An early warning method and system for tobacco weight and moisture in cigarette production process

Abstract

The present invention discloses a method and system for early warning of tobacco weight and moisture content in the cigarette production process. The method includes combining statistical methods with the establishment of a data optimization model to analyze and determine the weight and moisture content of tobacco in each process of the cigarette production process, and to issue early warnings for abnormal weight and moisture content. This method has the advantage of significantly reducing the model's reliance on process mechanisms and enabling judgment of production process data based on the data's inherent sensitivity. Furthermore, by utilizing the data optimization model to improve the statistical method, it can better adapt to specific process equipment and meet actual needs.

Description

Technical Field

[0001] The present invention relates to the technical field of cigarette production, and more particularly to an early warning method and system for tobacco cut weight and moisture content in a cigarette production process. Background Art

[0002] Currently, no technology related to early warning of tobacco weight and moisture content in the cigarette production process has been found. Currently, there is a lack of systematic solutions to regulate and issue early warnings for tobacco weight and moisture content in each process of the cigarette production process. Adjustments can only be made through manual operations, and there is a lack of stable and reliable methods and technologies.

[0003] Therefore, how to provide an early warning method and system for the weight and moisture of cut tobacco in the cigarette production process has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0004] The purpose of the present invention is to provide an early warning method, system, electronic device and computer-readable storage medium for the weight and moisture of cut tobacco in a cigarette production process.

[0005] The first aspect of the present invention discloses an early warning method for tobacco weight and moisture in a cigarette production process; the method comprises:

[0006] Step S1: Selecting the tobacco weight and moisture content of each process of the first and second cigarette production lines in normal batch operation for a preset time period from the historical operation database as sample data for establishing a statistical threshold calculation database;

[0007] Step S2: Selecting sample data of a predefined size from the statistical threshold calculation database;

[0008] Step S3: Calculate the statistics of the variance homogeneity test for the sample data of the same process and each size in two batches in the first production line to obtain a first statistic, and obtain the warning sample size and warning statistic threshold of the first production line based on the first statistic;

[0009] Step S4: Calculate the statistics of the variance homogeneity test for the sample data of the same process and each size in two batches in the second production line respectively to obtain a second statistic, and obtain the warning sample size and warning statistic threshold of the second production line based on the second statistic;

[0010] Step S5: Calculate the statistics of the variance homogeneity test for the sample data of the same process and each size of the first production line and the second production line respectively to obtain a third statistic, and obtain the warning sample size and warning statistic threshold between the production lines based on the third statistic;

[0011] Step S6: real-time collection of data on tobacco weight and moisture content of each process of the first production line and the second production line;

[0012] Step S7: Calculate the statistic of the variance homogeneity test of the warning sample size data of the first production line for the same process of two batches within the first production line to obtain a first real-time statistic. If the first real-time statistic is greater than the warning statistic threshold of the first production line, then there is a significant difference in the data of the same process of the two batches within the first production line, and an alarm is issued.

[0013] Step S8: Calculate the statistics of the homogeneity of variance test of the warning sample size of the second production line for the two batches of the same process in the second production line to obtain a second real-time statistic. If the second real-time statistic is greater than the warning statistic threshold of the second production line, then there is a significant difference in the data of the two batches of the same process, and an alarm is issued.

[0014] Step S9: Calculate the statistics of the variance homogeneity test of the data of the warning sample size between the production lines of the same process of the first production line and the second production line to obtain a third real-time statistic. If the third real-time statistic is greater than the warning statistic threshold between the production lines, there is a significant difference in the data of the same process of the first production line and the second production line, and an alarm is performed.

[0015] According to the method of the first aspect of the present invention, in step S2, the predefined size is 12 to 36 mm.

[0016] According to the method of the first aspect of the present invention, in step S3, the method of obtaining the warning sample size of the first production line based on the first statistic includes:

[0017] In different batches, obtain the sample size corresponding to the minimum significance level parameter of each batch;

[0018] Among the sample sizes corresponding to the smallest significance level parameters of all batches, the sample size with the smallest significance level parameter is selected.

[0019] According to the method of the first aspect of the present invention, in step S3, the method of obtaining the sample size corresponding to the minimum significance level parameter of each batch includes:

[0020] In the same batch, the significance level parameters are predefined in a round-robin manner, the critical value of the statistic for each sample size is calculated by interpolation, and the sample size corresponding to the minimum significance level parameter is selected when the true hypothesis is not discarded.

[0021] According to the method of the first aspect of the present invention, in step S3, the predefined significance level parameters include: 0.01, 0.02, 0.05, 0.1, 0.2 and 0.5.

[0022] The second aspect of the present invention discloses an early warning system for tobacco weight and moisture in a cigarette production process; the system comprises:

[0023] The first processing module is configured to select, from a historical operation database, the tobacco weight and moisture content of each process of the first and second cigarette production lines during normal batch operation for a preset time period as sample data for establishing a statistical threshold calculation database;

[0024] A second processing module is configured to select sample data of a predefined size from the statistic threshold calculation database;

[0025] a third processing module configured to calculate statistics of a variance homogeneity test for sample data of the same process and each size in two batches within the first production line, respectively, to obtain a first statistic, and to obtain a warning sample size and a warning statistic threshold for the first production line based on the first statistic;

[0026] a fourth processing module configured to calculate statistics of a variance homogeneity test for sample data of the same process and each size in two batches on the second production line to obtain a second statistic, and obtain a warning sample size and a warning statistic threshold for the second production line based on the second statistic;

[0027] a fifth processing module configured to calculate statistics of a variance homogeneity test for sample data of the same process and each size on the first production line and the second production line, respectively, to obtain a third statistic, and to obtain a warning sample size and a warning statistic threshold between the production lines based on the third statistic;

[0028] A sixth processing module is configured to collect data on the weight and moisture content of cut tobacco in each process of the first production line and the second production line in real time;

[0029] a seventh processing module configured to calculate a statistic of a variance homogeneity test for data of the first production line of the same process in two batches within the first production line, to obtain a first real-time statistic; if the first real-time statistic is greater than a threshold value of the warning statistic for the first production line, then a significant difference exists in the data of the same process in the two batches, and an alarm is initiated;

[0030] an eighth processing module configured to calculate a statistic of a variance homogeneity test for data of the second production line of the warning sample size for two batches of the same process within the second production line to obtain a second real-time statistic; if the second real-time statistic is greater than a warning statistic threshold for the second production line, a significant difference is present in the data of the two batches of the same process, and an alarm is initiated;

[0031] The ninth processing module is configured to calculate the statistic of the variance homogeneity test of the data of the warning sample size between the production lines of the same process of the first production line and the second production line to obtain a third real-time statistic. If the third real-time statistic is greater than the warning statistic threshold between the production lines, there is a significant difference in the data of the same process of the first production line and the second production line, and alarm processing is performed.

[0032] A third aspect of the present invention discloses an electronic device comprising a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of any one of the early warning methods for tobacco weight and moisture in a cigarette production process according to the first aspect of the present disclosure.

[0033] A fourth aspect of the present invention discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the early warning methods for tobacco weight and moisture in a cigarette production process according to the first aspect of the present disclosure.

[0034] According to the technical content disclosed in the present invention, the following beneficial effects are achieved: the dependence of the model on the process mechanism can be greatly reduced, and the production process data can be judged through the sensitivity of the data itself; in addition, by utilizing the data optimization model to improve the statistical method, it can be better adapted to the specified process equipment and meet actual needs.

[0035] Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

[0037] Figure 1 This is a flow chart of a method for early warning of tobacco weight and moisture in a cigarette production process according to an embodiment;

[0038] Figure 2 This is a structural diagram of an early warning system for tobacco weight and moisture in a cigarette production process according to an embodiment of the present invention;

[0039] Figure 3 FIG. 4 is a structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention.

[0041] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0042] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0043] In all examples shown and discussed herein, any specific values should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0044] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0045] Example 1:

[0046] The invention discloses an early warning method for tobacco cut weight and moisture in a cigarette production process. Figure 1 FIG. 1 is a flow chart of an early warning method for tobacco weight and moisture in a cigarette production process according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0047] Step S1: Selecting the tobacco weight and moisture content of each process of the first and second cigarette production lines in normal batch operation for a preset time period from the historical operation database as sample data for establishing a statistical threshold calculation database;

[0048] Step S2: Selecting sample data of a predefined size from the statistical threshold calculation database;

[0049] Step S3: Calculate the statistics of the variance homogeneity test for the sample data of the same process and each size in two batches in the first production line to obtain a first statistic, and obtain the warning sample size and warning statistic threshold of the first production line based on the first statistic;

[0050] Step S4: Calculate the statistics of the variance homogeneity test for the sample data of the same process and each size in two batches in the second production line respectively to obtain a second statistic, and obtain the warning sample size and warning statistic threshold of the second production line based on the second statistic;

[0051] Step S5: Calculate the statistics of the variance homogeneity test for the sample data of the same process and each size of the first production line and the second production line respectively to obtain a third statistic, and obtain the warning sample size and warning statistic threshold between the production lines based on the third statistic;

[0052] Step S6: real-time collection of data on tobacco weight and moisture content of each process of the first production line and the second production line;

[0053] Step S7: Calculate the statistic of the variance homogeneity test of the warning sample size data of the first production line for the same process of two batches within the first production line to obtain a first real-time statistic. If the first real-time statistic is greater than the warning statistic threshold of the first production line, then there is a significant difference in the data of the same process of the two batches within the first production line, and an alarm is issued.

[0054] Step S8: Calculate the statistics of the homogeneity of variance test of the warning sample size of the second production line for the two batches of the same process in the second production line to obtain a second real-time statistic. If the second real-time statistic is greater than the warning statistic threshold of the second production line, then there is a significant difference in the data of the two batches of the same process, and an alarm is issued.

[0055] Step S9: Calculate the statistics of the variance homogeneity test of the data of the warning sample size between the production lines of the same process of the first production line and the second production line to obtain a third real-time statistic. If the third real-time statistic is greater than the warning statistic threshold between the production lines, there is a significant difference in the data of the same process of the first production line and the second production line, and an alarm is performed.

[0056] In step S1, the tobacco weight and moisture content of each process of the first and second cigarette production lines in normal batch operation for a preset time are selected from the historical operation database as sample data for establishing a statistical threshold calculation database.

[0057] Specifically, from the historical operation database, the tobacco weight and moisture content of each process of the first and second cigarette production lines in normal batch operation for one month are selected as sample data to establish a statistical threshold calculation database.

[0058] In step S2, sample data of a predefined size is selected from the statistic threshold calculation database.

[0059] In some embodiments, in step S2, the predefined size is 12 to 36 mm.

[0060] In step S3, the statistics of the variance homogeneity test of the sample data of the same process and each size of two batches in the first production line are calculated respectively to obtain a first statistic, and the warning sample size and warning statistic threshold of the first production line are obtained based on the first statistic.

[0061] In some embodiments, in step S3, the method of obtaining the warning sample size of the first production line according to the first statistic includes:

[0062] In different batches, obtain the sample size corresponding to the minimum significance level parameter of each batch;

[0063] Among the sample sizes corresponding to the smallest significance level parameters of all batches, the sample size with the smallest significance level parameter is selected.

[0064] The method for obtaining the sample size corresponding to the minimum significance level parameter of each batch includes:

[0065] In the same batch, the significance level parameters are predefined in a round-robin manner, the critical value of the statistic for each sample size is calculated by interpolation, and the sample size corresponding to the minimum significance level parameter is selected when the true hypothesis is not discarded.

[0066] The predefined significance level parameters include: 0.01, 0.02, 0.05, 0.1, 0.2 and 0.5.

[0067] Specifically, the statistic F for the test of homogeneity of variance is, in is the variance of two sets of data, MAX(·) finds the maximum value function, and MIN(·) finds the minimum value function;

[0068] n is the number of samples, is the mean of the sample.

[0069] In summary, the solution proposed in the present invention can greatly reduce the model's dependence on the process mechanism, and can judge the production process data through the sensitivity of the data itself; in addition, by utilizing the data optimization model to improve the statistical method, it can better adapt to the specified process equipment and meet actual needs.

[0070] Example 2:

[0071] The invention discloses an early warning system for tobacco cut weight and moisture in a cigarette production process. Figure 2 FIG. 1 is a structural diagram of an early warning system for tobacco weight and moisture in a cigarette production process according to an embodiment of the present invention; FIG. Figure 2 As shown, the system 100 includes:

[0072] The first processing module 101 is configured to select, from a historical operation database, the tobacco weight and moisture content of each process of the first and second cigarette production lines during normal batch operation for a preset period of time as sample data for establishing a statistical threshold calculation database;

[0073] The second processing module 102 is configured to select sample data of a predefined size from the statistic threshold calculation database;

[0074] The third processing module 103 is configured to calculate statistics of a variance homogeneity test for sample data of the same process and each size in two batches on the first production line to obtain a first statistic, and obtain a warning sample size and a warning statistic threshold for the first production line based on the first statistic;

[0075] The fourth processing module 104 is configured to calculate statistics of a variance homogeneity test for sample data of the same process and each size in two batches on the second production line to obtain a second statistic, and obtain a warning sample size and a warning statistic threshold for the second production line based on the second statistic;

[0076] The fifth processing module 105 is configured to calculate statistics of a variance homogeneity test for sample data of the same process and each size on the first production line and the second production line, respectively, to obtain a third statistic, and obtain a warning sample size and a warning statistic threshold between the production lines based on the third statistic;

[0077] The sixth processing module 106 is configured to collect data on the weight and moisture content of cut tobacco in each process of the first production line and the second production line in real time;

[0078] The seventh processing module 107 is configured to calculate a statistic of a variance homogeneity test for the data of the first production line of the warning sample size of two batches of the same process within the first production line to obtain a first real-time statistic. If the first real-time statistic is greater than the warning statistic threshold of the first production line, a significant difference is observed in the data of the two batches of the same process, and an alarm is initiated.

[0079] The eighth processing module 108 is configured to calculate a statistic of a variance homogeneity test for the warning sample size of the second production line for two batches of the same process within the second production line to obtain a second real-time statistic. If the second real-time statistic is greater than a warning statistic threshold for the second production line, a significant difference is observed in the data of the two batches of the same process, and an alarm is initiated.

[0080] The ninth processing module 109 is configured to calculate the statistic of the variance homogeneity test of the data of the warning sample size between the production lines of the same process of the first production line and the second production line, and obtain a third real-time statistic. If the third real-time statistic is greater than the warning statistic threshold between the production lines, there is a significant difference in the data of the same process of the first production line and the second production line, and alarm processing is performed.

[0081] According to the system of the second aspect of the present invention, the first processing module 101 is specifically configured to select, from the historical operation database, the tobacco weight and moisture content of each process of the first and second cigarette production lines in normal batch operation for one month as sample data, as a database for establishing a statistical threshold calculation database.

[0082] According to the system of the second aspect of the present invention, the second processing module 102 is specifically configured such that the predefined size is 12 to 36 mm.

[0083] According to the system of the second aspect of the present invention, the third processing module 103 is specifically configured as follows: the method of obtaining the warning sample size of the first production line according to the first statistic includes:

[0084] In different batches, obtain the sample size corresponding to the minimum significance level parameter of each batch;

[0085] Among the sample sizes corresponding to the smallest significance level parameters of all batches, the sample size with the smallest significance level parameter is selected.

[0086] The method for obtaining the sample size corresponding to the minimum significance level parameter of each batch includes:

[0087] In the same batch, the significance level parameters are predefined in a round-robin manner, the critical value of the statistic for each sample size is calculated by interpolation, and the sample size corresponding to the minimum significance level parameter is selected when the true hypothesis is not discarded.

[0088] The predefined significance level parameters include: 0.01, 0.02, 0.05, 0.1, 0.2 and 0.5.

[0089] Specifically, the statistic F for the test of homogeneity of variance is, in is the variance of two sets of data, MAX(·) finds the maximum value function, and MIN(·) finds the minimum value function;

[0090] n is the number of samples, is the mean value of the sample.

[0091] Example 3:

[0092] This invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, it implements the steps of the early warning method for tobacco weight and moisture in the cigarette production process described in any one of the first embodiments of the present invention.

[0093] Figure 3 FIG. 1 is a structural diagram of an electronic device according to an embodiment of the present invention. Figure 3 As shown, the electronic device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, near field communication (NFC) or other technologies. The display screen of the electronic device can be a liquid crystal display or an electronic ink display screen, and the input device of the electronic device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the electronic device housing, or an external keyboard, touchpad or mouse.

[0094] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a structural diagram of the part related to the technical solution of the present disclosure, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0095] Example 4:

[0096] The present invention discloses a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements the steps of the early warning method for tobacco weight and moisture in a cigarette production process according to any one of the first embodiments of the present invention.

[0097] Please note that the technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification. The above embodiments only express several implementation methods of the present application. The description is relatively specific and detailed, but it cannot be understood as a limitation on the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, without departing from the concept of this application, several variations and improvements can be made, which all fall within the scope of protection of this application. Therefore, the scope of protection of the patent in this application shall be based on the attached claims.

[0098] Embodiments of the subject matter and functional operations described in this specification may be implemented in the following: digital electronic circuits, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or a combination of one or more of them. Embodiments of the subject matter described in this specification may be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier to be executed by a data processing device or to control the operation of the data processing device. Alternatively or additionally, the program instructions may be encoded on an artificially generated propagation signal, such as a machine-generated electrical, optical, or electromagnetic signal, which is generated to encode information and transmit it to a suitable receiver device for execution by the data processing device. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.

[0099] The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform the corresponding functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can be implemented as, special purpose logic circuitry, such as an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).

[0100] Computers suitable for executing computer programs include, for example, general-purpose and / or special-purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit will receive instructions and data from a read-only memory and / or random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or the computer will be operably coupled to such mass storage devices to receive data from them or to transmit data to them, or both. However, a computer does not necessarily have such devices. In addition, a computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name a few.

[0101] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and storage devices, including, for example, semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD ROM and DVD-ROM disks. The processor and memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0102] Although this specification includes many specific implementation details, these should not be interpreted as limiting the scope of any invention or the scope of protection claimed, but are mainly used to describe the features of specific embodiments of specific inventions. Certain features described in multiple embodiments within this specification may also be implemented in combination in a single embodiment. On the other hand, the various features described in a single embodiment may also be implemented separately in multiple embodiments or in any suitable sub-combination. In addition, although features may work in certain combinations as described above and even initially claimed as such, one or more features from the claimed combination may be removed from the combination in some cases, and the claimed combination may point to a sub-combination or a variation of the sub-combination.

[0103] Similarly, although operations are depicted in a particular order in the accompanying drawings, this should not be understood as requiring that these operations be performed in the particular order shown or performed sequentially, or that all illustrated operations be performed to achieve the desired results. In some cases, multitasking and parallel processing may be advantageous. In addition, the separation of various system modules and components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product, or packaged into multiple software products.

[0104] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the particular order shown or sequential sequence to achieve the desired results. In some implementations, multitasking and parallel processing may be advantageous.

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0106] Although some specific embodiments of the present invention have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It should be understood by those skilled in the art that modifications may be made to the above embodiments without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A method for early warning of tobacco weight and moisture in a cigarette production process, characterized in that: The method comprises: Step S1: Select the tobacco weight and moisture content of each process of the first and second cigarette production lines in normal batch operation for a preset time period from the historical operation database as sample data, and establish a statistical threshold calculation database; Step S2: Selecting sample data of a predefined size from the statistical threshold calculation database; Step S3: Calculate the statistics of the variance homogeneity test for the sample data of the same process and each size in two batches in the first production line to obtain a first statistic, and obtain the warning sample size and warning statistic threshold of the first production line based on the first statistic; Step S4: Calculate the statistics of the variance homogeneity test for the sample data of the same process and each size in two batches in the second production line respectively to obtain a second statistic, and obtain the warning sample size and warning statistic threshold of the second production line based on the second statistic; Step S5: Calculate the statistics of the variance homogeneity test for the sample data of the same process and each size of the first production line and the second production line respectively to obtain a third statistic, and obtain the warning sample size and warning statistic threshold between the production lines based on the third statistic; Step S6: real-time collection of data on tobacco weight and moisture content of each process of the first production line and the second production line; Step S7: Calculate the statistic of the variance homogeneity test of the warning sample size data of the first production line for the same process of two batches within the first production line to obtain a first real-time statistic. If the first real-time statistic is greater than the warning statistic threshold of the first production line, then there is a significant difference in the data of the same process of the two batches within the first production line, and an alarm is issued. Step S8: Calculate the statistics of the homogeneity of variance test of the warning sample size of the second production line for the two batches of the same process in the second production line to obtain a second real-time statistic. If the second real-time statistic is greater than the warning statistic threshold of the second production line, then there is a significant difference in the data of the two batches of the same process, and an alarm is issued. Step S9: Calculate the statistics of the variance homogeneity test of the data of the warning sample size between the production lines of the same process of the first production line and the second production line to obtain a third real-time statistic. If the third real-time statistic is greater than the warning statistic threshold between the production lines, there is a significant difference in the data of the same process of the first production line and the second production line, and an alarm is performed.

2. The early warning method for tobacco weight and moisture in a cigarette production process according to claim 1, characterized in that: In the step S2, the predefined size is 12 mm to 36 mm.

3. The early warning method for tobacco weight and moisture in a cigarette production process according to claim 1, characterized in that: In step S3, the method for obtaining the warning sample size of the first production line according to the first statistic includes: In different batches, obtain the sample size corresponding to the minimum significance level parameter of each batch; Among the sample sizes corresponding to the smallest significance level parameters of all batches, the sample size with the smallest significance level parameter is selected.

4. The early warning method for tobacco weight and moisture in a cigarette production process according to claim 3, characterized in that: In step S3, the method for obtaining the sample size corresponding to the minimum significance level parameter of each batch includes: In the same batch, the significance level parameters are predefined in a round-robin manner, the critical value of the statistic for each sample size is calculated by interpolation, and the sample size corresponding to the minimum significance level parameter is selected when the true hypothesis is not discarded.

5. The early warning method for tobacco weight and moisture in a cigarette production process according to claim 4, characterized in that: In step S3, the predefined significance level parameters include: 0.01, 0.02, 0.05, 0.1, 0.2 and 0.

5.

6. An early warning system for tobacco weight and moisture in a cigarette production process, characterized in that: The system comprises: The first processing module is configured to select, from a historical operation database, the weight and moisture content of cut tobacco from each process of the first and second cigarette production lines during normal batch operation for a preset period of time as sample data, and establish a statistical threshold calculation database; A second processing module is configured to select sample data of a predefined size from the statistic threshold calculation database; a third processing module configured to calculate statistics of a variance homogeneity test for sample data of the same process and each size in two batches within the first production line, respectively, to obtain a first statistic, and to obtain a warning sample size and a warning statistic threshold for the first production line based on the first statistic; a fourth processing module configured to calculate statistics of a variance homogeneity test for sample data of the same process and each size in two batches on the second production line to obtain a second statistic, and obtain a warning sample size and a warning statistic threshold for the second production line based on the second statistic; a fifth processing module configured to calculate statistics of a variance homogeneity test for sample data of the same process and each size on the first production line and the second production line, respectively, to obtain a third statistic, and to obtain a warning sample size and a warning statistic threshold between the production lines based on the third statistic; A sixth processing module is configured to collect data on the weight and moisture content of cut tobacco in each process of the first production line and the second production line in real time; a seventh processing module configured to calculate a statistic of a variance homogeneity test for data of the first production line of the same process in two batches within the first production line, to obtain a first real-time statistic; if the first real-time statistic is greater than a threshold value of the warning statistic for the first production line, then a significant difference exists in the data of the same process in the two batches, and an alarm is initiated; an eighth processing module configured to calculate a statistic of a variance homogeneity test for data of the second production line of the warning sample size for two batches of the same process within the second production line to obtain a second real-time statistic; if the second real-time statistic is greater than a warning statistic threshold for the second production line, a significant difference is present in the data of the two batches of the same process, and an alarm is initiated; The ninth processing module is configured to calculate the statistic of the variance homogeneity test of the data of the warning sample size between the production lines of the same process of the first production line and the second production line to obtain a third real-time statistic. If the third real-time statistic is greater than the warning statistic threshold between the production lines, there is a significant difference in the data of the same process of the first production line and the second production line, and alarm processing is performed.

7. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the steps of the early warning method for tobacco weight and moisture in a cigarette production process according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the early warning method for cut tobacco weight and moisture in a cigarette production process according to any one of claims 1 to 5 are implemented.

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