A care rhythm optimization method and a laundry care system

Abstract

This invention relates to the field of household appliance technology, and provides a method for optimizing the nursing rhythm and a clothing care system. The method includes: acquiring characteristic information of the patient; generating an initial flattening nursing strategy based on the characteristic information; acquiring the positional information of the nursing arm and the flattening arm; and pre-setting a dual-arm collaborative operation model to optimize the nursing rhythm sub-strategy in the initial flattening nursing strategy based on the positional information of the nursing arm and the flattening arm, thereby generating a dual-arm coordinated flattening nursing strategy. The nursing rhythm optimization method of this invention enables personalized care for clothing with different characteristics, improves the collaboration efficiency between the nursing arm and the flattening arm, and provides a more flexible flattening nursing strategy, resulting in a better nursing rhythm and ultimately a better nursing effect.

Description

Technical Field

[0001] This invention relates to the field of household appliance technology, and more specifically, to a method for optimizing care rhythm and a clothing care system. Background Technology

[0002] As living standards improve and personalized needs increase, consumers are demanding higher standards for garment care. Garment steamers, as common small appliances, use high-pressure steam generated by internal heating to continuously contact and soften clothing fibers, thereby smoothing out wrinkles and restoring garments to a flat state.

[0003] To enhance user experience, garment care machines often offer multiple care modes. Different garments can be cared for using the appropriate mode based on their specific needs.

[0004] In implementing the existing technology, the inventors discovered that: Current garment care machines typically execute fixed-time or fixed-cycle care procedures directly after the user selects a care mode, ignoring the characteristics of the garment. This fails to provide personalized care for garments with different characteristics, resulting in a rigid flattening strategy, poor care rhythm, and consequently, poor final care outcome.

[0005] In view of this, the present invention is hereby proposed. Summary of the Invention

[0006] The purpose of this invention is to propose a nursing rhythm optimization method and a clothing care system to solve the problem that existing nursing machines usually ignore the characteristics of clothing and directly execute a nursing procedure with a fixed time or fixed nursing schedule after the user selects a nursing mode. This cannot provide personalized care for clothing with different characteristics, and the flattening nursing strategy is relatively rigid, resulting in a poor nursing rhythm and ultimately a poor nursing effect.

[0007] To achieve the above objectives, the technical solution of the present invention is implemented as follows: A method for optimizing nursing rhythm, the method comprising the following steps: S1. Obtain the characteristic information of the nursing subject; S2. Generate an initial flattening nursing strategy based on the characteristics of the nursing subject; S3. Obtain the position and posture information of the nursing arm and the extended arm; S4. The preset dual-arm collaborative operation model optimizes the nursing rhythm sub-strategy in the initial flattening nursing strategy based on the positional information of the nursing arm and the flattening arm, and generates a dual-arm linkage flattening nursing strategy.

[0008] The nursing rhythm optimization method described in this invention can generate different initial flattening nursing strategies based on the different characteristic information of different nursing subjects. Then, a preset dual-arm collaborative operation model can optimize the nursing rhythm of the initial flattening nursing strategy based on the real-time acquired position and posture information of the nursing arm and the flattening arm, generating a more mature dual-arm linkage flattening nursing strategy. This allows for personalized care for clothing with different characteristics, improves the collaboration efficiency between the nursing arm and the flattening arm, and makes the flattening nursing strategy more flexible, resulting in a better nursing rhythm and ultimately a better nursing effect.

[0009] Furthermore, the characteristic information of the object being cared for includes at least one of length information, thickness information, and material information.

[0010] Furthermore, the method for constructing the preset dual-arm cooperative operation model includes the following steps: T1. Establish a dual-arm collaborative operation model; T2. In the dual-arm collaborative operation model, assign a first agent object corresponding to the nursing arm and a second agent object corresponding to the flattened arm. T3. Construct a reward function based on the motion relationship between the first and second intelligent agent objects; T4. Train a dual-arm coordinated operation model; T5. Optimize the trained dual-arm collaborative operation model and use it as a preset dual-arm collaborative operation model.

[0011] Furthermore, step T3 includes the following steps: Acquire the first number of movements, first movement speed, and first nursing effect evaluation data of the first intelligent agent object during the execution of nursing tasks; Acquire the second number of movements, the second movement speed, and the second nursing effect evaluation data of the second intelligent agent object during the execution of the nursing task; The first number of exercise sessions, the first exercise speed, and the first nursing effect evaluation data, as well as the second number of exercise sessions, the second exercise speed, and the second nursing effect evaluation data, are input into a predetermined reward function; The reward function is configured to calculate a reward value that characterizes the movement strategy of the corresponding agent object based on the number of movements, movement speed, and nursing effect evaluation data of at least one agent object.

[0012] Furthermore, in step T4, when training the dual-arm cooperative operation model, the motion strategy of the first agent object or the second agent object is stored in the experience pool according to the reward value based on the reward function.

[0013] Furthermore, in step T5, the trained dual-arm cooperative operation model is optimized using an empirical replay algorithm.

[0014] Furthermore, the rhythm includes: the number of times the arm extends, the speed of the arm extending, the number of times the nursing arm moves, and the speed of the nursing arm.

[0015] Furthermore, the pose information includes: the position information of the nursing arm, the spatial posture of the nursing arm, the position information of the flattened arm, the spatial posture of the flattened arm, the position information of the gripper on the flattened arm, and the spatial posture of the gripper on the flattened arm.

[0016] A second aspect of the present invention provides a garment care system, wherein the garment care system uses any one of the above-described garment care rhythm optimization methods, and the garment care system comprises: A housing, within which a receiving cavity is formed; A suspension device, located within the receiving cavity, is used to hang clothing to be cared for; A care component is disposed at least partially or entirely within the receiving cavity for performing care operations on the garments to be cared for within the receiving cavity; A clamping and flattening assembly is at least partially or entirely disposed within the receiving cavity, for clamping and flattening the garment to be cared for within the receiving cavity; The clamping and flattening component is set independently from the nursing component.

[0017] Furthermore, the garment care system includes: a first drive unit and a second drive unit; The first driving device drives the nursing component to move through the first transmission device, and the second driving device drives the clamping and flattening component to move through the second transmission device.

[0018] Compared with existing technologies, the nursing rhythm optimization method and clothing care system described in this invention have the following beneficial effects: The nursing rhythm optimization method and clothing care system described in this invention can generate different initial flattening nursing strategies based on the different characteristic information of different nursing objects. Then, a preset dual-arm collaborative operation model can optimize the nursing rhythm of the initial flattening nursing strategy based on the real-time acquired position and posture information of the nursing arm and the flattening arm, generating a more mature dual-arm linkage flattening nursing strategy. This enables personalized care for clothing with different characteristics, improves the cooperation efficiency between the nursing arm and the flattening arm, and makes the flattening nursing strategy more flexible, resulting in a better nursing rhythm and ultimately a better nursing effect. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a nursing rhythm optimization method according to an embodiment of the present invention; Figure 2This is a three-dimensional structural diagram of a garment care system according to an embodiment of the present invention; Explanation of reference numerals in the attached figures: 1. Housing; 101. Receiving cavity; 2. Care assembly; 3. Clamping and flattening assembly; 4. First guide rail assembly; 5. Second guide rail assembly; 6. Suspension device; 7. First drive device; 8. Second drive device. Detailed Implementation

[0020] It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present invention can be combined with each other. The descriptions of "first," "second," etc., mentioned in the embodiments of the present invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0021] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] Example 1

[0023] Existing nursing machines typically execute fixed-time or fixed-cycle nursing procedures directly after the user selects a nursing mode, ignoring the characteristics of the clothing. This fails to provide personalized care for clothing with different characteristics, resulting in a rigid flattening nursing strategy, poor nursing rhythm, and consequently, poor nursing outcomes.

[0024] To solve the above technical problems, such as Figure 1 As shown in the figure, this embodiment proposes a method for optimizing nursing rhythm, which includes the following steps: S1. Obtain the characteristic information of the nursing subject; S2. Generate an initial flattening nursing strategy based on the characteristics of the nursing subject; S3. Obtain the position and posture information of the nursing arm and the extended arm; S4. The preset dual-arm collaborative operation model optimizes the nursing rhythm sub-strategy in the initial flattening nursing strategy based on the positional information of the nursing arm and the flattening arm, and generates a dual-arm linkage flattening nursing strategy.

[0025] The nursing rhythm optimization method described in this embodiment has interconnected and inseparable steps S1 to S4. Step S1 obtains the characteristic information of the nursing object, which facilitates step S2 to formulate different initial flattening nursing strategies based on the different characteristic information of the nursing object, providing a preliminary action guide for the two-arm coordinated flattening nursing. Then, step S3 obtains the positional information of the nursing arm and the flattening arm, which facilitates step S4 to preset the two-arm collaborative operation model to optimize the nursing rhythm of the initial flattening nursing strategy based on the positional information of the nursing arm and the flattening arm, generating a two-arm coordinated flattening nursing strategy. The nursing rhythm optimization method described in this embodiment can provide personalized care for clothing with different characteristics, improve the collaboration efficiency between the nursing arm and the flattening arm, and make the flattening nursing strategy more flexible, thereby resulting in a better nursing rhythm and ultimately a better nursing effect.

[0026] The reason for needing to obtain the pose information of the nursing arm and the flattening arm is that the preset dual-arm collaborative operation model can generate an optimal rhythm strategy suitable for the current pose of the nursing arm and the flattening arm based on their pose information. Existing models typically assume the nursing arm and the flattening arm are at their origin; however, we do not assume they will return to their origin after completing their operations. In other words, in scenarios involving the care of multiple garments, the model generates a dual-arm coordinated flattening care strategy for the next garment based on the current pose information of the nursing arm and the flattening arm, rather than waiting for them to return to their origin before generating the corresponding flattening care strategy. This significantly accelerates the care rhythm and shortens the care cycle. Therefore, it is necessary to obtain the pose information of the current nursing arm and the flattening arm and input it into the preset dual-arm collaborative operation model. This allows the model to optimize the initial flattening care strategy's rhythm and generate a dual-arm coordinated flattening care strategy based on the current pose information of the nursing arm and the flattening arm.

[0027] Initial care strategies can include various sub-strategies, such as care pattern sub-strategies and care rhythm sub-strategies. For example, different types of clothing correspond to different care pattern sub-strategies; for instance, thicker clothing is neither flattened nor ironed, outerwear is ironed but not flattened, and innerwear is flattened and ironed. Care rhythm sub-strategies correspond to controlling the movement position, number of operations, and movement speed of the care arm or flattening arm.

[0028] Specifically, the characteristic information of the object being cared for includes at least one of length information, thickness information, and material information.

[0029] Preferably, in this embodiment, the characteristic information of the object being cared for includes length information, thickness information, and material information.

[0030] By collecting multi-dimensional features of clothing length, thickness, and material, a targeted initial flattening care strategy can be generated; this avoids clothing deformation, damage, or incomplete care caused by mismatched care parameters, significantly improving care quality and clothing protection.

[0031] Specifically, the length information of the object being cared for is obtained through a length measuring device; the thickness information of the object being cared for is obtained through a thickness measuring device; and the material information can be obtained through image recognition or sensor detection.

[0032] Specifically, the positional information of the nursing arm and the extended arm is obtained. This information is the basis for subsequent strategy formulation, ensuring that the nursing arm and the extended arm can accurately understand each other and their spatial relationship with the target patient.

[0033] Specifically, the rhythm includes: the number of times the arm extends, the speed of the arm extending, the number of times the nursing arm moves, and the speed of the nursing arm.

[0034] By comprehensively considering the number of movements of the outstretched arm, the speed of the outstretched arm, the number of movements of the nursing arm, and the speed of the nursing arm, more precise coordination can be achieved. This not only improves the synergy between the two arms but also ensures the quality of care and enhances adaptability.

[0035] Specifically, the position information includes: the position information of the nursing arm, the spatial posture of the nursing arm, the position information of the flattened arm, the spatial posture of the flattened arm, the position information of the gripper on the flattened arm, and the spatial posture of the gripper on the flattened arm.

[0036] Clearly defining the positional information of the nursing arm, its spatial posture, the positional information of the extended arm, the spatial posture of the extended arm, the positional information of the gripper on the extended arm, and the spatial posture of the gripper on the extended arm can not only further improve the accuracy of the coordination between the two arms, but also further ensure the quality of nursing care and further enhance adaptability.

[0037] Specifically, in step S3, the current position and posture information of the nursing arm and the flattening arm are acquired in real time through a high-precision sensor.

[0038] For example, high-precision sensors can be LiDAR, cameras with image processing algorithms, or pose sensors built into robots, and are not limited to these.

[0039] The pre-defined dual-arm collaborative operation model plays an important role in this solution.

[0040] Specifically, the construction steps of the preset dual-arm cooperative operation model include: T1. Establish a dual-arm collaborative operation model; T2. In the dual-arm collaborative operation model, assign a first agent object corresponding to the nursing arm and a second agent object corresponding to the flattened arm. T3. Construct a reward function based on the motion relationship between the first and second intelligent agent objects; T4. Train a dual-arm coordinated operation model; T5. Optimize the trained dual-arm collaborative operation model and use it as a preset dual-arm collaborative operation model.

[0041] The construction steps T1-T5 of the pre-defined dual-arm collaborative operation model are interconnected and inseparable. Step T1 focuses on model building, centering on the underlying architecture and providing the basic environment for subsequent agent interaction. Step T2 assigns agent objects, clearly defining the first agent object for the nursing arm and the second agent object for the corresponding extended arm, embodying the core idea of ​​multi-agent collaboration and ensuring a one-to-one correspondence between the model and real-world physical entities. Step T3 constructs the reward function, quantifying complex collaborative goals into calculable reward signals, a key design for guiding model learning. Step T4 (model training) and Step T5 (model optimization) form a complete machine learning closed loop, ensuring the model learns from data and continuously improves. The pre-defined dual-arm collaborative operation model is not only highly efficient in development but also easy to upgrade and expand. Furthermore, debugging and maintenance are very convenient.

[0042] The construction steps of the pre-set dual-arm collaborative operation model successfully transformed a complex robot collaborative control problem into a learnable, optimizable, and scalable intelligent decision-making system. It not only solved the problem of rigid nursing rhythm in traditional methods, but also endowed the system with intelligent capabilities of self-adaptation, self-learning, and anti-disturbance.

[0043] Specifically, in step T2, based on the pose information of the nursing arm and the flattened arm, a first intelligent agent object corresponding to the nursing arm and a second intelligent agent object corresponding to the flattened arm are assigned in the dual-arm collaborative operation model; these objects will represent the dual arms in performing simulated operations in the virtual environment.

[0044] Specifically, in step T3, to further improve the smoothness and efficiency of bi-arm collaboration, a reward function mechanism is introduced, considering the movement relationship between the first and second intelligent agents. This function should accurately reflect the effectiveness of bi-arm collaborative operation, such as coordination degree, nursing duration, and task completion quality. The reward function comprehensively scores the first and second intelligent agents based on multiple dimensions, including the number of movements, movement speed, and nursing effect, and provides corresponding rewards or penalties, thereby dynamically adjusting the interaction rhythm and movement strategy of the bi-arms.

[0045] More specifically, step T3 includes the following steps: Acquire the first number of movements, first movement speed, and first nursing effect evaluation data of the first intelligent agent object during the execution of nursing tasks; Acquire the second number of movements, the second movement speed, and the second nursing effect evaluation data of the second intelligent agent object during the execution of the nursing task; The first number of exercise sessions, the first exercise speed, and the first nursing effect evaluation data, as well as the second number of exercise sessions, the second exercise speed, and the second nursing effect evaluation data, are input into a predetermined reward function; The reward function is configured to calculate a reward value that characterizes the movement strategy of the corresponding agent object based on the number of movements, movement speed, and nursing effect evaluation data of at least one agent object.

[0046] The construction of the reward function in step T3 regarding the motion relationship between the first and second intelligent agents has the following advantages: First, acquire data on the number of movements, movement speed, and nursing effect evaluation of both the first and second intelligent agents during the execution of nursing tasks. This multi-dimensional data acquisition method can comprehensively and meticulously reflect the actual performance of both arms during the nursing process. The number of movements and movement speed reflect the frequency and speed of the arm movements, while the nursing effect evaluation data directly reflects the actual effect of arm collaboration on the patient. By comprehensively considering these data, the overall level of arm collaboration can be assessed more accurately.

[0047] Second, the reward function calculates reward values ​​based on this multi-dimensional data to represent the movement strategy of the corresponding intelligent agent. This allows the reward function to provide precise feedback based on the actual performance of the arms. If the number and speed of arm movements are reasonable and the care is effective, the reward function will award a higher reward value to encourage this effective movement strategy; conversely, if the arm movements are unreasonable or the care is ineffective, the reward function will award a lower reward value or even a penalty value to prompt the arms to adjust their movement strategy, thereby guiding the arms to improve towards more efficient and reasonable collaboration.

[0048] Third, during the execution of nursing tasks, real-time data of both arms is continuously acquired and input into a reward function for calculation. This allows the reward function to dynamically adjust the reward value in real time based on the actual performance of both arms at different stages, enabling the arms to dynamically optimize their collaboration based on the characteristics of different clothing, thereby improving the adaptability and flexibility of nursing care and adapting to complex and changing environments.

[0049] Specifically, in step T4, when training the dual-arm cooperative operation model, the motion strategy of the first agent object or the second agent object is stored in the experience pool according to the reward value based on the reward function.

[0050] This approach can improve learning efficiency on the one hand; on the other hand, the experience pool stores a variety of motion strategies corresponding to different reward values. When the model samples and learns from the experience pool, it can be exposed to a variety of different strategies, thereby avoiding the homogenization of strategies; in addition, it can also improve the stability and generalization ability of the model.

[0051] Specifically, in step T5, the trained dual-arm cooperative operation model is optimized using the experience replay algorithm.

[0052] In step T5, an experience replay algorithm is used to randomly extract historical experience from the experience pool for review, optimizing the trained dual-arm cooperative operation model to avoid overfitting and improve generalization ability. The model is optimized and adjusted to ensure stable and efficient operation in practical applications.

[0053] When faced with different care recipients or task scenarios, the reward function can guide both arms to quickly adapt and generate more mature and efficient linkage strategies, significantly improving the system's flexibility and adaptability.

[0054] The nursing rhythm optimization method proposed in this embodiment significantly improves the collaboration efficiency and accuracy of the two arms in nursing tasks through a refined preset dual-arm collaborative operation model, a dynamic reward function mechanism, and an efficient model training and optimization process, providing strong support for the development of intelligent nursing.

[0055] The nursing rhythm optimization method described in this invention can generate different initial flattening nursing strategies based on the different characteristic information of different nursing subjects. Then, a preset dual-arm collaborative operation model can optimize the nursing rhythm of the initial flattening nursing strategy based on the real-time acquired position and posture information of the nursing arm and the flattening arm, generating a more mature dual-arm linkage flattening nursing strategy. This can provide personalized care for clothing with different characteristics, improve the collaboration efficiency between the nursing arm and the flattening arm, and make the flattening nursing strategy more flexible, thereby resulting in a better nursing rhythm and ultimately a better nursing effect.

[0056] Example 2

[0057] In this embodiment, as Figure 2 As shown, a garment care system is proposed, which uses a care rhythm optimization method as described in any one of Embodiment 1. The garment care system includes: Housing 1, with a receiving cavity 101 formed inside the housing 1; The suspension device 6, located inside the receiving cavity 101, is used to hang clothing to be cared for. The care component 2 is at least partially or entirely disposed within the receiving cavity 101, and is used to iron or dry the garments to be cared for within the receiving cavity 101. The clamping and flattening assembly 3 is at least partially or entirely disposed within the receiving cavity 101, and is used to clamp and flatten the garment to be cared within the receiving cavity 101. The clamping and flattening component 3 is set independently from the nursing component 2.

[0058] The nursing component 2 and the clamping and flattening component 3 can each move up and down independently within the receiving cavity 101.

[0059] The clothing care system disclosed in this application is related to the care rhythm optimization method. The clamping and flattening component 3 and the care component 2 are set independently and work in coordination to improve the care effect.

[0060] In specific application scenarios, the nursing component 2 or the clamping and flattening component 3 can be manifested as a robotic arm or robotic hand equipped with different functional components.

[0061] The care component 2 can move from top to bottom or from bottom to top within the receiving cavity 101, and the clamping and flattening component 3 is positioned below the care component 2. This arrangement allows the care component 2 to move up and down within the receiving cavity 101, enabling more even ironing of the garments. Furthermore, positioning the clamping and flattening component 3 below the care component 2 ensures that the garments remain in an optimal flattened state during ironing, thereby improving the care effect, saving space, and facilitating operation.

[0062] Specifically, the nursing component 2 includes a nursing arm, and the clamping and flattening component 3 includes a flattening arm.

[0063] More specifically, grippers are provided on the flattening arm, which are capable of flattening clothing.

[0064] As a preferred example of this application, the clamping and flattening assembly 3 is always positioned lower than the nursing assembly 2 in the cavity 101 in the forward projection position.

[0065] The above settings ensure that during the care process, the garments are first clamped and flattened before ironing, avoiding deformation or ironing marks caused by unevenness in the garments during ironing. This guarantees the appearance and quality of the garments, ensures that the garments are fully unfolded and kept flat before ironing, improves the ironing effect, optimizes the operation process, and provides users with a better care experience.

[0066] Specifically, the garment care system includes: a first driving device and a second driving device; the first driving device drives the care component 2 to move through a first transmission device, and the second driving device drives the clamping and flattening component 3 to move through a second transmission device.

[0067] As a preferred example of this application, a first guide rail assembly 4 and a second guide rail assembly 5 are provided in the receiving cavity 101. The nursing assembly 2 slides up and down on the first guide rail assembly 4 under the action of the first driving device, and the clamping and flattening assembly 3 slides up and down on the second guide rail assembly 4 under the action of the second driving device.

[0068] By setting up the guide rail assembly, the care component 2 and the clamping and flattening component 3 can slide up and down on the guide rail, achieving precise positioning of the garments to be cared for. This ensures that the garment care system provides accurate and stable care for the garments, improving the care effect. At the same time, the care component 2 and the clamping and flattening component 3 can move flexibly within the receiving cavity 101 to adapt to garments of different sizes and shapes, achieving precise positioning, flexibility, convenient operation, and space saving, thus improving the overall performance and user experience of the garment care system.

[0069] As a preferred example of this application, the first guide rail assembly 4 and the second guide rail assembly 5 are arranged side by side.

[0070] This setting discloses the positional status of the first guide rail assembly 4 and the second guide rail assembly 5 in the two types of track components, which can enhance stability, optimize space utilization, simplify structure and improve ease of operation, thereby ensuring the appearance and quality of clothing.

[0071] As a preferred example of this application, the first driving device drives the nursing component 2 to move up and down on the first guide rail component 4 through the first transmission device, and the second driving device drives the clamping and flattening component 3 to move up and down on the second guide rail component 5 through the second transmission device.

[0072] The first drive device and the second drive device can be configured as motors; the first transmission device and the second transmission device can be configured as synchronous belt transmission structure or screw transmission structure.

[0073] Since the specific structures and assembly relationships of the first drive device, the second drive device, the first transmission device, and the second transmission device are all existing technologies, they will not be described in detail here.

[0074] In addition to the structure described above, the garment care system also includes other related components. Since the specific structure and assembly relationship of these components are existing technologies, they will not be described in detail here.

[0075] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A method of care rhythm optimization, characterized in that, The method for optimizing nursing rhythm includes the following steps: S1. Obtain the characteristic information of the nursing subject; S2. Generate an initial flattening nursing strategy based on the characteristics of the nursing subject; S3. Obtain the position and posture information of the nursing arm and the extended arm; S4. The preset dual-arm collaborative operation model optimizes the nursing rhythm sub-strategy in the initial flattening nursing strategy based on the positional information of the nursing arm and the flattening arm, and generates a dual-arm linkage flattening nursing strategy.

2. A method of care cadence optimization according to claim 1, wherein, The characteristic information of the object being cared for includes at least one of length information, thickness information, and material information.

3. A method of care cadence optimization according to claim 1, wherein, The method for constructing the preset dual-arm cooperative operation model includes the following steps: T1. Establish a dual-arm collaborative operation model; T2. In the dual-arm collaborative operation model, assign a first agent object corresponding to the nursing arm and a second agent object corresponding to the flattened arm. T3. Construct a reward function based on the motion relationship between the first and second intelligent agent objects; T4. Train a dual-arm coordinated operation model; T5. Optimize the trained dual-arm collaborative operation model and use it as a preset dual-arm collaborative operation model.

4. A method of care cadence optimization according to claim 3, wherein, Step T3 includes the following steps: Acquire the first number of movements, first movement speed, and first nursing effect evaluation data of the first intelligent agent object during the execution of nursing tasks; Acquire the second number of movements, the second movement speed, and the second nursing effect evaluation data of the second intelligent agent object during the execution of the nursing task; The first number of exercise sessions, the first exercise speed, and the first nursing effect evaluation data, as well as the second number of exercise sessions, the second exercise speed, and the second nursing effect evaluation data, are input into a predetermined reward function; The reward function is configured to calculate a reward value that characterizes the movement strategy of the corresponding agent object based on the number of movements, movement speed, and nursing effect evaluation data of at least one agent object.

5. A method of care cadence optimization according to claim 3, wherein, In step T4, when training the dual-arm cooperative operation model, the motion strategy of the first agent object or the second agent object is stored in the experience pool according to the reward value based on the reward function.

6. A method of care cadence optimization according to claim 3, wherein, In step T5, the trained dual-arm cooperative operation model is optimized using the empirical replay algorithm.

7. The method of pace of care optimization of claim 1, wherein, The rhythm includes: the number of times the arm extends, the speed of the arm extending, the number of times the nursing arm moves, and the speed of the nursing arm.

8. The method of pace of care optimization of claim 1, wherein, The pose information includes: the position information of the nursing arm, the spatial posture of the nursing arm, the position information of the flattened arm, the spatial posture of the flattened arm, the position information of the gripper on the flattened arm, and the spatial posture of the gripper on the flattened arm.

9. A laundry care system characterized in that, The garment care system uses a care rhythm optimization method according to any one of claims 1 to 8, and the garment care system includes: The housing (1) forms a receiving cavity (101) within the housing (1); A suspension device (6) is located inside the receiving cavity (101) and is used to hang clothing to be cared for; The nursing component (2) is at least partially or wholly disposed within the receiving cavity (101) for performing nursing operations on the garments to be cared for within the receiving cavity (101); The clamping and flattening assembly (3) is at least partially or entirely disposed within the receiving cavity (101) for clamping and flattening the garment to be cared for within the receiving cavity (101); The clamping and flattening component (3) is set independently from the nursing component (2).

10. A garment care system according to claim 9, wherein, The garment care system includes: a first drive unit and a second drive unit; The first driving device drives the nursing component (2) to move through the first transmission device, and the second driving device drives the clamping and flattening component (3) to move through the second transmission device.

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