An acupuncture training method and device and a storage medium

Abstract

The application relates to an acupuncture training method and device and a storage medium, and comprises the following steps: S1: an environment construction step: a virtual reality training environment is constructed, the virtual reality training environment comprises a learning unit for acupoint knowledge display and an examination unit for interactive operation; wherein the examination unit pre-stores a three-dimensional human acupoint coordinate model established based on standard anatomy data, the three-dimensional human acupoint coordinate model defines standard space coordinates of each acupoint and a permitted coordinate deviation range of the acupoint; S2: a knowledge display and operation collection step: the learning unit is controlled, and graphic and three-dimensional anatomical information of at least one target acupoint combination is displayed to a user; subsequently, the examination unit is controlled to load a three-dimensional human model corresponding to the target acupoint combination, and interactive operation data of the user for positioning the target acupoint combination through an interactive device is received; and the application can better realize acupuncture training.

Description

Technical Field

[0001] This invention relates to the field of training methods, and more specifically to an acupuncture training method, apparatus, and storage medium. Background Technology

[0002] Acupuncture and moxibustion is a general term for acupuncture and moxibustion techniques, including needle puncture and moxibustion.

[0003] Acupuncture, guided by Traditional Chinese Medicine theory, involves inserting needles into the patient's body at specific angles and using techniques such as twisting and lifting to stimulate specific points on the body to treat diseases. The insertion points are called acupoints, or simply points.

[0004] Moxibustion is a method of treating diseases by burning or fumigating certain acupoints on the body with pre-made moxa cones or moxa sticks, using heat stimulation. Mugwort is the most commonly used herb, hence the name "moxibustion with mugwort." Other methods include medicated moxibustion, willow twig moxibustion, lampwick moxibustion, and mulberry twig moxibustion.

[0005] Whether it's acupuncture or moxibustion, the most important thing is to accurately locate the acupoints on the human body. Understanding and memorizing the various acupoints on the human body is the foundation for learning traditional Chinese medicine acupuncture. Existing acupuncture training methods suffer from several shortcomings: traditional training often relies on physical models or textbooks, and the display of acupoint knowledge lacks intuitive three-dimensional anatomical visualization support, making it difficult for users to accurately understand the spatial location of acupoints; there is a lack of a quantitative evaluation system based on standard anatomical data, making it difficult to objectively measure the accuracy, efficiency, and consistency of acupoint location, resulting in highly subjective evaluation results; training programs are mostly standardized templates, failing to consider individual user ability differences, memory decay patterns, and TCM meridian theory, resulting in insufficient targeting; fixed acupoint coordinate models cannot adapt to users' location habits and operational characteristics, and the lack of real-time feedback mechanisms during location operations makes it difficult for users to perceive deviations and make adjustments in a timely manner, leading to low training efficiency and slow skill improvement; at the same time, the training algorithm parameters are fixed and cannot be dynamically optimized according to changes in user ability, further affecting the training effect. Therefore, an acupuncture training method, device, and storage medium are proposed. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an acupuncture training method, comprising: S1: Environment Construction Steps: Construct a virtual reality training environment, which includes a learning unit for displaying acupoint knowledge and an assessment unit for interactive operation; among them, the assessment unit has a three-dimensional human acupoint coordinate model pre-stored based on standard anatomical data. The three-dimensional human acupoint coordinate model defines the standard spatial coordinates of each acupoint and its allowable coordinate deviation range. S2: Knowledge Display and Operation Acquisition Steps: Control the learning unit to display the text, images, and three-dimensional anatomical information of at least one target acupoint combination to the user; subsequently, control the assessment unit to load the three-dimensional human body model corresponding to the target acupoint combination and receive interactive operation data from the user through the interactive device to locate the target acupoint combination. S3: Interactive data processing steps: Process the interactive operation data collected in step S2. For each positioning operation, parse the final positioning three-dimensional coordinates, operation time, and movement trajectory data of the interactive device, i.e., parsed data. Step S4: Dynamic Evaluation and Model Calibration: Based on the analytical data obtained in Step S3, perform dynamic evaluation and coordinate model calibration of acupoint location capability, and generate a capability evaluation report, specifically including: S41: For multiple positioning operations of the same acupoint by the user, the corresponding multiple final positioning three-dimensional coordinates are aggregated into a user positioning coordinate set; S42: Analyze the spatial distribution of the user positioning coordinate set and calculate the consistency evaluation value of the user positioning; if the consistency evaluation value meets the preset stability condition, calculate a calibration reference coordinate based on the user positioning coordinate set, and use the calibration reference coordinate to update the standard spatial coordinates of the corresponding acupoints in the three-dimensional human acupoint coordinate model. S43: Based on the user's accuracy in locating each acupoint, operation time, and consistency evaluation value, generate a quantitative assessment report on the user's mastery of the target acupoint combination. S5: Personalized training planning steps: Based on the generated ability assessment report, run a training planning algorithm that integrates the memory decay law and the relationship between acupoints to generate and output a personalized follow-up training plan for the user.

[0007] Furthermore, the spatial distribution of the user location coordinate set is analyzed to calculate the consistency evaluation value. The specific process includes: First, calculate the covariance matrix of all coordinate points in the user's location coordinate set in three-dimensional space; Next, the covariance matrix is ​​decomposed into eigenvalues ​​to obtain eigenvalues ​​along the three principal directions; Then, the three eigenvalues ​​are weighted and summed to calculate a comprehensive discreteness measure that characterizes the overall discreteness of the coordinate points. Finally, the comprehensive discrete metric value is used as the consistency evaluation value for user location.

[0008] Furthermore, the specific process of calculating the calibration reference coordinates and updating the standard space coordinates based on the coordinate set includes: S421: Obtain the calibration reference coordinates C by calculating the geometric center of the user's positioning coordinate set. c ; S422: Obtain the original standard spatial coordinates C0 of the acupoint to be updated in the three-dimensional human acupoint coordinate model; S423: Determine a historical weighting coefficient ,in ,and The value is set based on the number of times the acupoint has been successfully calibrated in history; the more times it has been calibrated, the higher the value. The smaller the value; S424: Updated new standard space coordinates The calculation is based on the original standard spatial coordinates C0 and the calibration reference coordinates C. c The weighted sum of , where the weight of C0 is . C c The weight is 1- The specific calculation formula is as follows: ; S425: Calculated Replace the original standard spatial coordinate C0 of the corresponding acupoint in the three-dimensional human acupoint coordinate model.

[0009] Furthermore, the specific process for generating the competency assessment report is as follows: For each acupoint in the target acupoint combination, generate a three-dimensional capability vector. Provide a quantitative description; Among them, the first component Acc represents the positioning accuracy score, which is calculated based on the success rate of the user's positioning coordinates of the acupoint falling within the coordinate deviation range defined in the three-dimensional human acupoint coordinate model. The second component, Eff, represents the operational efficiency score. Its value is calculated based on the average time taken by the user to perform a single positioning operation on the acupoint and the average length of the moving trajectory. The shorter the time and the more concise the trajectory, the higher the score. The third component Con represents the operational consistency score, and its value is directly taken from the user positioning consistency evaluation value corresponding to the acupoint calculated in step S42. The ability assessment report consists of three-dimensional ability vectors for all acupoints in the target acupoint combination.

[0010] Furthermore, in S5, the process by which the training planning algorithm generates a personalized subsequent training plan includes: S51: Based on the capability assessment report, select the weak acupoints in the target acupoint combination. The selection criteria are that the positioning accuracy score Acc or the operation consistency score Con of the capability vector of the weak acupoint is lower than the preset corresponding qualified threshold. S52: Calculate a dynamic training priority value for each selected weak acupoint Pi. The dynamic training priority value consists of a weighted sum of three parts: the first part is based on the degree of inadequacy of its location accuracy, the second part is based on the degree of inadequacy of its operational consistency, and the third part is based on the memory decay effect represented by the time interval since the last training of the selected weak acupoint. The specific calculation process is as follows: ; Among them, Acc i and Con i These are acupoints P i The positioning accuracy score and the operational consistency score, Since the last time the acupoint P i The time interval since the location operation was completed. The time constant characterizing the rate of memory decay, , and These are the weighting coefficients for each corresponding part; S53: Based on the theory of meridians in traditional Chinese medicine, find other acupoints that are on the same meridian as the weak acupoints with high priority in dynamic training or have a specific functional relationship. S54: Combine and sort high-priority weak acupoints and their related acupoints to form a structured training sequence, which can be used as a personalized follow-up training plan.

[0011] Furthermore, following S5, it also includes: S6: Training effect data collection steps: After the user executes the personalized follow-up training plan, collect their interaction data in the new training and update the capability assessment report; S7: Planning Algorithm Parameter Optimization Steps: Based on the differences in the capability assessment reports before and after the update, use regression analysis to optimize the weight coefficients in the training planning algorithm. and time constant Optimize and adjust.

[0012] Furthermore, in step S2, when the user performs acupoint location operations in the assessment unit, the method also includes a real-time feedback process: Real-time calculation of the Euclidean distance between the pointing point of the interactive device and the standard spatial coordinates of the target acupoint; The signal strength fed back to the interactive device is dynamically adjusted according to the Euclidean distance, and the signal strength is inversely proportional to the Euclidean distance; the feedback signal is a tactile vibration signal or an auditory prompt.

[0013] An acupuncture training device, used in acupuncture training methods, comprising: The environment building module is used to generate and manage the virtual reality training environment and the 3D human acupoint coordinate model in the environment building process. The interaction processing module is used to execute the knowledge display and operation collection steps and the interaction data processing steps to collect and parse the user's interaction operation data. The intelligent assessment module is used to perform dynamic assessment and model calibration steps and generate a capability assessment report; The planning and generation module is used to execute personalized training planning steps and subsequent optimization steps, and to generate and optimize personalized subsequent training plans. The real-time feedback module is used to perform the real-time feedback process.

[0014] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements S1 to S7 of an acupuncture training method. The present invention has the following advantages over the prior art: This project utilizes a virtual reality environment and a 3D human acupoint coordinate model based on standard anatomical data, combined with textual and 3D anatomical information displays, to make acupoint learning more intuitive and lower the learning threshold. During positioning operations, it calculates Euclidean distance in real time and dynamically provides tactile vibration or auditory cues, allowing for precise perception of training effects. A capability assessment report is generated through quantitative evaluation across three dimensions: positioning accuracy, operational efficiency, and consistency. This is combined with a dynamically calibrated coordinate model (updating standard spatial coordinates based on user positioning data) and incorporates memory decay laws and the connection to traditional Chinese medicine meridians to design personalized training programs. Furthermore, regression analysis can be used to optimize algorithm weight coefficients and time constants, making training more targeted and adaptable. The entire training process is closed-loop and intelligent, effectively improving the accuracy and stability of acupoint positioning while efficiently consolidating knowledge and accelerating the improvement of users' acupuncture skills. Attached Figure Description

[0015] Figure 1 This is the overall flowchart of the present invention. Detailed Implementation

[0016] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0017] like Figure 1 As shown, an acupuncture training method includes: S1: Environment Construction Steps: Construct a virtual reality training environment, which includes a learning unit for displaying acupoint knowledge and an assessment unit for interactive operation; among them, the assessment unit has a three-dimensional human acupoint coordinate model pre-stored based on standard anatomical data. The three-dimensional human acupoint coordinate model defines the standard spatial coordinates of each acupoint and its allowable coordinate deviation range. S2: Knowledge Display and Operation Acquisition Steps: Control the learning unit to display the text, images, and three-dimensional anatomical information of at least one target acupoint combination to the user; subsequently, control the assessment unit to load the three-dimensional human body model corresponding to the target acupoint combination and receive interactive operation data from the user through the interactive device to locate the target acupoint combination. S3: Interactive data processing steps: Process the interactive operation data collected in step S2. For each positioning operation, parse the final positioning three-dimensional coordinates, operation time, and movement trajectory data of the interactive device, i.e., parsed data. Step S4: Dynamic Evaluation and Model Calibration: Based on the analytical data obtained in Step S3, perform dynamic evaluation and coordinate model calibration of acupoint location capability, and generate a capability evaluation report, specifically including: S41: For multiple positioning operations of the same acupoint by the user, the corresponding multiple final positioning three-dimensional coordinates are aggregated into a user positioning coordinate set; S42: Analyze the spatial distribution of the user positioning coordinate set and calculate the consistency evaluation value of the user positioning; if the consistency evaluation value meets the preset stability condition, calculate a calibration reference coordinate based on the user positioning coordinate set, and use the calibration reference coordinate to update the standard spatial coordinates of the corresponding acupoints in the three-dimensional human acupoint coordinate model. S43: Based on the user's accuracy in locating each acupoint, operation time, and consistency evaluation value, generate a quantitative assessment report on the user's mastery of the target acupoint combination. S5: Personalized training planning steps: Based on the generated ability assessment report, run a training planning algorithm that integrates the memory decay law and the relationship between acupoints to generate and output a personalized follow-up training plan for the user.

[0018] The process of analyzing the spatial distribution of user location coordinate sets to calculate consistency evaluation values ​​includes: First, calculate the covariance matrix of all coordinate points in the user's location coordinate set in three-dimensional space; Next, the covariance matrix is ​​decomposed into eigenvalues ​​to obtain eigenvalues ​​along the three principal directions; Then, the three eigenvalues ​​are weighted and summed to calculate a comprehensive discreteness measure that characterizes the overall discreteness of the coordinate points. Finally, the comprehensive discrete metric value will be used as the consistency evaluation value for user positioning; By employing a standardized process of "calculating the three-dimensional covariance matrix, eigenvalue decomposition, and weighted summation," the overall three-dimensional spatial dispersion of the coordinates of multiple positioning of the same acupoint by a user is accurately quantified. This process avoids the one-sidedness of single-dimensional evaluation, objectively reflects the stability of positioning operations, and provides a scientific and reproducible quantitative basis for subsequent model calibration (determining whether stability conditions are met) and capability assessment (providing Con component data), ensuring the objectivity and accuracy of training evaluation.

[0019] Assuming a user performs six location operations on the "Hegu acupoint", the resulting three-dimensional coordinates (unit: cm) are as follows: P1(2.5,6.3,4.1), P2(2.6,6.2,4.2), P3(2.4,6.4,4.0), P4(2.7,6.1,4.3), P5(2.3,6.5,3.9), P6(2.8,6.0,4.4); The first step is to calculate the mean vector of the coordinate set. ,in ; ; ; The second step is to calculate the covariance matrix Cov in three-dimensional space. The covariance formula is: (n is the number of times to locate, here n=6); Calculate each element:

[0020] Similarly, we calculate Cov(x,z) = -0.034 and Cov(y,z) = -0.034, resulting in the final covariance matrix: ; The third step is to perform eigenvalue decomposition on the covariance matrix to obtain the eigenvalues ​​of the three principal directions. ; Step 4: Set eigenvalue weights (With a weighted sum of 1), calculate the comprehensive discrete metric value using the weighted summation formula: ; The results show that the overall dispersion of the user's six location coordinates is small, with a consistency evaluation value of 0.051, reflecting that the user's location operation of "Hegu acupoint" has good stability, providing a clear quantitative basis for subsequent judgment on whether the preset stability conditions of model calibration are met.

[0021] The specific process of calculating the calibration reference coordinates and updating the standard space coordinates based on the coordinate set includes: S421: Obtain the calibration reference coordinates C by calculating the geometric center of the user's positioning coordinate set. c; S422: Obtain the original standard spatial coordinates C0 of the acupoint to be updated in the three-dimensional human acupoint coordinate model; S423: Determine a historical weighting coefficient ,in ,and The value is set based on the number of times the acupoint has been successfully calibrated in history; the more times it has been calibrated, the higher the value. The smaller the value; S424: Updated new standard space coordinates The calculation is based on the original standard spatial coordinates C0 and the calibration reference coordinates C. c The weighted sum of , where the weight of C0 is . C c The weight is 1- The specific calculation formula is as follows: ; S425: Calculated Replace the original standard spatial coordinate C0 of the corresponding acupoint in the three-dimensional human acupoint coordinate model; The calibration reference coordinates are obtained by calculating the geometric center of the user's positioning coordinate set. Combined with a weighting coefficient λ that is dynamically adjusted according to the number of successful historical calibrations (λ decreases with more calibrations), the standard spatial coordinates of acupoints are updated using a weighted summation formula. This not only preserves the authority of the original coordinates C0 based on standard anatomical data, but also incorporates practical data from multiple stable user positioning. This enables dynamic calibration of the three-dimensional human acupoint coordinate model, allowing the model to adapt to the user's positioning habits and operating characteristics, improving the model's adaptability to individual users. Furthermore, the calibration process is quantifiable, controllable, and logically reproducible, providing a more accurate coordinate benchmark for subsequent personalized training.

[0022] For example, in the training scenario of the Hegu acupoint, after the user completes 6 positioning operations on the Hegu acupoint, the consistency evaluation value meets the preset stability condition and enters the coordinate calibration process. The first step is to calculate the calibration reference coordinates Cc: the calibration reference coordinates are the geometric center of the user's positioning coordinate set, i.e., the mean vector that has been calculated previously. ,therefore (Unit: cm); The second step is to obtain the original standard spatial coordinates C0: Query the three-dimensional human acupoint coordinate model, the original standard spatial coordinates of Hegu acupoint are C0=(2.5,6.3,4.2) (unit: cm); The third step is to determine the historical weight coefficient λ: It is known that the Hegu acupoint has been successfully calibrated once before. According to the rule "the more times it is calibrated, the smaller λ is", we set λ = 0.8 (0 < λ < 1) for one calibration. The fourth step is to calculate the updated standard spatial coordinates. Calculate each component of the three-dimensional coordinates according to the formula; x-axis components: =0.8×2.5+(1-0.8)×2.55=2.0+0.51=2.51(cm); y-axis component: =0.8×6.3+(1-0.8)×6.25=5.04+1.25=6.29(cm); z-axis component: =0.8×4.2+(1-0.8)×4.15=3.36+0.83=4.19(cm); Finally, the updated standard space coordinates are obtained. =(2.51, 6.29, 4.19) (unit: cm); Step 5, coordinate update: using =(2.51,6.29,4.19) Replace the original standard coordinates C0=(2.5,6.3,4.2) of Hegu acupoint in the model; This calibration process retains the core benchmark status of the original standard coordinates while incorporating stable user data with appropriate weights, making the model coordinates more aligned with the user's positioning habits and providing a more accurate reference benchmark for the user's subsequent Hegu acupoint training.

[0023] The specific process for generating a competency assessment report is as follows: For each acupoint in the target acupoint combination, generate a three-dimensional capability vector. Provide a quantitative description; Among them, the first component Acc represents the positioning accuracy score, which is calculated based on the success rate of the user's positioning coordinates of the acupoint falling within the coordinate deviation range defined in the three-dimensional human acupoint coordinate model. The second component, Eff, represents the operational efficiency score. Its value is calculated based on the average time taken by the user to perform a single positioning operation on the acupoint and the average length of the moving trajectory. The shorter the time and the more concise the trajectory, the higher the score. The third component Con represents the operational consistency score, and its value is directly taken from the user positioning consistency evaluation value corresponding to the acupoint calculated in step S42. The ability assessment report consists of three-dimensional ability vectors for all acupoints in the target acupoint combination; By constructing a three-dimensional capability vector, the mastery of each acupoint is quantitatively described from three core dimensions: positioning accuracy, operational efficiency, and operational consistency. This avoids the limitations of single-dimensional evaluation and comprehensively and accurately reflects the user's overall ability to locate acupoints. The generated capability assessment report has clear data and logical structure, providing a direct and reliable quantitative basis for subsequent screening of weak acupoints and calculation of training priorities, ensuring the pertinence and scientific nature of personalized training plans.

[0024] In the Hegu acupoint training scenario, the user has completed 6 positioning operations. The relevant data and model parameters are as follows: calibrated standard spatial coordinates of the Hegu acupoint. =(2.51,6.29,4.19) (unit: cm), the model defines the coordinate deviation range of this acupoint as x∈[2.31,2.71]cm, y∈[6.09,6.49]cm, z∈[3.99,4.39]cm, and the coordinates of the 6 positioning are still P1(2.5,6.3,4.1), P2(2.6,6.2,4.2), P3(2.4,6.4,4), P4(2.7,6.1,4.3), P5(2.3,6.5,3.9), P6(2.8,6.0,4.4); The first step is to calculate the positioning accuracy score (Acc): Acc represents the success rate of positioning coordinates falling within the deviation range, and the calculation formula is as follows: ; The coordinates were verified one by one: the coordinates of P1, P2, P3, and P4 were all within the deviation range and were considered qualified (4 times in total); P5 (y=6.5 exceeding [6.09, 6.49], z=3.9 exceeding [3.99, 4.39]) and P6 (x=2.8 exceeding [2.31, 2.71], y=6.0 exceeding [6.09, 6.49], z=4.4 exceeding [3.99, 4.39]) were considered unqualified (2 times in total). therefore Converted to a 10-point scale, this equates to Acc = 6.67 points; The second step is to calculate the operation efficiency score Eff: Eff is calculated by combining the average time and the average movement trajectory length. The operation time (in seconds) for 6 positioning operations is set as t1=3.2, t2=3, t3=3.1, t4=2.9, t5=3.3, t6=2.8, and the movement trajectory length (in cm) is set as L1=8.3, L2=8.1, L3=8.5, L4=7.9, L5=8.6, L6=7.8. First, calculate the average: average time spent. seconds, average trajectory length cm; Setting scoring rules: Time taken is worth 10 points, with a baseline value of [missing value]. For every 0.1 seconds exceeding the limit, 0.5 points will be deducted. ; Trajectory length is worth 10 points, benchmark value For every 0.1cm exceeding the limit, 0.5 points will be deducted. ; Calculated point; point; Eff is the average of the two, that is point; The third step is to determine the consistency score Con: Con is directly taken from the previously calculated consistency evaluation value of Hegu acupoint, i.e., Con=0.051; Finally, the three-dimensional capacity vector of the Hegu acupoint was obtained. =(6.67,9.38,0.051), this vector clearly quantifies the user's overall understanding of the Hegu acupoint: "the accuracy of positioning needs improvement, the operational efficiency is good, and the consistency is excellent." It constitutes the core data of the ability assessment report and provides a clear basis for subsequent personalized training planning.

[0025] In S5, the process by which the training planning algorithm generates a personalized subsequent training plan includes: S51: Based on the capability assessment report, select the weak acupoints in the target acupoint combination. The selection criteria are that the positioning accuracy score Acc or the operation consistency score Con of the capability vector of the weak acupoint is lower than the preset corresponding qualified threshold. S52: Calculate a dynamic training priority value for each selected weak acupoint Pi. The dynamic training priority value consists of a weighted sum of three parts: the first part is based on the degree of inadequacy of its location accuracy, the second part is based on the degree of inadequacy of its operational consistency, and the third part is based on the memory decay effect represented by the time interval since the last training of the selected weak acupoint. The specific calculation process is as follows: ; Among them, Acc i and Con i These are acupoints P i The positioning accuracy score and the operational consistency score, Since the last time the acupoint P i The time interval from the completion of the positioning operation to the present is a time constant characterizing the rate of memory decay. , and These are the weighting coefficients for each corresponding part; S53: Based on the theory of meridians in traditional Chinese medicine, find other acupoints that are on the same meridian as the weak acupoints with high priority in dynamic training or have a specific functional relationship. S54: Combine and sort high-priority weak acupoints and their related acupoints to form a structured training sequence as a personalized follow-up training plan; By identifying weak acupoints with inadequate positioning accuracy (Acc) or operational consistency (Con) scores based on competency assessment reports, and then dynamically calculating training priorities by combining the degree of inadequacy in positioning accuracy, operational consistency, and memory decay effect, a structured training sequence is formed by associating acupoints with the same meridians or related functions according to traditional Chinese medicine meridian theory. This approach precisely targets the user's weak points in acupoint positioning while incorporating memory principles and professional TCM theories, making personalized training programs targeted, scientific, and practical. This avoids the inefficiency caused by blind training and effectively improves training results and the speed of knowledge consolidation.

[0026] By identifying weak acupoints with inadequate positioning accuracy (Acc) or operational consistency (Con) scores based on competency assessment reports, and then dynamically calculating training priorities by combining the degree of inadequacy in positioning accuracy, operational consistency, and memory decay effect, a structured training sequence is formed by associating acupoints with the same meridians or related functions according to traditional Chinese medicine meridian theory. This approach precisely targets the user's weak points in acupoint positioning while incorporating memory principles and professional TCM theories, making personalized training programs targeted, scientific, and practical. This avoids the inefficiency caused by blind training and effectively improves training results and the speed of knowledge consolidation.

[0027] The training scenario for Hegu acupoint also includes data related to "Quchi acupoint" in the target acupoint combination. The specific process is as follows: The first step is to set the acceptable threshold: preset the acceptable positioning accuracy threshold Acc. th =0.7, Operational consistency qualification threshold Con th =0.06 (Con is a discrete measure; the smaller the value, the better the consistency. Therefore, exceeding this threshold is considered unacceptable). The second step is to screen weak acupoints: Given the three-dimensional capability vector of the Hegu acupoint (Pi=P1). (Acc is converted to the 0-1 interval for easier calculation), the three-dimensional capability vector of Quchi acupoint (Pi=P2). ; Judging the qualification: Hegu (LI4) acupoint Acc1=0.667<0.7, is a weak acupoint; Quchi (LI11) acupoint Con2=0.07>0.06, is a weak acupoint; The third step is to set the algorithm parameters: weight coefficients. The time constant characterizing the rate of memory decay Days (meaning that after 7 days the memory decays to 1 / e of the initial value, where e is a natural constant with a value of approximately 2.71828). Step 4: Determine the time interval ΔT: Check the training records. Since the last training of Hegu acupoint, ΔT1 = 3 days, and since the last training of Quchi acupoint, ΔT2 = 5 days. Step 5, Calculate dynamic training priority: Substitute into the formula to calculate the priority of Hegu acupoint: ; ; ; ; Calculate the priority of Quchi acupoint: 1-Acc2=1-0.72=0.28, 1-Con2=1-0.07=0.93, exp(-5 / 7)≈0.489; ; Step 6: Find related acupoints: According to the theory of meridians in traditional Chinese medicine, Hegu (LI4) and Quchi (LI11) both belong to the Large Intestine Meridian of Hand Yangming, and the two are related acupoints on the same meridian; Step 7: Form the training sequence: Sort by priority from high to low, Quchi (1.125) > Hegu (1.011). Combining the relationship with the same meridian, the final structured training sequence is: 1. Quchi location reinforcement training; 2. Hegu location reinforcement training; 3. Quchi + Hegu related location comprehensive training. This solution precisely targets two weak acupoints, integrates multiple influencing factors in priority calculation, aligns with traditional Chinese medicine theory in the association of acupoints, and has a clear training sequence logic, which can specifically improve users' ability to locate weak acupoints and consolidate their knowledge.

[0028] After S5, it also includes: S6: Training effect data collection steps: After the user executes the personalized follow-up training plan, collect their interaction data in the new training and update the capability assessment report; S7: Planning Algorithm Parameter Optimization Steps: Based on the differences in the capability assessment reports before and after the update, use regression analysis to optimize the weight coefficients in the training planning algorithm. and time constant Make optimizations and adjustments; By collecting new interaction data and updating the ability assessment report after the user executes the personalized training plan, and then using regression analysis to optimize the weight coefficients and time constants of the training planning algorithm based on the differences in the report, the algorithm parameters are dynamically iterated. This avoids the training plan from becoming out of sync with the user's skill improvement pace due to parameter solidification, and makes the subsequent training priority calculation more in line with the user's actual ability changes and memory patterns. This continuously improves the accuracy and adaptability of personalized training and ensures closed-loop iterative optimization of training effects.

[0029] For example, in training scenarios involving Hegu (LI4) and Quchi (LI11) acupoints, after completing a personalized training sequence (Quchi acupoint enhancement → Hegu acupoint enhancement → related comprehensive training), the user enters the parameter optimization process, with the specific steps as follows: The first step is to define the basic data before optimization: the algorithm parameters before optimization are as follows. Heaven; Hegu acupoint 3D capability vector before training: ; Quchi acupoint: ; The second step is to collect new training interaction data and update the ability assessment report: After the user completes 10 new training sessions, a new three-dimensional ability vector is obtained through parsing, and the Hegu acupoint is analyzed. (Accc increased to 0.75, Eff increased to 9.6, Con optimized to 0.04), Quchi acupoint (Acc increased to 0.78, Eff increased to 8.8, Con optimized to 0.055); The third step is to set the regression analysis objective and model: With the objective of minimizing the error between the calculated training priority of optimized parameters and the actual improvement in user capabilities, a linear regression model is constructed, with the objective function being... ,in Prioritize based on the actual degree of ability improvement (Hegu acupoint shows the greatest improvement, The lifting effect of the Quchi acupoint is relatively small. ); ( (The time interval after the new training is completed is set to 2 days). Step 4: Substitute the data to solve for the optimization parameters: and Substitute the objective function and perform regression analysis using the least squares method; Calculate the prediction priority before optimization: ; ; The sum of squared errors is ; The optimized parameters are obtained by regression iteration: sky; Step 5: Verify the optimization effect: Calculate the prediction priority using the optimized parameters. ; ; The optimized sum of squared errors is: The error is significantly reduced; Step 6, Parameter Application: Apply the optimized parameters... The training planning algorithm has been updated daily, and subsequent training will focus more on consolidating operational consistency and timely intervention for memory decay, adapting to the user's current ability level and improvement pace.

[0030] In step S2, when the user performs acupoint location operations in the assessment unit, the method also includes a real-time feedback process: Real-time calculation of the Euclidean distance between the pointing point of the interactive device and the standard spatial coordinates of the target acupoint; The signal strength fed back to the interactive device is dynamically adjusted according to the Euclidean distance, and the signal strength is inversely proportional to the Euclidean distance; the feedback signal is a tactile vibration signal or an auditory prompt. When users perform acupoint location operations, the Euclidean distance between the interactive device's pointing point and the target acupoint's standard spatial coordinates is calculated in real time. The intensity of tactile vibration or auditory cues is dynamically adjusted according to the rule that distance is inversely proportional to signal strength. This allows users to instantly perceive the magnitude of the location deviation and adjust the direction accordingly, avoiding blind operation, shortening the location error correction cycle, enhancing the real-time interactivity and operational guidance of training, helping users quickly establish a conditioned reflex of "deviation perception - real-time adjustment," improving the accuracy of acupoint location and operational proficiency, and making the training process more intuitive and efficient.

[0031] The training scenario for the Hegu acupoint, with the calibrated standard spatial coordinates of the Hegu acupoint as follows: =(2.51,6.29,4.19) (unit: cm), set real-time feedback rules: feedback type is tactile vibration signal, vibration intensity S (level 1-10, level 10 is the strongest) is inversely proportional to Euclidean distance D, the maximum effective feedback distance D0 = 0.5cm (the vibration intensity is 0 and there is no feedback beyond this distance), the intensity calculation formula is: ), where round is the rounding function; during the user's positioning operation, the five consecutive coordinates of the point pointed to by the interactive device are Q1(2.6,6.3,4.2), Q2(2.55,6.3,4.2), Q3(2.52,6.29,4.2), Q4(2.51,6.29,4.195), and Q5(2.51,6.29,4.19) (unit: cm); The first step is to calculate the points and The three-dimensional Euclidean distance is given by the formula: ; Calculate the Euclidean distance to Q1: cm; Calculate the Euclidean distance of Q2: cm; Calculate the Euclidean distance to Q3: cm; Calculate the Euclidean distance to Q4: cm; Calculate the Euclidean distance to Q5: cm; The second step is to calculate the vibration intensity based on the distance: class; class; class; class; class; The third step is to provide real-time feedback: When the user operates, the interactive device outputs tactile vibrations of corresponding intensity as the pointing point changes. In Q1, a relatively strong vibration of level 8 is felt, indicating that there is a slight deviation and adjusting to the center of the coordinates; in Q2, the vibration increases to level 9, confirming that the adjustment direction is correct; in Q3-Q5, the strongest vibration of level 10 is maintained, confirming that the Hegu acupoint has been accurately located. This real-time feedback mechanism allows users to instantly perceive positioning deviations without waiting for training to end, enabling rapid iteration and adjustments to operations. This significantly improves the accuracy and efficiency of single-positioning and enhances the user experience, helping users quickly establish a precise perception of the spatial location of the Hegu acupoint.

[0032] An acupuncture training device, used in acupuncture training methods, comprising: The environment building module is used to generate and manage the virtual reality training environment and the 3D human acupoint coordinate model in the environment building process. The interaction processing module is used to execute the knowledge display and operation collection steps and the interaction data processing steps to collect and parse the user's interaction operation data. The intelligent assessment module is used to perform dynamic assessment and model calibration steps and generate a capability assessment report; The planning and generation module is used to execute personalized training planning steps and subsequent optimization steps, and to generate and optimize personalized subsequent training plans. The real-time feedback module is used to perform the real-time feedback process.

[0033] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements S1 to S7 of an acupuncture training method.

[0034] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An acupuncture training method, characterized in that, include: S1: Environment Construction Steps: Construct a virtual reality training environment, which includes a learning unit for displaying acupoint knowledge and an assessment unit for interactive operation; among them, the assessment unit has a three-dimensional human acupoint coordinate model pre-stored based on standard anatomical data. The three-dimensional human acupoint coordinate model defines the standard spatial coordinates of each acupoint and its allowable coordinate deviation range. S2: Knowledge Display and Operation Acquisition Steps: Control the learning unit to display the text, images, and three-dimensional anatomical information of at least one target acupoint combination to the user; subsequently, control the assessment unit to load the three-dimensional human body model corresponding to the target acupoint combination and receive interactive operation data from the user through the interactive device to locate the target acupoint combination. S3: Interactive data processing steps: Process the interactive operation data collected in step S2. For each positioning operation, parse the final positioning three-dimensional coordinates, operation time, and movement trajectory data of the interactive device, i.e., parsed data. Step S4: Dynamic evaluation and model calibration: Based on the parsed data obtained in step S3, perform dynamic evaluation and coordinate model calibration of acupoint positioning ability, and generate an ability evaluation report; S5: Personalized training planning steps: Based on the generated ability assessment report, run a training planning algorithm that integrates the memory decay law and the relationship between acupoints to generate and output a personalized follow-up training plan for the user.

2. The acupuncture training method according to claim 1, characterized in that: The specific process of generating a capability assessment report includes: S41: For multiple positioning operations of the same acupoint by the user, the corresponding multiple final positioning three-dimensional coordinates are aggregated into a user positioning coordinate set; S42: Analyze the spatial distribution of the user positioning coordinate set and calculate the consistency evaluation value of the user positioning; if the consistency evaluation value meets the preset stability condition, calculate a calibration reference coordinate based on the user positioning coordinate set, and use the calibration reference coordinate to update the standard spatial coordinates of the corresponding acupoints in the three-dimensional human acupoint coordinate model. S43: Based on the user's accuracy in locating each acupoint, operation time, and consistency evaluation value, generate a quantitative assessment report on the user's mastery of the target acupoint combination.

3. The acupuncture training method according to claim 2, characterized in that: The process of analyzing the spatial distribution of user location coordinate sets to calculate consistency evaluation values ​​includes: First, calculate the covariance matrix of all coordinate points in the user's location coordinate set in three-dimensional space; Next, the covariance matrix is ​​decomposed into eigenvalues ​​to obtain eigenvalues ​​along the three principal directions; Then, the three eigenvalues ​​are weighted and summed to calculate a comprehensive discreteness measure that characterizes the overall discreteness of the coordinate points. Finally, the comprehensive discrete metric value is used as the consistency evaluation value for user location.

4. The acupuncture training method according to claim 3, characterized in that: The specific process of calculating the calibration reference coordinates and updating the standard space coordinates based on the coordinate set includes: S421: Obtain the calibration reference coordinates C by calculating the geometric center of the user's positioning coordinate set. c ; S422: Obtain the original standard spatial coordinates C0 of the acupoint to be updated in the three-dimensional human acupoint coordinate model; S423: Determine a historical weighting coefficient ,in ,and The value is set based on the number of times the acupoint has been successfully calibrated in history; the more times it has been calibrated, the higher the value. The smaller the value; S424: Updated new standard space coordinates The calculation is based on the original standard spatial coordinates C0 and the calibration reference coordinates C. c The weighted sum of , where the weight of C0 is . C c The weight is 1- ;; S425: Calculated Replace the original standard spatial coordinate C0 of the corresponding acupoint in the three-dimensional human acupoint coordinate model.

5. The acupuncture training method according to claim 4, characterized in that: The specific process for generating a competency assessment report is as follows: For each acupoint in the target acupoint combination, generate a three-dimensional capability vector. Provide a quantitative description; Among them, the first component Acc represents the positioning accuracy score, which is calculated based on the success rate of the user's positioning coordinates of the acupoint falling within the coordinate deviation range defined in the three-dimensional human acupoint coordinate model. The second component, Eff, represents the operational efficiency score. Its value is calculated based on the average time taken by the user to perform a single positioning operation on the acupoint and the average length of the moving trajectory. The shorter the time and the more concise the trajectory, the higher the score. The third component Con represents the operational consistency score, and its value is directly taken from the user positioning consistency evaluation value corresponding to the acupoint calculated in step S42. The ability assessment report consists of three-dimensional ability vectors for all acupoints in the target acupoint combination.

6. The acupuncture training method according to claim 5, characterized in that: In S5, the process by which the training planning algorithm generates a personalized subsequent training plan includes: S51: Based on the capability assessment report, select the weak acupoints in the target acupoint combination. The selection criteria are that the positioning accuracy score Acc or the operation consistency score Con of the capability vector of the weak acupoint is lower than the preset corresponding qualified threshold. S52: Calculate a dynamic training priority value for each selected weak acupoint Pi. The dynamic training priority value consists of a weighted sum of three parts: the first part is based on the degree of inadequacy of its location accuracy, the second part is based on the degree of inadequacy of its operational consistency, and the third part is based on the memory decay effect represented by the time interval since the last training of the selected weak acupoint. The specific calculation process is as follows: ； Among them, Acc i and Con i These are acupoints P i The positioning accuracy score and the operational consistency score, Since the last time the acupoint P i The time interval since the location operation was completed. The time constant characterizing the rate of memory decay, , and These are the weighting coefficients for each corresponding part; S53: Based on the theory of meridians in traditional Chinese medicine, find other acupoints that are on the same meridian as the weak acupoints with high priority in dynamic training or have a specific functional relationship. S54: Combine and sort high-priority weak acupoints and their related acupoints to form a structured training sequence, which can be used as a personalized follow-up training plan.

7. The acupuncture training method according to claim 6, characterized in that: After S5, it also includes: S6: Training effect data collection steps: After the user executes the personalized follow-up training plan, collect their interaction data in the new training and update the capability assessment report; S7: Planning Algorithm Parameter Optimization Steps: Based on the differences in the capability assessment reports before and after the update, use regression analysis to optimize the weight coefficients in the training planning algorithm. and time constant Optimize and adjust.

8. The acupuncture training method according to claim 7, characterized in that: In step S2, when the user performs acupoint location operations in the assessment unit, the method also includes a real-time feedback process: Real-time calculation of the Euclidean distance between the pointing point of the interactive device and the standard spatial coordinates of the target acupoint; The signal strength fed back to the interactive device is dynamically adjusted according to the Euclidean distance, and the signal strength is inversely proportional to the Euclidean distance; the feedback signal is a tactile vibration signal or an auditory prompt.

9. An acupuncture training device, wherein the device is used in the acupuncture training method according to any one of claims 1-8, characterized in that: include: The environment building module is used to generate and manage the virtual reality training environment and the 3D human acupoint coordinate model in the environment building process. The interaction processing module is used to execute the knowledge display and operation collection steps and the interaction data processing steps to collect and parse the user's interaction operation data. The intelligent assessment module is used to perform dynamic assessment and model calibration steps and generate a capability assessment report; The planning and generation module is used to execute personalized training planning steps and subsequent optimization steps, and to generate and optimize personalized subsequent training plans. The real-time feedback module is used to perform the real-time feedback process.

10. A computer-readable storage medium having a computer program stored thereon for performing the acupuncture psychological method of any one of claims 1-8, characterized in that, When the computer program is executed by the processor, it implements S1 to S7 of the acupuncture training method.

Images