Optimized guidance method for bed-making actions of hotel service skill competition

Abstract

The invention provides a hotel service skill match bed-making action optimization guidance method, which comprises the following steps: extracting a deviation value of a key joint angle from a quantitative deviation matrix, and calculating a transition sequence from a current action to a standard action by adopting a mapping method to obtain a preliminary optimization path sequence, the preliminary optimization path sequence describes a step-by-step path of action adjustment; a deviation part related to a bed making link is screened out from the guidance instruction sequence, residual unmatched joint angle information is processed through an iterative optimization method, a complete correction guidance path is obtained, and the complete correction guidance path covers all action elements needing to be adjusted; and outputting to a display interface according to the complete correction guidance path, verifying an application effect of the complete correction guidance path through a feedback loop method, and judging whether the initial deviation feature vector needs to be further finely adjusted to meet training requirements.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for optimizing bed-making movements in hotel service skills competitions. Background Technology

[0002] In the field of hotel service skills competitions, researching how to improve contestants' skill performance and training efficiency is of great significance. This area is not only a key manifestation of the standardization and professionalization of the service industry, but also an important way to promote the improvement of practitioners' professional qualities. Precise assessment and guidance of service skills through competition systems can effectively promote the overall improvement of service quality and set benchmarks for the industry. However, a common problem in current skills assessment and guidance is the lack of dynamic and personalized feedback mechanisms. Existing methods often rely on manual observation and subjective evaluation, making it difficult to capture subtle deviations in the execution of movements, let alone provide real-time, quantitative improvement suggestions for the specific problems of different contestants. This approach often falls short when facing complex service scenarios, especially in areas requiring high-precision operations, where the guidance effect is greatly reduced. Focusing on the technical level, the core challenge in this area lies in how to combine precise capture of motion data with real-time corrective guidance. First, capturing motion data needs to cover multi-dimensional body posture information, including joint angle changes and the rhythm of movements, but current technology often fails to collect this information comprehensively in dynamic environments, leading to an unreliable foundation for subsequent analysis. Because this problem remains unresolved, the generated corrective guidance lacks specificity. The system struggles to quickly calculate the optimized path from incorrect to standard movements based on the contestant's specific deviations. For example, in the specific task of making a bed, if the angle deviates from the standard value when folding the sheet, the system often cannot accurately identify the specific direction and degree of the deviation, let alone provide specific adjustment suggestions. This makes the skill improvement process inefficient and unintuitive. Therefore, how to accurately identify contestant movement deviations through technological means within the competition system and generate dynamic, quantifiable corrective guidance paths has become a key issue in improving the effectiveness of hotel service skills training. Summary of the Invention

[0003] This invention provides a method for optimizing bed-making actions in hotel service skills competitions, mainly including: Body posture data of the athlete during the bed-making process is collected using a sensor array. Posture analysis methods are employed to process multi-dimensional joint angle and rhythm information in the data, yielding an initial deviation feature vector. This initial deviation feature vector represents the preliminary difference between the athlete's movement and the standard posture. The initial deviation feature vector is compared with a preset standard movement template. If the comparison shows that the joint angle deviation exceeds a preset threshold, a data analysis model is used to determine the specific direction and degree of the rhythm deviation, resulting in a quantified deviation matrix. This quantified deviation matrix represents the numerical distribution of the movement deviation. Offset values ​​of key joint angles are extracted from the quantified deviation matrix, and a mapping method is used to calculate the transition sequence from the current movement to the standard movement, resulting in a preliminary optimized path sequence. This preliminary optimized path sequence describes the step-by-step path of movement adjustment. Personalized information from the athlete's historical data is integrated into the preliminary optimized path sequence. If rhythm deviations are repeatedly observed in the historical data, the step size parameter in the preliminary optimized path sequence is adjusted to determine a dynamic feedback adjustment vector. This dynamic feedback adjustment vector provides a correction direction based on historical patterns. After obtaining the dynamic feedback adjustment vector, the quantized deviation matrix and the dynamic feedback adjustment vector are fused through a real-time guidance module to generate a guidance instruction sequence that includes angle correction and rhythm optimization. This guidance instruction sequence integrates multiple adjustment information. Deviations related to the bed-making process are filtered from the guidance instruction sequence, and an iterative optimization method is used to process the remaining unmatched joint angle information to obtain a complete correction guidance path. This complete correction guidance path covers all motion elements that need adjustment. The complete correction guidance path is output to the display interface, and its application effect is verified through a feedback loop method to determine whether further fine-tuning of the initial deviation feature vector is needed to adapt to training requirements. Further, the specific direction and degree of rhythm deviation are determined through a data analysis model to obtain a quantized deviation matrix, which represents the numerical distribution of motion deviations. Further, a preliminary optimized path sequence is obtained, which describes the step-by-step path of motion adjustment. Further, the step size parameter in the preliminary optimized path sequence is adjusted to determine the dynamic feedback adjustment vector, which provides a correction direction based on historical patterns. Furthermore, a sequence of guidance instructions is generated, incorporating angle correction and rhythm optimization, which integrates various adjustment information. Further, a complete correction guidance path is obtained, covering all action elements requiring adjustment. Further, it is determined whether further fine-tuning of the initial deviation feature vector is needed to adapt to training requirements.

[0004] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This invention discloses a method for optimizing bed-making movements based on posture analysis and dynamic feedback. Addressing the problem of identifying and correcting posture deviations in the bed-making process, it integrates multi-dimensional joint angle data processing, rhythm deviation quantification, and personalized historical data adjustment to meet the business scenario requirements, forming a logically coherent solution. This invention collects posture data using a sensor array, generates an initial deviation feature vector, compares it with a standard template, quantifies the deviation matrix to determine the direction and degree of deviation, then extracts key offset values, calculates an optimized path sequence, adjusts step length parameters based on historical data, forms a dynamic feedback adjustment vector, and finally generates a sequence of guidance instructions including angle correction and rhythm optimization. The effectiveness is verified through iterative optimization and feedback loops to ensure the comprehensiveness and adaptability of the correction path. The core innovation of this invention lies in combining real-time data analysis with personalized historical patterns to dynamically generate precise guidance instructions, significantly improving the targeting and training efficiency of movement correction, and achieving intelligent support throughout the entire process from deviation identification to optimized guidance. Attached Figure Description

[0005] Figure 1 This is a flowchart illustrating an optimized guidance method for bed-making actions in a hotel service skills competition, as described in this invention. Detailed Implementation

[0006] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0007] like Figure 1 This embodiment of a method for optimizing bed-making actions in hotel service skills competitions may specifically include: This invention provides a method for motion optimization guidance based on body posture data analysis. It aims to provide precise corrective guidance by performing multi-dimensional analysis of an athlete's motion data during a specific training phase. The implementation process of this invention is described in detail below with reference to specific embodiments to make the objectives, technical solutions, and advantages of this invention clearer. In one embodiment, this method mainly focuses on collecting and analyzing the athlete's body posture data during the bed-making phase, thereby generating an optimized guidance path. The bed-making phase, as an action requiring high coordination and rhythm, demands high standards from the athlete in terms of joint angles, movement rhythm, and overall coordination. This embodiment uses sensor technology and data processing methods to gradually realize a complete process from data collection to guidance output, ensuring the scientific and personalized nature of motion optimization. Step S1: Collect the athlete's body posture data during the bed-making phase using a sensor array. Use a posture analysis method to process the multi-dimensional joint angle and rhythm information in the data to obtain an initial deviation feature vector. The initial deviation feature vector represents the initial difference between the athlete's movement and the standard posture. Specifically, sensor arrays can be deployed at key parts of the athlete's body, such as joints in the shoulders, elbows, wrists, hips, knees, and ankles, to capture dynamic posture data of the athlete in real time during the bed-making process. These sensors can be inertial measurement-based devices capable of recording joint angle changes and movement frequency information. The collected data includes multi-dimensional information, such as the angle values ​​of each joint in three-dimensional space and the speed of movement execution. The data acquisition frequency can be adjusted according to training needs, typically set to tens of acquisitions per second to ensure data continuity and accuracy. After acquiring the raw data, it needs to be preprocessed to remove noise and invalid information. For example, filtering techniques can be used to smooth the data and ensure the stability of joint angle values. Subsequently, posture analysis methods are used to extract features from the processed data. This process mainly focuses on the distribution of joint angles and the regularity of movement rhythm. By initially comparing the athlete's actual movement data with preset standard posture data, the difference between the two is calculated. These difference values ​​are integrated into a multi-dimensional vector, namely the initial deviation feature vector, to characterize the direction and degree of deviation of the athlete's movement overall. Step S11: During the data acquisition phase, the arrangement of the sensor array can be adjusted according to the training scenario. For example, in the bed-making stage, the focus is on the coordination of upper limbs and torso; therefore, the density of sensors can be increased in the shoulders and waist to improve data acquisition accuracy. Simultaneously, to accommodate athletes of different heights and body types, adjustable straps can be used to fix the sensors, ensuring a close fit and avoiding data deviation. Step S12: During posture analysis, the calculation of joint angles requires consideration of coordinate transformation in three-dimensional space. For example, the raw angle data acquired by the sensors can be converted into relative angle values ​​relative to the standard posture.This process can be achieved by creating a virtual human body model, mapping the athlete's actual movements onto the model, and thus visually displaying the deviation of each joint. The extraction of rhythm information relies on the analysis of the movement cycle. By identifying the start and end points of the movement, the frequency and duration of the movement are calculated and compared with a standard rhythm. Step S13, the generation of the initial deviation feature vector, is based on a comprehensive analysis of multi-dimensional data. For example, different weights are assigned to the angle deviation and rhythm deviation values ​​of each joint. The weights can be determined based on the importance of the joint in the bed-making movement. The angle deviations of the shoulder and elbow may have a greater impact on the overall movement, so they are given higher weights, while the deviation of the ankle has a smaller impact and is therefore given a lower weight. Through weighted calculation, a comprehensive vector is obtained, reflecting the overall deviation characteristics of the athlete's movements. This vector provides basic data support for subsequent in-depth analysis. In one possible implementation, the generation of the initial deviation feature vector can also be dynamically adjusted in conjunction with the athlete's training phase. For example, for beginners, the analysis can focus on the coarse deviations of major joints, resulting in lower-dimensional vectors that simplify subsequent processing. For advanced athletes, however, more subtle deviations need to be considered, leading to vectors containing more dimensional information and ensuring comprehensive analysis. This approach adapts to different training needs and improves the targeted nature of guidance. For instance, in actual training, suppose an athlete's shoulder angle deviates significantly from the standard value while making a bed, while the elbow and wrist deviations are smaller, and the rhythm is too fast. In the initial deviation feature vector generated after analyzing data collected by sensors, the shoulder deviation value dominates, and the rhythm deviation value is also prominent. This vector intuitively reflects the key areas of movement the athlete needs to adjust, providing a clear direction for subsequent comparison and optimization. Step S2: Compare the initial deviation feature vector with a preset standard movement template. If the comparison result shows that the joint angle deviation exceeds a preset threshold, the specific direction and degree of the rhythm deviation are determined through a data analysis model, resulting in a quantified deviation matrix. This quantified deviation matrix represents the numerical distribution of the movement deviation. Specifically, the preset standard movement template is constructed based on movement data from a large number of elite athletes, encompassing the standard angle range and ideal rhythmic pattern for each joint in the bed-making process. The initial deviation feature vector is compared dimension-by-dimensionally with the standard movement template to determine whether the angle deviation of each joint exceeds a preset threshold range. The threshold can be flexibly set according to training goals and athlete skill levels; for example, for high-level athletes, the threshold can be set smaller to pursue higher movement precision. During the comparison process, if the angle deviation of certain joints is found to exceed the threshold, the specific manifestations of these deviations are further analyzed. For example, the shoulder angle may be too high or too low, and the elbow angle may be too inward or too outward; the direction and magnitude of these deviations need to be recorded in detail.Meanwhile, the analysis of rhythm deviation is also crucial. By comparing the execution time and standard time of the movement, it can be determined whether the athlete's movement is too fast or too slow, thus determining the direction of rhythm adjustment. To integrate this deviation information into a systematic data structure, a data analysis model is used to process the deviation values ​​and generate a quantified deviation matrix. Each row of this matrix corresponds to a joint or a movement dimension, and each column represents the specific attributes of the deviation, such as deviation direction, deviation amplitude, and deviation frequency. The matrix format clearly displays the distribution of deviations in various aspects of the athlete's movements, providing data support for subsequent optimization path design. In step S21, when constructing the standard movement template, multiple template versions can be designed according to different training objectives. For example, for speed-priority training scenarios, the rhythm standard in the template can be set faster, while for accuracy-priority scenarios, the template focuses more on the precision of joint angles. Athletes can choose a suitable template for comparison based on their own needs to ensure that the analysis results are consistent with the training objectives. In step S22, during the comparison process, the threshold setting needs to be dynamically adjusted based on the actual training situation. For example, in the early stages of training, the threshold can be set relatively loosely, focusing only on large deviations in major joints; while in the later stages of training, the threshold is gradually tightened, focusing on more subtle deviations. This dynamic adjustment method can gradually improve the athlete's movement level, avoiding setting too high a standard from the beginning and causing excessive training difficulty. Step S23, the generation process of the quantified deviation matrix requires multi-level analysis of the deviation data. For example, the deviation value of each joint can be normalized first to ensure that the deviation values ​​of different joints are comparable under the same dimension. Subsequently, the normalized data is classified and organized according to the deviation direction and magnitude to form a matrix structure. Time dimension information can also be added to the matrix to record the distribution of deviations in the movement cycle, so as to analyze movement problems more comprehensively. In one possible implementation, the generation of the quantified deviation matrix can also be personalized in combination with the athlete's body characteristics. For example, for tall athletes, the deviation of joint angles may be affected by limb length, so a height ratio factor can be introduced when generating the matrix to correct the deviation value. This method can improve the accuracy of the matrix and ensure the scientific nature of subsequent analysis. For example, in practical applications, suppose a competitor's shoulder angle is too high (exceeding a threshold) and their elbow angle is too inward (but not exceeding a threshold) during the bed-making process, with an overall pace that is too fast. Through comparative analysis, the resulting quantitative deviation matrix marks shoulder deviation and pace deviation as key areas of focus. The matrix details the specific direction and magnitude of the shoulder deviation, as well as the degree of pace excess. This data provides a clear basis for subsequent movement adjustments, avoiding the inefficiency of blind adjustments.Step S3 involves extracting the offset values ​​of key joint angles from the quantified deviation matrix and using a mapping method to calculate the transition sequence from the current movement to the standard movement, obtaining a preliminary optimized path sequence. This preliminary optimized path sequence describes the gradual path of movement adjustment. Specifically, the quantified deviation matrix contains deviation information of the athlete's movement in various dimensions, with the offset values ​​of key joints being the focus of adjustment. Key joints are typically those parts that significantly affect the overall coordination of the bed-making movement, such as the shoulders, elbows, and waist. The offset values ​​of these joints, including the direction and magnitude of the deviation, are extracted from the matrix as the basis for subsequent calculations. After extracting the offset values, a mapping method is used to gradually adjust the current movement to the standard movement. This process can be viewed as path planning from the current state to the target state. The core of the mapping method lies in designing a series of intermediate states to make the movement adjustment process smooth and in line with the laws of human movement. For example, the shoulder angle can be gradually adjusted from the current high state to the standard value, with each adjustment controlled within a small range to avoid discomfort caused by sudden changes in movement. The transition sequence calculated using the mapping method is integrated into a preliminary optimized path sequence. This sequence contains multiple steps of movement adjustment, each step corresponding to an intermediate state, describing how joint angles and rhythms gradually approach standard values. The preliminary optimized path sequence not only focuses on the final goal but also emphasizes the operability of the adjustment process, ensuring that the athlete can gradually adapt to the adjustment rhythm. In step S31, when extracting key joint offset values, joints can be prioritized according to the importance of the movement. For example, in the bed-making phase, the offset values ​​of the shoulder and elbow may have higher priority because these joints directly affect the overall smoothness of the movement; while the offset values ​​of the wrist and ankle have lower priority and can be processed in subsequent adjustments. This prioritization method ensures that the adjustment process focuses on the most critical issues, improving efficiency. In step S32, the implementation of the mapping method can be designed in conjunction with the principles of human kinematics. For example, when calculating the transition sequence, it is necessary to consider the natural range of joint movement and the continuity of the movement, avoiding the design of adjustment steps that do not conform to the laws of human biomechanics. By simulating a dynamic model of human movement, the offset values ​​can be gradually decomposed into multiple small adjustments to ensure that each step conforms to the laws of motion. Step S33, the initial optimization of the path sequence generation can also incorporate time constraints. For example, for athletes with a faster pace, a longer adjustment period can be designed in the path sequence to gradually slow down the movement speed; while for athletes with a slower pace, a shorter adjustment period can be designed to gradually increase the movement frequency. This approach ensures that the path sequence matches the athlete's actual needs. In one possible implementation, the initial optimization of the path sequence generation can be validated using visual aids. For example, after designing the path sequence, the adjustment steps can be visualized using 3D simulation software, showing the athlete's movement patterns in each intermediate state, so that coaches or athletes can intuitively understand the direction of adjustment.This approach improves the practicality of the path sequence and reduces misunderstandings. For example, in actual training, suppose a competitor's shoulder angle is too high and their elbow angle is too inward. The offset value extracted from the quantified deviation matrix shows a large deviation in the shoulder angle. In the preliminary optimized path sequence calculated using the mapping method, the shoulder angle adjustment is divided into multiple small steps, each with a small adjustment range. Simultaneously, the elbow angle adjustment is also performed, ensuring that the overall coordination of the movement gradually improves. This sequence provides the competitor with a clear direction for adjustment, avoiding the discomfort caused by a large adjustment all at once. In another embodiment, for competitors with large rhythm deviations, the design of the preliminary optimized path sequence can focus on adjusting the movement cycle. For example, suppose the competitor's movements are too fast. The path sequence can be designed with a series of intermediate states that slow down the rhythm, gradually approaching the standard rhythm by extending the execution time of each movement. This approach helps the competitor gradually adapt to the correct rhythm and improves the stability of the movement. For example, for a competitor who moves too fast during bed-making, the preliminary optimized path sequence is designed with multiple adjustment steps, each step gradually extending the duration of the movement while fine-tuning the shoulder and elbow angles. In this way, athletes can gradually adjust to a standard rhythm without changing their movement habits, and the overall coordination of their movements is also improved. In one possible implementation, the preliminary optimized path sequence can also be adjusted in real time based on athlete feedback. For example, if an athlete feels uncomfortable with a certain adjustment step during training, they can record feedback information and fine-tune the adjustment range or step order in the path sequence. This method can improve the adaptability of the path sequence and ensure that the adjustment process matches the athlete's actual feelings. Through the above steps, from data collection to the generation of the preliminary optimized path sequence, this method realizes a comprehensive analysis and systematic adjustment of the athlete's movement deviations, laying the foundation for subsequent personalized optimization and guidance instruction generation. Subsequent steps will further integrate historical data and real-time feedback to ensure the accuracy and practicality of the guidance path. Step S4: For the preliminary optimized path sequence, personalized information from the athlete's historical data is integrated. If rhythm deviations recur in the historical data, the step size parameter in the preliminary optimized path sequence is adjusted to determine a dynamic feedback adjustment vector. The dynamic feedback adjustment vector provides a correction direction based on historical patterns. Specifically, while the initial optimized path sequence provides adjustment steps from the current movement to the standard movement, the differences in each athlete's physical condition, training habits, and movement characteristics mean that simply relying on a standardized path sequence may not fully meet individual needs. Therefore, it is necessary to incorporate the athlete's historical data for further optimization. Historical data can include the athlete's movement records, deviation patterns, and adjustment effects from past training sessions. This data is typically stored in the training management system and retrieved through a data interface. During the integration of historical data, a key focus is on the repetitiveness of rhythm deviations.For example, by analyzing historical data, it can be found that some athletes frequently move too fast or too slow during the bed-making process. This recurring deviation pattern reflects the athlete's habitual problems or limitations in physical coordination. If rhythm deviations are found to be recurring, the step length parameters in the initial optimized path sequence need to be adjusted. The step length parameter refers to the amplitude and time interval of each adjustment step in the path sequence. By adjusting the step length, the rhythm and intensity of the adjustment can be changed to better suit the athlete's actual situation. The adjusted step length parameters are integrated into a dynamic feedback adjustment vector. This vector not only includes the specific value of the step length adjustment but also the correction direction derived from historical patterns. For example, if historical data shows that athletes are prone to rebound during rhythm adjustments, a direction that gradually reduces the adjustment amplitude will be added to the vector to ensure a smoother adjustment process. In this way, the dynamic feedback adjustment vector can provide personalized correction basis for the generation of subsequent guidance instructions. In step S41, when extracting historical data, different data ranges can be selected according to different stages of the training cycle. For example, in the early stages of training, recent historical data can be extracted to reflect the athlete's current state; while in the later stages, data over a longer period can be extracted to analyze the athlete's long-term deviation patterns. This phased extraction method ensures the relevance of historical data and improves the targeting of adjustments. Step S42: When analyzing the repetitiveness of rhythm deviations, historical data can be processed using time series analysis. For example, comparing the athlete's rhythm data from multiple past training sessions can identify the frequency and scenarios of deviations. If deviations are found to mainly occur during training fatigue, it can be inferred that the deviations are related to decreased physical strength, and increasing rest intervals should be considered when adjusting step length parameters. This analysis method helps identify the underlying causes of deviations, providing a scientific basis for adjustments. Step S43: The generation of dynamic feedback adjustment vectors can also incorporate the athlete's physical feedback information. For example, if historical data shows that the athlete frequently feels discomfort when adjusting rhythm, a buffer adjustment factor can be added to the vector to reduce the intensity of the adjustment and ensure that the adjustment process does not place an additional burden on the athlete. This approach improves the comfort of adjustments and enhances the athlete's training confidence. In one possible implementation, the design of dynamic feedback adjustment vectors can incorporate the coach's experience input. For example, after analyzing historical data, coaches can offer targeted adjustment suggestions based on the athlete's specific situation. These suggestions are then transformed into vector correction parameters to ensure that the adjustments are more aligned with actual needs. This approach combines data analysis with human experience to improve the accuracy of adjustments. For instance, in actual training, suppose a athlete's historical data shows that they frequently move too quickly during the bed-making phase, especially in the latter half of the training session.After analysis, the step size parameter in the initial optimized path sequence was adjusted to a smaller amplitude, and the adjustment time interval was extended. The generated dynamic feedback adjustment vector included a progressive slowdown correction direction. This adjustment method helps athletes gradually overcome habitual deviations and improve the stability of their movements. Step S5: After obtaining the dynamic feedback adjustment vector, the quantized deviation matrix and the dynamic feedback adjustment vector are fused through the real-time guidance module to generate a guidance instruction sequence that includes angle correction and rhythm optimization. This guidance instruction sequence integrates multiple adjustment information. Specifically, the dynamic feedback adjustment vector provides personalized correction directions for movement adjustment, while the quantized deviation matrix contains detailed deviation information of the athlete's current movement. Through the real-time guidance module, these two parts of data are fused to generate a comprehensive guidance instruction sequence. This sequence not only includes joint angle correction steps but also specific methods for rhythm optimization, ensuring that the adjustment process covers all aspects of the movement. The fusion process of the real-time guidance module mainly includes two steps: data alignment and priority allocation. First, the deviation data in the quantized deviation matrix is ​​aligned with the correction parameters in the dynamic feedback adjustment vector to ensure consistency between the two in the time and movement dimensions. For example, the deviation data of the shoulder angle in the matrix needs to correspond to the step size parameter of the shoulder adjustment in the vector. Then, the adjustment information of different dimensions is weighted according to the severity of the deviation and the priority of the adjustment. For example, when the shoulder angle deviation is large, its adjustment instruction has a higher priority; when the rhythm deviation is small, its priority can be appropriately reduced. The guidance instruction sequence generated through fusion processing is an ordered set of adjustment steps, each containing specific adjustment goals and methods. For example, a step might require the athlete to reduce the shoulder angle by a certain range in the next movement while slowing down the movement rhythm. In this way, the guidance instruction sequence can provide clear adjustment guidance to the athlete, ensuring the comprehensiveness and systematic nature of movement optimization. In step S51, during data alignment, timestamp information can be used to ensure the synchronization of the quantized deviation matrix and the dynamic feedback adjustment vector. For example, the deviation data in the matrix is ​​divided according to the movement cycle and corresponds one-to-one with the adjustment parameters in the vector, ensuring that there is no data misalignment during the fusion process. This method can improve the accuracy of fusion and avoid confusion in adjustment instructions. In step S52, during priority allocation, the adjustment information of different dimensions can be dynamically sorted according to the training objective. For example, if the current training focus is on improving movement precision, the priority of joint angle adjustment can be set higher; if the focus is on rhythm coordination, the priority of rhythm adjustment can be increased accordingly. This dynamic sorting method ensures that the sequence of instructions aligns with the training objective. In step S53, the generation of the instruction sequence can also be optimized by incorporating real-time feedback.For example, during the integration process, if certain adjustment steps are found to be beyond the athlete's current ability, the instructions can be fine-tuned through the real-time guidance module to reduce the difficulty of adjustment and ensure the executability of the instructions. This approach improves the practicality of the instructions and avoids frustration caused by overly difficult instructions. In one possible implementation, the generation of the guidance instruction sequence can be combined with multimodal output methods. For example, in addition to textual descriptions of adjustment steps, instructions can also be delivered to the athlete through voice prompts or visual animations to ensure intuitiveness and ease of understanding. This approach can improve the athlete's execution efficiency and reduce misunderstandings. For example, in a practical application, suppose an athlete's quantification deviation matrix shows a large deviation in shoulder and elbow angles, and the dynamic feedback adjustment vector includes a step size parameter for progressive adjustment. After integration through the real-time guidance module, the generated guidance instruction sequence first instructs the athlete to focus on adjusting the shoulder angle in the next movement, while appropriately slowing down the pace, and then gradually adjusting the elbow angle. This sequential instruction design helps athletes complete movement optimization in an orderly manner. Step S6: Filter the deviations related to the bed-making stage from the instruction sequence, and use an iterative optimization method to process the remaining mismatched joint angle information to obtain a complete correction guidance path. This complete correction guidance path covers all movement elements that need adjustment. Specifically, the instruction sequence contains various adjustment information, but due to the special nature of the bed-making stage, some adjustment instructions may not be entirely relevant to the current training scenario. Therefore, it is necessary to filter the instruction sequence, extracting the deviations directly related to the bed-making stage, such as adjustment instructions for upper limb joint angles and movement rhythm, while temporarily suspending adjustment instructions unrelated to the lower limbs. After filtering out the relevant deviations, an iterative optimization method is used to process the remaining mismatched joint angle information. The core of the iterative optimization method is to gradually approach the optimal adjustment scheme, ensuring that all joint angles and movement dimensions are optimized through multiple iterations. For example, for mismatched wrist angle deviations, multiple small adjustments can be made to gradually approach the standard value, while observing the impact of the adjustments on the overall movement to ensure that no new deviations are caused. Through filtering and iterative optimization, the generated complete correction guidance path covers all movement elements that need adjustment. This path not only includes adjustments to key joints and rhythms during the bed-making process but also subtle adjustments to secondary joints, ensuring comprehensive movement optimization. Furthermore, the complete correction guidance path considers the order and dependencies of adjustments; for example, adjusting shoulder angles first, followed by elbows and wrists, ensuring coordinated movement adjustments. In step S61, during the screening process, screening rules can be designed based on the characteristics of the bed-making movements. For example, adjustment instructions related to upper limb joints and trunk movements can be prioritized, while instructions related to lower limb movements can be temporarily excluded. This rule design ensures that the screening results are highly relevant to the training scenario, improving the targeted nature of adjustments.Step S62: During iterative optimization, the rationality of each iteration can be verified by simulating the adjustment effect. For example, when adjusting the wrist angle, a virtual model can be used to simulate the adjusted movement shape to observe whether it will have an adverse effect on the shoulder or elbow. If a problem is found, the iteration parameters are adjusted to ensure the stability of the optimization process. This method can improve the reliability of the complete correction guidance path. Step S63: The generation of the complete correction guidance path can also be optimized by combining movement dependencies. For example, when adjusting multiple joint angles, the joints that have a greater impact on the overall movement can be processed first, followed by the secondary joints, to ensure that the adjustment process does not disrupt the overall coordination of the movement. This method can improve the practicality of the path and ensure that the athlete can execute the adjustments smoothly. In one possible implementation, the generation of the complete correction guidance path can be dynamically adjusted based on the athlete's real-time state. For example, during training, if it is found that the athlete's movement deviation is aggravated due to fatigue, the step order or amplitude in the path can be adjusted to reduce the difficulty of adjustment and ensure the feasibility of the path. This method can improve the adaptability of the path and enhance the training effect. For example, in actual training, assuming the instruction sequence includes adjustment instructions for the shoulder, elbow, and wrist, after filtering, it is determined that shoulder and elbow adjustments are directly related to the bed-making process, while wrist adjustments are secondary. During iterative optimization, the wrist angle is gradually adjusted while ensuring that it does not affect the adjustment effect of the shoulder and elbow. The final generated complete correction guidance path includes adjustment steps for all joints, with a reasonable adjustment order, ensuring that the athlete can gradually improve the quality of their movements. Step S7: The complete correction guidance path is output to the display interface, and the application effect of the complete correction guidance path is verified through a feedback loop method to determine whether further fine-tuning of the initial deviation feature vector is needed to adapt to training requirements. Specifically, after the complete correction guidance path is generated, the adjustment steps need to be presented intuitively to the athlete or coach through the display interface. The display interface can be a screen on the training equipment or an application interface on a mobile terminal. Each adjustment step in the path is displayed in the form of text, graphics, or animation to ensure that the athlete can clearly understand the adjustment direction and method. For example, a 3D animation can be used to show the intermediate state of shoulder angle adjustment to help the athlete intuitively feel the adjustment target. After outputting the path, a feedback loop method is used to verify its effectiveness. This method primarily includes two stages: real-time monitoring and effect evaluation. First, as the athlete performs the adjustment steps, a sensor array collects motion data in real time, recording the adjusted joint angles and rhythm information. Then, this data is compared with the target values ​​in the complete correction guidance path to evaluate whether the adjustment effect has met expectations. If some adjustment steps are found to be unsatisfactory, such as persistent deviations after shoulder angle adjustment, further analysis is needed to determine the cause and whether fine-tuning of the initial deviation feature vector is necessary.The process of fine-tuning the initial deviation feature vector can include readjusting weight allocation or adding new deviation dimensions. For example, if the rhythm adjustment is found to be ineffective, the weight of rhythm deviation can be increased in the initial deviation feature vector, or a new rhythm-related dimension can be added to ensure a more comprehensive subsequent analysis. This feedback loop continuously optimizes the guidance path, ensuring it adapts to the athlete's training needs. In step S71, when outputting to the display interface, different display modes can be designed according to the athlete's comprehension level. For example, for beginners, simplified text descriptions and intuitive animations can be used to ensure the adjustment steps are easy to understand; while for advanced athletes, more detailed data analysis charts can be provided to show the trend of deviation changes. This personalized display method improves the practicality of the path. In step S72, during the feedback loop, multiple rounds of verification can ensure the stability of the adjustment effect. For example, after the athlete completes one round of adjustment, the action can be repeated multiple times, and multiple sets of data can be collected for comparison to determine whether the adjustment effect is consistent. If significant fluctuations are found, further analysis of the reasons is needed to ensure the reliability of the path. This method improves the scientific rigor of the verification. Step S73: When fine-tuning the initial deviation feature vector, dynamic adjustments can be made based on the athlete's training progress. For example, in the early stages of training, fine-tuning can focus on deviations in major joints, while in the later stages, more subtle deviation dimensions can be gradually added to ensure comprehensive analysis. This approach ensures that the fine-tuning process remains consistent with the training objectives. In one possible implementation, the feedback loop method can also be optimized by combining real-time coaching guidance. For example, when verifying the adjustment effect, the coach can provide targeted improvement suggestions based on the athlete's actual performance. These suggestions are recorded and used to fine-tune the initial deviation feature vector, ensuring that the adjustment direction is more aligned with actual needs. This approach enhances the effectiveness of the feedback loop. For instance, in practical applications, assuming a complete correction guidance path is output to the display interface, the athlete adjusts their shoulder angle and movement rhythm according to the steps in the path. Verification using the feedback loop method reveals that the shoulder angle adjustment is effective, but the rhythm adjustment still has deviations. Further analysis leads to an increase in the weight of the rhythm dimension in the initial deviation feature vector, regenerating the guidance path to ensure more precise subsequent adjustments. This approach helps athletes gradually approach standard movements, improving training effectiveness. Through the above steps, from the fusion of historical data to the generation of a complete correction guidance path, and then to feedback loop verification, this method achieves personalized optimization and dynamic adjustment of the athlete's movements, ensuring the scientific nature and practicality of the guidance path, and providing comprehensive support for improving the quality of movements in the bed-making process.

[0008] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the present invention will not describe the various possible combinations separately. Furthermore, various different embodiments of the present invention can also be arbitrarily combined, as long as they do not violate the spirit of the present invention, and they should also be regarded as the content disclosed by the present invention.

Claims

1. A method for guiding a bedding-making action optimization in a hotel service skill competition, characterized by, The method comprises the following steps: Collecting the body posture data of the athlete in the bedding process through a sensor array, processing the multi-dimensional joint angle and rhythm information in the body posture data, and generating an initial deviation feature vector, which represents the preliminary difference between the athlete's action and the standard posture; Comparing the initial deviation feature vector with the preset standard action template to determine the joint angle deviation, and generating a quantitative deviation matrix, which represents the numerical distribution of action deviation; Extracting the offset value of the key joint angle from the quantitative deviation matrix, calculating the transition sequence from the current action to the standard action, and forming a preliminary optimization path sequence, which describes the step-by-step path of action adjustment; Fusing the personalized information in the athlete's historical data to adjust the step parameter in the preliminary optimization path sequence, generating a dynamic feedback adjustment vector, which provides a correction direction based on historical patterns; Integrating the quantitative deviation matrix and the dynamic feedback adjustment vector through a real-time guidance module to generate a guidance instruction sequence containing angle correction and rhythm optimization, which integrates multiple adjustment information; Screening the deviation part related to the bedding process in the guidance instruction sequence, processing the remaining unmatched joint angle information, and forming a complete correction guidance path, which covers all action elements that need to be adjusted; Outputting to the display interface according to the complete correction guidance path, verifying the application effect of the complete correction guidance path through a feedback loop, and determining whether the initial deviation feature vector needs to be fine-tuned to adapt to the training requirements.

2. The hotel service skill competition bed-making motion optimization guidance method according to claim 1, characterized in that, The method comprises the following steps: Real-time acquisition of dynamic data of multiple joint parts of the athlete in the bedding process through the sensor array; Decomposing the dynamic data to extract the change trajectory of joint angle and the fluctuation feature of action rhythm; Standardizing the change trajectory of joint angle and the fluctuation feature of action rhythm to generate the initial deviation feature vector.

3. The hotel service skill competition bed-making motion optimization guidance method according to claim 1, characterized in that, The method comprises the following steps: Comparing each joint angle data in the initial deviation feature vector with the corresponding standard value in the preset standard action template one by one; For each joint angle data, calculate its deviation amplitude and deviation direction from the standard value; According to the deviation amplitude and the deviation direction, construct a multi-dimensional deviation distribution structure to form the quantitative deviation matrix, which covers the deviation information of all joint parts.

4. The hotel service skill competition bed-making motion optimization guidance method according to claim 3, characterized in that, The method comprises the following steps: Screening the key joint angle data with larger deviation amplitude from the quantitative deviation matrix; For the key joint angle data, a gradual adjustment path from the current value to the standard value is constructed to generate the preliminary optimization path sequence.

5. The hotel service skill competition bed-making motion optimization guidance method according to claim 4, characterized in that, The personalized information in the fusion player historical data is adjusted to generate a dynamic feedback adjustment vector, including: Extracting the action pattern and deviation trend in the player historical data; According to the action pattern and the deviation trend, the step length parameter in the preliminary optimization path sequence is adjusted to generate the dynamic feedback adjustment vector.

6. The hotel service skill competition bed-making motion optimization guidance method according to claim 5, characterized in that, The real-time guidance module integrates the quantized deviation matrix and the dynamic feedback adjustment vector to generate a guidance instruction sequence containing angle correction and rhythm optimization, including: Input the quantized deviation matrix and the dynamic feedback adjustment vector into the real-time guidance module; The real-time guidance module performs weighted fusion on the quantized deviation matrix and the dynamic feedback adjustment vector to generate the guidance instruction sequence.

7. The hotel service skill competition bed-making motion optimization guidance method according to claim 6, characterized in that, The deviation part related to the laying bed link in the guidance instruction sequence is screened, and the remaining unmatched joint angle information is processed to form a complete correction guidance path, including: Extracting the deviation data directly related to the laying bed link from the guidance instruction sequence; Iteratively processing the remaining unmatched joint angle information to generate the complete correction guidance path.

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