Cut tobacco making equipment parameter self-adaptive adjustment system based on big data

Abstract

The invention, which relates to the technical field of tobacco primary processing equipment parameter adjustment, discloses a big-data-based primary processing equipment parameter adaptive adjustment system comprising a humidity sensing module, a humidity intelligent analysis module, a data correlation evaluation module, a data urgency degree analysis module, a data priority processing module and a feedback and optimization module. The data correlation evaluation module is used for evaluating the correlation between data needing to be processed by the system and the tobacco leaf cutting speed under the condition that the humidity increase degree of the tobacco leaves to be cut needs to reduce the cutting speed; according to the evaluation result, data needing to be processed by the system is divided into necessary data and unnecessary data; and the data urgency degree analysis module is used for evaluating the urgency degree of each piece of data in the necessary data in the cutting process of the tobacco leaves to be cut. According to the invention, the cutting problem caused by sudden humidity change can be effectively avoided, and the problems of sensor network communication delay and long processing time of mass data transmission are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of parameter adjustment of tobacco silk-making equipment, and in particular to a silk-making equipment parameter adaptive adjustment system based on big data. Background Art

[0002] The tobacco shredding equipment is a key mechanical equipment used for cutting, humidifying, drying and shredding tobacco leaves during tobacco processing. Through a series of mechanical operations and processing processes, the raw tobacco leaves are converted into shreds suitable for cigarette production. Parameter adjustment is to cope with the changes in the characteristics of different batches of raw materials and changes in the production environment, to ensure the stability of product quality and production efficiency; the specific parameters that need to be adjusted include cutting speed, cutting blade gap, humidification amount, humidification temperature, drying temperature, drying time, wind speed, air volume, etc. The precise adjustment of these parameters can optimize the cutting effect, moisture content and flexibility of tobacco leaves, thereby improving the stability of the shredding process and the uniformity of the finished product.

[0003] The parameter adjustment technology of silk-making equipment based on big data is an advanced technology that automatically optimizes the operating parameters of the equipment by collecting and analyzing massive data in the silk-making process. This technology first uses a sensor network and a data acquisition system to monitor the operating status of the equipment and the processing of tobacco leaves in real time, and collects key parameter data including cutting speed, humidification amount, drying temperature, etc. Then, through the big data analysis platform and machine learning algorithm, these data are processed and modeled to find the best relationship between each parameter and the silk-making effect. Then, the system calculates the optimal combination of equipment parameters based on the model, and automatically adjusts the operating parameters of the equipment through the control system to ensure that each batch of tobacco leaves can get the best processing effect under different raw material characteristics and environmental conditions. This technology can significantly improve production efficiency, ensure the consistency of product quality, reduce the need for manual intervention, reduce human errors and production fluctuations, and ultimately achieve a smarter and more efficient silk-making production process.

[0004] The specific working process of the existing silk-making equipment parameter adjustment system based on big data includes the following steps: First, various key parameter data including cutting speed, humidification amount, drying temperature, etc. are collected in real time through the sensor network installed on the silk-making equipment. These data are transmitted to the big data platform for storage through the data interface. Then, the system pre-processes these raw data, cleans noise and outliers, and then uses machine learning algorithms to analyze and model the data to establish the best relationship model between equipment parameters and silk-making effects. Based on this model, the system can calculate the optimal combination of equipment parameters in real time. Then, the control system automatically adjusts the operating parameters of the equipment, such as cutting speed and humidification amount, to ensure the optimization and stability of the silk-making process. Finally, the system continuously monitors and records the production effect, continuously updates and optimizes the model, gradually improves the accuracy of parameter adjustment and production efficiency, and finally achieves high-quality and stable silk production.

[0005] The prior art has the following deficiencies:

[0006] In the tobacco-making process, cutting speed is a key parameter that directly affects the uniformity and quality of tobacco leaf cutting. However, tobacco leaf humidity may suddenly increase due to changes in environmental conditions, fluctuations in storage conditions, or equipment failures. The system needs to quickly reduce the cutting speed to ensure cutting quality. However, since the system needs to process a large amount of data, it may fail to respond in time. Specific problems include communication delays in sensor networks, long transmission and processing time for large amounts of data, complex data preprocessing and cleaning, and insufficient processing speed of big data platforms or analysis algorithms. These problems make it impossible to quickly adjust the cutting speed, resulting in uneven thickness of the cut tobacco, affecting the subsequent humidification and drying process, and ultimately leading to reduced product quality, increased defective rates, and reduced production efficiency.

[0007] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0008] The purpose of the present invention is to provide a silk making equipment parameter adaptive adjustment system based on big data to solve the problems in the above-mentioned background technology.

[0009] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a parameter adaptive adjustment system for silk-making equipment based on big data, comprising a humidity sensing module, a humidity intelligent analysis module, a data correlation evaluation module, a data urgency analysis module, a data priority processing module and a feedback and optimization module;

[0010] The humidity sensing module monitors and analyzes the humidity data of the tobacco leaves to be cut in real time to determine whether the humidity of the tobacco leaves to be cut has increased;

[0011] The humidity intelligent analysis module analyzes the humidity data of the tobacco leaves to be cut monitored in real time when the humidity of the tobacco leaves to be cut is determined to have increased, and determines whether the cutting speed needs to be reduced due to the increase in humidity of the tobacco leaves to be cut;

[0012] The data correlation evaluation module evaluates the correlation between various data to be processed by the system and the tobacco leaf cutting speed when it is determined that the cutting speed needs to be reduced due to the increased humidity of the tobacco leaves to be cut, and divides the various data to be processed by the system into necessary data and non-essential data according to the evaluation results;

[0013] The data urgency analysis module evaluates the urgency of each of the necessary data when the tobacco leaves to be cut are in the cutting process, and further divides the necessary data into key data and secondary data according to the evaluation results;

[0014] Data priority processing module, the system processes key data first, then processes secondary data, and adjusts the cutting speed of the silk making equipment in real time according to the processing results of key data and secondary data;

[0015] The feedback and optimization module monitors the cutting effect of tobacco leaves in real time and feeds the results back to the system for self-learning and optimization, continuously updating the model and parameters to improve the system's response speed and decision-making accuracy.

[0016] Preferably, when it is determined that the humidity increase of the tobacco leaves to be cut requires a reduction in the cutting speed, the correlation information between each data to be processed by the system and the tobacco leaf cutting speed is obtained, including a data value variation coefficient and a historical correlation index. After the information is obtained, the data value variation coefficient and the historical correlation index between each data to be processed by the system and the tobacco leaf cutting speed are calibrated as and , The number indicating the coefficient of variation and historical correlation index of the data values ​​between the various data that the system needs to process and the tobacco leaf cutting speed. , is a positive integer, and a correlation evaluation model is constructed for the data value change coefficient and historical correlation index between each data that the system needs to process and the tobacco leaf cutting speed, and a correlation evaluation coefficient is generated. The generated correlation evaluation coefficient is compared with a preset correlation evaluation coefficient threshold to evaluate whether the various data that the system needs to process are correlated with the tobacco leaf cutting speed, and the various data that the system needs to process are divided into necessary data and non-essential data according to the evaluation results.

[0017] Preferably, the logic for obtaining the data value variation coefficient is as follows:

[0018] Obtain the data values ​​and corresponding time points of each data that the system needs to process in the past period of time, and mark the data values ​​and corresponding time points of each data that the system needs to process in the past period of time as and , Indicates the first Data in the past period of time The data value at the moment, Indicates the first Data in the past period of time The time point of the moment, , is a positive integer; the data change rate of each data that needs to be processed by the calculation system in each time interval, the specific calculation formula is as follows:

[0019] In the formula, Indicates the first The data in The rate of change of data within a time interval;

[0020] The computing system needs to process The specific calculation formula for the coefficient of variation of the data value of each data is as follows:

[0021] In the formula, Indicates the first The coefficient of variation of the data value of each data;

[0022] The logic for obtaining the historical correlation index is as follows:

[0023] Obtain the data values ​​of each data that the system needs to process in the past period of time and the cutting speed values ​​of the silk-making equipment at the corresponding time points, and calibrate the data values ​​of each data that the system needs to process in the past period of time and the cutting speed values ​​of the silk-making equipment at the corresponding time points as and , Indicates the first Data in the past period of time The data value at the moment, Indicates the first The cutting speed value of the wire making equipment at the corresponding time point in the past period of time. , is a positive integer;

[0024] The computing system needs to process The average value of the data in the past period of time and the first The average cutting speed value of the silk-making equipment corresponding to the data in the past period of time is calculated by the following formula:

[0025]

[0026] In the formula, The system needs to process The average value of the data in the past period of time. The system needs to process The average value of the cutting speed of the silk-making equipment corresponding to each data in the past period of time;

[0027] The computing system needs to process The covariance between the data values ​​of the continuous moments of the data in the past period of time and the cutting speed values ​​of the silk-making equipment at the corresponding time points is calculated by the following specific formula:

[0028] In the formula, The system needs to process The covariance between the data values ​​of a data set at consecutive moments in the past period of time and the cutting speed values ​​of the silk-making equipment at the corresponding time points;

[0029] The computing system needs to process The standard deviation of the data values ​​in the past period of time and the first The standard deviation of the cutting speed value of the wire making equipment corresponding to the data in the past period of time is calculated as follows:

[0030]

[0031] In the formula, The system needs to process The standard deviation of the data values ​​in the past period of time, The system needs to process The standard deviation of the cutting speed value of the silk-making equipment corresponding to the data in the past period of time;

[0032] The Pearson correlation coefficient formula is used to calculate the number of The historical correlation index between the data and the tobacco leaf cutting speed is calculated as follows:

[0033]

[0034] In the formula, The system needs to process The historical correlation index between the data and tobacco leaf cutting speed.

[0035] Preferably, a correlation evaluation model is constructed for the data value variation coefficient and historical correlation index between each data to be processed by the system and the tobacco leaf cutting speed, and a correlation evaluation coefficient is generated by weighted summation. , the generated correlation evaluation coefficient The correlation evaluation coefficient threshold is set By comparison, it is evaluated whether the data that the system needs to process is related to the tobacco leaf cutting speed, and the data that the system needs to process is divided into necessary data and non-essential data according to the evaluation results. The specific division is as follows:

[0036] like , the system needs to process If there is no correlation between the data and the tobacco leaf cutting speed, the data is classified as unnecessary data;

[0037] like , the system needs to process If there is a correlation between the data and the tobacco leaf cutting speed, the data is classified as necessary data.

[0038] Preferably, the urgent information of each data in the necessary data when the tobacco leaves to be cut are in the cutting process is obtained, including the data fluctuation frequency and the data continuous deviation coefficient. After obtaining, the data fluctuation frequency and the data continuous deviation coefficient of each data in the necessary data when the tobacco leaves to be cut are in the cutting process are calibrated as and , The number indicating the data fluctuation frequency and data continuous deviation coefficient of each of the necessary data when the tobacco leaves to be cut are in the cutting process, , is a positive integer, and an urgency assessment model is constructed for the data fluctuation frequency and data continuous deviation coefficient of each data in the necessary data when the tobacco leaves to be cut are in the cutting process, and an urgency coefficient is generated. The generated urgency coefficient is compared with a preset urgency coefficient threshold, and the urgency of each data in the necessary data when the tobacco leaves to be cut are in the cutting process is evaluated, and the necessary data is further divided into key data and secondary data according to the evaluation results.

[0039] Preferably, the logic for obtaining the data fluctuation frequency and the data continuous deviation coefficient is as follows:

[0040] Obtain the data values ​​and corresponding time points of each data in the necessary data at different times in the past period of time when the tobacco leaves to be cut are in the cutting process, and use the function to convert the data values ​​of each data in the necessary data at different times in the past period of time when the tobacco leaves to be cut are in the cutting process according to the time series To express, Indicates a point in time;

[0041] Calculate the data fluctuation frequency of each data in the necessary data when the tobacco leaves to be cut are in the cutting process. The specific calculation formula is as follows:

[0042] In the formula, is the data fluctuation frequency of each data in the necessary data when the tobacco leaves to be cut are in the cutting process, and are the starting point and end point of the integration interval respectively;

[0043] Calculate the average value of each data in the necessary data at different times in the past period of time when the tobacco leaves to be cut are in the cutting process. The specific calculation formula is as follows:

[0044] In the formula, is the average value of the data values ​​of each of the necessary data at different times in the past period of time when the tobacco leaves to be cut are in the cutting process;

[0045] Calculate the continuous deviation coefficient of each data in the necessary data when the tobacco leaves to be cut are in the cutting process. The specific calculation formula is as follows:

[0046] In the formula, is the continuous deviation coefficient of each data in the necessary data when the tobacco leaves to be cut are in the cutting process, and are the starting point and end point of the integration interval respectively.

[0047] Preferably, an urgency evaluation model is constructed for each of the necessary data, the data fluctuation frequency and the data continuous deviation coefficient when the tobacco leaves to be cut are in the cutting process, and the urgency coefficient is generated by weighted summation. , and the generated urgency coefficient With the pre-set urgency factor threshold Compare and evaluate the urgency of each data in the necessary data during the cutting process of the tobacco leaves to be cut, and further divide the necessary data into key data and secondary data according to the evaluation results, as follows:

[0048] like , the first of the necessary data If the urgency of a piece of data is low when the tobacco leaves to be cut are in the cutting process, the data is classified as secondary data;

[0049] like , the first of the necessary data If the urgency of a piece of data is high when the tobacco leaves to be cut are in the cutting process, the data will be classified as key data.

[0050] Preferably, the data priority processing module realizes data priority processing through the following steps: first, the system divides the necessary data into key data and secondary data according to the urgency assessment model; then, the key data is processed preferentially, including analyzing the data related to the cutting speed adjustment, and adjusting the cutting speed of the silk making equipment in real time according to the analysis results; after processing the key data, the secondary data is processed to ensure the timeliness and accuracy of the cutting speed adjustment, thereby optimizing the overall performance of the silk making process.

[0051] Preferably, the feedback and optimization module monitors the cutting effect of tobacco leaves in real time, including key indicators such as cutting thickness, uniformity and surface quality, and feeds back the monitoring results to the system; the system analyzes the collected data, adjusts and updates the model and parameters, thereby optimizing the system's response speed and decision-making accuracy; based on this adaptive mechanism, the feedback and optimization module enables the system to continuously improve the cutting effect and production efficiency under constantly changing production conditions, thereby ensuring the stability and consistency of product quality.

[0052] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0053] 1. The present invention realizes real-time monitoring and intelligent analysis of tobacco leaf humidity through a humidity sensing module and a humidity intelligent analysis module. When humidity suddenly increases due to changes in environmental conditions, fluctuations in storage conditions, or equipment failure, the system can quickly detect and analyze humidity data, thereby quickly determining whether the cutting speed needs to be adjusted. Through this real-time response and precise adjustment, the system can effectively avoid the problem of uneven cutting effects caused by sudden changes in humidity, ensure the consistency of tobacco thickness, and improve the accuracy and quality of the cutting process.

[0054] 2. The present invention processes and screens a large amount of data through a data correlation evaluation module and a data urgency analysis module, thereby optimizing the efficiency of data processing. The system screens out data that is highly correlated with the cutting speed adjustment by calculating the data value change coefficient and the historical correlation index, thereby reducing the amount of data that needs to be processed. At the same time, the urgency evaluation model further prioritizes key data to ensure the timeliness of the cutting speed adjustment. In this way, the system overcomes the problems of sensor network communication delays and long processing time for large amounts of data transmission, improves the efficiency of data processing and decision-making, thereby quickly responding to production changes and ensuring the stability and efficiency of the production process.

[0055] 3. The feedback and optimization module of the present invention monitors the cutting effect of tobacco leaves in real time, and feeds back the monitoring results to the system for self-learning and optimization. The system analyzes the collected data, continuously updates and adjusts the model and parameters, and adapts to the changing production conditions. This adaptive optimization mechanism enables the system to continuously improve the cutting effect and production efficiency, ensuring the stability and consistency of product quality. Through intelligent feedback and continuous optimization, the system not only reduces the possibility of manual intervention and human error, but also improves the overall production efficiency and consistency of product quality, significantly reduces the defective rate and production fluctuations, and improves production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0057] Figure 1 The present invention is a module schematic diagram of a silk making equipment parameter adaptive adjustment system based on big data. DETAILED DESCRIPTION

[0058] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0059] The present invention provides Figure 1 A parameter adaptive adjustment system for silk-making equipment based on big data is shown, comprising a humidity sensing module, a humidity intelligent analysis module, a data correlation evaluation module, a data urgency analysis module, a data priority processing module and a feedback and optimization module;

[0060] The humidity sensing module monitors and analyzes the humidity data of the tobacco leaves to be cut in real time to determine whether the humidity of the tobacco leaves to be cut has increased. If it is determined that the humidity of the tobacco leaves to be cut has not increased, there is no need to adjust the cutting speed of the silk-making equipment. If it is determined that the humidity of the tobacco leaves to be cut has increased, the humidity data of the tobacco leaves to be cut needs to be transmitted to the humidity intelligent analysis module.

[0061] The humidity sensing module monitors the humidity data of the tobacco leaves to be cut in real time through high-precision humidity sensors installed in the tobacco-making equipment. These sensors can capture the humidity changes on the surface and inside of the tobacco leaves and transmit these data to the system for analysis. Through continuous monitoring and analysis, the humidity sensing module can accurately determine whether the humidity of the tobacco leaves to be cut has increased, thereby providing basic data for subsequent intelligent adjustments.

[0062] The humidity data of the tobacco leaves to be cut can be monitored and analyzed in real time in the following ways: First, multiple high-precision humidity sensors distributed at different locations of the tobacco-making equipment are used to ensure full coverage and accurate measurement. Second, the data collected by the sensors is transmitted to the central data processing system in real time through the Internet of Things technology. The central system uses edge computing technology for preliminary processing to remove noise and outliers to ensure the accuracy and real-time nature of the data. Finally, the data is deeply analyzed through the big data analysis platform to identify the trends and characteristics of humidity changes.

[0063] The process of judging whether the humidity of the tobacco leaves to be cut has increased includes the following steps: First, the humidity sensing module compares the real-time collected humidity data with the preset humidity reference value. By setting a threshold, when the humidity data exceeds the threshold, the system determines that the humidity has increased. Secondly, the system combines historical data and current environmental conditions, and further verifies the trend and magnitude of the humidity increase through statistical analysis and machine learning algorithms. If the data analysis results show that the humidity continues to increase and reaches the warning level, the system will trigger the corresponding processing mechanism to ensure the stability of the production process and product quality.

[0064] The humidity intelligent analysis module analyzes the humidity data of the tobacco leaves to be cut monitored in real time when the humidity of the tobacco leaves to be cut is determined to have increased, and determines whether the cutting speed needs to be reduced due to the increase in humidity of the tobacco leaves to be cut;

[0065] When the humidity of the tobacco leaves to be cut increases, the humidity intelligent analysis module conducts a comprehensive analysis of the real-time monitored humidity data to evaluate the degree of humidity increase and determine whether the cutting speed needs to be reduced to ensure the quality and uniformity of tobacco leaf cutting. The module uses advanced data analysis technology and intelligent algorithms to ensure timely adjustment of cutting parameters when humidity changes to maintain the stability of the production process.

[0066] By analyzing the humidity data of the tobacco leaves to be cut that are monitored in real time, the degree of humidity change can be evaluated by calculating the standard deviation of the humidity data. The specific steps include: collecting humidity data over a period of time, calculating the average of these data, then calculating the square of the deviation of each data point from the average, finding the average of these squared deviations, and then taking the square root to get the standard deviation. By analyzing the standard deviation, the fluctuation of the humidity data can be determined, thereby evaluating the degree of humidity change.

[0067] To determine whether the increase in humidity of the tobacco leaves to be cut requires a reduction in cutting speed, the following methods can be used: Compare the calculated standard deviation of the humidity data with the preset standard deviation threshold. If the standard deviation exceeds the preset threshold, it indicates that the humidity has changed significantly, which may affect the cutting quality. At this time, it is necessary to evaluate the necessity of adjusting the cutting speed. In addition to standard deviation comparison, historical data and current environmental conditions can also be combined to predict the impact of humidity increase on cutting results through a model established by a machine learning algorithm. After comprehensive evaluation, it can be decided whether the cutting speed needs to be reduced to ensure cutting results and tobacco leaf quality.

[0068] The data correlation evaluation module evaluates the correlation between various data to be processed by the system and the tobacco leaf cutting speed when it is determined that the cutting speed needs to be reduced due to the increased humidity of the tobacco leaves to be cut, and divides the various data to be processed by the system into necessary data and non-essential data according to the evaluation results;

[0069] In this embodiment, when it is determined that the cutting speed needs to be reduced due to the increase in humidity of the tobacco leaves to be cut, the correlation information between each data to be processed by the system and the tobacco leaf cutting speed is obtained, including the data value variation coefficient and the historical correlation index. After the information is obtained, the data value variation coefficient and the historical correlation index between each data to be processed by the system and the tobacco leaf cutting speed are calibrated as and , The number indicating the coefficient of variation and historical correlation index of the data values ​​between the various data that the system needs to process and the tobacco leaf cutting speed. , is a positive integer, a correlation evaluation model is constructed for the data value variation coefficient and historical correlation index between each data to be processed by the system and the tobacco leaf cutting speed, a correlation evaluation coefficient is generated, the generated correlation evaluation coefficient is compared with a preset correlation evaluation coefficient threshold, whether the data to be processed by the system is correlated with the tobacco leaf cutting speed is evaluated, and the data to be processed by the system is divided into necessary data and non-essential data according to the evaluation result;

[0070] In this embodiment, the logic for obtaining the data value variation coefficient is as follows:

[0071] Obtain the data values ​​and corresponding time points of each data that the system needs to process in the past period of time, and mark the data values ​​and corresponding time points of each data that the system needs to process in the past period of time as and , Indicates the first Data in the past period of time The data value at the moment, Indicates the first Data in the past period of time The time point of the moment, , is a positive integer;

[0072] By installing high-precision sensors and data acquisition equipment in the silk-making equipment, the changes in various data can be monitored in real time, and these data values ​​and corresponding time points can be recorded. These sensors can be connected to the central data processing system through the Internet of Things (IoT) technology, and the data acquisition software can be used to continuously collect and store the data values ​​and time points at each moment, thereby realizing continuous monitoring of the data that the system needs to process.

[0073] The specific acquisition method is as follows: Data acquisition software can be used to continuously monitor and record various data. First, the sensor transmits the collected data to the data acquisition software in real time through the Internet of Things technology. The data acquisition software records and stores the sensor data at each time point, generating a time series containing data values ​​and corresponding time points. For example, a data acquisition script written in Python can use library functions (such as pandas and numpy) to create a data frame, record the readings and timestamps of each sensor, and regularly store the data in a database or file system for subsequent analysis and processing.

[0074] The calculation system needs to process the data change rate of each data in each time interval. The specific calculation formula is as follows:

[0075] In the formula, Indicates the first The data in The rate of change of data within a time interval;

[0076] The computing system needs to process The specific calculation formula for the coefficient of variation of the data value of each data is as follows:

[0077] In the formula, Indicates the first The coefficient of variation of the data value of each data point.

[0078] The system needs to process The coefficient of variation of the data value of The size reflects the degree of change of the data over a period of time. The correlation between the data and the tobacco leaf cutting speed is evaluated by comparing This is achieved by comparing the changing trend of historical cutting speed data. The larger the value, the more dramatic the change in the data, which may have a greater impact on the adjustment of the cutting speed. By performing a correlation analysis between the data and the change in cutting speed, the importance of the data in adjusting the cutting speed can be evaluated, thereby determining whether the data is necessary.

[0079] The logic for obtaining the historical correlation index is as follows:

[0080] Obtain the data values ​​of each data that the system needs to process in the past period of time and the cutting speed values ​​of the silk-making equipment at the corresponding time points, and calibrate the data values ​​of each data that the system needs to process in the past period of time and the cutting speed values ​​of the silk-making equipment at the corresponding time points as and , Indicates the first Data in the past period of time The data value at the moment, Indicates the first The cutting speed value of the wire making equipment at the corresponding time point in the past period of time. , is a positive integer;

[0081] The method for obtaining the data values ​​and corresponding cutting speed values ​​that the system needs to process is as follows: high-precision sensors and data acquisition equipment can be installed in the silk-making equipment to monitor the changes in various data in real time, and these data values ​​and corresponding time points can be recorded. At the same time, a data acquisition module is integrated into the cutting speed control system of the equipment to record the cutting speed value in real time. Through the Internet of Things (IoT) technology, these sensors and data acquisition modules can transmit all collected data to the central data processing system in real time, realizing comprehensive monitoring and recording of data values ​​and cutting speed values ​​at each moment. The specific acquisition method is not limited here and can be selected according to actual needs.

[0082] The specific acquisition method is as follows: The data acquisition software can be used to continuously monitor and record each data value and the corresponding cutting speed value. First, the high-precision sensor transmits the collected data values ​​to the data acquisition software in real time through the Internet of Things technology. At the same time, the integrated data acquisition module of the cutting speed control system also transmits the cutting speed value to the same data acquisition software in real time. The data acquisition software records and stores the sensor data and cutting speed value at each time point through programming, and generates a time series containing data values ​​and corresponding time points. For example, a data acquisition script written in Python can be used to create a data frame using the pandas library, record the readings and timestamps of each sensor, and store the data regularly in a database or file system for subsequent analysis and processing. Such a data acquisition system can ensure real-time performance and data accuracy, and provide a reliable data basis for subsequent correlation evaluation.

[0083] The computing system needs to process The average value of the data in the past period of time and the first The average cutting speed value of the silk-making equipment corresponding to the data in the past period of time is calculated by the following formula:

[0084]

[0085] In the formula, The system needs to process The average value of the data in the past period of time. The system needs to process The average value of the cutting speed of the silk-making equipment corresponding to each data in the past period of time;

[0086] The computing system needs to process The covariance between the data values ​​of the continuous moments of the data in the past period of time and the cutting speed values ​​of the silk-making equipment at the corresponding time points is calculated by the following specific formula:

[0087] In the formula, The system needs to process The covariance between the data values ​​of a data set at consecutive moments in the past period of time and the cutting speed values ​​of the silk-making equipment at the corresponding time points;

[0088] The computing system needs to process The standard deviation of the data values ​​in the past period of time and the first The standard deviation of the cutting speed value of the wire making equipment corresponding to the data in the past period of time is calculated as follows:

[0089]

[0090] In the formula, The system needs to process The standard deviation of the data values ​​in the past period of time, The system needs to process The standard deviation of the cutting speed value of the silk-making equipment corresponding to the data in the past period of time;

[0091] The Pearson correlation coefficient formula is used to calculate the number of The historical correlation index between the data and the tobacco leaf cutting speed is calculated as follows:

[0092]

[0093] In the formula, The system needs to process The historical correlation index between the data and tobacco leaf cutting speed.

[0094] The Pearson correlation coefficient formula is used to measure the linear correlation between two variables. The numerator of the formula is the covariance, which reflects the degree of common change of the two variables; the denominator is the product of the standard deviations of the two variables, which is used to standardize the covariance. The calculation result ranges from -1 to 1. A positive value indicates a positive correlation, and a negative value indicates a negative correlation. The closer the absolute value is to 1, the stronger the correlation.

[0095] The Pearson correlation coefficient formula is used to calculate the number of The historical correlation index between individual data and tobacco leaf cutting speed is because this coefficient can accurately quantify the strength and direction of the linear relationship between the two. The use of the Pearson correlation coefficient can effectively filter out irrelevant or less influential data, and only retain key data that has a significant impact on the adjustment of the cutting speed. This ensures that when the system adjusts the cutting speed, it gives priority to data that is closely related to the cutting effect, thereby improving the accuracy and real-time nature of decision-making. In addition, the calculation method of the Pearson correlation coefficient is relatively simple and efficient, which is suitable for real-time processing needs in a big data environment, and can quickly identify and respond to changes in the production process, thereby improving overall production efficiency and product quality.

[0096] The system needs to process Historical correlation index between data and tobacco leaf cutting speed The size of reflects the strength and direction of the linear correlation between the data and the cutting speed. A higher historical correlation index indicates that the data has a significant correlation with the cutting speed in history. Therefore, when evaluating, such data is considered to have an important impact on the cutting speed adjustment and is necessary data; on the contrary, a lower correlation index indicates that the data has little impact on the cutting speed adjustment and can be treated as non-essential data. Through this correlation evaluation, the most valuable data for cutting speed adjustment can be effectively screened out, improving the accuracy and efficiency of system decision-making.

[0097] In this embodiment, a correlation evaluation model is constructed based on the data value variation coefficient and historical correlation index between each data to be processed by the system and the tobacco leaf cutting speed, and the correlation evaluation coefficient is generated by weighted summation. , the generated correlation evaluation coefficient The correlation evaluation coefficient threshold is set By comparison, it is evaluated whether the data that the system needs to process is related to the tobacco leaf cutting speed, and the data that the system needs to process is divided into necessary data and non-essential data according to the evaluation results. The specific division is as follows:

[0098] like , the system needs to process If there is no correlation between the data and the tobacco leaf cutting speed, the data is classified as unnecessary data;

[0099] like , the system needs to process If there is a correlation between the data and the tobacco leaf cutting speed, the data is classified as necessary data.

[0100] The pre-set relevance evaluation coefficient threshold can be determined by analyzing historical data, including model training and verification using machine learning algorithms. First, collect and organize historical data, including various data values, change rate coefficients, historical relevance indexes, and corresponding cutting speed adjustment records. Then, use supervised learning algorithms (such as random forests, support vector machines, etc.) to train these data and build a prediction model. Through cross-validation and model optimization, determine the optimal relevance evaluation coefficient threshold that can accurately classify necessary and non-essential data. Finally, embed this threshold into the system for real-time evaluation of the correlation between new data and cutting speed.

[0101] The correlation evaluation model is constructed for the data value change coefficient and historical correlation index between each data that the system needs to process and the tobacco leaf cutting speed in order to comprehensively evaluate the importance of each data to the cutting speed adjustment. Specifically, the change rate coefficient of each data is first calculated to reflect the fluctuation of the data in the past period of time; then the historical correlation index of each data is calculated to measure its linear correlation with the cutting speed. Then, these two parameters are weighted and summed, and the formula is: ,in and The weight coefficients are determined based on actual conditions. By adjusting these weights, the impact of the rate of change and historical relevance on the evaluation results can be balanced. The generated correlation evaluation coefficient (CCEC) takes into account the current fluctuations and historical impacts of the data, which helps to more accurately determine the relevance of the data to the cutting speed adjustment.

[0102] The data urgency analysis module evaluates the urgency of each of the necessary data when the tobacco leaves to be cut are in the cutting process, and further divides the necessary data into key data and secondary data according to the evaluation results;

[0103] In this embodiment, the urgent information of each data in the necessary data when the tobacco leaves to be cut are in the cutting process is obtained, including the data fluctuation frequency and the data continuous deviation coefficient. After obtaining, the data fluctuation frequency and the data continuous deviation coefficient of each data in the necessary data when the tobacco leaves to be cut are in the cutting process are calibrated as and , The number indicating the data fluctuation frequency and data continuous deviation coefficient of each of the necessary data when the tobacco leaves to be cut are in the cutting process, , is a positive integer, and an urgency assessment model is constructed for the data fluctuation frequency and data continuous deviation coefficient of each data in the necessary data when the tobacco leaves to be cut are in the cutting process, and an urgency coefficient is generated. The generated urgency coefficient is compared with a preset urgency coefficient threshold, and the urgency of each data in the necessary data when the tobacco leaves to be cut are in the cutting process is evaluated, and the necessary data is further divided into key data and secondary data according to the evaluation results.

[0104] In this embodiment, the logic for obtaining the data fluctuation frequency and the data continuous deviation coefficient is as follows:

[0105] Obtain the data values ​​and corresponding time points of each data in the necessary data at different times in the past period of time when the tobacco leaves to be cut are in the cutting process, and use the function to convert the data values ​​of each data in the necessary data at different times in the past period of time when the tobacco leaves to be cut are in the cutting process according to the time series To express, Indicates a point in time;

[0106] The data values ​​of each data in the necessary data at different times in the past period of time when the tobacco leaves to be cut are in the cutting process and the corresponding time points are obtained as follows: high-precision sensors and data acquisition equipment can be installed in the silk making equipment to monitor and record the changes of the necessary data in real time. Specifically, high-precision sensors can continuously collect various data values ​​of the tobacco leaves to be cut during the cutting process and record the corresponding time points. These sensors transmit real-time data to the central data processing system through the Internet of Things technology. After receiving these data, the data acquisition software uses programming technology (such as Python combined with the pandas library) to record and store the sensor data at each time point to generate a time series containing data values ​​and time points. For example, the data acquisition software can be set to collect data once every second and save these data in a database or file system to ensure that the data values ​​and time points at different times in the past period of time are fully and accurately recorded, providing a reliable data basis for subsequent analysis.

[0107] Calculate the data fluctuation frequency of each data in the necessary data when the tobacco leaves to be cut are in the cutting process. The specific calculation formula is as follows:

[0108] In the formula, is the data fluctuation frequency of each data in the necessary data when the tobacco leaves to be cut are in the cutting process, and They are the starting point and end point of the integration interval, which represent the start time and end time of a period of time in the past and are used to calculate the fluctuation frequency of the data in this period of time.

[0109] The frequency of data fluctuations of each of the necessary data when the tobacco leaves to be cut are in the cutting process reflects how frequently these data change during the process. The higher the frequency of data fluctuations, the more drastic the data changes, which means that the data has a greater real-time impact on the cutting process, and therefore the higher the urgency is assessed. Specifically, data with high fluctuation frequencies often require more timely responses and adjustments, because they have a more direct and significant impact on cutting quality and equipment operation. The system must give priority to these high-urgency data to ensure the stability of the cutting process and product quality. Therefore, by calculating the frequency of data fluctuations, the urgency of the necessary data in the cutting process can be effectively assessed, and then key data and secondary data can be divided to optimize the real-time adjustment and response efficiency of the system.

[0110] Calculate the average value of each data in the necessary data at different times in the past period of time when the tobacco leaves to be cut are in the cutting process. The specific calculation formula is as follows:

[0111] In the formula, is the average value of the data values ​​of each of the necessary data at different times in the past period of time when the tobacco leaves to be cut are in the cutting process;

[0112] Calculate the continuous deviation coefficient of each data in the necessary data when the tobacco leaves to be cut are in the cutting process. The specific calculation formula is as follows:

[0113] In the formula, is the continuous deviation coefficient of each data in the necessary data when the tobacco leaves to be cut are in the cutting process, and They are the starting point and end point of the integration interval, which represent the start time and end time of a period of time in the past and are used to calculate the continuous deviation coefficient of the data in this period of time.

[0114] The size of the continuous deviation coefficient (SDC) of each data in the necessary data when the tobacco leaves to be cut are in the cutting process reflects the degree of continuous deviation of the data from its average value over a period of time, and has a direct relationship with the urgency of evaluating the necessary data in the cutting process. A higher SDC indicates that the data has had greater fluctuations and deviations over a period of time, which means that its impact on the cutting process is more significant and the urgency is higher; if this data is not processed in time, it may have a greater impact on the cutting quality. Therefore, by calculating and analyzing SDC, the system can identify those data with higher urgency in the cutting process. These data need to be processed first to ensure the stability of the cutting process and the quality of the tobacco leaves, while data with lower urgency can be processed as secondary data.

[0115] In this embodiment, the urgency evaluation model is constructed for each of the necessary data, the data fluctuation frequency and the data continuous deviation coefficient when the tobacco leaves to be cut are in the cutting process, and the urgency coefficient is generated by weighted summation. , and the generated urgency coefficient With the pre-set urgency factor threshold Compare and evaluate the urgency of each data in the necessary data during the cutting process of the tobacco leaves to be cut, and further divide the necessary data into key data and secondary data according to the evaluation results, as follows:

[0116] like , the first of the necessary data If the urgency of a piece of data is low when the tobacco leaves to be cut are in the cutting process, the data is classified as secondary data;

[0117] like , the first of the necessary data If the urgency of a piece of data is high when the tobacco leaves to be cut are in the cutting process, the data will be classified as key data.

[0118] The process of constructing the urgency evaluation model and generating the urgency coefficient by weighted summation includes the following steps: First, calculate the fluctuation frequency of each data in the necessary data and coefficient of persistence , respectively reflecting the short-term fluctuation of the data and the degree of long-term deviation from the average value. Then, the weighted summation method is used to and Combined to generate an urgency coefficient that comprehensively reflects the urgency of the data , the formula is as follows: in, and is the weight coefficient, which reflects the relative importance of fluctuation frequency and persistent deviation in the evaluation model. The setting of the weight coefficient can be adjusted based on historical data analysis and expert experience to ensure the accuracy and applicability of the model.

[0119] The threshold of the urgency coefficient set in advance can be determined in the following ways: First, use historical data for statistical analysis to calculate the distribution of the urgency coefficient under different circumstances in the past, so as to identify the relationship between the urgency coefficient and the impact of the cutting process. The specific method can be analyzed by software, such as using Python's pandas library to process historical data, and using the statistical functions in the scipy library to calculate the distribution and critical value of the urgency coefficient. Secondly, through expert experience combined with historical analysis results, a reasonable urgency coefficient threshold (T) is determined so that it can effectively distinguish between high-urgency and low-urgency data. For example, the urgency coefficient can be clustered and analyzed by the clustering algorithm (such as K-means) in the scikit-learn library to obtain the optimal threshold range. The threshold set in this way can ensure that in actual operation, key data with high urgency are processed first, ensuring the stability of the cutting process and the quality of tobacco leaves.

[0120] Data priority processing module, the system processes key data first, then processes secondary data, and adjusts the cutting speed of the silk making equipment in real time according to the processing results of key data and secondary data;

[0121] In this embodiment, the data priority processing module implements data priority processing through the following steps: first, the system divides the necessary data into key data and secondary data according to the urgency assessment model; then, the key data is processed first, including analyzing the data related to the cutting speed adjustment, and adjusting the cutting speed of the silk making equipment in real time according to the analysis results; after processing the key data, the secondary data is processed to ensure the timeliness and accuracy of the cutting speed adjustment, thereby optimizing the overall performance of the silk making process.

[0122] The data priority processing module prioritizes key data, including analyzing and processing data that is highly relevant to the cutting speed adjustment, and adjusting the cutting speed of the silk-making equipment in real time according to the analysis results; specific methods include: using high-precision sensors and data acquisition systems to monitor key data in real time, and realizing data analysis through software programming, such as using Python or R language to perform statistical analysis and modeling on the data, and using algorithms to identify patterns and trends that are closely related to the cutting speed adjustment, and then generating adjustment instructions based on these analysis results, which are transmitted to the equipment control system in real time to adjust the cutting speed; after processing the key data, the same method is used to process the secondary data to ensure the timeliness and accuracy of the cutting speed adjustment, thereby optimizing the overall performance of the silk-making process and improving production efficiency and product quality.

[0123] Feedback and optimization module monitors the cutting effect of tobacco leaves in real time and feeds the results back to the system for self-learning and optimization, continuously updating models and parameters to improve the system's response speed and decision-making accuracy;

[0124] In this embodiment, the feedback and optimization module monitors the cutting effect of tobacco leaves in real time, including key indicators such as cutting thickness, uniformity and surface quality, and feeds back the monitoring results to the system; the system analyzes the collected data, adjusts and updates the model and parameters, thereby optimizing the system's response speed and decision-making accuracy; based on this adaptive mechanism, the feedback and optimization module enables the system to continuously improve the cutting effect and production efficiency under constantly changing production conditions, thereby ensuring the stability and consistency of product quality.

[0125] The feedback and optimization module monitors the cutting effect of tobacco leaves in real time, including key indicators such as cutting thickness, uniformity and surface quality, and feeds back these monitoring results to the system; the specific methods include using high-precision sensors and cameras to monitor the cutting effect in real time, and transmitting the collected data to the central processing system through the data interface; the system uses machine learning algorithms to analyze the collected data, identify and predict the changing trends of cutting effects, and adjust and update models and parameters, such as using machine learning libraries in Python or R (such as scikit-learn or TensorFlow) for modeling and prediction; through such an adaptive mechanism, the system can continuously optimize the cutting effect and production efficiency under changing production conditions to ensure the stability and consistency of product quality.

[0126] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0127] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0128] It should be noted that, in this article, if there are first and second, etc., relational terms are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or still includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0129] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0130] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. 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.

[0131] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0132] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components 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 through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0133] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0134] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0135] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0136] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A fiber-making equipment parameter adaptive adjustment system based on big data, characterized in that: It includes humidity perception module, humidity intelligent analysis module, data correlation evaluation module, data urgency analysis module, data priority processing module and feedback and optimization module; The humidity sensing module monitors and analyzes the humidity data of the tobacco leaves to be cut in real time to determine whether the humidity of the tobacco leaves to be cut has increased; The humidity intelligent analysis module analyzes the humidity data of the tobacco leaves to be cut monitored in real time when the humidity of the tobacco leaves to be cut is determined to have increased, and determines whether the cutting speed needs to be reduced due to the degree of humidity increase of the tobacco leaves to be cut; The data correlation evaluation module evaluates the correlation between various data to be processed by the system and the tobacco leaf cutting speed when it is determined that the cutting speed needs to be reduced due to the increased degree of humidity of the tobacco leaves to be cut, and divides the various data to be processed by the system into necessary data and non-essential data according to the evaluation results; The data urgency analysis module evaluates the urgency of each of the necessary data when the tobacco leaves to be cut are in the cutting process, and further divides the necessary data into key data and secondary data according to the evaluation results; Data priority processing module, the system processes key data first, then processes secondary data, and adjusts the cutting speed of the silk making equipment in real time according to the processing results of key data and secondary data; The feedback and optimization module monitors the cutting effect of tobacco leaves in real time and feeds the results back to the system for self-learning and optimization, continuously updating the model and parameters to improve the system's response speed and decision-making accuracy.

2. According to the big data-based adaptive adjustment system for silk-making equipment parameters according to claim 1, it is characterized in that: When it is determined that the humidity of the tobacco leaves to be cut increases and the cutting speed needs to be reduced, the correlation information between the various data to be processed by the system and the tobacco leaf cutting speed is obtained, including the data value variation coefficient and the historical correlation index. After obtaining, the data value variation coefficient and the historical correlation index between the various data to be processed by the system and the tobacco leaf cutting speed are calibrated as and , The number indicating the coefficient of variation and historical correlation index of the data values ​​between the various data that the system needs to process and the tobacco leaf cutting speed. , is a positive integer, and a correlation evaluation model is constructed for the data value change coefficient and historical correlation index between each data that the system needs to process and the tobacco leaf cutting speed, and a correlation evaluation coefficient is generated. The generated correlation evaluation coefficient is compared with a preset correlation evaluation coefficient threshold to evaluate whether the various data that the system needs to process are correlated with the tobacco leaf cutting speed, and the various data that the system needs to process are divided into necessary data and non-essential data according to the evaluation results.

3. The fiber-making equipment parameter adaptive adjustment system based on big data according to claim 2 is characterized in that: The logic for obtaining the data value change coefficient is as follows: Obtain the data values ​​and corresponding time points of each data that the system needs to process in the past period of time, and mark the data values ​​and corresponding time points of each data that the system needs to process in the past period of time as and , Indicates the first Data in the past period of time The data value at the moment, Indicates the first Data in the past period of time The time point of the moment, , is a positive integer; the data change rate of each data that needs to be processed by the calculation system in each time interval, the specific calculation formula is as follows: (1) In formula (1), Indicates the first The data in The rate of change of data within a time interval; The computing system needs to process The data value variation coefficient of each data, the specific calculation formula is as follows: (2) In formula (2), Indicates the first The coefficient of variation of the data value of each data; The logic for obtaining the historical correlation index is as follows: Obtain the data values ​​of each data that the system needs to process in the past period of time and the cutting speed values ​​of the silk-making equipment at the corresponding time points, and calibrate the data values ​​of each data that the system needs to process in the past period of time and the cutting speed values ​​of the silk-making equipment at the corresponding time points as and , Indicates the first Data in the past period of time The data value at the moment, Indicates the first The cutting speed value of the wire making equipment at the corresponding time point in the past period of time. , is a positive integer; The computing system needs to process The average value of the data in the past period of time and the first The average cutting speed value of the silk-making equipment corresponding to the data in the past period of time is calculated by the specific calculation formulas such as formula (3) and formula (4): (3) (4) In formula (3) and formula (4), The system needs to process The average value of the data in the past period of time. The system needs to process The average value of the cutting speed of the silk-making equipment corresponding to each data in the past period of time; The computing system needs to process The covariance between the data values ​​of the continuous moments of the data in the past period of time and the cutting speed value of the silk-making equipment at the corresponding time point is calculated by the formula (5): (5) In formula (5), The system needs to process The covariance between the data values ​​of the data at consecutive moments in the past period of time and the cutting speed values ​​of the silk-making equipment at the corresponding time points; The computing system needs to process The standard deviation of the data values ​​in the past period of time and the first The standard deviation of the cutting speed value of the silk-making equipment corresponding to each data in the past period of time is calculated by the formula (6) and (7): (6) (7) In formula (6) and formula (7), The system needs to process The standard deviation of the data values ​​in the past period of time, The system needs to process The standard deviation of the cutting speed value of the silk-making equipment corresponding to the data in the past period of time; The Pearson correlation coefficient formula is used to calculate the number of The historical correlation index between the data and the tobacco leaf cutting speed is calculated by the formula (8): (8) In formula (8), The system needs to process The historical correlation index between the data and tobacco leaf cutting speed.

4. The fiber-making equipment parameter adaptive adjustment system based on big data according to claim 3 is characterized in that: A correlation evaluation model is constructed based on the data value change coefficient and historical correlation index between each data that needs to be processed by the system and the tobacco leaf cutting speed, and the correlation evaluation coefficient is generated by weighted summation. , the generated correlation evaluation coefficient The correlation evaluation coefficient threshold is set By comparison, it is evaluated whether the data that the system needs to process is related to the tobacco leaf cutting speed, and the data that the system needs to process is divided into necessary data and non-essential data according to the evaluation results. The specific division is as follows: like , the system needs to process If there is no correlation between the data and the tobacco leaf cutting speed, the data is classified as unnecessary data; like , the system needs to process If there is a correlation between the data and the tobacco leaf cutting speed, the data is classified as necessary data.

5. The fiber-making equipment parameter adaptive adjustment system based on big data according to claim 1, characterized in that: The urgent information of each data in the necessary data when the tobacco leaves to be cut are in the cutting process is obtained, including the data fluctuation frequency and the data continuous deviation coefficient. After obtaining, the data fluctuation frequency and the data continuous deviation coefficient of each data in the necessary data when the tobacco leaves to be cut are in the cutting process are calibrated as and , The number indicating the data fluctuation frequency and data continuous deviation coefficient of each of the necessary data when the tobacco leaves to be cut are in the cutting process, , is a positive integer, and an urgency assessment model is constructed for the data fluctuation frequency and data continuous deviation coefficient of each data in the necessary data when the tobacco leaves to be cut are in the cutting process, and an urgency coefficient is generated. The generated urgency coefficient is compared with a preset urgency coefficient threshold, and the urgency of each data in the necessary data when the tobacco leaves to be cut are in the cutting process is evaluated, and the necessary data is further divided into key data and secondary data according to the evaluation results.

6. The fiber-making equipment parameter adaptive adjustment system based on big data according to claim 5, characterized in that: The logic for obtaining data fluctuation frequency and data continuous deviation coefficient is as follows: Obtain the data values ​​and corresponding time points of each data in the necessary data at different times in the past period of time when the tobacco leaves to be cut are in the cutting process, and use the function to convert the data values ​​of each data in the necessary data at different times in the past period of time when the tobacco leaves to be cut are in the cutting process according to the time series To express, Indicates a point in time; The data fluctuation frequency of each data in the necessary data when the tobacco leaves to be cut are in the cutting process is calculated. The specific calculation formula is as follows: (9) In formula (9), is the data fluctuation frequency of each data in the necessary data when the tobacco leaves to be cut are in the cutting process, and are the starting point and end point of the integration interval respectively; Calculate the average value of each data in the necessary data at different times in the past period of time when the tobacco leaves to be cut are in the cutting process. The specific calculation formula is as follows: (10) In formula (10), is the average value of the data values ​​of each of the necessary data at different times in the past period of time when the tobacco leaves to be cut are in the cutting process; The data continuous deviation coefficient of each data in the necessary data when the tobacco leaves to be cut are in the cutting process is calculated. The specific calculation formula is as follows: (11) In formula (11), is the continuous deviation coefficient of each data in the necessary data when the tobacco leaves to be cut are in the cutting process, and are the starting point and end point of the integration interval respectively.

7. The fiber-making equipment parameter adaptive adjustment system based on big data according to claim 6 is characterized in that: The urgency evaluation model is constructed for each of the necessary data, including the data fluctuation frequency and the data continuous deviation coefficient during the cutting process of the tobacco leaves to be cut, and the urgency coefficient is generated by weighted summation. , and the generated urgency coefficient With the pre-set urgency factor threshold Compare and evaluate the urgency of each data in the necessary data during the cutting process of the tobacco leaves to be cut, and further divide the necessary data into key data and secondary data according to the evaluation results, as follows: like , the first of the necessary data If the urgency of a piece of data is low when the tobacco leaves to be cut are in the cutting process, the data is classified as secondary data; like , the first of the necessary data If the urgency of a piece of data is high when the tobacco leaves to be cut are in the cutting process, the data will be classified as key data.

8. The fiber-making equipment parameter adaptive adjustment system based on big data according to claim 1, characterized in that: The data priority processing module realizes data priority processing through the following steps: first, the system divides the necessary data into key data and secondary data according to the urgency assessment model; then, the key data is processed first, including analyzing the data related to the cutting speed adjustment, and adjusting the cutting speed of the silk making equipment in real time according to the analysis results; after processing the key data, the secondary data is processed to ensure the timeliness and accuracy of the cutting speed adjustment, thereby optimizing the overall performance of the silk making process.

9. The fiber-making equipment parameter adaptive adjustment system based on big data according to claim 1, characterized in that: The feedback and optimization module monitors the cutting effect of tobacco leaves in real time, including key indicators such as cutting thickness, uniformity and surface quality, and feeds back the monitoring results to the system; the system analyzes the collected data, adjusts and updates the model and parameters, thereby optimizing the system's response speed and decision-making accuracy; based on this adaptive mechanism, the feedback and optimization module enables the system to continuously improve the cutting effect and production efficiency under changing production conditions, ensuring the stability and consistency of product quality.

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