Method and device for controlling moisture stability of cut tobacco, electronic equipment and storage medium

Abstract

This invention discloses a method, apparatus, electronic device, and storage medium for controlling the stability of stem moisture content. The specific scheme involves: acquiring first moisture data corresponding to the stem to be processed, furnace temperature data corresponding to the drying equipment, and second moisture data at the current moment; adjusting the second moisture data based on a first model predictive controller to determine the predicted stem flow rate corresponding to the adjusted second moisture data; inputting the first moisture data and furnace temperature data into the second model predictive controller to calculate the predicted stem flow rate offset value; determining the target predicted stem flow rate based on the predicted stem flow rate and the predicted stem flow rate offset value; determining the stem flow rate at the inlet of the drying equipment at the next moment based on the target predicted stem flow rate, thereby controlling the third moisture data corresponding to the stem flow rate. This invention achieves accurate adjustment of the stem flow rate while ensuring the stability of the third moisture data.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, electronic device, and storage medium for controlling the moisture stability of stem fibers. Background Technology

[0002] In the tobacco processing process, hot air is heated by the combustion furnace of the drying equipment, and the tobacco stems are dried in the drying tower of the drying equipment.

[0003] Currently, the main method is to manually adjust and control the flow rate of the stalks at the inlet of the drying equipment based on the observed moisture content at the outlet, in order to ensure the stability of the outlet moisture content. However, this method not only requires frequent adjustments to the inlet stalk flow rate, leading to increased time and raw material costs, but also, relying on manual experience to adjust the inlet stalk flow rate cannot accurately guarantee the stability of the outlet moisture content. Summary of the Invention

[0004] This invention provides a method, device, electronic device, and storage medium for controlling the moisture stability of stem fibers, which not only achieves accurate adjustment of stem fiber flow rate but also ensures the stability of third-party moisture data.

[0005] According to one aspect of the present invention, a method for controlling the moisture stability of stem fibers is provided, the method comprising:

[0006] The first moisture data, the furnace temperature data of the drying equipment, and the second moisture data are obtained at the current moment. The first moisture data is the moisture data of the skeins to be processed at the inlet of the drying equipment, and the second moisture data is the moisture data of the skeins to be processed at the outlet of the drying equipment.

[0007] Based on the first model prediction controller, the second moisture data is adjusted and controlled to determine the predicted filament flow rate corresponding to the adjusted second moisture data. The predicted filament flow rate is the predicted filament flow rate at the inlet of the drying equipment at the next moment.

[0008] The first moisture data and the furnace temperature data are input into the second model prediction controller to calculate the predicted stem flow rate offset value, which is used to characterize the offset value of the predicted stem flow rate.

[0009] Based on the predicted stem flow rate and the predicted stem flow rate offset, the target predicted stem flow rate is determined. The stem flow rate at the inlet of the drying equipment at the next moment is then determined based on the target predicted stem flow rate. This is used to control the third moisture data corresponding to the stem flow rate, where the third moisture data is the moisture data corresponding to the outlet of the drying equipment at the next moment.

[0010] According to another aspect of the present invention, a stem moisture stability control device is provided, the device comprising:

[0011] The data acquisition module is used to acquire the first moisture data, the furnace temperature data and the second moisture data of the filaments to be processed at the current moment. The first moisture data is the moisture data of the filaments to be processed at the inlet of the drying equipment, and the second moisture data is the moisture data of the filaments to be processed at the outlet of the drying equipment.

[0012] The predicted stem flow rate determination module is used to adjust and control the second moisture data based on the first model prediction controller, and determine the predicted stem flow rate corresponding to the adjusted second moisture data. The predicted stem flow rate is the predicted stem flow rate at the inlet of the drying equipment at the next moment.

[0013] The offset value determination module is used to input the first moisture data and the furnace temperature data into the second model prediction controller to calculate the predicted stem flow rate offset value, which is used to characterize the offset value of the predicted stem flow rate.

[0014] The stem flow rate determination module is used to determine the target predicted stem flow rate based on the predicted stem flow rate and the predicted stem flow rate offset value, and to determine the stem flow rate at the inlet of the drying equipment at the next moment based on the target predicted stem flow rate, so as to control the third moisture data corresponding to the stem flow rate, wherein the third moisture data is the moisture data corresponding to the outlet of the drying equipment at the next moment.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] At least one processor; and

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

[0018] 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 stem moisture stability control method of any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement the stem moisture stability control method of any embodiment of the present invention.

[0020] The technical solution of this invention provides data for predicting the stem flow rate at the next moment by acquiring the first moisture data, second moisture data, and furnace temperature data of the stems to be processed at the current moment. The second moisture data is adjusted by a first model prediction controller to obtain the predicted stem flow rate corresponding to the adjusted second moisture data. The first moisture data and furnace temperature data are processed by a second model prediction controller to obtain the predicted stem flow rate offset value corresponding to the predicted stem flow rate. Based on the predicted stem flow rate and the predicted stem flow rate offset value, the target predicted stem flow rate is obtained. The stem flow rate at the inlet of the drying equipment is adjusted using the target predicted stem flow rate to determine the stem flow rate at the inlet of the drying equipment at the next moment while maintaining the stability of the third moisture data. This solves the problem in the prior art of relying on repeated manual adjustment of the inlet stem flow rate, which consumes a lot of time and raw material costs. This invention achieves accurate adjustment of the stem flow rate while ensuring the moisture stability at the outlet of the drying equipment.

[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 flowchart of a method for controlling the moisture stability of stem fibers according to an embodiment of the present invention;

[0024] Figure 2 This is a flowchart of a method for controlling the moisture stability of stem fibers according to an embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram illustrating the logical relationship between outlet moisture and various parameters provided in an embodiment of the present invention;

[0026] Figure 4 This is a schematic diagram of variable information of the MPC controller provided in an embodiment of the present invention;

[0027] Figure 5 This is an example diagram of a visualization image provided in an embodiment of the present invention;

[0028] Figure 6This is an example diagram of another visualization image provided in an embodiment of the present invention;

[0029] Figure 7 This is a schematic diagram of a stem moisture stability control device provided in an embodiment of the present invention;

[0030] Figure 8 This is a schematic diagram of the structure of an electronic device for implementing the method for controlling the moisture stability of stem fibers according to an embodiment of the present invention. Detailed Implementation

[0031] 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.

[0032] 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.

[0033] Example 1

[0034] Figure 1 This is a flowchart of a stem moisture stability control method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where the stem flow rate is adjusted to ensure the stability of the third moisture data. This method can be executed by a stem moisture stability control device, which can be implemented in hardware and / or software. This stem moisture stability control device can be configured in electronic devices such as mobile phones, computers, or servers. Figure 1 As shown, the method includes:

[0035] S110. Obtain the first moisture data, the furnace temperature data, and the second moisture data corresponding to the filaments to be processed at the current time.

[0036] The first moisture data refers to the moisture content of the tobacco stems to be processed at the inlet of the drying equipment, and the second moisture data refers to the moisture content of the tobacco stems to be processed at the outlet of the drying equipment. The tobacco stems to be processed can be understood as the tobacco stems currently being conveyed on the conveyor belt for drying. The drying equipment is used to dry the tobacco stems to be processed; for example, the drying equipment can be an airflow drying machine. Correspondingly, the furnace temperature data can be the hot air temperature inside the drying equipment.

[0037] Specifically, during the drying process of the stalks to be processed, the stalks can be conveyed to a drying equipment for drying. During this process, when stalks to be processed are detected at the inlet of the drying equipment, the moisture data of the stalks not yet entering the drying equipment (i.e., the first moisture data) can be obtained using a corresponding moisture detection device. Furthermore, the furnace temperature data corresponding to the drying equipment during the drying process is determined. When stalks to be processed are detected at the outlet of the drying equipment, the moisture data of the dried stalks (i.e., the second moisture data) is detected, so that the stalk flow rate at the inlet of the drying equipment can be adjusted based on the first moisture data, the second moisture data, and the furnace temperature data.

[0038] Optionally, acquiring the first moisture data of the stalk to be processed, the furnace temperature data of the drying equipment, and the second moisture data at the current moment includes: acquiring the first moisture data of the stalk to be processed when the presence of the stalk to be processed is detected at the inlet of the drying equipment; acquiring the furnace temperature data of the drying equipment based on the temperature acquisition device when the drying power of the drying equipment is a preset value; and acquiring the second moisture data of the stalk to be processed when the presence of the stalk to be processed is detected at the outlet of the drying equipment.

[0039] The preset value can be a pre-set standard value for the wire drying power. The temperature acquisition device can be a device used to acquire furnace temperature data.

[0040] Specifically, when unprocessed filaments are detected at the inlet of the filament drying equipment, the first moisture data corresponding to the unprocessed filaments is obtained through a moisture detection device. Similarly, when unprocessed filaments are detected at the outlet of the filament drying equipment, the second moisture data corresponding to the unprocessed filaments can be obtained. The drying power of the filament drying equipment is adjusted to a preset value, and the internal furnace temperature data of the filament drying equipment is determined using a temperature acquisition device. Based on this, data support can be provided for subsequent adjustments to the filament flow rate.

[0041] Optionally, obtaining the first moisture data corresponding to the stem to be processed includes: obtaining the moisture data to be processed corresponding to the stem to be processed based on a moisture detection device; and performing filtering and noise reduction processing on the moisture data to be processed to obtain the first moisture data corresponding to the stem to be processed.

[0042] The moisture data to be processed can be the original moisture data corresponding to the filaments to be processed, obtained at the inlet of the filament drying equipment by a moisture detection device. The moisture detection device is used to obtain the moisture data of the filaments to be processed. The filtering and noise reduction processing can be performed by filtering the filaments to be processed using an appropriate filter. Optionally, the filter can be one or more of a median filter, a curve fitting filter, and a low-pass filter, etc. This embodiment does not limit the type of filter.

[0043] Specifically, when the presence of stems to be processed is detected at the inlet of the drying equipment, the moisture content data is obtained using a moisture detection device. This moisture content data is then filtered and noise-reduced using a corresponding filter to obtain initial moisture data free of noise and interference. This ensures the accuracy of the initial moisture data, providing accurate data support for subsequent determination of the target and prediction of stem flow rate.

[0044] S120. Based on the first model prediction controller, the second moisture data is adjusted and controlled to determine the predicted stem flow rate corresponding to the adjusted second moisture data.

[0045] The predicted stem flow rate is the predicted stem flow rate at the inlet of the drying equipment at the next moment. Stem flow rate can be understood as the mass or volume of stems processed during drying per unit time. The first model predictive controller can be a model predictive controller used for feedback control of the second moisture data. Optionally, the first model predictive controller is an MPC (Model-based Predictive Control) controller.

[0046] Specifically, the current second moisture data is input into the first model predictive controller. The second moisture data is adjusted and controlled based on the logical relationship between the second moisture data and the filament flow rate at the inlet of the drying equipment. While ensuring the second moisture data remains stable within a fixed range, the corresponding predicted filament flow rate is determined based on feedback from the first model predictive controller. This predicted filament flow rate is then used to determine the filament flow rate at the inlet of the drying equipment at the next moment.

[0047] S130. Input the first moisture data and the furnace temperature data into the second model prediction controller to calculate the predicted filament flow rate offset value.

[0048] The predicted stem flow rate offset value is used to characterize the offset value of the predicted stem flow rate. The second model predictive controller can be a model predictive controller used for feedforward control of the first moisture data and the furnace temperature data. Optionally, the second model predictive controller is an MPC (Model-based Predictive Control) controller.

[0049] Specifically, the current moisture data and furnace temperature data are input into the second model prediction controller. Based on the logical relationship between the moisture data and furnace temperature data and the filament flow rate at the inlet of the drying equipment, the offset value relative to the predicted filament flow rate is calculated, i.e., the predicted filament flow rate offset value.

[0050] S140. Based on the predicted filament flow rate and the predicted filament flow rate offset value, determine the target predicted filament flow rate, and determine the filament flow rate at the inlet of the drying equipment at the next moment based on the target predicted filament flow rate, so as to control the third moisture data corresponding to the filament flow rate.

[0051] The third moisture data is the moisture data corresponding to the outlet of the drying equipment at the next moment. The target predicted wire flow rate is the predicted value obtained by summing the predicted wire flow rate and the predicted wire flow rate offset.

[0052] Specifically, when the predicted filament flow rate is obtained from the first model predictive controller and the predicted filament flow rate offset value is obtained from the second model predictive controller, the predicted filament flow rate and the predicted filament flow rate offset value are summed to obtain the target predicted filament flow rate. Based on the target predicted filament flow rate, the filament flow rate at the inlet of the drying equipment is adjusted to achieve automatic control of the third moisture data, so that the third moisture data is stabilized within a fixed value range.

[0053] Optionally, the skewer flow rate at the inlet of the drying equipment at the next moment is adjusted based on the target predicted skewer flow rate to control the third moisture data corresponding to the skewer flow rate, including: obtaining the skewer flow rate to be processed corresponding to the inlet of the drying equipment; when it is determined that the third moisture data does not exceed the preset outlet moisture range, adjusting the skewer flow rate to be processed based on the target predicted skewer flow rate, and using the adjusted skewer flow rate to be processed as the skewer flow rate at the inlet of the drying equipment at the next moment.

[0054] The flow rate of the stalks to be processed is the current flow rate of the stalks at the inlet of the drying equipment. The preset outlet moisture range can be a pre-set fixed range of moisture values ​​that the stalks to be processed at the outlet of the drying equipment should maintain. That is, when the third moisture value does not exceed the preset outlet moisture range, the third moisture value can be considered stable.

[0055] Specifically, the current stem flow rate at the inlet of the drying equipment is obtained, i.e., the stem flow rate to be processed. Since there is a logical relationship between the third moisture data and the stem flow rate, the stem flow rate to be processed can be adjusted based on the target predicted stem flow rate, while observing whether the third moisture data is within the preset outlet moisture range. That is, if the third moisture data does not exceed the preset outlet moisture range, the stem flow rate at the inlet of the drying equipment at the next moment is determined based on the target stem flow rate.

[0056] The technical solution of this embodiment obtains the first moisture data, second moisture data, and furnace temperature data of the skein to be processed at the current moment, providing data basis for predicting the skein flow rate at the next moment. The second moisture data is adjusted and controlled by a first model prediction controller to obtain the predicted skein flow rate corresponding to the adjusted second moisture data. The first moisture data and furnace temperature data are processed by a second model prediction controller to obtain the predicted skein flow rate offset value corresponding to the predicted skein flow rate. Based on the predicted skein flow rate and the predicted skein flow rate offset value, the target predicted skein flow rate is obtained. The skein flow rate at the inlet of the skein drying equipment is adjusted using the target predicted skein flow rate to determine the skein flow rate at the inlet of the skein drying equipment at the next moment while maintaining the stability of the third moisture data. This solves the problem in the prior art of relying on repeated manual adjustment of the inlet skein flow rate, which consumes a lot of time and raw material costs. This invention achieves accurate adjustment of the skein flow rate while ensuring the moisture stability at the outlet of the skein drying equipment.

[0057] Example 2

[0058] Figure 2 This is a flowchart of a method for controlling the moisture stability of stem fibers according to Embodiment 2 of the present invention. This embodiment, based on the above embodiments, requires parameter adjustment of the first and second model prediction controllers to be trained before data processing based on the first and second model prediction controllers. 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 2 As shown, the method includes:

[0059] S210. Obtain multiple batches of sample stems, and when the current batch of sample stems is detected at the outlet of the drying equipment, obtain the second sample moisture data of the sample stems.

[0060] The sample stems can be stems from different batches. The batch refers to the production batch of the stems. The second sample moisture data can be understood as the moisture content of the sample stems at the outlet of the drying equipment.

[0061] Specifically, before adjusting and optimizing the parameters of the first model predictive controller to be trained, it is necessary to obtain sample stems from multiple production batches to train the model based on the second moisture data corresponding to the sample stems. To improve the accuracy of the model, as many sample stems as possible can be obtained to acquire second sample moisture data corresponding to different production batches, thereby improving the data processing accuracy of the first model predictive controller based on this second sample moisture data.

[0062] S220. Based on the second sample moisture data and the preset outlet moisture range, adjust the first model parameters corresponding to the first model prediction controller to be trained, so as to obtain the first model prediction controller.

[0063] The first model predictive controller to be trained is a model predictive controller that provides feedback control over the moisture data of the second sample. Optionally, the first model predictive controller to be trained is an MPC controller. The preset outlet moisture range can be a pre-set fixed range of moisture values ​​that the sample filaments at the outlet of the drying equipment should maintain. The first model parameters are parameters that are adjusted according to actual control requirements. For example, the first model parameters can be weight values ​​and priorities for controlling the moisture data of the second sample.

[0064] Specifically, if the moisture content of the second sample does not exceed the preset outlet moisture content range, the first model parameters of the first model predictive controller to be trained are adjusted to obtain the adjusted first model parameters, and the first model predictive controller is obtained based on the adjusted model parameters.

[0065] For example, let's take an MPC controller as the first predictive controller to be trained and the outlet moisture data as the second sample moisture data as an example. The MPC controller performs feedback control in response to the outlet moisture, outputting the sample stem flow rate. During this process, the first model parameters controlling the outlet moisture, such as weight values ​​and priorities, are adjusted to obtain optimal first model parameters, so that the stem flow rate of the stem to be processed can be processed subsequently based on the optimal first model parameters. Here, the sample stem flow rate is the predicted stem flow rate corresponding to the sample stem. In addition, the sample stem flow rate output by the MPC controller can be sent to a Programmable Logic Controller (PLC) to adjust and control the stem flow rate at the next time step based on the PLC.

[0066] S230. For multiple batches of sample stems, when the presence of the current batch of sample stems is detected at the inlet of the drying equipment, the first sample moisture data and the sample oven temperature data of the sample stems are obtained.

[0067] The moisture data for the first sample can be understood as the moisture content of the sample filament at the inlet of the drying equipment. The temperature data inside the drying oven is the temperature of the hot air applied to the sample filament by the drying equipment.

[0068] Specifically, when adjusting and optimizing the parameters of the second model predictive controller to be trained, the model can be trained based on the first sample moisture data and sample oven temperature data corresponding to sample filaments from multiple production batches. That is, when the presence of sample filaments of the current batch is detected at the entrance of the drying equipment, the first sample moisture data of the sample filaments is obtained based on the moisture detection device, and the sample oven temperature data is obtained using the temperature acquisition device.

[0069] S240. Based on the second model to be trained, the predictive controller processes the moisture data of the first sample and the temperature data inside the sample furnace to determine the sample stem flow offset value corresponding to the sample stem.

[0070] The second model predictive controller to be trained can be understood as a model predictive controller that performs feedforward control on the moisture data and furnace temperature data of the first sample. Optionally, the second model predictive controller to be trained is an MPC controller. The sample filament flow rate offset value is the offset value relative to the sample filament flow rate.

[0071] Specifically, the second model predictive controller to be trained performs feedforward control on the moisture data of the first sample and the temperature data inside the furnace of the sample, so as to determine the sample stem flow offset value corresponding to the sample stem while ensuring the stability of the moisture data of the second sample, and adjust the parameters of the second model based on the sample stem offset value.

[0072] S250. Based on the sample filament flow offset value and the preset filament flow offset range, the second model parameters of the second model predictive controller to be trained are adjusted to obtain the second model predictive controller.

[0073] The preset stem flow rate offset range can be understood as a pre-defined allowable range of deviation for the sample stem flow rate. The second model parameter is used to adjust parameters according to actual control requirements. For example, the second model parameter could be a parameter controlling the sample stem flow rate offset value.

[0074] Specifically, the second model parameters of the second model predictive controller to be trained are optimized and adjusted according to the sample stem flow offset value and the preset stem flow offset range, so as to obtain the second model predictive controller based on the optimized second model parameters while ensuring the stability of the second sample moisture data.

[0075] For example, taking an MPC controller as the predictive controller for the second model to be trained, and the first sample moisture data as inlet moisture and the sample furnace temperature data as hot air temperature as an example, the MPC controller performs feedforward control in response to the inlet moisture and hot air temperature, outputting a sample filament flow rate offset value. During this process, the second model parameters controlling the sample filament flow rate offset value, such as the step size, are adjusted to obtain optimal second model parameters, so that the filament flow rate of the filament to be processed can be subsequently processed based on these optimal second model parameters. Alternatively, the sample filament flow rate offset value output by the MPC controller can be sent to a Programmable Logic Controller (PLC) so that the PLC can integrate the sample filament flow rate and the sample filament flow rate offset value to adjust and control the filament flow rate at the next time step.

[0076] Optionally, the method further includes: visualizing the moisture data of the first sample, the temperature data inside the sample furnace, the flow rate offset value of the sample stems, and the preset flow rate offset range of the stems to obtain a visual image corresponding to the sample stems, and adjusting the parameters of the second model based on the visual image to obtain the second model prediction controller.

[0077] The visualized image can be an image that visualizes the data associated with the training process of the predictive controller of the second model to be trained.

[0078] Specifically, the moisture data of the first sample from multiple batches, the temperature data inside the sample furnace, the flow rate offset value of the sample stems, and the preset flow rate offset range of the sample stems are visualized to obtain corresponding visualization images. This allows for the adjustment of the second model parameters based on the data changes in the visualization images, thus obtaining the second model predictive controller.

[0079] For example, referring to the above example, the moisture data of the second sample is taken as the outlet moisture, the moisture data of the first sample is taken as the inlet moisture, and the furnace temperature data is taken as the hot air temperature. See [link to relevant documentation]. Figure 3 , Figure 3 This is a schematic diagram illustrating the logical relationship between export moisture content and various parameters. According to... Figure 3 The logical relationships between various data points are used to determine the control variables, operating variables, disturbance variables, and corresponding parameter information of each variable in the MPC controller. Figure 4 As shown. Figure 4 This is a schematic diagram of the variable information for the MPC controller. According to... Figure 4 The variable information in the data is used to train the MPC controller based on sample stroma data. The MPC controller consists of two parts: one is a feedback output in response to outlet moisture; the other is a feedforward output in response to changes in inlet moisture and hot air temperature. Based on this process, corresponding visualization images can be generated, such as... Figure 5As shown. According to Figure 5 It can be seen that the outlet moisture setting was adjusted multiple times according to the preset outlet moisture range during this process. After adjustment and control by the MPC controller, the standard deviation of the overall outlet moisture was 0.12 and the range was 0.61, ensuring the stability of the outlet moisture. In addition, the production batch of the sample stem wire was changed during this process, and the inlet moisture of the two production batches differed greatly, resulting in a certain deviation in outlet moisture for a short period of time. However, the MPC controller made a logical and rapid adjustment to ensure the stability of the outlet moisture.

[0080] based on Figure 3 and Figure 4 The information in the sample filaments, and the process of training the MPC controller, can also be described as follows: Figure 6 As shown. In Figure 6 In this process, filtering and noise reduction of the inlet moisture content were added, and the outlet moisture setpoint was adjusted multiple times according to the preset outlet moisture range. Based on this, after adjusting the MPC controller, the standard deviation of the overall outlet moisture content was 0.12, the range was 0.91, and... Figure 6 It can be observed that by controlling the actual flow rate of the stems, the outlet moisture content is maintained at a stable level.

[0081] It should be noted that, in Figure 5 and Figure 6 The actual filament flow rate is used to characterize the actual sample filament flow rate at the inlet of the filament drying equipment.

[0082] The technical solution of this embodiment involves acquiring multiple batches of sample stems. When the presence of the current batch of sample stems is detected at the outlet of the drying equipment, second sample moisture data of the sample stems is obtained. Based on the second sample moisture data and a preset outlet moisture range, the first model parameters corresponding to the first model predictive controller to be trained are adjusted to obtain the first model predictive controller. This allows for subsequent adjustment of the stem flow rate of the processed stems based on the first model predictive controller. For multiple batches of sample stems, when the presence of the current batch of sample stems is detected at the inlet of the drying equipment, first sample moisture data and furnace temperature data of the sample stems are acquired. These data are then input into the second model predictive controller to be trained for processing to obtain a sample stem flow rate offset value. Based on the sample stem flow rate offset value and a preset flow rate offset range, the second model parameters of the second model predictive controller to be trained are adjusted to obtain the second model predictive controller. This facilitates subsequent adjustment of the stem flow rate of the processed stems based on the second model predictive controller. This invention improves the control accuracy of the first and second model predictive controllers by adjusting and optimizing the model parameters in the first and second model predictive controllers to be trained by using the moisture data of the first sample, the moisture data of the second sample, and the temperature data inside the sample oven corresponding to multiple batches of sample stems. This ensures accurate adjustment of stem flow rate and stable control of moisture at the outlet of the drying equipment during subsequent processing.

[0083] Example 3

[0084] Figure 7 This is a schematic diagram of a stem moisture stability control device provided in Embodiment 3 of the present invention. Figure 7 As shown, the device includes: a data acquisition module 310, a predicted filament flow determination module 320, an offset value determination module 330, and a filament flow determination module 340.

[0085] Data acquisition module 310 is used to acquire the first moisture data corresponding to the stalks to be processed, the furnace temperature data corresponding to the drying equipment, and the second moisture data at the current moment. The first moisture data is the moisture data of the stalks to be processed at the inlet of the drying equipment, and the second moisture data is the moisture data of the stalks to be processed at the outlet of the drying equipment. Predicted stalk flow rate determination module 320 is used to adjust the second moisture data based on the first model prediction controller and determine the predicted stalk flow rate corresponding to the adjusted second moisture data. The predicted stalk flow rate is the predicted stalk flow rate at the inlet of the drying equipment at the next moment. The offset value determination module 330 is used to input the first moisture data and the furnace temperature data into the second model prediction controller to calculate the predicted filament flow rate offset value, which is used to characterize the offset value of the predicted filament flow rate; the filament flow rate determination module 340 is used to determine the target predicted filament flow rate based on the predicted filament flow rate and the predicted filament flow rate offset value, so as to determine the filament flow rate at the inlet of the drying equipment at the next moment based on the target predicted filament flow rate, so as to control the third moisture data corresponding to the filament flow rate, wherein the third moisture data is the moisture data corresponding to the outlet of the drying equipment at the next moment.

[0086] The technical solution of this embodiment obtains the first moisture data, second moisture data, and furnace temperature data of the skein to be processed at the current moment, providing data basis for predicting the skein flow rate at the next moment. The second moisture data is adjusted and controlled by a first model prediction controller to obtain the predicted skein flow rate corresponding to the adjusted second moisture data. The first moisture data and furnace temperature data are processed by a second model prediction controller to obtain the predicted skein flow rate offset value corresponding to the predicted skein flow rate. Based on the predicted skein flow rate and the predicted skein flow rate offset value, the target predicted skein flow rate is obtained. The skein flow rate at the inlet of the skein drying equipment is adjusted using the target predicted skein flow rate to determine the skein flow rate at the inlet of the skein drying equipment at the next moment while maintaining the stability of the third moisture data. This solves the problem in the prior art of relying on repeated manual adjustment of the inlet skein flow rate, which consumes a lot of time and raw material costs. This invention achieves accurate adjustment of the skein flow rate while ensuring the moisture stability at the outlet of the skein drying equipment.

[0087] Based on the above embodiments, optionally, the data acquisition module includes: a first moisture data acquisition unit, used to acquire first moisture data corresponding to the skeins to be processed when the presence of skeins to be processed is detected at the inlet of the drying equipment; an oven temperature data acquisition unit, used to acquire oven temperature data in the drying equipment based on a temperature acquisition device when the drying power of the drying equipment is a preset value; and a second moisture data acquisition unit, used to acquire second moisture data of the skeins to be processed when the presence of skeins to be processed is detected at the outlet of the drying equipment.

[0088] Optionally, the first moisture data acquisition unit includes: a moisture data determination subunit, used to acquire the moisture data to be processed corresponding to the stem to be processed based on the moisture detection device; and a data noise reduction processing subunit, used to perform filtering and noise reduction processing on the moisture data to be processed to obtain the first moisture data corresponding to the stem to be processed.

[0089] Optionally, the device further includes: a first model parameter adjustment module, used to acquire multiple batches of sample stems, and when the presence of the current batch of sample stems is detected at the outlet of the drying equipment, to acquire second sample moisture data of the sample stems; based on the second sample moisture data and a preset outlet moisture range, to adjust the first model parameters corresponding to the first model prediction controller to be trained, so as to obtain the first model prediction controller.

[0090] Optionally, the device further includes: a second model parameter adjustment module, used to acquire first sample moisture data and sample oven temperature data of sample filaments when the presence of sample filaments of the current batch is detected at the inlet of the drying equipment for multiple batches of sample filaments; process the first sample moisture data and sample oven temperature data based on the second model prediction controller to be trained to determine the sample filament flow rate offset value corresponding to the sample filaments; and adjust the second model parameters of the second model prediction controller to be trained based on the sample filament flow rate offset value and a preset filament flow rate offset range to obtain the second model prediction controller.

[0091] Optionally, the device further includes: a data visualization processing module, used to visualize the moisture data of the first sample, the temperature data inside the sample furnace, the flow offset value of the sample stems, and the preset flow offset range of the stems to obtain a visualization image corresponding to the sample stems, and to adjust the parameters of the second model based on the visualization image to obtain the second model prediction controller.

[0092] Optionally, the stem flow rate determination module includes: a stem flow rate determination unit for obtaining the stem flow rate at the inlet of the drying equipment; and a stem flow rate adjustment unit for adjusting the stem flow rate based on the target predicted stem flow rate when the third moisture data does not exceed the preset outlet moisture range, and using the adjusted stem flow rate as the stem flow rate at the inlet of the drying equipment at the next moment.

[0093] The stem moisture stability control device provided in the embodiments of the present invention can execute the stem moisture stability control method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0094] Example 4

[0095] Figure 8This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 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.

[0096] like Figure 8 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.

[0097] 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.

[0098] 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 stem moisture stability control method.

[0099] In some embodiments, the stem moisture stability 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 stem moisture stability control method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the stem moisture stability control method by any other suitable means (e.g., by means of firmware).

[0100] 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.

[0101] Computer programs for implementing the stem moisture stability 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 the processor of a general-purpose computer, 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.

[0102] Example 5

[0103] Embodiment 5 of the present invention also provides a computer-readable storage medium storing computer instructions for causing a processor to execute a method for controlling the moisture stability of stem fibers, the method comprising:

[0104] The system acquires the first moisture data corresponding to the skewer to be processed, the furnace temperature data corresponding to the skewer drying equipment, and the second moisture data at the current moment. The first moisture data is the moisture data of the skewer to be processed at the inlet of the skewer drying equipment, and the second moisture data is the moisture data of the skewer to be processed at the outlet of the skewer drying equipment. Based on the first model predictive controller, the second moisture data is adjusted and controlled to determine the predicted skewer flow rate corresponding to the adjusted second moisture data. The predicted skewer flow rate is the predicted skewer flow rate at the inlet of the skewer drying equipment at the next moment. The first moisture data and the furnace temperature data are input into the second model predictive controller to calculate the predicted skewer flow rate offset value, which is used to characterize the offset value of the predicted skewer flow rate. Based on the predicted skewer flow rate and the predicted skewer flow rate offset value, the target predicted skewer flow rate is determined. Based on the target predicted skewer flow rate, the skewer flow rate at the inlet of the skewer drying equipment at the next moment is determined to control the third moisture data corresponding to the skewer flow rate. The third moisture data is the moisture data corresponding to the outlet of the skewer drying equipment at the next moment.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 controlling the moisture stability of stem fibers, characterized in that, include: The first moisture data, the furnace temperature data, and the second moisture data of the filaments to be processed at the current moment are obtained. The first moisture data is the moisture data of the filaments to be processed at the inlet of the drying equipment, and the second moisture data is the moisture data of the filaments to be processed at the outlet of the drying equipment. The second moisture data is adjusted and controlled based on the first model predictive controller to determine the predicted stem flow rate corresponding to the adjusted second moisture data. The predicted stem flow rate is the predicted stem flow rate at the inlet of the drying equipment at the next moment. The first model predictive controller is a model predictive controller used for feedback control of the second moisture data. The first moisture data and the furnace temperature data are input into the second model predictive controller to calculate the predicted stem flow rate offset value, which is used to characterize the offset value of the predicted stem flow rate; the second model predictive controller is a model predictive controller used to perform feedforward control on the first moisture data and the furnace temperature data. Based on the predicted stem flow rate and the predicted stem flow rate offset value, a target predicted stem flow rate is determined, and the stem flow rate at the inlet of the drying equipment at the next moment is determined based on the target predicted stem flow rate, so as to control the third moisture data corresponding to the stem flow rate, wherein the third moisture data is the moisture data corresponding to the outlet of the drying equipment at the next moment. The step of inputting the first moisture data and the furnace temperature data into the second model prediction controller to calculate the predicted filament flow rate offset value includes: The first moisture data and the furnace temperature data at the current moment are input into the second model prediction controller. Based on the logical relationship between the first moisture data and the furnace temperature data and the filament flow rate at the inlet of the filament drying equipment, the offset value relative to the predicted filament flow rate is calculated to obtain the predicted filament flow rate offset value. Determining the target predicted stalk flow rate based on the predicted stalk flow rate and the predicted stalk flow rate offset value includes: The predicted stalk flow rate and the predicted stalk flow rate offset value are summed to obtain the target predicted stalk flow rate.

2. The method according to claim 1, characterized in that, The acquisition of the first moisture data, the furnace temperature data of the drying equipment, and the second moisture data corresponding to the filaments to be processed at the current time includes: When the presence of the filament to be processed is detected at the inlet of the drying equipment, the first moisture data corresponding to the filament to be processed is obtained; When the drying power of the wire drying equipment is a preset value, the furnace temperature data in the wire drying equipment is obtained based on the temperature acquisition device. When the presence of the filaments to be processed is detected at the outlet of the drying equipment, the second moisture data of the filaments to be processed is obtained.

3. The method according to claim 2, characterized in that, The step of obtaining the first moisture data corresponding to the stem to be processed includes: The moisture content data corresponding to the stems to be treated is obtained using a moisture detection device. The moisture data to be processed is filtered and noise-reduced to obtain the first moisture data corresponding to the stem to be processed.

4. The method according to claim 1, characterized in that, Also includes: Multiple batches of sample stems are obtained, and when the current batch of sample stems is detected at the outlet of the drying equipment, the second sample moisture data of the sample stems is obtained. Based on the second sample moisture data and the preset outlet moisture range, the parameters of the first model corresponding to the first model prediction controller to be trained are adjusted to obtain the first model prediction controller.

5. The method according to claim 4, characterized in that, Also includes: For multiple batches of sample stems, when the presence of the current batch of sample stems is detected at the inlet of the drying equipment, the first sample moisture data and the sample oven temperature data of the sample stems are obtained. The second model prediction controller to be trained processes the moisture data of the first sample and the temperature data inside the furnace of the sample to determine the sample stem flow rate offset value corresponding to the sample stem. Based on the sample filament flow offset value and the preset filament flow offset range, the second model parameters of the second model prediction controller to be trained are adjusted to obtain the second model prediction controller.

6. The method according to claim 5, further comprising: The moisture data of the first sample, the temperature data inside the furnace of the sample, the flow rate offset value of the sample stems, and the preset flow rate offset range of the stems are visualized to obtain a visualization image corresponding to the sample stems. The parameters of the second model are then adjusted based on the visualization image to obtain the second model prediction controller.

7. The method according to claim 1, characterized in that, The step of adjusting the stem flow rate at the inlet of the drying equipment at the next moment based on the target predicted stem flow rate, in order to control the third moisture data corresponding to the stem flow rate, includes: Obtain the flow rate of the filaments to be processed at the inlet of the filament drying equipment; When it is determined that the third moisture data does not exceed the preset outlet moisture range, the flow rate of the filaments to be processed is adjusted based on the target predicted filament flow rate, and the adjusted filament flow rate is used as the filament flow rate at the inlet of the drying equipment at the next moment.

8. A device for controlling the moisture stability of stem fibers, characterized in that, include: The data acquisition module is used to acquire the first moisture data, the furnace temperature data and the second moisture data of the filaments to be processed at the current moment. The first moisture data is the moisture data of the filaments to be processed at the inlet of the drying equipment, and the second moisture data is the moisture data of the filaments to be processed at the outlet of the drying equipment. The predicted stem flow rate determination module is used to adjust and control the second moisture data based on the first model prediction controller, and determine the predicted stem flow rate corresponding to the adjusted second moisture data. The predicted stem flow rate is the predicted stem flow rate at the inlet of the drying equipment at the next moment. The first model prediction controller is a model prediction controller used for feedback control of the second moisture data. The offset value determination module is used to input the first moisture data and the furnace temperature data into the second model prediction controller to calculate the predicted stem flow rate offset value, which is used to characterize the offset value of the predicted stem flow rate; the second model prediction controller is a model prediction controller used to perform feedforward control on the first moisture data and the furnace temperature data. The stem flow rate determination module is used to determine the target predicted stem flow rate based on the predicted stem flow rate and the predicted stem flow rate offset value, so as to determine the stem flow rate at the inlet of the drying equipment at the next moment based on the target predicted stem flow rate, so as to control the third moisture data corresponding to the stem flow rate, wherein the third moisture data is the moisture data corresponding to the outlet of the drying equipment at the next moment. The offset value determination module is specifically used for: The first moisture data and the furnace temperature data at the current moment are input into the second model prediction controller. Based on the logical relationship between the first moisture data and the furnace temperature data and the filament flow rate at the inlet of the filament drying equipment, the offset value relative to the predicted filament flow rate is calculated to obtain the predicted filament flow rate offset value. The stem flow rate determination module is specifically used for: The predicted stalk flow rate and the predicted stalk flow rate offset value are summed to obtain the target predicted stalk flow rate.

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, the computer program being executed by the at least one processor to enable the at least one processor to perform the stem moisture stability 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 are used to cause a processor to execute the method for controlling the moisture stability of stem fibers as described in any one of claims 1-7.

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