Model-based cut thread control method, apparatus, device, and storage medium

Abstract

This invention discloses a model-based shredding control method, apparatus, equipment, and storage medium, relating to the field of tobacco production control technology. The method includes: during production, acquiring the target material characteristics of a target batch of tobacco shreds, and generating a target material characteristic set based on these characteristics; inputting the target material characteristic set into a process parameter matching model, which outputs a preferred process parameter set and a target quality; performing feedforward control based on the actual process parameters and the preferred process parameter set; dynamically correcting the process parameters based on the actual tobacco shred quality and the target quality; and shredding based on the corrected process parameters. This method achieves the determination of the shredder's production process parameters by the process parameter matching model, ensuring consistency between the actual shredding process parameters and the preferred process parameters through feedforward control and dynamic correction, thereby stabilizing the quality of the shredded tobacco. Shredding based on the corrected process parameters improves the accuracy of each stage of the shredding process.

Description

Technical Field

[0001] This invention relates to the field of tobacco production control technology, and in particular to a model-based shredding control method, apparatus, equipment, and storage medium. Background Technology

[0002] The shredding process is a crucial step in tobacco processing, and the stability of the shredding width and the shredding quality directly affect the filling value, combustibility, sensory quality, and consumption rate of subsequent cigarette products.

[0003] Current tobacco shredders primarily operate by manually pre-setting and adjusting single parameters such as the cutter roller speed and feed height, resulting in a crude control mode and slow response. Improving the precision of shredding control and enhancing the stability of tobacco shred quality have become urgent problems to be solved. Summary of the Invention

[0004] This invention provides a model-based method, apparatus, device, and storage medium for tobacco shredding control, in order to solve the problem of insufficient precision in tobacco shredding control and unstable tobacco quality caused by current manual adjustment equipment.

[0005] According to one aspect of the present invention, a model-based shredding control method is provided, comprising:

[0006] During production, the target material characteristics of the target batch of tobacco are obtained, and a target material characteristic set is generated based on the target material characteristics.

[0007] The target material characteristic set is input into the process parameter matching model, and the process parameter matching model outputs the optimal process parameter set and the expected quality target.

[0008] Feedforward control is performed based on the actual process parameters and the preferred set of process parameters;

[0009] The process parameters are dynamically adjusted based on the actual tobacco quality and the expected quality target; the tobacco is then shredded based on the adjusted process parameters.

[0010] According to another aspect of the present invention, a model-based shredding control device is provided, comprising:

[0011] The material characteristic acquisition module is used to acquire the target material characteristics of the target batch of tobacco during production and generate a target material characteristic set based on the target material characteristics.

[0012] The model processing module is used to input the target material characteristic set into the process parameter matching model, and the process parameter matching model outputs the preferred process parameter set and the expected quality target;

[0013] The feedforward control module is used to perform feedforward control based on the actual process parameters and the preferred process parameter set;

[0014] The shredding control module is used to dynamically correct process parameters based on the actual tobacco shred quality and the expected quality target; and to shred tobacco according to the corrected process parameters.

[0015] According to another aspect of the present invention, an electronic device is provided, characterized in that the electronic device comprises:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the model-based shredding control method according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the model-based shredding control method according to any embodiment of the present invention.

[0020] The technical solution of this invention involves obtaining the target material characteristics of a target batch of tobacco shreds during production, generating a target material characteristic set based on these characteristics, inputting the target material characteristic set into a process parameter matching model, which outputs a preferred process parameter set and a target quality. Feedforward control is then performed based on the actual process parameters and the preferred process parameter set. Dynamic correction of the process parameters is performed based on the actual tobacco shred quality and the target quality. Finally, the shredded tobacco is cut according to the corrected process parameters. Compared to the current situation where manual adjustment of equipment leads to inaccurate shredding control and unstable tobacco shred quality, the technical solution provided by this invention can obtain a preferred process parameter set and a target quality based on the target material characteristics of the target batch of tobacco shreds through a process parameter matching model. Feedforward control is performed based on the actual process parameters and the preferred process parameter set, and dynamic correction of the process parameters is performed based on the actual tobacco shred quality and the target quality. This allows the process parameter matching model to determine the production process parameters of the shredder. Through feedforward control and dynamic correction, the actual shredding process parameters are made consistent with the preferred process parameters, resulting in stable tobacco shred quality. Shredding is then performed based on the corrected process parameters, improving the accuracy of each step in the shredding process.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic flowchart of a model-based shredding control method provided in an embodiment of the present invention;

[0024] Figure 2 This is a flowchart illustrating another model-based shredding control method provided in an embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram of a model-based shredding control device provided in an embodiment of the present invention;

[0026] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the model-based shredding control method of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims and accompanying drawings of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products or devices.

[0029] The inventors discovered that the shredding process is a crucial step in tobacco processing, as the stability of the shredding width and the quality of the shreds directly affect the filling value, combustibility, sensory quality, and consumption rate of subsequent cigarette products. Current shredding machines primarily operate by manually pre-setting and adjusting single parameters such as the speed of the cutter roller and the chain conveyor speed, resulting in a crude control mode and delayed response. In actual production, numerous parameters affect shredding quality and are interconnected. For example, the speed of the cutter roller, the chain conveyor speed, and the number of cutters affect the shredding width; while the pressure of the cutter gate, blade wear, material moisture content, and slippage affect the shredding morphology (such as slippage, adhesion, and uneven width) and stability. Currently, adjustments are largely made retrospectively based on personal experience, making it difficult to achieve dynamic optimization and stable control during the process. This leads to large fluctuations in shredding quality within and between batches, hindering the continuous improvement of the premium product rate. How to utilize historical data to mine the optimal process and combine it with real-time sensing and automatic control technologies to ensure the shredding process remains consistently stable at its optimal state has become a pressing technical problem to be solved.

[0030] This invention provides a model-based shredding control method. First, in the offline stage, historical big data and deep learning technology are used to construct an optimal combination of process parameters that adapts to factors such as actual production material characteristics, environmental temperature and humidity, and production line layout, forming a process parameter matching model with an ideal process center value. Then, in production, the process parameter matching model is used as a feedforward controller to set the optimal process baseline in real time based on the current material characteristics; simultaneously, a feedback controller is used to fine-tune process fluctuations, thereby achieving intelligent, adaptive, and stable control of shredding quality. The technical solution of this application is described in detail below through embodiments.

[0031] Figure 1 This is a flowchart illustrating a model-based shredding control method provided in an embodiment of the present invention. This embodiment is applicable to equipment control in tobacco shredding processes. The method can be executed by a model-based shredding control device, which can be implemented in hardware and / or software. Figure 1 As shown, the method includes:

[0032] Step S101: During production, the target material characteristics of the target batch of tobacco are obtained, and a target material characteristic set is generated based on the target material characteristics.

[0033] Optionally, before obtaining the target material characteristics of the target batch of tobacco, the following steps may also be taken:

[0034] Based on historical data, preprocessing is performed to obtain material property sets, process parameter sets, and quality parameter sets;

[0035] The deep learning model is trained by using the material property set as input data and the process parameter set and quality parameter set as output data to obtain the process parameter matching model. The input of the process parameter matching model is the material property set, and the output is the process parameter set and quality parameter set.

[0036] The material characteristic set includes: tobacco leaf grade, real-time moisture content, ambient temperature and humidity, or production layout. The process parameter set includes: the optimal cutter roller speed, chain conveying speed, cutter gate pressure, or cutter gate height obtained after actual production adjustments corresponding to the material characteristic set. The quality parameter set includes: the target value of shredding width, width standard deviation, or comprehensive quality score obtained by shredding based on the process parameter set.

[0037] It can collect and clean historical production data to build a structured, high-quality dataset. Each data record includes:

[0038] Input characteristics (X): The material characteristic set includes: tobacco leaf grade, real-time moisture content, ambient temperature and humidity, production line (production layout), material flow rate, etc.

[0039] Output Label (Y): Optimal process and quality parameter set, comprising two parts: process parameter set and quality parameter set. Process parameter set: Optimal cutter roller speed, chain conveying speed, cutter gate pressure, cutter gate height, material level height, cutter gate clearance, chain conveying friction, and cutting force, obtained after actual production adjustments under the given material characteristics. Quality parameter set: Target shredding width, width standard deviation, and overall quality score achieved under the above optimal process parameters.

[0040] Based on the aforementioned dataset, a deep learning algorithm was used to train a process parameter matching model. The learning objective of the process parameter matching model is to establish a precise mapping relationship from "material properties (X)" to "optimal process and quality parameter set (Y)".

[0041] The above implementation method utilizes historical big data to train an intelligent model construction method that can directly output recommended process parameters such as cutter roller speed, chain conveying speed, and cutter gate pressure, as well as expected quality targets, based on material characteristics input such as grade, moisture content, ambient temperature and humidity, and production line.

[0042] Furthermore, the trained model is cross-validated to ensure that the process parameters of its predicted output are reasonable and the quality parameters meet the standards.

[0043] The above implementation method can obtain input features (X) and output labels (Y) as training data by classifying historical big data, and obtain a process parameter matching model through deep learning.

[0044] This enables the process parameter matching model to accurately output the optimal set of process parameters and the expected quality targets.

[0045] Step S102: Input the target material characteristic set into the process parameter matching model, and the process parameter matching model outputs the preferred process parameter set and the expected quality target.

[0046] When online production starts, the material characteristic set of the target batch (which can be the current batch) is acquired in real time. This real-time data is input into the offline-trained process parameter matching model. The process parameter matching model outputs in real time the optimal process parameter set and expected quality target corresponding to the target batch of tobacco. The system automatically sets the recommended parameter combination as the feedforward setpoints to achieve accurate initial settings. During production, the system continuously monitors the actual process parameters and detects the actual shredded quality online.

[0047] Step S103: Perform feedforward control based on the actual process parameters and the preferred process parameter set.

[0048] Step S104: Dynamically correct the process parameters based on the actual tobacco quality and the expected quality target; cut the tobacco into shreds based on the corrected process parameters.

[0049] Optionally, the process parameters are dynamically adjusted based on the actual tobacco quality and the expected quality target, including:

[0050] The actual tobacco quality is compared with the expected quality target; if the comparison result shows a continuous deviation, the process parameters are dynamically corrected.

[0051] The actual process parameters are compared with the optimal set of process parameters output by the process parameter matching model. PID control is used to ensure that the equipment's operating state closely matches the set values. The actual shredding quality is compared with the expected quality target output by the process parameter matching model. If a persistent deviation occurs (e.g., the difference between the actual shredding quality and the expected quality target exceeds a threshold within one minute), the deviation signal is fed back to the controller, and the process parameter set values ​​are dynamically corrected. For example, the knife gate pressure may be fine-tuned to compensate for unmodeled disturbances.

[0052] Furthermore, after shredding according to the revised process parameters, the process also includes:

[0053] During production, the corrected process parameters, the target material feature set, and the expected quality target are output in real time; the process parameter matching model is optimized based on the corrected process parameters, the target material feature set, and the expected quality target generated during production.

[0054] Control commands drive actuators such as frequency converters and servo systems to achieve precise parameter adjustments. This process can be displayed in real-time on a human-machine interface, including material characteristics, model-recommended values, actual values, and quality trends.

[0055] Furthermore, real-time production data (material characteristics x real-time, final process parameters adopted, and final quality parameters achieved) is used as new samples and stored in the historical database. The intelligent matching model is fine-tuned using the new data, enabling the process parameter matching model to continuously optimize and adapt to new raw material characteristics or equipment conditions, forming a closed loop of production-learning-optimization.

[0056] The model-based shredding control method provided in this invention involves the following steps during production: acquiring the target material characteristics of a target batch of tobacco shreds; generating a target material characteristic set based on these characteristics; inputting the target material characteristic set into a process parameter matching model, which outputs a preferred process parameter set and a target quality; performing feedforward control based on the actual process parameters and the preferred process parameter set; dynamically correcting the process parameters based on the actual tobacco shred quality and the target quality; and shredding the tobacco shreds based on the corrected process parameters. Compared to the current situation where manual adjustment of equipment leads to inaccurate shredding control and unstable tobacco quality, the model-based shredding control method provided in this invention can obtain an optimal set of process parameters and expected quality targets based on the target material characteristics of the target batch of tobacco through a process parameter matching model. Feedforward control is then performed based on the actual process parameters and the optimal set of process parameters, and the process parameters are dynamically corrected based on the actual tobacco quality and the expected quality targets. This allows the production process parameters of the shredder to be determined by the process parameter matching model. Through feedforward control and dynamic correction, the actual shredding process parameters are made consistent with the optimal process parameters, thereby stabilizing the quality of the shredded tobacco. Shredding is then performed based on the corrected process parameters, improving the accuracy of each stage of the shredding process.

[0057] Figure 2 The flowchart illustrates a model-based shredding control method provided in this embodiment of the invention. As a further explanation of the above embodiments, the method includes:

[0058] Step S201: Preprocess historical data to obtain material characteristic set, process parameter set and quality parameter set.

[0059] Step S202: Use the material characteristic set as input data and the process parameter set and quality parameter set as output data to train the deep learning model and obtain the process parameter matching model.

[0060] The process parameter matching model takes a set of material characteristics as input and outputs a set of process parameters and a set of quality parameters. The set of material characteristics includes: tobacco leaf grade, real-time moisture content, ambient temperature and humidity, or production layout. The set of process parameters includes: the optimal cutter roller speed, chain conveying speed, cutter gate pressure, or cutter gate height obtained after actual production adjustments corresponding to the set of material characteristics. The set of quality parameters includes: the target shredding width, width standard deviation, or comprehensive quality score obtained by shredding based on the set of process parameters.

[0061] Step S203: During production, the target material characteristics of the target batch of tobacco are obtained, and a target material characteristic set is generated based on the target material characteristics.

[0062] Step S204: Input the target material characteristic set into the process parameter matching model, and the process parameter matching model outputs the preferred process parameter set and the expected quality target.

[0063] Step S205: Perform feedforward control based on the actual process parameters and the preferred process parameter set.

[0064] Step S206: Compare the actual tobacco shred quality with the expected quality target; if the comparison result shows a continuous deviation, dynamically correct the process parameters. Cut the tobacco shreds according to the corrected process parameters.

[0065] Step S207: Output the corrected process parameters, the target material feature set, and the expected quality target in real time.

[0066] Step S208: Optimize the process parameter matching model based on the corrected process parameters generated during the production process, the target material feature set, and the expected quality target.

[0067] The above implementation method automatically matches and outputs a finite combination of process parameters based on real-time material characteristics and process parameter matching models, improving the scientific nature and consistency of quality standards. By pre-setting the optimal combination of process parameters according to material characteristics, it overcomes quality fluctuations caused by changes in incoming materials, giving the production process strong initial adaptability. Combined with real-time feedback adjustments, it improves batch-to-batch consistency. The model's recommended values ​​serve as a high-precision starting point, and combined with closed-loop feedback to eliminate random disturbances, it ensures that process parameters continuously operate within the optimal range, significantly improving the uniformity of shredded width and reducing the defect rate.

[0068] Figure 3 This is a schematic diagram of a model-based shredding control device provided in an embodiment of the present invention. This embodiment is applicable to equipment control in the tobacco shredding process. The model-based shredding control device can be implemented in hardware and / or software. Figure 3As shown, the model-based shredding control device includes: a material feature acquisition module 31, a model processing module 32, a feedforward control module 33, and a shredding control module 34.

[0069] The material characteristic acquisition module 31 is used to acquire the target material characteristics of the target batch of tobacco shreds during production and generate a target material characteristic set based on the target material characteristics.

[0070] Model processing module 32 is used to input the target material characteristic set into the process parameter matching model, and the process parameter matching model outputs the preferred process parameter set and the expected quality target;

[0071] Feedforward control module 33 is used to perform feedforward control based on actual process parameters and the preferred process parameter set;

[0072] The shredding control module 34 is used to dynamically correct process parameters based on the actual tobacco shred quality and the expected quality target; and to shred tobacco according to the corrected process parameters.

[0073] Based on the above embodiments, optionally, a model training module is also included, which is used to preprocess historical data before obtaining the target material characteristics of the target batch of tobacco shreds to obtain a set of material characteristics, a set of process parameters, and a set of quality parameters.

[0074] The deep learning model is trained by using the material property set as input data and the process parameter set and quality parameter set as output data to obtain the process parameter matching model. The input of the process parameter matching model is the material property set, and the output is the process parameter set and quality parameter set.

[0075] Based on the above embodiments, optionally, the material characteristic set includes: tobacco leaf grade, real-time moisture content, ambient temperature and humidity, or production layout.

[0076] Based on the above embodiments, optionally, the process parameter set includes: the optimal cutter roller speed, chain conveying speed, cutter gate pressure, or cutter gate height obtained after actual production adjustment corresponding to the material characteristic set.

[0077] Based on the above embodiments, optionally, the quality parameter set includes: the target value of the shredding width, the standard deviation of the width, or the comprehensive quality score obtained by shredding according to the process parameter set.

[0078] Based on the above embodiments, optionally, the shredding control module 34 is used for:

[0079] Compare the actual tobacco quality with the stated expected quality target;

[0080] If the comparison results show a continuous deviation, then the process parameters are dynamically corrected.

[0081] In addition to the above embodiments, optionally, an output module and a model optimization module may also be included.

[0082] The output module is used to output the corrected process parameters, the target material feature set, and the expected quality target in real time.

[0083] The model optimization module is used to optimize the process parameter matching model based on the corrected process parameters generated during production, the target material feature set, and the expected quality target.

[0084] The model-based shredding control device provided in this embodiment of the invention includes a material feature acquisition module 31, used to acquire the target material characteristics of a target batch of tobacco shreds during production and generate a target material characteristic set based on the target material characteristics; a model processing module 32, used to input the target material characteristic set into a process parameter matching model, the process parameter matching model outputting a preferred process parameter set and an expected quality target; a feedforward control module 33, used to perform feedforward control based on the actual process parameters and the preferred process parameter set; and a shredding control module 34, used to dynamically correct the process parameters based on the actual tobacco shred quality and the expected quality target; and to shred the tobacco shreds based on the corrected process parameters. Compared to the current situation where manual adjustment of equipment leads to inaccurate shredding control and unstable tobacco quality, the model-based shredding control device provided in this invention can obtain an optimal set of process parameters and expected quality targets based on the target material characteristics of the target batch of tobacco through a process parameter matching model. Feedforward control is performed based on the actual process parameters and the optimal set of process parameters, and the process parameters are dynamically corrected based on the actual tobacco quality and the expected quality targets. This allows the production process parameters of the shredder to be determined by the process parameter matching model. Through feedforward control and dynamic correction, the actual shredding process parameters are made consistent with the optimal process parameters, thereby stabilizing the quality of the shredded tobacco. Shredding is performed according to the corrected process parameters, improving the accuracy of each step of the shredding process.

[0085] The model-based shredding control device provided in this embodiment of the invention can execute the model-based shredding control method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0086] Figure 4This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0087] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0088] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as a camera, ultrasonic sensor, infrared sensor, etc.; output unit 17, such as various types of speakers, etc.; storage unit 18, such as a disk, solid-state drive, etc.; and communication unit 19, such as a network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0089] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as model-based slicing control methods.

[0090] In some embodiments, the model-based shredding control method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the model-based shredding control method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the model-based shredding control method by any other suitable means (e.g., by means of firmware).

[0091] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0092] The processor (CPU) can be a multi-core high-performance processor for performing complex rule matching and logical calculations. The RAM capacity is no less than 16GB for loading the rule base and intermediate processing data. The storage device can be a solid-state drive (SSD) for storing the structured rule base, historical competition data, process log tables, and generated audit logs and scoring reports. Networking devices can be used to connect to the database server or receive referee record data submitted from clients.

[0093] Computer programs for implementing the model-based shredding control method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0094] This invention also provides a computer-readable storage medium storing computer instructions for causing a processor to execute a model-based shredding control method, the method comprising:

[0095] During production, the target material characteristics of the target batch of tobacco are obtained, and a target material characteristic set is generated based on the target material characteristics.

[0096] The target material characteristic set is input into the process parameter matching model, and the process parameter matching model outputs the optimal process parameter set and the expected quality target.

[0097] Feedforward control is performed based on the actual process parameters and the preferred set of process parameters;

[0098] The process parameters are dynamically adjusted based on the actual tobacco quality and the expected quality target; the tobacco is then shredded based on the adjusted process parameters.

[0099] Based on the above embodiments, optionally, before obtaining the target material characteristics of the target batch of tobacco, the method further includes:

[0100] Based on historical data, preprocessing is performed to obtain material property sets, process parameter sets, and quality parameter sets;

[0101] The deep learning model is trained by using the material property set as input data and the process parameter set and quality parameter set as output data to obtain the process parameter matching model. The input of the process parameter matching model is the material property set, and the output is the process parameter set and quality parameter set.

[0102] Based on the above embodiments, optionally, the material characteristic set includes: tobacco leaf grade, real-time moisture content, ambient temperature and humidity, or production layout.

[0103] Based on the above embodiments, optionally, the process parameter set includes: the optimal cutter roller speed, chain conveying speed, cutter gate pressure, or cutter gate height obtained after actual production adjustment corresponding to the material characteristic set.

[0104] Based on the above embodiments, optionally, the quality parameter set includes: the target value of the shredding width, the standard deviation of the width, or the comprehensive quality score obtained by shredding according to the process parameter set.

[0105] Based on the above embodiments, optionally, dynamic correction of process parameters can be performed according to the actual tobacco quality and the expected quality target, including:

[0106] Compare the actual tobacco quality with the stated expected quality target;

[0107] If the comparison results show a continuous deviation, then the process parameters are dynamically corrected.

[0108] Based on the above embodiments, optionally, after shredding according to the modified process parameters, the process further includes:

[0109] The corrected process parameters, the target material feature set, and the expected quality target are output in real time.

[0110] The process parameter matching model is optimized based on the corrected process parameters generated during production, the target material feature set, and the expected quality target.

[0111] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0112] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0113] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0114] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0115] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0116] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A model-based shredding control method, characterized in that, include: During production, the target material characteristics of the target batch of tobacco are obtained, and a target material characteristic set is generated based on the target material characteristics. The target material characteristic set is input into the process parameter matching model, and the process parameter matching model outputs the optimal process parameter set and the expected quality target. Feedforward control is performed based on the actual process parameters and the preferred set of process parameters; The process parameters are dynamically adjusted based on the actual tobacco quality and the expected quality target; the tobacco is then shredded based on the adjusted process parameters.

2. The method according to claim 1, characterized in that, Before obtaining the target material characteristics of the target batch of tobacco, the following steps are also included: Based on historical data, preprocessing is performed to obtain material property sets, process parameter sets, and quality parameter sets; The deep learning model is trained by using the material property set as input data and the process parameter set and quality parameter set as output data to obtain the process parameter matching model. The input of the process parameter matching model is the material property set, and the output is the process parameter set and quality parameter set.

3. The method according to claim 2, characterized in that, The material property set includes: tobacco leaf grade, real-time moisture content, ambient temperature and humidity, or production layout.

4. The method according to claim 2, characterized in that, The process parameter set includes: the optimal cutter roller speed, chain conveying speed, cutter gate pressure, or cutter gate height corresponding to the material characteristic set, obtained after actual production adjustment.

5. The method according to claim 2, characterized in that, The quality parameter set includes: the target value of the shredding width, the standard deviation of the width, or the comprehensive quality score obtained by shredding according to the process parameter set.

6. The method according to claim 1, characterized in that, Dynamically adjusting process parameters based on actual tobacco quality and the expected quality target, including: The actual tobacco quality is compared with the expected quality target. If the comparison results show a continuous deviation, then the process parameters are dynamically corrected.

7. The method according to claim 1, characterized in that, After shredding according to the revised process parameters, the process also includes: The corrected process parameters, the target material feature set, and the expected quality target are output in real time. The process parameter matching model is optimized based on the corrected process parameters generated during production, the target material feature set, and the expected quality target.

8. A model-based shredding control device, characterized in that, include: The material characteristic acquisition module is used to acquire the target material characteristics of the target batch of tobacco during production and generate a target material characteristic set based on the target material characteristics. The model processing module is used to input the target material characteristic set into the process parameter matching model, and the process parameter matching model outputs the preferred process parameter set and the expected quality target; The feedforward control module is used to perform feedforward control based on the actual process parameters and the preferred process parameter set; The shredding control module is used to dynamically correct process parameters based on the actual tobacco shred quality and the expected quality target; and to shred the tobacco shreds according to the corrected process parameters.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the model-based shredding control method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the model-based shredding control method according to any one of claims 1-7.

Images