Acupuncture teaching auxiliary platform and method based on intelligent analysis

Abstract

The invention discloses an acupuncture teaching auxiliary platform and method based on intelligent analysis. The method comprises the steps that S1, a preprocessing action signal data set is output; s2, obtaining a delay parameter set and an embedded dimension value; s3, forming a phase space reconstruction trajectory set; s4, generating an action track fragment object set; s5, constructing a bilateral preference sequence according to the dynamic similarity index and the stability difference index; s6, an improved stable marriage algorithm is executed on the basis of the bilateral preference sequence, and a comprehensive stability measurement index value is obtained; and S7, mapping a preset scoring interval according to the comprehensive stability measurement index value to generate a stability scoring result, and pushing the stability scoring result and the corresponding action gap information to the teaching platform to complete feedback. According to the invention, unstable paragraphs and control weak points existing in student operation can be accurately identified, and teaching intervention is more targeted.

Description

Technical Field

[0001] The present invention relates to the technical field of acupuncture teaching, and in particular to an acupuncture teaching assistance platform and method based on intelligent analysis. Background Art

[0002] With the advancement of medical education informatization and visualization technology, acupuncture, as a highly operational diagnosis and treatment technique in traditional Chinese medicine, the teaching quality directly affects clinical efficacy and patient safety. Traditional acupuncture teaching usually relies on teachers' oral explanations combined with practical demonstrations for guidance. Students master the operation steps of needle insertion, retention, and needle withdrawal through imitation learning. However, this "master-apprentice" teaching method has significant limitations: on the one hand, the teaching process lacks an accurate and quantitative feedback mechanism, and it is difficult for students to timely understand the specific differences between their own actions and standard actions during the operation process; on the other hand, the subjective evaluation of teachers accounts for a high proportion in teaching, and the evaluation criteria are not unified, which easily leads to significant differences in teaching results.

[0003] In recent years, some acupuncture teaching institutions have tried to introduce sensor devices and computer vision technology to collect and analyze the actions during acupuncture. By recording the hand trajectory and force application, it is used to assist teaching evaluation. However, existing methods mostly focus only on the data analysis of a single modality such as displacement or posture, ignoring the coupling relationship between the three-dimensional spatial posture change and axial force application during acupuncture operation, resulting in a single evaluation dimension and poor result interpretability. In addition, existing technologies generally fail to achieve the dynamic segmentation of the entire action process and the accurate extraction of stage characteristics, making it difficult to conduct fine-grained analysis on each link of "needle insertion - retention - needle withdrawal" during the acupuncture process, lacking quantitative support for core indicators such as dynamic stability and action trajectory consistency.

[0004] More critically, most existing teaching evaluation tools compare the actions of students and experts by using methods based on template matching or similarity scoring. They fail to conduct comprehensive modeling by combining the complexity of the dynamic evolution process, and also fail to integrate a bilateral preference matching mechanism to globally analyze the matching stability between the "student - standard" action pairs. This leads to the feedback results being limited to static difference judgments, making it difficult to reflect the process stability and overall consistency of action execution, and restricting the teaching system's ability to accurately judge the skill level of students and generate personalized improvement suggestions.

[0005] In summary, there is an urgent need for a teaching assistance method that can simultaneously integrate multi-modal data, multi-stage process division, and dynamic stability evaluation mechanisms to improve teaching efficiency and evaluation scientificity. Summary of the Invention

[0006] An object of the present invention is to provide an acupuncture teaching assistance platform and method based on intelligent analysis. The present invention can accurately identify the unstable paragraphs and weak control points existing in the student's operation, and the teaching intervention is more targeted.

[0007] An acupuncture teaching auxiliary method based on intelligent analysis according to an embodiment of the present invention comprises the following steps: S1. Collect multimodal action data sets for the whole process of acupuncture teaching, perform preprocessing operations, and output preprocessed action signal data sets; S2. Calculate the delay time parameter and the embedding dimension parameter for the preprocessed action signal data set to obtain a delay parameter set and an embedding dimension value; S3. Perform phase space reconstruction on the preprocessed motion signal data set according to the delay parameter set and the embedding dimension value to form a phase space reconstruction trajectory set; S4. Based on the phase space reconstruction trajectory set, the action segments are divided according to the acupuncture insertion stage, the stay stage and the needle removal stage, and the local dynamic features are extracted to generate the action trajectory segment object set; S5. construct a standard motion trajectory segment object set according to the teaching reference library, use the motion trajectory segment object set and the standard motion trajectory segment object set as the matching parties, and construct a bilateral preference sequence according to the dynamic similarity index and the stability difference index; S6. Execute the improved stable marriage algorithm based on the bilateral preference sequence to obtain a comprehensive stability measurement index value; S7. Generate a stability score result based on the preset score interval mapped by the comprehensive stability measurement index value, and push the stability score result and the corresponding action gap information to the teaching platform to complete the feedback.

[0008] Optionally, the S1 includes the following steps: S11. Use a multi-modal acquisition device to synchronously collect signals during the entire acupuncture teaching process, obtain three-axis displacement information, three-axis posture information, and axial force information, and form a multi-modal action original data set ; ;in, Indicates Single-frame multimodal action data at sampling time, Indicates the sampling timestamp, Represents the three-axis displacement vector, reflecting the position of the hand or needle in space. Indicates the three-axis attitude angle, corresponding to the pitch angle, yaw angle and roll angle, representing the attitude state of the needle body. It represents the instantaneous force along the needle axis, reflecting the mechanical response during the insertion process. Indicates the total number of sampling frames; S12. Perform wavelet threshold denoising on the original multimodal action data set, retain the signal main frequency characteristics, and perform linear interpolation resampling to align all modal data to a unified time resolution , and through timestamp consistency alignment, an action signal dataset after time alignment is formed. The action signal dataset after time alignment is used as the preprocessed action signal dataset .

[0009] Optionally, the S2 includes the following steps: S21. For the triaxial displacement information, triaxial attitude information, and axial force information in the preprocessed action signal dataset, respectively, use the average mutual information function to estimate the optimal delay time parameter. By calculating the statistical correlation of the signal at different time intervals , find the time point when the information independence first appears during the time evolution of the signal. Based on the time point when the information independence first appears, determine the delay time parameter of the triaxial displacement information , the delay time parameter of the triaxial attitude information , and the delay time parameter of the axial force information respectively. The three together form the delay time parameter set ; ; Among them, represents the joint probability distribution of different time values of the signal at the time interval , . , are their respective marginal distributions, is the candidate delay time parameter; so that the average mutual information function first reaches the local minimum corresponding to as the optimal delay time parameter of the current signal; S22. After obtaining the delay time parameter set , for each type of signal component, use the false nearest neighbor ratio function to determine the optimal embedding dimension. By comparing the relative distance changes between adjacent trajectory points in the reconstructed phase space under different embedding dimensions, identify the proportion of false nearest neighbors. When the false nearest neighbor ratio at a certain embedding dimension drops below the threshold, it is considered that the original nonlinear structure can be fully unfolded at this embedding dimension. Respectively obtain the embedding dimension of the triaxial displacement information, the embedding dimension of the triaxial attitude information, and the embedding dimension of the axial force information. The three together form the embedding dimension set : ; Among them, is the reconstructed vector of dimension , is its nearest neighbor, is the delay time parameter, indicating in the -dimensional case, whether there is a false neighbor for the th vector; when the false neighbor ratio function value is less than the threshold , the current is selected as the embedding dimension.

[0010] Optionally, the S3 includes the following steps: S31. Based on the delay time parameter set and the embedding dimension set, for the preprocessed action signal data set, construct a multi-modal collaborative reconstruction phase space that integrates the dynamic stability constraints of acupuncture actions, and introduce the dynamic interaction between the triaxial displacement information, triaxial attitude information, and axial force information as the collaborative reconstruction constraint condition on the basis of the independent reconstruction of a single signal; S32. For the data at the th moment in the preprocessed action signal data set, construct a collaborative phase space state vector that integrates the multi-dimensional information coupling of acupuncture actions : ; where represents the value of the triaxial displacement information at the th moment, where is the delay time parameter of the triaxial displacement information, , used to construct the time-delay embedding sequence of the triaxial displacement information, is the embedding dimension of the triaxial displacement information, indicating how many delayed embedding components are used to construct the state space trajectory of this signal, represents the value of the triaxial attitude information at the th moment, is the delay time parameter of the triaxial attitude information, , used to construct the time-delay embedding sequence of the attitude signal, is the embedding dimension of the triaxial attitude information, indicating the dimension used when constructing the phase space of this signal, represents the value of the axial force information at the th moment, is the delay time parameter of the axial force, , used to construct the time-delay embedding sequence of the force signal, is the embedding dimension of the axial force information; Collaborative phase space state vector It is formed by splicing sub-vectors obtained by embedding three modal signals under their respective optimal delay times and embedding dimensions, and is used to capture the cooperative dynamic characteristics of acupuncture actions in terms of spatial movement, posture change, and mechanical feedback; S33. Calculate the trajectory neighborhood density distribution of multi-dimensional information of acupuncture actions in the cooperative phase space based on the cooperative phase space state vector . The trajectory neighborhood density distribution reflects the stability of the trainee's acupuncture action in the dynamic state space: ; where is the volume of the neighborhood sphere centered on the current cooperative phase space state vector , is the Heaviside step function, and is the size of the set of cooperative phase space state vectors; in the topic of analyzing the stability of acupuncture actions in acupuncture teaching; S34. Calculate the local Lyapunov exponent of the cooperative phase space trajectory according to the trajectory neighborhood density distribution to analyze the local stability of the dynamic evolution process of acupuncture actions: ; where is the sampling interval, is the frame number increment of the trajectory advancing along the time axis, represents the average of all point pairs in the neighborhood of the current point; in the topic of analyzing the stability of acupuncture actions in acupuncture teaching, the local Lyapunov exponent is used to measure the local dynamic stability of the trainee's acupuncture action in real time; S35. Combine the cooperative phase space state vector , the trajectory neighborhood density distribution and the local Lyapunov exponent to jointly form a set of phase space reconstruction trajectories with constraints on the dynamic stability of acupuncture actions.

[0011] Selected, the said S4 includes the following steps: S41. Identify three key stages during the whole acupuncture process, including the needle insertion stage, the staying stage, and the needle withdrawal stage, based on the phase space reconstruction trajectory set and the first derivative of the three-axis displacement information in the axial direction. By analyzing the change trend of the displacement in the time series, determine the interval boundaries where the speed continuously increases, stabilizes at zero, or continuously decreases, dynamically divide the time period indices corresponding to the stages, and classify the time period indices into the needle insertion stage index set, the staying stage index set, and the needle withdrawal stage index set respectively. Extract the corresponding collaborative phase space state vector sequences from the phase space reconstruction trajectory set according to the three index sets to form the needle insertion stage trajectory sequence, the staying stage trajectory sequence, and the needle withdrawal stage trajectory sequence respectively, thus constituting a set of stage trajectory sequences; S42. For each stage trajectory sequence in the set of stage trajectory sequences, subtract adjacent two collaborative phase space state vectors in chronological order to obtain the state transition direction vector sequence within the current stage. The state transition direction vector sequence reflects the evolution direction trend of the acupuncture action in the collaborative phase space within this stage; S43. Within each stage, calculate the trajectory curvature sequence of the trajectory of this stage using the included angle information between adjacent state transition direction vectors. The larger the trajectory curvature value, the more obvious the bending change of the trajectory in this stage, which reflects the instability degree of the acupuncture action trajectory and is used to measure the local non-linearity degree of the evolution trajectory of the action in the collaborative phase space; S44. Combine the collaborative phase space state vector sequence, the state transition direction vector sequence, and the trajectory curvature sequence corresponding to each stage to form the needle insertion stage action trajectory segment object. The needle insertion stage action trajectory segment object includes the needle insertion stage trajectory sequence, the state transition direction vector sequence, and the needle insertion stage curvature sequence. The staying stage action trajectory segment object includes the staying stage trajectory, the staying stage direction vector, and the staying stage curvature sequence. The needle withdrawal stage action trajectory segment object includes the needle withdrawal stage trajectory, the needle withdrawal stage direction vector, and the needle withdrawal stage curvature sequence; S45. Combine the action trajectory segment objects corresponding to the needle insertion stage, the staying stage, and the needle withdrawal stage to form a set of action trajectory segment objects.

[0012] Optionally, the S5 includes the following steps: S51. Call the standard acupuncture action data from the teaching reference library. The standard acupuncture action data consists of multi-modal information during the operation of acupuncture experts, including three-axis displacement information, three-axis attitude information, and axial force information, and is in the same format as the preprocessed action signal data set of the trainees, denoted as the standard action original data set ; S52. Execute the same processing steps as the trainee data processing flow on the standard action original data set and finally generate a set of standard action trajectory segment objects , including the standard motion trajectory segment objects of the needle insertion stage, the staying stage, and the needle withdrawal stage, which are respectively composed of the standard collaborative state vector sequence, the standard state transition direction vector sequence, and the standard trajectory curvature sequence; S53. Take the set of trainee motion trajectory segment objects and the set of standard motion trajectory segment objects as the two sides of the matching respectively, and construct the trainee-side preference sequence. The preference degree of the trainee motion trajectory segment for the standard motion trajectory segment is jointly determined by two dimensions: one is the structural similarity between the collaborative state vector sequences, which is used to measure the consistency of the motion trajectory evolution path, and the other is the difference between the average trajectory curvatures, which is used to reflect the deviation of the motion trajectory in terms of stability; the two form the trainee-side preference scoring result through weighted combination, and generate the trainee-side preference sequence according to the preference scores from high to low; S54. Construct the standard-side preference sequence based on the same method. When generating preferences for the standard motion trajectory segment, the preference score is also formed based on the structural similarity of the collaborative state vector sequence and the difference in average trajectory curvature, and the standard-side preference sequence is generated according to the preference score ranking. The trainee-side preference sequence and the standard-side preference sequence together constitute the bilateral preference sequence.

[0013] Optionally, the S6 includes the following steps: S61. Based on the bilateral preference sequence and for the specific requirements of the stability analysis of acupuncture teaching acupuncture actions, construct an improved stable marriage algorithm that incorporates the constraints of acupuncture action dynamics characteristics and the adaptive adjustment of the stage action stability weight. The improved stable marriage algorithm introduces a dynamic update mechanism for the dynamic preference between acupuncture action segments, and updates the preference list in real time during each round of matching to meet the requirements for the evaluation of action stability in the acupuncture teaching process; S62. In the round of matching process, for each trainee motion trajectory segment object , dynamically update the preference value according to its historical matching status with each standard motion trajectory segment object : ; Among them, , are respectively the collaborative state vector sequences of the th trainee motion trajectory segment object and the th standard motion trajectory segment object in the matching edge, reflecting the structural similarity degree between acupuncture motion trajectories, represents the cosine similarity of the trajectory sequences, , respectively represent the average trajectory curvatures of the corresponding trajectory segments, is the fixed weight of the trajectory structure similarity, is the dynamic weight, which is adaptively adjusted as the number of matching rounds increases. represents the object of the trainee's action trajectory segment in the previous round of matching. For the object of the standard action trajectory segment the cumulative historical number of rejections or acceptances is the adjustment factor for the influence of the historical number of rejections or acceptances on the preference value update. S63. In each round of stable matching process of the round of matching, the local stability values of each matching edge are calculated and recorded in real time: ; Among them, is the local Lyapunov exponent, which represents the object of the trainee's action trajectory segment for the object of the standard action trajectory segment the average value of the local Lyapunov exponents, which is used to characterize the difference in local dynamic stability between the two trajectory segments in the cooperative phase space. The introduction of the exponential function is used to emphasize the decisive role of the local stability characteristics of the acupuncture action dynamics in the stable marriage matching process. S64. When the absolute value of the difference in the local stability value corresponding to the matching edge between adjacent rounds of matching is lower than the preset convergence threshold , the improved stable marriage algorithm stops iterating, determines the set of final stable matching relationships , and outputs the corresponding global one-to-many stable matching relationship. S65. Weighted aggregation is performed on the local stability values of all matching edges in the set of final stable matching relationships to obtain the comprehensive stability metric value for the stability analysis of acupuncture actions in acupuncture teaching: ; Among them, is the number of matching edges in the set of final stable matching relationships.

[0014] Optionally, the S7 includes the following steps: S71. According to the comprehensive stability metric value , combined with the teaching evaluation requirements at different stability levels in the acupuncture teaching scenario, set the stability scoring interval and establish a hierarchical scoring rule to convert the numerical stability metric result into a discrete level feedback to form the stability scoring result ; S72. For the stability scoring result , combined with the set of final stable matching relationships For the matching pairs, calculate the set of difference vectors between each pair of trainee trajectories and the standard trajectory in the collaborative phase space and extract the following feedback information based on the set of difference vectors: Structural deviation description: Determine the overall deviation degree of the trajectory according to the average Euclidean distance value between the collaborative state vectors; Stability defect localization: Locate the critical time period of unstable fluctuations based on the difference in local Lyapunov exponents; Force control anomaly detection: Identify the physical channel where instability occurs by combining the direction of the vector with the largest difference in the embedding dimension of the axial force; S73. Integrate the stability scoring results , the structural deviation description, the stability defect localization, and the force control anomaly detection results to generate a structured evaluation report for teaching feedback, and push it to the trainee side and the teacher side through the interaction interface of the teaching platform to achieve a feedback closed-loop.

[0015] Optionally, the grading scoring rules are based on the value range of the comprehensive stability metric to divide the interval, and five levels are set correspondingly: A, B, C, D, E. The specific rules are as follows: Level A: , the structure of the acupuncture action trajectory is higher than the first coincidence threshold, the dynamic stability is extremely high, and the action execution is highly consistent with the standard action; Level B: , the structure of the acupuncture action trajectory is higher than the second coincidence threshold, and there are small dynamic deviations locally; Level C: , the structure of the acupuncture action trajectory is higher than the third coincidence threshold, and the local action path or force control rhythm needs to be corrected; Level D: , the structure of the acupuncture action trajectory is lower than the third coincidence threshold, and there are multi-stage trajectory deviations or severe unstable points; Level E: , the structure of the acupuncture action trajectory is lower than the fourth coincidence threshold, and there are significant teaching risks or operation misunderstandings.

[0016] An acupuncture teaching assistance platform based on intelligent analysis, which is applied to the above-mentioned acupuncture teaching assistance method based on intelligent analysis, includes the following modules: Multimodal action acquisition module, which is used to collect the three-axis displacement information, three-axis attitude information, and axial force information of trainees during the acupuncture teaching process, and output a multimodal action original data set; Signal preprocessing and synchronization module, which is used to perform wavelet threshold filtering, linear interpolation resampling, and timestamp alignment operations on the multimodal action original data set to generate a preprocessed action signal data set; A delay parameter and embedding dimension estimation module is used to calculate a delay time parameter set and an embedding dimension set for each modal information in the preprocessed motion signal data set based on an average mutual information function and a pseudo neighbor ratio function; The collaborative phase space reconstruction module is used to perform multi-dimensional embedding reconstruction of each modal signal according to the delay time parameter set and the embedding dimension set, construct a collaborative phase space state vector sequence integrating the three-axis displacement, three-axis posture and axial force, and output a phase space reconstruction trajectory set; The motion trajectory segmentation and feature extraction module is used to divide the needle insertion, retention and needle removal into three stages according to the derivative change trend of the three-axis displacement information, and extract the coordinated state vector sequence, state transfer direction vector sequence and trajectory curvature sequence in each stage to construct the student's motion trajectory segment object set; The standard action processing module is used to extract standard acupuncture action data from the teaching reference library and perform processing steps consistent with the student data, generate a set of standard action trajectory fragment objects, and construct a bilateral preference sequence based on structural similarity and stability indicators; Improved stable marriage matching module, used to execute the improved stable marriage algorithm that introduces local Lyapunov exponent and historical matching feedback adjustment, generate the final stable matching relationship set under multiple rounds of preference iteration and dynamic weight update mechanism, and calculate the local stability value of each pair of matching edges, and summarize them to form a comprehensive stability measurement index value; The teaching feedback generation module is used to generate stability scoring results based on the preset scoring interval mapped by the comprehensive stability metric index value, and generate a structured evaluation report based on the difference vector set, structural offset, stability defects and force control anomalies. The report is pushed to the student and teacher ends through the teaching platform interface to achieve a closed feedback loop.

[0017] The beneficial effects of the present invention are: Based on the traditional motion signal reconstruction method, the present invention introduces the coupling relationship between three-axis displacement information, three-axis posture information and axial force information. By constructing a collaborative phase space state vector and calculating the trajectory neighborhood density and local Lyapunov exponent, it comprehensively reflects the temporal evolution law and stability characteristics in the motion process, and significantly enhances the quantitative modeling capability of the core indicators of motion stability teaching while maintaining data integrity.

[0018] The present invention proposes to construct a bilateral preference sequence based on structural similarity + average trajectory curvature difference, and on this basis designs a stable marriage matching algorithm that dynamically adjusts weights and historical feedback factors, which can realize an adaptive preference update mechanism and multiple rounds of matching iterations, effectively improving the global stability of action clip matching, and is significantly better than traditional matching methods based on static scoring.

[0019] The present invention structuredly models the core indicators of local Lyapunov difference and collaborative state vector difference obtained in the matching results, generates a feedback report with multiple dimensions including "structural offset description", "stability defect location" and "force control anomaly detection", and sets a five-level scoring system based on the stability measurement indicator Stotal, to achieve a closed-loop output from quantitative indicators to teaching suggestions, which can accurately identify unstable sections and control weaknesses in students' operations, making teaching intervention more targeted. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 A flowchart of an acupuncture teaching auxiliary platform and method based on intelligent analysis proposed by the present invention; Figure 2 A multi-modal collaborative phase space state vector construction and local Lyapunov exponent calculation flow chart of an acupuncture teaching auxiliary platform and method based on intelligent analysis proposed by the present invention; Figure 3 This is a flowchart of an improved stable marriage algorithm matching that introduces dynamic weights and local stability control mechanisms into an acupuncture teaching auxiliary platform and method based on intelligent analysis proposed by the present invention. DETAILED DESCRIPTION

[0021] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0022] refer to Figure 1 , an acupuncture teaching auxiliary method based on intelligent analysis, comprising the following steps: S1. Collect multimodal action data sets for the whole process of acupuncture teaching, perform preprocessing operations, and output preprocessed action signal data sets; S2. Calculate the delay time parameter and the embedding dimension parameter for the preprocessed action signal data set to obtain a delay parameter set and an embedding dimension value; S3. Perform phase space reconstruction on the preprocessed motion signal data set according to the delay parameter set and the embedding dimension value to form a phase space reconstruction trajectory set; S4. Based on the phase space reconstruction trajectory set, the action segments are divided according to the acupuncture insertion stage, the stay stage and the needle removal stage, and the local dynamic features are extracted to generate the action trajectory segment object set; S5. Construct a set of standard action trajectory segment objects according to the teaching reference library. Take the set of action trajectory segment objects and the set of standard action trajectory segment objects as the two sides of the matching. Construct a bilateral preference sequence based on the dynamic similarity index and the stability difference index; S6. Execute an improved stable marriage algorithm based on the bilateral preference sequence to obtain the value of the comprehensive stability metric; S7. Map the preset scoring interval according to the value of the comprehensive stability metric to generate a stability scoring result, and push the stability scoring result and the corresponding action gap information to the teaching platform to complete the feedback.

[0023] In this embodiment, S1 includes the following steps: S11. Use a multi-modal acquisition device to synchronously collect signals throughout the acupuncture teaching process, obtain three-axis displacement information, three-axis attitude information, and axial force information, and form a multi-modal action raw data set ; ; wherein, represents the single-frame multi-modal action data at the th sampling moment, represents the sampling timestamp, represents the three-axis displacement vector, reflecting the position of the hand or the needle body in space, represents the three-axis attitude angles, corresponding to the pitch angle, yaw angle, and roll angle respectively, characterizing the attitude state of the needle body, represents the instantaneous force along the needle axis direction, reflecting the mechanical response during the insertion process, represents the total number of sampling frames; S12. Perform wavelet threshold denoising processing on the multi-modal action raw data set, retain the main frequency characteristics of the signal, and perform linear interpolation resampling to align all modal data to a unified time resolution , and through timestamp consistency alignment, form an action signal data set after time alignment, and use the action signal data set after time alignment as the preprocessed action signal data set .

[0024] In this embodiment, S2 includes the following steps: S21. For the three-axis displacement information, three-axis attitude information, and axial force information in the preprocessed action signal data set, respectively use the average mutual information function to estimate the optimal delay time parameter, and find the time point when the signal first appears information independence by calculating the statistical correlation of the signal at different time intervals , and determine the delay time parameter of the three-axis displacement information respectively based on the time point when the signal first appears information independence The delay time parameter of the three-axis attitude information And the delay time parameter of the axial force information , the three together form a set of delay time parameters ; ; Among them, Represents the different moment values of the signal at the time interval , , The joint probability distribution of, , Are their respective marginal distributions, Is the candidate delay time parameter; so that the average mutual information function Reaches the local minimum for the first time corresponding to As the optimal delay time parameter of the current signal; S22. After obtaining the set of delay time parameters , for each type of signal component, use the pseudo-nearest neighbor ratio function To determine the optimal embedding dimension, identify the proportion of pseudo-nearest neighbors by comparing the relative distance changes between neighboring trajectory points in the reconstructed phase space under different embedding dimensions. When the pseudo-nearest neighbor ratio at a certain embedding dimension drops below the threshold, it is considered that the original non-linear structure can be fully unfolded at this embedding dimension, and the embedding dimensions of the three-axis displacement information , the embedding dimension of the three-axis attitude information And the embedding dimension of the axial force information Are obtained respectively, and the three together form a set of embedding dimensions : ; Among them, Is the reconstructed vector with dimension , Is its nearest neighbor, Is the delay time parameter, Indicates that in the -dimensional case, whether there are pseudo-nearest neighbors for the -th vector; when the pseudo-nearest neighbor ratio function Value is less than the threshold , the current Is selected as the embedding dimension.

[0025] In this embodiment, S3 includes the following steps: S31. Based on the set of delay time parameters and the set of embedding dimensions, for the preprocessed action signal dataset, construct a multi-modal collaborative reconstruction phase space that incorporates the dynamic stability constraints of acupuncture actions. Introduce the dynamic interaction between the three-axis displacement information, three-axis attitude information, and axial force information as the collaborative reconstruction constraint condition on the basis of the independent reconstruction of a single signal; S32. For the data at the th moment in the preprocessed action signal dataset, construct a collaborative phase space state vector that incorporates the multi-dimensional information coupling of acupuncture actions : ; where, represents the value of the three-axis displacement information at the th moment, where is the delay time parameter of the three-axis displacement information, , which is used to construct the time-delay embedding sequence of the three-axis displacement information, is the embedding dimension of the three-axis displacement information, indicating how many delayed embedding components are used to construct the state space trajectory of this signal, represents the value of the three-axis attitude information at the th moment, is the delay time parameter of the three-axis attitude information, , which is used to construct the time-delay embedding sequence of the attitude signal, is the embedding dimension of the three-axis attitude information, indicating the dimension used when constructing the phase space of this signal, represents the value of the axial force information at the th moment, is the delay time parameter of the axial force, , which is used to construct the time-delay embedding sequence of the force signal, is the embedding dimension of the axial force information; The collaborative phase space state vector is formed by splicing the sub-vectors embedded by the three modal signals under their respective optimal delay times and embedding dimensions, and is used to capture the collaborative dynamic characteristics of acupuncture actions in terms of spatial motion, attitude change, and mechanical feedback; S33. Calculate the trajectory neighborhood density distribution of the multi-dimensional information of acupuncture actions in the collaborative phase space based on the collaborative phase space state vector , and the trajectory neighborhood density distribution reflects the stability degree of the trainee's acupuncture actions in the dynamic state space: ; ; where, is centered on the current collaborative phase space state vector and The volume of the neighborhood sphere of is the Heaviside step function, The size of the state vector set of the collaborative phase space; In the topic of stability analysis of acupuncture movements in acupuncture teaching; S34. Distribution based on trajectory neighborhood density Computing local Lyapunov exponents of cooperative phase space trajectories , analyze the local stability of the dynamic evolution process of acupuncture action: ; in, is the sampling interval, is the frame increment of the track along the time axis, Represents the average of all point pairs in the neighborhood of the current point; In the topic of acupuncture movement stability analysis in acupuncture teaching, local Lyapunov exponent Used to measure the local dynamic stability of students' acupuncture movements in real time; S35. The coordinated phase space state vector , trajectory neighborhood density distribution and the local Lyapunov exponent Together they form a set of phase space reconstruction trajectories with acupuncture action dynamic stability constraints. .

[0026] In this implementation, S4 includes the following steps: S41. According to the phase space reconstruction trajectory set and the first-order derivative of the three-axis displacement information in the axial direction, the three key stages in the whole acupuncture process are identified, including the needle insertion stage, the dwelling stage and the needle removal stage. By analyzing the change trend of the displacement in the time series, the interval boundary of the speed continuously rising, stabilizing to zero or continuously decreasing is determined, and the time period index of the corresponding stage is dynamically divided, and the time period index is respectively classified into the needle insertion stage index set, the dwelling stage index set and the needle removal stage index set. According to the three index sets, the corresponding cooperative phase space state vector sequence is extracted from the phase space reconstruction trajectory set, and the needle insertion stage trajectory sequence, the dwelling stage trajectory sequence and the needle removal stage trajectory sequence are respectively formed to form a stage trajectory sequence set; S42. For each stage trajectory sequence in the stage trajectory sequence set, the state vectors of two adjacent cooperative phase spaces are subtracted in time order to obtain a state transfer direction vector sequence in the current stage, and the state transfer direction vector sequence reflects the evolution direction trend of the acupuncture action in the cooperative phase space in the stage; S43. In each stage, the trajectory curvature sequence of the trajectory in this stage is calculated using the included angle information between adjacent state transition direction vectors. The larger the trajectory curvature value, the more obvious the bending change of the trajectory in this stage, reflecting the instability degree of the acupuncture action trajectory, and is used to measure the local non-linearity degree of the evolution trajectory of the action in the coordination phase space; S44. The coordination phase space state vector sequence, state transition direction vector sequence, and trajectory curvature sequence corresponding to each stage are jointly composed into an object of the action trajectory segment in the needle insertion stage. The object of the action trajectory segment in the needle insertion stage includes the trajectory sequence in the needle insertion stage, the state transition direction vector sequence, and the curvature sequence in the needle insertion stage. The object of the action trajectory segment in the staying stage includes the trajectory in the staying stage, the direction vector in the staying stage, and the curvature sequence in the staying stage. The object of the action trajectory segment in the needle withdrawal stage includes the trajectory in the needle withdrawal stage, the direction vector in the needle withdrawal stage, and the curvature sequence in the needle withdrawal stage; S45. The action trajectory segment objects corresponding to the needle insertion stage, staying stage, and needle withdrawal stage are combined to form a set of action trajectory segment objects.

[0027] In this embodiment, S5 includes the following steps: S51. Call the standard acupuncture action data from the teaching reference library. The standard acupuncture action data is composed of multi-modal information during the operation of acupuncture experts, including three-axis displacement information, three-axis attitude information, and axial force information, and is consistent with the format of the preprocessed action signal data set of the trainees, denoted as the standard action original data set ; S52. Perform the same processing steps as the trainee data processing flow on the standard action original data set Finally, generate a set of standard action trajectory segment objects , including the standard action trajectory segment objects in the needle insertion stage, staying stage, and needle withdrawal stage, which are respectively composed of the standard coordination state vector sequence, standard state transition direction vector sequence, and standard trajectory curvature sequence; S53. Take the set of trainee action trajectory segment objects and the set of standard action trajectory segment objects as the two parties for matching respectively, and construct the trainee-side preference sequence. The preference degree of the trainee action trajectory segment for the standard action trajectory segment is jointly determined by two dimensions: one is the structural similarity between the coordination state vector sequences, which is used to measure the consistency of the action trajectory evolution path, and the other is the difference between the average trajectory curvatures, which is used to reflect the deviation of the action trajectory in terms of stability; the two are combined by weighting to form the trainee-side preference scoring result, and the trainee-side preference sequence is generated by arranging in descending order according to the preference score; S54. The standard side preference sequence is constructed in the same way. When generating preferences, the standard action trajectory segments also form preference scores based on the structural similarity of the collaborative state vector sequences and the difference in average trajectory curvature, and generate the standard side preference sequence according to the preference scores. The trainee side preference sequence and the standard side preference sequence together constitute the bilateral preference sequence.

[0028] In this embodiment, S6 includes the following steps: S61. Based on the bilateral preference sequence and for the specific requirements of analyzing the stability of acupuncture actions in acupuncture teaching, an improved stable marriage algorithm that incorporates the constraints of acupuncture action dynamics characteristics and adaptively adjusts the weights of stage action stability is constructed. The improved stable marriage algorithm introduces a dynamic update mechanism for the dynamic preferences between acupuncture action segments, and updates the preference list in real time during each round of matching to meet the requirements for evaluating action stability in the acupuncture teaching process; S62. In the round of matching, for each trainee action trajectory segment object , the preference value is dynamically updated according to its historical matching status with each standard action trajectory segment object : ; Among them, , are respectively the collaborative state vector sequences of the th trainee action trajectory segment object and the th standard action trajectory segment object in the matching edge, reflecting the structural similarity between acupuncture action trajectories, represents the cosine similarity of the trajectory sequences, , respectively represent the average trajectory curvature of the corresponding trajectory segments, is the fixed weight of the trajectory structure similarity, is the dynamic weight, which is adaptively adjusted as the number of matching rounds increases, represents the cumulative historical number of times that the trainee action trajectory segment object rejected or accepted the standard action trajectory segment object in the previous round of matching, is the adjustment factor for the influence of the historical rejection or acceptance times on the preference value update; S63. In each round of stable matching in the round of matching, the local stability values of each matching edge are calculated and recorded in real time: ; Among them, It represents the local Lyapunov exponent and denotes the object of the trainee's action trajectory segment. For the object of the standard action trajectory segment The mean value of the local Lyapunov exponent is used to characterize the difference in local dynamic stability between the two trajectory segments in the cooperative phase space. The introduction of the exponential function is used to emphasize the decisive role of the local stability characteristics of the acupuncture action dynamics in the stable marriage matching process. S64. When the absolute value of the difference in the local stability value corresponding to the matching edge between adjacent two rounds of matching is lower than the preset convergence threshold the improved stable marriage algorithm stops iterating, determines the set of final stable matching relationships and outputs the corresponding global one-to-many stable matching relationship. S65. Perform weighted aggregation on the local stability values of all matching edges in the set of final stable matching relationships to obtain the comprehensive stability metric value for the analysis of the stability of acupuncture actions in acupuncture teaching : ; where is the number of matching edges in the set of final stable matching relationships.

[0029] In this embodiment, S7 includes the following steps: S71. According to the comprehensive stability metric value and combining with the teaching evaluation requirements at different stability levels in the acupuncture teaching scenario, set the stability scoring interval and establish a hierarchical scoring rule to convert the numerical stability metric result into a discrete grade feedback, forming the stability scoring result ; S72. For the stability scoring result and combining with the matching pairs in the set of final stable matching relationships calculate the set of difference vectors between each pair of trainee trajectories and the standard trajectory in the cooperative phase space, and extract the following feedback information based on the set of difference vectors: Structure deviation description: Judge the overall deviation degree of the trajectory according to the average Euclidean distance value between the cooperative state vectors; Stability defect location: Locate the key time period of unstable fluctuations according to the difference in local Lyapunov exponents; Force control anomaly detection: Combine the direction of the vector with the largest difference in the axial force embedding dimension to identify the physical channel where instability occurs; S73. Take the stability scoring result Integrate the structural deviation description, stability defect location, and force control anomaly detection results to generate a structured evaluation report for teaching feedback, and push it to the student side and teacher side through the interaction interface of the teaching platform to achieve a feedback closed-loop.

[0030] In this embodiment, the hierarchical scoring rule is based on the value range of the comprehensive stability metric to divide the interval and correspondingly set five levels: A, B, C, D, E. The specific rules are as follows: Level A: , the structure of the acupuncture movement trajectory is higher than the first coincidence threshold, the dynamic stability is extremely high, and the action execution is highly consistent with the standard action; Level B: , the structure of the acupuncture movement trajectory is higher than the second coincidence threshold, and there are small dynamic deviations locally; Level C: , the structure of the acupuncture movement trajectory is higher than the third coincidence threshold, and the action path or force control rhythm needs to be locally corrected; Level D: , the structure of the acupuncture movement trajectory is lower than the third coincidence threshold, and there are multi-stage trajectory offsets or severe instability points; Level E: , the structure of the acupuncture movement trajectory is lower than the fourth coincidence threshold, and there are significant teaching risks or operation misunderstandings.

[0031] An intelligent analysis-based acupuncture teaching assistance platform, which is applied to an intelligent analysis-based acupuncture teaching assistance method, includes the following modules: The multi-modal action acquisition module is used to collect the three-axis displacement information, three-axis attitude information, and axial force information of the students during the acupuncture teaching process, and output a multi-modal action original data set; The signal preprocessing and synchronization module is used to perform wavelet threshold filtering, linear interpolation resampling, and timestamp alignment operations on the multi-modal action original data set to generate a preprocessed action signal data set; The delay parameter and embedding dimension estimation module is used to calculate the delay time parameter set and embedding dimension set for each modal information in the preprocessed action signal data set based on the average mutual information function and the pseudo-nearest neighbor ratio function respectively; The collaborative phase space reconstruction module is used to perform multi-dimensional embedding reconstruction on each modal signal according to the delay time parameter set and embedding dimension set, construct a collaborative phase space state vector sequence integrating three-axis displacement, three-axis attitude, and axial force, and output a phase space reconstruction trajectory set; The motion trajectory segmentation and feature extraction module is used to divide the needle insertion, retention and needle removal into three stages according to the derivative change trend of the three-axis displacement information, and extract the coordinated state vector sequence, state transfer direction vector sequence and trajectory curvature sequence in each stage to construct the student's motion trajectory segment object set; The standard action processing module is used to extract standard acupuncture action data from the teaching reference library and perform processing steps consistent with the student data, generate a set of standard action trajectory fragment objects, and construct a bilateral preference sequence based on structural similarity and stability indicators; Improved stable marriage matching module, used to execute the improved stable marriage algorithm that introduces local Lyapunov exponent and historical matching feedback adjustment, generate the final stable matching relationship set under multiple rounds of preference iteration and dynamic weight update mechanism, and calculate the local stability value of each pair of matching edges, and summarize them to form a comprehensive stability measurement index value; The teaching feedback generation module is used to generate stability scoring results based on the preset scoring interval mapped by the comprehensive stability metric index value, and generate a structured evaluation report based on the difference vector set, structural offset, stability defects and force control anomalies. The report is pushed to the student and teacher ends through the teaching platform interface to achieve a closed feedback loop.

[0032] Example 1

[0033] In Training Room No. 6 of the Experimental Teaching Center of the College of Acupuncture and Massage of A City University of Traditional Chinese Medicine, the acupuncture simulation teaching session started on time. The theme of the training course was "Standard Operation Procedure Training of Zusanli Acupoint". The participants were 14 undergraduate students from the 21st grade of the "Basic Operation Class of Acupuncture". The operation supervision and data analysis were supported by the intelligent acupuncture teaching platform VerAI-TCM V1.4. The main lecturer was Associate Professor Gao Haitao, who has 11 years of teaching experience.

[0034] Trainee B performed the first needle insertion training at operating station No. 3. The platform equipment access number was "APT-TCM-003", the acquisition serial number was "LZM-1", and the system multimodal collector started working, continuously recording timestamps, three-axis position vectors, posture angles, and axial forces. The sampling frequency was set to 120 Hz, the total action duration was 32.4 seconds, and the total number of sampled frames was 3888.

[0035] The platform automatically identified the needle insertion stage, with the starting time frame being the 48th frame. The system prompted "the action entered the fast stage". Later, the system detected that its Z-axis displacement acceleration was higher than the upper limit threshold of the standard sample (standard: 0.42 m / s², measured value: 0.61 m / s²), and the posture angle θ fluctuated continuously by more than 5.8 degrees within 1.6 seconds. The local Lyapunov exponent value rose to 0.0387 (the standard threshold is below 0.025). The system background automatically marked this period as "highly unstable segment" in the "local unstable state list".

[0036] Meanwhile, the matching engine performs trajectory matching based on the standard sample "STD-FZL2020". The system log shows that within the frame number range of #1208 - #1452, the mean value of the state vector difference is 1.183 (unitless), the structural offset score is 3.6 (score range: 0 - 10). The system automatically executes the 3rd round of matching of the improved stable marriage algorithm, outputs the updated preference value Pi,j(r) = 2.73, and finally confirms that the deviation range of the paired action sequence from the standard sample trajectory is moderate, generating a rating level "C".

[0037] The system pops up a window to prompt the feedback report. The teacher - end report ID is "FBK - 003 - LZM1", and it contains the following structural content: Action paragraph: 9:02:21 - 9:02:25; Abnormal type: Loss of control of attitude stability; Cause analysis: Attitude control error + sudden change in axial force application; Suggestion: During the needle insertion, the left - wrist control angle needs to be stabilized within ±3° in the tangent direction, and the continuous force application should not exceed 0.4N / s.

[0038] After receiving the push, the student - end enters the retraining mode of the simulation operation console.

[0039] The second - round training number is "LZM - 2". The platform samples 3620 frames of data again, and the total operation duration is 29.6 seconds. After the platform reconstructs the phase space, the monitored data shows that the mean value of the overall Lyapunov exponent drops to 0.0198, the stability of needle insertion is significantly improved, the attitude angle change is controlled within 2.1°, and the standard deviation of the trajectory curvature drops to 0.0075 (compared with 0.0152 in the first time). The scoring system updates the stability metric value Stotal to 0.803, and the stability level is upgraded to "B+".

[0040] In the overall class summary analysis, the average increase in Stotal of the second - round operation of the intelligent system group (7 people using the method of the present invention) is 0.174, while that of the traditional teaching group (oral guidance by teachers) is only 0.062 on average; the "average positioning time of the problem paragraph" marked by the system is 0.46 seconds, and the average time visually estimated by the teacher group is 3.2 seconds, with an obvious difference in accuracy (95.1% vs 62.8%).

[0041] Table 1 is the comparison summary of partial data exported from the experimental platform Student ID First Stotal Score Second Stotal Score Number of Feedback for Problem Sections Average Duration for Error Location (seconds) LZM 0.578 0.803 2 0.42 DXT 0.613 0.782 1 0.53 AXL 0.547 0.762 2 0.49 FYR 0.598 0.801 1 0.37 Table 2 is the data of the comparison with the traditional group Student ID First Stotal Score Second Stotal Score B 0.591 0.624 C 0.572 0.618 Example 1 fully verified the three key problems solved by the technology of the present invention: First, there is a lack of dynamic process modeling in traditional teaching, making it difficult to identify abnormal fluctuations in specific time periods during operations; second, teachers' oral feedback is lagging and rough, lacking an accurate dynamic positioning mechanism; third, there is a lack of a data-driven scoring closed-loop, making it difficult to quantify the improvement effect. The present invention effectively fills the above-mentioned technical gaps through three algorithmic links of collaborative phase space analysis, stable marriage matching, and structured feedback, significantly improving the teaching real-time performance, accuracy, and the self-correction efficiency of trainees.

[0042] Based on the traditional action signal reconstruction method, the present invention introduces the coupling relationship of triaxial displacement information, triaxial attitude information, and axial force information. By constructing a collaborative phase space state vector and calculating the trajectory neighborhood density and local Lyapunov exponent, it comprehensively reflects the temporal evolution law and stability characteristics during the action process, and significantly enhances the quantitative modeling ability of the core indicators of action stability while maintaining data integrity.

[0043] The present invention proposes to construct a bilateral preference sequence based on structural similarity + average trajectory curvature difference, and on this basis, designs a stable marriage matching algorithm that dynamically adjusts weights and historical feedback factors, which can achieve an adaptive preference update mechanism and multi-round matching iterations, effectively improving the global stability of action segment matching, and significantly superior to traditional static scoring-based matching methods.

[0044] The present invention generates a feedback report containing multiple dimensions such as "structural deviation description", "stability defect positioning", and "force control anomaly detection" by structuring and modeling the core indicators of local Lyapunov difference and collaborative state vector difference obtained in the matching results, and sets a five-level scoring system based on the stable metric Stotal to achieve a closed-loop output from quantitative indicators to teaching suggestions, which can accurately identify unstable segments and weak control points in trainees' operations, making teaching intervention more targeted.

[0045] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. An acupuncture teaching auxiliary method based on intelligent analysis, characterized in that: The steps include: S1. Collect multimodal action data sets for the whole process of acupuncture teaching, perform preprocessing operations, and output preprocessed action signal data sets; S2. Calculate the delay time parameter and the embedding dimension parameter for the preprocessed action signal data set to obtain a delay parameter set and an embedding dimension value; S3. Perform phase space reconstruction on the preprocessed motion signal data set according to the delay parameter set and the embedding dimension value to form a phase space reconstruction trajectory set; S4. Based on the phase space reconstruction trajectory set, the action segments are divided according to the acupuncture insertion stage, the stay stage and the needle removal stage, and the local dynamic features are extracted to generate the action trajectory segment object set; S5. construct a standard motion trajectory segment object set according to the teaching reference library, use the motion trajectory segment object set and the standard motion trajectory segment object set as the matching parties, and construct a bilateral preference sequence according to the dynamic similarity index and the stability difference index; S6. Execute the improved stable marriage algorithm based on the bilateral preference sequence to obtain a comprehensive stability measurement index value; S7. Generate a stability score result based on the preset score interval mapped by the comprehensive stability measurement index value, and push the stability score result and the corresponding action gap information to the teaching platform to complete the feedback.

2. The acupuncture teaching auxiliary method based on intelligent analysis according to claim 1, characterized in that: The S1 comprises the following steps: S11. Use a multi-modal acquisition device to synchronously collect signals during the entire acupuncture teaching process, obtain three-axis displacement information, three-axis posture information, and axial force information, and form a multi-modal action original data set ; S12. Perform wavelet threshold denoising on the original multimodal action data set, retain the signal main frequency characteristics, and perform linear interpolation resampling to align all modal data to a unified time resolution , and align the timestamps to form a time-aligned action signal dataset. As a preprocessed action signal dataset .

3. The acupuncture teaching auxiliary method based on intelligent analysis according to claim 2, characterized in that: The S2 comprises the following steps: S21. For the three-axis displacement information, three-axis posture information and axial force information in the preprocessed motion signal data set, the average mutual information function is used to calculate the displacement information. Estimate the optimal delay time parameters by calculating the signal at different time intervals The statistical correlation under the condition of time is used to find the time point when the signal first appears information independence during the time lapse process, and the delay time parameters of the three-axis displacement information are determined based on the time point when the information independence first appears. , delay time parameters of three-axis attitude information Delay time parameters with axial force information , the three together constitute the delay time parameter set ; S22. Obtaining the delay time parameter set Then, for each type of signal component, the pseudo neighbor ratio function is used The optimal embedding dimension is determined, and the proportion of pseudo-neighbors is identified by comparing the relative distance changes between adjacent trajectory points in the reconstructed phase space under different embedding dimensions. When the pseudo-neighbor ratio under a certain embedding dimension drops below the threshold, it is considered that the embedding dimension can fully expand the original nonlinear structure, and the embedding dimensions of the three-axis displacement information are obtained respectively. , the embedding dimension of the three-axis attitude information Embedding dimension of axial force information , the three together constitute the embedding dimension set .

4. The acupuncture teaching auxiliary method based on intelligent analysis according to claim 3 is characterized in that: The S3 comprises the following steps: S31. Based on the delay time parameter set and the embedding dimension set, a multi-modal collaborative reconstruction phase space integrating the dynamic stability constraints of acupuncture movements is constructed for the pre-processed motion signal data set. On the basis of independent reconstruction of a single signal, the dynamic interaction between the three-axis displacement information, the three-axis posture information and the axial force information is introduced as a collaborative reconstruction constraint condition; S32. For the pre-processed action signal data set The data at each moment is used to construct a collaborative phase space state vector that integrates the multi-dimensional information coupling of acupuncture movements. : ； in, Indicates the three-axis displacement information in The value at the moment, is the delay time parameter of the three-axis displacement information, , used to construct the time-delay embedding sequence of the three-axis displacement information, is the embedding dimension of the three-axis displacement information, indicating how many delayed embedding components are used to construct the state space trajectory of the signal. Indicates the three-axis attitude information in The value of the moment, is the delay time parameter of the three-axis attitude information, , used to construct the time-delay embedding sequence of the gesture signal, is the embedding dimension of the three-axis attitude information, indicating the dimension used to construct the signal phase space. Indicates the axial force information in The value of the moment, is the delay time parameter of the axial force, , used to construct the time-delay embedded sequence of force signals, is the embedding dimension of the axial force information; S33. In the cooperative phase space state vector Based on the calculation of the trajectory neighborhood density distribution of acupuncture action multidimensional information in the collaborative phase space , the trajectory neighborhood density distribution reflects the stability of the students’ acupuncture movements in the dynamic state space: ; in, The current cooperative phase space state vector Centered The volume of the neighborhood sphere of is the Heaviside step function, The size of the state vector set of the collaborative phase space; In the topic of stability analysis of acupuncture movements in acupuncture teaching; S34. Distribution based on trajectory neighborhood density Computing local Lyapunov exponents of cooperative phase space trajectories , analyze the local stability of the dynamic evolution process of acupuncture action: ; in, is the sampling interval, is the frame increment of the track along the time axis, Represents the average of all point pairs in the neighborhood of the current point; S35. The coordinated phase space state vector , trajectory neighborhood density distribution and the local Lyapunov exponent Together they form a set of phase space reconstruction trajectories with acupuncture action dynamic stability constraints .

5. The acupuncture teaching auxiliary method based on intelligent analysis according to claim 4 is characterized in that: The S4 comprises the following steps: S41. According to the phase space reconstruction trajectory set and the first-order derivative of the three-axis displacement information in the axial direction, the three key stages in the whole acupuncture process are identified, including the needle insertion stage, the dwelling stage and the needle removal stage. By analyzing the change trend of the displacement in the time series, the interval boundary of the speed continuously rising, stabilizing to zero or continuously decreasing is determined, and the time period index of the corresponding stage is dynamically divided, and the time period index is respectively classified into the needle insertion stage index set, the dwelling stage index set and the needle removal stage index set. According to the three index sets, the corresponding cooperative phase space state vector sequence is extracted from the phase space reconstruction trajectory set, and the needle insertion stage trajectory sequence, the dwelling stage trajectory sequence and the needle removal stage trajectory sequence are respectively formed to form a stage trajectory sequence set; S42. For each stage trajectory sequence in the stage trajectory sequence set, the state vectors of two adjacent cooperative phase spaces are subtracted in time order to obtain a state transfer direction vector sequence in the current stage, and the state transfer direction vector sequence reflects the evolution direction trend of the acupuncture action in the cooperative phase space in the stage; S43. In each stage, the angle information between the adjacent state transfer direction vectors is used to calculate the trajectory curvature sequence of the trajectory of that stage. The larger the trajectory curvature value, the more obvious the curvature change of the trajectory of that stage, which reflects the instability of the acupuncture action trajectory and is used to measure the local nonlinearity of the evolution trajectory of the action in the collaborative phase space. S44. The state vector sequence of the coordinated phase space, the state transfer direction vector sequence and the trajectory curvature sequence corresponding to each stage are collectively formed into a needle insertion stage motion trajectory fragment object, wherein the needle insertion stage motion trajectory fragment object includes the needle insertion stage trajectory sequence, the state transfer direction vector sequence and the needle insertion stage curvature sequence, the stay stage motion trajectory fragment object includes the stay stage trajectory, the stay stage direction vector and the stay stage curvature sequence, and the needle removal stage motion trajectory fragment object includes the needle removal stage trajectory, the needle removal stage direction vector and the needle removal stage curvature sequence; S45. Combine the motion track segment objects corresponding to the needle insertion phase, the stay phase, and the needle removal phase to form a motion track segment object set.

6. The acupuncture teaching auxiliary method based on intelligent analysis according to claim 5, characterized in that: The S5 comprises the following steps: S51. Call the standard acupuncture action data from the teaching reference library. The standard acupuncture action data consists of multimodal information during the operation of acupuncture experts, including three-axis displacement information, three-axis posture information and axial force information. It is consistent with the format of the student's pre-processed action signal data set and is recorded as the standard action original data set. ; S52. Standard action original data set Execute the processing steps consistent with the student data processing flow, and finally generate a set of standard action trajectory fragment objects , including standard motion trajectory fragment objects of the needle insertion stage, the stay stage and the needle removal stage, which are respectively composed of a standard collaborative state vector sequence, a standard state transfer direction vector sequence and a standard trajectory curvature sequence; S53. Collect the student's motion trajectory segment objects The student-side preference sequence is constructed by taking the set of standard action trajectory fragments as the matching parties. The preference degree of the student's action trajectory fragments to the standard action trajectory fragments is jointly determined by two dimensions: the first is the structural similarity between the collaborative state vector sequences, which is used to measure the consistency of the action trajectory evolution path; the second is the difference between the average trajectory curvatures, which is used to reflect the deviation of the action trajectory in stability; the two are weightedly combined to form the student-side preference score result, and the student-side preference sequence is generated by arranging the preference scores from high to low; S54. The standard side preference sequence is constructed based on the same method. When generating preferences, the standard action trajectory fragments also form a preference score based on the structural similarity of the collaborative state vector sequence and the difference in the average trajectory curvature, and the standard side preference sequence is generated by sorting according to the preference score. The student side preference sequence and the standard side preference sequence together constitute a bilateral preference sequence.

7. The acupuncture teaching auxiliary method based on intelligent analysis according to claim 6, characterized in that: The S6 comprises the following steps: S61. Based on the bilateral preference sequence, aiming at the specific needs of acupuncture action stability analysis in acupuncture teaching, an improved stable marriage algorithm is constructed that incorporates the dynamic feature constraints of acupuncture actions and the adaptive adjustment of the weight of staged action stability. The improved stable marriage algorithm introduces a dynamic update mechanism of dynamic preferences between acupuncture action segments, and updates the preference list in real time during each round of matching, so as to meet the requirements for action stability evaluation in the process of acupuncture teaching; S62. In the first round of matching, for each student's motion trajectory segment object , according to its relationship with each standard action track fragment object Dynamically update the preference value based on the historical matching status between : ; in, , are the first The student's motion trajectory fragment object and the The collaborative state vector sequence of standard motion trajectory fragment objects reflects the structural similarity between acupuncture motion trajectories. represents the cosine similarity of the trajectory sequence, , They represent the average trajectory curvature of the corresponding trajectory segments, is the fixed weight of trajectory structure similarity, It is a dynamic weight, which is adaptively adjusted as the number of matching rounds increases. Represents the student's motion trajectory fragment object in the previous round of matching For standard motion track fragment objects The cumulative number of rejections or acceptances. is the adjustment factor for the impact of the number of historical rejections or acceptances on the update of preference values; S63. In each round of stable matching, the local stability value of each matching edge is Perform real-time calculation and recording: ; in, is the local Lyapunov index, representing the student's motion trajectory segment object For standard motion track fragment objects The local Lyapunov exponent mean is used to characterize the difference in local dynamic stability between two trajectory segments in the collaborative phase space. The introduction of the exponential function is used to emphasize the decisive role of the local stability characteristics of acupuncture action dynamics in the process of stabilizing marriage matching. S64. When the local stability value corresponding to the matching edge The absolute value of the difference between two consecutive rounds of matching is lower than the preset convergence threshold When , the improved stable marriage algorithm stops iterating and determines the final stable matching relationship set , output the corresponding global one-to-many stable matching relationship; S65. Final stable matching relationship set The local stability values ​​of all matching edges in the training set are weighted and summarized to obtain the comprehensive stability measurement index value for the stability analysis of acupuncture movements in acupuncture teaching. : ； in, is the number of matching edges in the final stable matching relationship set.

8. The acupuncture teaching auxiliary method based on intelligent analysis according to claim 7 is characterized in that: The S7 comprises the following steps: S71. Based on comprehensive stability measurement index value , combined with the teaching evaluation requirements of different stability levels in acupuncture teaching scenarios, set the stability scoring range and establish the grading scoring rules, convert the numerical stability measurement results into discrete grade feedback, and form the stability scoring results ; S72. Stability scoring results , combined with the final stable matching relationship set The difference vector set between each pair of student trajectories and the standard trajectory is calculated in the cooperative phase space. , and extract the following feedback information based on the difference vector set: Structural deviation description: The overall deviation of the trajectory is determined based on the average Euclidean distance between the collaborative state vectors; Stability defect location: locate the key period of unstable fluctuations based on the difference of local Lyapunov exponents; Force control anomaly detection: Combine the direction of the vector with the largest difference in the axial force embedding dimension to identify the physical channel where instability occurs; S73. The stability score result , structural offset description, stability defect location and force control anomaly detection results are integrated to generate a structured evaluation report for teaching feedback, which is pushed to the students and teachers through the interactive interface of the teaching platform to achieve a closed feedback loop.

9. The acupuncture teaching auxiliary method based on intelligent analysis according to claim 8, characterized in that: The grading scoring rules are based on the comprehensive stability measurement index value The value range is divided into intervals, and five levels are set accordingly: A, B, C, D, E. The specific rules are as follows: A Level: ,The structure of the trajectory of acupuncture action is higher than the first matching threshold, the dynamic stability is extremely high, and the action execution is highly consistent with the standard action; B Grade: ,The structure of the trajectory of the acupuncture movement is higher than the second matching threshold, and there is a small dynamic deviation locally; C Grade: , the acupuncture action trajectory structure is higher than the third matching threshold, and the action path or force control rhythm needs to be locally corrected; D Level: ,The structure of the trajectory of the acupuncture action is lower than the third matching threshold, and there are multi-stage trajectory deviations or severe unstable points; E-level: The structure of the acupuncture movement trajectory is lower than the fourth matching threshold, and there are significant teaching risks or operational errors.

10. An acupuncture teaching auxiliary platform based on intelligent analysis, applied to an acupuncture teaching auxiliary method based on intelligent analysis as claimed in any one of claims 1 to 9, characterized in that: Includes the following modules: The multimodal motion acquisition module is used to collect the three-axis displacement information, three-axis posture information and axial force information of students during acupuncture teaching, and output the original data set of multimodal motion; The signal preprocessing and synchronization module is used to perform wavelet threshold filtering, linear interpolation resampling and timestamp alignment operations on the original multimodal action data set to generate a preprocessed action signal data set; A delay parameter and embedding dimension estimation module is used to calculate a delay time parameter set and an embedding dimension set for each modal information in the preprocessed motion signal data set based on an average mutual information function and a pseudo neighbor ratio function; The collaborative phase space reconstruction module is used to perform multi-dimensional embedding reconstruction of each modal signal according to the delay time parameter set and the embedding dimension set, construct a collaborative phase space state vector sequence integrating the three-axis displacement, three-axis posture and axial force, and output a phase space reconstruction trajectory set; The motion trajectory segmentation and feature extraction module is used to divide the needle insertion, retention and needle removal into three stages according to the derivative change trend of the three-axis displacement information, and extract the coordinated state vector sequence, state transfer direction vector sequence and trajectory curvature sequence in each stage to construct the student's motion trajectory segment object set; The standard action processing module is used to extract standard acupuncture action data from the teaching reference library and perform processing steps consistent with the student data, generate a set of standard action trajectory fragment objects, and construct a bilateral preference sequence based on structural similarity and stability indicators; Improved stable marriage matching module, used to execute the improved stable marriage algorithm that introduces local Lyapunov exponent and historical matching feedback adjustment, generate the final stable matching relationship set under multiple rounds of preference iteration and dynamic weight update mechanism, and calculate the local stability value of each pair of matching edges, and summarize them to form a comprehensive stability measurement index value; The teaching feedback generation module is used to generate stability scoring results based on the preset scoring interval mapped by the comprehensive stability metric index value, and generate a structured evaluation report based on the difference vector set, structural offset, stability defects and force control anomalies. The report is pushed to the student and teacher ends through the teaching platform interface to achieve a closed feedback loop.

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