Method and system for preventing and controlling scratches on cleaner of spinning frame

Abstract

The invention relates to the technical field of spinning frame cleaning, in particular to a spinning frame cleaner cotton hanging prevention and control method and system. The method comprises the following steps: acquiring current spindle rotating speed and real-time power consumption data of a cleaner air suction motor; based on the current spindle rotating speed, a power consumption reference range corresponding to a preset air suction motor is searched, and the power consumption reference range is a target power consumption interval of the cleaner under different spindle rotating speeds; comparing the real-time power consumption data with the lower limit value of the searched power consumption reference range; when the real-time power consumption data is lower than the value obtained by subtracting a preset deviation threshold value from the lower limit value, it is judged that the cleaner has the abnormal risk that the flying capture efficiency is reduced; in response to the abnormal risk, an enhanced cleaning mode is performed. The technical problems that in the high-speed operation environment of an existing spinning frame cleaner, flyings are accumulated in a cleaning blind area and cannot be directly detected, so that a system cannot respond in time, equipment abrasion and jamming are caused, and the product quality is reduced are solved.

Description

Technical Field

[0001] This application relates to the field of ring spinning machine cleaning technology, and more specifically, to a method and system for preventing and controlling smudges on a ring spinning machine cleaner. Background Technology

[0002] In the modern textile industry, the continuous and efficient operation of spinning machines is crucial for ensuring production capacity and product quality. To prevent short fibers, cotton dust, and other "flying flowers" from forming "scratches" that can lead to yarn breakage, defects, or equipment malfunctions, spinning machines are generally equipped with automatic cleaners. Existing cleaners typically rely on their own sensors to directly detect the amount of scratches and adjust their operating modes accordingly.

[0003] However, in actual production, as the spindle speed of the spinning machine increases, a stronger "local high-speed rotating airflow field" is generated. This airflow field makes it easier for lightweight fly waste to bypass the cleaner's air intake and accumulate in traditional cleaning "blind spots" such as behind the cleaner's movement guide rail and in the inner corners of the frame. Because these "blind spots" are not directly monitored by the cleaner's sensors, the readings of the cleaner's own fly waste sensors remain consistently low, causing the system to erroneously maintain operation in "standard energy-saving mode." This increases the actual risk of fly waste accumulation, but the system remains unaware of it. Over time, the accumulated fly waste will compact and clump together under the influence of oil mist and static electricity, forming a dirt layer. This can eventually cause continuous friction with key transmission components of the spinning machine, leading to increased load, accelerated wear, and even jamming, resulting in product quality problems and equipment damage.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] This application discloses a method for preventing and controlling fly shavings in a ring spinning machine cleaner, which aims to solve the technical problem that existing ring spinning machine cleaners cannot be directly detected due to the accumulation of fly shavings in the cleaning blind area under high-speed operation, resulting in the system's inability to respond in time, and thus causing equipment wear, jamming, and a decline in product quality.

[0006] The technical solution of this application is as follows: In a first aspect, this application discloses a method for preventing and controlling smudging in a spinning machine cleaner, comprising: Obtain the current spindle speed and the real-time power consumption data of the cleaner's suction motor; Based on the current spindle speed, find the corresponding preset power consumption reference range of the suction motor. The power consumption reference range is the target power consumption range of the cleaner at different spindle speeds. Compare the real-time power consumption data with the lower limit of the power consumption reference range being looked up; When the real-time power consumption data is lower than the lower limit minus the preset deviation threshold, it is determined that there is an abnormal risk of decreased fly shaving capture efficiency in the cleaner. In response to unusual risks, an enhanced cleaning mode is implemented.

[0007] Through the above technical solution, this application effectively solves the problem in existing technologies where cleaners cannot detect the accumulation of fly ash in "blind spots" under high-speed airflow by indirectly monitoring the power consumption data of the suction motor, rather than directly detecting the amount of fly ash. The method can proactively identify abnormal risks of decreased fly ash capture efficiency and promptly activate an enhanced cleaning mode, thereby effectively preventing fly ash caking, equipment wear, and product quality problems, achieving intelligent and proactive control of the cleaning status of the spinning machine.

[0008] Furthermore, after determining that there is an abnormal risk of decreased fly shaving capture efficiency in the cleaner, it also includes: Initiate a diagnostic cleaning operation, causing the cleaner to move at a preset uniform speed over the surfaces of key components of the spinning machine; During diagnostic cleaning operations, real-time power consumption data of the suction motor is collected at a frequency higher than the preset frequency. Based on the fluctuation characteristics of real-time power consumption data within a sliding window, determine whether there is an abrasion layer on the surface of key components; Based on the assessment of the abrasion layer, corresponding pre-set targeted intervention and control strategies are implemented.

[0009] Through the aforementioned technical solution, after identifying potential risks, this application introduces a deeper diagnostic mechanism. By employing specific cleaning operations and high-frequency power consumption data acquisition, it can accurately determine whether an abrasive layer exists on the surface of critical components. This allows the system to extend beyond preventing lint buildup to preventing equipment wear caused by accumulated lint, and to take targeted measures based on the specific condition of the abrasive layer, avoiding indiscriminate cleaning and improving the accuracy and efficiency of maintenance.

[0010] Furthermore, determining whether an abrasion layer exists on the surface of critical components includes: Frequency domain analysis is performed on the real-time power consumption data within the sliding window to extract the frequency energy characteristics of power consumption energy within a preset frequency range. The frequency energy features are matched with multiple preset single feature patterns to obtain the matching degree of each single feature pattern. When the matching degree of each single feature pattern is lower than the preset threshold, if the frequency energy feature is determined to meet the preset combination conditions of composite feature patterns, the erosion layer is determined to be a composite erosion layer. When the frequency energy characteristics do not meet the combination conditions of the composite characteristic modes, based on the matching degree of each individual characteristic mode, if it is determined that the frequency energy characteristics are in the transition region between adjacent characteristic modes, the erosion layer is determined to have progressive physical properties.

[0011] Through the above technical solution, this application provides a method for refined identification of wear layer types. By performing frequency domain analysis on power consumption data and matching it with a preset pattern, it is possible to distinguish between composite wear layers and wear layers with progressive physical properties. This detailed classification helps to more accurately understand the wear mechanism, providing a more precise basis for subsequent intervention strategies, thereby achieving more effective equipment maintenance.

[0012] Furthermore, the method also includes: When the frequency energy characteristics neither meet the combination conditions of the composite characteristic mode nor are they in the transition region between adjacent characteristic modes, analyze the frequency distribution pattern of the frequency energy characteristics. The frequency distribution pattern is compared with a preset abnormal feature fingerprint database, which includes frequency distribution patterns unique to the wear layer caused by new materials or new processes. When the frequency distribution pattern does not match any frequency distribution pattern in the abnormal feature fingerprint database, the wear layer is determined to be a single wear layer pattern that is not preset, and a new pattern warning is triggered. New mode early warning includes displaying new mode early warning information to the operation interface, sending new mode early warning information to the bound mobile terminal, or uploading new mode early warning information to the central control system and triggering subsequent processing. Based on the publish-subscribe mechanism of message queues, the new mode early warning information is synchronized to multiple system subscription queues, including the maintenance management system, spare parts management system and quality control system.

[0013] Through the above technical solution, this application further enhances the system's adaptability and robustness. When encountering an unknown erosion layer mode, the system can identify and trigger a new mode warning, and synchronize the warning information to multiple related systems through a message queue mechanism. This ensures that even when faced with abnormal wear caused by new materials or processes, the system can respond promptly and initiate cross-departmental collaboration, greatly enhancing the equipment's fault warning and management capabilities.

[0014] Furthermore, the method also includes: Obtain the frequency distribution pattern corresponding to a single erosion layer mode, and continuously monitor the changing characteristics of the frequency distribution pattern; By comparing the frequency distribution pattern with the historical frequency distribution pattern, the variation characteristics of specific frequency components can be identified. These variation characteristics include the trend of energy increase or decrease and the drift of the main peak frequency. Based on the characteristics of the changes, determine the urgency and scope of impact of the new model's early warning information; Based on the level of urgency and the scope of impact, a pre-defined tiered early warning strategy will be implemented.

[0015] Through the above technical solution, this application provides dynamic tracking and graded early warning capabilities for newly identified erosion layer modes. By continuously monitoring changes in their frequency distribution morphology and comparing them with historical data, the evolution trend and potential hazards of the new modes can be assessed, thereby determining the urgency and scope of the early warning and implementing corresponding graded early warning strategies. This enables the system to manage and respond to unknown risks more precisely, avoiding excessive or insufficient intervention.

[0016] Furthermore, after executing the enhanced cleaning mode, it includes: Dynamically track power consumption trends based on real-time power consumption data of the suction motor; A short-term fitting analysis within a sliding window is performed on the power consumption change trend to identify whether the power consumption recovery rate has reached the preset recovery slope threshold. If the power consumption recovery rate remains below the preset recovery slope threshold for more than a set duration, it is determined that the response effect of the enhanced cleaning mode is insufficient, and the operating parameters of the enhanced cleaning mode are adjusted. The operating parameters include the cleaner's operating speed, suction strength, and cleaning path coverage.

[0017] Through the above technical solution, this application introduces a real-time evaluation and adaptive adjustment mechanism for the enhanced cleaning mode. By dynamically tracking the rate of power consumption recovery, the system can determine whether the enhanced cleaning is effective. If the effect is unsatisfactory, the system automatically adjusts the cleaner's operating parameters, such as speed, suction intensity, and cleaning path coverage, ensuring that the cleaning mode can be optimized according to the actual situation, thereby improving cleaning efficiency and resource utilization.

[0018] Furthermore, the method also includes: When a preset number of enhanced cleaning modes are all judged to have insufficient response, the cross-cycle diagnostic mode is triggered. Record the operating parameters, power consumption trends, and judgment results of each enhanced cleaning mode to construct a diagnostic dataset; Based on the diagnostic dataset, we analyzed the operating parameters of several enhanced cleaning modes with insufficient response effects, identified common operating state characteristics, and extracted the parameter combination features that constitute the abnormal triggering mode. Based on the abnormal triggering pattern, candidate combinations of operating parameters are generated and used to predictively correct the operating parameters of subsequent enhanced cleaning modes.

[0019] Through the aforementioned technical solution, this application introduces a higher-level "cross-cycle diagnostic model," designed to learn from multiple cases of ineffective enhanced cleaning. By constructing a diagnostic dataset and analyzing its common features, the system can identify parameter combinations leading to anomalies and generate predictive correction schemes accordingly. This enables the system to learn from experience, achieving intelligent optimization and proactive adjustment of cleaning patterns, effectively avoiding repeated ineffective cleaning.

[0020] Furthermore, the method also includes: Anomaly triggering patterns are tagged and stored to build an anomaly triggering pattern tag library. The anomaly triggering pattern tag includes the corresponding parameter combination features, matching rules and correction suggestions. Before each execution of the enhanced cleaning mode, the abnormal triggering mode tag library is dynamically matched based on the current running parameters to determine whether there are any recorded similar triggering modes. When similar triggering patterns exist, extract their corresponding correction suggestions and preload and adjust the operating parameters of the enhanced cleaning mode.

[0021] Through the above technical solution, this application further optimizes the anomaly handling process by constructing an anomaly triggering pattern tag library, transforming historical experience into reusable knowledge. Before each execution of the enhanced cleaning mode, the system can intelligently match and preload verified correction suggestions, thereby significantly shortening the problem-solving time, improving the efficiency and accuracy of system response, and achieving more intelligent fault prevention and handling.

[0022] Furthermore, the method also includes: When any tag in the abnormal triggering mode tag library is matched and applied to the enhanced cleaning mode, the response effect and correction parameters of the enhanced cleaning mode are recorded. Based on the response results, determine whether the corrected parameters are effective; If the result is deemed valid, the parameter group of the correction suggestion in the corresponding tag is updated to increase the tag's matching priority. If the enhanced cleaning mode is deemed to have insufficient response after applying the same label's correction suggestions a preset number of times, the label's matching priority will be reduced or it will be marked as an invalid label, and a cleanup operation will be performed in the label library.

[0023] Through the above technical solution, this application introduces a self-learning and adaptive update mechanism into the anomaly triggering mode tag library. The system can evaluate the effectiveness of the correction parameters based on the actual application effect and dynamically adjust the priority of tags or mark them as invalid. This ensures that the knowledge of the tag library is always up-to-date and effective, avoids the repeated application of invalid or outdated correction suggestions, and thus continuously improves the system's intelligence level and maintenance efficiency.

[0024] Secondly, this application also discloses a yarn spinning machine cleaner anti-spinning control system, comprising: The data acquisition module is used to acquire the current spindle speed and the real-time power consumption data of the cleaner's suction motor; The data lookup module is used to find the corresponding preset power consumption reference range of the suction motor based on the current spindle speed. The power consumption reference range is the target power consumption range of the cleaner at different spindle speeds. The comparison module is used to compare real-time power consumption data with the lower limit of the power consumption reference range being searched. The judgment module is used to determine that there is an abnormal risk of decreased fly shaving capture efficiency in the cleaner when the real-time power consumption data is lower than the lower limit value minus the preset deviation threshold. The execution module is used to execute an enhanced cleaning mode in response to abnormal risks.

[0025] Through the above technical solution, this application provides a system entity for implementing the above-mentioned method for preventing and controlling the smudging of the cleaning device in a spinning machine. Through modular design, the system can efficiently acquire and process power consumption data, and determine abnormal risks based on preset logic, thereby triggering an enhanced cleaning mode. The system provides reliable hardware and software support for the practical application of the method, ensuring the automated and intelligent management of spinning machine cleaning.

[0026] Beneficial effects

[0027] This application discloses a method for preventing and controlling fly shavings in a spinning frame cleaner. It acquires real-time power consumption data of the current spindle speed and the cleaner's suction motor, comparing this data with a preset lower limit of the power consumption reference range based on the spindle speed. When the real-time power consumption data is lower than the preset threshold, it determines that the cleaner has an abnormal risk of decreased fly shavings capture efficiency and responds by executing an enhanced cleaning mode. This method overcomes the technical problem in existing technologies where the cleaner relies on its own sensors to directly detect fly shavings, leading to the accumulation of lightweight fly shavings in the cleaning "blind zone" by bypassing the suction port when the spinning frame spindle speed increases and a local high-speed rotating airflow field is generated, making it undetectable. By monitoring subtle fluctuations in the suction system load and indirect indicators such as the motor power consumption curve, this application can inversely infer the degree of deterioration in the external airflow environment and the risk of decreased capture efficiency, achieving early warning without relying on direct fly shavings monitoring. This effectively solves the problems of long-term fly shavings accumulation, compaction, and friction with key transmission components leading to increased load, accelerated wear, and even jamming, significantly improving the stability of the spinning frame operation and product quality, avoiding equipment damage, and possessing significant practical value and technological advancement. Attached Figure Description

[0028] Figure 1 A flowchart illustrating a method for preventing and controlling snagging on a spinning machine cleaner, as provided in this application.

[0029] Figure 2 A flowchart of a yarn spinning machine cleaner anti-spinning control system provided in this application.

[0030] In the diagram: 1. Data acquisition module; 2. Data search module; 3. Comparison module; 4. Judgment module; 5. Execution module. Detailed Implementation

[0031] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0032] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0033] In the modern textile industry, the continuous and efficient operation of spinning machines is crucial for ensuring production capacity and product quality. Traditional spinning machine cleaners typically rely on their own sensors to directly detect the amount of fly ash (or lint) and adjust their operating modes accordingly. However, in actual production, as the spinning machine spindle speed increases, a stronger localized high-speed rotating airflow is generated, causing lightweight fly ash to more easily bypass the cleaner's air intake and accumulate in cleaning blind spots. Because these blind spots are not directly monitored by the cleaner's sensors, the fly ash sensor readings remain consistently low, causing the system to erroneously maintain standard energy-saving operation, thus increasing the actual risk of fly ash accumulation. Long-term accumulation of fly ash can compact and form a deposit layer, potentially causing continuous friction with critical transmission components of the spinning machine, leading to increased load, accelerated wear, and even jamming, resulting in product quality problems and equipment damage.

[0034] Reference Figure 1 In response, this application proposes a method for preventing and controlling smudging in a spinning machine cleaner, comprising: S1000: Obtain the current spindle speed and the real-time power consumption data of the cleaner's suction motor; S2000: Based on the current spindle speed, find the corresponding preset power consumption reference range of the suction motor. The power consumption reference range is the target power consumption range of the cleaner at different spindle speeds. S3000: Compares real-time power consumption data with the lower limit of the power consumption reference range being looked up; S4000: When the real-time power consumption data is lower than the lower limit minus the preset deviation threshold, it is determined that there is an abnormal risk of decreased fly shaving capture efficiency in the cleaner. S5000: In response to abnormal risks, executes an enhanced cleaning mode.

[0035] Specifically, spindle speed refers to the rotational speed of the spindle used for winding yarn in a spinning frame. It directly affects yarn production efficiency and quality and is closely related to the airflow environment around the spinning frame. The cleaner's suction motor is the core component providing suction in the cleaner; its power consumption data indirectly reflects the load on the suction system and the efficiency of fly shavings capture. Real-time power consumption data refers to the instantaneous energy consumption data of the suction motor collected at a specific point in time or within a continuous period. The power consumption reference range is a pre-set range of suction motor power consumption based on the normal operating conditions of the spinning frame at different spindle speeds, used to measure whether the suction system is operating normally. The preset deviation threshold is the allowable error value used to determine whether the power consumption data deviates from the normal range. "Abnormal risk of decreased fly shavings capture efficiency" refers to a reduced ability of the cleaner to capture fly shavings, leading to an increased possibility of fly shavings accumulating on the equipment. Enhanced cleaning mode is a more powerful cleaning operation than regular cleaning mode, designed to more effectively remove fly shavings by adjusting the cleaner's operating parameters (such as suction intensity and operating speed).

[0036] The core of the method for preventing and controlling fly waste in the cleaner of a spinning frame disclosed in this application lies in achieving early warning and proactive intervention against the risk of fly waste accumulation through the monitoring and analysis of indirect indicators. First, the current spindle speed and the real-time power consumption data of the cleaner's suction motor are acquired. As one implementation method, the current spindle speed can be directly obtained through the data interface of the spinning frame's main control system. For example, the main control system can periodically send the spindle speed data to the cleaner control unit via industrial Ethernet or RS485 bus. The real-time power consumption data of the cleaner's suction motor can be obtained by connecting an energy metering module in series in the suction motor's power supply line. This module can measure the motor's voltage and current in real time, calculate the instantaneous power consumption, and then transmit the power consumption data to the cleaner control unit via analog or digital signals. For example, a smart meter or power sensor can be used, with its output connected to the analog input port of the cleaner control unit, or digital communication can be achieved via the Modbus protocol.

[0037] Secondly, based on the current spindle speed, search for the corresponding preset power consumption reference range of the suction fan motor. This power consumption reference range is the target power consumption interval of the cleaner at different spindle speeds. Specifically, a corresponding relationship table or model between the spindle speed and the power consumption reference range of the suction fan motor can be established in advance through experiments or historical data analysis. For example, during the commissioning stage of the spinning frame, the cleaner can be operated under the condition of no lint accumulation and normal suction at different typical spindle speeds, and the power consumption data of the suction fan motor can be recorded. Then, a reasonable power consumption fluctuation range can be set as the reference range based on these data. This corresponding relationship can be stored in the memory of the cleaner control unit, for example, in the form of a lookup table. After obtaining the current spindle speed, the control unit can retrieve the corresponding power consumption reference range from the lookup table according to this speed value.

[0038] Next, compare the real-time power consumption data with the lower limit value of the searched power consumption reference range. As an implementation manner, after receiving the real-time power consumption data, the cleaner control unit will perform a numerical comparison between it and the lower limit value of the power consumption reference range corresponding to the current spindle speed obtained from the lookup table. For example, if the real-time power consumption data is P_real and the lower limit value is P_lower, then a judgment of P_real < P_lower is made. When the real-time power consumption data is lower than the lower limit value minus the preset deviation threshold, it is judged that there is an abnormal risk of a decrease in the lint capture efficiency of the cleaner. The preset deviation threshold is an allowable power consumption fluctuation range used to avoid misjudgment due to normal fluctuations. For example, this deviation threshold can be set based on historical data statistical analysis or set as a certain percentage of the lower limit value of the power consumption reference range according to empirical values. When the real-time power consumption data is lower than (lower limit value - preset deviation threshold), it indicates that the load of the suction fan motor may abnormally decrease, which usually means that the suction port is blocked by lint, resulting in a decrease in the suction volume, or the external air flow environment deteriorates, making it difficult to effectively capture lint, thus reducing the lint capture efficiency of the cleaner. For example, if the lower limit value is 100W and the preset deviation threshold is 5W, when the real-time power consumption is lower than 95W, the system will issue a judgment of abnormal risk.

[0039] Finally, in response to the above abnormal risk, execute an enhanced cleaning mode. Once the system determines that there is an abnormal risk of a decrease in the lint capture efficiency, the cleaner control unit will immediately trigger the enhanced cleaning mode. As an implementation manner, the enhanced cleaning mode can include increasing the speed of the suction fan motor to increase the suction intensity, or adjusting the running speed and cleaning path of the cleaner so that it performs more intensive or longer cleaning in key areas. For example, the cleaner can be instructed to pass through the areas prone to dust accumulation at a slower speed, or perform multiple reciprocating cleanings in specific areas to ensure that the lint is completely removed.

[0040] In another embodiment of the present application, it is further proposed that after step S4000, it further includes: S4100: Initiate diagnostic cleaning operation, causing the cleaner to move at a preset uniform speed over the surface of key components of the spinning machine; S4200: During diagnostic cleaning operations, real-time power consumption data of the suction motor is collected at a frequency higher than the preset frequency; S4300: Based on the fluctuation characteristics of real-time power consumption data within a sliding window, determine whether there is an abrasion layer on the surface of key components; S4400: Based on the assessment of the wear layer, execute the corresponding preset targeted intervention and control strategy.

[0041] Specifically, diagnostic cleaning refers to a process where, upon detecting an abnormal risk, the cleaner does not immediately perform routine enhanced cleaning. Instead, it moves at a controlled, pre-set, uniform speed, precisely passing its cleaning head or air intake over the surfaces of key components inside the spinning machine that are closely related to fly shavings capture efficiency. These key component surfaces may include, but are not limited to, the inner walls of the air intake channel, the edges of the air intake, the deflector, and the filter surface where fly shavings may accumulate or where wear may occur. The purpose of this operation is to "scan" the physical state of these surfaces by observing subtle changes in the power consumption of the air intake motor during the cleaner's movement, thereby obtaining more detailed diagnostic information.

[0042] During diagnostic cleaning operations, real-time power consumption data of the suction motor is collected at a frequency higher than a preset frequency to capture minute fluctuations in power consumption over a short period. Compared to the power consumption sampling frequency during normal operation, a higher sampling frequency provides finer data granularity, enabling the system to capture power consumption fluctuations caused by instantaneous changes in airflow resistance due to surface unevenness, abrasion, or local blockage. Based on the fluctuation characteristics of real-time power consumption data within a sliding window, the presence of an abrasion layer on the surface of key components is determined. A sliding window is a data processing technique that performs localized analysis of the data within a fixed-size window by moving it across a continuous stream of power consumption data. Fluctuation characteristics can be understood as changes in the amplitude, frequency, variance, or specific patterns of power consumption data. For example, when the cleaner passes over an abraded surface, airflow resistance changes irregularly, resulting in unique high-frequency or irregular fluctuation patterns in the suction motor's power consumption. By analyzing these fluctuation patterns, the presence and extent of an abrasion layer on the surface of key components can be inferred. Based on the determination of the abrasion layer, corresponding preset targeted intervention control strategies are executed. This means that once the presence of an abrasive layer is detected, the system will no longer simply perform general enhanced cleaning, but will take more precise and effective measures based on the type, location, or severity of the abrasion. For example, if it is determined to be minor abrasion, the cleaner's operating parameters can be adjusted, such as increasing the number of cleaning cycles in specific areas or changing the suction intensity; if it is determined to be severe abrasion, a maintenance alarm may be triggered, recommending the replacement of damaged parts, or guiding the cleaner to perform a deep cleaning operation along a specific path to avoid further equipment damage or efficiency loss.

[0043] In some preferred embodiments, the following specific example illustrates the situation: Suppose that during the operation of a spinning machine, the real-time power consumption data of its cleaner's suction motor is consistently lower than the preset lower limit of the power consumption reference range minus a preset deviation threshold. The system initially determines that there is an abnormal risk of decreased fly ash capture efficiency. In this case, the system will not immediately execute the general enhanced cleaning mode, but instead initiate a diagnostic cleaning operation. The cleaner moves its suction nozzle sequentially over the inner wall of the spinning machine's suction channel and the surface of the suction nozzle at a preset uniform speed of 5 cm / s. During this diagnostic process, the power consumption data of the suction motor is collected at a frequency of once per millisecond, far higher than the once-per-second collection frequency during normal operation. The system then performs sliding window analysis on this high-frequency power consumption data; for example, using a sliding window containing 100 data points, it calculates the variance and spectral characteristics of the power consumption data within the window. If a significant increase in the variance of the power consumption data is found in a specific region, and a significant energy peak appears in the frequency range of 200Hz to 500Hz, this may be identified as a characteristic of a slight abrasion layer on the surface of the suction nozzle. Based on this assessment, the system will execute targeted intervention control strategies. For example, it might trigger a command to have the cleaner perform three reciprocating sweeps with a specific friction brush head in the suction nozzle area to attempt to remove the abrasive layer, rather than simply increasing the suction intensity. If the abrasive layer is severe, a maintenance alarm may be triggered, prompting the operator to check or replace the suction nozzle.

[0044] Another embodiment of this application further proposes that S4300 includes the following steps: S4310: Performs frequency domain analysis on real-time power consumption data within the sliding window and extracts frequency energy characteristics of power consumption energy within a preset frequency range; S4320: Match the frequency energy characteristics with multiple preset single feature patterns to obtain the matching degree of each single feature pattern; S4330: When the matching degree of each single feature pattern is lower than the preset threshold, if the frequency energy feature is judged to meet the preset combination conditions of composite feature patterns, the erosion layer is determined to be a composite erosion layer. S4340: When the frequency energy characteristics do not meet the combination conditions of the composite characteristic modes, based on the matching degree of each individual characteristic mode, if it is determined that the frequency energy characteristics are in the transition region between adjacent characteristic modes, the wear layer is determined to have progressive physical properties.

[0045] Frequency domain analysis involves converting real-time power consumption data from the time domain to the frequency domain using mathematical methods such as Fourier transform to reveal the energy distribution of different frequency components within the data. This step aims to more precisely capture the periodic or aperiodic patterns hidden in the power consumption fluctuations of the induced draft motor, patterns that are often associated with the type and extent of the wear layer. The extracted frequency energy characteristics can be understood as the energy intensity or distribution pattern of the power consumption signal within a specific frequency range, such as a specific frequency peak, bandwidth, or energy percentage.

[0046] The system matches frequency energy characteristics with multiple preset single-feature patterns to obtain the matching degree of each single-feature pattern. Specifically, a single-feature pattern is pre-trained using experiments or historical data and represents the typical power consumption fingerprint of different types of single wear layers (e.g., fiber entanglement, dust accumulation, oil adhesion, etc.) in the frequency domain. The matching degree can be calculated using correlation coefficients, Euclidean distance, or other similarity metric algorithms to quantify the similarity between the current frequency energy characteristics and each single-feature pattern. When the matching degree of each single-feature pattern is lower than a preset threshold, if the frequency energy characteristics are determined to meet the combination conditions of a preset composite feature pattern, the wear layer is identified as a composite wear layer. This means that when the characteristics of a single wear layer are not obvious or multiple wear types exist simultaneously, the system will further check for signs of composite wear. A composite feature pattern refers to a frequency energy characteristic composed of two or more single feature patterns combined in a specific way. The combination conditions can include the coexistence of specific frequency components, energy ratio relationships, or phase relationships. Identifying it as a composite wear layer helps to identify more complex fault causes.

[0047] When the frequency energy characteristics do not meet the combination conditions of composite characteristic modes, based on the matching degree of each individual characteristic mode, if the frequency energy characteristics are determined to be in the transition region between adjacent characteristic modes, the erosion layer is determined to have progressive physical properties. The transition region refers to a state where the current frequency energy characteristics do not match many individual characteristic modes, but do not fully conform to any single or composite mode; rather, it is a state between two or more modes. This usually indicates that the erosion layer is evolving from one type to another, or that its physical properties are undergoing a slow change, such as gradually transforming from a loose dust accumulation to a compacted fibrous layer. Determining that the erosion layer has progressive physical properties helps predict its development trend.

[0048] In another embodiment of this application, the method further includes: S4350: When the above frequency energy characteristics neither meet the combination conditions of composite characteristic modes nor are in the transition region between adjacent characteristic modes, analyze the frequency distribution pattern of the frequency energy characteristics. Among them, frequency distribution pattern refers to the energy distribution characteristics of power consumption data in the frequency domain, such as the energy intensity of specific frequency components, the location of the main peak frequency, bandwidth, etc.

[0049] S4360: Compare the frequency distribution pattern with a preset abnormal feature fingerprint database, which includes frequency distribution patterns unique to the wear layer caused by new materials or new processes. An anomalous feature fingerprint database is a collection of known but atypical etching patterns, including frequency distribution patterns unique to etching layers induced by new materials or processes. These patterns may be special fingerprints discovered in historical data analysis but not yet classified as conventional single or composite patterns.

[0050] S4370: When the frequency distribution pattern does not match any frequency distribution pattern in the abnormal feature fingerprint database, the etched layer is determined to be a single etched layer pattern that is not preset, and a new pattern warning is triggered. The new pattern warning includes displaying the new pattern warning information to the operation interface, sending the new pattern warning information to the bound mobile terminal, or uploading the new pattern warning information to the central control system and triggering subsequent processing. This means that the system has identified a completely new, previously unknown type of erosion layer.

[0051] New mode warnings can take many forms, such as displaying the warning information to the user interface so that operators can be aware of the abnormal situation in a timely manner; sending the warning information to the bound mobile terminal so that managers can receive the notification remotely; or uploading the warning information to the central control system and triggering subsequent processing for higher-level decision-making and resource scheduling.

[0052] S4380: Based on a message queue publish-subscribe mechanism, it synchronizes new mode early warning information to multiple system subscription queues, including the maintenance management system, spare parts management system, and quality control system.

[0053] To ensure the timely and accurate delivery of early warning information for the new pattern, a publish-subscribe mechanism based on message queues can be used to synchronize the early warning information to multiple system subscription queues. System subscription queues may include a maintenance management system for scheduling inspection and maintenance plans for the new erosion layer; a spare parts management system for assessing the need to stockpile new spare parts or adjust spare parts inventory strategies; and a quality control system for analyzing the impact of the new erosion layer on product quality and tracing its causes.

[0054] This application's solution effectively overcomes the shortcomings of existing diagnostic methods when facing novel or unpredictable anomalies by introducing the ability to identify unknown erosion layer patterns. Specifically, when the frequency energy characteristics obtained through frequency domain analysis cannot match known composite feature patterns or are in the transition region between adjacent feature patterns, the system no longer simply determines that there is no anomaly or that it is a misjudgment, but further analyzes its frequency distribution pattern. By comparing this pattern with a pre-set anomaly feature fingerprint database, special erosion layers caused by specific new materials or processes but not yet included in the conventional classification can be identified. Furthermore, if even the anomaly feature fingerprint database cannot match, it indicates that the system has detected a completely new and unknown erosion layer pattern. At this time, the system can promptly trigger a new pattern warning and synchronize this critical information to multiple relevant departments such as the maintenance management system, spare parts management system, and quality control system through a message queue publish-subscribe mechanism. It is precisely because of this multi-level identification and extensive information synchronization mechanism that the system can provide early warning and rapid response to unknown anomalies, thereby avoiding potential production problems or equipment damage caused by information lag or diagnostic blind spots.

[0055] In one specific implementation: Suppose a textile factory introduces a new type of synthetic fiber. During the high-speed operation of a spinning machine, tiny particles of this fiber rub against the surface of a key component of a cleaning device, creating an abrasion layer that has not been previously observed. When the real-time power consumption data of the cleaning device's suction motor is collected and analyzed in the frequency domain, the extracted frequency energy characteristics do not conform to known composite feature patterns, nor are they in the transition region between single feature patterns. At this point, the system further analyzes the frequency distribution pattern of this frequency energy characteristic and compares it with a preset abnormal feature fingerprint database. Because the abrasion layer caused by this new fiber is entirely new, its frequency distribution pattern does not match any pattern in the fingerprint database. The system then determines that the abrasion layer is an unpreset single abrasion layer pattern and immediately triggers a new pattern warning. This warning information is displayed in a prominent position on the operating interface in real time and is also sent to the mobile terminals of workshop supervisors and equipment engineers via SMS or application notification. In addition, this warning information is also published to the central control system via a message queue, and further distributed by the central control system to the maintenance management system, spare parts management system, and quality control system. Upon receiving the information, the maintenance management system immediately dispatches engineers to conduct on-site inspections of the cleaner and collect samples of the abraded layer for analysis. The spare parts management system assesses whether it is necessary to procure or develop specialized cleaning tools or spare parts for the new abraded layer. The quality control system traces the batch and supplier of the new fiber, analyzing its potential impact on the quality of the final product. In this way, even when faced with entirely new anomalies, the system can achieve rapid identification, multi-party collaboration, and early intervention, effectively preventing the further deterioration of the lint problem.

[0056] In another embodiment of this application, the method further includes: S4390: Obtain the frequency distribution pattern corresponding to a single erosion layer mode and continuously monitor the changing characteristics of this frequency distribution pattern; S4391: Compare the frequency distribution pattern with the historical frequency distribution pattern to identify the variation characteristics of specific frequency components. The variation characteristics include the trend of energy increase or decrease and the drift of the main peak frequency. S4392: Determine the urgency and scope of impact of the new model's early warning information based on the characteristics of the changes; S4393: Implement a pre-defined tiered early warning strategy based on the level of urgency and the scope of impact.

[0057] Specifically, after identifying a single, unpreset abrasion layer pattern and triggering a new pattern warning, the system continuously acquires the frequency distribution pattern of the real-time power consumption data of the suction motor corresponding to that single abrasion layer pattern. Here, "frequency distribution pattern" refers to the energy distribution of the power consumption signal at different frequencies obtained through frequency domain analysis (e.g., Fourier transform), reflecting the energy response characteristics of the abrasion layer at different vibration frequencies. "Continuous monitoring" of this frequency distribution pattern means that the system periodically or continuously collects and analyzes power consumption data to track its dynamic changes.

[0058] Furthermore, the system compares the currently acquired frequency distribution pattern with previously recorded historical frequency distribution patterns. This comparison allows for the identification of "change characteristics of specific frequency components." These change characteristics are key indicators for judging the development trend of the erosion layer, specifically including "energy increase / decrease trends" and "peak frequency drift." "Energy increase / decrease trends" refer to whether the intensity of power consumption energy increases or decreases within a specific frequency range, which is usually related to the severity or extent of erosion. "Peak frequency drift" refers to whether the frequency point with the highest energy concentration in the power consumption spectrum has shifted, which may indicate a change in the nature, location, or morphology of the erosion.

[0059] Therefore, based on the identified frequency distribution patterns, the system can "determine the urgency and scope of impact of new pattern warning information." For example, if the energy increases sharply within the critical frequency range and the main peak frequency shifts significantly, it may indicate that the abrasive layer is rapidly deteriorating, indicating a high degree of urgency and a potentially wide impact. Conversely, if the changes are not significant or tend to stabilize, the urgency may be low.

[0060] Ultimately, based on the determined urgency and scope of impact, the system will "execute a pre-defined tiered early warning strategy." This means that early warnings are no longer simple notifications, but rather trigger different levels of response based on the severity and potential impact of the problem. For example, for the high-urgency, wide-ranging erosion layer pattern, the highest-level early warning may be triggered, including immediate shutdown for inspection and sending emergency notifications to senior management; while for the low-urgency, small-ranging pattern, only routine maintenance reminders or log entries may be triggered.

[0061] The proposed solution achieves in-depth understanding of the evolution patterns of newly identified single erosion layer modes through dynamic monitoring and trend analysis. By comparing current and historical frequency distribution patterns and identifying key change characteristics such as energy increase / decrease trends and peak frequency shifts, the system can accurately assess the development trend of the erosion layer. It is precisely this refined dynamic assessment that enables the system to intelligently determine the urgency and scope of impact of early warning information based on the actual development of the erosion layer, thus avoiding a one-size-fits-all approach to early warning for all new modes.

[0062] Through the above technical solution, this application enables dynamic and refined management of unpredictable single abrasion layer patterns. This solution not only promptly detects emerging abrasion problems, but more importantly, it intelligently adjusts the warning level and response strategy based on the development trend and potential hazards of the abrasion layer. This significantly improves the intelligence level and response efficiency of the ring spinning machine cleaner's anti-scratching control. This helps avoid over-intervention or under-response, optimizes the allocation of maintenance resources, and effectively reduces the risk of equipment failure due to abrasion layer deterioration, ensuring the continuity and stability of ring spinning machine production.

[0063] In some preferred embodiments, the following specific example illustrates the situation: Suppose the system identifies a new single abrasion layer pattern using the method described above and triggers a new pattern warning. Subsequently, the system continuously monitors the frequency distribution of the suction motor power consumption corresponding to this abrasion layer pattern. For example, in the initial stage, the energy at a specific frequency f1 is low, and the peak frequency is stable. The system marks this as a low-urgency warning. However, in subsequent monitoring periods, the system finds that the energy at frequency f1 begins to increase continuously at a rate of 5% per hour, and the peak frequency gradually drifts from f1 to f2. Based on preset rules (e.g., energy growth exceeding a threshold and peak frequency drift), the system determines that this change indicates the abrasion layer is rapidly deteriorating, its urgency level is changing from low to high, and the impact range is expanding from localized to potentially affecting the entire cleaner's performance. Based on this judgment, the system no longer simply sends a regular warning message but immediately triggers the highest-level tiered warning strategy, such as displaying a red emergency warning on the operator interface and notifying the maintenance supervisor via SMS or email, recommending immediate shutdown for inspection or adjustment of the cleaner's operating parameters to prevent further equipment damage. This ability to dynamically adjust the warning level enables the system to respond more promptly and accurately to newly emerging anomalies.

[0064] In another embodiment of this application, it is further proposed that after S5000, the following is included: S6000: Dynamically tracks power consumption trends based on real-time power consumption data of the suction motor; S7000: Performs short-term fitting analysis within a sliding window on the power consumption change trend to identify whether the power consumption recovery rate has reached the preset recovery slope threshold. S8000: When the power consumption recovery rate remains below the preset recovery slope threshold for a duration exceeding the set time, it is determined that the response effect of the enhanced cleaning mode is insufficient, and the operating parameters of the enhanced cleaning mode are adjusted. The operating parameters include the cleaner's operating speed, suction strength, and cleaning path coverage.

[0065] Specifically, after the enhanced cleaning mode is activated, the system continuously acquires real-time power consumption data of the cleaner's suction motor. This real-time power consumption data is used to dynamically track the power consumption trend of the suction motor. Tracking the power consumption trend can be achieved by continuously sampling and recording power consumption values, forming a power consumption time series.

[0066] The short-term fitting analysis of power consumption trends within a sliding window involves using methods such as linear regression, multinomial fitting, or other time series analysis to analyze power consumption data collected within a specific time window to calculate the rate of power consumption recovery. The rate of power consumption recovery is a key indicator of the cleaner's suction capacity recovery, typically represented by the slope of power consumption increase over time. A preset recovery slope threshold can be obtained based on empirical data, equipment performance standards, or through machine learning model training, defining the minimum acceptable rate of power consumption recovery. When the power consumption recovery rate remains below the preset recovery slope threshold for a duration exceeding a set time, the system determines that the enhanced cleaning mode's response is insufficient. The set time is a preset time parameter used to avoid misjudgments caused by instantaneous fluctuations, ensuring that the slow power consumption recovery is a persistent issue. Once insufficient response is determined, the system automatically adjusts the operating parameters of the enhanced cleaning mode. These operating parameters include cleaner operating speed, suction intensity, and cleaning path coverage. The cleaner's operating speed can be adjusted to change its movement speed on the spinning machine; the suction intensity can be adjusted by changing the speed of the suction motor or the opening of the damper; the cleaning path coverage can be adjusted by changing the cleaning density or the number of repetitions in key areas of the spinning machine. These parameters are adjusted to optimize cleaning performance in order to achieve the expected rate of power recovery.

[0067] This application's solution effectively addresses the potential issue of insufficient response in the basic enhanced cleaning mode by introducing a dynamic evaluation and adjustment mechanism for the enhanced cleaning mode's response. Specifically, by continuously monitoring the real-time power consumption data of the suction motor, the recovery status of the cleaner's suction capacity can be directly reflected. Identifying the power consumption recovery rate allows for a quantitative assessment of the cleaner's efficiency in removing fly shavings and restoring normal operation. When the power consumption recovery rate remains below a preset threshold, it indicates that the cleaner has failed to effectively remove fly shavings or that suction efficiency is recovering slowly. In this case, the system can promptly determine that the enhanced cleaning mode's response is insufficient. It is precisely this real-time performance evaluation that allows the system to specifically adjust operating parameters such as the cleaner's running speed, suction intensity, and cleaning path coverage, thereby optimizing the cleaning strategy and ensuring that the cleaner can more effectively remove fly shavings, restore its normal fly shaving capture efficiency, and prevent the persistent or recurring fly shaving problem.

[0068] In some preferred embodiments, a specific example is given below: Suppose that after the spinning machine cleaner executes the enhanced cleaning mode, the system continuously collects real-time power consumption data of the suction motor. Within a certain time period, the system, through sliding window fitting analysis, finds that the power consumption recovery slope of the suction motor is 0.5W / s, while the preset recovery slope threshold is 1.0W / s. If this state below the threshold persists for more than a preset 30-second duration, the system will determine that the enhanced cleaning mode's response is insufficient. At this time, the system can automatically adjust operating parameters, for example, reducing the cleaner's operating speed from 10m / min to 8m / min, increasing the suction intensity from 70% to 85%, and increasing the cleaning path coverage, for example, by having the cleaner perform two round trips in key areas. Through these adjustments, it is hoped that the system can more effectively remove lint, ensuring that the suction motor's power consumption recovery rate reaches or exceeds the preset threshold, thereby restoring the cleaner's normal lint capture efficiency.

[0069] In another embodiment of this application, the method further includes: S9000: When a preset number of enhanced cleaning modes are all judged to have insufficient response, the cross-cycle diagnostic mode is triggered. S10000: Records the operating parameters, power consumption trends, and judgment results of each enhanced cleaning mode to build a diagnostic dataset; S11000: Based on the diagnostic dataset, analyze the operating parameters of multiple enhanced cleaning modes with insufficient response effects, identify common operating state characteristics, and extract the parameter combination features that constitute the abnormal triggering mode. S12000: Generates candidate combinations of operating parameters based on the abnormal triggering mode, and uses them to predictively correct the operating parameters of subsequent enhanced cleaning modes.

[0070] Specifically, when the system detects that the enhanced cleaning mode has been judged as having insufficient response effectiveness a preset number of times consecutively, it indicates that there may be a deeper or recurring problem, and at this time, a cross-cycle diagnostic mode will be automatically triggered. This mode aims to learn from historical data to achieve more intelligent parameter correction. After triggering the cross-cycle diagnostic mode, the system will record in detail the operating parameters of each enhanced cleaning mode (including cleaner operating speed, suction intensity, and cleaning path coverage), the power consumption trend of the suction motor, and the final response effectiveness judgment result, and collect this data to build a diagnostic dataset. This diagnostic dataset contains detailed historical performance of the system executing enhanced cleaning modes under different operating conditions.

[0071] Based on the constructed diagnostic dataset, the system will conduct an in-depth analysis of the operational parameters of all enhanced cleaning modes identified as having insufficient response. The purpose of this analysis is to identify common operational characteristics among these failure cases; for example, whether certain combinations of operational parameters consistently lead to poor cleaning results. Through this analysis, the parameter combination characteristics constituting the abnormal triggering patterns can be extracted. These characteristics represent potential causes or conditions leading to insufficient response from the enhanced cleaning modes. Once the abnormal triggering patterns are identified, the system will generate a series of candidate operational parameter combinations based on these patterns. These candidate parameter combinations are optimized or adjusted to avoid re-triggering the identified abnormal patterns and are used to predictively correct the operational parameters of subsequent enhanced cleaning modes, thereby improving cleaning effectiveness.

[0072] This application's solution, by introducing a cross-cycle diagnostic mode, effectively addresses the problem that relying solely on single or short-term parameter adjustments cannot completely resolve insufficient continuous cleaning effectiveness. When the enhanced cleaning mode fails to respond effectively multiple times, the system no longer simply performs minor parameter adjustments but instead triggers the cross-cycle diagnostic mode to deeply mine and analyze historical operational data. By recording and constructing a diagnostic dataset, the system accumulates rich operational experience. Based on this dataset, the system can identify common operational state characteristics that lead to repeated failures of the enhanced cleaning mode and extract abnormal triggering patterns. This ability to learn from historical failures enables the system to generate more targeted candidate combinations of operational parameters, thereby predictively correcting subsequent enhanced cleaning modes. This transforms the cleaner's stain prevention and control from a passive response to proactive prevention, significantly improving the system's intelligence and adaptability.

[0073] In some preferred embodiments: Suppose that the spinning machine cleaner is judged to have insufficient response three times consecutively when executing enhanced cleaning mode. At this point, the system will trigger a cross-cycle diagnostic mode. This mode collects the operating parameters (e.g., cleaner speed, suction intensity, cleaning path coverage), suction motor power consumption trends, and final judgment results for these three enhanced cleaning modes and all previous enhanced cleaning modes judged to have insufficient response. By analyzing this historical data, the system may identify an abnormal triggering pattern; for example, when the cleaner speed is above a certain threshold and the suction intensity is below a certain threshold, the enhanced cleaning mode's response is consistently insufficient. Based on this identified abnormal triggering pattern, the system can generate a set of candidate operating parameter combinations. For example, it may suggest automatically reducing the cleaner speed or increasing the suction intensity in similar situations in the future to avoid recurring insufficient response. These candidate parameter combinations will be used to pre-load and adjust the operating parameters of subsequent enhanced cleaning modes, thereby improving the cleaning success rate and efficiency.

[0074] In another embodiment of this application, the method further includes: S13000: Store abnormal triggering patterns in a tagged manner and build an abnormal triggering pattern tag library. The abnormal triggering pattern tag includes the corresponding parameter combination features, matching rules and correction suggestions. S14000: Before each execution of the enhanced cleaning mode, dynamically match the abnormal triggering mode tag library based on the current running parameters to determine whether there are any recorded similar triggering modes; S15000: When similar triggering modes exist, extract their corresponding correction suggestions and preload and adjust the operating parameters of the enhanced cleaning mode.

[0075] Specifically, the tagged storage of abnormal triggering patterns refers to encapsulating and persistently storing specific combinations of operating parameters identified through cross-cycle diagnostic patterns that lead to insufficient response in the enhanced cleaning mode, along with their corresponding matching rules and recommended correction suggestions, as an independent "abnormal triggering pattern tag." The abnormal triggering pattern tag library is a centralized collection of these tags, designed to build a knowledge base that the system can query and learn from. Here, parameter combination features can be understood as the combined state of specific operating parameters (such as cleaner operating speed, suction intensity, and cleaning path coverage) that cause the abnormality; matching rules are the logical conditions used to determine whether the current operating parameters match a specific abnormal triggering pattern tag in the library; and correction suggestions are the specific adjustment schemes recommended by the system for optimizing the operating parameters of the enhanced cleaning mode for that abnormal pattern.

[0076] In practical applications, before each enhanced cleaning mode is executed, the system obtains the current operating parameters and dynamically matches them against all tags in the abnormal triggering mode tag library. This matching process aims to determine whether there are recorded similar triggering modes, that is, to identify whether the enhanced cleaning mode to be executed is similar to a mode that has been proven ineffective in the past. If the match is successful, that is, "a similar triggering mode exists," the system will "extract its corresponding correction suggestions" and immediately "preload and adjust the operating parameters of the enhanced cleaning mode." "Preload and adjust" means that the operating parameters of the enhanced cleaning mode have been optimized based on historical experience before the enhanced cleaning mode is actually started, rather than adjusting them after the mode is executed and the effect is found to be poor.

[0077] This application's solution transforms valuable experience gained from historical diagnostics into reusable knowledge assets by constructing and utilizing an anomaly triggering pattern tag library. When the system is about to execute the enhanced cleaning mode, it no longer simply runs according to the default or last adjusted parameters, but proactively and forward-lookingly compares itself with historical anomaly patterns. It is precisely this dynamic matching and pre-loading adjustment mechanism that enables the system to intelligently correct its operating parameters before potential problems occur, i.e., at the very beginning of the enhanced cleaning mode. This effectively avoids repeatedly trying known ineffective parameter combinations, thus significantly reducing multiple iterations and diagnostic processes caused by insufficient response from the enhanced cleaning mode, greatly improving the efficiency and accuracy of problem solving.

[0078] In some preferred embodiments, a specific example is given below. Suppose that during a spinning machine operation, the real-time power consumption data of the cleaner's suction motor is consistently below a preset lower limit. The system determines there is an anomaly risk and triggers an enhanced cleaning mode. After executing this mode, the system finds that the power consumption recovery rate is consistently below a preset recovery slope threshold, indicating that the enhanced cleaning mode's response is insufficient. After diagnosing similar situations multiple times, the system identifies that when the cleaner's operating speed is set to X, the suction intensity is set to Y, and the cleaning path coverage is Z, it often leads to insufficient response, and marks this parameter combination as "abnormal trigger mode A". Simultaneously, the system analysis reveals that increasing the suction intensity by 10% can effectively improve the cleaning effect in this mode. Therefore, this information is stored as a "correction suggestion" along with "abnormal trigger mode A" in the abnormal trigger mode tag library.

[0079] Subsequently, when the system faces a situation requiring the execution of enhanced cleaning mode again, it first checks the operating parameters to be used before the mode starts (for example, if the system defaults or calculates based on other logic that the parameters to be used happen to be X, Y, and Z). At this time, the system dynamically matches the abnormal trigger mode tag library and finds that the current parameters are highly similar to "abnormal trigger mode A". Based on the matching result, the system immediately extracts the correction suggestion corresponding to "abnormal trigger mode A", namely "increase the suction intensity by 10%", and preloads and adjusts the suction intensity parameter of the upcoming enhanced cleaning mode. Therefore, when this enhanced cleaning mode starts, its suction intensity has already been adjusted to Y+10%, thus running with more optimized parameters from the very beginning, effectively improving cleaning efficiency and avoiding the recurrence of insufficient response.

[0080] In another embodiment of this application, the method further includes: S16000: When any tag in the abnormal triggering mode tag library is matched and applied to the enhanced cleaning mode, record the response effect and correction parameters of the enhanced cleaning mode; S17000: Determines whether the correction parameters are effective based on the response effect; S18000: If determined to be valid, update the parameter group of the correction suggestion in the corresponding tag and increase the matching priority of the tag; S19000: If the enhanced cleaning mode is deemed to have insufficient response after applying the same label's correction suggestions a preset number of times, the matching priority of the label will be reduced or it will be marked as an invalid label, and a cleanup operation will be performed in the label library.

[0081] The recording of the enhanced cleaning mode's response and correction parameters refers to the system collecting key performance indicators during the mode's operation after its completion. These indicators include the recovery rate of the suction motor's real-time power consumption, the final stable power consumption value, the cleaner's operating speed, suction intensity, and cleaning path coverage. This data is used for subsequent evaluation. The effectiveness of the correction parameters can be determined based on preset evaluation criteria. For example, if the suction motor's power consumption recovery rate reaches a preset recovery slope threshold and remains stable for a certain period, the correction parameters are considered effective. Updating the parameter group of the correction suggestions in the corresponding tag involves fine-tuning or optimizing the correction suggestions associated with that tag based on the successful correction experience, making them more aligned with actual needs. Improving the tag's matching priority can be achieved by increasing its weight or adjusting its ranking in the matching algorithm, ensuring that the validated and effective correction suggestions are prioritized when encountering similar abnormal patterns in the future. If the enhanced cleaning mode is deemed ineffective after applying the same correction suggestion for a preset number of times—for example, if the suction power consumption fails to recover effectively after three consecutive applications of a certain correction suggestion—it indicates that the correction suggestion may no longer be applicable or may have defects. In this case, the system will lower the matching priority of the tag, reducing its probability of being selected in subsequent matches, or directly mark it as an invalid tag and remove it from the tag library to ensure the real-time nature and effectiveness of the tag library.

[0082] This application's solution addresses the issue of potentially ineffective or suboptimal correction suggestions in existing solutions by introducing a dynamic evaluation and feedback mechanism for correction suggestions in the anomaly triggering pattern tag library. Specifically, after an anomaly triggering pattern tag is applied, the system records the actual response effect of the enhanced cleaning mode and the correction parameters used. By judging the response effect, the effectiveness of the correction can be objectively evaluated. If the correction is effective, the correction suggestion corresponding to that tag is optimized and updated, and its matching priority is increased, enabling the system to prioritize and apply validated effective correction schemes when similar anomaly patterns occur in the future. Conversely, if corrections are continuously ineffective, their priority is reduced or they are marked as ineffective, thereby avoiding the repeated application of ineffective strategies and prompting the system to explore new correction schemes or clean up the tag library, ensuring the real-time nature and effectiveness of the tag library.

[0083] In some preferred embodiments, the following specific example illustrates the situation: Suppose that during a spinning machine operation, the real-time power consumption data of the cleaner's suction motor consistently falls below a preset deviation threshold. The system determines there is an anomaly risk and triggers an enhanced cleaning mode. At this point, the system identifies an anomaly trigger mode tag named "Suction Vent Blockage" by matching the anomaly trigger mode tag library. This tag suggests increasing the cleaner's operating speed by 10% and increasing the suction intensity. After applying these corrective parameters, the system records the operation of the enhanced cleaning mode. If the suction motor's power consumption subsequently recovers rapidly and stabilizes within the normal range, it indicates that the corrective parameters are effective. The system will update the correction suggestions in the "Suction Vent Blockage" tag accordingly, such as fine-tuning parameter combinations, and increasing the tag's matching priority in the tag library, making it a preferred choice in similar future situations. Conversely, if after three consecutive applications of the "Suction Vent Blockage" tag's correction suggestions, the power consumption recovery rate fails to reach the preset recovery slope threshold, the system will determine that the correction suggestion's response is insufficient. At this point, the matching priority of the "air intake blockage" label will be reduced, or it may even be marked as an invalid label and cleared, prompting the system to try other correction solutions or trigger deeper diagnosis when it encounters a similar situation again, thereby ensuring the continuous optimization and adaptability of the prevention and control strategy.

[0084] This application also discloses a yarn spinning machine cleaner anti-spinning control system, comprising: Data acquisition module 1 is used to acquire the current spindle speed and the real-time power consumption data of the cleaner's suction motor; Data lookup module 2 is used to look up the power consumption reference range of the corresponding preset suction motor based on the current spindle speed. The power consumption reference range is the target power consumption range of the cleaner at different spindle speeds. Comparison module 3 is used to compare real-time power consumption data with the lower limit of the power consumption reference range being searched; The judgment module 4 is used to determine that there is an abnormal risk of decreased fly shaving capture efficiency in the cleaner when the real-time power consumption data is lower than the lower limit value minus the preset deviation threshold. Execution module 5 is used to execute an enhanced cleaning mode in response to abnormal risks.

[0085] Specifically, data acquisition module 1 is used to acquire the current spindle speed and the real-time power consumption data of the cleaner's suction motor. In a preferred embodiment, data acquisition module 1 can be a software module integrated into the cleaner control unit, reading spindle speed data from the data interface of the spinning machine's main control system by calling the underlying driver or application programming interface (API), for example, through industrial Ethernet or RS485 bus communication. Simultaneously, this module can also receive real-time power consumption data from the power metering module (e.g., a smart meter or power sensor) in the suction motor's power supply line via an analog input port or a digital communication protocol (e.g., Modbus protocol). In another embodiment, data acquisition module 1 can be a separate hardware unit, containing a dedicated sensor interface and a data processing chip, responsible for data acquisition and preliminary processing, and then transmitting the processed data to other modules of the system. For example, this hardware unit can employ a microcontroller, integrating an analog-to-digital converter (ADC) for acquiring analog signals and possessing a communication interface for receiving and transmitting digital signals.

[0086] The data lookup module 2 is used to find the corresponding preset power consumption reference range of the suction motor based on the current spindle speed. The power consumption reference range is the target power consumption range of the cleaner at different spindle speeds. In one implementation, the data lookup module 2 can be a software module with built-in memory to store a table or model of the correspondence between spindle speed and suction motor power consumption reference range, established in advance through experiments or historical data analysis. When the data acquisition module 1 provides the current spindle speed, this module can retrieve the corresponding power consumption reference range from the stored correspondence based on that speed value. For example, this correspondence can be stored in the form of a lookup table, and efficient retrieval can be achieved through hash mapping or binary search algorithms. In another implementation, the data lookup module 2 can be a hardware logic unit that achieves rapid matching of power consumption reference ranges through pre-programmed lookup logic circuits.

[0087] The comparison module 3 compares the real-time power consumption data with the lower limit of the power consumption reference range. In one implementation, the comparison module 3 can be a software logic unit that receives the real-time power consumption data provided by the data acquisition module 1 and the lower limit of the power consumption reference range provided by the data lookup module 2, and performs a precise numerical comparison operation. For example, this module can perform a logical judgment of "real-time power consumption data < (lower limit value - preset deviation threshold)". In another implementation, the comparison module 3 can be a dedicated comparator circuit that directly compares the input analog or digital signals and outputs a comparison result signal.

[0088] The judgment module 4 is used to determine the abnormal risk of decreased fly ash capture efficiency in the cleaner when the real-time power consumption data is lower than the lower limit minus a preset deviation threshold. In one implementation, the judgment module 4 can be a software algorithm module that receives the output of the comparison module 3 and performs logical judgment based on the preset deviation threshold. This module can implement complex judgment algorithms. For example, by setting an allowable power consumption fluctuation range, the abnormal risk judgment is triggered only when the real-time power consumption remains below this range for a preset duration, thereby improving the accuracy and robustness of the judgment and avoiding false alarms caused by instantaneous fluctuations. In another implementation, the judgment module 4 can be a hardware logic unit based on a programmable gate array (FPGA) or application-specific integrated circuit (ASIC) to achieve high-speed, real-time abnormal risk judgment.

[0089] The execution module 5 is used to execute an enhanced cleaning mode in response to abnormal risks. In one implementation, the execution module 5 can be a software control module that receives abnormal risk signals from the judgment module 4 and immediately triggers the enhanced cleaning mode. This module can communicate with the cleaner's actuators (e.g., the suction motor driver, the cleaner's moving mechanism) to send control commands to adjust the cleaner's operating parameters, such as increasing the suction motor speed to increase suction intensity, or adjusting the cleaner's operating speed and cleaning path to perform more intensive or longer cleaning in critical areas. For example, the cleaner can be instructed to pass through areas prone to dust accumulation at a slower speed, or to perform multiple reciprocating cleaning cycles in specific areas to ensure thorough removal of fly ash. In another implementation, the execution module 5 can be a hardware controller that directly outputs control signals to drive the cleaner's actuators, enabling a rapid response to the enhanced cleaning mode.

[0090] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for preventing and controlling smudging in a spinning machine cleaner, characterized in that, include: Obtain the current spindle speed and the real-time power consumption data of the cleaner's suction motor; Based on the current spindle speed, find the corresponding preset power consumption reference range of the suction motor. The power consumption reference range is the target power consumption range of the cleaner at different spindle speeds. The real-time power consumption data is compared with the lower limit of the power consumption reference range being searched. When the real-time power consumption data is lower than the lower limit value minus the preset deviation threshold, it is determined that there is an abnormal risk of decreased fly shaving capture efficiency in the cleaner; In response to the aforementioned abnormal risks, an enhanced cleaning mode is executed.

2. The method for preventing and controlling smudging in a spinning machine cleaner according to claim 1, characterized in that, After determining that the cleaner has an abnormal risk of decreased fly shaving capture efficiency, the method further includes: Initiate a diagnostic cleaning operation, causing the cleaner to move at a preset uniform speed over the surfaces of key components of the spinning machine; During the diagnostic cleaning operation, real-time power consumption data of the suction motor is collected at a frequency higher than the preset frequency; Based on the fluctuation characteristics of the real-time power consumption data within the sliding window, it is determined whether there is an abrasion layer on the surface of the key component; Based on the determination result of the abrasion layer, a corresponding preset targeted intervention and control strategy is executed.

3. The method for preventing and controlling smudging in a spinning machine cleaner according to claim 2, characterized in that, The determination of whether an abrasion layer exists on the surface of the key component includes: Frequency domain analysis is performed on the real-time power consumption data within the sliding window to extract the frequency energy characteristics of the power consumption energy within a preset frequency range; The frequency energy features are matched with multiple preset single feature patterns to obtain the matching degree of each single feature pattern. When the matching degree of each of the single feature patterns is lower than the preset threshold, if it is determined that the frequency energy feature satisfies the preset combination condition of the composite feature pattern, the erosion layer is determined to be a composite erosion layer. When the frequency energy feature does not meet the combination conditions of the composite feature mode, based on the matching degree of each of the individual feature modes, if it is determined that the frequency energy feature is in the transition region between adjacent feature modes, the abrasive layer is determined to have progressive physical properties.

4. The method for preventing and controlling smudging in a spinning machine cleaner according to claim 3, characterized in that, The method further includes: When the frequency energy feature neither satisfies the combination conditions of the composite feature mode nor is it in the transition region between adjacent feature modes, analyze the frequency distribution pattern of the frequency energy feature. The frequency distribution pattern is compared with a preset abnormal feature fingerprint database, which includes frequency distribution patterns unique to the wear layer caused by new materials or new processes. When the frequency distribution pattern does not match any frequency distribution pattern in the abnormal feature fingerprint database, the abrasion layer is determined to be a single abrasion layer pattern without a preset pattern, and a new pattern warning is triggered. The new pattern warning includes displaying the new pattern warning information to the operation interface, sending the new pattern warning information to the bound mobile terminal, or uploading the new pattern warning information to the central control system and triggering subsequent processing. Based on the publish-subscribe mechanism of message queues, the early warning information of the new mode is synchronized to multiple system subscription queues, including the maintenance management system, the spare parts management system, and the quality control system.

5. The method for preventing and controlling smudging in a spinning machine cleaner according to claim 4, characterized in that, The method further includes: Obtain the frequency distribution pattern corresponding to the single erosion layer mode, and continuously monitor the changing characteristics of the frequency distribution pattern; By comparing the frequency distribution pattern with the historical frequency distribution pattern, the variation characteristics of specific frequency components can be identified. These variation characteristics include the trend of energy increase or decrease and the drift of the main peak frequency. Based on the aforementioned change characteristics, the urgency and scope of impact of the new model's early warning information are determined; Based on the level of urgency and the scope of impact, a pre-defined tiered early warning strategy is implemented.

6. The method for preventing and controlling smudging in a spinning machine cleaner according to claim 1, characterized in that, After executing the enhanced cleaning mode, the following is included: Dynamically track power consumption trends based on real-time power consumption data of the suction motor; A short-term fitting analysis within a sliding window is performed on the power consumption change trend to identify whether the power consumption recovery rate reaches a preset recovery slope threshold. When the power consumption recovery rate remains below the preset recovery slope threshold for a duration exceeding a set time, it is determined that the response effect of the enhanced cleaning mode is insufficient, and the operating parameters of the enhanced cleaning mode are adjusted. The operating parameters include the cleaner's operating speed, suction strength, and cleaning path coverage.

7. The method for preventing and controlling smudging in a spinning machine cleaner according to claim 6, characterized in that, The method further includes: When a preset number of enhanced cleaning modes are all judged to have insufficient response, the cross-cycle diagnostic mode is triggered. Record the operating parameters, power consumption trends, and judgment results of each enhanced cleaning mode to construct a diagnostic dataset; Based on the diagnostic dataset, the operating parameters of multiple enhanced cleaning modes with insufficient response effects are analyzed, common operating state characteristics are identified, and parameter combination characteristics constituting abnormal triggering modes are extracted. Based on the abnormal triggering mode, candidate combinations of operating parameters are generated and used to predictively correct the operating parameters of subsequent enhanced cleaning modes.

8. The method for preventing and controlling smudging in a spinning machine cleaner according to claim 7, characterized in that, The method further includes: The abnormal triggering patterns are tagged and stored to construct an abnormal triggering pattern tag library. The abnormal triggering pattern tags include corresponding parameter combination features, matching rules and correction suggestions. Before each execution of the enhanced cleaning mode, the abnormal triggering mode tag library is dynamically matched based on the current running parameters to determine whether there are any recorded similar triggering modes. When similar triggering patterns exist, extract their corresponding correction suggestions and preload and adjust the operating parameters of the enhanced cleaning mode.

9. The method for preventing and controlling smudging in a spinning machine cleaner according to claim 8, characterized in that, The method further includes: When any tag in the abnormal triggering mode tag library is matched and applied to the enhanced cleaning mode, the response effect and correction parameters of the enhanced cleaning mode are recorded; Based on the response effect, determine whether the correction parameter is effective; If the result is deemed valid, the parameter group of the correction suggestion in the corresponding tag is updated to increase the matching priority of that tag; If the enhanced cleaning mode is deemed to have insufficient response after applying the same label's correction suggestions a preset number of times, the matching priority of the label will be reduced or it will be marked as an invalid label, and a cleanup operation will be performed in the label library.

10. A smudge prevention and control system for a yarn spinning machine cleaner, characterized in that, include: The data acquisition module is used to acquire the current spindle speed and the real-time power consumption data of the cleaner's suction motor; The data lookup module is used to look up the corresponding preset power consumption reference range of the suction motor based on the current spindle speed. The power consumption reference range is the target power consumption range of the cleaner at different spindle speeds. The comparison module is used to compare the real-time power consumption data with the lower limit of the power consumption reference range being searched. The judgment module is used to determine that when the real-time power consumption data is lower than the lower limit value minus the preset deviation threshold, there is an abnormal risk of decreased fly shaving capture efficiency in the cleaner. An execution module is used to execute an enhanced cleaning mode in response to the aforementioned abnormal risks.

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