Tobacco process parameter determination method and device, electronic equipment and storage medium

Abstract

The application discloses a tobacco process parameter determination method and device, electronic equipment and a storage medium. By obtaining the corresponding to-be-processed tobacco leaves under different reference dimensions, and determining the initial attribute parameters corresponding to the to-be-processed tobacco leaves, for each to-be-processed tobacco leaf, the initial process parameters and environmental parameters corresponding to each production process before the to-be-processed tobacco leaf in the preset production process are determined, the initial attribute parameters, the initial process parameters and the environmental parameters are integrated and processed to obtain to-be-processed data, feature extraction is performed on the to-be-processed data to obtain at least one to-be-processed feature corresponding to the to-be-processed data, the at least one to-be-processed feature is input into a target moisture prediction model to obtain target predicted moisture data corresponding to the to-be-processed tobacco leaf, and the initial process parameters are adjusted based on the target predicted moisture data and a preset moisture threshold to obtain target process parameters corresponding to the to-be-processed tobacco leaf, so that the purpose of improving the stability of tobacco production and preparation is achieved.

Description

Technical Field

[0001] This invention relates to the field of tobacco preparation technology, and in particular to a method, apparatus, electronic device, and storage medium for determining tobacco process parameters. Background Technology

[0002] In the tobacco production process, the drying process plays a crucial role in the stability of tobacco production. Current approaches to the drying process primarily focus on analyzing the correlation between the process parameters and the tobacco production results, thereby adjusting the process parameters to ensure the stability of the tobacco production outcome.

[0003] However, the above methods mainly focus on the process parameters of the tobacco drying process, while neglecting the influence of the preceding processes on the drying process, thus failing to accurately guarantee the stability of the tobacco preparation results. Based on this, the present invention proposes a technical solution to adjust the process parameters of the preceding processes in the tobacco drying process to achieve moisture control before the drying process. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for determining tobacco process parameters. By adjusting the process parameters in the tobacco production process, the stability of tobacco production and preparation is improved.

[0005] According to one aspect of the present invention, a method for determining tobacco processing parameters is provided, the method comprising:

[0006] Obtain the tobacco leaves to be processed under different reference dimensions, and determine the initial attribute parameters corresponding to the tobacco leaves to be processed; wherein, the reference dimensions include at least one of tobacco leaf batch, tobacco leaf grade, and tobacco leaf production area;

[0007] For each tobacco leaf to be processed, determine the initial process parameters and environmental parameters corresponding to each production process before the preset production process;

[0008] The initial attribute parameters, initial process parameters, and environmental parameters are integrated and processed to obtain the data to be processed;

[0009] Feature extraction is performed on the data to be processed to obtain at least one feature corresponding to the data to be processed.

[0010] Input at least one feature to be processed into the target moisture prediction model to obtain the target predicted moisture data corresponding to the tobacco leaf to be processed;

[0011] Based on the target predicted moisture data and the preset moisture threshold, the initial process parameters are adjusted to obtain the target process parameters corresponding to the tobacco leaves to be treated.

[0012] According to another aspect of the present invention, a tobacco processing parameter determining apparatus is provided, the apparatus comprising:

[0013] The tobacco leaf acquisition module is used to acquire the tobacco leaves to be processed under different reference dimensions and determine the initial attribute parameters of the tobacco leaves to be processed; wherein, the reference dimensions include at least one of tobacco leaf batch, tobacco leaf grade, and tobacco leaf production area;

[0014] The parameter determination module is used to determine the initial process parameters and environmental parameters corresponding to each production process before the preset production process for each tobacco leaf to be processed.

[0015] The data integration module is used to integrate and process initial attribute parameters, initial process parameters, and environmental parameters to obtain the data to be processed.

[0016] The feature extraction module is used to extract features from the data to be processed, and obtain at least one feature corresponding to the data to be processed.

[0017] The model prediction module is used to input at least one feature to be processed into the target moisture prediction model to obtain the target predicted moisture data corresponding to the tobacco leaf to be processed.

[0018] The parameter adjustment module is used to adjust the initial process parameters based on the target predicted moisture data and the preset moisture threshold to obtain the target process parameters corresponding to the tobacco leaves to be processed.

[0019] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0020] At least one processor; and

[0021] A memory that is communicatively connected to at least one processor; wherein,

[0022] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the tobacco process parameter determination method of any embodiment of the present invention.

[0023] 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 the tobacco process parameter determination method of any embodiment of the present invention.

[0024] The technical solution of this invention involves acquiring the tobacco leaves to be processed under different reference dimensions and determining the initial attribute parameters corresponding to the tobacco leaves to be processed. The reference dimensions include at least one of tobacco leaf batch, tobacco leaf grade, and tobacco leaf production area. For each tobacco leaf to be processed, the initial process parameters and environmental parameters corresponding to each production step before the preset production step are determined. The initial attribute parameters, initial process parameters, and environmental parameters are integrated to obtain the data to be processed. Feature extraction is performed on the data to be processed to obtain at least one feature corresponding to the data to be processed. The at least one feature to be processed is input into a target moisture prediction model to obtain the target predicted moisture data corresponding to the tobacco leaves to be processed. Furthermore, based on the target predicted moisture data and a preset moisture threshold, the initial process parameters are adjusted to obtain the target process parameters corresponding to the tobacco leaves to be processed. This solves the problem in the prior art that only focuses on the preset production step while ignoring the influence of each production step before the preset production step, thus failing to guarantee the stability of tobacco production. This invention achieves moisture control before the preset production step by adjusting the process parameters corresponding to the preceding steps, thereby improving the stability of tobacco production.

[0025] 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

[0026] 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.

[0027] Figure 1 This is a flowchart of a method for determining tobacco processing parameters provided in an embodiment of the present invention;

[0028] Figure 2 This is an example diagram of the preceding process in the tobacco preparation process provided in this embodiment of the invention;

[0029] Figure 3 This is an example diagram of the data acquisition device provided in an embodiment of the present invention;

[0030] Figure 4 This is a flowchart of a target moisture prediction model training method provided in an embodiment of the present invention;

[0031] Figure 5This is a schematic diagram of a tobacco processing parameter determination device provided in an embodiment of the present invention;

[0032] Figure 6 This is a schematic diagram of the structure of an electronic device that implements the tobacco process parameter determination method of this invention. Detailed Implementation

[0033] 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.

[0034] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0035] Example 1

[0036] Figure 1 This is a flowchart of a method for determining tobacco process parameters provided in an embodiment of the present invention. This embodiment is applicable to situations where the initial process parameters corresponding to various production processes before the drying process need to be adjusted. This method can be executed by a tobacco process parameter determining device, which can be implemented in hardware and / or software. This device can be configured in electronic devices such as mobile phones, computers, or servers. Figure 1 As shown, the method includes:

[0037] S110. Obtain the tobacco leaves to be processed under different reference dimensions, and determine the initial attribute parameters corresponding to the tobacco leaves to be processed.

[0038] The reference dimensions include at least one of the following: tobacco leaf batch, tobacco leaf grade, and tobacco leaf production region.

[0039] Typically, after tobacco leaves are harvested in the field, they are packaged to facilitate subsequent processing. The tobacco leaves obtained after unpacking can be considered as the leaves to be processed. Correspondingly, after unpacking, the leaves will have initial temperature and humidity, which can be used as the initial attribute parameters of the leaves to be processed. Furthermore, the initial attributes of the leaves to be processed may differ under different reference dimensions; therefore, the initial attribute parameters corresponding to the leaves to be processed under the current reference dimension can be obtained.

[0040] Specifically, the unpacked tobacco leaves are considered as tobacco leaves to be processed. These leaves can be categorized based on reference dimensions such as batch size, grade, and production region to obtain the corresponding tobacco leaves for each reference dimension. The batch size can be the first batch, second batch, third batch, etc. The grade can be A, B, C, etc. The production region can be an administrative region or a region designated based on actual needs, such as region A, region B, and region C. This is just an example of reference dimensions and does not impose limitations on the specific content of each dimension. Furthermore, after obtaining the tobacco leaves to be processed, appropriate equipment can be used to measure the temperature and humidity to obtain the initial attribute parameters of the tobacco leaves.

[0041] S120. For each tobacco leaf to be processed, determine the initial process parameters and environmental parameters corresponding to each production process before the preset production process.

[0042] The preset production process can be a production process pre-set during tobacco preparation; optionally, the preset production process can be a tobacco drying process. It should be noted that the tobacco preparation process can include multiple production processes. Since this invention only focuses on the tobacco drying process and the production processes preceding it, they can be combined... Figure 2 Please provide an explanation. See also... Figure 2In the tobacco production process, after the unpacking process, there are also loosening and rehydration processes, premixing processes, feeding processes, leaf storage processes, shredding processes, and drying processes. Correspondingly, when the preset production process is the drying process, the various production processes include: loosening and rehydration, premixing, feeding, leaf storage, and shredding. In each of these production processes, the initial process parameters are different. Initial process parameters can be understood as the parameter values ​​corresponding to various methods of processing tobacco. Taking loosening and rehydration as an example, initial process parameters can include rehydration temperature, rehydration time, and rehydration method. Taking the leaf storage process as an example, initial process parameters can be the storage temperature, storage humidity, and storage time of the tobacco in the storage cabinet. Environmental parameters can be understood as weather data when the tobacco leaves are being processed, such as air temperature, air pressure, humidity, and wind speed.

[0043] Specifically, the initial process parameters differ depending on the reference dimension for the tobacco leaves to be processed. Therefore, the tobacco leaves to be processed under at least one reference dimension can be determined. For example, the tobacco leaves to be processed might be first-batch, grade B, and from region b, or a blend of first-batch, grade C, and from region c. Thus, the initial process parameters corresponding to the tobacco leaves to be processed can be determined based on their reference dimension. Optionally, ETL data can be used to obtain the production plan data for the tobacco leaves to be processed from the corresponding production management system, i.e., the initial process parameters. Furthermore, the environmental parameters corresponding to the processing of the tobacco leaves can be obtained. This can be achieved by using OpenAPI services to collect surrounding weather data from the internet, i.e., environmental parameters.

[0044] Optionally, it also includes: constructing a data acquisition device to obtain initial attribute parameters, initial process parameters, and environmental parameters based on the data acquisition device; wherein the data acquisition device includes: a data acquisition module, a data processing module, a communication module, a data storage module, and a data display module.

[0045] The data acquisition device can be a device that acquires all parameter data corresponding to the processing of the tobacco leaves to be processed. Optionally, the data acquisition device can acquire the initial attribute parameters, initial process parameters, and environmental parameters corresponding to the tobacco leaves to be processed.

[0046] Specifically, initial attribute parameters, initial process parameters, and environmental parameters can be collected using the data acquisition module in the data acquisition device. Further, after acquiring the aforementioned parameter data, the data can be transmitted to the data processing module for processing, and then transmitted to the data storage module. The data processing module is connected to the data display module, which can display the processed initial attribute parameters, initial process parameters, and environmental parameters on the corresponding terminal devices. Additionally, the data storage module is connected to the communication module, enabling the subsequent input of the processed initial attribute parameters, initial process parameters, and environmental parameters into the target moisture prediction model for predictive analysis.

[0047] For example, see Figure 3 , Figure 3 This is an example diagram of a data acquisition device provided in an embodiment of the present invention, wherein the MAX125CEAX+D data acquisition extension corresponds to the data acquisition module, the ARM946T embedded processor corresponds to the data processing module, the communication interface module corresponds to the communication module, the storage module corresponds to the data storage module, and the LCD touch screen corresponds to the data display module. Therefore, in Figure 3 The data acquisition device shown includes: an embedded processor, a communication interface module, a storage module, a data acquisition expansion module, a switching power supply, an LCD touchscreen, and a real-time clock. The switching power supply and the real-time clock are bidirectionally connected to the ARM946T embedded processor. Additionally, in... Figure 3 In this system, the embedded processor has a bidirectional communication interface module connected to the LCD touchscreen; the embedded processor has a unidirectional data acquisition extension; the communication interface module has a bidirectional connection between the storage module and the embedded processor; and the data acquisition device has a bidirectional connection with the full moisture prediction system via an API interface. The full moisture prediction system is the target moisture prediction model mentioned above.

[0048] Among them, Figure 3 In this context, the MAX125CEAX+D data acquisition module is expanded into the MAX125CEAX+D data acquisition unit. The MAX125CEAX+D data acquisition unit features multi-channel, high-speed, and simultaneous sampling capabilities. It can automatically acquire non-electrical or electrical signals from sensors and other devices under test, and send them to the embedded processor for analysis and processing.

[0049] exist Figure 3In this example, the embedded processor is the ARM946T embedded microprocessor, which includes an arithmetic logic unit (ALU), a control unit, and registers. The ALU performs various arithmetic and logical operations. The control unit fetches instructions from the registers one by one, decodes and analyzes them, and then issues control commands to the entire machine for fetching, executing, and storing data to ensure the correct completion of the processor's required functions. Registers are used to accelerate computation and processing speed, and to temporarily store data involved in the computation or intermediate results.

[0050] exist Figure 3 The communication interface module supports multiple hardware interfaces, including Wi-Fi, Ethernet, Bluetooth, and RS-232 serial communication. It supports widely applicable protocols and communication protocols from various third-party devices. Specifically, it allows communication with third-party devices by writing XML files according to their communication protocols and copying these files to a designated directory on the gateway.

[0051] exist Figure 3 The storage module includes FLASH chips and SDRAM chips. The FLASH chips are used to store the embedded operating system, applications, network server, and the file system required to support various services. Since the embedded operating system requires dynamic RAM, SDRAM needs to be expanded on an external bus. Additionally, the storage module supports short-term storage of industrial equipment data, which can be forwarded as needed.

[0052] Optionally, the initial process parameters and environmental parameters corresponding to each production step before the preset production step for the tobacco leaves to be processed are determined, including: determining each production step before the preset production step for the tobacco leaves to be processed based on the preset production step; and acquiring the environmental parameters and initial process parameters corresponding to each production step based on the data acquisition device.

[0053] Specifically, the production processes preceding the pre-set production process for the tobacco leaves to be processed can be determined based on the pre-set production process and all corresponding production processes. For example, if the pre-set production process is drying, the subsequent production processes could be loosening and rehydration, pre-mixing, feeding, leaf storage, and cutting. Furthermore, the data acquisition module of the data acquisition device collects the environmental parameters and initial process parameters corresponding to each production process. Additionally, the data acquisition device can also collect initial attribute parameters. For example, initial process parameters corresponding to each production process can be obtained from the corresponding production management system using ETL data; initial attribute parameters can be collected from PLCs or instruments using an industrial data acquisition gateway; and environmental parameters can be collected using OpenAPI services.

[0054] S130. Integrate and process the initial attribute parameters, initial process parameters, and environmental parameters to obtain the data to be processed.

[0055] The data to be processed can be obtained after processing the initial attribute parameters, initial process parameters, and environmental parameters. In other words, the data to be processed includes the initial attribute parameters, initial process parameters, and environmental parameters.

[0056] Specifically, to facilitate subsequent processing, the initial attribute parameters, initial process parameters, and environmental parameters can be integrated first, and the integrated data can be preprocessed using big data fusion processing. Optionally, the initial attribute parameters, initial process parameters, and environmental parameters can be filtered and cleaned to ensure the accuracy of the above parameter data; data correction and noise reduction can also be performed on the above parameter data to ensure the consistency and clarity of the parameter data.

[0057] Optionally, the initial attribute parameters, initial process parameters, and environmental parameters are integrated and processed to obtain the data to be processed, including: integrating the initial attribute parameters, initial process parameters, and environmental parameters to obtain the data to be screened; performing data preprocessing on the data to be screened to obtain the data to be modeled; wherein, the data preprocessing includes at least one of data screening, data cleaning, data correction, and data denoising; performing data modeling on the data to be modeled to obtain the data model to be used, so as to obtain the data to be processed based on the data model to be used.

[0058] The data to be screened can include all initial attribute parameters, initial process parameters, and environmental parameters. The data to be modeled can be the data obtained after preprocessing the data to be screened. The data model to be used can be a model used to describe and represent the data to be modeled. For example, the data model to be used can be an entity-relationship model, an object-oriented model, a hierarchical model, etc., and this embodiment does not impose any restrictions on this.

[0059] Specifically, by integrating initial attribute parameters, initial process parameters, and environmental parameters, data to be screened can be obtained. This data can contain various attributes and characteristics, allowing for preprocessing to facilitate subsequent data modeling. Data preprocessing can include at least one of the following: data screening, data cleaning, data correction, and data denoising. Data screening filters out unsuitable data based on specific conditions; data cleaning handles inaccurate, incomplete, or incorrectly formatted data; data correction corrects erroneous or abnormal data; and data denoising removes noise from the data to improve its clarity. Furthermore, a data modeling method can be determined based on actual needs and the characteristics of the data to be modeled, leading to a usable data model. Finally, the data to be processed is obtained from this usable data model.

[0060] For example, after integrating and preprocessing the initial attribute parameters, initial process parameters, and environmental parameters, the data to be modeled can be obtained. Further, a data mainline is determined based on the characteristics of the data to be modeled. The data mainline can be understood as a data link centered on business operations and supporting business applications. The data mainline can be used to decouple, reconstruct, and reuse the data to be modeled. Based on the data mainline, the data to be modeled is categorized and stored in the corresponding databases, and this data is then abstracted to obtain object models. Object models can be understood as abstract digital models of various physical world objects; for example, a feeder or a conveyor belt can be an object model. The data mainline is populated based on each object model, allowing the data to be modeled to converge on the data mainline. Based on the above method, accurate data to be processed can be provided at the appropriate time and in the correct manner, based on the data model to be used.

[0061] S140. Extract features from the data to be processed to obtain at least one feature corresponding to the data to be processed.

[0062] The features to be processed can be obtained by processing the data using feature engineering. Furthermore, feature engineering typically requires manual intervention, but can also be performed using automated tools.

[0063] Specifically, feature engineering can be used to extract features from the data to be processed; that is, mathematical or statistical methods can be used to transform the data into at least one feature. Additionally, all features can be filtered to obtain those that match the predictions of the target moisture prediction model. Optionally, statistical or machine learning methods can be used to filter the features. Examples include stepwise regression and decision trees.

[0064] S150. Input at least one feature to be processed into the target moisture prediction model to obtain the target predicted moisture data corresponding to the tobacco leaves to be processed.

[0065] The target moisture prediction model can be a model for predicting the moisture content of the tobacco leaves to be treated. The target predicted moisture data can be the moisture content of the tobacco leaves to be treated before the preset production process.

[0066] Specifically, the target moisture prediction model can predict at least one feature to be processed to obtain the target predicted moisture data of the tobacco leaves before the drying process, so that the initial process parameters of each production process can be adjusted based on the target predicted moisture data.

[0067] S160. Based on the target predicted moisture data and the preset moisture threshold, the initial process parameters are adjusted to obtain the target process parameters corresponding to the tobacco leaves to be treated.

[0068] The preset moisture threshold can be the standard moisture value corresponding to a preset production process. The target process parameters can be the process parameters obtained by adjusting the initial process parameters.

[0069] Specifically, by comparing and analyzing the target predicted moisture data with the preset moisture threshold, the process parameters that need to be adjusted in the initial process parameters are obtained based on the target predicted moisture data. The initial process parameters are then updated based on these adjusted parameters to obtain the target process parameters for the tobacco leaves to be processed. Based on this, moisture control can be achieved for each production step before the preset production steps, thereby improving moisture stability before the drying process, reducing the temperature range of the drying machine cylinder, and ensuring the stability of the tobacco preparation results.

[0070] Optionally, based on the target predicted moisture data and the preset moisture threshold, the initial process parameters are adjusted to obtain the target process parameters corresponding to the tobacco leaves to be treated. This includes: determining the process parameters to be adjusted for each production step before the preset production step based on the target predicted moisture data and the preset moisture threshold; wherein, the preset moisture threshold is the moisture standard value corresponding to the preset production step; and adjusting the initial process parameters based on the process parameters to be adjusted to obtain the target process parameters corresponding to the tobacco leaves to be treated.

[0071] Among them, the process parameters to be adjusted can be understood as the parameters in the initial process parameters that need to be adjusted.

[0072] Specifically, based on the target predicted moisture data and the preset moisture standard values ​​corresponding to the production processes, appropriate software is used to analyze and derive the parameters in the initial process parameters that need adjustment, i.e., the process parameters to be adjusted. These process parameters may exist in each production process. Furthermore, the initial process parameters are adjusted based on the process parameters to be adjusted to obtain the target process parameters corresponding to the tobacco leaves to be treated.

[0073] Optionally, after obtaining the target process parameters corresponding to the tobacco leaves to be processed, the method further includes: sending the target process parameters to the target terminal to generate and display a process parameter prediction report based on the target terminal.

[0074] The target terminal can be a device used to process and display the target process parameters. Optionally, the target terminal can be an electronic device such as a mobile phone, computer, or server; this embodiment does not impose any limitations on this. The process parameter prediction report can be a report that displays the target process parameters in the form of a report.

[0075] Specifically, after determining the target process parameters, these parameters can be sent to the target terminal device and displayed to the relevant user in the form of data visualization. The target terminal can then generate a corresponding process parameter prediction report based on the obtained parameters and display it on its interface for easy user understanding and use.

[0076] For example, based on software design and development steps such as problem identification, requirements analysis, architecture design, software coding, and system testing, corresponding software can be designed to process target process parameters and generate process parameter prediction reports. This software can have report processing capabilities and feature chart controls with flexible parameter passing and excellent interactive effects. Furthermore, the software can be deployed on the target terminal, allowing users to click on the corresponding chart controls to generate process parameter prediction reports. Optionally, the process parameter prediction report can include at least one chart type. For example, bar charts, line charts, pie charts, area charts, rose charts, donut charts, scatter plots, bubble charts, radar charts, and combination charts. Business charts may include rainbow charts, residual analysis charts, normal distribution charts, and predicted probability scatter plots. Additionally, the process parameter prediction report can be stored so that users can query the target process parameters from different reference dimensions.

[0077] The technical solution of this embodiment obtains the tobacco leaves to be processed under different reference dimensions and determines the initial attribute parameters corresponding to the tobacco leaves to be processed. The reference dimensions include at least one of tobacco leaf batch, tobacco leaf grade, and tobacco leaf production area. For each tobacco leaf to be processed, the initial process parameters and environmental parameters corresponding to each production step before the preset production step are determined. The initial attribute parameters, initial process parameters, and environmental parameters are integrated to obtain the data to be processed. Features are extracted from the data to be processed to obtain at least one feature corresponding to the data to be processed. The at least one feature to be processed is input into the target moisture prediction model to obtain the target predicted moisture data corresponding to the tobacco leaves to be processed. Furthermore, based on the target predicted moisture data and the preset moisture threshold, the initial process parameters are adjusted to obtain the target process parameters corresponding to the tobacco leaves to be processed. This solves the problem in the prior art that only focuses on the preset production step while ignoring the influence of each production step before the preset production step, thus failing to guarantee the stability of tobacco production. This invention achieves moisture control before the preset production step by adjusting the process parameters corresponding to the preceding steps, thereby improving the stability of tobacco production.

[0078] Example 2

[0079] Figure 4 This is a flowchart illustrating a target moisture prediction model training method provided in this embodiment of the invention. This embodiment, based on the above embodiments, requires determining and training the moisture prediction model to be trained before using the target moisture prediction model for prediction. Specific implementation details can be found in the technical solution of this embodiment. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here. Figure 4 As shown, the method includes:

[0080] S210. Obtain historical parameter data corresponding to the tobacco leaves to be used under different reference dimensions.

[0081] The historical parameter data includes at least one of the historical attribute parameters, initial process parameters, and historical environmental parameters corresponding to the tobacco leaves to be used.

[0082] The historical parameter data can include historical attribute parameters, initial process parameters, and historical environmental parameters corresponding to the tobacco leaves to be used. Historical attribute parameters can include the initial temperature and humidity parameters of the tobacco leaves before each production process, as well as the temperature and humidity parameters of the tobacco leaves during each production process. Initial process parameters are related to the reference dimension corresponding to the tobacco leaves to be used and can be obtained by looking up the corresponding initial process parameters in a table based on at least one reference dimension corresponding to the tobacco leaves. Historical environmental parameters can include the ambient temperature and humidity and the weather data at the time of processing the tobacco leaves to be used in each production process.

[0083] Specifically, before training the moisture prediction model, historical parameter data corresponding to the tobacco leaves to be used under different reference dimensions can be obtained from the corresponding data model or database to train the model based on the historical parameter data. To improve the accuracy of the model, as much historical parameter data as possible should be obtained. This historical parameter data may include at least one of the following: historical attribute parameters of the tobacco leaves to be used, initial process parameters, and historical environmental parameters.

[0084] It should be noted that historical attribute parameters can be obtained from PLCs or instruments based on industrial data acquisition gateways. These industrial data acquisition gateways can perform edge computing optimization on historical attribute parameters to generate concise parameter data; for example, the parameter data format can be real-time SD values.

[0085] S220. Conduct correlation analysis on historical parameter data to determine the moisture prediction model to be trained.

[0086] Correlation analysis can be used to analyze the relationship between historical parameter data and moisture content. The moisture prediction model to be trained can be selected based on the correlation analysis and actual business needs. Optionally, the moisture prediction model to be trained can be a regression analysis model, a time series analysis model, or a mathematical model trained based on unsupervised learning. This embodiment does not impose any restrictions on this.

[0087] Specifically, because different prediction models process historical parameter data differently, their output results also differ. Therefore, correlation analysis can be performed on the historical parameter data. Based on the relationship between historical parameter data and moisture content, a corresponding mathematical model can be determined, and this mathematical model can be used as the moisture prediction model to be trained.

[0088] For example, users can select or input expressions through the corresponding interface to build a mathematical model and manually configure and optimize it. Multiple different mathematical models can be built, outputting the target predicted moisture data in real time. Simultaneously, mathematical models are recorded by version and type so that a suitable mathematical model can be selected as the moisture prediction model to be trained based on actual needs and the target predicted moisture data output by the model.

[0089] S230. Extract features from historical parameter data to determine at least one feature to be input.

[0090] The input features can be feature information corresponding to historical parameter data.

[0091] Specifically, feature engineering can be used to extract features from historical parameter data to obtain at least one input feature corresponding to the historical parameter data. Then, the moisture prediction model can be trained based on the input feature to obtain the target moisture prediction model.

[0092] S240. Input at least one feature to be input into the prediction model to be trained to obtain the model parameter values ​​of the prediction model to be trained, and obtain the target moisture prediction model based on the model parameter values.

[0093] The model parameter values ​​can be obtained by fitting the input features and the prediction model to be trained.

[0094] Specifically, at least one feature to be input is fed into the prediction model to be trained, and the feature data corresponding to the input feature is fitted based on the prediction model to obtain the model parameter values ​​of the prediction model to be trained. Further, the target moisture prediction model can be determined based on the model parameter values ​​of the prediction model to be trained.

[0095] The technical solution of this embodiment obtains historical parameter data corresponding to the tobacco leaves to be used under different reference dimensions; and performs correlation analysis on the historical parameter data to determine the moisture prediction model to be trained. Next, feature extraction is performed on the historical parameter data to determine at least one input feature, and this at least one input feature is input into the prediction model to be trained to obtain the model parameter values ​​of the prediction model to be trained. Based on the model parameter values, the target moisture prediction model is obtained, thus realizing the determination and training of the prediction model to be trained. This allows the initial process parameters corresponding to each production process to be adjusted based on the target moisture prediction model, thereby achieving moisture control before the preset process.

[0096] Example 3

[0097] Figure 5This is a schematic diagram of a tobacco processing parameter determination device provided in an embodiment of the present invention. Figure 5 As shown, the device includes: a tobacco leaf acquisition module 310, a parameter determination module 320, a data integration module 330, a feature extraction module 340, a model prediction module 350, and a parameter adjustment module 360.

[0098] The tobacco leaf acquisition module 310 is used to acquire tobacco leaves to be processed under different reference dimensions and determine the initial attribute parameters corresponding to the tobacco leaves to be processed; wherein, the reference dimensions include at least one of tobacco leaf batch, tobacco leaf grade, and tobacco leaf production area; the parameter determination module 320 is used to determine the initial process parameters and environmental parameters corresponding to each production process before the preset production process for each tobacco leaf to be processed; the data integration module 330 is used to integrate the initial attribute parameters, initial process parameters, and environmental parameters to obtain the data to be processed; the feature extraction module 340 is used to extract features from the data to be processed to obtain at least one feature to be processed corresponding to the data to be processed; the model prediction module 350 is used to input at least one feature to be processed into the target moisture prediction model to obtain the target predicted moisture data corresponding to the tobacco leaves to be processed; the parameter adjustment module 360 ​​is used to adjust the initial process parameters based on the target predicted moisture data and the preset moisture threshold to obtain the target process parameters corresponding to the tobacco leaves to be processed.

[0099] The technical solution of this embodiment obtains the tobacco leaves to be processed under different reference dimensions and determines the initial attribute parameters corresponding to the tobacco leaves to be processed. The reference dimensions include at least one of tobacco leaf batch, tobacco leaf grade, and tobacco leaf production area. For each tobacco leaf to be processed, the initial process parameters and environmental parameters corresponding to each production step before the preset production step are determined. The initial attribute parameters, initial process parameters, and environmental parameters are integrated to obtain the data to be processed. Features are extracted from the data to be processed to obtain at least one feature corresponding to the data to be processed. The at least one feature to be processed is input into the target moisture prediction model to obtain the target predicted moisture data corresponding to the tobacco leaves to be processed. Furthermore, based on the target predicted moisture data and the preset moisture threshold, the initial process parameters are adjusted to obtain the target process parameters corresponding to the tobacco leaves to be processed. This solves the problem in the prior art that only focuses on the preset production step while ignoring the influence of each production step before the preset production step, thus failing to guarantee the stability of tobacco production. This invention achieves moisture control before the preset production step by adjusting the process parameters corresponding to the preceding steps, thereby improving the stability of tobacco production.

[0100] Based on the above embodiments, optionally, before the parameter determination module, the device further includes a data acquisition device construction module, which is used to construct a data acquisition device to obtain initial attribute parameters, initial process parameters, and environmental parameters based on the data acquisition device; wherein, the data acquisition device includes: a data acquisition module, a data processing module, a communication module, a data storage module, and a data display module.

[0101] Optionally, the parameter determination module includes: a production process determination unit, used to determine the various production processes corresponding to the tobacco leaves to be processed before the preset production processes based on the preset production processes; and a parameter acquisition unit, used to acquire the environmental parameters and initial process parameters corresponding to each production process based on the data acquisition device.

[0102] Optionally, the data integration module includes: a data acquisition unit for integrating initial attribute parameters, initial process parameters, and environmental parameters to obtain data to be screened; a data preprocessing unit for preprocessing the data to be screened to obtain data to be modeled; wherein, data preprocessing includes at least one of data screening, data cleaning, data correction, and data denoising; and a data acquisition unit for acquiring data to be processed to obtain a data model to be used, so as to acquire data to be processed based on the data model to be used.

[0103] Optionally, the parameter adjustment module includes: a process parameter determination unit, used to determine the process parameters to be adjusted corresponding to each production process before the preset production process based on the target predicted moisture data and the preset moisture threshold; wherein, the preset moisture threshold is the moisture standard value corresponding to the preset production process; and a parameter adjustment unit, used to adjust the initial process parameters based on the process parameters to be adjusted to obtain the target process parameters corresponding to the tobacco leaves to be treated.

[0104] Optionally, after the parameter adjustment module, the device further includes a parameter display module, which is used to send the target process parameters to the target terminal to generate and display a process parameter prediction report based on the target terminal.

[0105] Optionally, the device further includes a model training module, which comprises: a historical parameter data acquisition unit for acquiring historical parameter data corresponding to the tobacco leaves to be used under different reference dimensions; wherein the historical parameter data includes at least one of historical attribute parameters, initial process parameters, and historical environmental parameters corresponding to the tobacco leaves to be used; a correlation analysis unit for performing correlation analysis on the historical parameter data to determine the moisture prediction model to be trained; a feature determination unit for extracting features from the historical parameter data to determine at least one feature to be input; and a target moisture prediction model determination unit for inputting at least one feature to be input into the prediction model to be trained to obtain the model parameter values ​​of the prediction model to be trained, so as to obtain the target moisture prediction model based on the model parameter values.

[0106] The tobacco process parameter determination device provided in this embodiment of the invention can execute the tobacco process parameter determination method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0107] Example 4

[0108] Figure 6 This 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.

[0109] like Figure 6 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 may 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.

[0110] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as 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.

[0111] 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 the tobacco process parameter determination method.

[0112] In some embodiments, the tobacco process parameter determination 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 tobacco process parameter determination method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the tobacco process parameter determination method by any other suitable means (e.g., by means of firmware).

[0113] 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.

[0114] Computer programs for implementing the tobacco process parameter determination method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are performed. 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.

[0115] Example 5

[0116] This invention also provides a computer-readable storage medium storing computer instructions for causing a processor to execute a method for determining tobacco process parameters, the method comprising:

[0117] Obtain the tobacco leaves to be processed under different reference dimensions and determine the initial attribute parameters corresponding to the tobacco leaves to be processed; wherein, the reference dimensions include at least one of tobacco leaf batch, tobacco leaf grade, and tobacco leaf production area; for each tobacco leaf to be processed, determine the initial process parameters and environmental parameters corresponding to each production process before the preset production process; integrate the initial attribute parameters, initial process parameters, and environmental parameters to obtain the data to be processed; extract features from the data to be processed to obtain at least one feature to be processed corresponding to the data to be processed; input the at least one feature to be processed into the target moisture prediction model to obtain the target predicted moisture data corresponding to the tobacco leaves to be processed; based on the target predicted moisture data and the preset moisture threshold, adjust the initial process parameters to obtain the target process parameters corresponding to the tobacco leaves to be processed.

[0118] 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.

[0119] 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).

[0120] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include 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.

[0121] 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.

[0122] 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.

[0123] 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 method for determining tobacco processing parameters, characterized in that, include: Obtain the tobacco leaves to be processed under different reference dimensions, and determine the initial attribute parameters corresponding to the tobacco leaves to be processed; wherein, the reference dimensions include at least one of tobacco leaf batch, tobacco leaf grade, and tobacco leaf production area; For each tobacco leaf to be processed, the initial process parameters and environmental parameters corresponding to each production process before the preset production process are determined; wherein, the preset production process is the drying process, and the initial process parameters corresponding to each production process before the preset production process are different. The initial attribute parameters, the initial process parameters, and the environmental parameters are integrated and processed to obtain the data to be processed; Feature extraction is performed on the data to be processed to obtain at least one feature corresponding to the data to be processed. At least one of the features to be processed is input into the target moisture prediction model to obtain the target predicted moisture data corresponding to the tobacco leaf to be processed; wherein, the target predicted moisture data is used to characterize the moisture content of the tobacco leaf to be processed before the preset production process; Based on the target predicted moisture data and the preset moisture threshold, the initial process parameters are adjusted to obtain the target process parameters corresponding to the tobacco leaves to be treated. The step of adjusting the initial process parameters based on the target predicted moisture data and the preset moisture threshold to obtain the target process parameters corresponding to the tobacco leaves to be treated includes: Based on the target predicted moisture data and the preset moisture threshold, the process parameters to be adjusted for each production step preceding the preset production step are determined; wherein, the preset moisture threshold is the moisture standard value corresponding to the preset production step. The initial process parameters are adjusted based on the process parameters to be adjusted to obtain the target process parameters corresponding to the tobacco leaves to be processed.

2. The method according to claim 1, characterized in that, Also includes: A data acquisition device is constructed to acquire the initial attribute parameters, the initial process parameters, and the environmental parameters based on the data acquisition device; The data acquisition device includes: a data acquisition module, a data processing module, a communication module, a data storage module, and a data display module.

3. The method according to claim 1, characterized in that, The determination of the initial process parameters and environmental parameters corresponding to each production step before the preset production step for the tobacco leaves to be processed includes: Based on the preset production process, determine the various production processes corresponding to the tobacco leaves to be processed before the preset production process; Based on the data acquisition device, the environmental parameters and initial process parameters corresponding to each production process are obtained.

4. The method according to claim 1, characterized in that, The process of integrating the initial attribute parameters, the initial process parameters, and the environmental parameters to obtain the data to be processed includes: The initial attribute parameters, the initial process parameters, and the environmental parameters are integrated to obtain the data to be screened; The data to be screened is preprocessed to obtain the data to be modeled; wherein, the data preprocessing includes at least one of data screening, data cleaning, data correction and data denoising. Data modeling is performed on the data to be modeled to obtain a data model to be used, and the data to be processed is obtained based on the data model to be used.

5. The method according to claim 1, characterized in that, After obtaining the target process parameters corresponding to the tobacco leaves to be processed, the process further includes: The target process parameters are sent to the target terminal to generate and display a process parameter prediction report based on the target terminal.

6. The method according to claim 1, characterized in that, Also includes: Obtain historical parameter data corresponding to the tobacco leaves to be used under different reference dimensions; wherein, the historical parameter data includes at least one of the historical attribute parameters, initial process parameters, and historical environmental parameters corresponding to the tobacco leaves to be used; Correlation analysis was performed on the historical parameter data to determine the moisture prediction model to be trained; Feature extraction is performed on historical parameter data to determine at least one feature to be input; At least one of the input features is input into the prediction model to be trained to obtain the model parameter values ​​of the prediction model to be trained, so as to obtain the target moisture prediction model based on the model parameter values.

7. A device for determining tobacco processing parameters, characterized in that, include: The tobacco leaf acquisition module is used to acquire the tobacco leaves to be processed under different reference dimensions and determine the initial attribute parameters corresponding to the tobacco leaves to be processed; wherein, the reference dimensions include at least one of tobacco leaf batch, tobacco leaf grade, and tobacco leaf production area; The parameter determination module is used to determine the initial process parameters and environmental parameters corresponding to each production process before the preset production process for each tobacco leaf to be processed; wherein, the preset production process is the drying process, and the initial process parameters corresponding to each production process before the preset production process are different. The data integration module is used to integrate the initial attribute parameters, the initial process parameters, and the environmental parameters to obtain the data to be processed. The feature extraction module is used to extract features from the data to be processed to obtain at least one feature corresponding to the data to be processed. The model prediction module is used to input at least one of the features to be processed into the target moisture prediction model to obtain the target predicted moisture data corresponding to the tobacco leaves to be processed; wherein, the target predicted moisture data is used to characterize the moisture content of the tobacco leaves to be processed before the preset production process; The parameter adjustment module is used to adjust the initial process parameters based on the target predicted moisture data and the preset moisture threshold to obtain the target process parameters corresponding to the tobacco leaves to be treated. The parameter adjustment module includes: a process parameter determination unit, used to determine the process parameters to be adjusted corresponding to each production process before the preset production process based on the target predicted moisture data and the preset moisture threshold; wherein the preset moisture threshold is the moisture standard value corresponding to the preset production process; and a parameter adjustment unit, used to adjust the initial process parameters based on the process parameters to be adjusted to obtain the target process parameters corresponding to the tobacco leaves to be processed.

8. 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, the computer program being executed by the at least one processor to enable the at least one processor to perform the tobacco process parameter determination method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the method for determining tobacco process parameters as described in any one of claims 1-6.

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