A laundry spin control method and system based on unbalance detection

Abstract

The application provides a laundry dehydration control method and system based on load unbalance detection, which is applied to the field of data processing; through collecting multi-dimensional operation state parameters such as rotation speed fluctuation amplitude, current fluctuation amplitude, torque fluctuation amplitude and vibration amplitude, and combining with a preset weight relationship to construct a comprehensive load unbalance characteristic value, the application avoids the misjudgment problem caused by relying on a single detection signal for load unbalance judgment, improves the accuracy and reliability of load unbalance state recognition, and simultaneously, after detecting the load unbalance, instead of directly executing a fixed control strategy, a load unbalance change curve is generated according to the comprehensive load unbalance characteristic value, the inner drum rotation speed is dynamically adjusted by analyzing the load unbalance change process, and further, the load unbalance trend is obtained, so that the continuous tracking and prediction of the load unbalance state evolution process are realized.

Description

Technical Field

[0001] This invention relates to the field of data processing, and in particular to a washing and dehydration control method and system based on off-center load detection. Background Technology

[0002] In order to improve the dehydration efficiency, existing washing machines typically require the inner drum to operate at a higher speed during the spin-drying process. However, due to differences in the type, quantity, and distribution of clothes, the clothes are prone to uneven loading in the inner drum, resulting in greater vibration and noise during the spin-drying process, which may even affect the overall stability and service life of the machine in severe cases. Summary of the Invention

[0003] The present invention aims to solve the problem of how to improve the accuracy of off-center load detection and achieve more reasonable and efficient dehydration control based on off-center load status, and provides a washing and dehydration control method and system based on off-center load detection.

[0004] The present invention employs the following technical means to solve the technical problem: This invention provides a washing and spin-drying control method based on off-center load detection, comprising: Based on the preset spin-drying program of the smart washing machine, the operating status parameters of the smart washing machine during the spin-drying acceleration process are collected. According to the preset weight relationship of the operating status parameters, the comprehensive off-center load characteristic value of the smart washing machine is constructed. The operating status parameters specifically include the speed fluctuation amplitude, current fluctuation amplitude, torque fluctuation amplitude and vibration amplitude. Determine whether the comprehensive off-center load characteristic value is less than a preset off-center load threshold; If not, then based on the comprehensive off-center load characteristic value, a corresponding off-center load change curve is generated. Through the off-center load change curve, the inner drum speed of the smart washing machine is dynamically adjusted to obtain the off-center load trend of the smart washing machine. The off-center load trend specifically includes a stabilization trend and a mitigation trend. Determine whether the off-center loading trend has detected a risk of deterioration; If detected, the preset distribution mode of the smart washing machine is activated. Based on the combination of actions of the distribution mode, the clothes in the drum of the smart washing machine are redistributed. The off-center load status of the smart washing machine is rechecked. According to the off-center load status, the safe speed range of the smart washing machine is dynamically limited. The combination of actions specifically includes forward rotation, reverse rotation and intermittent pause.

[0005] 2. The washing and spin-drying control method based on off-center load detection according to claim 1, characterized in that, the step of dynamically adjusting the inner drum speed of the intelligent washing machine through the off-center load change curve further includes: Based on the off-carrier peak of the off-carrier variation curve, the interval time between adjacent off-carrier peaks is collected, and the corresponding response feature sequence is constructed according to the interval time. Determine whether the response feature sequence matches the dynamic distribution state of the clothing during the inner tub rotation process; If so, then based on the offset carrier peak, the corresponding offset carrier peak data is obtained, and the distribution balance of the clothes is identified through the offset carrier peak data. Based on the distribution balance, the micro-speed adjustment action of the smart washing machine is activated to dynamically increase the inner drum speed of the smart washing machine. The offset carrier peak data specifically includes the number of peaks, the duration of peaks, and the distribution density of peaks.

[0006] Furthermore, before the step of obtaining the off-center load trend of the smart washing machine, the method further includes: The operation data of the inner barrel in several rotation cycles is obtained. Based on the operation data in each rotation cycle, a corresponding set of cycle states is constructed. The set of cycle states is divided into several adjacent state segments. The state change features corresponding to each state segment are extracted. Determine whether the state change features can match the degree of correlation between changes in different state segments; If possible, the characteristic regions corresponding to the changes in operating status are identified, a state evolution sequence is generated based on the characteristic regions, the similarity deviation between adjacent state evolution sequences is calculated, the evolution direction of the inner tub's operating status is collected based on the similarity deviation, and a corresponding state evolution result is formed through the evolution direction to obtain the off-load trend of the smart washing machine. The characteristic regions specifically include concentrated change regions and dispersed change regions.

[0007] Furthermore, the step of re-checking the off-center load state of the smart washing machine and dynamically limiting the safe speed range of the smart washing machine based on the off-center load state also includes: Obtain the vibration response data corresponding to the off-center load state, and based on the vibration response data, obtain the vibration change characteristics of the current inner tub in different speed ranges, construct the correspondence between the speed range and the vibration change characteristics, form the speed response model corresponding to the current operating stage, and identify the speed segment corresponding to the current operating speed according to the speed response model. Determine whether the speed range belongs to a preset sensitive range; If so, the speed range is divided into a restricted range, the restricted range is removed from the operable speed range, and a safe speed range is regenerated based on the remaining operable speed range. The inner tub is dynamically controlled to avoid the restricted range through the safe speed range.

[0008] Furthermore, the step of determining whether the comprehensive off-center load characteristic value is less than a preset off-center load threshold also includes: Obtain the difference between the comprehensive off-center load characteristic value and the preset off-center load threshold, and obtain the off-center load margin corresponding to the current off-center load state based on the difference; Determine whether the off-center load margin is greater than a preset margin threshold; If not, the inner tub of the smart washing machine is marked as a critical stable operating state. Based on the off-center load margin, a corresponding operating protection coefficient is generated. Through the operating protection coefficient, the rotation speed increase rate of the inner tub is limited, the preset rotation speed range is dynamically narrowed, and the off-center load margin change record sequence is established. The change amplitude of the off-center load margin is statistically analyzed within several consecutive sampling periods.

[0009] Furthermore, the step of determining whether the off-center loading trend detects a risk of deterioration also includes: Obtain multiple continuous trend segments corresponding to the off-center loading trend, and construct the corresponding off-center loading evolution trajectory based on the connection relationship between each trend segment; Determine whether the off-center load evolution trajectory meets the preset deterioration conditions; If so, the abnormal evolution segment in the off-center load evolution trajectory is located, the corresponding operational feature information of the abnormal evolution segment is extracted, the corresponding off-center load disturbance image is constructed based on the operational feature information, and the dominant influencing factor of the current off-center load state is identified based on the off-center load disturbance image.

[0010] Furthermore, the step of collecting the operating status parameters of the smart washing machine during the spin-drying acceleration process based on the preset spin-drying program of the smart washing machine, and constructing the comprehensive off-load characteristic value of the smart washing machine according to the preset weight relationship of the operating status parameters, further includes: Obtain several parameter change sequences corresponding to the running status parameters, construct corresponding parameter response trajectories based on the changes of each parameter change sequence within a continuous sampling period, perform correlation mapping processing on the parameter response trajectories, and obtain the collaborative change relationship between each parameter response trajectories; Determine whether the cooperative change relationship satisfies the preset cooperative stability condition; If so, a stable linkage relationship is generated between the response trajectories of each parameter. Based on the stable linkage relationship, the corresponding collaborative feature set is extracted, the duration of each collaborative feature within the continuous sampling period is calculated, and the corresponding collaborative credibility index is constructed based on the duration of the duration.

[0011] The present invention also provides a washing and spin-drying control system based on off-center load detection, comprising: The module is used to collect the operating status parameters of the smart washing machine during the spin-drying acceleration process based on the preset spin-drying program of the smart washing machine, and construct the comprehensive off-center load characteristic value of the smart washing machine according to the preset weight relationship of the operating status parameters. The operating status parameters specifically include the speed fluctuation amplitude, current fluctuation amplitude, torque fluctuation amplitude and vibration amplitude. The judgment module is used to determine whether the comprehensive off-center load characteristic value is less than a preset off-center load threshold. The execution module is used to generate a corresponding off-center load change curve based on the comprehensive off-center load characteristic value if not otherwise, and dynamically adjust the inner drum speed of the smart washing machine through the off-center load change curve to obtain the off-center load trend of the smart washing machine. The off-center load trend specifically includes a stabilization trend and a mitigation trend. The second judgment module is used to determine whether the off-center loading trend has detected a risk of deterioration. The second execution module is used to activate the preset distribution mode of the smart washing machine if detected, redistribute the clothes in the drum of the smart washing machine based on the combination of actions of the distribution mode, recheck the off-center load state of the smart washing machine, and dynamically limit the safe speed range of the smart washing machine according to the off-center load state. The combination of actions specifically includes forward rotation, reverse rotation and intermittent pause.

[0012] Furthermore, the execution module also includes: The construction unit is used to collect the interval time between adjacent off-carrier peaks based on the off-carrier peaks of the off-carrier change curve, and construct the corresponding response feature sequence according to the interval time. The judgment unit is used to determine whether the response feature sequence matches the dynamic distribution state of the clothes during the rotation of the inner tub; The execution unit is configured to, if so, acquire corresponding off-carrier peak data based on the off-carrier peak, identify the distribution balance of the clothes through the off-carrier peak data, and activate the micro-speed adjustment action of the smart washing machine based on the distribution balance to dynamically increase the inner drum speed of the smart washing machine. The off-carrier peak data specifically includes the number of peaks, peak duration, and peak distribution density.

[0013] Furthermore, it also includes: The extraction module is used to obtain the running data of the inner barrel in several rotation cycles, construct the corresponding cycle state set based on the running data in each rotation cycle, divide the cycle state set into several adjacent state segments, and extract the state change features corresponding to each state segment. The third judgment module is used to determine whether the state change features can match the degree of correlation between changes in different state segments; The third execution module is used to identify the feature region corresponding to the change in operating state if possible, generate a state evolution sequence based on the feature region, calculate the similarity deviation between adjacent state evolution sequences, collect the evolution direction of the inner tub's operating state based on the similarity deviation, form a corresponding state evolution result through the evolution direction, and obtain the off-load trend of the smart washing machine. The feature region specifically includes a concentrated change region and a dispersed change region.

[0014] This invention provides a washing and dehydration control method and system based on off-center load detection, which has the following beneficial effects: This invention collects multi-dimensional operating state parameters such as speed fluctuation amplitude, current fluctuation amplitude, torque fluctuation amplitude, and vibration amplitude, and constructs a comprehensive off-center load characteristic value by combining them with a preset weight relationship. This avoids the misjudgment problem caused by relying on a single detection signal for off-center load judgment, and improves the accuracy and reliability of off-center load state identification. At the same time, after detecting off-center load, it does not directly execute a fixed control strategy, but generates an off-center load change curve based on the comprehensive off-center load characteristic value. By analyzing the off-center load change process, it dynamically adjusts the inner drum speed and further obtains the off-center load trend, realizing continuous tracking and prediction of the off-center load state evolution process. Attached Figure Description

[0015] Figure 1 This is a schematic flowchart of an embodiment of the washing and dehydration control method based on off-center load detection of the present invention; Figure 2 This is a structural block diagram of an embodiment of the washing and dehydration control system based on off-center load detection of the present invention. Detailed Implementation

[0016] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. The realization of the purpose, functional features, and advantages of the invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings.

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Reference Appendix Figure 1 The washing and dehydration control method based on off-center load detection in one embodiment of the present invention includes: S1: Based on the preset spin-drying program of the smart washing machine, collect the operating status parameters of the smart washing machine during the spin-drying acceleration process, and construct the comprehensive off-center load characteristic value of the smart washing machine according to the preset weight relationship of the operating status parameters. The operating status parameters specifically include the speed fluctuation amplitude, current fluctuation amplitude, torque fluctuation amplitude and vibration amplitude. S2: Determine whether the comprehensive off-center load characteristic value is less than the preset off-center load threshold; S3: If not, then based on the comprehensive off-center load characteristic value, generate the corresponding off-center load change curve, and dynamically adjust the inner drum speed of the smart washing machine through the off-center load change curve to obtain the off-center load trend of the smart washing machine, wherein the off-center load trend specifically includes a stabilization trend and a mitigation trend. S4: Determine whether the risk of deterioration of the off-center loading trend has been detected; S5: If detected, the preset distribution mode of the smart washing machine is activated. Based on the combination of actions of the distribution mode, the clothes in the drum of the smart washing machine are redistributed. The off-center load status of the smart washing machine is rechecked. According to the off-center load status, the safe speed range of the smart washing machine is dynamically limited. The combination of actions specifically includes forward rotation, reverse rotation and intermittent pause.

[0019] In this embodiment, the system collects operating status parameters of the smart washing machine during the spin-drying acceleration process based on a pre-set spin-drying program. These parameters specifically include the amplitude of speed fluctuation, current fluctuation, torque fluctuation, and vibration. Based on a pre-set weighting relationship for these different operating status parameters, a comprehensive off-center load characteristic value for the smart washing machine is constructed. The system then determines whether this comprehensive off-center load characteristic value is less than a pre-set off-center load threshold to execute corresponding steps. For example, if the system determines that the comprehensive off-center load characteristic value is indeed less than the pre-set off-center load threshold, the system considers that no obvious off-center load accumulation has formed inside the washing machine drum. The system will continue to collect operating status parameters during the spin-drying acceleration process, and... The system recalculates the comprehensive off-center load characteristic value according to a preset sampling period to monitor the off-center load status in real time. During the continued acceleration process, the system can also determine the off-center load safety margin based on the difference between the current comprehensive off-center load characteristic value and the off-center load threshold. When the off-center load safety margin remains within a preset range, the inner drum is allowed to operate according to the normal acceleration curve to improve dehydration efficiency and shorten dehydration time. For example, if the system determines that the comprehensive off-center load characteristic value of the smart washing machine is not less than the preset off-center load threshold, the system will consider that there is a significant off-center load accumulation phenomenon in the washing machine drum. The system will generate a corresponding off-center load change curve based on this comprehensive off-center load characteristic value, and dynamically adjust the inner drum speed of the smart washing machine through the off-center load change curve to obtain intelligent washing... The system analyzes the off-center loading trend of the washing machine, which includes a stabilizing trend and a mitigating trend. By optimizing the rotation speed in real time based on changes in off-center loading, the system reduces the probability of the inner drum entering a high-vibration zone under off-center loading conditions, minimizing mechanical shock and vibration accumulation. This improves the operational stability of the spin-drying process and reduces overall machine noise and component wear. The stabilizing trend reflects that the off-center loading condition has not worsened, while the mitigating trend reflects that the distribution of clothing is gradually improving. By identifying the off-center loading trend, the system can shift from traditional "post-event response" to "trend judgment," allowing for proactive control strategies before the off-center loading becomes severe, reducing unnecessary distribution operations and shutdowns. The system then determines whether these off-load trends have a risk of deterioration and executes the corresponding steps accordingly. For example, if the system determines that the off-load trend of the smart washing machine has not a risk of deterioration, the system will consider that although the current off-load state has reached or exceeded the preset off-load threshold, its change process is within a controllable range and does not show a trend of continuous aggravation, rapid spread or unstable development. The system will continuously collect the operating status parameters during the dehydration process and periodically update the comprehensive off-load characteristic value, off-load change curve and off-load trend information. When the subsequent detection results still do not show a risk of deterioration, the system will continue to execute the current dehydration control strategy and gradually release some of the operating restrictions caused by off-load, so that the inner drum can smoothly transition to the target dehydration speed.For example, when the system detects a worsening risk of unbalanced load in the smart washing machine, it considers the current unbalanced load state to be difficult to control. The system then activates the washing machine's pre-set distribution mode, using a combination of actions based on this mode. These actions include forward rotation, reverse rotation, and intermittent pauses to redistribute the clothes inside the drum, re-checking the unbalanced load state. Based on different unbalanced load states, the system dynamically limits the washing machine's safe speed range. By analyzing the re-check results, the system can accurately determine whether the unbalanced load state has been alleviated, avoiding blindly entering the high-speed spin-drying stage due to poor distribution. This improves the consistency between the unbalanced load detection results and the actual distribution of clothes, enhancing the targeting and reliability of subsequent spin-drying control. Simultaneously, when the unbalanced load is small, the safe speed range can be appropriately expanded to ensure spin-drying efficiency; when the unbalanced load is large, the safe speed range is correspondingly narrowed to reduce vibration risk. Dynamic adjustment of the safe speed range avoids problems such as severe vibration, increased noise, and damage to mechanical components caused by the inner drum operating within an unsuitable speed range, ensuring both operational safety and overall machine stability.

[0020] It should be noted that, based on the comprehensive off-center load characteristic value, a corresponding off-center load change curve is generated. Using this off-center load change curve, the inner drum speed of the smart washing machine is dynamically adjusted to obtain the off-center load trend of the smart washing machine. Specifically: When the system detects that the comprehensive off-center load characteristic value is not less than the preset off-center load threshold, it indicates that the clothes in the current bin have formed a relatively obvious off-center load accumulation state. At this time, the system will not take control measures directly based on the single detection result, but will continuously acquire the corresponding comprehensive off-center load characteristic value according to the preset sampling period, and associate the comprehensive off-center load characteristic values ​​corresponding to different time points in chronological order to generate the corresponding off-center load change curve. For example, if the comprehensive off-center load characteristic values ​​obtained in several consecutive sampling periods are 0.72, 0.78, 0.85, 0.91 and 0.98 respectively, the system will generate an off-center load change curve with an upward trend based on the above data. By analyzing the change direction, change rate and fluctuation characteristics of the off-center load change curve, the system can more intuitively grasp the development process of the current off-center load state, rather than just knowing whether an off-center load phenomenon exists. Compared with the traditional single-point detection method, this method can reflect the continuous evolution characteristics of the off-center load state, thereby improving the accuracy of off-center load state identification. After generating the off-center load change curve, the system further dynamically adjusts the inner drum speed of the smart washing machine based on the curve. For example, when the off-center load change curve continues to rise, it indicates that the degree of clothing accumulation in the drum is increasing. The system then appropriately reduces the inner drum speed increase rate, allowing the clothing to redistribute over a longer speed transition phase. When the off-center load change curve flattens out, it indicates that the current off-center load state is stabilizing. The system maintains the current speed and continues to observe. When the off-center load change curve gradually decreases, it indicates that the clothing in the drum is redistributing, and the off-center load state is improving. At this point, the system can gradually restore the normal speed increase strategy. During the above dynamic speed adjustment process, the system continuously updates the off-center load change curve and obtains the corresponding off-center load trend based on the curve changes. For example, if the off-center load change curve remains within a small fluctuation range for a long time, it is determined to be a stable trend. If the off-center load change curve continues to decrease and the decrease gradually increases, it is determined to be a mitigation trend. By obtaining the off-center load trend, the system can anticipate the development direction of the off-center load state, providing a basis for whether to activate the distribution mode or adjust the safe speed range, thereby achieving more reasonable and efficient spin-drying control.

[0021] It should be added that, by activating the preset distribution mode of the smart washing machine, and based on the combined actions of the distribution mode, the clothes inside the drum of the smart washing machine are redistributed, the off-center load status of the smart washing machine is rechecked, and the safe speed range of the smart washing machine is dynamically limited according to the off-center load status, specifically: When the system detects a worsening risk of uneven load, it indicates that the uneven load on the clothes in the drum is continuously intensifying. Continuing to operate at increased speed according to the current spin-drying program could lead to rapidly increasing vibration, increased machine shaking, or even triggering a protective shutdown. Therefore, the system activates the pre-set distribution mode of the smart washing machine and uses a combination of actions corresponding to this mode to redistribute the clothes in the drum. These actions include forward rotation, reverse rotation, and intermittent pauses. Specifically, the system first controls the inner drum to rotate forward at a low speed, causing initial displacement of the clothes gathered in localized areas. Then, it stops rotating for a preset period, allowing the clothes to relax under gravity. The clothes fall naturally; then the inner drum is controlled to rotate in the opposite direction, causing the clothes that were originally tangled or piled up in the same area to change direction and reposition; then the relative displacement effect between the clothes is further enhanced by intermittent pauses; by repeatedly performing the above combination of actions, the clothes in the drum are gradually changed from a concentrated distribution to a relatively uniform distribution; for example, when only one thick blanket that has absorbed water is put in the washing machine, the blanket is prone to concentrate on one side of the inner drum during high-speed rotation, forming a serious off-center load. After the system detects that the off-center load trend is worsening, it can use a combination of multiple rounds of forward rotation, pause, reverse rotation and pause again to make the blanket gradually unfold and spread to different areas in the drum, thereby reducing the degree of off-center load; After completing the distribution operation, the system does not immediately restore the original spin speed. Instead, it re-checks the current off-center load status. During the re-check, the system re-collects operating status parameters such as speed fluctuation amplitude, current fluctuation amplitude, torque fluctuation amplitude, and vibration amplitude, and recalculates the comprehensive off-center load characteristic value to determine the actual improvement effect after the distribution operation. Based on the off-center load status obtained from the re-check, the system dynamically limits the safe speed range of the smart washing machine. For example, if the re-check result shows that the off-center load status has been significantly improved, and the comprehensive off-center load characteristic value has decreased from 0.95 to 0.62, the system can set the safe speed range to 800 rpm to 1200 rpm, allowing the inner drum to continue to increase towards the target spin speed. If the re-check result shows that the off-center load status has improved but still... If the overall off-center load characteristic value decreases significantly from 0.95 to 0.82, the system can limit the safe speed range to 800 rpm to 1000 rpm to avoid entering the high-speed range that is prone to severe vibration. If the retest results show that the off-center load condition is not significantly improved, for example, if the overall off-center load characteristic value remains above 0.90, the system can further narrow the safe speed range, or even limit it to below 800 rpm. By dynamically adjusting the safe speed range according to the actual off-center load condition after retesting, the dehydration process can always operate within a speed range that matches the current distribution of clothing. This not only effectively reduces vibration and noise, but also avoids mechanical shock caused by blindly high-speed dehydration, improving the overall stability of the machine and the safety of dehydration control.

[0022] In addition, based on the preset spin-drying program of the smart washing machine, the operating status parameters of the smart washing machine during the spin-drying acceleration process are collected. According to the preset weighting relationship of the operating status parameters, a comprehensive off-load characteristic value of the smart washing machine is constructed, specifically: After the smart washing machine enters the preset spin-drying program, the system first controls the inner drum to gradually increase its speed according to a predetermined speed-up strategy, and continuously collects multiple parameters reflecting the operating status throughout the spin-drying speed-up process. These operating status parameters specifically include speed fluctuation amplitude, current fluctuation amplitude, torque fluctuation amplitude, and vibration amplitude. The speed fluctuation amplitude reflects the stability of the inner drum's rotation speed; the current fluctuation amplitude reflects changes in the load driven by the motor; the torque fluctuation amplitude reflects the fluctuation of the motor's output power; and the vibration amplitude reflects the vibration state of the entire machine during operation. Since different operating status parameters have different sensitivities to off-center load conditions, the system pre-sets corresponding weight relationships for each operating status parameter and fuses these parameters based on these weight relationships to construct a comprehensive off-center load characteristic value that comprehensively reflects the current degree of off-center load on the clothes. By employing a multi-parameter fusion approach, misjudgments caused by the influence of random factors on a single parameter can be avoided, improving the accuracy and stability of off-center load condition identification. For example, during a certain dehydration acceleration stage, the system collected data showing a rotational speed fluctuation amplitude of 0.35, a current fluctuation amplitude of 0.28, a torque fluctuation amplitude of 0.40, and a vibration amplitude of 0.55, with pre-set weights of 25%, 20%, 25%, and 30%, respectively. The system then weights these parameters according to the pre-set weights to obtain a comprehensive off-center load characteristic value. Because the vibration and torque fluctuation amplitudes are relatively large, the resulting comprehensive off-center load characteristic value is also relatively high, indicating a possible significant aggregation of clothing within the drum. Conversely, when multiple pieces of clothing of relatively uniform weight are placed in the drum, the collected rotational speed fluctuation amplitude, current fluctuation amplitude, torque fluctuation amplitude, and vibration amplitude are all at lower levels. The resulting comprehensive off-center load characteristic value after weighted fusion is also relatively small, indicating a more uniform distribution of clothing and a lower degree of off-center load. Therefore, by weighting and fusing multiple operating parameters to construct a comprehensive off-center load characteristic value, the actual distribution of clothing during the dehydration process can be more comprehensively reflected, providing a reliable data foundation for subsequent off-center load judgment, rotational speed adjustment, and distribution control.

[0023] In this embodiment, step S3, which dynamically adjusts the inner drum speed of the smart washing machine based on the off-center load variation curve, further includes: S31: Based on the off-carrier peak of the off-carrier change curve, collect the interval time between adjacent off-carrier peaks, and construct the corresponding response feature sequence according to the interval time. S32: Determine whether the response feature sequence matches the dynamic distribution state of the clothing during the inner tub rotation process; S33: If so, then based on the offset carrier peak, obtain the corresponding offset carrier peak data, identify the distribution balance of the clothes through the offset carrier peak data, and activate the micro-speed change action of the smart washing machine based on the distribution balance to dynamically increase the inner drum speed of the smart washing machine. The offset carrier peak data specifically includes the number of peaks, peak duration and peak distribution density.

[0024] In this embodiment, the system collects the interval time between adjacent off-carrier peaks based on the off-carrier peaks of the off-carrier change curve. Based on different interval time periods, it constructs corresponding response feature sequences. The system then determines whether these response feature sequences match the dynamic distribution state of the clothing during the inner tub's rotation, and executes corresponding steps accordingly. For example, when the system determines that these response feature sequences cannot match the dynamic distribution state of the clothing during the inner tub's rotation, the system considers that the off-carrier peak interval pattern extracted based on the off-carrier change curve can no longer accurately reflect the actual movement characteristics of the clothing inside the tub, i.e., the clothing distribution state has changed abnormally. The system marks the current operating state as an abnormal distribution state and further extracts the corresponding abnormal peak segment in the current off-carrier change curve, analyzing the peak density, peak growth rate, and peak duration of the abnormal peak segment to determine the main abnormal source causing the matching failure. Simultaneously, it re-establishes the response feature sequence corresponding to the current operating cycle and reduces the reference weight of historical response feature sequences in subsequent analysis processes to avoid interference from old distribution patterns in the current judgment result. For example, when the system determines that these response feature sequences can match the dynamic distribution state of the clothing during the inner tub's rotation, the system considers... Currently, the off-center carrier peak interval pattern extracted based on the off-center load variation curve can accurately reflect the actual movement characteristics of clothes inside the drum. The system acquires corresponding off-center carrier peak data based on the off-center carrier peaks. The off-center carrier peak data specifically includes the number of peaks, peak duration, and peak distribution density. Through this off-center carrier peak data, the system identifies the distribution balance of clothes. Based on different distribution balances, it activates the micro-speed adjustment action of the smart washing machine, dynamically increasing the inner drum speed. By analyzing the number of peaks, peak duration, and peak distribution density to identify the distribution balance of clothes, the system can transform the originally difficult-to-quantify distribution state of clothes. As an analyzable and comparable indicator of uniformity, a higher degree of uniformity is indicated when the clothing is evenly distributed, while a lower degree of uniformity is indicated when there is localized clustering or uneven loading of clothing. Simultaneously, micro-speed adjustments are activated based on different degrees of uniformity, dynamically increasing the inner drum's rotational speed to ensure the speed increase is coordinated with the current clothing distribution. When the clothing distribution uniformity is high, the system can appropriately increase the speed increment to improve dehydration efficiency. When the clothing distribution uniformity is relatively low, micro-speed adjustments are used to further shift and redistribute the clothing within the drum, and the inner drum's rotational speed is increased more gradually.

[0025] It should be noted that the response feature sequence is used to characterize the aggregation and dispersion patterns of clothing during the rotation of the inner tub; subsequently, the stable interval and sensitive interval corresponding to the off-center load state are determined based on the off-center load response feature sequence, wherein the stable interval is used to characterize the operating stage where the distribution of clothing changes little, and the sensitive interval is used to characterize the operating stage where the position of clothing is prone to migration.

[0026] It should be added that, based on the aforementioned offset carrier peak, corresponding offset carrier peak data is obtained. Through this offset carrier peak data, the distribution uniformity of the clothes is identified. Based on this distribution uniformity, the micro-speed adjustment action of the smart washing machine is activated, dynamically increasing the inner drum speed of the smart washing machine. Specifically: After the system completes the construction of the off-center load change curve and identifies the off-center carrier peaks, it does not directly control based on a single peak. Instead, it performs structured analysis on each off-center carrier peak to obtain the corresponding off-center carrier peak data. The off-center carrier peak data specifically includes the number of peaks, peak duration, and peak distribution density. Among them, the number of peaks is used to characterize the number of times the off-center load state is activated per unit time, reflecting the frequency of significant shift of clothing in the bin. The peak duration is used to characterize the stability of a single off-center load state, reflecting the persistence of clothing aggregation. The peak distribution density is used to characterize the concentration of multiple off-center carrier peaks on the time axis, reflecting the uniformity of clothing distribution changes. By comprehensively analyzing the above multi-dimensional peak data, the system constructs a clothing distribution balance index to characterize the uniformity and stability of the overall distribution of clothing in the bin. For example, during a certain dehydration stage, the system detects three off-carrier peaks within a 10-second time window, with an average peak duration of approximately 1.2 seconds. The peaks are relatively evenly distributed along the time axis, indicating that while the clothing exhibits some periodic shift, the overall distribution is relatively stable, resulting in a high degree of distribution balance. Conversely, if the system detects six off-carrier peaks within the same time window, with longer peak durations and overlapping peaks exhibiting a high-density distribution, it indicates significant localized aggregation and repeated shifts in the clothing within the drum, resulting in a lower degree of distribution balance. Through this method, the system can transform abstract off-carrier peak information into quantifiable clothing distribution balance, thus more accurately reflecting the actual operating status within the drum. After obtaining the distribution balance, the system controls the intelligent washing machine to perform micro-speed adjustments based on different levels of balance, dynamically increasing the inner drum speed. For example, when the distribution balance is high, the system increases the inner drum speed with small increments, such as 20 rpm per control cycle, allowing the clothes to gradually enter a higher speed spin-drying stage while maintaining a stable state. When the distribution balance is moderate, the system uses micro-speed adjustments (such as alternating short-term small speed increases and brief periods of holding speed) to further redistribute the clothes, preventing localized agglomeration. When the distribution balance is low, the system reduces the speed increment or even pauses the speed increase, using micro-speed adjustments to disturb the clothes in the drum and gradually restore a uniform distribution. Through this dynamic incremental control method based on distribution balance, the risk of uneven load re-agglomeration can be reduced while ensuring spin-drying efficiency, improving the stability and safety of the spin-drying process.

[0027] In this embodiment, before step S3 of obtaining the off-center load trend of the smart washing machine, the method further includes: S301: Obtain the running data of the inner barrel in several rotation cycles, construct the corresponding cycle state set based on the running data in each rotation cycle, divide the cycle state set into several adjacent state segments, and extract the state change features corresponding to each state segment. S302: Determine whether the state change feature can match the degree of correlation between changes in different state segments; S303: If possible, identify the feature region corresponding to the change in operating state, generate a state evolution sequence based on the feature region, calculate the similarity deviation between adjacent state evolution sequences, collect the evolution direction of the inner tub's operating state based on the similarity deviation, form the corresponding state evolution result through the evolution direction, and obtain the off-load trend of the smart washing machine. The feature region specifically includes concentrated change region and dispersed change region.

[0028] In this embodiment, the system acquires the operating data of the inner tub within several rotation cycles. Based on the operating data within each rotation cycle, it constructs a corresponding set of cycle states. These sets of cycle states are divided into several adjacent state segments. The system extracts the state change features corresponding to each state segment. Then, the system determines whether these state change features can match the degree of correlation between changes in different state segments, and executes the corresponding steps accordingly. For example, when the system determines that the state change features corresponding to each state segment cannot match the degree of correlation between changes in different state segments, the system considers that the distribution of clothes in the tub has undergone a discontinuous change. For example, the clothes may undergo concentrated flipping, sudden local aggregation, or rapid dispersion in a short period of time, disturbing the originally relatively smooth state evolution process. The system will then re-divide the time boundaries of the cycle state sets to match the granularity of the adjacent state segments with the rhythm of the current operating state changes. At the same time, it will reduce the weight of the original state change features in the subsequent correlation analysis and re-extract the local change features within each state segment to construct a new set of state change features. For example, when the system determines that the state change features corresponding to each state segment... Features can match the degree of correlation between changes in different state segments. At this time, the system will consider the clothes in the drum to still be inertial changes. The system will identify the feature regions corresponding to the changes in operating state. The feature regions specifically include concentrated change regions and dispersed change regions. Based on these feature regions, a state evolution sequence is generated, and the similarity deviation between adjacent state evolution sequences is calculated. Based on different similarity deviations, the evolution direction of the inner drum's operating state is collected. Different evolution directions form corresponding state evolution results, and the off-center load trend of the smart washing machine is obtained. By comparing the similarity and deviation between different state evolution sequences, the system can more accurately reflect the changing trend and magnitude of the operating state, thereby effectively reducing misjudgments caused by instantaneous fluctuations, occasional vibrations, or local disturbances, and improving the stability and reliability of the off-center load state analysis results. At the same time, by introducing the evolution direction and state evolution results as intermediate analysis objects, the system can predict the future development direction of the off-center load state from the evolution process of the operating state, rather than being limited to the detection results of the current off-center load state. For example, it can identify in advance whether the off-center load state tends to stabilize, gradually alleviate, or has a tendency to deteriorate.

[0029] It should be noted that the process involves identifying the feature regions corresponding to changes in operating status, generating a state evolution sequence based on these feature regions, calculating the similarity deviation between adjacent state evolution sequences, collecting the evolution direction of the inner tub's operating status based on the similarity deviation, forming a corresponding state evolution result through the evolution direction, and obtaining the off-center load trend of the smart washing machine. Specifically: Based on the changes in operating data within continuous rotation cycles, the system identifies regions with concentrated and dispersed changes in operating status. Concentrated change regions characterize segments where the operating status changes significantly within a short period, while dispersed change regions characterize segments where the operating status remains relatively stable over a longer period. Subsequently, the system correlates different characteristic regions in chronological order to generate corresponding state evolution sequences. For example, within a certain dehydration stage, the system sequentially identifies the change process of "dispersed change region → concentrated change region → dispersed change region → concentrated change region," thus forming a corresponding state evolution sequence. Next, the system calculates the similarity deviation between adjacent state evolution sequences. The similarity deviation characterizes the degree of difference between two adjacent state evolution sequences. A small similarity deviation indicates high similarity between adjacent state evolution sequences, suggesting good continuity in the inner drum's operating status. A large similarity deviation indicates significant differences between adjacent state evolution sequences, suggesting a substantial change in the inner drum's operating status. After obtaining the similarity deviation, the system further collects the evolution direction of the inner tub's operating state based on the similarity deviation. For example, when the similarity deviation between multiple consecutive state evolution sequences gradually decreases, it indicates that the operating state is becoming more consistent, and the system identifies this change direction as a stable evolution direction. When the similarity deviation between multiple consecutive state evolution sequences shows a continuous downward trend, and the frequency of concentrated change areas gradually decreases, it indicates that the distribution of clothes inside the tub is gradually becoming more balanced, and the system identifies this change direction as a mitigating evolution direction. Subsequently, the system generates corresponding state evolution results based on different evolution directions. For example, for a stable evolution direction, it generates a "stable operating state" state evolution result. The system determines the "improved operating status" state evolution result based on the state evolution result. Finally, the system obtains the load imbalance trend of the smart washing machine based on the state evolution result. When the state evolution result corresponds to a stable operating status, the load imbalance trend can be determined as a stable trend. When the state evolution result corresponds to an improved operating status, the load imbalance trend can be determined as a mitigation trend. For example, when the system finds that the similarity deviation of adjacent state evolution sequences gradually decreases from 0.42 to 0.31, 0.19 and 0.08 in several consecutive rotation cycles, it indicates that the difference in operating status is gradually decreasing and the distribution of clothes is becoming more balanced. At this time, the system can determine the current load imbalance trend as a mitigation trend, providing a basis for further increasing the spin speed.

[0030] In this embodiment, step S5, which involves rechecking the off-center load state of the smart washing machine and dynamically limiting the safe rotation speed range of the smart washing machine based on the off-center load state, further includes: S51: Obtain the vibration response data corresponding to the off-center load state, obtain the vibration change characteristics of the inner tub in different speed ranges based on the vibration response data, construct the correspondence between the speed range and the vibration change characteristics, form the speed response model corresponding to the current operating stage, and identify the speed segment corresponding to the current operating speed according to the speed response model. S52: Determine whether the speed range belongs to a preset sensitive range; S53: If so, the speed range is divided into a restricted range, the restricted range is removed from the operable speed range, and a safe speed range is regenerated based on the remaining operable speed range. The inner tub is dynamically controlled to avoid the restricted range through the safe speed range.

[0031] In this embodiment, the system acquires vibration response data corresponding to the off-center load state. Based on this vibration response data, it obtains the vibration change characteristics of the inner drum in different speed ranges, constructs the correspondence between speed ranges and vibration change characteristics, and forms a speed response model corresponding to the current operating stage. According to this speed response model, it identifies the speed segment corresponding to the current operating speed. Then, the system determines whether the speed segment belongs to a pre-set sensitive segment to execute the corresponding steps. For example, when the system determines that the speed segment corresponding to the current operating speed does not belong to a pre-set sensitive segment, the system will consider that the current operating state still has the conditions to continue the dehydration process, and there is no need to take strong intervention measures such as slowing down to avoid, speed jumping, or redistribution. The system will store the vibration response data, vibration change characteristics, and operating results corresponding to the current non-sensitive operating segment into the speed response model as the data basis for subsequent model self-optimization. As the operating data is continuously accumulated, the system can gradually improve the correspondence between different speed segments and vibration change characteristics, and improve the accuracy of identifying sensitive and non-sensitive segments. For example, when the system determines that the current operating speed does not belong to a pre-set sensitive segment, it will determine that the current operating speed does not belong to a pre-set sensitive segment. If the corresponding speed range falls within a pre-defined sensitive range, the system will consider the current operating state unsuitable for continuing the spin-drying process. The system will then classify this speed range as a restricted range and remove it from the operable speed range. Based on the remaining operable speed ranges, a new safe speed range is generated. By dynamically controlling the inner drum to avoid the restricted range within different safe speed ranges, the system ensures that spin-drying control is no longer limited to a preset fixed speed strategy but can be dynamically adjusted according to the current operating state. When different load conditions, clothing types, and load imbalances correspond to different sensitive ranges, the system can still re-plan the safe speed range to match a suitable operating interval for the current condition. Simultaneously, by dynamically controlling the inner drum to avoid the restricted range within different safe speed ranges, the inner drum always prioritizes operation within a relatively stable vibration response speed range during acceleration, stabilization, and subsequent spin-drying. This not only reduces vibration and noise but also lowers the cyclic load on the suspension system, bearing assemblies, and drive system, reducing mechanical wear and structural fatigue.

[0032] It should be noted that the aforementioned speed range is divided into restricted ranges, and these restricted ranges are removed from the operable speed range. Based on the remaining operable speed ranges, a new safe speed range is generated. The inner tub is then dynamically controlled to avoid the restricted ranges within this safe speed range. Specifically: When the system determines, based on the speed response model, that the current operating speed corresponds to a pre-defined sensitive range, it indicates that the vibration response characteristics within that range have shown a significant amplification trend, suggesting a strong coupling effect between the off-center load and the inner drum rotation. In this case, continuing the original spin-drying program could lead to a rapid increase in vibration amplitude, intensified machine shaking, and even triggering the machine's protection mechanism. Therefore, the system first classifies the current speed range as a restricted range and removes it from the original operable speed range. For example, a smart washing machine's preset spin-drying speed range is 800 rpm to 1200 rpm. If the vibration response analysis reveals that the vibration response in the 950rpm to 1050rpm range consistently exceeds the preset safety standard, the system will classify the 950rpm to 1050rpm range as a restricted section and remove it from the original operating range. At this point, the original operating speed range is reclassified into two usable operating sections: 800rpm to 950rpm and 1050rpm to 1200rpm. Subsequently, the system will regenerate the corresponding safe speed range based on the remaining usable speed sections, so that subsequent dehydration control is based on the current actual operating state, rather than continuing to rely on a fixed preset speed curve. After regenerating the safe speed range, the system dynamically controls the inner drum's running path based on the new safe speed range to actively avoid restricted sections. For example, when the inner drum is currently running at 900 rpm and the target speed for the spin-drying program is 1100 rpm, the system will not follow the original speed increase path sequentially through speed points such as 950 rpm, 1000 rpm, and 1050 rpm. Instead, it will adjust the speed increase strategy based on the regenerated safe speed range. When the system confirms that 950 rpm to 1050 rpm is a restricted section, it can avoid this section by quickly bridging it or through a phased transition. For example, it can first maintain stable operation within the 920 rpm to 940 rpm range for a period of time, and then directly increase the speed to the safe speed range above 1060 rpm after the vibration condition meets the requirements; or it can operate at 950 rpm... After the clothes are redistributed stably below a certain depth, the system quickly enters the operable range above 1050 rpm. For example, if the system detects multiple sensitive ranges simultaneously, such as 900 rpm to 980 rpm and 1080 rpm to 1120 rpm being restricted ranges, the system can regenerate three safe speed ranges: 800 rpm to 900 rpm, 980 rpm to 1080 rpm, and 1120 rpm to 1200 rpm. Based on the current off-center load, the system selects the optimal operating path. Through this method, the system can dynamically plan safe operating ranges based on actual vibration response characteristics, ensuring the inner drum always avoids sensitive speed areas that easily amplify vibration and worsen off-center loads. This improves the overall stability, safety, and adaptability to different load conditions while maintaining dehydration efficiency.

[0033] In this embodiment, step S2, which determines whether the comprehensive off-center load characteristic value is less than a preset off-center load threshold, further includes: S21: Obtain the difference between the comprehensive off-center load characteristic value and the preset off-center load threshold, and obtain the off-center load margin corresponding to the current off-center load state based on the difference; S22: Determine whether the off-center load margin is greater than a preset margin threshold; S23: If not, mark the inner tub of the smart washing machine as a critical stable operating state, generate a corresponding operating protection coefficient according to the off-center load margin, limit the speed increase rate of the inner tub through the operating protection coefficient, dynamically narrow the preset speed range, establish the off-center load margin change record sequence, and statistically analyze the change amplitude of the off-center load margin in several consecutive sampling periods.

[0034] In this embodiment, the system obtains the difference between the comprehensive off-center load characteristic value and the preset off-center load threshold. Based on this difference, it obtains the off-center load margin corresponding to the current off-center load state. Then, the system determines whether the off-center load margin is greater than the preset margin threshold to execute the corresponding steps. For example, when the system determines that the off-center load margin corresponding to the current off-center load state is greater than the preset margin threshold, the system considers the current off-center load state to be a high safety margin state, and there is no need to take strong intervention measures such as distribution, speed limiting, or rebalancing immediately. The system will continuously collect operating status parameters such as speed fluctuation amplitude, current fluctuation amplitude, torque fluctuation amplitude, and vibration amplitude, and periodically update the comprehensive off-center load characteristic value and the corresponding off-center load margin. When the off-center load margin continues to remain above the margin threshold during subsequent operation, the system continues to execute the current operation release strategy. When it detects that the off-center load margin is gradually decreasing and approaching the margin threshold, it enters the early warning monitoring state in advance, increases the operating status sampling frequency, and strengthens the tracking and analysis of off-center load change trends so as to adjust the control in time before the off-center load state deteriorates. Control strategies are implemented; for example, when the system determines that the off-load margin corresponding to the current off-load state is not greater than the preset margin threshold, the system will consider the current off-load state to be a low margin state, requiring strong intervention measures such as distribution, speed limiting, or rebalancing. The system will mark the inner drum of the smart washing machine as a critical stable operating state, generate corresponding operating protection coefficients based on different off-load margins, limit the speed increase rate of the inner drum through these operating protection coefficients, dynamically narrow the preset speed range, establish a record sequence of off-load margin changes, and statistically analyze the change amplitude of the off-load margin over several consecutive sampling periods. The system generates corresponding operating protection coefficients based on different off-load margins and uses these operating protection coefficients to limit the speed increase rate of the inner drum and dynamically narrow the preset speed range, so that the speed control strategy can match the risk level of the current off-load state. When the off-load margin is small, the system increases the operating protection intensity, reduces the acceleration rate, and compresses the operating speed range; when the off-load margin is relatively large but still in a low margin state, it appropriately retains some operating capacity.

[0035] It should be noted that the inner drum of the intelligent washing machine is marked as a critical stable operating state. Based on the off-center load margin, a corresponding operating protection coefficient is generated. Through the operating protection coefficient, the rotational speed increase rate of the inner drum is limited, the preset rotational speed range is dynamically narrowed, and a record sequence of off-center load margin changes is established. The change amplitude of the off-center load margin is statistically analyzed within several consecutive sampling periods. Specifically: The system first marks the inner drum of the smart washing machine as being in a critical stable operating state, representing the transitional phase between a safe and risky operating state. Then, the system generates a corresponding operating protection coefficient based on the current off-center load margin. A smaller off-center load margin indicates less remaining safety space, resulting in a higher operating protection coefficient; conversely, a load margin closer to the margin threshold results in a relatively lower operating protection coefficient. Next, the system uses the operating protection coefficient to limit the rate of increase in the inner drum's rotation speed and dynamically narrows the preset rotation speed range. For example, the original spin-drying program allows the inner drum to rotate between 800 rpm and 1200 rpm. The system operates within the specified rpm range, with a 50 rpm increase per control cycle. When the system determines that the current off-center load margin is low, it can adjust the allowable operating speed range to 800 rpm to 1000 rpm based on the operating protection coefficient, and reduce the speed increase per control cycle from 50 rpm to 20 rpm, allowing the inner drum to operate in a smoother speed increase process, thereby reducing the probability of further deterioration of the off-center load condition. For example, when the off-center load margin further decreases, the system can further compress the speed range to 800 rpm to 900 rpm and temporarily postpone entering the higher speed range to ensure operational stability. After completing the operation protection control, the system further establishes a record sequence of off-center load margin changes to continuously record the corresponding changes in off-center load margin within different sampling periods. For example, if the system records off-center load margins of 0.18, 0.16, 0.14, 0.13, and 0.12 in five consecutive sampling periods, a corresponding off-center load margin change record sequence is formed. Subsequently, the system statistically analyzes the change range of the off-center load margin in multiple consecutive sampling periods to analyze the development trend of the current off-center load state. When the system detects that the off-center load margin is continuously decreasing and the rate of decrease is gradually increasing, it indicates that the off-center load state of the clothes in the bin is approaching the risk boundary. At this time, the system can further increase the operation protection coefficient and continue to compress the load. The system can determine the speed range; when the system detects that the off-center load margin change gradually decreases or even begins to rise, it indicates that the distribution of clothing is stabilizing and the current protective measures have taken effect. At this time, the system can maintain the current operating strategy or appropriately relax some operating restrictions. For example, if the off-center load margin sequence gradually rises from 0.12 to 0.15, 0.18, and 0.22, it indicates that the off-center load condition has improved, and the system can gradually restore some of the restricted speed range and appropriately increase the speed growth rate. By establishing a record sequence of off-center load margin changes and continuously analyzing its change range, the system can not only focus on the current off-center load condition but also grasp the development process of the off-center load condition, thereby achieving more refined and dynamic dehydration control.

[0036] In this embodiment, step S4, which determines whether the off-center load trend has a risk of deterioration, further includes: S41: Obtain multiple continuous trend segments corresponding to the off-center loading trend, and construct the corresponding off-center loading evolution trajectory based on the connection relationship between each trend segment; S42: Determine whether the off-center load evolution trajectory meets the preset deterioration conditions; S43: If so, locate the abnormal evolution segment in the off-center load evolution trajectory, extract the operation feature information corresponding to the abnormal evolution segment, construct the corresponding off-center load disturbance image based on the operation feature information, and identify the dominant influencing factor of the current off-center load state based on the off-center load disturbance image.

[0037] In this embodiment, the system acquires multiple continuous trend segments corresponding to the off-center load trend. Based on the connection relationship between each trend segment, it constructs corresponding off-center load evolution trajectories. Then, the system determines whether these off-center load evolution trajectories meet preset deterioration conditions to execute corresponding steps. For example, when the system determines that these off-center load evolution trajectories do not meet the preset deterioration conditions, the system considers the overall evolution process to be still within a controllable range, and the off-center load state does not show characteristics of continuous aggravation, rapid spread, or unstable development. The system extracts stable trajectory segments from the off-center load evolution trajectories and constructs corresponding trajectory reference templates based on the stable trajectory segments. It then matches and analyzes these trajectory reference templates with multiple normal operation trajectory templates stored in the historical operation process to determine the operating mode to which the current off-center load state belongs. For example, when the system determines that these off-center load evolution trajectories have already met the preset deterioration conditions, the system considers the overall evolution process to be difficult to control, and the system will... The system extracts operational characteristic information corresponding to abnormal evolution segments in the off-center load evolution trajectory. Based on different operational characteristic information, it constructs corresponding off-center load disturbance images and identifies the dominant influencing factors of the current off-center load state based on these images. The system extracts operational characteristic information corresponding to abnormal evolution segments and constructs corresponding off-center load disturbance images based on different operational characteristic information. This can transform the originally scattered operational data into disturbance characterization results with clear characteristic attributes. Through off-center load disturbance images, the system can more comprehensively reflect the changing patterns of the operating state within abnormal evolution segments, such as vibration enhancement characteristics, load transfer characteristics, center of gravity shift characteristics, and periodic fluctuation characteristics. At the same time, by identifying the main causes of off-center load deterioration, such as local accumulation of clothing, entanglement of large clothing items, continuous shift of the center of gravity, or abnormal fluctuations in the operating state, the system can adopt more matched control strategies for different influencing factors, rather than using a uniform distribution or speed limiting method.

[0038] It should be noted that, in locating the abnormal evolution segment in the off-center load evolution trajectory, extracting the corresponding operational feature information of the abnormal evolution segment, constructing the corresponding off-center load disturbance image based on the operational feature information, and identifying the dominant influencing factors of the current off-center load state based on the off-center load disturbance image, specifically: The system first performs segmented analysis on the off-center load evolution trajectory and locates the abnormal evolution segments in the time dimension. The so-called abnormal evolution segment refers to the time interval in which the off-center load characteristics suddenly increase, the vibration response is abnormally amplified, or the rate of change deviates significantly from the normal evolution law during the connection of continuous trend segments. For example, in a certain dehydration acceleration process, the system detects that between the 6th and 8th trend segments, the comprehensive off-center load characteristic value jumps rapidly from 0.82 to 0.96, and the vibration amplitude simultaneously shows a nonlinear increase. This interval is then identified as an abnormal evolution segment for subsequent in-depth analysis. After locating the abnormal evolution segment, the system further extracts the corresponding operational characteristic information for that segment. This operational characteristic information includes, but is not limited to, speed fluctuation characteristics, current fluctuation characteristics, torque mutation characteristics, and vibration amplification characteristics. For example, in the aforementioned abnormal segment, the speed fluctuation amplitude increases from 0.30 to 0.55, the current fluctuation exhibits periodic spikes, the vibration amplitude rapidly increases from 0.60 to 0.90, and the inner tank load response shows a significant unbalanced change. The system structures and organizes these multi-dimensional operational characteristics to form a feature set describing the abnormal evolution process, thereby avoiding the information loss problem caused by single-parameter analysis. Subsequently, the system constructs an off-center load disturbance image based on the aforementioned operational characteristic information to comprehensively characterize the off-center load formation mechanism in the abnormal evolution section. The off-center load disturbance image can be understood as a multi-dimensional feature mapping result of the current abnormal state, which integrates and expresses the correlation between different operational characteristics. For example, when the vibration amplitude continues to rise and the current fluctuation increases synchronously, a "mechanical vibration-dominated disturbance image" can be constructed; when the speed fluctuation is significant but the current change is small, a "speed control mismatch disturbance image" can be constructed; when multiple parameters fluctuate violently at the same time, a "multi-factor coupling disturbance image" can be constructed. Finally, the system identifies the dominant influencing factors of the current off-center load state based on the off-center load disturbance image, thereby locating the root cause of the off-center load deterioration. For example, in a certain spin cycle, if the disturbance image shows that the vibration amplitude increase is dominant and accompanied by abnormal local current peaks, the system determines that the dominant influencing factor is "mechanical imbalance caused by local concentration of clothes in the drum". In another scenario, if the current fluctuation is dominant and the vibration change is weak, the dominant influencing factor is determined to be "abnormal motor load distribution or control response lag". In this way, the system can not only identify whether the off-center load has deteriorated, but also further clarify the core factors that cause the deterioration, thereby providing more targeted decision-making basis for subsequent distributed control, speed limiting strategy adjustment or speed range reconstruction, and improving the accuracy and adaptability of spin-drying control.

[0039] In this embodiment, step S1, which involves collecting the operating status parameters of the smart washing machine during the spin-drying acceleration process based on the preset spin-drying program of the smart washing machine and constructing the comprehensive off-load characteristic value of the smart washing machine according to the preset weighting relationship of the operating status parameters, further includes: S11: Obtain several parameter change sequences corresponding to the running status parameters, construct corresponding parameter response trajectories based on the changes of each parameter change sequence within a continuous sampling period, perform correlation mapping processing on the parameter response trajectories, and obtain the collaborative change relationship between each parameter response trajectories; S12: Determine whether the cooperative change relationship satisfies the preset cooperative stability condition; S13: If so, generate a stable linkage relationship between the response trajectories of each parameter, extract the corresponding collaborative feature set according to the stable linkage relationship, calculate the duration of each collaborative feature in the continuous sampling period, and construct the corresponding collaborative credibility index based on the duration of the duration.

[0040] In this embodiment, the system acquires several parameter change sequences corresponding to the operating state parameters. Based on the changes of each parameter change sequence within a continuous sampling period, it constructs corresponding parameter response trajectories. These parameter response trajectories are then correlated and mapped to obtain the cooperative change relationship between them. The system then determines whether this cooperative change relationship meets a pre-set cooperative stability condition and executes the corresponding steps. For example, if the system determines that the cooperative change relationship between the parameter response trajectories does not meet the pre-set cooperative stability condition, it considers that the distribution of clothing inside the bin is undergoing non-uniform reconstruction, such as local entanglement, eccentric stacking, or instantaneous center of gravity shift. This causes the responses of various physical quantities to no longer synchronously reflect the same stable operating state, resulting in the cooperative change relationship deviating from the expected stable range. The system marks the current operating state as a cooperative instability state and performs quantitative analysis on the degree of deviation between the parameter response trajectories to identify the main source parameters of instability. For example, if the vibration response trajectory deviates significantly while the current and rotational speed changes are relatively stable, it indicates that the instability is mainly caused by mechanical side eccentric loading. For example, abnormal fluctuations in the synchronization of current and torque indicate that instability may be related to sudden changes in the drive load. For instance, when the system determines that the coordinated change relationship between the response trajectories of each parameter meets the pre-set coordinated stability conditions, the system assumes that the distribution of clothes in the bin is undergoing uniform reconstruction. The system generates a stable linkage relationship between the response trajectories of each parameter, extracts the corresponding set of coordinated features based on this stable linkage relationship, calculates the duration of each coordinated feature within a continuous sampling period, and constructs a corresponding coordinated reliability index based on these durations. Compared to relying solely on parameter consistency judgment at a single moment, this method introduces the dynamic evaluation dimension of "duration," enabling the system to distinguish between short-term and sustained stable states, avoiding misjudgments caused by accidental fluctuations or short-term consistency, thereby improving the reliability and robustness of coordinated stability judgment. Simultaneously, this coordinated reliability index comprehensively reflects the stability and continuity of the linkage relationship between various operating state parameters. A longer duration indicates higher coordinated reliability, suggesting a more obvious trend of uniform reconstruction of the distribution of clothes in the bin; conversely, a shorter duration indicates insufficient stability.

[0041] It should be noted that a stable linkage relationship is generated between the response trajectories of the various parameters. Based on this stable linkage relationship, a corresponding set of collaborative features is extracted, the duration of each collaborative feature within the continuous sampling period is calculated, and a corresponding collaborative reliability index is constructed based on the duration of the duration. Specifically: The system generates stable linkage relationships between the response trajectories of each parameter, which are used to characterize the consistency and coupling stability of multidimensional operating parameters in the time dimension. For example, if the changes of each parameter show a synchronous slow decrease or synchronous slight fluctuation in multiple consecutive sampling periods, the system can determine that the stable linkage relationship is established, indicating that the clothes in the bucket are undergoing a relatively uniform redistribution process, rather than a local off-center load abrupt change. After generating a stable linkage relationship, the system further extracts a corresponding set of collaborative features based on this linkage relationship. The set of collaborative features includes features such as synchronous change of multiple parameters, consistency of fluctuation amplitude, and consistency of response rhythm. For example, in a certain dehydration acceleration stage, if the speed fluctuation, current change, and vibration amplitude all show a synchronous slight decreasing trend, then the "synchronous attenuation feature" can be extracted. When multiple parameters change in the same direction over multiple cycles, the "directional consistency feature" can be extracted. When the parameter change rhythm is basically consistent and there is no obvious lag, the "temporal synchronization feature" can be extracted. These collaborative features together constitute a structured expression of the stable linkage relationship, enabling the system to characterize the stability of the current operating state from multiple dimensions. Subsequently, the system calculates the duration of each coordinating feature within consecutive sampling periods to characterize the length of time that the coordinating feature remains stable. For example, if the "synchronization decay feature" persists for 10 consecutive sampling periods, its duration is 10 periods; if the "temporal synchronization feature" deviates after only 5 periods, its duration is 5 periods. By statistically analyzing the duration of different coordinating features, the system can distinguish between short-term and long-term stable states, thereby avoiding misjudging instantaneous consistency as a true stable linkage relationship. Finally, the system constructs a coordination reliability index based on the duration of each coordination feature to quantify the reliability of the current stable linkage relationship. For example, when the duration of multiple key coordination features is relatively long (e.g., more than 15 sampling periods), the coordination reliability index is high, indicating that the clothes in the drum have formed a relatively stable and uniform distribution. The system can appropriately increase the inner drum speed or shorten the speed-up interval. For example, in a certain spin-drying process, if the "synchronous decay feature" is maintained for 18 periods and the "directional consistency feature" is maintained for 16 periods, the system calculates a high coordination reliability. At this time, it can be judged that the current spin-drying state is stable and it is allowed to continue to increase the speed steadily. Conversely, when the duration of coordination features is short and frequent interruptions occur, the coordination reliability is low. The system needs to maintain or reduce the speed-up strategy to prevent the off-center load state from fluctuating again. In this way, the system can transform the stability of multiple parameters into a quantifiable index, thereby improving the precision and reliability of spin-drying control.

[0042] Reference Appendix Figure 2 A washing and spin-drying control system based on off-center load detection in one embodiment of the present invention includes: The construction module 10 is used to collect the operating status parameters of the smart washing machine during the spin-drying acceleration process based on the preset spin-drying program of the smart washing machine, and construct the comprehensive off-center load characteristic value of the smart washing machine according to the preset weight relationship of the operating status parameters. The operating status parameters specifically include the speed fluctuation amplitude, current fluctuation amplitude, torque fluctuation amplitude and vibration amplitude. The judgment module 20 is used to determine whether the comprehensive off-center load characteristic value is less than a preset off-center load threshold. The execution module 30 is used to generate a corresponding off-center load change curve based on the comprehensive off-center load characteristic value if no, and dynamically adjust the inner drum speed of the smart washing machine through the off-center load change curve to obtain the off-center load trend of the smart washing machine. The off-center load trend specifically includes a stabilization trend and a mitigation trend. The second judgment module 40 is used to determine whether the off-center loading trend has detected a risk of deterioration. The second execution module 50 is used to activate the preset distribution mode of the smart washing machine if detected, redistribute the clothes in the drum of the smart washing machine based on the combination of actions of the distribution mode, recheck the off-center load state of the smart washing machine, and dynamically limit the safe speed range of the smart washing machine according to the off-center load state. The combination of actions specifically includes forward rotation, reverse rotation and intermittent pause.

[0043] In this embodiment, the construction module 10 collects the operating status parameters of the smart washing machine during the spin-drying acceleration process based on the pre-set spin-drying program of the smart washing machine. The operating status parameters specifically include the amplitude of speed fluctuation, current fluctuation, torque fluctuation, and vibration amplitude. According to the pre-set weighting relationship of different operating status parameters, a comprehensive off-center load characteristic value of the smart washing machine is constructed. Then, the judgment module 20 judges whether the comprehensive off-center load characteristic value is less than a pre-set off-center load threshold to execute the corresponding steps. For example, when the system determines that the comprehensive off-center load characteristic value of the smart washing machine is indeed less than the pre-set off-center load threshold, the system will consider that no obvious off-center load accumulation phenomenon has formed in the washing machine drum, and the system will continue to collect the operating status during the spin-drying acceleration process. The system calculates the parameters and recalculates the comprehensive off-center load characteristic value according to a preset sampling period to monitor the off-center load status in real time. During the continued acceleration process, the system can also determine the off-center load safety margin based on the difference between the current comprehensive off-center load characteristic value and the off-center load threshold. When the off-center load safety margin remains within a preset range, the inner tub is allowed to operate according to the normal acceleration curve to improve dehydration efficiency and shorten dehydration time. For example, if the system determines that the comprehensive off-center load characteristic value of the smart washing machine is not less than the preset off-center load threshold, the execution module 30 will consider that there is a significant off-center load accumulation phenomenon in the washing machine tub. The system will generate a corresponding off-center load change curve based on this comprehensive off-center load characteristic value, and dynamically adjust the inner tub speed of the smart washing machine through the off-center load change curve to obtain... The system monitors the off-center load trend in intelligent washing machines, which includes a stabilization trend and a mitigation trend. By optimizing the spin speed in real time based on changes in off-center load, the system reduces the probability of the inner drum entering a high-vibration zone under off-center load conditions. This reduces mechanical impact and vibration accumulation, thereby improving the operational stability of the spin-drying process and reducing overall machine noise and component wear. The stabilization trend reflects that the off-center load condition has not worsened further, while the mitigation trend reflects that the distribution of clothes is gradually improving. By identifying the off-center load trend, the system can shift from traditional "post-event response" to "trend judgment," allowing for proactive control strategies before the off-center load becomes severe, reducing unnecessary distribution operations and downtime. The second judgment module 40 then judges whether these off-load trends have detected a risk of deterioration, and executes the corresponding steps accordingly. For example, when the system determines that the off-load trend of the smart washing machine has not detected a risk of deterioration, the system will consider that although the current off-load state has reached or exceeded the preset off-load threshold, its change process is within a controllable range and does not show a trend of continuous aggravation, rapid spread or unstable development. The system will continuously collect the operating status parameters during the dehydration process and periodically update the comprehensive off-load characteristic value, off-load change curve and off-load trend information. When the subsequent detection results still do not show a risk of deterioration, the system will continue to execute the current dehydration control strategy and gradually release some of the operating restrictions caused by the off-load, so that the inner drum can smoothly transition to the target dehydration speed.For example, when the system detects a risk of deterioration in the off-center load trend of the smart washing machine, the second execution module 50 will consider the current off-center load change process difficult to control. The system will activate the smart washing machine's pre-set distribution mode and, based on the distribution mode, a combination of actions including forward rotation, reverse rotation, and intermittent pauses, redistribute the clothes in the washing machine drum, recheck the off-center load status, and dynamically limit the safe speed range of the smart washing machine according to different off-center load states. By analyzing the recheck results, the system can accurately determine whether the current off-center load status has been alleviated, avoiding blindly entering the high-speed spin-drying stage due to poor distribution effect. This improves the consistency between the off-center load detection results and the actual distribution of clothes, enhancing the pertinence and reliability of subsequent spin-drying control. At the same time, when the off-center load is small, the safe speed range can be appropriately expanded to ensure spin-drying efficiency; when the off-center load is large, the safe speed range is correspondingly narrowed to reduce vibration risk. By dynamically adjusting the safe speed range, the system can avoid the inner drum operating in an unsuitable speed range, which could cause severe vibration, increased noise, and damage to mechanical parts. This ensures operational safety while also considering spin-drying efficiency and overall machine stability. ;

[0044] In this embodiment, the execution module further includes: The construction unit is used to collect the interval time between adjacent off-carrier peaks based on the off-carrier peaks of the off-carrier change curve, and construct the corresponding response feature sequence according to the interval time. The judgment unit is used to determine whether the response feature sequence matches the dynamic distribution state of the clothes during the rotation of the inner tub; The execution unit is configured to, if so, acquire corresponding off-carrier peak data based on the off-carrier peak, identify the distribution balance of the clothes through the off-carrier peak data, and activate the micro-speed adjustment action of the smart washing machine based on the distribution balance to dynamically increase the inner drum speed of the smart washing machine. The off-carrier peak data specifically includes the number of peaks, peak duration, and peak distribution density.

[0045] In this embodiment, the system collects the interval time between adjacent off-carrier peaks based on the off-carrier peaks of the off-carrier change curve. Based on different interval time periods, it constructs corresponding response feature sequences. The system then determines whether these response feature sequences match the dynamic distribution state of the clothing during the inner tub's rotation, and executes corresponding steps accordingly. For example, when the system determines that these response feature sequences cannot match the dynamic distribution state of the clothing during the inner tub's rotation, the system considers that the off-carrier peak interval pattern extracted based on the off-carrier change curve can no longer accurately reflect the actual movement characteristics of the clothing inside the tub, i.e., the clothing distribution state has changed abnormally. The system marks the current operating state as an abnormal distribution state and further extracts the corresponding abnormal peak segment in the current off-carrier change curve, analyzing the peak density, peak growth rate, and peak duration of the abnormal peak segment to determine the main abnormal source causing the matching failure. Simultaneously, it re-establishes the response feature sequence corresponding to the current operating cycle and reduces the reference weight of historical response feature sequences in subsequent analysis processes to avoid interference from old distribution patterns in the current judgment result. For example, when the system determines that these response feature sequences can match the dynamic distribution state of the clothing during the inner tub's rotation, the system considers... Currently, the off-center carrier peak interval pattern extracted based on the off-center load variation curve can accurately reflect the actual movement characteristics of clothes inside the drum. The system acquires corresponding off-center carrier peak data based on the off-center carrier peaks. The off-center carrier peak data specifically includes the number of peaks, peak duration, and peak distribution density. Through this off-center carrier peak data, the system identifies the distribution balance of clothes. Based on different distribution balances, it activates the micro-speed adjustment action of the smart washing machine, dynamically increasing the inner drum speed. By analyzing the number of peaks, peak duration, and peak distribution density to identify the distribution balance of clothes, the system can transform the originally difficult-to-quantify distribution state of clothes. As an analyzable and comparable indicator of uniformity, a higher degree of uniformity is indicated when the clothing is evenly distributed, while a lower degree of uniformity is indicated when there is localized clustering or uneven loading of clothing. Simultaneously, micro-speed adjustments are activated based on different degrees of uniformity, dynamically increasing the inner drum's rotational speed to ensure the speed increase is coordinated with the current clothing distribution. When the clothing distribution uniformity is high, the system can appropriately increase the speed increment to improve dehydration efficiency. When the clothing distribution uniformity is relatively low, micro-speed adjustments are used to further shift and redistribute the clothing within the drum, and the inner drum's rotational speed is increased more gradually.

[0046] In this embodiment, it also includes: The extraction module is used to obtain the running data of the inner barrel in several rotation cycles, construct the corresponding cycle state set based on the running data in each rotation cycle, divide the cycle state set into several adjacent state segments, and extract the state change features corresponding to each state segment. The third judgment module is used to determine whether the state change features can match the degree of correlation between changes in different state segments; The third execution module is used to identify the feature region corresponding to the change in operating state if possible, generate a state evolution sequence based on the feature region, calculate the similarity deviation between adjacent state evolution sequences, collect the evolution direction of the inner tub's operating state based on the similarity deviation, form a corresponding state evolution result through the evolution direction, and obtain the off-load trend of the smart washing machine. The feature region specifically includes a concentrated change region and a dispersed change region.

[0047] In this embodiment, the system acquires the operating data of the inner tub within several rotation cycles. Based on the operating data within each rotation cycle, it constructs a corresponding set of cycle states. These sets of cycle states are divided into several adjacent state segments. The system extracts the state change features corresponding to each state segment. Then, the system determines whether these state change features can match the degree of correlation between changes in different state segments, and executes the corresponding steps accordingly. For example, when the system determines that the state change features corresponding to each state segment cannot match the degree of correlation between changes in different state segments, the system considers that the distribution of clothes in the tub has undergone a discontinuous change. For example, the clothes may undergo concentrated flipping, sudden local aggregation, or rapid dispersion in a short period of time, disturbing the originally relatively smooth state evolution process. The system will then re-divide the time boundaries of the cycle state sets to match the granularity of the adjacent state segments with the rhythm of the current operating state changes. At the same time, it will reduce the weight of the original state change features in the subsequent correlation analysis and re-extract the local change features within each state segment to construct a new set of state change features. For example, when the system determines that the state change features corresponding to each state segment... Features can match the degree of correlation between changes in different state segments. At this time, the system will consider the clothes in the drum to still be inertial changes. The system will identify the feature regions corresponding to the changes in operating state. The feature regions specifically include concentrated change regions and dispersed change regions. Based on these feature regions, a state evolution sequence is generated, and the similarity deviation between adjacent state evolution sequences is calculated. Based on different similarity deviations, the evolution direction of the inner drum's operating state is collected. Different evolution directions form corresponding state evolution results, and the off-center load trend of the smart washing machine is obtained. By comparing the similarity and deviation between different state evolution sequences, the system can more accurately reflect the changing trend and magnitude of the operating state, thereby effectively reducing misjudgments caused by instantaneous fluctuations, occasional vibrations, or local disturbances, and improving the stability and reliability of the off-center load state analysis results. At the same time, by introducing the evolution direction and state evolution results as intermediate analysis objects, the system can predict the future development direction of the off-center load state from the evolution process of the operating state, rather than being limited to the detection results of the current off-center load state. For example, it can identify in advance whether the off-center load state tends to stabilize, gradually alleviate, or has a tendency to deteriorate.

[0048] In this embodiment, the second execution module further includes: The acquisition unit is used to acquire vibration response data corresponding to the off-center load state, acquire vibration change characteristics of the inner tub in different speed ranges based on the vibration response data, construct the correspondence between the speed range and the vibration change characteristics, form a speed response model corresponding to the current operating stage, and identify the speed segment corresponding to the current operating speed according to the speed response model. The second judgment unit is used to determine whether the speed range belongs to a preset sensitive range; The second execution unit is used to, if so, divide the speed range into a restricted range, remove the restricted range from the operable speed range, regenerate a safe speed range based on the remaining operable speed range, and dynamically control the inner drum to avoid the restricted range through the safe speed range.

[0049] In this embodiment, the system acquires vibration response data corresponding to the off-center load state. Based on this vibration response data, it obtains the vibration change characteristics of the inner drum in different speed ranges, constructs the correspondence between speed ranges and vibration change characteristics, and forms a speed response model corresponding to the current operating stage. According to this speed response model, it identifies the speed segment corresponding to the current operating speed. Then, the system determines whether the speed segment belongs to a pre-set sensitive segment to execute the corresponding steps. For example, when the system determines that the speed segment corresponding to the current operating speed does not belong to a pre-set sensitive segment, the system will consider that the current operating state still has the conditions to continue the dehydration process, and there is no need to take strong intervention measures such as slowing down to avoid, speed jumping, or redistribution. The system will store the vibration response data, vibration change characteristics, and operating results corresponding to the current non-sensitive operating segment into the speed response model as the data basis for subsequent model self-optimization. As the operating data is continuously accumulated, the system can gradually improve the correspondence between different speed segments and vibration change characteristics, and improve the accuracy of identifying sensitive and non-sensitive segments. For example, when the system determines that the current operating speed does not belong to a pre-set sensitive segment, it will determine that the current operating speed does not belong to a pre-set sensitive segment. If the corresponding speed range falls within a pre-defined sensitive range, the system will consider the current operating state unsuitable for continuing the spin-drying process. The system will then classify this speed range as a restricted range and remove it from the operable speed range. Based on the remaining operable speed ranges, a new safe speed range is generated. By dynamically controlling the inner drum to avoid the restricted range within different safe speed ranges, the system ensures that spin-drying control is no longer limited to a preset fixed speed strategy but can be dynamically adjusted according to the current operating state. When different load conditions, clothing types, and load imbalances correspond to different sensitive ranges, the system can still re-plan the safe speed range to match a suitable operating interval for the current condition. Simultaneously, by dynamically controlling the inner drum to avoid the restricted range within different safe speed ranges, the inner drum always prioritizes operation within a relatively stable vibration response speed range during acceleration, stabilization, and subsequent spin-drying. This not only reduces vibration and noise but also lowers the cyclic load on the suspension system, bearing assemblies, and drive system, reducing mechanical wear and structural fatigue.

[0050] In this embodiment, the determination module further includes: The second acquisition unit is used to acquire the difference between the comprehensive off-center load characteristic value and the preset off-center load threshold, and based on the difference, acquire the off-center load margin corresponding to the current off-center load state. The third judgment unit is used to determine whether the off-center load margin is greater than a preset margin threshold. The third execution unit is used to mark the inner drum of the smart washing machine as a critical stable operating state if no, generate a corresponding operating protection coefficient according to the off-center load margin, limit the speed increase rate of the inner drum through the operating protection coefficient, dynamically narrow the preset speed range, establish the off-center load margin change record sequence, and statistically analyze the change amplitude of the off-center load margin in several consecutive sampling periods.

[0051] In this embodiment, the system obtains the difference between the comprehensive off-center load characteristic value and the preset off-center load threshold. Based on this difference, it obtains the off-center load margin corresponding to the current off-center load state. Then, the system determines whether the off-center load margin is greater than the preset margin threshold to execute the corresponding steps. For example, when the system determines that the off-center load margin corresponding to the current off-center load state is greater than the preset margin threshold, the system considers the current off-center load state to be a high safety margin state, and there is no need to take strong intervention measures such as distribution, speed limiting, or rebalancing immediately. The system will continuously collect operating status parameters such as speed fluctuation amplitude, current fluctuation amplitude, torque fluctuation amplitude, and vibration amplitude, and periodically update the comprehensive off-center load characteristic value and the corresponding off-center load margin. When the off-center load margin continues to remain above the margin threshold during subsequent operation, the system continues to execute the current operation release strategy. When it detects that the off-center load margin is gradually decreasing and approaching the margin threshold, it enters the early warning monitoring state in advance, increases the operating status sampling frequency, and strengthens the tracking and analysis of off-center load change trends so as to adjust the control in time before the off-center load state deteriorates. Control strategies are implemented; for example, when the system determines that the off-load margin corresponding to the current off-load state is not greater than the preset margin threshold, the system will consider the current off-load state to be a low margin state, requiring strong intervention measures such as distribution, speed limiting, or rebalancing. The system will mark the inner drum of the smart washing machine as a critical stable operating state, generate corresponding operating protection coefficients based on different off-load margins, limit the speed increase rate of the inner drum through these operating protection coefficients, dynamically narrow the preset speed range, establish a record sequence of off-load margin changes, and statistically analyze the change amplitude of the off-load margin over several consecutive sampling periods. The system generates corresponding operating protection coefficients based on different off-load margins and uses these operating protection coefficients to limit the speed increase rate of the inner drum and dynamically narrow the preset speed range, so that the speed control strategy can match the risk level of the current off-load state. When the off-load margin is small, the system increases the operating protection intensity, reduces the acceleration rate, and compresses the operating speed range; when the off-load margin is relatively large but still in a low margin state, it appropriately retains some operating capacity.

[0052] In this embodiment, the second determination module further includes: The second construction unit is used to obtain multiple continuous trend segments corresponding to the off-center loading trend, and construct the corresponding off-center loading evolution trajectory based on the connection relationship between each trend segment; The fourth judgment unit is used to determine whether the off-center load evolution trajectory meets the preset deterioration conditions; The fourth execution unit is used to locate the abnormal evolution segment in the off-center load evolution trajectory if the condition is met, extract the operation feature information corresponding to the abnormal evolution segment, construct the corresponding off-center load disturbance image based on the operation feature information, and identify the dominant influencing factor of the current off-center load state based on the off-center load disturbance image.

[0053] In this embodiment, the system acquires multiple continuous trend segments corresponding to the off-center load trend. Based on the connection relationship between each trend segment, it constructs corresponding off-center load evolution trajectories. Then, the system determines whether these off-center load evolution trajectories meet preset deterioration conditions to execute corresponding steps. For example, when the system determines that these off-center load evolution trajectories do not meet the preset deterioration conditions, the system considers the overall evolution process to be still within a controllable range, and the off-center load state does not show characteristics of continuous aggravation, rapid spread, or unstable development. The system extracts stable trajectory segments from the off-center load evolution trajectories and constructs corresponding trajectory reference templates based on the stable trajectory segments. It then matches and analyzes these trajectory reference templates with multiple normal operation trajectory templates stored in the historical operation process to determine the operating mode to which the current off-center load state belongs. For example, when the system determines that these off-center load evolution trajectories have already met the preset deterioration conditions, the system considers the overall evolution process to be difficult to control, and the system will... The system extracts operational characteristic information corresponding to abnormal evolution segments in the off-center load evolution trajectory. Based on different operational characteristic information, it constructs corresponding off-center load disturbance images and identifies the dominant influencing factors of the current off-center load state based on these images. The system extracts operational characteristic information corresponding to abnormal evolution segments and constructs corresponding off-center load disturbance images based on different operational characteristic information. This can transform the originally scattered operational data into disturbance characterization results with clear characteristic attributes. Through off-center load disturbance images, the system can more comprehensively reflect the changing patterns of the operating state within abnormal evolution segments, such as vibration enhancement characteristics, load transfer characteristics, center of gravity shift characteristics, and periodic fluctuation characteristics. At the same time, by identifying the main causes of off-center load deterioration, such as local accumulation of clothing, entanglement of large clothing items, continuous shift of the center of gravity, or abnormal fluctuations in the operating state, the system can adopt more matched control strategies for different influencing factors, rather than using a uniform distribution or speed limiting method.

[0054] In this embodiment, the construction module further includes: The third construction unit is used to obtain several parameter change sequences corresponding to the running status parameters, construct corresponding parameter response trajectories based on the changes of each parameter change sequence in a continuous sampling period, perform correlation mapping processing on the parameter response trajectories, and obtain the cooperative change relationship between each parameter response trajectories. The fifth judgment unit is used to determine whether the cooperative change relationship satisfies the preset cooperative stability condition; The fifth execution unit is used to generate a stable linkage relationship between the response trajectories of each parameter if the condition is met, extract the corresponding set of collaborative features based on the stable linkage relationship, calculate the duration of each collaborative feature in the continuous sampling period, and construct the corresponding collaborative reliability index based on the duration of the duration.

[0055] In this embodiment, the system acquires several parameter change sequences corresponding to the operating state parameters. Based on the changes of each parameter change sequence within a continuous sampling period, it constructs corresponding parameter response trajectories. These parameter response trajectories are then correlated and mapped to obtain the cooperative change relationship between them. The system then determines whether this cooperative change relationship meets a pre-set cooperative stability condition and executes the corresponding steps. For example, if the system determines that the cooperative change relationship between the parameter response trajectories does not meet the pre-set cooperative stability condition, it considers that the distribution of clothing inside the bin is undergoing non-uniform reconstruction, such as local entanglement, eccentric stacking, or instantaneous center of gravity shift. This causes the responses of various physical quantities to no longer synchronously reflect the same stable operating state, resulting in the cooperative change relationship deviating from the expected stable range. The system marks the current operating state as a cooperative instability state and performs quantitative analysis on the degree of deviation between the parameter response trajectories to identify the main source parameters of instability. For example, if the vibration response trajectory deviates significantly while the current and rotational speed changes are relatively stable, it indicates that the instability is mainly caused by mechanical side eccentric loading. For example, abnormal fluctuations in the synchronization of current and torque indicate that instability may be related to sudden changes in the drive load. For instance, when the system determines that the coordinated change relationship between the response trajectories of each parameter meets the pre-set coordinated stability conditions, the system assumes that the distribution of clothes in the bin is undergoing uniform reconstruction. The system generates a stable linkage relationship between the response trajectories of each parameter, extracts the corresponding set of coordinated features based on this stable linkage relationship, calculates the duration of each coordinated feature within a continuous sampling period, and constructs a corresponding coordinated reliability index based on these durations. Compared to relying solely on parameter consistency judgment at a single moment, this method introduces the dynamic evaluation dimension of "duration," enabling the system to distinguish between short-term and sustained stable states, avoiding misjudgments caused by accidental fluctuations or short-term consistency, thereby improving the reliability and robustness of coordinated stability judgment. Simultaneously, this coordinated reliability index comprehensively reflects the stability and continuity of the linkage relationship between various operating state parameters. A longer duration indicates higher coordinated reliability, suggesting a more obvious trend of uniform reconstruction of the distribution of clothes in the bin; conversely, a shorter duration indicates insufficient stability.

[0056] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A washing and spin-drying control method based on off-center load detection, characterized in that, Includes the following steps: Based on the preset spin-drying program of the smart washing machine, the operating status parameters of the smart washing machine during the spin-drying acceleration process are collected. According to the preset weight relationship of the operating status parameters, the comprehensive off-center load characteristic value of the smart washing machine is constructed. The operating status parameters specifically include the speed fluctuation amplitude, current fluctuation amplitude, torque fluctuation amplitude and vibration amplitude. Determine whether the comprehensive off-center load characteristic value is less than a preset off-center load threshold; If not, then based on the comprehensive off-center load characteristic value, a corresponding off-center load change curve is generated. Through the off-center load change curve, the inner drum speed of the smart washing machine is dynamically adjusted to obtain the off-center load trend of the smart washing machine. The off-center load trend specifically includes a stabilization trend and a mitigation trend. Determine whether the off-center loading trend has detected a risk of deterioration; If detected, the preset distribution mode of the smart washing machine is activated. Based on the combination of actions of the distribution mode, the clothes in the drum of the smart washing machine are redistributed. The off-center load status of the smart washing machine is rechecked. According to the off-center load status, the safe speed range of the smart washing machine is dynamically limited. The combination of actions specifically includes forward rotation, reverse rotation and intermittent pause.

2. The washing and dehydration control method based on off-center load detection according to claim 1, characterized in that, The step of dynamically adjusting the inner drum speed of the smart washing machine based on the off-center load change curve further includes: Based on the off-carrier peak of the off-carrier variation curve, the interval time between adjacent off-carrier peaks is collected, and the corresponding response feature sequence is constructed according to the interval time. Determine whether the response feature sequence matches the dynamic distribution state of the clothing during the inner tub rotation process; If so, then based on the offset carrier peak, the corresponding offset carrier peak data is obtained, and the distribution balance of the clothes is identified through the offset carrier peak data. Based on the distribution balance, the micro-speed adjustment action of the smart washing machine is activated to dynamically increase the inner drum speed of the smart washing machine. The offset carrier peak data specifically includes the number of peaks, the duration of peaks, and the distribution density of peaks.

3. The washing and dehydration control method based on off-center load detection according to claim 1, characterized in that, Before the step of obtaining the off-center load trend of the smart washing machine, the method further includes: The operation data of the inner barrel in several rotation cycles is obtained. Based on the operation data in each rotation cycle, a corresponding set of cycle states is constructed. The set of cycle states is divided into several adjacent state segments. The state change features corresponding to each state segment are extracted. Determine whether the state change features can match the degree of correlation between changes in different state segments; If possible, the characteristic regions corresponding to the changes in operating status are identified, a state evolution sequence is generated based on the characteristic regions, the similarity deviation between adjacent state evolution sequences is calculated, the evolution direction of the inner tub's operating status is collected based on the similarity deviation, and a corresponding state evolution result is formed through the evolution direction to obtain the off-load trend of the smart washing machine. The characteristic regions specifically include concentrated change regions and dispersed change regions.

4. The washing and dehydration control method based on off-center load detection according to claim 1, characterized in that, The step of re-inspecting the off-center load state of the smart washing machine and dynamically limiting the safe speed range of the smart washing machine based on the off-center load state further includes: Obtain the vibration response data corresponding to the off-center load state, and based on the vibration response data, obtain the vibration change characteristics of the current inner tub in different speed ranges, construct the correspondence between the speed range and the vibration change characteristics, form the speed response model corresponding to the current operating stage, and identify the speed segment corresponding to the current operating speed according to the speed response model. Determine whether the speed range belongs to a preset sensitive range; If so, the speed range is divided into a restricted range, the restricted range is removed from the operable speed range, and a safe speed range is regenerated based on the remaining operable speed range. The inner tub is dynamically controlled to avoid the restricted range through the safe speed range.

5. The washing and dehydration control method based on off-center load detection according to claim 1, characterized in that, The step of determining whether the comprehensive off-center load characteristic value is less than the preset off-center load threshold further includes: Obtain the difference between the comprehensive off-center load characteristic value and the preset off-center load threshold, and obtain the off-center load margin corresponding to the current off-center load state based on the difference; Determine whether the off-center load margin is greater than a preset margin threshold; If not, the inner tub of the smart washing machine is marked as a critical stable operating state. Based on the off-center load margin, a corresponding operating protection coefficient is generated. Through the operating protection coefficient, the rotation speed increase rate of the inner tub is limited, the preset rotation speed range is dynamically narrowed, and the off-center load margin change record sequence is established. The change amplitude of the off-center load margin is statistically analyzed within several consecutive sampling periods.

6. The washing and dehydration control method based on off-center load detection according to claim 1, characterized in that, The step of determining whether the off-center loading trend has detected a risk of deterioration further includes: Obtain multiple continuous trend segments corresponding to the off-center loading trend, and construct the corresponding off-center loading evolution trajectory based on the connection relationship between each trend segment; Determine whether the off-center load evolution trajectory meets the preset deterioration conditions; If so, the abnormal evolution segment in the off-center load evolution trajectory is located, the corresponding operational feature information of the abnormal evolution segment is extracted, the corresponding off-center load disturbance image is constructed based on the operational feature information, and the dominant influencing factor of the current off-center load state is identified based on the off-center load disturbance image.

7. The washing and dehydration control method based on off-center load detection according to claim 1, characterized in that, The step of collecting the operating status parameters of the smart washing machine during the spin-drying acceleration process based on the preset spin-drying program of the smart washing machine, and constructing the comprehensive off-load characteristic value of the smart washing machine according to the preset weight relationship of the operating status parameters, further includes: Obtain several parameter change sequences corresponding to the running status parameters, construct corresponding parameter response trajectories based on the changes of each parameter change sequence within a continuous sampling period, perform correlation mapping processing on the parameter response trajectories, and obtain the collaborative change relationship between each parameter response trajectories; Determine whether the cooperative change relationship satisfies the preset cooperative stability condition; If so, a stable linkage relationship is generated between the response trajectories of each parameter. Based on the stable linkage relationship, the corresponding collaborative feature set is extracted, the duration of each collaborative feature within the continuous sampling period is calculated, and the corresponding collaborative credibility index is constructed based on the duration of the duration.

8. A washing and spin-drying control system based on off-center load detection, characterized in that, include: The module is used to collect the operating status parameters of the smart washing machine during the spin-drying acceleration process based on the preset spin-drying program of the smart washing machine, and construct the comprehensive off-center load characteristic value of the smart washing machine according to the preset weight relationship of the operating status parameters. The operating status parameters specifically include the speed fluctuation amplitude, current fluctuation amplitude, torque fluctuation amplitude and vibration amplitude. The judgment module is used to determine whether the comprehensive off-center load characteristic value is less than a preset off-center load threshold. The execution module is used to generate a corresponding off-center load change curve based on the comprehensive off-center load characteristic value if not otherwise, and dynamically adjust the inner drum speed of the smart washing machine through the off-center load change curve to obtain the off-center load trend of the smart washing machine. The off-center load trend specifically includes a stabilization trend and a mitigation trend. The second judgment module is used to determine whether the off-center loading trend has detected a risk of deterioration. The second execution module is used to activate the preset distribution mode of the smart washing machine if detected, redistribute the clothes in the drum of the smart washing machine based on the combination of actions of the distribution mode, recheck the off-center load state of the smart washing machine, and dynamically limit the safe speed range of the smart washing machine according to the off-center load state. The combination of actions specifically includes forward rotation, reverse rotation and intermittent pause.

9. The washing and spin-drying control system based on off-center load detection according to claim 8, characterized in that, The execution module further includes: The construction unit is used to collect the interval time between adjacent off-carrier peaks based on the off-carrier peaks of the off-carrier change curve, and construct the corresponding response feature sequence according to the interval time. The judgment unit is used to determine whether the response feature sequence matches the dynamic distribution state of the clothes during the rotation of the inner tub; The execution unit is configured to, if so, acquire corresponding off-carrier peak data based on the off-carrier peak, identify the distribution balance of the clothes through the off-carrier peak data, and activate the micro-speed adjustment action of the smart washing machine based on the distribution balance to dynamically increase the inner drum speed of the smart washing machine. The off-carrier peak data specifically includes the number of peaks, peak duration, and peak distribution density.

10. The washing and spin-drying control system based on off-center load detection according to claim 8, characterized in that, Also includes: The extraction module is used to obtain the running data of the inner barrel in several rotation cycles, construct the corresponding cycle state set based on the running data in each rotation cycle, divide the cycle state set into several adjacent state segments, and extract the state change features corresponding to each state segment. The third judgment module is used to determine whether the state change features can match the degree of correlation between changes in different state segments; The third execution module is used to identify the feature region corresponding to the change in operating state if possible, generate a state evolution sequence based on the feature region, calculate the similarity deviation between adjacent state evolution sequences, collect the evolution direction of the inner tub's operating state based on the similarity deviation, form a corresponding state evolution result through the evolution direction, and obtain the off-load trend of the smart washing machine. The feature region specifically includes a concentrated change region and a dispersed change region.

Images