected to a third network switch through the bus network. The second network switch is cascaded with a fourth network switch, and the fourth network switch is connected to the silk-making PLC. By setting up various monitoring devices and management devices, the control data required for tobacco silk-making can be obtained in real time. The data is collected by the database. The main PCServer can effectively control the entire tobacco silk-making process based on the processing under big data analysis, thereby optimizing the system. The system is a system composed of hardware devices for realizing tobacco silk-making control, but it is not specifically disclosed how it is analyzed.

[0005] The patent application with publication number CN103653224A discloses a tobacco silk control method based on a new group processing process. By establishing a "minimum basic equipment group" model, the entire line is divided into various "minimum basic equipment groups" as the basis for achieving various process requirements, and a supporting design method for realizing the new group processing process based on this model is provided. The tobacco silk control method does not provide data analysis for tobacco silk control. Summary of the invention

[0006] In view of the above, the purpose of the present invention is to provide a tobacco silk making control and early warning platform based on big data analysis, which solves the problems of the existing tobacco silk making process that it is impossible to know the specific situation of tobacco in the production process, whether there are other external factors affecting the tobacco silk making process, and the low qualified rate of the products produced.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] A tobacco silk production control and early warning platform based on big data analysis, comprising an operation terminal, a control system, an early warning platform and a cleaning system, wherein the operation terminal is bidirectionally connected with the control system, the early warning platform and the cleaning system respectively, and the control system and the early warning platform are bidirectionally connected;

[0009] The management and control system is used to control the collection of all data generated by the tobacco silk making process and perform preliminary processing on the data; the operation terminal is used to receive the data after preliminary processing, and classify and count the cleaned data for display; the cleaning system is used to obtain the data after preliminary processing from the operation terminal for node calculation, screening and cleaning of the data; the early warning platform is used to monitor according to the collected data, and generate and output fault warnings.

[0010] In one embodiment, the control system includes: a data acquisition module, a digital signal conversion module, a control processing center, a data comparison module, a data analysis module, a data feedback module and a historical comprehensive database;

[0011] The control and processing center controls the data acquisition module to collect data. After the collected data is converted from analog to digital signals by the digital signal conversion module, it is compared with the historical data in the historical comprehensive database through the data comparison module to determine data abnormalities. The abnormal data obtained by comparison is preliminarily analyzed by the data analysis module and then fed back to the operation terminal and early warning platform through the data feedback module.

[0012] In one embodiment, in the data comparison module, the current data is compared with the lowest value and the highest value in the historical data. When the current data is between the lowest value and the highest value, the next node data is detected. Otherwise, it is considered that data anomalies may occur, and the current data is input into the data analysis module for preliminary analysis.

[0013] In one embodiment, the operation terminal includes a device control module, a data acquisition module, a data recording module, a data storage module, a data statistics module and a data display module;

[0014] The data fed back by the management and control system is received by the equipment control module, acquired by the data acquisition module, recorded by the data recording module, and stored in the data storage module. The stored data is classified and statistically analyzed by the data statistics module, and the statistical results are displayed through the data display module.

[0015] In one embodiment, the cleaning system includes a data receiving module, a cleaning processing platform, a node computing module, a data screening module, a data cleaning module, and a data sending module;

[0016] After the data from the operation terminal is received by the data receiving module, it enters the cleaning processing platform for preliminary cleaning, and then passes through the node computing module, data screening module, and data cleaning module in sequence for stream computing data cleaning, data screening, and data processing, and then is sent to the operation terminal through the data sending module.

[0017] In one embodiment, the early warning platform includes a real-time monitoring module, a data input module, a model building module, a budget deduction module, a data comparison module, a data output module, a fault warning module and a fault processing module;

[0018] The data detected by the real-time detection module is received by the data input module and then input into the model building module. The deduction model established by the model building module is deduced by the budget deduction module and then input into the data comparison module. After comparative analysis, possible problems are found. The fault warning module generates early warning information and issues an alarm based on the comparative analysis results. The fault handling module handles the fault based on the early warning information.

[0019] In one embodiment, the model building module builds a deduction model based on historical data and a deep learning model, which is used to deduce production status and energy consumption based on data.

[0020] In one embodiment, the budget deduction module uses the established deduction model to deduce the production status and energy consumption of the input data to obtain a deduction result.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] By setting up an early warning platform, using the real-time monitoring module for detection, cooperating with the data input module, model building module, budget deduction module, data comparison module, data output module, fault warning module and fault processing module, real-time feedback can be carried out, and early warning of possible problems can be given in time, which can not only handle them in time, but also avoid the loss of raw materials, greatly improving the qualified rate of tobacco silk making.

[0023] By setting up an operating terminal and using the equipment control module for real-time control, in conjunction with the data acquisition module, data recording module, data storage module, data statistics module and data display module, it is possible to sort out some of the measured data and find out the corresponding causes of the problems, which can not only solve the problems in a timely manner, but also avoid the problems from occurring next time.

[0024] By setting up a management and control system and a cleaning system, cooperating with the PLC in the data acquisition module for data collection, cooperating with the digital signal conversion module, the management and control processing center, the data comparison module, the data analysis module, the data feedback module and the historical comprehensive database, the corresponding unqualified products can be screened out in time, greatly reducing the workload of screening. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 A principle block diagram of a tobacco silk production control and early warning platform based on big data analysis provided in an embodiment;

[0027] Figure 2 A principle block diagram of an operation terminal provided in an embodiment;

[0028] Figure 3 A block diagram of the control system provided in the embodiment;

[0029] Figure 4 A principle block diagram of the early warning platform provided for the embodiment;

[0030] Figure 5 A functional block diagram of a cleaning system provided in an embodiment;

[0031] Figure 6 A logical judgment diagram provided for an embodiment;

[0032] In the figure: 1-operation terminal, 11-equipment control module, 12-data acquisition module, 13-data recording module, 14-data storage module, 15-data statistics module, 16-data display module, 2-control system, 21-data acquisition module, 22-digital signal conversion module, 23-control processing center, 24-data comparison module, 25-data analysis module, 26-data feedback module, 27-historical comprehensive database, 3-early warning platform, 31-real-time monitoring module, 32-data input module, 33-model building module, 34-budget deduction module, 35-data comparison module, 36-data output module, 37-fault warning module, 38-fault handling module, 4-cleaning system, 41-data receiving module, 42-cleaning processing platform, 43-node calculation module, 44-data screening module, 45-data cleaning module, 46-data sending module. DETAILED DESCRIPTION

[0033] To make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation methods described herein are only used to explain the present invention and do not limit the scope of protection of the present invention.

[0034] See also Figure 1-6 The embodiment provides a tobacco silk processing control and early warning platform based on big data analysis, including an operation terminal 1, a control system 2, an early warning platform 3 and a cleaning system 4. The operation terminal 1 is bidirectionally connected with the control system 2, the early warning platform 3 and the cleaning system 4, and the control system 2 is bidirectionally connected with the early warning platform 3.

[0035] The control system 2 conducts comprehensive process quality data mining, and through analysis and exploration of deeper quality problems and optimal process parameters, ensures that the quality of the entire silk production process is controlled. The control system 2 includes a data acquisition module 21, a digital signal conversion module 22, a control processing center 23, a data comparison module 24, a data analysis module 25, a data feedback module 26 and a historical comprehensive database 27. The output end of the data acquisition module 21 is connected to the input end of the digital signal conversion module 22, the output end of the digital signal conversion module 22 is connected to the input end of the control processing center 23, the output end of the control processing center 23 is connected to the input end of the data comparison module 24, the output end of the data comparison module 24 is connected to the input end of the data feedback module 26, and the output end of the historical comprehensive database 27 is connected to the input end of the data comparison module 24. Among them, in the data comparison module 24, the current data X is compared with the lowest value Z and the highest value T in the historical data. When the current data is between the lowest value and the highest value, the next node data is detected. Otherwise, it is considered that data abnormality may occur, and the current data is input into the data analysis module for preliminary analysis.

[0036] The cleaning system 4 implements a distributed cluster architecture to realize data collection, stream computing data cleaning, and real-time data storage, realizes dynamic scheduling and elastic computing of computing nodes, and improves the stability of data cleaning and the speed of real-time data computing. The cleaning system 4 includes a data receiving module 41, a cleaning processing platform 42, a node computing module 43, a data screening module 44, a data cleaning module 45, and a data sending module 46. The output end of the data receiving module 41 is connected to the input end of the cleaning processing platform 42, the output end of the cleaning processing platform 42 is connected to the input end of the node computing module 43, the output end of the node computing module 43 is connected to the input end of the data screening module 44, the output end of the data screening module 44 is connected to the input end of the data cleaning module 45, and the output end of the data cleaning module 45 is connected to the input end of the data sending module 46.

[0037] By setting up a control system 2 and a cleaning system 4, cooperating with the PLC in the data acquisition module 21 for data collection, cooperating with the digital signal conversion module 22, the control processing center 23, the data comparison module 24, the data analysis module 25, the data feedback module 26 and the historical comprehensive database 27, the corresponding unqualified products can be screened out in time, which greatly reduces the workload of screening.

[0038] The operation terminal 1 includes an equipment control module 11, a data acquisition module 12, a data recording module 13, a data storage module 14, a data statistics module 15 and a data display module 16. The output end of the equipment control module 11 is connected to the input end of the data acquisition module 12, the output end of the data acquisition module 12 is connected to the input end of the data recording module 13, the output end of the data recording module 13 is connected to the input end of the data storage module 14, the output end of the data storage module 14 is connected to the input end of the data statistics module 15, and the output end of the data statistics module 15 is connected to the input end of the data display module 16. By providing the operation terminal 1, the equipment control module 11 is used for real-time control, and the data acquisition module 12, the data recording module 13, the data storage module 14, the data statistics module 15 and the data display module 16 are coordinated, so that some measured data can be sorted out and the corresponding cause of the problem can be found, which can not only solve the problem in time, but also avoid the next problem.

[0039] The management and control system 2 and the early warning platform 3 are bidirectionally connected. The early warning platform 3 comprehensively analyzes the quality data of the silk-making process, explores the internal connection and development and change rules between the data, studies the process quality early warning model, builds a multi-level process quality early warning system, and improves the online early warning analysis and processing capabilities. The early warning platform 3 includes a real-time monitoring module 31, a data input module 32, a model building module 33, a budget deduction module 34, a data comparison module 35, a data output module 36, a fault warning module 37 and a fault processing module 38. The output end of the real-time monitoring module 31 is connected to the input end of the data input module 32, the output end of the data input module 32 is connected to the input end of the model building module 33, the output end of the model building module 33 is connected to the input end of the budget deduction module 34, the output end of the budget deduction module 34 is connected to the input end of the data comparison module 35, and the data comparison module 35 The output end of the data comparison module 35 is connected to the input end of the fault warning module 37, the fault warning module 37 is bidirectionally connected to the data input module 32, and the output end of the fault warning module 37 is connected to the input end of the fault processing module 38. By providing an early warning platform 3, the real-time monitoring module 31 is used for detection, and the data input module 32, the model building module 33, the budget deduction module 34, the data comparison module 35, the data output module 36, the fault warning module 37 and the fault processing module 38 are coordinated to perform real-time feedback, so that possible problems can be warned in time, which can not only be handled in time, but also avoid the loss of raw materials, greatly improving the qualified rate of tobacco silk making. At the same time, the contents not described in detail in this specification belong to the existing technology known to those skilled in the art.

[0040] When working, firstly, the data is collected by the data acquisition module 21, and then converted by the digital signal conversion module 22, and then transmitted to the control and processing center 23, and compared with the historical comprehensive database 27 by the data comparison module 24, and then analyzed by the data analysis module 25 and the data feedback module 26, and finally fed back to the early warning platform 3, wherein the data is calculated by the node calculation module 43 in the cleaning processing platform 42, and screened and cleaned by the data screening module 44 and the data cleaning module 45, and the control system 2 and the cleaning system 4 are provided, and the PLC in the data acquisition module 21 is used for collection, and the digital signal conversion module 22, the control and processing center 23, the data comparison module 24, the data analysis module 25, the data feedback module 26 and the historical comprehensive database 27 are used, so that the corresponding unqualified products can be screened out in time, which greatly reduces the workload of screening, and then the model building module 33 is established for the detected data, and the deduction model is established according to the budget deduction module 34. If there are certain problems, timely feedback is given to the fault warning module 37 and the fault processing module 38 for processing. Early warning and timely processing are carried out. By setting up an early warning platform 3, a real-time monitoring module 31 is used for detection, and a data input module 32, a model building module 33, a budget deduction module 34, a data comparison module 35, a data output module 36, a fault warning module 37 and a fault processing module 38 are used to provide real-time feedback, and early warning of possible problems can be given in time. Not only can timely processing be carried out, but also the loss of raw materials can be avoided, which greatly improves the qualified rate of tobacco silk making. At the same time, the equipment control module 11 is used for real-time control, and the data recording module 13, the data storage module 14 and the data statistics module 15 are used for recording, storage and statistics, and finally displayed in the form of a chart. By setting up an operation terminal 1, the equipment control module 11 is used for real-time control, and the data acquisition module 12, the data recording module 13, the data storage module 14, the data statistics module 15 and the data display module 16 are used to organize some of the measured data and find out the corresponding cause of the problem, which can not only solve the problem in time, but also avoid the next problem.

[0041] The specific implementation methods described above have described in detail the technical solutions and beneficial effects of the present invention. It should be understood that the above is only the most preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, supplements and equivalent substitutions made within the scope of the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A tobacco silk production control and early warning platform based on big data analysis, characterized in that: It includes an operation terminal, a control system, an early warning platform and a cleaning system. The operation terminal is bidirectionally connected with the control system, the early warning platform and the cleaning system respectively, and the control system is bidirectionally connected with the early warning platform; The control system is used to control the collection of all data generated in the tobacco silk making process and perform preliminary processing on the data. The control system includes a data acquisition module, a digital signal conversion module, a control processing center, a data comparison module, a data analysis module, a data feedback module and a historical comprehensive database. The control processing center controls the data acquisition module to collect data. After the collected data is converted from analog signals to digital signals by the digital signal conversion module, it is compared with the historical data in the historical comprehensive database through the data comparison module to determine the data abnormality. After the abnormal data obtained by comparison is preliminarily analyzed by the data analysis module, it is fed back to the operation terminal and the early warning platform through the data feedback module; The operation terminal is used to receive the data after preliminary processing, and display the cleaned data after classification and statistics. The operation terminal includes an equipment control module, a data acquisition module, a data recording module, a data storage module, a data statistics module and a data display module. The data fed back by the management and control system is received by the equipment control module, acquired by the data acquisition module, recorded by the data recording module, and stored in the data storage module. The stored data is classified and statistically analyzed by the data statistics module, and the statistical results are displayed by the data display module; The cleaning system is used to obtain the data after preliminary processing from the operation terminal to perform node calculation, screening and cleaning of the data. The cleaning system includes a data receiving module, a cleaning processing platform, a node calculation module, a data screening module, a data cleaning module and a data sending module. After the data of the operation terminal is received by the data receiving module, it enters the cleaning processing platform for preliminary cleaning, and then passes through the node calculation module, the data screening module, and the data cleaning module in sequence to perform flow computing data cleaning, data screening and data cleaning, and then is sent to the operation terminal through the data sending module; The early warning platform is used to monitor according to the collected data, and generate and output fault warnings. The early warning platform includes a real-time monitoring module, a data input module, a model building module, a budget deduction module, a data comparison module, a data output module, a fault warning module and a fault processing module. The data detected by the real-time detection module is received by the data input module and input into the model building module. The deduction model established by the model building module is deduced by the budget deduction module and then input into the data comparison module. After comparative analysis, possible problems are obtained. The fault warning module generates early warning information and issues an alarm based on the comparative analysis results, and the fault processing module performs fault processing based on the early warning information. Among them, the model building module establishes a deduction model based on historical data and a deep learning model, which is used to deduce production status and energy consumption based on data.

2. The tobacco silk processing control and early warning platform based on big data analysis according to claim 1 is characterized in that: The budget deduction module uses the established deduction model to deduce the production status and energy consumption of the input data to obtain the deduction result.

3. The tobacco silk processing control and early warning platform based on big data analysis according to claim 1 is characterized in that: In the data comparison module, the current data is compared with the lowest and highest values ​​in the historical data. When the current data is between the lowest and highest values, the next node data is tested. Otherwise, it is considered that data anomalies may occur, and the current data is input into the data analysis module for preliminary analysis.

Images