A digital analysis-based control method and system for flax fiber carding machine

Abstract

The application relates to the technical field of combing machine control, and discloses a hemp fiber combing machine control method and system based on digital analysis, which comprises the following modules: an information acquisition module, which is used for synchronously acquiring quality data and operation energy consumption data; a state analysis module, which is used for jointly digitally analyzing the data, constructing a state feature set containing quality feature components and energy consumption feature components, and dividing a combing process into a first operation state and a second operation state according to the weight relationship of the feature components; a control decision module, which is used for calling a corresponding parameter subset according to the operation state, generating a continuously adjusted first control strategy and a constraint adjusted second control strategy; and an execution control module, which is used for executing adjustment on the operation parameters of the combing process according to the selected strategy. Through data joint digital analysis and state recognition, the application solves the problem that quality stability and energy consumption control are difficult to coordinate in the combing process.

Description

Technical Field

[0001] This invention relates to the field of combing machine control technology, and more specifically, to a control method and system for a hemp fiber combing machine based on digital analysis. Background Technology

[0002] The combing process is a crucial step in fiber processing and production, typically involving adjustments to the drafting rhythm, coordinated actions of actuators, and maintenance of continuous operation. In actual production, the combing process's operational status fluctuates with changes in raw material conditions, operating load, and working conditions, placing high demands on the responsiveness and adjustment methods of the control system.

[0003] Existing control methods for combing processes mostly employ fixed process parameters or adjustments based on single operating data, such as adjusting actuators according to changes in speed, current, or power. These control methods typically focus on changes in a single operating quantity, lacking analysis of the correlation between multiple operating data sources, and thus failing to reflect the overall changing characteristics of the combing process's operating status.

[0004] With the development of sensing and automation technologies, the types of operational data available during the combing process are gradually increasing, including data related to changes in combing quality and changes in operational energy consumption. However, in existing technologies, this operational data is mostly processed independently or used only as monitoring data. Control decisions still mainly rely on single judgment conditions or fixed control rules, making it difficult to reflect the dynamic relationship between changes in quality and energy consumption in a timely manner.

[0005] In the absence of systematic digital analysis of operational data, when the combing process simultaneously exhibits trends in both quality and energy consumption, existing control methods struggle to differentiate the control priorities under different operating conditions. This can easily lead to control response lag or mismatched adjustment strategies, thereby affecting the stable and consistent operation of the combing process. Summary of the Invention

[0006] In view of this, the present invention proposes a combing process control method and system based on digital analysis, which aims to solve the problem that the control method in the prior art is difficult to switch the control focus according to the changes in the operating state, resulting in the difficulty of balancing the stability of the combing process and energy consumption control.

[0007] In one aspect, the present invention proposes a control system for a hemp fiber combing machine based on digital analysis, comprising: The information acquisition module synchronously collects quality data and energy consumption data reflecting the operating status of the combing process within the same sampling period; An information storage module, which is connected to the information acquisition module, includes a first storage unit and a second storage unit. The first storage unit stores the quality data and operating energy consumption data acquired in the current sampling period, and the second storage unit stores the combing process status data in the historical operating periods. The state analysis module, which is connected to the information storage module, performs joint digital analysis on the quality data and operating energy consumption data within a predetermined sampling window to obtain a state feature set including quality feature components and energy consumption feature components. Based on the change weights of the quality feature components and energy consumption feature components in the state feature set, the combing process is divided into a first operating state and a second operating state. A control decision module is connected to the state analysis module. The control decision module has a set of candidate control parameters. The control parameters in the set of candidate control parameters are divided into a first parameter subset and a second parameter subset according to the way they participate in different control paths of the combing process. When the combing process is in the first operating state, the first parameter subset is invoked to combine and adjust the operating parameters in the combing process according to the continuous adjustment path to generate the first control strategy; When the combing process is in the second operating state, the second parameter subset is invoked to adjust the operating parameters of the combing process according to the constraint adjustment path, thereby generating a second control strategy. An execution control module, which is connected to the control decision module, performs corresponding adjustments to at least one operating parameter in the combing process according to the first control strategy or the second control strategy.

[0008] Furthermore, when performing joint digital analysis on the quality data and operating energy consumption data, the status analysis module includes: Within the predetermined sampling window, the quality data and operating energy consumption data are reconstructed into time series to obtain the quality feature sequence and the energy consumption feature sequence. Based on the quality feature sequence and energy consumption feature sequence, the corresponding change amplitude features and fluctuation features are extracted to obtain the quality feature component set and the energy consumption feature component set. The set of quality characteristic components and the set of energy consumption characteristic components are combined to form a set of state characteristics used to characterize the operating characteristics of the combing process.

[0009] Furthermore, after forming the set of state features, the state analysis module includes: Within the same sampling window, the set of quality feature components and the set of energy consumption feature components are normalized respectively; Based on the normalized set of quality feature components and the set of energy consumption feature components, the cumulative change and instantaneous fluctuation of each feature component within the sampling window are calculated respectively, and the quality change description vector and the energy consumption change description vector are constructed. Alignment analysis is performed on the quality change description vector and the energy consumption change description vector to obtain the participation weight of each feature component in the state feature set.

[0010] Furthermore, after obtaining the participation weights of each feature component, the state analysis module includes: Within the same sampling window, the participation weights corresponding to the quality change description vector are summarized to form a quality weight set, and the participation weights corresponding to the energy consumption change description vector are summarized to form an energy consumption weight set. Based on the distribution of the quality weight set and energy consumption weight set in the state feature set, a state structure description reflecting the relative occupancy relationship between the quality feature components and the energy consumption feature components is constructed. When the proportion of the quality weight set is greater than that of the energy consumption weight set, the combing process is divided into the first operating state; When the proportion of the energy consumption weight set is greater than that of the quality weight set, the combing process is divided into the second operating state; When the proportion of the quality weight set is equal to the proportion of the energy consumption weight set, the operating state corresponding to the previous sampling window is adopted.

[0011] Furthermore, when the combing process is divided into a first operating state, the control decision module's invocation of the first parameter subset includes: Based on the set of state features within the current sampling window, determine the participation order of each control parameter in the continuous adjustment path within the first parameter subset; According to the order of participation, the control parameters in the first parameter subset are adjusted sequentially, and the corresponding quality data is reacquired after each adjustment. Based on the changes in quality data within several consecutive sampling windows, the control parameters in the first parameter subset are combined and corrected to obtain the first control strategy.

[0012] Furthermore, when the combing process is divided into a second operating state, the control decision module's invocation of the second parameter subset includes: Based on the set of state features within the current sampling window, determine the constraint adjustment range corresponding to each control parameter in the second parameter subset; Within the constraint adjustment range, the control parameters in the second parameter subset are adjusted independently, and the range of change of the control parameters is limited during the adjustment process. When the control parameters corresponding to the operating energy consumption data no longer change within multiple consecutive sampling windows, the current values ​​of the control parameters in the second parameter subset are maintained to obtain the second control strategy.

[0013] Further, when the execution control module receives the first control policy or the second control policy, it includes: Upon receiving the first control strategy, the corresponding operating parameters are adjusted sequentially according to the combined adjustment order determined in the first control strategy, and a sampling waiting period is inserted between two adjacent adjustment operations to obtain the updated quality data. Upon receiving the second control strategy, the corresponding operating parameters are subjected to a limiting adjustment operation according to the constraint adjustment range determined in the second control strategy, and the operating parameter values ​​are kept unchanged within a series of sampling windows. When the first control strategy and the second control strategy are switched, the execution control module keeps the current operating parameters unchanged, and after completing a full sampling window, it executes the adjustment operation corresponding to the current control strategy again.

[0014] Furthermore, during the execution of the first control strategy or the second control strategy, the execution control module further includes: If a change in the running status type output by the status analysis module is detected within any sampling window, the adjustment operation within the current sampling window is paused. After completing the data acquisition of the current sampling window, the first control strategy or the second control strategy is reselected according to the updated running status type, and the adjustment operation corresponding to the selected control strategy is executed.

[0015] Furthermore, the execution control module also includes: An anomaly handling unit is used to stop executing the first control strategy and the second control strategy and keep the current operating parameters unchanged when the quality data or operating energy consumption data is detected to be missing, fluctuating abnormally, or unable to be updated within multiple consecutive sampling windows. After the quality data and operating energy consumption data are restored to data that can be used for status analysis, the status analysis module is re-triggered to determine the operating status, and the control strategy corresponding to the current operating status is continued to be executed according to the determination result.

[0016] Compared with existing technologies, the advantages of this invention are as follows: By synchronously collecting quality data and operating energy consumption data within the same sampling period and performing joint digital analysis, a state feature set containing both quality characteristic components and energy consumption characteristic components is constructed. Based on the changing weights of the two types of characteristic components in the state feature set, the system dynamically determines whether the combing process is in a first operating state centered on quality control or a second operating state centered on energy consumption constraints, thereby achieving precise matching and adaptive switching of control strategies. By dividing the control parameters into a first parameter subset and a second parameter subset corresponding to continuous adjustment paths and constraint adjustment paths respectively, the system adopts a differentiated control mechanism under different operating states. In the first operating state, the quality indicators are gradually optimized through combined continuous adjustment; in the second operating state... During operation, the system maintains stable energy consumption through sub-item constraint adjustment, thereby improving the targeting and efficiency of control. Through normalization, weight alignment, and anomaly handling mechanisms in state analysis, the system exhibits good adaptability and stability under dynamic operating conditions, avoiding frequent state switching due to short-term data fluctuations. It can also pause adjustment and maintain parameter stability when data is abnormal, resuming the corresponding control strategy after data recovery. Furthermore, the execution control module maintains the current parameters unchanged during strategy switching and executes the new strategy after a complete sampling window. Combined with a closed-loop feedback mechanism of the sampling waiting period, it prevents parameter mutations, improves the smoothness and overall stability of the combing process, and achieves synergistic optimization of quality control and energy consumption constraints. This provides a reliable solution for the intelligent and refined control of the hemp fiber combing process.

[0017] On the other hand, this application also provides a control method for a hemp fiber combing machine based on digital analysis, used to implement the above-mentioned control system for a hemp fiber combing machine based on digital analysis, comprising: Simultaneously collect quality data and operating energy consumption data within the same sampling period; It also includes a first storage unit and a second storage unit. The first storage unit stores the quality data and operating energy consumption data collected in the current sampling period, and the second storage unit stores the combing process status data in the historical operating periods. Within a predetermined sampling window, joint digital analysis is performed on the quality data and operating energy consumption data to obtain a state feature set including quality characteristic components and energy consumption characteristic components. Based on the change weights of the quality characteristic components and energy consumption characteristic components in the state feature set, the combing process is divided into a first operating state and a second operating state. It also includes a set of candidate control parameters, wherein the control parameters in the set of candidate control parameters are divided into a first parameter subset and a second parameter subset according to the way they participate in different control paths of the combing process; When the combing process is in the first operating state, the first parameter subset is invoked to combine and adjust the operating parameters in the combing process according to the continuous adjustment path to generate the first control strategy; When the combing process is in the second operating state, the second parameter subset is invoked to adjust the operating parameters of the combing process according to the constraint adjustment path, thereby generating a second control strategy. According to the first control strategy or the second control strategy, at least one operating parameter in the combing process is adjusted accordingly.

[0018] It is understandable that the above-mentioned control method and system for a hemp fiber combing machine based on digital analysis has the same beneficial effects, and will not be elaborated further here. Attached Figure Description

[0019] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A functional block diagram of a hemp fiber combing machine control system based on digital analysis is provided for an embodiment of the present invention; Figure 2 A flowchart illustrating a control method for a hemp fiber combing machine based on digital analysis, provided as an embodiment of the present invention. Detailed Implementation

[0020] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0021] See Figure 1 As shown, this application proposes a control system for a hemp fiber combing machine based on digital analysis, comprising: The information acquisition module synchronously collects quality data and energy consumption data reflecting the operating status of the combing process within the same sampling period; The information storage module is connected to the information acquisition module and includes a first storage unit and a second storage unit. The first storage unit stores the quality data and operating energy consumption data acquired in the current sampling period, and the second storage unit stores the combing process status data in the historical operating periods. The state analysis module, which is connected to the information storage module, performs joint digital analysis on quality data and operating energy consumption data within a predetermined sampling window to obtain a state feature set including quality feature components and energy consumption feature components. Based on the change weights of quality feature components and energy consumption feature components in the state feature set, the combing process is divided into a first operating state and a second operating state. The control decision module is connected to the state analysis module. The control decision module has a set of candidate control parameters. The control parameters in the candidate control parameter set are divided into a first parameter subset and a second parameter subset according to the way they participate in different control paths of the combing process. When the combing process is in the first operating state, the first parameter subset is called, and the operating parameters in the combing process are combined and adjusted according to the continuous adjustment path to generate the first control strategy. When the combing process is in the second operating state, the second parameter subset is called to adjust the operating parameters of the combing process according to the constraint adjustment path to generate the second control strategy. The execution control module, which is connected to the control decision module, performs corresponding adjustments to at least one operating parameter in the combing process according to the first control strategy or the second control strategy.

[0022] Specifically, the information acquisition module is used to synchronously collect multi-source operating status information during the combing process within the same sampling period. Quality data reflects the quality change characteristics of the combed hemp fiber sliver under the current operating condition. These quality change characteristics can originate from indicators such as evenness and fiber length distribution obtained from online quality detection devices, or from surrogate quantities corresponding to quality changes, such as main motor current fluctuation characteristics or machine vibration response characteristics. Operating energy consumption data reflects the energy consumption level of the combing equipment under the current operating conditions, which can be obtained through a main motor power detection device or energy consumption metering unit. Synchronously collecting quality data and operating energy consumption data within the same sampling period avoids data misalignment at different time scales from interfering with subsequent analysis results. The first storage unit in the information storage module stores the real-time quality data and operating energy consumption data collected within the current sampling period, providing an immediate analysis basis for the status analysis module. The second storage unit stores the combing process status data within historical operating periods to support status change trend analysis and the inheritance judgment of historical stable states when the operating status is unclear. The state analysis module performs joint digital analysis on quality data and operational energy consumption data within a predetermined sampling window. This joint digital analysis involves simultaneously incorporating quality characteristic components reflecting quality changes and energy consumption characteristic components reflecting energy consumption changes into the analysis process. By comprehensively processing the variation amplitude and fluctuation characteristics of different characteristic components within the sampling window, a state feature set is formed to comprehensively characterize the operational features of the combing process. Based on this, the state analysis module further determines the operational state of the combing process according to the weighted relationship between each quality characteristic component and the energy consumption characteristic component in the state feature set. The weighted change characterizes the relative influence of different characteristic components on the current operational state. When the weighted change corresponding to a quality characteristic component dominates the state structure, it indicates that the current operational state is more affected by quality changes; when the weighted change corresponding to an energy consumption characteristic component dominates, it indicates that the current operational state is more affected by energy consumption changes. Based on the above weighted change relationship, the state analysis module divides the combing process into a first operational state or a second operational state. Based on the operating state type output by the state analysis module, the control decision module selects a corresponding subset of parameters from the candidate control parameter set to participate in the control decision. The first parameter subset corresponds to the set of operating parameters that have a synergistic regulatory effect on combing quality, and the second parameter subset corresponds to the set of operating parameters that have a constraining regulatory effect on operating energy consumption.When the combing process is in its first operating state, the control decision module generates a first control strategy through a continuous adjustment path. This first control strategy refers to the operating parameter control strategy generated by the control decision module based on the continuous adjustment path when the combing process is determined to be in its first operating state. The operating parameter control strategy includes at least the participation order of each operating parameter in the first parameter subset, the linkage adjustment relationship between the operating parameters, and the combined correction result formed based on quality data feedback. This guides the execution control module to perform correlated adjustments on the combing process operating parameters. The continuous adjustment path refers to a control method that continuously correlates and adjusts multiple operating parameters in the first parameter subset according to a predetermined participation order. When iteratively adjusting the operating parameters according to the participation order, each adjustment of the operating parameter is based on feedback decisions using quality data acquired within the current sampling window. This quality data includes the cumulative impact of previous parameter adjustments, ensuring the overall performance of the combing process... The quality indicators gradually converge to the expected state, enabling multiple operating parameters to form a continuous adjustment process with feedback relationships within adjacent sampling windows. When the combing process is in the second operating state, the control decision module generates a second control strategy through a constraint adjustment path. The second control strategy refers to the operating parameter control strategy generated by the control decision module based on the constraint adjustment path when the combing process is determined to be in the second operating state. The operating parameter control strategy includes at least the constraint adjustment range corresponding to each operating parameter in the second parameter subset, the independent adjustment rules of each operating parameter, and the condition for maintaining changes in operating energy consumption data. It is used to guide the execution control module to adjust the operating parameters of the combing process separately within a limited range. The constraint adjustment path refers to adjusting the operating parameters in the second parameter subset separately within a predetermined safe adjustment range and limiting the magnitude of parameter changes to avoid instability in the operation of the combing process during energy consumption control. The execution control module performs specific adjustment operations on the corresponding operating parameters in the combing process according to the first or second control strategy output by the control decision module. When the operating state or control strategy changes, the current operating parameters remain unchanged until the current sampling window is completed before executing the adjustment operation corresponding to the new control strategy. This avoids the impact of parameter abrupt changes on the stability of the combing process, ensuring the continuity and stability of the combing process. For example, in one embodiment, when the system detects a continuous increase in the weighting of the stripe fluctuation characteristics and surrogate quality characteristics corresponding to the quality data within multiple consecutive sampling windows, while the change in operating energy consumption data is relatively small, the state analysis module determines the combing process to be in the first operating state.At this point, the control decision module calls the first parameter subset and adjusts the cylinder speed and clamp spacing sequentially according to the continuous adjustment path. After each parameter adjustment, the corresponding quality data is reacquired, and the subsequent parameter adjustment range is corrected according to the quality change trend, thus forming the first control strategy for quality regulation. When the weight of the change in operating energy consumption data gradually increases and the quality change tends to stabilize in the subsequent sampling window, the state analysis module switches the combing process to the second operating state. The control decision module then calls the second parameter subset and performs a limit adjustment on the main motor speed or auxiliary drive parameters under the constraint adjustment path to generate the second control strategy. The execution control module then performs the corresponding adjustment operation, thereby realizing differentiated control of the combing process under different operating states.

[0023] In some embodiments of this application, the status analysis module, when performing joint digital analysis of quality data and operating energy consumption data, includes: Within a predetermined sampling window, time series reconstruction is performed on the quality data and the operating energy consumption data to obtain the quality feature sequence and the energy consumption feature sequence. Based on the quality feature sequence and energy consumption feature sequence, the corresponding change amplitude features and fluctuation features are extracted to obtain the set of quality feature components and the set of energy consumption feature components. The quality characteristic component set and the energy consumption characteristic component set are combined to form a state characteristic set used to characterize the operation characteristics of the combing process.

[0024] Specifically, time series reconstruction refers to organizing and mapping continuously collected quality data and operational energy consumption data in chronological order within a predetermined sampling window. This transforms discrete sampling points into a time series structure that reflects the dynamic evolution of the combing process. The quality feature sequence describes the changes in combing quality-related indicators or quality proxy quantities over time, while the energy consumption feature sequence describes the energy consumption changes of the combing equipment within the corresponding time period. After obtaining the quality and energy consumption feature sequences, the state analysis module further extracts amplitude and fluctuation features from the time series. The amplitude feature characterizes the overall degree of change of each sequence within the sampling window, while the fluctuation feature characterizes the stability and fluctuations of each sequence within the sampling window, thus forming a set of quality feature components and a set of energy consumption feature components, respectively. Subsequently, the state analysis module combines the quality and energy consumption feature sets, mapping feature components from different sources and with different physical meanings to the same analytical framework. This allows them to collectively characterize the comprehensive operational characteristics of the combing process within the current sampling window, thereby forming a state feature set. By using a set of state features, the overall operating status of the combing process can be characterized without relying on a single quality indicator or a single energy consumption indicator. For example, in one embodiment, quality data includes the stripe fluctuation value obtained by the stripe detection device and the instantaneous fluctuation characteristics of the main motor current. Operating energy consumption data includes the average power value and power change amplitude of the main drive motor of the combing equipment within the sampling window. Within a predetermined sampling window, the state analysis module first reconstructs the above-mentioned quality data and operating energy consumption data into a time series, forming corresponding quality feature sequences and energy consumption feature sequences. Subsequently, it extracts the stripe fluctuation amplitude and current fluctuation amplitude from the quality feature sequences as quality feature components, and extracts the power change amplitude and power stability index from the energy consumption feature sequences as energy consumption feature components, respectively forming a set of quality feature components and a set of energy consumption feature components. The state analysis module combines the above-mentioned set of quality feature components and the set of energy consumption feature components to form a set of state features used to characterize the current operating characteristics of the combing process, thereby simultaneously reflecting the changes in combing quality and operating energy consumption within the same analytical framework, providing a data foundation for subsequent operating status determination.

[0025] In some embodiments of this application, the state analysis module, after forming a set of state features, includes: Within the same sampling window, the set of quality feature components and the set of energy consumption feature components are normalized respectively; Based on the normalized set of quality feature components and energy consumption feature components, the cumulative change and instantaneous fluctuation of each feature component within the sampling window are calculated respectively, and the quality change description vector and energy consumption change description vector are constructed. Alignment analysis is performed on the description vectors of quality change and energy consumption change to obtain the participation weights of each feature component in the state feature set.

[0026] Specifically, alignment analysis can be implemented by calculating the correlation between corresponding feature components in the quality change description vector and the energy consumption change description vector. For example, it involves calculating the correlation coefficient or similarity index of the corresponding components within the sampling window and normalizing the results. The normalized result serves as the weight for the corresponding feature component in the state feature set. Normalization refers to mapping feature components with different dimensions and value ranges in the quality and energy consumption feature sets to comparable numerical intervals within the same sampling window. This eliminates the scale effect caused by differences in physical quantities, allowing subsequent analysis to reflect the true relative changes of each feature component. After normalization, the state analysis module quantifies the changes of each feature component within the sampling window based on the normalized quality and energy consumption feature sets. The cumulative change represents the overall trend of the corresponding feature component throughout the sampling window, while the instantaneous fluctuation represents the short-term fluctuation of the corresponding feature component within the sampling window. By combining these two types of quantification results, the quality change description vector and the energy consumption change description vector are constructed respectively. Subsequently, the state analysis module performs alignment analysis on the quality change description vector and the energy consumption change description vector. This alignment analysis is used to compare the correspondence between the quality-side change features and the energy consumption-side change features in terms of time evolution and change trend, thereby determining the degree of participation of each feature component in the change of the combing process state within the current sampling window, and quantifying this degree of participation as a participation weight, so that the influence relationship between different quality feature components and energy consumption feature components in the state feature set can be clearly expressed. For example, in one embodiment, the quality characteristic components include stripe fluctuation amplitude and current fluctuation amplitude, and the energy consumption characteristic components include the average power change of the main motor and the power fluctuation amplitude. The state analysis module first normalizes the above characteristic components; then it calculates the cumulative change of stripe fluctuation amplitude within the sampling window and the corresponding instantaneous fluctuation, forming a quality change description vector; at the same time, it calculates the cumulative change of the main motor power change and the instantaneous fluctuation, forming an energy consumption change description vector; by aligning and comparing the change trends of the two types of description vectors within the same sampling window, it is determined that the participation weight of stripe fluctuation-related features in the current operating state is higher than that of power change-related features, thus indicating that the current combing process operating state is more affected by quality changes, providing a basis for subsequent operating state determination.

[0027] In some embodiments of this application, after obtaining the participation weights of each feature component, the state analysis module includes: Within the same sampling window, the participation weights corresponding to the quality change description vector are summarized to form a quality weight set, and the participation weights corresponding to the energy consumption change description vector are summarized to form an energy consumption weight set. Based on the distribution of the quality weight set and the energy consumption weight set in the state feature set, a state structure description reflecting the relative occupancy relationship between the quality feature components and the energy consumption feature components is constructed. When the proportion of the quality weight set is greater than that of the energy consumption weight set, the combing process is classified as the first operating state; When the proportion of the energy consumption weight set is greater than that of the quality weight set, the combing process is divided into the second operating state; When the proportion of the quality weight set is equal to the proportion of the energy consumption weight set, the operating state corresponding to the previous sampling window is adopted.

[0028] Specifically, after obtaining the participation weights of each feature component, the state analysis module first structures the calculation process of the participation weights. The participation weights are used to quantify the influence of each quality feature component and energy consumption feature component on the changes in the combing process's operating state within the current sampling window. In some embodiments of this application, the calculation of the participation weights is determined based on the correspondence between the changing behavior of each feature component in the quality change description vector and the energy consumption change description vector and the overall operating state change. That is, by comprehensively analyzing the cumulative change and instantaneous fluctuation of each feature component within the sampling window, its relative contribution to the current operating state is evaluated. The state analysis module can calculate the change index of each feature component in the quality change description vector and the energy consumption change description vector respectively, and perform correlation analysis between the change index and the overall change trend in the state feature set to obtain initial weight values ​​reflecting the participation degree of the feature components. Subsequently, the initial weight values ​​are normalized to ensure that the participation weights corresponding to different feature components are within a unified dimension, thereby obtaining the participation weight of each feature component in the state feature set. By employing the above method, it can be ensured that the participation weights not only reflect the magnitude of change of a single feature component, but also the degree of correlation between the change of that feature component and the overall operating state of the combing process. After obtaining the participation weights of each feature component, the state analysis module summarizes the participation weights from the quality change description vector within the same sampling window to form a quality weight set, and summarizes the participation weights from the energy consumption change description vector to form an energy consumption weight set. The quality weight set is used to characterize the overall influence of the quality feature component in the current operating state, and the energy consumption weight set is used to characterize the overall influence of the energy consumption feature component in the current operating state. Based on the relative distribution of the quality weight set and the energy consumption weight set in the state feature set, the state analysis module further constructs a state structure description reflecting the relative occupancy relationship between the quality feature component and the energy consumption feature component, to characterize whether the current combing process operating state is mainly dominated by quality change factors or energy consumption change factors. When the proportion of the quality weight set in the state structure description is greater than that of the energy consumption weight set, the state analysis module classifies the combing process into the first operating state; when the proportion of the energy consumption weight set in the state structure description is greater than that of the quality weight set, the state analysis module classifies the combing process into the second operating state; when the proportion of the quality weight set is equal to that of the energy consumption weight set, the state analysis module uses the operating state corresponding to the previous sampling window as the current operating state to avoid frequent switching of operating states due to short-term feature fluctuations, thereby improving the stability of operating state determination. For example, in one embodiment, the quality change description vector includes the strip fluctuation amplitude and the main motor current fluctuation amplitude, and the energy consumption change description vector includes the main motor average power change and power fluctuation amplitude.The state analysis module first calculates the cumulative change and instantaneous fluctuation of each characteristic component within the sampling window, and then performs correlation analysis between the changes and the overall operating state trend of the combing process to obtain the initial participation weights for each characteristic component. Subsequently, the initial participation weights are normalized to form participation weights corresponding to stripe fluctuations, current fluctuations, power changes, and power fluctuations. Within the same sampling window, the state analysis module summarizes the participation weights corresponding to stripe fluctuations and current fluctuations to form a quality weight set, and summarizes the participation weights corresponding to power changes and power fluctuations to form an energy consumption weight set. When the sum of the quality weight set is higher than the energy consumption weight set, the system determines that the current combing process operating state is mainly affected by quality change factors, thus entering the first operating state; when the sum of the energy consumption weight set is higher than the quality weight set, the system determines that the current combing process operating state is mainly affected by energy consumption change factors, thus entering the second operating state.

[0029] In some embodiments of this application, when the combing process is divided into a first operating state, the control decision module's invocation of the first parameter subset includes: Based on the set of state features within the current sampling window, determine the participation order of each control parameter in the continuous adjustment path within the first parameter subset; According to the order of participation, the control parameters in the first parameter subset are adjusted sequentially, and the corresponding quality data is reacquired after each adjustment; Based on the changes in quality data within several consecutive sampling windows, the control parameters in the first parameter subset are combined and corrected to obtain the first control strategy.

[0030] Specifically, when the combing process is divided into the first operating state, the control decision module determines the participation order of the control parameters in the continuous adjustment path based on the participation weights of each feature component output by the state analysis module within the current sampling window. In some embodiments of this application, each control parameter corresponds to at least one quality feature component, and the control decision module uses the participation weight of the quality feature component as the sorting basis for the control parameter in the continuous adjustment path. When determining the participation order, if the participation weight of the quality feature component corresponding to a certain control parameter is greater than the participation weight of the quality feature component corresponding to another control parameter, the control decision module determines the former as the first adjustment parameter and the latter as the subsequent adjustment parameter; when the participation weights of the quality feature components corresponding to multiple control parameters have the same value, the control decision module keeps the corresponding participation order in the previous sampling window unchanged, thereby forming a continuous and consistent adjustment path between adjacent sampling windows. When adjusting the control parameters in the first parameter subset sequentially according to the participation order, after completing the adjustment of any control parameter, the control decision module triggers the information acquisition module to re-acquire the quality data corresponding to that control parameter and stores the acquired quality data in the first storage unit to record the quality change results before and after the adjustment of the single control parameter. Subsequently, the next control parameter is adjusted to ensure that each quality change can be traced back to the corresponding control parameter adjustment. Within several consecutive sampling windows, the control decision module compares and analyzes the quality data before and after each control parameter adjustment. When it detects that the direction of quality change corresponding to multiple control parameters remains consistent during continuous adjustment, the control decision module combines and corrects the adjustment order and method of each control parameter in the first parameter subset, enabling multiple control parameters to form a stable collaborative adjustment relationship in the continuous adjustment path, and generates a first control strategy accordingly. For example, in one embodiment, the state analysis module calculates that the participation weight of the quality feature component corresponding to the stripe change is 0.42, and the participation weight of the quality feature component corresponding to the fracture rate change is 0.27 within the current sampling window. Based on this, the control decision module determines the control parameter corresponding to the stripe change as the preliminary adjustment parameter, and re-collects quality data after completing the adjustment of this control parameter; subsequently, the control parameter corresponding to the fracture rate change is adjusted. In subsequent sampling windows, when the weight relationship between the two quality feature components remains unchanged, the control decision module maintains the adjustment order and modifies the combination adjustment method of multiple control parameters to form the first control strategy.

[0031] In some embodiments of this application, when the combing process is divided into a second operating state, the control decision module's invocation of the second parameter subset includes: Based on the set of state features within the current sampling window, determine the constraint adjustment range corresponding to each control parameter in the second parameter subset; Within the constraint adjustment range, the control parameters in the second parameter subset are adjusted independently, and the range of change of the control parameters is limited during the adjustment process; When the control parameters corresponding to the running energy consumption data no longer change within multiple consecutive sampling windows, the current values ​​of the control parameters in the second parameter subset are maintained to obtain the second control strategy.

[0032] Specifically, when the combing process is divided into the second operating state, the control decision module determines the corresponding constraint adjustment range for each control parameter in the second parameter subset based on the set of state features within the current sampling window. In some embodiments of this application, the constraint adjustment range is determined by the combing process state data recorded in the historical operating cycle. The combing process state data in the historical operating cycle includes the changes in operating energy consumption data under different operating parameter values, thereby establishing the upper and lower boundary ranges for adjustment of each control parameter. After determining the constraint adjustment range, the control decision module performs independent adjustments to each control parameter in the second parameter subset. During the independent adjustment process, the current value of any control parameter is only allowed to change within the corresponding constraint adjustment range, and the adjustment amount of the control parameter must not exceed the boundary limit of the constraint adjustment range between two adjacent sampling windows, thereby avoiding the overlapping effects between multiple control parameters. When performing independent adjustments within a continuous sampling window, the control decision module synchronously monitors the operating energy consumption data corresponding to each control parameter. When the operating energy consumption data corresponding to a certain control parameter no longer changes within its constraint adjustment range within multiple consecutive sampling windows, or when the value of the control parameter remains unchanged between adjacent sampling windows, the control decision module determines that the control parameter has reached a stable value state and maintains the current value of the control parameter without further adjustment. Once all control parameters in the second parameter subset have entered a state of maintaining unchanged values, the control decision module determines the current combination of control parameter values ​​as the second control strategy and outputs the second control strategy to the execution control module for subsequent operation control. For example, in one embodiment, the second parameter subset includes the fan speed parameter and the auxiliary drive speed parameter. Based on the combing process status data within historical operating cycles, the control decision module determines the constraint adjustment range of the fan speed parameter as n1 and n2, and determines the constraint adjustment range of the auxiliary drive speed parameter as m1 and m2. Within the current sampling window, the control decision module first adjusts the fan speed parameter within the range of n1 and n2, and then acquires the corresponding operating energy consumption data in the next sampling window. Subsequently, it independently adjusts the auxiliary drive speed parameter within the range of m1 and m2, and acquires the corresponding operating energy consumption data. In subsequent continuous sampling windows, when the operating energy consumption data corresponding to the fan speed parameter remains consistent across multiple sampling windows, and the fan speed parameter value does not change, the control decision module maintains the current value of the fan speed parameter unchanged. When the operating energy consumption data corresponding to the auxiliary drive speed parameter also no longer changes within its constraint adjustment range, the control decision module simultaneously maintains the current value of the auxiliary drive speed parameter unchanged and determines this set of parameter values ​​as the second control strategy.

[0033] In some embodiments of this application, when the execution control module receives a first control policy or a second control policy, it includes: Upon receiving the first control strategy, the corresponding operating parameters are adjusted sequentially according to the combined adjustment order determined in the first control strategy, and a sampling waiting period is inserted between two adjacent adjustment operations to obtain the updated quality data. Upon receiving the second control strategy, the corresponding operating parameters are subjected to a limiting adjustment operation according to the constraint adjustment range determined in the second control strategy, and the operating parameter values ​​are kept unchanged within a series of sampling windows. When switching between the first control strategy and the second control strategy, the execution control module keeps the current operating parameters unchanged, and after completing a full sampling window, it executes the adjustment operation corresponding to the current control strategy again.

[0034] Specifically, after receiving the first or second control strategy output by the control decision module, the execution control module does not directly adjust all operating parameters simultaneously. Instead, it differentiates and processes the adjustment method, adjustment order, and adjustment timing of the operating parameters according to the type of the current control strategy.

[0035] Upon receiving the first control strategy, the execution control module sequentially adjusts the operating parameters in the first parameter subset according to the adjustment sequence determined in the first control strategy. After each adjustment of an operating parameter, the execution control module enters a sampling waiting period. During this period, it does not adjust other operating parameters but waits for the combing process to complete a sampling window of data acquisition under the current parameter values ​​to obtain the quality data corresponding to this adjustment operation. The sampling waiting period ensures that the acquired quality data reflects the actual operating state after the current operating parameter adjustment, thus providing a basis for subsequent operating parameter adjustments.

[0036] Upon receiving the second control strategy, the execution control module no longer adjusts the operating parameters sequentially according to the combination order. Instead, it performs a limiting adjustment operation on the corresponding operating parameters based on the pre-determined constraint adjustment ranges for each operating parameter in the second control strategy. The limiting adjustment operation means that the value of any operating parameter is only allowed to change within its corresponding constraint adjustment range, and after completing one adjustment, the value of that operating parameter remains unchanged for several consecutive sampling windows. During the holding phase, the execution control module only collects data and does not perform any new adjustment operations on the operating parameters, in order to determine whether the current value needs further adjustment.

[0037] When switching between the first and second control strategies, the execution control module does not immediately readjust the operating parameters. Instead, it maintains the current operating parameter values ​​unchanged until the data acquisition process within the current sampling window is completed. After completing a full sampling window, the execution control module selects the corresponding adjustment method based on the switched control strategy type and executes the operating parameter adjustment operation that matches the current control strategy. This avoids multiple changes in operating parameters within the same sampling window due to strategy switching.

[0038] For example, in one embodiment, when the state analysis module determines that the combing process is in the first operating state, the control decision module generates a first control strategy. The execution control module, according to the order determined in the first control strategy, first performs an adjustment operation on the cylinder speed parameter; then it enters a sampling waiting period, during which the corresponding quality data is collected. After the sampling window is completed, the execution control module performs the next adjustment operation on the clamp spacing parameter, and then enters the sampling waiting period again.

[0039] When the operating status type output by the status analysis module in the subsequent sampling window changes from the first operating status to the second operating status, the execution control module keeps the current cylinder speed parameter and clamp spacing parameter unchanged until the current sampling window ends. After the sampling window is completed, the execution control module performs a limiting adjustment operation on the fan speed parameter according to the constraint adjustment range determined in the second control strategy, and keeps the fan speed parameter value unchanged in multiple consecutive sampling windows, only collecting and monitoring the operating energy consumption data.

[0040] In some embodiments of this application, the execution control module further includes the following during the execution of the first control strategy or the second control strategy: If a change in the running status type output by the status analysis module is detected within any sampling window, the adjustment operation within the current sampling window is paused. After completing the data acquisition for the current sampling window, the first or second control strategy is reselected based on the updated operating status type, and the adjustment operation corresponding to the selected control strategy is executed.

[0041] Specifically, during the execution of the first or second control strategy, the execution control module continuously receives the operating status type output from the status analysis module and monitors the operating status type within the current sampling window. When the operating status type output by the status analysis module changes from the first operating status to the second operating status, or vice versa, within the same sampling window, the execution control module immediately suspends any unfinished operating parameter adjustments within the current sampling window. After suspending the adjustment, the execution control module maintains the current operating parameter values ​​unchanged and only performs data acquisition until the quality data and operating energy consumption data within the current sampling window are acquired. By completing the data acquisition for this sampling window, the acquired data fully reflects the operating status of the combing process under the same operating parameter values, thus avoiding unclear data correspondences due to changes in operating parameters within the same sampling window. After the current sampling window ends, the execution control module, based on the updated operating state type output by the state analysis module at the end of the sampling window, reselects either the first or second control strategy. Upon entering the next sampling window, it executes operating parameter adjustment operations matching the current operating state type according to the adjustment method corresponding to the reselected control strategy. For example, in one embodiment, during the combined adjustment of operating parameters in the first parameter subset according to the first control strategy, the execution control module receives an indication signal from the state analysis module indicating that the operating state type has switched from the first operating state to the second operating state within a certain sampling window. At this time, the execution control module immediately stops any unexecuted operating parameter adjustment operations within that sampling window, only completing the acquisition of quality data and operating energy consumption data within that sampling window. After completing the sampling window, the execution control module switches to the second control strategy based on the updated operating state type and performs adjustment operations on the operating parameters according to the limiting adjustment method corresponding to the second control strategy in subsequent sampling windows.

[0042] In some embodiments of this application, the execution control module further includes: An anomaly handling unit is used to stop executing the first control strategy and the second control strategy and keep the current operating parameters unchanged when missing, abnormally fluctuating or unable to update quality data or operating energy consumption data is detected within multiple consecutive sampling windows. After the quality data and operating energy consumption data are restored to be usable for status analysis, the status analysis module is re-triggered to determine the operating status, and the control strategy corresponding to the current operating status is executed according to the determination result.

[0043] Specifically, during the execution of the first or second control strategy, the execution control module continuously monitors the integrity and update status of the information transmitted from the information acquisition module within each sampling window. When missing, abnormal, or unupdated quality or energy consumption data is detected in multiple consecutive sampling windows, the anomaly handling unit determines that the current sampled data does not meet the conditions for state analysis. At this point, the execution control module stops executing the operating parameter adjustment operations corresponding to the first and second control strategies and maintains the operating parameter values ​​at the end of the current sampling window unchanged. After stopping the adjustment operation, the execution control module only performs data acquisition and data integrity checks, and no longer triggers state analysis or control decisions based on abnormal data, thereby avoiding erroneous adjustments to the combing process operating parameters when the data is unreliable. The anomaly handling unit continuously monitors the update status of quality and energy consumption data in subsequent sampling windows. When the quality and energy consumption data are detected to be continuous, updatable, and meet the requirements of state analysis, the anomaly handling unit removes the abnormal state flag. After the abnormal state is resolved, the execution control module re-triggers the state analysis module to determine the operating state based on the recovered quality data and operating energy consumption data. According to the determined operating state type, it invokes the first or second control strategy corresponding to that operating state to continue adjusting the operating parameters during the combing process, thus achieving consistent control before and after the abnormality. For example, in one embodiment, if the execution control module fails to acquire complete operating energy consumption data within multiple consecutive sampling windows while performing amplitude limiting adjustments to the operating parameters according to the second control strategy, the abnormality handling unit determines that the sampled data does not meet the state analysis conditions and stops executing the second control strategy, maintaining the current operating parameter values ​​unchanged. Subsequently, in subsequent sampling windows, when the operating energy consumption data and quality data return to a continuously updated state, the execution control module re-triggers the state analysis module to complete the operating state determination and continues to execute the control strategy corresponding to the current operating state based on the determination result.

[0044] In another preferred embodiment based on the above embodiments, see [reference] Figure 2 As shown, this embodiment provides a control method for a hemp fiber combing machine based on digital analysis, including: S1: Simultaneously collect quality data and operating energy consumption data within the same sampling period; S2: It also includes a first storage unit and a second storage unit. The first storage unit stores the quality data and operating energy consumption data collected in the current sampling period, and the second storage unit stores the combing process status data in the historical operating periods. S3: Perform joint digital analysis on quality data and operating energy consumption data within a predetermined sampling window to obtain a set of state features including quality characteristic components and energy consumption characteristic components. Based on the change weights of quality characteristic components and energy consumption characteristic components in the set of state features, divide the combing process into a first operating state and a second operating state. S4: It also includes a set of candidate control parameters. The control parameters in the set of candidate control parameters are divided into a first parameter subset and a second parameter subset according to the way they participate in different control paths of the combing process. When the combing process is in the first operating state, the first parameter subset is called, and the operating parameters in the combing process are combined and adjusted according to the continuous adjustment path to generate the first control strategy. When the combing process is in the second operating state, the second parameter subset is called to adjust the operating parameters of the combing process according to the constraint adjustment path to generate the second control strategy. S5: According to the first control strategy or the second control strategy, perform corresponding adjustments on at least one operating parameter in the combing process.

[0045] Understandably, by synchronously acquiring quality data and operational energy consumption data within the same sampling period and performing joint digital analysis on the two types of data within a predetermined sampling window, the operational status of the combing process no longer relies on a single indicator for judgment. Instead, it is based on the changing weights of quality characteristic components and energy consumption characteristic components to classify the state, thereby distinguishing whether the current combing process is in an operational state primarily focused on quality control or one primarily focused on energy consumption constraints. Based on this, candidate control parameters are divided into a first parameter subset and a second parameter subset according to different control paths. Control strategies are generated using either continuous adjustment paths or constraint adjustment paths for different operational states, ensuring that the adjustment method of the operating parameters matches the current operational state. This avoids excessive energy consumption constraints during quality-sensitive phases or ineffective linkage adjustments to quality parameters during energy-constrained phases. Therefore, it enables orderly switching and coordinated control between quality control and energy consumption control during the combing process, improving the targeting and stability of the control process, reducing unnecessary repeated parameter adjustments, and enhancing the overall operational controllability of the combing process.

[0046] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A control system for a hemp fiber combing machine based on digital analysis, characterized in that, include: The information acquisition module synchronously collects quality data and energy consumption data reflecting the operating status of the combing process within the same sampling period; An information storage module, which is connected to the information acquisition module, includes a first storage unit and a second storage unit. The first storage unit stores the quality data and operating energy consumption data acquired in the current sampling period, and the second storage unit stores the combing process status data in the historical operating periods. The state analysis module, which is connected to the information storage module, performs joint digital analysis on the quality data and operating energy consumption data within a predetermined sampling window to obtain a state feature set including quality feature components and energy consumption feature components. Based on the change weights of the quality feature components and energy consumption feature components in the state feature set, the combing process is divided into a first operating state and a second operating state. A control decision module is connected to the state analysis module. The control decision module has a set of candidate control parameters. The control parameters in the set of candidate control parameters are divided into a first parameter subset and a second parameter subset according to the way they participate in different control paths of the combing process. When the combing process is in the first operating state, the first parameter subset is invoked to combine and adjust the operating parameters in the combing process according to the continuous adjustment path to generate the first control strategy; When the combing process is in the second operating state, the second parameter subset is invoked to adjust the operating parameters of the combing process according to the constraint adjustment path, thereby generating a second control strategy. An execution control module, which is connected to the control decision module, performs corresponding adjustments to at least one operating parameter in the combing process according to the first control strategy or the second control strategy.

2. The control system for a hemp fiber combing machine based on digital analysis according to claim 1, characterized in that, When performing joint digital analysis on the quality data and operating energy consumption data, the status analysis module includes: Within the predetermined sampling window, the quality data and operating energy consumption data are reconstructed into time series to obtain the quality feature sequence and the energy consumption feature sequence. Based on the quality feature sequence and energy consumption feature sequence, the corresponding change amplitude features and fluctuation features are extracted to obtain the quality feature component set and the energy consumption feature component set. The set of quality characteristic components and the set of energy consumption characteristic components are combined to form a set of state characteristics used to characterize the operating characteristics of the combing process.

3. The control system for a hemp fiber combing machine based on digital analysis according to claim 2, characterized in that, After forming the set of state features, the state analysis module includes: Within the same sampling window, the set of quality feature components and the set of energy consumption feature components are normalized respectively; Based on the normalized set of quality feature components and the set of energy consumption feature components, the cumulative change and instantaneous fluctuation of each feature component within the sampling window are calculated respectively, and the quality change description vector and the energy consumption change description vector are constructed. Alignment analysis is performed on the quality change description vector and the energy consumption change description vector to obtain the participation weight of each feature component in the state feature set.

4. The control system for a hemp fiber combing machine based on digital analysis according to claim 3, characterized in that, After obtaining the participation weights of each feature component, the state analysis module includes: Within the same sampling window, the participation weights corresponding to the quality change description vector are summarized to form a quality weight set, and the participation weights corresponding to the energy consumption change description vector are summarized to form an energy consumption weight set. Based on the distribution of the quality weight set and energy consumption weight set in the state feature set, a state structure description reflecting the relative occupancy relationship between the quality feature components and the energy consumption feature components is constructed. When the proportion of the quality weight set is greater than that of the energy consumption weight set, the combing process is divided into the first operating state; When the proportion of the energy consumption weight set is greater than that of the quality weight set, the combing process is divided into the second operating state; When the proportion of the quality weight set is equal to the proportion of the energy consumption weight set, the operating state corresponding to the previous sampling window is adopted.

5. The control system for a hemp fiber combing machine based on digital analysis according to claim 4, characterized in that, When the combing process is divided into a first operating state, the control decision module's invocation of the first parameter subset includes: Based on the set of state features within the current sampling window, determine the participation order of each control parameter in the continuous adjustment path within the first parameter subset; According to the order of participation, the control parameters in the first parameter subset are adjusted sequentially, and the corresponding quality data is reacquired after each adjustment. Based on the changes in quality data within several consecutive sampling windows, the control parameters in the first parameter subset are combined and corrected to obtain the first control strategy.

6. The control system for a hemp fiber combing machine based on digital analysis according to claim 5, characterized in that, When the combing process is divided into a second operating state, the control decision module's invocation of the second parameter subset includes: Based on the set of state features within the current sampling window, determine the constraint adjustment range corresponding to each control parameter in the second parameter subset; Within the constraint adjustment range, the control parameters in the second parameter subset are adjusted independently, and the range of change of the control parameters is limited during the adjustment process. When the control parameters corresponding to the operating energy consumption data no longer change within multiple consecutive sampling windows, the current values ​​of the control parameters in the second parameter subset are maintained to obtain the second control strategy.

7. The control system for a hemp fiber combing machine based on digital analysis according to claim 6, characterized in that, When the execution control module receives the first control policy or the second control policy, it includes: Upon receiving the first control strategy, the corresponding operating parameters are adjusted sequentially according to the combined adjustment order determined in the first control strategy, and a sampling waiting period is inserted between two adjacent adjustment operations to obtain the updated quality data. Upon receiving the second control strategy, the corresponding operating parameters are subjected to a limiting adjustment operation according to the constraint adjustment range determined in the second control strategy, and the operating parameter values ​​are kept unchanged within a series of sampling windows. When the first control strategy and the second control strategy are switched, the execution control module keeps the current operating parameters unchanged, and after completing a full sampling window, it executes the adjustment operation corresponding to the current control strategy again.

8. The control system for a hemp fiber combing machine based on digital analysis according to claim 7, characterized in that, During the execution of the first control strategy or the second control strategy, the execution control module further includes: If a change in the running status type output by the status analysis module is detected within any sampling window, the adjustment operation within the current sampling window is paused. After completing the data acquisition of the current sampling window, the first control strategy or the second control strategy is reselected according to the updated running status type, and the adjustment operation corresponding to the selected control strategy is executed.

9. The control system for a hemp fiber combing machine based on digital analysis according to claim 8, characterized in that, The execution control module further includes: An anomaly handling unit is used to stop executing the first control strategy and the second control strategy and keep the current operating parameters unchanged when the quality data or operating energy consumption data is detected to be missing, fluctuating abnormally, or unable to be updated within multiple consecutive sampling windows. After the quality data and operating energy consumption data are restored to data that can be used for status analysis, the status analysis module is re-triggered to determine the operating status, and the control strategy corresponding to the current operating status is continued to be executed according to the determination result.

10. A control method for a hemp fiber combing machine based on digital analysis, used to implement the hemp fiber combing machine control system based on digital analysis as described in any one of claims 1-9, characterized in that, include: Simultaneously collect quality data and operating energy consumption data within the same sampling period; It also includes a first storage unit and a second storage unit. The first storage unit stores the quality data and operating energy consumption data collected in the current sampling period, and the second storage unit stores the combing process status data in the historical operating periods. Within a predetermined sampling window, joint digital analysis is performed on the quality data and operating energy consumption data to obtain a state feature set including quality characteristic components and energy consumption characteristic components. Based on the change weights of the quality characteristic components and energy consumption characteristic components in the state feature set, the combing process is divided into a first operating state and a second operating state. It also includes a set of candidate control parameters, wherein the control parameters in the set of candidate control parameters are divided into a first parameter subset and a second parameter subset according to the way they participate in different control paths of the combing process; When the combing process is in the first operating state, the first parameter subset is invoked to combine and adjust the operating parameters in the combing process according to the continuous adjustment path to generate the first control strategy; When the combing process is in the second operating state, the second parameter subset is invoked to adjust the operating parameters of the combing process according to the constraint adjustment path, thereby generating a second control strategy. According to the first control strategy or the second control strategy, at least one operating parameter in the combing process is adjusted accordingly.

Images