A golf ai diagnosis method based on target ball path backstepping club defect

Abstract

The application discloses a golf AI diagnosis method based on target ball path reverse pushing club defects, comprising the following steps: S1: target ball path representation and tolerance band construction; S2: deviation calculation and error type determination; S3: two-stage defect diagnosis; S4: executable prescription generation; S5: individualized model updating.The application has the beneficial effects that: the application relies on target strategy, ball path and action multi-source data fusion to construct an integrated diagnosis system. With the user target ball path as the core constraint, the training needs are matched, the two-stage diagnosis engine is used to determine the club defect type and the deviation contribution weight, the output quantitative and verifiable correction prescription is output, the training data is iterated to form a "target-execution-feedback-optimization" closed loop, and the application has wide application scenarios and significant industrialization value.

Description

Technical Field

[0001] This invention relates to the field of intelligent diagnostic technology for golf training, specifically to an AI diagnostic method for golf based on the backswing defects of the target ball path. Background Technology

[0002] With the popularization and professionalization of golf, scientific training has become key to improving players' competitive level. Current golf training assessment systems are mainly divided into two categories: one is swing motion analysis, which extracts player posture, trajectory, and other motion indicators through video capture, IMU sensors, or motion capture technology, and outputs swing scores or generalized motion cues; the other is ball trajectory / hit data, which uses radar, high-speed cameras, and other equipment to acquire data such as ball speed, launch angle, and sideslip, and only quantitatively evaluates the hit result.

[0003] The core teaching logic of professional coaches is based on the constraint of "target ball trajectory / target landing point," combined with the deviation between the actual ball trajectory and the target, to infer the root cause of the deviation and provide targeted adjustment suggestions. However, existing techniques generally have the following defects and shortcomings: Lack of "goal constraints": Simply recording actions or shot results without considering the user's strategic intent and goal setting results in a low degree of alignment between the output suggestions and the user's actual training needs; Weak explanatory power: It only outputs isolated technical indicators, fails to clarify the causal relationship between ball trajectory deviation and swing defects, and is unable to answer the core question of "why the deviation occurred"; Difficulty in distinguishing between errors and defects: No tolerance standard has been established for the target ball path, making it impossible to determine whether the deviation is a normal fluctuation or a substantial defect that needs correction, and also impossible to define the degree of correction; The suggestion is not feasible: the output adjustment suggestions are described in an abstract way, lacking quantifiable range, implementation priority and effect verification methods, making it difficult for users to implement them. Fragmented multi-source data: Swing action data, ball trajectory data, and strategy target data are not effectively integrated, failing to form a complete closed loop of "target-execution-feedback-optimization", thus limiting training efficiency.

[0004] Therefore, there is an urgent need for an intelligent diagnostic method that can incorporate target ball trajectory constraints, possess defect causal attribution capabilities, output actionable suggestions, and form a closed-loop optimization, in order to fill the gap in existing golf training technologies in terms of accurate diagnosis and personalized guidance. Summary of the Invention

[0005] The purpose of this invention is to provide a golf AI diagnostic method based on the target ball path to reverse the swing defect, in order to solve the problems of lack of target constraints, ambiguous defect attribution, unexecutable suggestions and data fragmentation in existing golf training assessment systems.

[0006] This invention achieves the above objectives through the following technical solution: a golf AI diagnostic method based on target ball trajectory backswing swing defects, comprising the following core steps: (I) Target ball trajectory representation and tolerance zone construction (1) Definition of target ball path parameters: The target ball path parameters T include the target landing area, target take-off direction, target take-off angle (optional), target curve trend and target strategy constraints (optional); they are obtained through three methods: manual input by the user, automatic scene matching or recommendation of the best ball path in history; (2) Construction of constraint set: The target ball path is represented as a constraint set, including the target landing area T_land, the target flight corridor T_corridor, the target curve type T_curve and the allowable curvature range; (3) Tolerance band construction: Based on the type of club, hitting distance, player skill level, wind conditions and terrain features, set the directional deviation threshold, curve deviation threshold and landing point deviation threshold to form an acceptable deviation tolerance band Tol, which is used for error judgment and prescription range constraint.

[0007] (II) Deviation Calculation and Error Type Determination (1) Error vector calculation: Collect actual ball path parameters Y, which include actual takeoff direction, sideslip, curve trend and landing point, and collect and output through radar equipment, high-speed camera or golf simulator, or estimate based on swing action parameters; calculate the error vector E of the actual ball path relative to the target ball path, including direction error E_dir, curve error E_curve and distance / land error E_land; (2) Error type classification: Based on the characteristics of the error vector E, the error type label C_err is output, including straight push right / right curve / straight push left / left curve, transition curve, short hit / long hit and landing point deviation, etc.

[0008] (III) Two-stage defect diagnosis engine (1) Coarse diagnosis (rules + prior): A prior rule set is established based on the ball path deviation pattern, and the candidate defect set R_cand and the initial probability p0 are output; the prior rule set includes: When E_dir and E_curve point to the same side and the curve error E_curve is greater than the set threshold, the candidate defect is either abnormal pole-to-path control or abnormal face-to-path ratio. When the takeoff direction error E_dir is greater than the set threshold and the curve error E_curve is less than the set threshold, the candidate defect is the path dominance anomaly or the target line setting error. When the error vector fluctuation amplitude of multiple shots to the same target exceeds a set threshold, the candidate defect is timing abnormality or shot point stability problem; (2) Detailed diagnosis (causal mapping + counterfactual testing): Construct an action-result mapping model F: Y_hat=F(X,C_context), where X represents the swing action parameters, including clubface attitude, path direction, release timing, and body coordination parameters, and C_context represents the scene context parameters; when X is missing, the "weakly observed action variable" X is used as an approximation. Counterfactual test: Apply a small correction ΔX_i to the action variables corresponding to the candidate defect set R_cand, and calculate the predicted ball path change ΔY_i through the mapping model F; Output filtering: Select the defect combination that best explains the current error vector E and has the smallest correction amount ΔX_i as the final defect set R, and output the contribution weight W and confidence level Conf of each defect.

[0009] (iv) Executable prescription generation (1) Construction of correction suggestions: Based on the defect set R, contribution weight W and tolerance band Tol, a set of correction suggestions P is generated, and specific correction directions and quantitative magnitudes are given for different defect types, such as the specific angle adjustment amount for opening / closing of the rod surface problem; (2) Suggestion ranking optimization: Rank the suggestions based on contribution weight W, expected benefit ΔY and implementation difficulty D, and output 1 main suggestion and 1-2 auxiliary suggestions; (3) Validation standard setting: Generate a verifiable standard, including the next set of training targets T', tolerance band Tol' and the criteria for achieving the standard, wherein the criteria for achieving the standard is that the landing points of N consecutive balls are within the target area T'_land.

[0010] (v) Individualized model update and closed-loop optimization (1) Data collection: Collect multiple sets of ball-hitting data of the same user under the same target constraints, including target ball path parameters T, actual ball path parameters Y, swing action parameters X, and prescription execution feedback; (2) Model iteration: Based on the collected data, the individualized action-result mapping model F_user is iteratively updated to learn the user's sensitivity to the pole face, path and timing parameters, and to construct an individualized "psychological-action-result" model; (3) Closed-loop verification: If the new ball data Y_new meets the criteria, the training difficulty will be automatically increased, the tolerance band Tol will be reduced, or the target ball path parameter T will be updated; if it does not meet the criteria, the defect contribution weight W will be recalculated based on Y_new and the error vector E_new, and the prescription amplitude will be adjusted to form a closed-loop optimization.

[0011] Compared with the prior art, the beneficial effects of the present invention are: 1. Introduce target ball path constraints to align with user strategy intent: Using the user-defined target ball path as the core constraint, and combining it with scene characteristics to construct tolerance standards, the diagnostic results are highly matched with training requirements, solving the problem of low fitting of existing system suggestions; 2. Defect attribution is explainable and causal relationships are clear: Through a two-stage diagnostic engine, not only are the types of swing defects output, but also the contribution weight and confidence level of each defect to the deviation are provided, clearly answering "why the deviation occurred", filling the gap of weak explanatory ability of existing technologies; 3. Prescription suggestions are executable and verifiable: Output suggestions for quantification range, priority, expected effect and verification standards. Users can directly implement them and verify the training effect through clear achievement conditions, thus solving the problem of abstract suggestions. 4. Closed-loop optimization improves individualized accuracy: The individualized model is updated iteratively through training data to continuously optimize the accuracy of diagnosis and prescription, while forming a complete closed loop of "goal-execution-feedback-optimization" to improve training efficiency; 5. Multi-source data fusion with high industrial value: Integrating target strategies, ball trajectory data, and action data to form an integrated diagnostic solution is a core capability of golf AI coaches. It has a wide range of applications and significant industry promotion value and industrialization potential. Attached Figure Description

[0012] Figure 1 This is a flowchart of the diagnostic method of the present invention; Figure 2 This is a schematic diagram of the two-stage diagnosis in this invention; Figure 3 This is a schematic diagram of the closed loop of prescription generation and verification in this invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] In the description of this invention, it should be understood that the terms "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0015] Combination Figures 1 to 3 The golf AI diagnostic method shown includes the following steps: S1: Target ball path representation and tolerance band construction. The target ball path parameters are represented as a set of constraints, and an acceptable deviation tolerance band is constructed by combining the scenario and user characteristics. S2: Deviation calculation and error type determination. Based on target constraints and tolerance band, calculate the error vector of the actual ball path relative to the target ball path, and classify and output error type labels. S3: Two-stage defect diagnosis, through "rule-based coarse diagnosis + causal mapping and counterfactual testing fine diagnosis", to reverse the set of swing defects, the contribution weight of each defect and the confidence level; S4: Executable prescription generation, based on defect attribution results, tolerance band and implementation difficulty, generates modification suggestions for content quantification range, priority, expected effect and validation standards; S5: Personalized model update, based on multi-ball training data of the same user, iteratively updates the "action-result" personalized mapping model to optimize diagnostic and prescription accuracy.

[0016] Example 1: Data Input and Deployment: Users manually input the target ball path parameters T through the mobile application, including the target landing area (distance 150 yards, left and right deviation ±5 yards) and the target curve trend (slight rightward curve); the actual ball path parameters Y are collected through a golf simulator, including the actual takeoff direction deviating 8 yards to the right, curve error 3 yards, and landing distance 145 yards. Target representation and tolerance band construction: The constraint set includes the target landing area T_land (145-155 yards, ±5 yards left and right), the target flight corridor T_corridor (±3 yards in the takeoff direction), and the target curve type T_curve (slight rightward curve, curvature ≤2°); the tolerance band Tol is set as the direction deviation threshold ±3 yards, the curve deviation threshold ±2 yards, and the landing point deviation threshold ±5 yards; Deviation Calculation and Error Judgment: The error vector E is calculated as follows: Direction error E_dir = +8 yards (right deviation), Curve error E_curve = +1 yard (excessive right curve), and Landing error E_land = -5 yards (short hit); The error type label C_err is "straight push right + excessive right curve + short hit"; Two-stage diagnosis: Coarse diagnosis: Based on the rule "E_dir and E_curve point to the same side and the curve error is greater than the threshold", the candidate defects R_cand are "insufficient pole-face closure" and "abnormal face-to-path ratio", with initial probabilities p0 of 0.6 and 0.4 respectively; Detailed diagnosis: Construct an action-result mapping model F (based on an industry-standard action-ball path mapping database), and apply corrections to the action variables corresponding to candidate defects: increase clubface closure by 2° (ΔX1), and adjust the face-to-path ratio by 10% (ΔX2); calculate ΔY1 (direction deviation reduced by 6 yards, curve deviation reduced by 0.8 yards) and ΔY2 (direction deviation reduced by 3 yards, curve deviation reduced by 0.3 yards) through the model; filter and output defect set R as "insufficient clubface closure", contribution weight W=0.75, confidence level Conf=0.92; Prescription generation: Output correction suggestions P: Main suggestion "Increase clubface closure angle by 2°, move right index finger forward by 1cm when gripping the club", auxiliary suggestion "Move center of gravity forward by 5cm at impact to increase hitting distance"; verification standard is "5 consecutive balls land within 145-155 yards, ±3 yards to the left and right"; Closed-loop optimization: After the user trains according to the recommendations, the new ball path data Y_new shows a directional deviation of 2 yards, a curve deviation of 1 yard, and a landing point of 152 yards, which meets the target conditions; the system automatically reduces the tolerance band Tol (directional deviation threshold ±2 yards) and updates the target ball path parameter T to "distance 155 yards, slight right curve".

[0017] Example 2: Data Input and Deployment: The user inputs the target ball path parameters T (distance 180 yards, straight flight); the swing video is captured by a high-speed camera, and the motion data is collected by an IMU sensor to obtain the swing motion parameters X (clubface opening 3°, path deviates 5° to the right, release timing delay 0.2s); the actual ball path parameters Y are collected by radar equipment (direction deviates 10 yards to the right, curve deviation 0.5 yards, landing point 175 yards). Target representation and tolerance band construction: The constraint set includes the target landing area T_land (175-185 yards, ±3 yards left and right), the target flight corridor T_corridor (±2 yards in the takeoff direction), and the target curve type T_curve (straight line, curvature = 0°); the tolerance band Tol is set as the direction deviation threshold ±2 yards, the curve deviation threshold ±1 yard, and the landing point deviation threshold ±5 yards; Deviation calculation and error judgment: The error vector E is the direction error E_dir=+10 yards, the curve error E_curve=+0.5 yards, and the landing point error E_land=-5 yards; the error type label C_err is "straight push right + short hit"; Two-stage diagnosis: Preliminary diagnosis: Based on the rule "large takeoff direction error and small curve error", the candidate defects R_cand are "path dominance anomaly" and "target line setting error", with initial probabilities p0 of 0.7 and 0.3 respectively; Detailed diagnosis: Based on the collected action parameters X, an individualized action-result mapping model F_user is constructed, and corrections are applied to the action variables corresponding to the candidate defects: the path is adjusted to the left by 4° (ΔX1), and the target line is recalibrated (ΔX2); counterfactual testing yields ΔY1 (direction deviation reduced by 9 yards, landing point increased by 3 yards) and ΔY2 (direction deviation reduced by 2 yards, landing point unchanged); the output defect set R is "path deviation to the right is abnormal", with a contribution weight W=0.88 and a confidence level Conf=0.95; Prescription generation: Output correction suggestions P: The main suggestion is "Adjust the swing path 4° to the left, achieved by adjusting the stance (moving the left foot forward 10cm)", and the auxiliary suggestion is "Close the clubface by 2° to improve flight stability". The quantitative range is clear and the implementation difficulty is low. The verification standard is "6 consecutive balls with a direction deviation of ≤2 yards and a landing point of 175-185 yards". Closed-loop optimization: After user training, the action parameter X is updated to (clubface opening 1°, path deviates 1° to the right, release timing delay 0.1s), and the new ball path data Y_new meets the qualification conditions; the system updates the individualized model F_user to strengthen the mapping relationship between "path adjustment and direction deviation".

[0018] Example 3: Data Input and Deployment: The user starts a "10-ball training round" and sets the target ball path parameter T (distance 120 yards, slight left curve); after each ball training, the system automatically collects the actual ball path parameter Y and swing action parameter X (optional). Training and diagnosis for the first 3 goals: First ball: Y1 (direction 2 yards to the left, curve deviation 3 yards, landing 118 yards), error vector E1 conforms to "excessive left curve", the diagnosed defect is "excessive clubface opening", and it is recommended to "close the clubface by 1.5°"; Second ball: Y2 (direction 1 yard to the left, curve deviation 2 yards, landing 121 yards), the error has been reduced but not up to standard. The defect weight has been updated and the adjustment suggestion is "clubface closed 2°". Ball #3: Y3 (direction 0.5 yards to the left, curve deviation 1 yard, landing point 122 yards), meets the qualification requirements, system reduces tolerance band Tol; Subsequent training and optimization: During training for balls 4-10, the system updates the defect weights and individualized models for each ball, and the user consistently meets the target after ball 8; after the round ends, a training report is generated, including the defect improvement trend, optimal motion parameters, and the training target for the next stage (distance 125 yards, slight left curve). Model iteration: The training data of 10 balls (T, Y, X, prescription feedback) were fed back to the system, and the diagnostic accuracy of the individualized model F_user was improved by 12%, and the confidence of defect attribution was improved to an average of 0.94.

[0019] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0020] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A golf AI diagnostic method based on target ball trajectory reverse swing defects, characterized in that, Includes the following steps: S1: Target ball path representation and tolerance band construction. The target ball path parameters are represented as a set of constraints, and an acceptable deviation tolerance band is constructed by combining the scenario and user characteristics. S2: Deviation calculation and error type determination. Based on target constraints and tolerance band, calculate the error vector of the actual ball path relative to the target ball path, and classify and output error type labels. S3: Two-stage defect diagnosis, through "rule-based coarse diagnosis + causal mapping and counterfactual testing fine diagnosis", to reverse the set of swing defects, the contribution weight of each defect and the confidence level; S4: Executable prescription generation, based on defect attribution results, tolerance band and implementation difficulty, generates modification suggestions for content quantification range, priority, expected effect and validation standards; S5: Personalized model update, based on multi-ball training data of the same user, iteratively updates the "action-result" personalized mapping model to optimize diagnostic and prescription accuracy.

2. The golf AI diagnostic method for target ball trajectory back-pushing swing defects according to claim 1, characterized in that: The target trajectory parameters include the target landing area, target takeoff direction, target takeoff angle, target curve trend, and optional target strategy constraints; the constraint set includes the target landing area T_land, target flight corridor T_corridor, target curve type T_curve, and allowable curvature range.

3. The golf AI diagnostic method for target ball trajectory back-pushing swing defects according to claim 1, characterized in that: The tolerance band is constructed based on the type of club, hitting distance, player skill level, wind conditions, and terrain features. It sets directional deviation thresholds, curve deviation thresholds, and landing point deviation thresholds to form an acceptable deviation threshold set Tol, which is used for error judgment and prescription range constraint.

4. The golf AI diagnostic method for target ball trajectory backswing defects according to claim 1, characterized in that: The error vector E includes direction error E_dir, curve error E_curve, and distance to landing point error E_land; the error type label C_err includes straight push to the right, right curve, straight push to the left, left curve, overshoot curve, short hit, long hit, and landing point deviation.

5. The golf AI diagnostic method for target ball path back-pushing swing defects according to claim 1, characterized in that, The coarse diagnostic steps are as follows: A prior rule set is established based on the ball path deviation pattern, and the candidate defect set R_cand and the initial probability p0 are output; the prior rule set includes: When E_dir and E_curve point to the same side and the curve error E_curve is greater than the set threshold, the candidate defect is either abnormal pole-to-path control or abnormal face-to-path ratio. When the takeoff direction error E_dir is greater than the set threshold and the curve error E_curve is less than the set threshold, the candidate defect is the path dominance anomaly or the target line setting error. When the error vector fluctuation of multiple shots to the same target exceeds a set threshold, the candidate defect is either timing abnormality or shot point stability problem.

6. The golf AI diagnostic method for target ball path back-pushing swing defects according to claim 1, characterized in that, The detailed diagnostic steps are as follows: (1) Construct an action-result mapping model F: Y_hat=F(X,C_context), where X is the swing action parameter, including clubface posture, path direction, release timing and body coordination parameters, and C_context is the scene context parameter; when X is missing, the "weakly observed action variable" X is used as an approximation; (2) Counterfactual test: Apply a small correction ΔX_i to the action variable corresponding to the candidate defect set R_cand, and calculate the predicted ball path change ΔY_i through the mapping model F; (3) Output filtering: Select the defect combination that best explains the current error vector E and has the smallest correction amount ΔX_i as the final defect set R, and output the contribution weight W and confidence level Conf of each defect.

7. The golf AI diagnostic method for identifying swing defects based on the target ball path as described in claim 1, characterized in that, The prescription generation steps are as follows: (1) Based on the defect set R, contribution weight W and tolerance band Tol, generate a set of correction suggestions P, and give specific correction directions and quantification ranges for different defect types; (2) Rank the suggestions based on contribution weight W, expected return ΔY and implementation difficulty D, and output 1 main suggestion and 1-2 auxiliary suggestions; (3) Generate verifiable criteria, including the next set of training targets T', tolerance band Tol', and achievement conditions, wherein the achievement conditions are that the landing points of N consecutive balls are within the target area T'_land.

8. The golf AI diagnostic method for target ball trajectory backswing defects according to claim 1, characterized in that, The individualized model update steps are as follows: collect multiple sets of ball-hitting data of the same user under the same target constraints, including target ball path parameters T, actual ball path parameters Y, swing action parameters X and prescription execution feedback, iteratively update the individualized action-result mapping model F_user, learn the user's sensitivity to clubface, path and timing parameters, and construct an individualized "psychological-action-result" model.

9. The golf AI diagnostic method for target ball path back-pushing swing defects according to claim 1, characterized in that: The target ball path parameters are obtained through one of the following methods: manual input by the user, automatic scene matching, or recommendation of the best historical ball path; the actual ball path parameters are obtained through radar equipment, high-speed cameras, or golf simulators, or estimated based on one of the following methods: swing action parameters.

10. The golf AI diagnostic method for identifying swing defects based on the target ball path as described in claim 1, characterized in that, The prescription verification closed-loop mechanism is as follows: if the new shot data Y_new meets the criteria, the training difficulty is automatically increased, the tolerance band Tol is reduced, or the target ball path parameters T are updated; if the criteria are not met, the defect contribution weight W is recalculated based on Y_new and the error vector E_new, and the prescription magnitude is adjusted to form a closed-loop optimization.

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