A notebook computer hinge control method based on data processing

Abstract

The present application relates to the technical field of device control, in particular to a notebook computer rotating shaft control method based on data processing, comprising: determining corresponding torque fluctuation missing amount based on fluctuation amplitude of the torque curve and phase difference change between the torque curve and the rotating speed curve; determining corresponding torque compensation demand amount based on the torque fluctuation missing amount and standard fluctuation range distribution of the torque curve; determining corresponding angle adjustment resistance amount based on overall offset of the angle position curve and dynamic coupling deviation between the torque curve and the rotating speed curve. Through dynamic compensation of torque fluctuation and angle position, the present application can effectively reduce torque fluctuation missing amount and angle adjustment resistance, not only improve response speed of the rotating shaft, but also optimize positioning accuracy of the rotating shaft and reduce angle deviation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of device control, specifically a notebook computer hinge control method based on data processing. BACKGROUND

[0002] The hinge of a notebook computer is a mechanical component that connects the screen and keyboard parts of the notebook. Its main function is to allow the screen to open and close within a certain range. It enables the notebook screen to remain fixedly connected to the keyboard part and allows users to adjust the angle of the screen as needed.

[0003] Currently, traditional hinge control methods typically use fixed control parameters and cannot dynamically adjust according to factors such as temperature, load, and rotational speed. This makes traditional methods less adaptable to environmental changes during device operation, resulting in suboptimal system performance, especially in cases of large temperature and load fluctuations, which can significantly compromise control effectiveness. Moreover, traditional methods often rely on simple control algorithms and lack real-time compensation mechanisms for torque fluctuations and angular positions. This can result in slow response speed and low positioning accuracy of the hinge, especially when there are missing amounts of fluctuations and angular adjustment obstructions, making it difficult for the system to maintain high-precision control.

[0004] In addition, traditional methods often do not consider the vibration problem in the hinge system. Abnormal vibration frequencies can cause system failure or performance degradation, and traditional methods cannot detect and correct vibration problems in real time without a vibration spectrum analysis module, increasing the risk of failure. SUMMARY

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a notebook computer hinge control method based on data processing, comprising:

[0006] During the operation of the hinge, the torque curve, rotational speed curve, temperature curve, and angular position curve in each monitoring time period are obtained;

[0007] Based on the fluctuation amplitude of the torque curve and the phase difference change between the torque curve and the rotational speed curve, the corresponding torque fluctuation missing amount is determined;

[0008] Based on the torque fluctuation missing amount and the standard fluctuation range distribution of the torque curve, the corresponding torque compensation demand amount is determined;

[0009] Based on the overall offset of the angular position curve and the dynamic coupling deviation between the torque curve and the rotational speed curve, the corresponding angular adjustment obstruction amount is determined;

[0010] determine an initial adjustment coefficient of a current monitoring time period based on the torque compensation demand, the angle adjustment obstruction and a cumulative deviation value between an angle position curve and a torque curve in a historical monitoring time period;

[0011] determine a dynamic adjustment weight based on a correlation change trend between the temperature curve and the rotation speed curve, adjust the initial adjustment coefficient based on the dynamic adjustment weight to determine a final adjustment coefficient, and perform remote control of the rotating shaft based on the final adjustment coefficient.

[0012] Preferably, the torque fluctuation missing amount is determined based on the fluctuation amplitude of the torque curve and the phase difference change between the torque curve and the rotation speed curve, including:

[0013] In the current monitoring time period, the torque curve and the rotation speed curve are synchronously collected and processed, the fluctuation amplitude of the torque curve and the phase difference change between the fluctuation amplitude and the rotation speed curve are calculated based on the collected curve data to obtain a curve feature data set;

[0014] Based on the curve feature data set, a torque fluctuation deviation evaluation model is constructed based on torque-rotation speed reference feature data of the equipment under standard working conditions, and a deviation amount of an actual value and a reference value of the torque fluctuation in the current monitoring time period is calculated through the torque fluctuation deviation evaluation model.

[0015] Taking the deviation amount as a core index, a corresponding torque fluctuation missing amount is determined based on correction coefficients of associated parameters such as equipment operating load and environmental temperature.

[0016] Preferably, the torque fluctuation deviation evaluation model is constructed, including:

[0017] Based on the torque-rotation speed reference feature data, torque fluctuation amplitude reference values and phase difference reference change rules under different loads and environmental temperatures are extracted;

[0018] Taking the curve feature data set as a sample, a deviation mapping relationship between sample features and reference features is established based on corresponding load and environmental temperature parameters;

[0019] The deviation mapping relationship is trained and optimized to obtain the torque fluctuation deviation evaluation model.

[0020] Preferably, the process of obtaining the torque compensation demand includes:

[0021] Based on the distribution characteristics of the torque fluctuation missing amount in the historical monitoring time period, a torque compensation reference model is established.

[0022] In the current monitoring time period, a difference between the real-time torque value and the torque compensation benchmark model predicted value is slidingly averaged to determine a torque deviation value at the current time;

[0023] The torque deviation value and the torque fluctuation missing amount are nonlinearly fused to determine a torque compensation demand amount in the current monitoring time period.

[0024] Preferably, based on the overall offset amount of the angle position curve and the dynamic coupling deviation condition between the torque curve and the rotation speed curve, a corresponding angle adjustment resistance amount is determined, including:

[0025] Feature extraction is performed on the angle position curve, the torque curve, and the rotation speed curve to obtain initial offset features and coupling features; wherein the initial offset features are characteristic information reflecting the deviation of the angle position curve from the benchmark trajectory, and the coupling features are characteristic information reflecting the interaction relationship between the torque curve and the rotation speed curve;

[0026] If the initial offset features and the coupling features meet the feature validity condition, an offset weight coefficient is determined based on an offset peak value and an offset frequency in the initial offset features; the offset peak value, the offset frequency, and the offset weight coefficient are fused and calculated to obtain the overall offset amount of the angle position curve; at the same time, the dynamic coupling deviation condition between the torque curve and the rotation speed curve is analyzed according to the coupling features;

[0027] Based on the overall offset amount and the dynamic coupling deviation condition, and based on a preset resistance amount correlation model, an initial angle adjustment resistance amount of the target device is determined;

[0028] Based on a curve fluctuation feature in the initial offset features and a deviation fluctuation coefficient in the dynamic coupling deviation condition, the initial angle adjustment resistance amount is corrected to obtain a target angle adjustment resistance amount;

[0029] When it is detected that the environmental temperature exceeds a preset threshold, a vibration spectrum analysis module is introduced; an abnormal vibration frequency component of the shaft bearing is obtained through the vibration spectrum analysis module; the abnormal vibration frequency component and the angle adjustment resistance amount are associated to calculate a degree of association, and a weight coefficient of the angle adjustment resistance amount is dynamically corrected.

[0030] Preferably, the dynamic coupling deviation condition between the torque curve and the rotation speed curve is analyzed according to the coupling features, including:

[0031] Based on phase difference data and amplitude ratio data in the coupling features, a real-time coupling coefficient is determined;

[0032] If the real-time coupling coefficient is within a preset coupling range, the difference between the real-time coupling coefficient and the range reference value is calculated to obtain the dynamic coupling deviation.

[0033] If the real-time coupling coefficient exceeds the preset coupling range, the preset deviation extreme value is determined as the dynamic coupling deviation situation.

[0034] Preferably, the process of obtaining the initial adjustment coefficient includes:

[0035] The integral value of the angle tracking error is calculated based on the cumulative error of the angle position curve in the preceding time period of the current monitoring time period.

[0036] During the current monitoring period, the ratio of the variance of the temperature curve to the variance of the torque curve is used as the thermodynamic coupling coefficient.

[0037] The initial adjustment coefficient for the current monitoring period is determined by fusing the integral value of the angle tracking error, the thermal coupling coefficient, the torque compensation requirement, and the angle adjustment resistance.

[0038] Preferably, the process of obtaining dynamically adjusted weights includes:

[0039] Using the inflection point of the temperature curve within the current monitoring period as the dividing point, the time period is divided into a thermally stable zone and a thermally sensitive zone.

[0040] The frequency domain features of the rotational speed curve are extracted within the thermally stable region, and the time-frequency domain features of the torque curve are extracted within the thermally sensitive region.

[0041] Cross-domain matching is performed between the frequency domain characteristics of the thermally stable region and the time-frequency domain characteristics of the thermally sensitive region to calculate the dynamic adjustment weight.

[0042] Preferably, the initial adjustment coefficient is adjusted based on the dynamic adjustment weight to determine the final adjustment coefficient, and the shaft is remotely controlled based on the final adjustment coefficient, including:

[0043] The dynamic adjustment weights are subjected to adaptive filtering; the filtered dynamic adjustment weights are then subjected to asymmetric weighting with the initial adjustment coefficients to determine the final adjustment coefficients.

[0044] The target torque curve is obtained by dynamically compensating the preset reference torque curve based on the final adjustment coefficient; the target torque curve is compared with the real-time torque curve by a closed-loop control system, and a compensation control signal is output; the shaft actuator is driven to complete the angle closed-loop adjustment based on the compensation control signal.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] The present application can adapt to different working environments through dynamic adjustment of temperature, load, rotating speed and other factors, especially in the process of equipment operation, the system can automatically adjust the control parameters with the change of temperature, load and other conditions, to ensure the optimization of the performance of the rotating shaft, and through dynamic compensation of torque fluctuation and angle position, the torque fluctuation loss and the hindrance of angle adjustment can be effectively reduced, the response speed of the rotating shaft is improved, the positioning accuracy of the rotating shaft is optimized, and the angle deviation is reduced.

[0047] The present application can continuously adjust the angle of the rotating shaft by adopting a closed-loop control system, outputting a compensation control signal through differential comparison of the real-time torque curve and the target torque curve, realizing accurate adjustment of the angle, and ensuring that the system can maintain the best working state at any time, and by establishing a torque compensation reference model and an angle adjustment hindrance model based on historical data, the system can gradually optimize the adjustment coefficient in the long-term operation process, thereby improving the reliability and efficiency of the system.

[0048] The present application can better cope with the control problem of the rotating shaft in a high-temperature environment by dividing the time period into a thermal stable zone and a thermal sensitive zone through inflection point analysis of the temperature curve, using the frequency domain characteristics of the rotating speed curve in the thermal stable zone and using the time-frequency domain characteristics of the torque curve in the thermal sensitive zone to calculate the dynamic adjustment weight, and avoiding performance degradation caused by temperature fluctuations, and by introducing a vibration spectrum analysis module, the system can detect abnormal vibration frequencies of the rotating shaft bearing, and dynamically correct the weight of the angle adjustment hindrance according to the vibration information, thereby providing protection for the long-term reliability of the rotating shaft and avoiding system failure or performance degradation caused by vibration. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 The figure shows the step flowchart of the overall method in an embodiment of the present application. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0051] Embodiment one, please refer to Figure 1 The present application provides a technical solution: a notebook computer rotating shaft control method based on data processing, comprising:

[0052] S1, in the process of rotating shaft operation, the torque curve, rotating speed curve, temperature curve and angle position curve in each monitoring time period are obtained;

[0053] S2, determine the corresponding torque fluctuation missing amount based on the fluctuation amplitude of the torque curve and the phase difference change between it and the speed curve;

[0054] S3, determine the corresponding torque compensation demand amount based on the torque fluctuation missing amount and the standard fluctuation range distribution of the torque curve;

[0055] S4, determine the corresponding angle adjustment resistance amount based on the overall offset of the angle position curve and the dynamic coupling deviation between the torque curve and the speed curve;

[0056] S5, determine the initial adjustment coefficient of the current monitoring time period based on the torque compensation demand amount, the angle adjustment resistance amount, and the cumulative deviation value between the angle position curve and the torque curve in the historical monitoring time period;

[0057] S6, determine the dynamic adjustment weight based on the correlation change trend between the temperature curve and the speed curve; adjust the initial adjustment coefficient based on the dynamic adjustment weight to determine the final adjustment coefficient, and perform remote control of the shaft based on the final adjustment coefficient.

[0058] It should be noted that during the operation of the shaft, the following data needs to be continuously monitored: the torque curve represents the torque value of the shaft at different time points, reflecting the torque that the shaft bears; the speed curve represents the speed of the shaft, reflecting the movement speed of the shaft in each time period; the temperature curve represents the temperature change of the shaft, which is usually related to the running load and friction; the angle position curve represents the angle position of the shaft at each time point, which is usually related to the angle of rotation or displacement; the torque fluctuation amplitude refers to the degree of change of the torque curve in unit time, reflecting the fluctuation of the shaft during operation; the phase difference with the speed curve refers to the time difference or relative offset between the torque curve and the speed curve; if the speed changes quickly and the torque reacts slowly, it may indicate that the system is unstable or fluctuates; based on these factors, by analyzing the fluctuation amplitude and the phase difference change, the torque fluctuation missing amount can be determined, i.e. the system fails to provide sufficient torque in time in a certain time period, resulting in decreased efficiency or insufficient stability; by analyzing the torque fluctuation missing amount and the standard fluctuation range distribution of the torque curve, a standard fluctuation range is usually designed, and the torque compensation amount required in the current period can be calculated; the purpose of this step is to ensure that the shaft does not lose efficiency or damage equipment due to fluctuations during operation;

[0059] The overall offset based on the angular position curve means whether the angle change of the rotating shaft within a certain time period exceeds the expected range; at the same time, the dynamic coupling deviation of the torque curve and the speed curve, that is, the torque and speed of the rotating shaft do not change according to the predetermined mode, may affect the accuracy of the angle adjustment; this step calculates the angle adjustment obstruction by analyzing these factors, that is, the factors that hinder normal angle adjustment; combined with the torque compensation demand, the angle adjustment obstruction and the cumulative deviation value of the angular position curve and the torque curve in the historical monitoring data, the initial adjustment coefficient of the current period can be calculated; the purpose of this step is to set a preliminary adjustment coefficient for subsequent adjustment; based on the associated change trend of the temperature curve and the speed curve, that is, the change relationship between temperature and speed, the influence of temperature on system performance is analyzed; generally, temperature rise may cause increased friction, thereby affecting torque or angle control; the dynamic adjustment weight reflects the influence of temperature change on the adjustment coefficient; in other words, the system will dynamically adjust the adjustment strategy according to the changes of temperature and speed; the dynamic adjustment weight is applied to the initial adjustment coefficient to determine the final adjustment coefficient through adjustment; this is the final parameter used for remote control of the rotating shaft; based on the final adjustment coefficient, the system can remotely control the operation of the rotating shaft to achieve the required stability, torque output and angle accuracy.

[0060] In an optional embodiment, based on the fluctuation amplitude of the torque curve and the phase difference change between the torque curve and the speed curve, the corresponding torque fluctuation missing amount is determined, including:

[0061] In the current monitoring time period, the torque curve and the speed curve are synchronously collected and processed, the fluctuation amplitude of the torque curve and the phase difference change between the fluctuation amplitude and the speed curve are calculated based on the collected curve data, and a curve feature data set is obtained;

[0062] Based on the curve feature data set, based on the torque-speed reference feature data of the device under standard working conditions, a torque fluctuation deviation evaluation model is constructed, and the deviation amount of the actual value and the reference value of the torque fluctuation in the current monitoring time period is calculated through the torque fluctuation deviation evaluation model;

[0063] Taking the deviation amount as the core index, based on the correction coefficient of the associated parameters such as device operating load and environmental temperature, the corresponding torque fluctuation missing amount is determined.

[0064] It should be noted that, in the current monitoring time period, the torque and speed data of the rotating shaft need to be synchronously collected; these two curves reflect the performance of the rotating shaft, the torque curve describes the moment change of the rotating shaft, and the speed curve describes the rotation speed of the rotating shaft; the two are usually related, the change of the speed will affect the output of the torque, and the fluctuation of the torque may affect the stability of the speed; by calculating the relationship between the fluctuation amplitude and the phase difference change, a curve characteristic data set describing the dynamic interaction between the torque and the speed is obtained, which contains the fluctuation amplitude, phase difference and other information in each time period; the standard working condition refers to the ideal relationship between the torque and the speed of the equipment under normal and ideal working conditions; usually, these standard data are obtained through the design parameters of the equipment, theoretical analysis or typical data in historical operation; under the standard working condition, the torque-speed relationship of the equipment is regarded as the ideal reference; these reference data can be calculated during the design, or historical data of the equipment under good running environment; based on these standard data, a torque fluctuation deviation evaluation model is established; the model can evaluate whether the current system is in a normal working state by comparing the current torque fluctuation amplitude with the reference value under the standard working condition, and calculate the deviation, that is, the difference between the current fluctuation and the standard value.

[0065] The deviation is the difference between the actual torque fluctuation value in the current monitoring time period and the reference value; this deviation indicates the gap between the current running state of the equipment and the expected standard working condition; a larger deviation usually indicates that the equipment has abnormal fluctuations, which may be caused by load, temperature, wear and tear and other factors; in order to solve the influence of external factors such as load and environmental temperature on the equipment, the expected value under the standard working condition may need to be corrected; for example, in a high temperature environment, the friction of the equipment may increase, causing the torque output to change, or under heavy load, the response speed and stability of the equipment may be affected; these correction coefficients are usually obtained through empirical formula, experimental data or online monitoring data; by combining the correction coefficient with the deviation, the torque fluctuation loss can be determined; that is, the additional torque required by the equipment under the current environmental and load conditions to make up for the performance decline caused by fluctuation and external factors.

[0066] In an optional embodiment, the torque fluctuation deviation evaluation model is constructed, comprising:

[0067] Based on the torque-speed reference characteristic data, the torque fluctuation amplitude reference value and the phase difference reference change rule under different loads and environmental temperatures are extracted;

[0068] Taking the curve characteristic data set as a sample, a deviation mapping relationship between the sample characteristics and the reference characteristics is established based on the corresponding load and environmental temperature parameters;

[0069] The deviation mapping relationship is trained and optimized to obtain the torque fluctuation deviation evaluation model.

[0070] It should be noted that the torque-speed reference characteristic data refers to the typical data of the relationship between torque and speed of the device under different loads and environmental temperatures under ideal or standard working conditions; these data are usually obtained from calculations during device design or through experiments in standard test environments; the torque fluctuation amplitude reference value refers to the normal range of torque fluctuation of the device under different load and environmental temperature conditions under standard working conditions; the amplitude of torque fluctuation under each load and temperature condition is usually measured, and the maximum and minimum values are recorded; these reference values reflect the stability of the device under various working environments; the phase difference reference variation law is to record the variation of the phase difference between the torque and speed curves under different conditions; this helps to understand the dynamic response behavior of the device under different working conditions; based on experimental data or simulation data under standard working conditions, the torque fluctuation amplitude and phase difference variation law under different loads and environmental temperatures are extracted; these laws will serve as a reference for subsequent modeling and evaluation; in actual operation, real-time data of the device (such as real-time acquisition data of torque and speed) are recorded as curve characteristic data sets; this data set contains the real-time performance of the torque fluctuation amplitude and speed fluctuation amplitude of the device under a specific load and environmental temperature;

[0071] The load and environmental temperature of the device will affect the performance of the device in actual operation; therefore, the load and environmental temperature of the device need to be recorded when collecting real-time data, which helps to understand the fluctuations in the data; each sample feature (such as the fluctuation amplitude of the curve, the phase difference, etc.) is compared with the corresponding reference feature (the fluctuation amplitude and phase difference under standard working conditions) to calculate the deviation; the goal of this process is to establish a mapping relationship that reflects the difference between the actual fluctuation of the device and the standard fluctuation under different load and environmental temperature conditions; the deviation mapping can be regarded as a mapping relationship from the actual data (sample features) to the reference data (standard features), which describes the deviation of the current state of the device from the ideal state; this mapping relationship is established according to the load and environmental temperature of the device and other parameters; in order to make the deviation mapping relationship effective in evaluating torque fluctuation, the mapping relationship usually needs to be trained; the training process is based on a large number of sample features and corresponding reference features, and learns the law of deviation mapping through machine learning algorithms (such as regression analysis, neural networks, etc.); the optimization goal is to improve the prediction accuracy of the model, so that it can accurately evaluate the torque fluctuation deviation of the device under different working environments;

[0072] The deviation mapping relationship obtained through training can be used to evaluate the torque fluctuation deviation of the equipment during real-time monitoring. The evaluation model can calculate the torque fluctuation deviation of the equipment by inputting real-time data such as torque, speed, load and ambient temperature, and predict whether the equipment is in normal working condition. As the amount of data increases and the operating conditions of the equipment become more diverse, the evaluation model may need to be further optimized to adapt to new operating environments or different load conditions of the equipment. This optimization usually relies on the continuous accumulation of real-time data and improves the evaluation accuracy by adjusting the model parameters.

[0073] In an optional embodiment, the process of obtaining the torque compensation requirement includes:

[0074] Based on the distribution characteristics of torque fluctuation missing data within historical monitoring periods, a torque compensation benchmark model is established.

[0075] During the current monitoring period, the difference between the real-time torque value and the torque compensation benchmark model prediction value is processed by moving average to determine the torque deviation value at the current moment;

[0076] The torque deviation value and the torque fluctuation missing amount are nonlinearly fused to determine the torque compensation requirement for the current monitoring period.

[0077] It should be noted that the historical monitoring period refers to the past period since the equipment started operating. During this period, torque fluctuation data of the equipment was recorded, especially the missing torque fluctuation. This data helps to understand the typical fluctuation patterns and regularities of the equipment in long-term operation. During the process of monitoring the equipment torque, data gaps may occur; for example, torque fluctuations may not be fully recorded due to sensor errors, signal loss, etc. The missing torque fluctuation refers to the amount of torque fluctuation that was not recorded during the monitoring period. By analyzing the distribution characteristics of these missing values, we can better understand the fluctuation patterns of the equipment and potential compensation needs. By analyzing the distribution characteristics of missing values ​​in historical data, we can infer the torque fluctuation compensation benchmark for the equipment under different operating conditions. This benchmark model describes how to predict the amount of torque to be compensated when the equipment experiences missing torque fluctuations. Based on historical data, the model can understand which conditions (such as load, temperature, etc.) may lead to missing torque fluctuations, and the typical distribution patterns of these missing values. The core of this step is to build a benchmark model through historical data analysis, which can provide an expected torque compensation value in the absence of real-time data.

[0078] The real-time torque value is the actual torque data of the equipment at the current time, which is usually collected in real time by a sensor; due to environmental changes, load fluctuations and other reasons, the real-time torque value may be different from the ideal state; the torque compensation reference model prediction value is the ideal torque value under the current conditions based on historical data and model algorithm prediction; it represents the normal torque fluctuation range that the equipment should produce under the current load, temperature and other conditions; the moving average is a common signal smoothing method, which removes short-term fluctuations by averaging data over a period of time to obtain more stable results; in this step, the difference between the real-time torque value and the reference prediction value is processed by moving average to obtain a smooth torque deviation value, which reflects the difference between the current operating state and the expected state of the equipment; the torque deviation value is the difference between the real-time torque value and the compensation reference model prediction value, which is obtained after smoothing, indicating the deviation degree of the equipment at the current time, i.e. whether the equipment is in normal working condition;

[0079] The relationship between the torque deviation value and the torque fluctuation missing amount may be nonlinear; this means that the relationship between the torque fluctuation missing amount and the deviation value may not be a simple linear function; for example, the deviation value may increase with the increase of the missing amount, but the increase may show an accelerating or decelerating trend; in order to more accurately reflect this complex relationship, a nonlinear fusion method is used to combine the torque deviation value and the fluctuation missing amount; by nonlinearly fusing the torque deviation value and the torque fluctuation missing amount, an accurate compensation demand can be obtained; this indicates the torque amount that the equipment needs to correct through compensation measures in the current monitoring period to ensure the normal operation of the equipment; specifically, if the torque fluctuation missing amount is large and the deviation value also deviates from the reference value, the compensation demand will be large; if the missing amount is small or the deviation value is close to zero, the compensation demand will be low; through nonlinear fusion, the compensation amount required by the equipment can be more accurately predicted and adjusted.

[0080] In an optional embodiment, the corresponding angle adjustment resistance amount is determined based on the overall offset of the angle position curve and the dynamic coupling deviation between the torque curve and the speed curve, comprising:

[0081] The angle position curve, the torque curve and the speed curve are feature extracted to obtain initial offset features and coupling features; wherein the initial offset features are characteristic information reflecting the deviation of the angle position curve from the reference trajectory, and the coupling features are characteristic information reflecting the interaction relationship between the torque curve and the speed curve;

[0082] If the initial offset feature and the coupling feature meet the feature validity condition, an offset weight coefficient is determined based on an offset peak value and an offset frequency in the initial offset feature; the offset peak value, the offset frequency, and the offset weight coefficient are fused and calculated to obtain an overall offset of the angle position curve; meanwhile, a dynamic coupling deviation between the torque curve and the rotation speed curve is analyzed according to the coupling feature;

[0083] Based on the overall offset and the dynamic coupling deviation, and based on a preset obstruction quantity correlation model, an initial angle adjustment obstruction quantity of the target device is determined;

[0084] Based on a curve fluctuation feature in the initial offset feature and a deviation fluctuation coefficient in the dynamic coupling deviation, the initial angle adjustment obstruction quantity is corrected to obtain a target angle adjustment obstruction quantity;

[0085] When it is detected that the ambient temperature exceeds a preset threshold, a vibration spectrum analysis module is introduced; an abnormal vibration frequency component of the shaft bearing is obtained through the vibration spectrum analysis module; a correlation degree between the abnormal vibration frequency component and the angle adjustment obstruction quantity is calculated to dynamically correct a weight coefficient of the angle adjustment obstruction quantity.

[0086] It should be noted that the initial offset feature is used to describe the situation that the angle position curve deviates from the reference trajectory; specifically, the initial offset feature includes: an offset peak value representing the maximum value of the angle position curve deviating from the reference trajectory, i.e., the farthest point of deviation; an offset frequency representing the frequency of such deviation, reflecting the occurrence frequency and change period of the deviation; the coupling feature reflects the interaction relationship between the torque curve and the rotation speed curve; by analyzing the dynamic changes of the torque and rotation speed curves, the coupling between them can be understood; for example, whether there is some synchronicity or opposition between the changes of torque and rotation speed, etc.; before subsequent calculations, it is necessary to check whether the initial offset feature and the coupling feature meet the validity condition; the validity condition usually involves whether the feature is sufficient to reflect the dynamic changes of the system, or whether there is sufficient change amplitude for effective compensation; if the initial offset feature is valid, an offset weight coefficient can be calculated using the offset peak value and the offset frequency; the role of this coefficient is to provide a basis for the compensation strategy in the angle adjustment process; the higher the offset peak value and the offset frequency, the more serious the deviation of the system from the reference trajectory, and thus more compensation is needed; by fusing and calculating the offset peak value, the offset frequency, and the offset weight coefficient, an overall offset can be obtained, which represents the degree of deviation of the current angle position of the system;

[0087] According to the coupling feature, the dynamic coupling deviation between the torque curve and the rotation speed curve can be further analyzed; such deviation reflects the inconsistency or asynchronization in time between the torque and rotation speed curves, which can affect the overall performance of the equipment; by analyzing the coupling feature between the torque and rotation speed, the dynamic changes of the load and rotation speed of the equipment during operation can be evaluated, so as to identify potential problems in the system operation; based on the overall deviation and the dynamic coupling deviation, a preset resistance amount correlation model can be used to determine the initial angle adjustment resistance amount of the equipment; according to the current deviation of the equipment and the coupling relationship, the model estimates the resistance force, such as friction and inertia, that the equipment may encounter during angle adjustment; according to the curve fluctuation feature in the initial deviation feature and the deviation fluctuation coefficient in the dynamic coupling deviation, the initial angle adjustment resistance amount is corrected; the fluctuation feature and the deviation fluctuation coefficient reflect the change trend of the equipment during angle adjustment; by analyzing these fluctuations, the resistance amount can be dynamically adjusted to make the angle adjustment more accurate; the corrected angle adjustment resistance amount is the adjustment amount that the equipment should take during actual operation, to compensate for the deviation of the angle position and the coupling effect of the system;

[0088] When the environmental temperature of the equipment exceeds the preset threshold value, the operating characteristics of the equipment may change, especially the friction and inertia of the mechanical parts may be affected by temperature changes; this may increase the difficulty of angle adjustment, so the angle adjustment resistance amount needs to be dynamically corrected; when the temperature exceeds the threshold value, a vibration spectrum analysis module is introduced to analyze the abnormal vibration frequency components of the shaft bearing; vibration spectrum analysis can detect whether there is abnormal vibration in the equipment, which may affect the accuracy and stability of angle adjustment; the correlation degree between the abnormal vibration frequency components and the angle adjustment resistance amount is calculated; through this calculation, the influence degree of abnormal vibration on the angle adjustment of the equipment can be understood; based on this influence, the weight coefficient of the angle adjustment resistance amount is dynamically adjusted to ensure that the equipment can still operate normally under high temperature and abnormal vibration conditions.

[0089] In an optional embodiment, the dynamic coupling deviation between the torque curve and the rotation speed curve is analyzed according to the coupling feature, including:

[0090] Based on the phase difference data and the amplitude ratio data in the coupling feature, a real-time coupling coefficient is determined;

[0091] If the real-time coupling coefficient is in the preset coupling interval, the difference between the real-time coupling coefficient and the interval reference value is calculated to obtain the dynamic coupling deviation;

[0092] If the real-time coupling coefficient exceeds the preset coupling interval, the preset deviation extreme value is determined as the dynamic coupling deviation.

[0093] It should be noted that the coupling features generally include phase difference and amplitude ratio, which reflect the dynamic relationship between two variables (such as torque and speed, temperature and pressure, etc.); the phase difference indicates the time offset between the two signals, and the amplitude ratio represents their relative strength or amplitude; the calculation of the real-time coupling coefficient: the change of the phase difference indicates the synchronization or opposition of the two; for example, in a mechanical system, if the phase difference between the speed and the torque is large, it may mean that their coupling degree is weak, and vice versa; the amplitude ratio reflects the relative change amplitude of the two; if the amplitude ratio is close to 1, it means that the change amplitudes of the two are close, indicating that the coupling strength is large; if the amplitude ratio difference is large, the coupling is weak; based on these feature data, the real-time coupling coefficient can be calculated by some algorithm (such as Fourier transform, correlation analysis, etc.), which reflects the coupling degree of the two signals at the current time; the preset coupling interval is usually set according to historical data, equipment working characteristics or theoretical model; it represents the coupling coefficient range of the device or system in normal and effective work; for example, if the coupling coefficient of the speed and the torque fluctuates between 0.8 and 1.2, this interval is considered as the preset coupling interval;

[0094] If the calculated real-time coupling coefficient is within the preset coupling interval, it means that the system is in a normal working state, and the coupling relationship between the two remains stable; if the real-time coupling coefficient exceeds the preset coupling interval, it means that the coupling relationship of the system has changed, which may affect the performance of the device or cause failure; if the real-time coupling coefficient is within the preset coupling interval, the difference between the real-time coupling coefficient and the reference value of the interval can be further calculated; this difference reflects the deviation between the current coupling state and the ideal coupling state; the interval reference value can be the median value of the coupling interval or the optimal coupling coefficient; the dynamic coupling deviation represents the deviation of the system during operation, which may be caused by external factors (such as load change, environmental temperature fluctuation) or internal factors (such as mechanical wear, component aging); if the real-time coupling coefficient exceeds the preset coupling interval, it means that the coupling relationship of the device or system has a large abnormal deviation, which may mean that the system is in a fault or abnormal running state; in this case, the preset deviation extreme value is used as the "dynamic coupling deviation condition"; the deviation extreme value represents the maximum acceptable deviation range of the system coupling state, and the deviation beyond this range may have a greater impact on the performance of the system, or even cause failure; this processing method helps to ensure that if the coupling coefficient appears obvious abnormality during system operation, measures can be taken to adjust or shut down in time to avoid damage to the equipment.

[0095] In an optional embodiment, the process of obtaining the initial adjustment coefficient includes:

[0096] Calculate an angle tracking error integral value based on accumulated errors of the angle position curve in a previous time period of the current monitoring time period;

[0097] In the current monitoring time period, the ratio of the temperature curve variance to the torque curve variance is taken as the thermal coupling coefficient;

[0098] The angle tracking error integral value, the thermal coupling coefficient, the torque compensation demand, and the angle adjustment resistance are fused to determine the initial adjustment coefficient of the current monitoring time period.

[0099] It should be noted that in the control system, the goal is to make the angle of the device as close to the set angle as possible; the angle position is usually controlled by an actuator or motor, but due to the imperfections of the system or the influence of external factors, the actual angle may deviate from the target angle; the tracking error refers to the difference between the actual angle and the desired angle, usually expressed in the form of deviation; the cumulative error (error integral) is the accumulation of errors over a period of time, representing the sum of long-term deviations; by integrating these errors, the angle tracking error integral value is obtained, which helps to assess whether the system has long-term deviations, such as the angle failing to accurately track the target angle, and the error will gradually increase; the angle tracking error integral value is actually the cumulative value of the angle error in the previous time period within the current monitoring time period; it can be used to judge the adjustment accuracy of the system throughout the process; in many mechanical systems, there is a close coupling between temperature and torque; for example: during the operation of the device, the temperature will fluctuate with the change in load, and excessive temperature may affect the performance of the material, lubrication and friction, and thus affect the torque; when the device is working, due to changes in load or friction, the output of the torque will fluctuate;

[0100] The variance represents the degree of data change, the greater the variance, the more intense the data fluctuation; the degree of temperature change, the greater the variance, the more intense the temperature change; the degree of torque output fluctuation, the greater the variance, the greater the torque change; the thermal coupling coefficient is the ratio of the temperature curve variance to the torque curve variance; if the temperature fluctuation is large and the torque fluctuation is small, the thermal-mechanical coupling of the system is strong; if the torque fluctuation is large and the temperature change is small, the mechanical fluctuation has a greater impact and the thermal coupling is weak; the thermal coupling coefficient is used to measure the influence of temperature fluctuation on mechanical behavior, helping to understand the degree of influence of thermal effect on the control system; different system characteristics are fused to determine the adjustment coefficient according to multiple factors; the angle tracking error integral value, the thermal coupling coefficient, the torque compensation demand and the angle adjustment resistance are fused, which may be obtained by weighting, algorithm optimization, model calculation and the like to obtain an initial adjustment coefficient for adjusting the behavior of the system; the initial adjustment coefficient is a key parameter of the control system, which determines how the system adjusts the angle in the current time period to correct the error and overcome the interference of heat and mechanics; the determination of the adjustment coefficient needs to consider: the accumulation of angle error, which indicates that the long-term angle deviation needs stronger adjustment; thermal coupling influence, if the temperature change is intense, the temperature compensation may need to be increased; torque compensation demand, if the load change is large, the torque compensation demand is large, and stronger torque adjustment is needed; angle adjustment resistance, if the resistance is large, stronger adjustment force is needed; finally, the initial adjustment coefficient determines how the system adjusts according to the current state to achieve more accurate angle control.

[0101] In an optional embodiment, the process of obtaining the dynamic adjustment weight comprises:

[0102] The time period is divided into a thermal stable zone and a thermal sensitive zone with the inflection point of the temperature curve in the current monitoring time period as the dividing point;

[0103] The frequency domain features of the speed curve are extracted in the thermal stable zone, and the time-frequency domain features of the torque curve are extracted in the thermal sensitive zone;

[0104] The frequency domain features of the thermal stable zone and the time-frequency domain features of the thermal sensitive zone are cross-domain matched to calculate the dynamic adjustment weight.

[0105] It is worth noting that the inflection point refers to the place where the slope of the curve changes, usually where the rate of temperature change changes; the temperature may accelerate at some moments or tend to be stable; the inflection point reflects the pattern of heat change, which has important influence on the performance of the system; when the temperature change tends to be stable or the change is small, the system can be considered to be in a stable state and is not easily affected by external temperature changes, and this interval is called the thermal stable zone; in the stage of rapid temperature change or fluctuation, the system may be more strongly affected by heat, and the temperature change has a greater disturbance on the mechanical performance, which is called the thermal sensitive zone; the monitoring time period is divided into two intervals according to the inflection point of the temperature, which are the thermal stable zone and the thermal sensitive zone; the system performance may be different in each interval, so it needs to be analyzed separately; frequency domain analysis is to convert time domain signals into frequency domain signals, usually using methods such as Fourier transform (FFT); frequency domain analysis can help identify periodic components and different frequency influences in the signal; in the thermal stable zone, the dynamic characteristics of the system are relatively stable due to the small temperature change, and the regularity of the system in this interval can be extracted by frequency domain analysis of the speed curve, such as the periodicity of the speed, the harmonic component, etc.

[0106] Time-frequency domain analysis combines the advantages of time domain and frequency domain, and is suitable for processing non-stationary signals, i.e. signals whose characteristics change over time; a common time-frequency analysis method is short-time Fourier transform (STFT), which can display both the time variation and frequency components of the signal; in the thermal sensitive zone, the behavior of the mechanical system is greatly affected due to the rapid temperature change, and the torque curve may become more complex, containing more transient changes and irregular fluctuations; through time-frequency domain feature extraction, the dynamic process of these complex changes can be revealed, and the influence of temperature fluctuations on torque can be identified; since the characteristics of the thermal stable zone and the thermal sensitive zone are different: the former focuses on the frequency domain regularity in the stable state, and the latter focuses on the time-frequency variation in the unstable state, therefore the characteristics of the two zones need to be matched and integrated; cross-domain matching means comparing, correlating and integrating the characteristics of the two intervals, finding the relationship between them, and adjusting according to these relationships; this may involve certain mathematical or algorithmic models, such as optimization algorithms, neural networks, etc.; dynamic adjustment weight is a key adjustment factor in control systems, used to adjust the response sensitivity or control strategy of the system; it is dynamically calculated based on the current state (such as temperature, speed, torque, etc.) to cope with the uncertainty and changes of the system; in the process of cross-domain matching, by comparing the frequency domain characteristics of the thermal stable zone and the time-frequency domain characteristics of the thermal sensitive zone, the system can derive a "dynamic adjustment weight", which can reflect the current working state of the system and how to optimize the adjustment in different intervals.

[0107] In an optional embodiment, the initial adjustment coefficient is adjusted based on a dynamic adjustment weight, a final adjustment coefficient is determined, and the shaft remote control is performed based on the final adjustment coefficient, comprising:

[0108] The dynamic adjustment weight is adaptively filtered, and the filtered dynamic adjustment weight and the initial adjustment coefficient are asymmetrically weighted to determine the final adjustment coefficient;

[0109] The preset reference torque curve is dynamically compensated based on the final adjustment coefficient to obtain a target torque curve; the target torque curve and the real-time torque curve are differentially compared through a closed-loop control system to output a compensation control signal; and the compensation control signal is used to drive the shaft actuator to complete angle closed-loop adjustment.

[0110] It should be noted that adaptive filtering is a commonly used technique in the field of signal processing, and its core idea is to automatically adjust the parameters of the filter according to the changes of the input signal, so as to optimize the quality of the output signal; here, the purpose of adaptive filtering is to process the noise or instability in the dynamic adjustment weight, so that the weight value is more stable, and the too violent fluctuation is removed, so as to ensure that the control system can respond more smoothly and accurately; through filtering, the dynamic adjustment weight will be more robust, avoiding the influence of fluctuations caused by environmental changes or sensor errors, so as to ensure that the system can be more accurately adjusted; asymmetric weighting operation means that when two weight values are weighted, different weight coefficients are assigned to them; in this step, the filtered dynamic adjustment weight and the initial adjustment coefficient are combined through weighting, and the filtered weight may occupy a larger proportion because it has been optimized and can better reflect the current system state; through this weighting operation, the final adjustment coefficient will be more in line with the current actual working conditions, and can dynamically adjust the control effect; the preset reference torque curve is an ideal and reference torque curve, which usually represents the best operating state of the system under certain standard conditions; in actual operation, the performance of the system may be affected by various factors, such as temperature change, load change, etc., and there is a difference with the reference torque curve; through dynamic compensation, the reference torque curve can be corrected according to the actual adjustment coefficient, so that the compensated curve is closer to the current actual demand; the result of dynamic compensation is to obtain a target torque curve, which can reflect the current dynamic state of the system, and is an adjusted version of the reference curve under ideal conditions;

[0111] The closed-loop control system is an automatic adjustment system that continuously monitors the output (in this case, the real-time torque curve) and compares it to the target value (the target torque curve), outputting a control signal to reduce the difference between the two. The real-time torque curve is the torque curve measured by the system during actual operation and may be affected by actual working conditions, errors, and noise. The target torque curve is compared to the real-time torque curve by taking the difference between the two, i.e., calculating the error. This error represents the current deviation of the system from the target state. The result of the difference comparison is the control error, which is adjusted by a compensation control signal to adjust the system output so that the real-time torque curve is as close as possible to the target torque curve. The compensation control signal is a control signal used to adjust the output of the system. It is calculated based on the difference between the target torque and the real-time torque and is used to compensate for system deviation. The actuator is a device that can adjust its movement or position according to the control signal. In this case, the shaft actuator will adjust the angle of the shaft according to the compensation control signal. Angle closed-loop regulation is a control method that adjusts through feedback. The system continuously monitors the change in angle and adjusts through the control signal until the angle of the shaft reaches the desired target. Through closed-loop control, the system can continuously adjust and correct the angle of the shaft, ensuring accurate positioning of the mechanical system.

[0112] The embodiments of the present application are described in detail above with reference to the accompanying drawings, but the present application is not limited thereto, and various changes can be made within the knowledge of those skilled in the art without departing from the spirit of the present application.

Claims

1. A method for controlling the hinge of a laptop computer based on data processing, characterized in that, include: During the operation of the shaft, the torque curve, speed curve, temperature curve, and angle position curve are acquired for each monitoring time period. Based on the fluctuation amplitude of the torque curve and the change in the phase difference between the fluctuation amplitude and the speed curve, the corresponding torque fluctuation missing amount is determined. Based on the missing torque fluctuation amount and the standard fluctuation range distribution of the torque curve, the corresponding torque compensation requirement is determined. Based on the overall offset of the angle position curve and the dynamic coupling deviation between the torque curve and the speed curve, the corresponding angle adjustment resistance is determined. Based on the torque compensation requirement, the angle adjustment resistance, and the cumulative deviation between the angle position curve and the torque curve during the historical monitoring period, the initial adjustment coefficient for the current monitoring period is determined. Based on the correlation trend between the temperature curve and the rotation speed curve, the dynamic adjustment weight is determined; The initial adjustment coefficient is adjusted based on the dynamic adjustment weight to determine the final adjustment coefficient, and the shaft is remotely controlled based on the final adjustment coefficient.

2. The method for controlling the hinge of a laptop computer based on data processing according to claim 1, characterized in that, Based on the fluctuation amplitude of the torque curve and the phase difference change between the fluctuation amplitude and the speed curve, the corresponding torque fluctuation missing amount is determined, including: During the current monitoring period, the torque curve and the speed curve are synchronously acquired and processed. Based on the acquired curve data, the fluctuation amplitude of the torque curve and the phase difference change between the fluctuation amplitude and the speed curve are calculated to obtain the curve feature dataset. Based on the curve feature dataset, and using the torque-speed reference feature data of the equipment under standard operating conditions, a torque fluctuation deviation assessment model is constructed. The deviation between the actual torque fluctuation value and the reference value during the current monitoring period is calculated using the torque fluctuation deviation assessment model. Using the aforementioned deviation as the core indicator, and based on the correction coefficients of the equipment operating load and ambient temperature-related parameters, the corresponding torque fluctuation missing amount is determined.

3. The method for controlling the hinge of a laptop computer based on data processing according to claim 2, characterized in that, Construct a torque fluctuation deviation assessment model, including: Based on the torque-speed reference feature data, the reference values ​​of torque fluctuation amplitude and phase difference reference variation are extracted under different loads and ambient temperatures; Using the aforementioned curve feature dataset as a sample, a deviation mapping relationship between the sample features and the baseline features is established based on the corresponding load and ambient temperature parameters. The deviation mapping relationship is trained and optimized to obtain a torque fluctuation deviation evaluation model.

4. The laptop hinge control method based on data processing according to claim 3, characterized in that, The process of obtaining the torque compensation requirement includes: Based on the distribution characteristics of the torque fluctuation missing amount during the historical monitoring period, a torque compensation benchmark model is established. During the current monitoring period, the difference between the real-time torque value and the predicted value of the torque compensation benchmark model is processed by moving average to determine the torque deviation value at the current moment. The torque deviation value and the torque fluctuation missing amount are nonlinearly fused to determine the torque compensation requirement for the current monitoring period.

5. The laptop computer hinge control method based on data processing according to claim 4, characterized in that, Based on the overall offset of the angle position curve and the dynamic coupling deviation between the torque curve and the speed curve, the corresponding angle adjustment resistance is determined, including: Feature extraction is performed on the angular position curve, torque curve, and speed curve to obtain initial offset features and coupling features; wherein, the initial offset features are feature information reflecting the deviation of the angular position curve from the reference trajectory, and the coupling features are feature information reflecting the interaction relationship between the torque curve and the speed curve; If the initial offset feature and coupling feature satisfy the feature validity condition, the offset weight coefficient is determined based on the offset peak value and offset frequency in the initial offset feature; the offset peak value, offset frequency and offset weight coefficient are fused and calculated to obtain the overall offset of the angle position curve; at the same time, the dynamic coupling deviation between the torque curve and the speed curve is analyzed according to the coupling feature. Based on the overall offset and the dynamic coupling deviation, and based on the preset resistance amount correlation model, the initial angle adjustment resistance amount of the target device is determined; Based on the curve fluctuation characteristics in the initial offset features and the deviation fluctuation coefficient in the dynamic coupling deviation, the initial angle adjustment resistance is corrected to obtain the target angle adjustment resistance. When the ambient temperature exceeds a preset threshold, a vibration spectrum analysis module is introduced; the abnormal vibration frequency components of the shaft bearing are obtained through the vibration spectrum analysis module; the correlation between the abnormal vibration frequency components and the angle adjustment resistance is calculated, and the weighting coefficient of the angle adjustment resistance is dynamically corrected.

6. The laptop computer hinge control method based on data processing according to claim 5, characterized in that, The dynamic coupling deviation between the torque curve and the speed curve is analyzed based on the coupling characteristics, including: Based on the phase difference data and amplitude ratio data in the coupling characteristics, the real-time coupling coefficient is determined; If the real-time coupling coefficient is within a preset coupling range, the difference between the real-time coupling coefficient and the range reference value is calculated to obtain the dynamic coupling deviation. If the real-time coupling coefficient exceeds the preset coupling range, the preset deviation extreme value is determined as the dynamic coupling deviation situation.

7. The method for controlling the hinge of a laptop computer based on data processing according to claim 6, characterized in that, The process of obtaining the initial adjustment coefficient includes: The integral value of the angle tracking error is calculated based on the cumulative error of the angle position curve in the preceding time period of the current monitoring time period. During the current monitoring period, the ratio of the variance of the temperature curve to the variance of the torque curve is used as the thermodynamic coupling coefficient. The initial adjustment coefficient for the current monitoring period is determined by fusing the integral value of the angle tracking error, the thermal coupling coefficient, the torque compensation requirement, and the angle adjustment resistance.

8. The laptop computer hinge control method based on data processing according to claim 7, characterized in that, The process of obtaining dynamically adjusted weights includes: Using the inflection point of the temperature curve within the current monitoring period as the dividing point, the time period is divided into a thermally stable zone and a thermally sensitive zone. The frequency domain features of the rotational speed curve are extracted within the thermally stable region, and the time-frequency domain features of the torque curve are extracted within the thermally sensitive region. Cross-domain matching is performed between the frequency domain characteristics of the thermally stable region and the time-frequency domain characteristics of the thermally sensitive region to calculate the dynamic adjustment weight.

9. A laptop hinge control method based on data processing according to claim 8, characterized in that, The initial adjustment coefficient is adjusted based on the dynamic adjustment weight to determine the final adjustment coefficient. Remote control of the rotating shaft is then performed based on the final adjustment coefficient, including: The dynamic adjustment weights are subjected to adaptive filtering; the filtered dynamic adjustment weights are then subjected to asymmetric weighting with the initial adjustment coefficients to determine the final adjustment coefficients. The target torque curve is obtained by dynamically compensating the preset reference torque curve based on the final adjustment coefficient; the target torque curve is compared with the real-time torque curve by a closed-loop control system, and a compensation control signal is output; the shaft actuator is driven to complete the angle closed-loop adjustment based on the compensation control signal.

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