Digital control method and system for vibration sweeping servo motor

Abstract

The invention discloses a digital control method and system for a sweep vibration servo motor, and relates to the technical field of motor energy saving.The method comprises the steps that when a driving end makes contact with an acting object for the first time, a user is reminded to hold the working face of the driving end to make contact with the acting object at preset pressure and move to the working face track to fully cover the acting object according to a specified functional area sequence; storing a working face moving track; performing flatness analysis on the surface of the acting object based on the working surface moving track; when the acting object is in contact for the second time or above, contact pressure is obtained, in combination with the surface flatness of the acting object, a sample set of acting objects of the same type is retrieved, mode statistics is conducted, a control array set is obtained, and any control array of the control array set comprises the amplitude, the vibration frequency, the swing angle and the torque; and performing energy consumption minimum optimization on the control array set to obtain a target control array to execute energy-saving control of the sweep vibration servo motor. The technical problem that in the prior art, a sweeping vibration servo motor is large in energy consumption is solved.

Description

Technical Field

[0001] This invention relates to the field of motor energy-saving technology, specifically to a digital control method and system for a sweeping servo motor. Background Technology

[0002] Sweeping servo motors are widely used in surface treatment equipment such as cleaning brushes and toothbrushes. Their core function is to achieve efficient surface treatment through high-frequency vibration and oscillation. Existing technologies typically use preset fixed vibration parameters or rely on manual adjustment by the user. This fails to adapt to actual working conditions and causes sweeping servo motors to often operate at power far exceeding actual needs, resulting in a significant increase in energy consumption. Summary of the Invention

[0003] This application provides a digital control method and system for a sweeping servo motor, which addresses the technical problem of high energy consumption in the operation of sweeping servo motors in the prior art.

[0004] In view of the above problems, this application provides a digital control method and system for a sweeping servo motor.

[0005] In a first aspect, this application provides a digital control method for a sweeping servo motor, the method comprising: When the drive end first contacts the object, remind the user to hold the drive end working surface and contact the object with the preset pressure and move it in the order of the specified functional areas until the working surface trajectory fully covers the object. Then, store the working surface movement trajectory. The preset pressure represents the pressure that will not cause the drive end working surface to deform, and the specified functional areas represent the functional area division of the object surface. The surface smoothness of the object is analyzed based on the working surface movement trajectory to obtain the surface smoothness of the object. When the object is contacted a second or more times, the contact pressure is obtained. Combined with the surface flatness of the object, a sample set of similar objects is retrieved, and mode statistics are performed to obtain a control array set. Each control array in the control array set includes amplitude, frequency, swing angle, and torque. The energy consumption of the control array set is minimized to obtain the target control array for energy-saving control of the sweep servo motor.

[0006] Secondly, this application provides a digital control system for a sweep servo motor, comprising: The trajectory storage module is used to store the working surface movement trajectory when the drive end first contacts the object and reminds the user to hold the working surface of the drive end and move it in the order of the specified functional areas until the working surface trajectory fully covers the object. The preset pressure represents the pressure that will not cause deformation of the working surface of the drive end, and the specified functional areas represent the functional area division of the surface of the object. The flatness analysis module is used to perform flatness analysis on the surface of the object based on the moving trajectory of the working surface, and to obtain the flatness of the surface of the object. The control array acquisition module is used to obtain the contact pressure when the object is contacted a second or more times, and to retrieve a sample set of similar objects by combining the surface flatness of the object, performing mode statistics, and obtaining a control array set. Each control array in the control array set includes amplitude, frequency, swing angle, and torque. The energy-saving optimization module is used to perform energy-saving optimization on the control array set to obtain the target control array for energy-saving control of the sweep servo motor.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application proposes a digital control method and system for a sweeping servo motor. During the initial contact, the system guides the user to collect standardized surface trajectory data. Combined with a machine learning model, it intelligently analyzes the flatness of the surface being worked on. In subsequent operations, it dynamically generates the optimal combination of vibration parameters by integrating real-time pressure and historical sample data. This significantly improves the equipment's adaptability to surfaces of different materials, shapes, and functional zones. Compared to traditional methods, this solution significantly eliminates the problem of high energy consumption in sweeping servo motors caused by fixed preset parameters, achieving a balance between stable performance and reduced energy consumption in complex surface operation scenarios. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart illustrating a digital control method for a sweeping servo motor provided in an embodiment of this application.

[0010] Figure 2 This is a schematic diagram of the structure of a digital control system for a sweeping servo motor provided in an embodiment of this application.

[0011] The components represented by each number in the attached diagram are explained below: The system includes a trajectory storage module 100, a flatness analysis module 200, a control array acquisition module 300, and an energy-saving optimization module 400. Detailed Implementation

[0012] This application provides a digital control method and system for a sweeping servo motor, which addresses the technical problem of high energy consumption in the operation of sweeping servo motors in the prior art.

[0013] The technical solutions of the embodiments of this application 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 this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, methods, products, or devices.

[0015] Example 1, as Figure 1 As shown, this application provides a digital control method for a sweep servo motor, wherein the method includes: S10: When the drive end first contacts the object, remind the user to hold the drive end working surface and contact the object with a preset pressure. Move the drive end working surface in the specified functional area sequence until the working surface trajectory fully covers the object. Store the working surface movement trajectory. The preset pressure represents the pressure that will not cause deformation of the drive end working surface. The specified functional area represents the functional area division of the object surface.

[0016] When the sweeping vibration equipment first comes into contact with the object, the existing technology lacks standardized operation guidance, resulting in a serious lack of effectiveness in trajectory acquisition. At the same time, excessive contact pressure can easily cause deformation of the working surface or damage to the object, while insufficient contact pressure will lead to the loss of key mechanical feedback in the trajectory data.

[0017] Step S10 in the method provided in this application embodiment includes: When the drive end first contacts the object, the user is reminded to hold the working surface of the drive end in contact with the object, and the first contact pressure is collected by the pressure sensor. When the first pressure deviation between the first contact pressure and the preset pressure is greater than the pressure deviation threshold, the user is reminded to reduce the contact pressure. When the first pressure deviation between the first contact pressure and the preset pressure is less than the pressure deviation threshold, the user is reminded to increase the contact pressure. When the first pressure deviation between the first contact pressure and the preset pressure is equal to the pressure deviation threshold, the user is reminded to start moving, and the movement trajectory of the working surface is stored by the drive end positioner.

[0018] In this embodiment of the application, when the drive end of the device driven by the user using a sweeping servo motor first contacts the object, the user is reminded to hold the working surface of the drive end to contact the object, and at the same time, the first contact pressure is collected by a pressure sensor.

[0019] When the initial contact pressure deviates from the preset pressure by more than a pressure deviation threshold, the user is prompted to reduce the contact pressure. Initial pressure deviation = Initial contact pressure - Preset pressure. The preset pressure is a pre-defined contact pressure value suitable for the current operating conditions. The pressure deviation threshold is a pre-defined threshold representing the degree of deviation between the contact pressure and the preset pressure. When the initial pressure deviation exceeds the pressure deviation threshold, it indicates that the current contact pressure is too high. Excessive contact pressure may lead to insufficient driving force and wear of the sweep servo motor. When the first contact pressure deviates from the preset pressure by less than the pressure deviation threshold, the user is prompted to increase the contact pressure. When the first contact pressure deviates from the preset pressure by a first pressure deviation threshold, the user is alerted to start moving, and the movement trajectory of the working surface is stored by the drive end positioner.

[0020] In application example one, when the cleaning brush is driven by a sweeping servo motor, the driving surface is the working surface where the bristles contact the surface to be cleaned. A prompting tone can be used. If the user has not yet brought the working surface into contact with the object being cleaned, such as the surface of the item, a voice prompt saying "Please contact the working surface" is used to prompt the user. The prompting tone stops when the user brings the working surface into contact with the object being cleaned. Simultaneously, a pressure sensor located at the base of each bristle tuft collects the first contact pressure, where one pressure sensor is installed at the base of each bristle tuft. For example, the preset pressure is set to 2N, and the pressure deviation threshold is set to 0.5N. When the first pressure deviation collected by the pressure sensor is greater than the pressure deviation threshold, a voice prompt saying "Pressure too high" is used to prompt the user. When the first pressure deviation is less than the pressure deviation threshold, a continuous voice prompt saying "Pressure too low" is used to prompt the user. When the first pressure deviation equals the pressure deviation threshold, a voice prompt saying "Please start cleaning" prompts the user to begin cleaning. Simultaneously, the working surface movement trajectory during the user's use of the cleaning brush is stored by the drive-end positioner.

[0021] Application Example 2: When the toothbrush is driven by a sweeping servo motor, the driving surface is the working surface of the toothbrush that contacts the teeth. A prompting tone can be used. If the user has not yet brought the working surface into contact with the teeth, a voice prompt saying "Please contact your teeth" is used. The prompting tone stops when the user brings the working surface into contact with the teeth. Simultaneously, a pressure sensor located at the base of each bristle tuft collects the first contact pressure. For example, the preset pressure is set to 0.8N, and the pressure deviation threshold is set to 0.1N. When the first pressure deviation collected by the pressure sensor is greater than the pressure deviation threshold, a voice prompt saying "Pressure too high" is used to alert the user. When the first pressure deviation is less than the pressure deviation threshold, a continuous voice prompt saying "Pressure too low" is used to alert the user. When the first pressure deviation equals the pressure deviation threshold, a voice prompt saying "Please start brushing" is used to alert the user to start brushing. At the same time, the working surface movement trajectory during the user's use of the toothbrush is stored by the drive-end positioner.

[0022] By pre-setting a pressure threshold to guide users to maintain zero-deformation contact, the trajectory data is collected under physical protection, providing a high-fidelity trajectory data foundation for flatness analysis, while avoiding damage to equipment or objects due to uncontrolled pressure, thus improving the reliability of subsequent analysis from the source.

[0023] S20: Based on the moving trajectory of the working surface, perform a flatness analysis on the surface of the object to obtain the flatness of the surface of the object.

[0024] Traditional methods rely on complex algorithms to determine surface flatness, which has the disadvantage of high computing power requirements, resulting in low processing efficiency and a lack of timely and effective objective basis for adjusting vibration parameters.

[0025] Step S20 in the method provided in this application embodiment includes: Based on the type of object being processed, a flatness analysis model is selected from the flatness analysis model library. Using the selected flatness analysis model, the movement trajectory of the working surface is processed, and the flatness of the surface of the object is output. The selected flatness analysis model is generated by machine learning training through multiple sets of data. Each set of data includes working surface movement trajectory recording data and labels that identify the flatness of the surface of the object being processed. The selected flatness analysis model is generated through machine learning training using multiple sets of data. Each set of data includes working surface movement trajectory recording data and labels indicating the flatness of the surface of the object being processed, including: Collect data on the movement trajectory of several user-handheld drive terminals when the working surfaces of the terminals contact the object with a preset pressure and move in sequence according to the specified functional areas until the working surface trajectory fully covers the object. Obtain the standard working surface movement trajectory pre-stored by the administrator, wherein the standard working surface movement trajectory represents the movement trajectory of the hand-held drive end working surface when it contacts the object with a preset pressure and moves in the order of the specified functional areas until the working surface trajectory fully covers the flat surface object. Based on the standard working surface movement trajectory, the trajectory shape is compared by traversing the recorded data of the several working surface movement trajectories to obtain several trajectory shape similarities, which are set as several labels to identify the surface flatness of the object. Specifically, based on the standard working surface movement trajectory, the trajectory shape is compared by traversing the recorded data of several working surface movement trajectories to obtain several trajectory shape similarities, which are then set as several labels to identify the surface flatness of the object, including: From the standard working surface movement trajectory, extract the standard working surface movement trajectory of the first functional area up to the standard working surface movement trajectory of the Nth functional area; The curvature of the standard working surface movement trajectory of the first functional area is calculated by traversing the standard working surface movement trajectory of the Nth functional area to obtain the curvature set of the standard working surface movement trajectory of the first functional area up to the curvature set of the standard working surface movement trajectory of the Nth functional area. Traverse the curvature set of the standard working surface movement trajectory of the first functional area up to the curvature set of the standard working surface movement trajectory of the Nth functional area to extract the maximum curvature value, and obtain the curvature threshold of the first functional area up to the curvature threshold of the Nth functional area; From the moving trajectory recording data of the plurality of working surfaces, extract the moving trajectory recording data of the first working surface and perform partition curvature maximum value extraction to obtain the curvature parameters of the first functional area up to the curvature parameters of the Nth functional area; When the curvature parameter of the first functional area is less than or equal to the curvature threshold of the first functional area, k is incremented by one, and the initial value of k is 0. When the curvature parameter of the first functional area is greater than the curvature threshold of the first functional area, k remains unchanged, and the initial value of k is equal to 0. Until the curvature parameters of the Nth functional area have been traversed, the k value is obtained, and it is compared with the N value to obtain the first trajectory shape similarity, which is then added to the several trajectory shape similarities. The data recordings of the movement trajectories of the working surfaces and the labels indicating the flatness of the surface of the object being worked on are added to the multiple sets of data.

[0026] In this embodiment, when there is secondary or higher contact with the object, a flatness analysis model is selected from the flatness analysis model library based on the type of object. The selected flatness analysis model is then used to process the movement trajectory of the working surface and output the surface flatness of the object.

[0027] The selected flatness analysis model is generated through machine learning training using multiple sets of data. Each set of data used for training includes data recording the movement trajectory of the working surface and labels indicating the flatness of the surface of the object being processed.

[0028] Specifically, data is collected on the movement trajectories of several working surfaces as users hold the drive end and apply preset pressure to the object, moving it according to a specified functional area sequence until the working surface trajectory fully covers the object. Optionally, when the drive end is a cleaning brush, data is collected on the movement trajectories of several working surfaces as users hold the cleaning brush and apply preset pressure to the surface to be cleaned, moving it according to a specified functional area sequence until the working surface trajectory fully covers the surface to be cleaned. The specified functional area is a pre-named partition of the area to be cleaned; for example, for a cleaning cabinet, the specified functional area includes multiple partitions such as the cabinet surface, cabinet door, and cabinet top. When the drive end is a toothbrush, data is collected on the movement trajectories of several working surfaces as users hold the toothbrush and apply preset pressure to the tooth surface to be cleaned, moving it according to a specified functional area sequence until the working surface trajectory fully covers the surface to be cleaned. The specified functional area is for each tooth, such as the left central incisor, left canine, and left first premolar.

[0029] Obtain the standard working surface movement trajectory pre-stored by the administrator. The administrator is the production designer of the equipment driven by the sweeping servo motor. The administrator will pre-store the standard working surface movement trajectory for later use. The standard working surface movement trajectory represents the movement trajectory of the handheld drive end working surface when it contacts the object with a preset pressure and moves in the specified functional area sequence until the working surface trajectory fully covers the object on the flat surface. Application Example 1: If a cleaning brush is driven by a sweeping servo motor and is prepared for cleaning a cabinet, a standard working surface movement trajectory might be the movement trajectory of first contacting the first functional area (e.g., the cabinet surface), then the second functional area (e.g., the cabinet door), then the third functional area (e.g., the cabinet top), until the working surface of the cleaning brush fully covers the surface of the cabinet to be cleaned. Application Example 2: If a toothbrush is driven by a sweeping servo motor and is prepared for cleaning a user's teeth, a standard working surface movement trajectory might be the movement trajectory of first contacting the first functional area (e.g., the left central incisor), then the second functional area (e.g., the left canine), then the third functional area (e.g., the left first premolar), until the working surface of the toothbrush fully covers all the user's teeth to be cleaned.

[0030] Extract the standard working surface movement trajectory from the standard working surface movement trajectory of the first functional area up to the standard working surface movement trajectory of the Nth functional area.

[0031] Traverse the standard working surface movement trajectory of the first functional area up to the standard working surface movement trajectory of the Nth functional area, and use the discrete curvature estimation algorithm in differential geometry to calculate the curvature, thereby obtaining the curvature set of the standard working surface movement trajectory of the first functional area up to the standard working surface movement trajectory of the Nth functional area.

[0032] Traverse the curvature set of the standard working surface movement trajectory in the first functional area up to the curvature set of the standard working surface movement trajectory in the Nth functional area, extract the maximum curvature value, which is the curvature of the working surface under standard conditions, and obtain the curvature threshold of the first functional area up to the curvature threshold of the Nth functional area.

[0033] From the movement trajectory recording data of several working faces, extract the movement trajectory recording data of the first working face and perform partition curvature maximum value extraction to obtain the curvature parameters of the first functional area up to the curvature parameters of the Nth functional area.

[0034] When the curvature parameter of the first functional area is less than or equal to the curvature threshold of the first functional area, k is incremented by one, and the initial value of k is 0. When the curvature parameter of the first functional area is greater than the curvature threshold of the first functional area, k remains unchanged, and the initial value of k is equal to 0. The process continues until the curvature parameters of the Nth functional area are traversed, obtaining the k value. This k value is then compared to the N value to obtain the first trajectory shape similarity, which is then added to several other trajectory shape similarities. Trajectory shape similarity = k ÷ N. For example, traditional methods of calculating similarity are overly complex and inefficient. This application only needs to roughly assess the degree of unevenness, and since the curvature performance of each partition generally follows a consistent trend, comparing one curvature parameter per partition greatly improves comparison efficiency. A higher first trajectory shape similarity indicates that the trajectory similarity is closer to the pre-stored standard case.

[0035] Several working face movement trajectory records and several labels indicating the surface flatness of the object being worked on are added to multiple sets of data.

[0036] Machine learning was employed to train a selected flatness analysis model. Specifically, a three-layer structure was used: the input layer received data recording the movement trajectory of the work surface; the fully connected layer had 32 nodes activated using the ReLU function; and the output layer output the surface flatness of the object, which included trajectory shape similarity. Multiple sets of data were used to train the constructed flatness analysis model, each set including work surface movement trajectory data and labels indicating the surface flatness of the object. A flatness analysis model was trained using multiple sets of data of the same type; for example, when training the flatness analysis model for a cleaning brush cleaning cabinet, multiple sets of data from different cabinets were used; similarly, when training the flatness analysis model for a toothbrush brushing teeth, multiple sets of data from different teeth were used. The model parameters were repeatedly adjusted until convergence was achieved; for example, training was considered complete when the accuracy of the output surface flatness was above 90% when inputting a set of work surface movement trajectory data. Multiple flatness analysis models for different object types were trained using the same method and stored in a flatness analysis model library.

[0037] Based on the type of object being cleaned, a flatness analysis model is selected from the flatness analysis model library. Using the selected flatness analysis model, the movement trajectory of the working surface is processed, and the flatness of the object's surface is output. Application Example 1: When a cleaning brush is driven by a sweeping servo motor and is prepared for cleaning a cabinet, a flatness analysis model matching the cleaning brush's cleaning of the cabinet is retrieved from the flatness analysis model library, and the cabinet's flatness is analyzed. Application Example 2: When a toothbrush is driven by a sweeping servo motor and is prepared for cleaning a user's teeth, a flatness analysis model matching the toothbrush's brushing motion is retrieved from the flatness analysis model library, and the tooth's flatness is analyzed.

[0038] Intelligent smoothness analysis is constructed based on trajectory data. By matching a machine learning model library, trajectory shape features, such as curvature changes, are transformed into smoothness parameters. Model training uses similarity comparison between standard trajectories and real trajectories to ensure that the analysis results objectively reflect the undulation characteristics of functional areas. The method provided in this application can overcome the limitations of human experience and complex calculations, providing a calculable basis for dynamic adaptation of vibration parameters.

[0039] S30: When the object is contacted a second or more times, the contact pressure is obtained. Combined with the surface flatness of the object, a sample set of similar objects is retrieved, and mode statistics are performed to obtain a control array set. Each control array in the control array set includes amplitude, frequency, swing angle, and torque.

[0040] During secondary and multiple operations, existing technologies neglect the coupling relationship between real-time pressure and surface characteristics, leading to blind parameter generation. They rely solely on adjusting the sweeping servo motor parameters based on the current pressure or flatness, failing to adapt to complex surface variations. The inability to effectively mine historical sample data renders optimization experience from similar scenarios unusable. Vibration parameters such as amplitude, frequency, oscillation angle, and torque require coordinated optimization, but traditional methods struggle to quickly pinpoint the optimal combination of multiple parameters, resulting in fluctuating operational performance and increased energy consumption.

[0041] Step S30 in the method provided in this application embodiment includes: Using the surface flatness of the object as a constraint, a first sample set of objects of the same type is collected, and mode analysis is performed on the motor torque to obtain the motor torque set; Traverse the motor torque set, and with the contact pressure as a constraint, collect a second set of samples of the same type of action object. Perform ternary array mode analysis on amplitude, frequency and swing angle to obtain a ternary array set. Specifically, the process involves traversing the motor torque set, using the contact pressure as a constraint, collecting a second set of samples of the same type of action, and performing a ternary array mode analysis on the amplitude, frequency, and oscillation angle to obtain a ternary array set, including: Extract the first motor torque sample set from the second sample set of similar objects. The amplitude normalization process is performed on the sample set of objects with the same torque as the first motor to obtain the set of amplitude feature values; The vibration frequency normalization process is performed on the sample set of objects with the same torque of the first motor to obtain a set of vibration frequency feature values; The swing angle is normalized by performing a swing angle feature value set on the sample set of objects with similar torque to the first motor. A three-dimensional coordinate set is constructed based on the one-to-one correspondence of the amplitude feature value set, the frequency feature value set, and the swing angle feature value set; Perform mode analysis on the three-dimensional coordinate set to obtain a first motor torque ternary array set, and add it to the ternary array set; Based on the motor torque set, it is combined with the corresponding ternary array set to generate a quaternary array set, which is set as the control array set.

[0042] In this embodiment, the surface flatness of the object being acted upon is used as a constraint to collect a first set of samples of the same type of object, and mode analysis is performed on the motor torque. Preferably, a box plot is used for analysis. A box plot is created on the sweep servo motor torques corresponding to all the first set of samples of the same type of object being acted upon, such as all being cabinets to be cleaned or all being teeth to be cleaned, and all having a surface flatness greater than 0.7. Outliers are removed, and the mode is taken, such as the mode of the motor torque of the cabinet to be cleaned and the mode of the motor torque of the teeth to be cleaned, and then added to the motor torque set.

[0043] Traverse the set of motor torques, using contact pressure as a constraint, and collect a second set of samples of the same type of action. For example, traverse the set of motor torques, filter out motor torques with a contact pressure of 2N, and collect the corresponding amplitude, frequency, and oscillation angle as the second set of samples of the same type of action.

[0044] From the second set of samples of the same type of object, extract the first set of samples of the same type of object related to motor torque. Extract samples of the same type of object, such as cabinets to be cleaned or teeth to be cleaned.

[0045] For example, the amplitude characteristic value = amplitude ÷ maximum amplitude, where the maximum amplitude is the maximum amplitude in the sample set of objects acting on the same type of torque as the first motor. For example, when the amplitude is 3mm and the maximum amplitude is 5mm, the amplitude characteristic value = 3 ÷ 5 = 0.6. The same method is used to normalize all amplitudes in the sample set of objects acting on the same type of torque as the first motor to obtain the set of amplitude characteristic values.

[0046] Using the same approach, the vibration frequency characteristic value is obtained. The vibration frequency characteristic value = vibration frequency ÷ maximum vibration frequency, where the maximum vibration frequency is the highest vibration frequency in the sample set of objects acting on the same type of torque as the first motor. For example, when the vibration frequency is 12,000 times / minute and the maximum vibration frequency is 20,000 times / minute, the vibration frequency characteristic value = 12,000 ÷ 20,000 = 0.6. The same method is used to normalize the vibration frequencies of all objects acting on the same type of torque as the first motor in the sample set to obtain the set of vibration frequency characteristic values.

[0047] Using the same approach, the swing angle feature value is obtained. The swing angle feature value = swing angle ÷ maximum swing angle, where the maximum swing angle is the maximum swing angle in the sample set of objects acting on the same type of torque as the first motor. For example, when the swing angle is 15° and the maximum swing angle is 20°, the swing angle feature value = 15 ÷ 20 = 0.75. The swing angles of all objects acting on the same type of torque as the first motor are then normalized to obtain the set of swing angle feature values.

[0048] A three-dimensional coordinate set is constructed based on a one-to-one correspondence of amplitude feature value set, frequency feature value set, and oscillation angle feature value set. Each three-dimensional coordinate is a one-to-one correspondence of (amplitude feature value, frequency feature value, and oscillation angle feature value).

[0049] Perform mode analysis on the three-dimensional coordinate set to obtain the first motor torque ternary array set, and add it to the ternary array set.

[0050] Based on the motor torque set, a quaternion array set is generated by combining it with the corresponding ternary array set, which is set as the control array set. Each control array includes torque, amplitude, frequency, and oscillation angle.

[0051] By integrating multidimensional data and filtering similar sample sets, high-frequency torque parameters are extracted through mode analysis. Then, with real-time pressure as a constraint, ternary mode statistics are performed on amplitude, frequency, and oscillation angle to generate a control array set that takes into account mainstream experience and current operating conditions. This provides a reliable basis for subsequent optimization.

[0052] S40: Perform energy-saving optimization on the control array set to obtain the target control array and execute energy-saving control of the sweep servo motor.

[0053] Traditional energy consumption optimization schemes face a dilemma: blindly reducing power can affect work performance, while maintaining high power results in energy waste. Existing technologies lack refined optimization of multi-dimensional vibration parameter combinations, making it impossible to select the lowest energy consumption combination while ensuring work performance.

[0054] Step S40 in the method provided in this application embodiment includes: From the control array set, a first control array is extracted, wherein the first control array has a first amplitude, a first frequency, a first oscillation angle, and a first torque; Using the first amplitude, the first frequency, the first swing angle, and the first torque, retrieve energy consumption record data of the same model of motor, perform lumped value evaluation, obtain the first control array energy consumption fitting value, and add it to the energy consumption fitting value set. Based on the set of energy consumption fitted values, minimum values ​​are selected, and the target control array is configured to perform energy-saving control of the sweeping servo motor.

[0055] In this embodiment, a first control array is randomly extracted from the control array set as the current processing target. The first control array has a first amplitude, a first frequency, a first oscillation angle, and a first torque.

[0056] Using the first amplitude, first frequency, first swing angle, and first torque, energy consumption record data of the same model of motor is retrieved, and lumped value evaluation is performed to obtain the energy consumption fitting value of the first control array, which is then added to the energy consumption fitting value set. Specifically, multiple energy consumption record data of the same model of motor with the same amplitude, frequency, swing angle, and torque as the first amplitude, first frequency, first swing angle, and first torque are retrieved, lumped value evaluation is performed, multiple energy consumption record data are made into a box plot and discrete points are deleted, and the median of the remaining data is taken as the energy consumption fitting value of the first control array. Multiple control array energy consumption fitting values ​​are obtained using the same method and added to the energy consumption fitting value set.

[0057] Minimum value selection is performed based on the set of energy consumption fitting values. The minimum energy consumption fitting value corresponding to the current action object, contact pressure, and surface flatness is selected. The target control array corresponding to the minimum energy consumption fitting value is configured, and energy-saving control of the sweeping servo motor is executed.

[0058] This application utilizes a multi-dimensional parameter combination to retrieve historical energy consumption data for motors of the same model. It employs lumped value analysis to quantify the energy consumption levels of each array and uses the minimum value to filter and pinpoint the target array with the lowest energy consumption to meet operational requirements. This approach achieves precise control of energy consumption while ensuring operational effectiveness, resulting in a significant reduction in the energy consumption of sweeping servo motors.

[0059] Example 2, as Figure 2 As shown, based on the same inventive concept as the digital control method for a sweeping servo motor provided in Embodiment 1, this embodiment of the invention also provides a digital control system for a sweeping servo motor, comprising: The trajectory storage module 100 is used to store the working surface movement trajectory when the driving end first contacts the object and reminds the user to hold the working surface of the driving end and move it in the order of the specified functional areas until the working surface trajectory fully covers the object. The preset pressure represents the pressure that will not cause the working surface of the driving end to deform, and the specified functional areas represent the functional area division of the surface of the object. The flatness analysis module 200 is used to perform flatness analysis on the surface of the object based on the moving trajectory of the working surface, and to obtain the flatness of the surface of the object. The control array acquisition module 300 is used to obtain the contact pressure when the object is contacted for the second or more times, and to retrieve a sample set of similar objects by combining the surface flatness of the object, performing mode statistics, and obtaining a control array set. Each control array in the control array set includes amplitude, frequency, swing angle, and torque. The energy-saving optimization module 400 is used to perform energy-saving optimization on the control array set to obtain the target control array for energy-saving control of the sweep servo motor.

[0060] In one embodiment, the trajectory storage module 100 is further configured to: When the drive end first contacts the object, the user is reminded to hold the working surface of the drive end in contact with the object, and the first contact pressure is collected by the pressure sensor. When the first pressure deviation between the first contact pressure and the preset pressure is greater than the pressure deviation threshold, the user is reminded to reduce the contact pressure. When the first pressure deviation between the first contact pressure and the preset pressure is less than the pressure deviation threshold, the user is reminded to increase the contact pressure. When the first pressure deviation between the first contact pressure and the preset pressure is equal to the pressure deviation threshold, the user is reminded to start moving, and the movement trajectory of the working surface is stored by the drive end positioner.

[0061] In one embodiment, the flatness analysis module 200 is further used for: Based on the type of object being processed, a flatness analysis model is selected from the flatness analysis model library. Using the selected flatness analysis model, the movement trajectory of the working surface is processed, and the flatness of the surface of the object is output. The selected flatness analysis model is generated by machine learning training through multiple sets of data. Each set of data includes working surface movement trajectory recording data and labels that identify the flatness of the surface of the object being processed. The selected flatness analysis model is generated through machine learning training using multiple sets of data. Each set of data includes working surface movement trajectory recording data and labels indicating the flatness of the surface of the object being processed, including: Collect data on the movement trajectory of several user-handheld drive terminals when the working surfaces of the terminals contact the object with a preset pressure and move in sequence according to the specified functional areas until the working surface trajectory fully covers the object. Obtain the standard working surface movement trajectory pre-stored by the administrator, wherein the standard working surface movement trajectory represents the movement trajectory of the hand-held drive end working surface when it contacts the object with a preset pressure and moves in the order of the specified functional areas until the working surface trajectory fully covers the flat surface object. Based on the standard working surface movement trajectory, the trajectory shape is compared by traversing the recorded data of the several working surface movement trajectories to obtain several trajectory shape similarities, which are set as several labels to identify the surface flatness of the object. Specifically, based on the standard working surface movement trajectory, the trajectory shape is compared by traversing the recorded data of several working surface movement trajectories to obtain several trajectory shape similarities, which are then set as several labels to identify the surface flatness of the object, including: From the standard working surface movement trajectory, extract the standard working surface movement trajectory of the first functional area up to the standard working surface movement trajectory of the Nth functional area; The curvature of the standard working surface movement trajectory of the first functional area is calculated by traversing the standard working surface movement trajectory of the Nth functional area to obtain the curvature set of the standard working surface movement trajectory of the first functional area up to the curvature set of the standard working surface movement trajectory of the Nth functional area. Traverse the curvature set of the standard working surface movement trajectory of the first functional area up to the curvature set of the standard working surface movement trajectory of the Nth functional area to extract the maximum curvature value, and obtain the curvature threshold of the first functional area up to the curvature threshold of the Nth functional area; From the moving trajectory recording data of the plurality of working surfaces, extract the moving trajectory recording data of the first working surface and perform partition curvature maximum value extraction to obtain the curvature parameters of the first functional area up to the curvature parameters of the Nth functional area; When the curvature parameter of the first functional area is less than or equal to the curvature threshold of the first functional area, k is incremented by one, and the initial value of k is 0. When the curvature parameter of the first functional area is greater than the curvature threshold of the first functional area, k remains unchanged, and the initial value of k is equal to 0. Until the curvature parameters of the Nth functional area have been traversed, the k value is obtained, and it is compared with the N value to obtain the first trajectory shape similarity, which is then added to the several trajectory shape similarities. The data recordings of the movement trajectories of the working surfaces and the labels indicating the flatness of the surface of the object being worked on are added to the multiple sets of data.

[0062] In one embodiment, the control array acquisition module 300 is further configured to: Using the surface flatness of the object as a constraint, a first sample set of objects of the same type is collected, and mode analysis is performed on the motor torque to obtain the motor torque set; Traverse the motor torque set, and with the contact pressure as a constraint, collect a second set of samples of the same type of action object. Perform ternary array mode analysis on amplitude, frequency and swing angle to obtain a ternary array set. Specifically, the process involves traversing the motor torque set, using the contact pressure as a constraint, collecting a second set of samples of the same type of action, and performing a ternary array mode analysis on the amplitude, frequency, and oscillation angle to obtain a ternary array set, including: Extract the first motor torque sample set from the second sample set of similar objects. The amplitude normalization process is performed on the sample set of objects with the same torque as the first motor to obtain the set of amplitude feature values; The vibration frequency normalization process is performed on the sample set of objects with the same torque of the first motor to obtain a set of vibration frequency feature values; The swing angle is normalized by performing a swing angle feature value set on the sample set of objects with similar torque to the first motor. A three-dimensional coordinate set is constructed based on the one-to-one correspondence of the amplitude feature value set, the frequency feature value set, and the swing angle feature value set; Perform mode analysis on the three-dimensional coordinate set to obtain a first motor torque ternary array set, and add it to the ternary array set; Based on the motor torque set, it is combined with the corresponding ternary array set to generate a quaternary array set, which is set as the control array set.

[0063] In one embodiment, the energy-saving optimization module 400 is also used for: From the control array set, a first control array is extracted, wherein the first control array has a first amplitude, a first frequency, a first oscillation angle, and a first torque; Using the first amplitude, the first frequency, the first swing angle, and the first torque, retrieve energy consumption record data of the same model of motor, perform lumped value evaluation, obtain the first control array energy consumption fitting value, and add it to the energy consumption fitting value set. Based on the set of energy consumption fitted values, minimum values ​​are selected, and the target control array is configured to perform energy-saving control of the sweeping servo motor.

[0064] In summary, the embodiments of this application have at least the following technical effects: This application proposes a digital control method and system for a sweeping servo motor. By guiding the user to collect standardized surface trajectory data upon initial contact, and combining this with a machine learning model to intelligently analyze the flatness of the surface being worked on, the system dynamically generates the optimal combination of vibration parameters in subsequent operations by integrating real-time pressure and historical sample data. This significantly improves the device's adaptability to surfaces of different materials, shapes, and functional zones. Specifically, this application uses a standardized pressure guidance mechanism to ensure that a complete movement trajectory is collected upon initial contact, protecting the integrity of the surface being worked on and providing a reliable data foundation for subsequent analysis. Regarding energy consumption optimization, by combining mode statistics of similar samples with an energy consumption optimization algorithm, the lowest energy consumption combination that meets operational requirements is selected from a multi-dimensional control array, solving the energy waste problem caused by traditional high-power continuous operation. Compared to traditional methods, this solution significantly eliminates the problem of high energy consumption of the sweeping servo motor due to fixed preset parameters, achieving a technical effect of balancing stable operation and reduced energy consumption in complex surface operation scenarios.

[0065] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0066] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0067] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A digital control method for a sweeping servo motor, characterized in that, include: When the drive end first contacts the object, remind the user to hold the drive end working surface and contact the object with the preset pressure and move it in the order of the specified functional areas until the working surface trajectory fully covers the object. Then, store the working surface movement trajectory. The preset pressure represents the pressure that will not cause the drive end working surface to deform, and the specified functional areas represent the functional area division of the object surface. The surface flatness of the object is analyzed based on the working surface movement trajectory to obtain the surface flatness of the object. When the object is contacted a second or more times, the contact pressure is obtained. Combined with the surface flatness of the object, a sample set of similar objects is retrieved, and mode statistics are performed to obtain a control array set. Each control array in the control array set includes amplitude, frequency, swing angle, and torque. The control array set is optimized for minimum energy consumption to obtain the target control array for energy-saving control of the sweep servo motor.

2. The method as described in claim 1, characterized in that, When the drive end first contacts the object, the user is prompted to hold the drive end working surface and move it with a preset pressure until the working surface fully covers the object. The movement trajectory of the working surface is then stored, including: When the drive end first contacts the object, the user is reminded to hold the working surface of the drive end in contact with the object, and the first contact pressure is collected by the pressure sensor. When the first pressure deviation between the first contact pressure and the preset pressure is greater than the pressure deviation threshold, the user is reminded to reduce the contact pressure. When the first pressure deviation between the first contact pressure and the preset pressure is less than the pressure deviation threshold, the user is reminded to increase the contact pressure. When the first pressure deviation between the first contact pressure and the preset pressure is equal to the pressure deviation threshold, the user is reminded to start moving, and the movement trajectory of the working surface is stored by the drive end positioner.

3. The method as described in claim 1, characterized in that, Based on the working surface movement trajectory, a flatness analysis is performed on the surface of the object to obtain the surface flatness, including: Based on the type of object being processed, a flatness analysis model is selected from the flatness analysis model library. Using the selected flatness analysis model, the movement trajectory of the working surface is processed, and the flatness of the surface of the object is output. The selected flatness analysis model is generated by machine learning training using multiple sets of data. Each set of data includes working surface movement trajectory recording data and labels indicating the flatness of the surface of the object being processed.

4. The method as described in claim 3, characterized in that, The selected flatness analysis model is generated through machine learning training using multiple sets of data. Each set of data includes working surface movement trajectory recording data and labels indicating the flatness of the surface of the object being processed, including: Collect data on the movement trajectory of several user-handheld drive terminals when the working surfaces of the terminals contact the object with a preset pressure and move in sequence according to the specified functional areas until the working surface trajectory fully covers the object. Obtain the standard working surface movement trajectory pre-stored by the administrator, wherein the standard working surface movement trajectory represents the movement trajectory of the hand-held drive end working surface when it contacts the object with a preset pressure and moves in the order of the specified functional areas until the working surface trajectory fully covers the flat surface object. Based on the standard working surface movement trajectory, the trajectory shape is compared by traversing the recorded data of the several working surface movement trajectories to obtain several trajectory shape similarities, which are set as several labels to identify the surface flatness of the object. The data recordings of the movement trajectories of the working surfaces and the labels indicating the flatness of the surface of the object being worked on are added to the multiple sets of data.

5. The method as described in claim 4, characterized in that, Based on the standard working surface movement trajectory, the trajectory shape is compared by traversing the recorded data of the several working surface movement trajectories to obtain several trajectory shape similarities, which are set as several labels to identify the surface flatness of the object, including: From the standard working surface movement trajectory, extract the standard working surface movement trajectory of the first functional area up to the standard working surface movement trajectory of the Nth functional area; The curvature of the standard working surface movement trajectory of the first functional area is calculated by traversing the standard working surface movement trajectory of the Nth functional area to obtain the curvature set of the standard working surface movement trajectory of the first functional area up to the curvature set of the standard working surface movement trajectory of the Nth functional area. Traverse the curvature set of the standard working surface movement trajectory of the first functional area up to the curvature set of the standard working surface movement trajectory of the Nth functional area to extract the maximum curvature value, and obtain the curvature threshold of the first functional area up to the curvature threshold of the Nth functional area; From the moving trajectory recording data of the plurality of working surfaces, extract the moving trajectory recording data of the first working surface and perform partition curvature maximum value extraction to obtain the curvature parameters of the first functional area up to the curvature parameters of the Nth functional area; When the curvature parameter of the first functional area is less than or equal to the curvature threshold of the first functional area, k is incremented by one, and the initial value of k is 0. When the curvature parameter of the first functional area is greater than the curvature threshold of the first functional area, k remains unchanged, and the initial value of k is equal to 0. The process continues until the curvature parameters of the Nth functional area are traversed, the k value is obtained, and it is compared with the N value to obtain the first trajectory shape similarity, which is then added to the several trajectory shape similarities.

6. The method as described in claim 1, characterized in that, Upon secondary or subsequent contact with the object, the contact pressure is obtained. Combined with the surface smoothness of the object, a sample set of similar objects is retrieved, and mode statistics are performed to obtain a control array set, including: Using the surface flatness of the object as a constraint, a first sample set of objects of the same type is collected, and mode analysis is performed on the motor torque to obtain the motor torque set; Traverse the motor torque set, and with the contact pressure as a constraint, collect a second set of samples of the same type of action object. Perform ternary array mode analysis on amplitude, frequency and swing angle to obtain a ternary array set. Based on the motor torque set, it is combined with the corresponding ternary array set to generate a quaternary array set, which is set as the control array set.

7. The method as described in claim 6, characterized in that, Traversing the motor torque set, and using the contact pressure as a constraint, a second set of samples of the same type of action is collected. A ternary array mode analysis is performed on the amplitude, frequency, and oscillation angle to obtain a ternary array set, including: Extract the first motor torque sample set from the second sample set of similar objects. The amplitude normalization process is performed on the sample set of objects with the same torque as the first motor to obtain the set of amplitude feature values; The vibration frequency normalization process is performed on the sample set of objects with the same torque of the first motor to obtain a set of vibration frequency feature values; The swing angle is normalized by performing a swing angle feature value set on the sample set of objects with similar torque to the first motor. A three-dimensional coordinate set is constructed based on the one-to-one correspondence of the amplitude feature value set, the frequency feature value set, and the swing angle feature value set; Perform mode analysis on the three-dimensional coordinate set to obtain the first motor torque ternary array set, and add it to the ternary array set.

8. The method as described in claim 1, characterized in that, The energy consumption of the control array set is minimized to obtain the target control array for energy-saving control of the sweep servo motor, including: From the control array set, a first control array is extracted, wherein the first control array has a first amplitude, a first frequency, a first oscillation angle, and a first torque; Using the first amplitude, the first frequency, the first swing angle, and the first torque, retrieve energy consumption record data of the same model of motor, perform lumped value evaluation, obtain the first control array energy consumption fitting value, and add it to the energy consumption fitting value set. Based on the set of energy consumption fitted values, minimum values ​​are selected, and the target control array is configured to perform energy-saving control of the sweeping servo motor.

9. A digital control system for a sweeping servo motor, characterized in that, A digital control method for implementing a sweeping servo motor according to any one of claims 1-8, the system comprising: The trajectory storage module is used to store the working surface movement trajectory when the drive end first contacts the object and reminds the user to hold the working surface of the drive end and move it in the order of the specified functional areas until the working surface trajectory fully covers the object. The preset pressure represents the pressure that will not cause deformation of the working surface of the drive end, and the specified functional areas represent the functional area division of the surface of the object. The flatness analysis module is used to perform flatness analysis on the surface of the object based on the moving trajectory of the working surface, and to obtain the flatness of the surface of the object. The control array acquisition module is used to obtain the contact pressure when the object is contacted a second or more times, and to retrieve a sample set of similar objects by combining the surface flatness of the object, performing mode statistics, and obtaining a control array set. Each control array in the control array set includes amplitude, frequency, swing angle, and torque. The energy-saving optimization module is used to perform energy-saving optimization on the control array set to obtain the target control array for energy-saving control of the sweep servo motor.

10. The system as described in claim 9, characterized in that, The trajectory storage module performs the following steps: When the drive end first contacts the object, the user is reminded to hold the working surface of the drive end in contact with the object, and the first contact pressure is collected by the pressure sensor. When the first pressure deviation between the first contact pressure and the preset pressure is greater than the pressure deviation threshold, the user is reminded to reduce the contact pressure. When the first pressure deviation between the first contact pressure and the preset pressure is less than the pressure deviation threshold, the user is reminded to increase the contact pressure. When the first pressure deviation between the first contact pressure and the preset pressure is equal to the pressure deviation threshold, the user is reminded to start moving, and the movement trajectory of the working surface is stored by the drive end positioner.

Images