University table tennis teaching method and system based on actual combat module

Abstract

This invention discloses a college table tennis teaching method and system based on a practical module, belonging to the field of intelligent sports teaching analysis technology. It includes collecting complete ball-hitting process data from students in real matches, including ball trajectory, body response timing, and multi-point tactile feedback; simulating the competitive situation based on this data to deduce a map of students' technical weaknesses; comparing this map with an ideal competitive model to generate a description of technical compensation behavior and key tactical vulnerabilities; performing stress amplification calculations on the technical compensation behavior under pressure to form a tactical error prediction including stress state parameters; and designing a corrective task with progressive competitive logic based on key tactical vulnerabilities, including tactical reconstruction and competitive adaptability enhancement schemes. This invention achieves in-depth diagnosis of technical and tactical vulnerabilities and accurate prediction of stress errors under competitive conditions.

Description

Technical Field

[0001] This invention belongs to the field of intelligent sports teaching analysis technology, specifically a college table tennis teaching method and system based on a practical module. Background Technology

[0002] Currently, university table tennis instruction commonly employs methods such as decomposed movement practice, multi-ball training, and video playback analysis. These techniques rely on the coach's experience and observation, and utilize motion sensors and video analysis systems to record the student's swing trajectory and basic posture data. The core analytical logic involves statically comparing the collected movement data with a pre-defined standardized movement model to identify deviations in the movement's appearance.

[0003] The main drawback of existing technical solutions lies in the severe disconnect between their analytical scenarios and real continuous combat environments. These methods fail to effectively capture the complete decision-making and adaptation processes of trainees handling uncertain incoming balls, making technical connections, and executing tactical combinations in dynamic rounds. Their analysis remains at the level of correcting isolated, explicit action errors, failing to diagnose the hidden technical compensation behaviors developed by trainees under pressure, nor revealing the structural vulnerabilities these compensation behaviors create in the entire tactical system. Furthermore, conventional methods lack effective mechanisms to quantitatively assess the dynamic correlation and evolutionary patterns between technical deformation and the risk of tactical errors under high-pressure situations.

[0004] A teaching analysis method that can deeply integrate with adversarial scenarios is needed. This method should be able to identify not only surface-level technical weaknesses based on real-world dynamic data, but also deeply analyze the true effectiveness of technology in the adversarial system, and proactively predict the specific tactical error risks caused by technical compensation under specific pressure conditions. This would provide a precise basis for designing corrective training with a clear adversarial progression logic. Summary of the Invention

[0005] This invention aims to solve at least one of the technical problems existing in the prior art;

[0006] Therefore, this invention proposes a college table tennis teaching method based on practical modules, including:

[0007] In a real competitive scenario, the complete hitting process data of the trainee is obtained. The complete hitting process data includes the trajectory of the ball, the timing of the body response, and multi-point tactile feedback.

[0008] Based on the complete ball-hitting process data, the technical performance in the simulated competitive situation is used to deduce the technical weaknesses of the students under the current competitive intensity.

[0009] The map of technological weaknesses is compared with the adversarial fitness of an ideal adversarial model to generate a description of technological compensation behavior and key tactical vulnerabilities.

[0010] The description of the technical compensation behavior is subjected to stress amplification calculation under stress environment to form a tactical error prediction that includes stress state parameters;

[0011] Based on the aforementioned key tactical vulnerabilities, a corrective task with progressive adversarial logic is designed, which includes a tactical reconstruction scheme and an adversarial adaptive enhancement scheme.

[0012] Preferably, the step of simulating technical performance in a competitive situation based on the complete ball-hitting process data and deducing the student's technical weaknesses under the current competitive intensity includes:

[0013] Identify the preparation phase, initiation phase, and ball-hitting completion phase in the body response time sequence, and establish an adversarial response model for each phase;

[0014] In the preparation phase, visual capture data and predicted body posture are extracted, and an initial reaction capability profile is generated by simulating decision delays under different combinations of incoming ball speed and spin.

[0015] During the initiation phase, the muscle activation sequence and force perception in the multi-point tactile feedback are combined to analyze the step initiation efficiency and arm swing trajectory stability under counter-pressure, and generate a motion execution efficiency profile.

[0016] During the ball-hitting completion phase, based on the antagonistic deviation between the incoming ball trajectory and the outgoing ball trajectory, as well as the body's return speed, the ball-hitting quality maintenance capability and continuous antagonistic capability are calculated, and a technical maintenance capability profile is generated.

[0017] By integrating the initial reaction capability profile, the motion execution efficiency profile, and the technology maintenance capability profile, a three-dimensional adversarial capability assessment space is constructed.

[0018] In the adversarial capability assessment space, regions where various capabilities are below a preset adversarial threshold are identified. These regions are then connected and mapped to form a map of technical weaknesses with spatial distribution characteristics under a specific adversarial intensity.

[0019] Preferably, in the preparation phase, visual capture data and predicted body posture are extracted, and an initial reaction capability profile is generated by simulating decision delays under different combinations of incoming ball speed and spin, including:

[0020] The ball image sequence and the trajectory of the student's gaze point change are separated from the complete ball-hitting process data to form a visual capture data stream;

[0021] Simultaneously acquire the student's torso angle, center of gravity preparation position, and racket face orientation at each time point of the incoming ball image sequence to form a predictive body posture sequence;

[0022] A virtual ball interference library containing speed mutations and rotation combinations was established. The visual capture data stream and the predicted body posture sequence were input sequentially to test the student's decision-making mechanism.

[0023] Calculate the time increment between the appearance of the stimulus and the body's effective response action when facing each type of virtual ball interference, and obtain a set of decision delay spectra;

[0024] The distribution patterns and abrupt change points of the decision delay spectrum under different interference types were analyzed, and an initial reaction capability profile was drawn with interference type as the horizontal axis and delay time as the vertical axis. The initial reaction capability profile revealed the vulnerability of trainees to different incoming balls in the confrontation.

[0025] Preferably, the step of comparing the technological weakness map with the ideal adversarial model to generate a description of technological compensation behavior and key tactical vulnerabilities includes:

[0026] Obtain a preset ideal adversarial model, which defines the optimal range and dynamic balance relationship of various technical indicators under standard adversarial pressure;

[0027] The map of technical weaknesses is superimposed on the ideal adversarial model, and the deviation between the actual data of the trainees and the ideal interval is compared point by point to generate a local fitness difference field.

[0028] In the local fitness difference field, regions where the deviation exceeds the tolerance threshold are identified, and the correlation and temporal induced relationship between the regions where the deviation exceeds the tolerance threshold are analyzed.

[0029] For deviations that have a triggering relationship, trace their original performance in the complete ball-hitting process data, describe the unconventional action or decision-making patterns that students spontaneously form to compensate for a certain technical deficiency and that may trigger a chain of problems, and form a description of technical compensation behavior.

[0030] For isolated deviations or those that serve as the starting point of a triggering chain, their destructive impact on overall tactical coherence, tempo control, and scoring efficiency is analyzed and defined as critical tactical vulnerabilities.

[0031] Preferably, the step of performing stress amplification calculations on the description of the technical compensation behavior under stress conditions to form a tactical error prediction including stress state parameters includes:

[0032] The description of the technical compensation behavior is analyzed to extract non-standard action features, unconventional force exertion patterns, and redundant decision-making processes.

[0033] A stress response model was established, which defined the amplification factors of confrontation intensity, game duration, and key score situations on trainees' physiological tension and cognitive load.

[0034] The non-standard movement characteristics are input into the stress model to calculate the possible technical deformation range and movement failure probability of the non-standard movement characteristics under simulated high-pressure situations, and to generate movement error risk parameters.

[0035] The unconventional power generation mode and redundant decision-making process are input into the stress response model to calculate the additional energy consumption rate and decision delay growth curve under simulated continuous confrontation and fatigue accumulation, thereby generating performance decay risk parameters.

[0036] By combining the aforementioned action error risk parameters and the aforementioned effectiveness decay risk parameters, a tactical error sequence with temporal order and developmental pattern is deduced under specific pressure scenarios, which is directly or indirectly caused by technical compensation behavior. This is known as tactical error prediction.

[0037] Preferably, the establishment of the stress model defines the amplification factors of the intensity of confrontation, the duration of the game, and the key score situation on the physiological tension and cognitive load of the trainees, including:

[0038] Set up basic resistance intensity units and define the mapping relationship between resistance intensity levels and trainees' heart rate increase and muscle tension increase;

[0039] Set a basic competition duration unit and define a correlation function between the cumulative effect of time and the student's attention fluctuation cycle and reaction speed baseline offset;

[0040] Typical key score scenarios are set up, including leading, stalemate and trailing, and the impact weight of each scenario on the participants' risk preference and decision conservatism coefficient is defined;

[0041] Based on the mapping relationship, the correlation function, and the influence weight, a multi-dimensional stress response surface is constructed.

[0042] On the stress response surface, a corresponding amplification factor is configured for each dimension, so that the input original technical compensation features can be mapped into a quantified risk value with stress state parameters according to the real-time simulated stress state.

[0043] Preferably, the step of designing a corrective task with progressive adversarial logic based on the aforementioned key tactical vulnerability includes a tactical reconstruction scheme and an adversarial adaptive enhancement scheme, comprising:

[0044] To address the tactical chain disrupted by the aforementioned critical tactical vulnerabilities, a reconstruction exercise for basic tactical units is designed. This reconstruction exercise focuses on restoring the standard execution flow of the basic tactical units under no-confrontation or low-confrontation conditions.

[0045] After the basic tactical units are stabilized, a single counter-disruption factor is introduced, and adaptive fine-tuning exercises are designed to enable trainees to learn how to deal with the single counter-disruption factor while maintaining core tactical movements.

[0046] Gradually increase the number, intensity, or unpredictability of the counter-disruptive factors, design tactical execution exercises under complex counter-environment scenarios, and improve the robustness of tactics in complex environments.

[0047] Based on the aforementioned tactical error predictions, high-incidence error scenarios are extracted, and tactical decision-making and execution reinforcement exercises are designed under specific pressure situations.

[0048] The reconstruction exercises, adaptive fine-tuning exercises, tactical execution exercises under complex adversarial situations, and tactical decision-making and execution enhancement exercises under pressure situations are arranged in order of increasing adversarial intensity and increasing adversarial complexity to form a tactical reconstruction scheme with progressive logic.

[0049] Simultaneously, based on the description of the aforementioned technical compensatory behavior, an adversarial adaptive enhancement scheme is designed to establish a new and more efficient body kinetic chain and decision-making mode. This adversarial adaptive enhancement scheme works in sync with the tactical reconstruction scheme during the training phase.

[0050] Preferably, the introduction of a single adversarial perturbation factor and the design of adaptive fine-tuning exercises include:

[0051] Select a specific velocity or spin change pattern from the virtual incoming ball interference library as the initial disturbance factor;

[0052] The ball feeding program is designed to keep the incoming ball stable in 80% of the normal ball paths, and to introduce the initial disturbance factor at 20% of the preset nodes.

[0053] Trainees are required to maintain standard movements for regular ball trajectories when executing pre-set tactics, and to respond to balls that introduce disturbances using specific, finely tuned, pre-trained techniques.

[0054] The system collects real-time data on trainees' success rate and return quality when dealing with disturbed balls, and dynamically adjusts the frequency and location of disturbances accordingly.

[0055] When the difference between the student's success rate under the initial disturbance factors and the success rate of the regular ball path is less than the set threshold, the adaptive fine-tuning practice is deemed complete, and the student proceeds to the next complexity level of practice.

[0056] Preferably, the method further includes:

[0057] After a teaching cycle is completed, collect data on the trainees' technical performance throughout the new real-world combat scenarios.

[0058] From the full-process technical performance data, identify the evolution of the original technical weakness map and whether any new technical weakness areas have emerged;

[0059] The evolution and newly emerging weak areas are compared with previously generated tactical error predictions to assess the accuracy of the predictions.

[0060] Based on the comparison results, the parameter sensitivity of the adversarial response model used to generate a map of technical weaknesses is adjusted in reverse, and the amplification factor in the stress model is corrected.

[0061] By using the adjusted model and coefficients, a new round of analysis and prediction is conducted on the subsequent performance of the same student in adversarial situations, forming a closed loop for teaching evaluation.

[0062] Preferably, the present invention also includes a table tennis teaching system based on actual combat, the system including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, it implements the steps of the college table tennis teaching method based on the actual combat module described above.

[0063] Compared with the prior art, the beneficial effects of the present invention are:

[0064] By comparing the fitness of trainees' technical weakness maps with a dynamic ideal adversarial model, this technique can systematically identify non-standard technical movement patterns that trainees passively develop to maintain their returns under continuous adversarial pressure. Unlike conventional static movement comparisons, this comparison process evaluates the effectiveness and stability of technical movements within a simulated tactical adversarial flow, thereby revealing the causal relationship between surface technical deficiencies and deep-seated tactical vulnerabilities. This method expands the analytical dimension from evaluating a single technical movement to assessing the technical functionality and tactical coherence within the adversarial system.

[0065] By applying simulated stress parameters to identified compensatory techniques and performing stress-amplification calculations, the probability and type of errors resulting from specific compensatory techniques can be quantitatively predicted under different adversarial intensities and psychological loads. This computational model establishes a dynamic mapping relationship between compensatory techniques, environmental stress, and tactical errors, enabling training interventions to be precisely designed to target specific technical risks under specific stress thresholds. This achieves a shift in training paradigm from correcting existing errors to preventing predictable ones.

[0066] Based on the descriptions of key tactical vulnerabilities and technological compensation behaviors generated through dynamic comparison and stress calculation, corrective tasks with a clear progressive adversarial logic can be designed. These tasks directly target and reinforce identified weaknesses in the tactical chain and predictable error patterns, enabling technological correction and tactical application to proceed simultaneously under simulated real adversarial pressure. The training program thus possesses clear scenario-specific and stress-adaptive objectives, effectively connecting technical learning with practical application. Attached Figure Description

[0067] Figure 1 This is a flowchart illustrating the steps of the college table tennis teaching method based on practical modules described in this invention.

[0068] Figure 2 Flowchart for generating the initial reaction capability profile;

[0069] Figure 3 A flowchart generated for predicting tactical errors;

[0070] Figure 4 Grouped bar charts showing the success rate and return quality of different disturbance combinations during students' table tennis practice;

[0071] Figure 5 Grouped bar chart for sensitivity adjustment of parameters in a table tennis teaching confrontation response model. Detailed Implementation

[0072] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0073] See Figure 1In a real table tennis match setting, high-speed cameras, inertial sensors, and pressure sensors are used to collect complete ball-hitting process data from the participants. This data includes the ball trajectory, body response timing, and multi-point tactile feedback from key body parts and the racket. Based on the collected complete ball-hitting process data, computer simulation technology is used to construct a competitive scenario, reproducing the participants' technical performance. Analysis is then used to deduce the participants' technical weaknesses under the current competitive intensity. This weakness map is compared with a pre-set ideal competitive model. By calculating the competitive adaptability, the technical compensation behaviors adopted by the participants to compensate for weaknesses are identified, and key tactical vulnerabilities that are highly destructive to the tactical system are located. The identified technical compensation behaviors are quantitatively analyzed, simulating their stress amplification effect under pressure, and tactical error predictions including specific stress state parameters are calculated. Based on the key tactical vulnerabilities identified, a series of corrective tasks with progressive competitive logic are designed. These tasks include tactical reconstruction schemes aimed at fixing vulnerabilities and enhancement schemes aimed at improving overall competitive adaptability.

[0074] In one embodiment of the present invention, see [reference] Figure 2Based on the complete ball-hitting process data, the technical performance in a simulated competitive situation is analyzed to deduce the technical weaknesses of the trainees under the current competitive intensity. The process specifically includes: First, identifying three key periods from the body response sequence: the preparation phase, the initiation phase, and the ball-hitting completion phase, and establishing a competitive response model for each phase. In the preparation phase, visual capture data and predicted body posture are extracted. By simulating decision delays under different combinations of incoming ball speed and spin, an initial reaction capability profile is generated. Specifically, the ball image sequence and the trainee's gaze point change trajectory are separated from the complete ball-hitting process data to form a visual capture data stream; simultaneously, the trainee's torso angle, center of gravity preparation position, and racket face orientation are acquired at each time stamp in the incoming ball image sequence to form a predicted body posture sequence. A virtual incoming ball interference library containing various speed mutations and spin combinations is established. The visual capture data stream and predicted body posture sequence are input sequentially to test the trainee's decision-making mechanism. The time increment from the appearance of the stimulus to the body's effective response action when facing each type of virtual incoming ball interference is calculated, resulting in a set of decision delay spectra. The distribution patterns and abrupt change points of the decision delay spectrum under different interference types were analyzed, and an initial reaction capability profile was plotted with interference type as the horizontal axis and delay time as the vertical axis. This profile revealed the trainees' vulnerability to different incoming balls during confrontation. In the initiation phase, combining the muscle activation sequence and force perception from the multi-point tactile feedback, the footwork initiation efficiency and arm swing trajectory stability under confrontational pressure were analyzed to generate a motion execution efficiency profile. In the ball-hitting completion phase, based on the confrontational deviation between the incoming and outgoing ball trajectories and the body return speed, the ball-hitting quality maintenance ability and continuous confrontation ability were calculated to generate a technical maintenance ability profile. Finally, the initial reaction capability profile, the motion execution efficiency profile, and the technical maintenance ability profile were merged to construct a three-dimensional confrontation capability assessment space. In this assessment space, regions where various capabilities are below preset confrontation thresholds were identified. These regions were connected and mapped to form a technical weakness map with spatial distribution characteristics under specific confrontation intensities.

[0075] In practical implementation, based on the complete ball-hitting process data, the technical performance in a simulated competitive situation is analyzed to deduce the student's technical weaknesses under the current competitive intensity. This process is illustrated through a specific example scenario. In this example scenario, during multi-ball practice against a ball machine, the student faces attacks primarily consisting of topspin and sidespin. High-speed cameras, inertial measurement units, and intelligent racket sensors deployed on the side of the table and key parts of the student's body simultaneously collect complete ball-hitting process data, including the ball trajectory, body response sequence, electromyographic signals from the wrist, forearm, and shoulder muscles, and racket face pressure feedback. The data processing system first identifies the precise time boundaries of the preparation, initiation, and completion phases from the body response sequence and initializes a competitive response model for each phase. The preparation phase competitive response model focuses on perception and decision-making, the initiation phase competitive response model focuses on action execution, and the completion phase competitive response model focuses on maintaining and maintaining the effect.

[0076] In some embodiments, during the preparation phase, the system separates the ball image sequence and the student's gaze point change trajectory obtained through an eye tracker from the complete hitting process data, forming a visual capture data stream. The ball image sequence records the ball's movement from after crossing the net to before hitting at a rate of 200 frames per second, while the gaze point change trajectory records the movement coordinates and dwell time of the student's eye focus. Simultaneously, the system synchronously acquires, at each corresponding timestamp of the ball image sequence, the angle of the student's torso relative to the reference plane collected by the inertial measurement unit, the coordinates of the center of gravity in preparation position collected by the pressure-sensing floor, and the racket face orientation angle collected by the built-in sensor of the smart racket, forming a predicted body posture sequence. The system internally has a pre-set virtual ball interference library, which includes various speed change and spin combination modes such as speed step increase, sudden speed decrease, topspin combined with left side spin, topspin combined with right side spin, and backspin suddenly topspin. The system sequentially inputs the visual capture data stream and the predicted body posture sequence into the preparation phase adversarial response model. The model simulates the student's decision-making mechanism when a ball change matching the interference library occurs in real adversarial play. The system calculates the time increment between the moment the interference feature is identified in the visual capture data stream and the start time of the step or backswing action recorded in the adversarial response model during the start phase when facing each type of virtual ball interference, and obtains a set of decision delay spectra.

[0077] Understandingly, analyzing the distribution patterns and abrupt changes in the decision delay spectrum under different interference types involves statistically plotting an initial reaction capability profile with interference type on the horizontal axis and delay time on the vertical axis. This initial reaction capability profile, presented as a curve, visually reveals the vulnerable areas of trainees' responses to different incoming balls during the exercise. Generating the initial reaction capability profile involves calculating the average delay and standard deviation for each interference type; the peak points in the profile correspond to the interference type with the highest reaction vulnerability. (Decision delay time) The calculation formula, derived from the model, is as follows:

[0078]

[0079] in: This represents the timestamp when a specific interference feature is identified by the model in the visual capture data stream. This represents the start timestamp of the corresponding effective response action recorded in the adversarial response model during the startup phase. This formula is only used to clarify the logical relationship of the time points in the delay calculation.

[0080] In its implementation, during the initiation phase, the system combines the muscle activation sequence and force perception revealed by the electromyographic signals on the muscle surface from multi-point tactile feedback. It analyzes the timeliness of footwork initiation and the stability of the arm swing trajectory when the ball's landing point changes under pressure, generating a motion execution efficiency profile. This profile quantifies the execution quality from decision-making to action completion. During the shot completion phase, the system calculates the shot quality maintenance ability and continuous resistance ability based on the angle and distance of the resistance deviation between the incoming and outgoing ball trajectories, as well as the time required for the center of gravity to return to the ready position after the shot. This generates a technique maintenance ability profile, reflecting the level of maintenance of both the single shot effect and the prepared state for continuous shots. By integrating the initial reaction ability profile, the motion execution efficiency profile, and the technique maintenance ability profile, a three-dimensional resistance ability assessment space is constructed. The three axes of this three-dimensional space represent the speed of perception and decision-making, the accuracy of action execution, and the ability to maintain the effect.

[0081] In some embodiments, within the adversarial capability assessment space, the system identifies regions where various capability index values ​​are below a preset adversarial threshold. This preset threshold is dynamically set based on the trainee's skill level and training objectives. The systems then spatially connect and temporally map these regions, forming a map of technical weaknesses with spatial distribution characteristics under specific adversarial intensities. For example, when dealing with "upward spin combined with leftward spin" interference, a high-delay region may simultaneously appear in the initial reaction capability dimension, while an area with insufficient arm swing trajectory stability may appear in the motion execution efficiency dimension. These two regions are connected in three-dimensional space, mapping to a comprehensive technical weakness for this type of rotational combination.

[0082] In one embodiment of the present invention, the technical weakness map is compared with an ideal adversarial model to generate a description of technical compensation behavior and key tactical vulnerabilities. The process specifically includes: obtaining a preset ideal adversarial model, which defines the optimal range and dynamic balance relationship of various technical indicators under standard adversarial pressure; overlaying the technical weakness map onto the ideal adversarial model, comparing the deviation of the student's actual data from the ideal range point by point, and generating a local fitness difference field; identifying regions in the local fitness difference field where the deviation exceeds the tolerance threshold, and analyzing the correlation and temporal induced relationships between these regions; for deviation regions with induced relationships, tracing their original performance in the complete shot flow data, describing the unconventional actions or decision-making patterns spontaneously formed by the student to compensate for a certain technical deficiency, which may trigger a chain reaction, thus forming a description of technical compensation behavior; and analyzing the destructive impact of isolated deviation regions or those serving as the starting point of an induced chain on overall tactical coherence, rhythm control, and scoring efficiency, defining them as key tactical vulnerabilities.

[0083] In practical implementation, the technical weakness map is compared with the ideal adversarial model to generate a description of technical compensation behavior and key tactical vulnerabilities. This process is illustrated through a specific example scenario. In this scenario, the system acquires a preset ideal adversarial model. The ideal adversarial model defines the optimal range and dynamic balance relationship of various technical indicators under standard adversarial pressure. For example, for the technical scenario of "responding to topspin combined with left side spin," the ideal adversarial model specifies that the optimal range for initial reaction delay is 100 to 120 milliseconds, the optimal range for the standard deviation of the arm swing trajectory angular velocity is less than 5 degrees / second, and the optimal range for the return ball landing point control deviation is within a 30-centimeter radius of the target area. The system overlays the technical weakness map formed by the trainee when responding to topspin combined with left side spin onto the ideal adversarial model. The technical weakness map marks areas with an initial reaction delay of 150 milliseconds, a standard deviation of the arm swing trajectory angular velocity of 8 degrees / second, and a return ball landing point control deviation of 50 centimeters.

[0084] In some embodiments, the system compares the difference between the student's actual data and the boundary of the ideal interval point by point to generate a local fitness difference field. For the initial reaction delay index, the difference between the student's actual value of 150 milliseconds and the upper limit of the ideal interval of 120 milliseconds is 30 milliseconds; for the standard deviation index of the arm swing trajectory angular velocity, the difference between the student's actual value of 8 degrees / second and the upper limit of the ideal interval of 5 degrees / second is 3 degrees / second. After assigning a weighting coefficient to each index difference, the system calculates a scalarized deviation. The local fitness difference field spatially represents the degree and distribution of deviation from the ideal state across different technical dimensions. The comprehensive deviation of each technical index node in the local fitness difference field... The calculation can be derived from the following formula:

[0085]

[0086] in: This indicates the total number of technical indicators involved in the evaluation. Indicates the first The preset weighting coefficients for each technical indicator. Indicates that the students are in the The actual measured values ​​of each technical indicator. In the ideal adversarial model, the first... The optimal range boundary value for each technical indicator.

[0087] In its implementation, the system identifies regions in the local fitness difference field where the deviation exceeds a preset tolerance threshold, which is dynamically adjusted according to the training phase. Analyzing the correlation and temporal induced relationships among these regions exceeding the tolerance threshold, the system discovers, through temporal correlation analysis of complete shot data, that the peak time point of the initial reaction delay region is earlier than the starting time point of the region with insufficient arm trajectory stability, and that the two exhibit a stable sequential order across multiple consecutive shot cycles. This indicates that the initial reaction delay region has an induced relationship with the region with insufficient arm trajectory stability.

[0088] Understandably, for deviation areas with induced relationships, the system traces their original performance in the complete hitting process data, describing the spontaneous movement patterns formed by the student to compensate for the technical deficiency of initial reaction delay. After the student's visual judgment of the ball's spin is slightly delayed, in order to gain hitting time, their initiation phase involves unconventional movements such as premature shoulder forward thrust and hasty forearm contraction to compensate for insufficient backswing time. This unconventional movement disrupts the ideal kinetic chain, directly leading to an increase in the standard deviation of the arm swing trajectory angular velocity. The system describes this chain reaction as "a compensatory shoulder forward thrust and rapid contraction pattern induced by delayed prediction," forming a description of technical compensatory behavior. For another deviation area where the return ball landing point control deviates beyond the threshold but has no significant temporal correlation with other areas, the system analyzes its impact on overall tactical coherence. The student's return ball landing point deviation is large and consistently biased towards the opponent's backhand side at a large angle, making the tactical intention easy for the opponent to predict, disrupting the rhythm of continuous attack and scoring efficiency. The system defines this as "a vulnerability in tactical intentions that are easily predicted due to the singularity of landing point control," i.e., a key tactical vulnerability.

[0089] In one embodiment of the present invention, see [reference] Figure 3The process involves performing stress amplification calculations on the description of the technical compensation behavior under pressure conditions to form a tactical error prediction that includes stress state parameters. Specifically, this includes: parsing the description of the technical compensation behavior and extracting non-standard movement features, unconventional force exertion patterns, and redundant decision-making processes. A stress response model is established, defining amplification coefficients for the physiological tension and cognitive load of trainees based on the intensity of confrontation, match duration, and key score scenarios. Specifically, a basic unit of confrontation intensity is set, and a mapping relationship is defined between the confrontation intensity level and the trainee's heart rate increase and muscle tension increase; a basic unit of match duration is set, and a correlation function is defined between the time cumulative effect and the trainee's attention fluctuation cycle and reaction speed baseline offset; typical key score scenarios are set, including leading, stalemate, and lagging, and the influence weights of each scenario on the trainee's risk preference and decision conservatism coefficient are defined. Based on the mapping relationship, the correlation function, and the influence weights, a multi-dimensional stress response surface is constructed; on this stress response surface, a corresponding amplification coefficient is configured for each dimension, so that the input original technical compensation features can be mapped into a quantified risk value with stress state parameters according to the real-time simulated stress state. The non-standard movement characteristics are input into the stress model to calculate the potential technical deformation and failure probability under simulated high-pressure conditions, generating movement error risk parameters. The unconventional force exertion patterns and redundant decision-making processes are also input into the stress model to calculate the rate of additional energy consumption and decision delay growth curves under simulated continuous confrontation and fatigue accumulation, generating effectiveness decay risk parameters. By combining these movement error risk parameters and effectiveness decay risk parameters, a tactical error sequence with temporal progression and developmental patterns, directly or indirectly caused by technical compensation behavior, is deduced under specific stress scenarios—that is, tactical error prediction.

[0090] In practical implementation, the description of the technical compensation behavior is subjected to stress amplification calculations under pressure conditions to form a tactical error prediction that includes stress state parameters. This process is illustrated through a specific example scenario. In the example scenario, the system analyzes the technical compensation behavior description "the shoulder compensatory forward thrust and rapid contraction pattern induced by delayed prediction," extracting non-standard movement features, unconventional force generation patterns, and redundant decision-making steps. Non-standard movement features include premature and excessive forward movement of the shoulder joint during the backswing phase. Unconventional force generation patterns are manifested in the reliance on rapid forearm contraction rather than complete trunk twisting and leg extension to transmit power. Redundant decision-making steps are reflected in an additional fine-tuning of the racket face angle just before the shot.

[0091] In some embodiments, the system establishes a stress model that defines the amplification factors of confrontation intensity, match duration, and key score scenarios on trainees' physiological tension and cognitive load. The basic confrontation intensity unit is set as the pace and speed of a standard match round, and a mapping relationship is defined between the confrontation intensity level and the trainees' heart rate increase and muscle tension increase. For example, when the confrontation intensity level increases by one level, the baseline heart rate increase increases by 5%, and the baseline amplitude of the electromyographic signal of the trapezius muscle in the shoulder increases by 10%. The basic match duration unit is set as the standard time of a game, and a correlation function is defined between the cumulative effect of time and the trainees' attention fluctuation cycle and reaction speed baseline offset. The correlation function stipulates that for every basic match duration unit, the effective concentration cycle of attention shortens by 15%, and the baseline delay of reaction speed in standard decision-making tasks increases by 20 milliseconds. Typical key score scenarios are defined, including leading, stalemate, and trailing, and the influence weight of each scenario on trainees' risk preference and decision conservatism coefficient is defined. For example, in the "trail" scenario, an increased risk preference weight makes trainees more inclined to adopt more aggressive tactics, but at the same time, a decreased decision conservatism coefficient may lead to a decrease in the execution rate of technical action details.

[0092] In practical implementation, a multi-dimensional stress response surface is constructed based on the mapping relationship between the intensity of confrontation and physiological indicators, the correlation function between the cumulative effect of match duration and cognitive indicators, and the influence weight of key score situations on psychological indicators. On the stress response surface, corresponding amplification coefficients are configured for the dimensions of confrontation intensity, match duration, and key score situations. , , This allows the original technical compensation features to be mapped into quantified risk values ​​with stress state parameters, based on the simulated stress state. The model is based on the real-time simulated adversarial intensity index. Duration of the match Current Score Scenario Index and the basic value of technical compensation characteristics The calculation formula is as follows:

[0093]

[0094] in: This represents the quantified value of the technical compensation characteristics measured under pressure-free baseline conditions. , , These are the magnification factors for the corresponding dimensions. , , This is the normalized pressure state index.

[0095] It is understandable that inputting the non-standard movement characteristic "premature and excessive forward shoulder movement" into the stress model allows for the calculation of the potential technical deformation and failure probability under simulated high-pressure situations, generating error risk parameters. For example, in simulating the stress state of "high intensity confrontation at the end of a match and trailing in the score," the model calculates that the forward shoulder angle will increase by 40% from the baseline, leading to swing trajectory distortion and increasing the probability of the ball hitting the net from 15% to 45%. Inputting unconventional power generation patterns and redundant decision-making processes into the stress model allows for the calculation of the additional energy consumption rate and decision delay growth curve under simulated continuous confrontation and fatigue accumulation, generating efficiency decay risk parameters. For example, in simulating the stress state of "entering a crucial point after a long rally," the model calculates that due to the reliance on rapid forearm power, the energy consumption rate of related muscle groups is 1.8 times that of the standard power generation pattern, while the redundant racket face fine-tuning decision-making process introduces an additional 80 millisecond delay under fatigue conditions.

[0096] Optionally, the system integrates action error risk parameters and efficiency decay risk parameters to deduce a sequence of tactical errors directly or indirectly caused by technical compensation behaviors under specific pressure scenarios. Based on the rising point of action error probability, the inflection point of action quality decline due to energy consumption, and the cumulative effect of decision delay, the system predicts tactical errors such as excessive forward shoulder thrust leading to a return ball going out of bounds in the third consecutive attack, or inability to cope with sudden speed changes in the opponent due to increased decision delay after the fifth rally. Specifically, the tactical error prediction is described as a chain of error events with temporal sequence and developmental patterns that may occur sequentially under specific pressure situations, such as "third-pole attack going out of bounds" and "fifth-pole error responding to sudden ball changes."

[0097] In one embodiment of the present invention, based on the aforementioned critical tactical vulnerability, a corrective task with progressive adversarial logic is designed. This corrective task includes a tactical reconstruction scheme and an adversarial adaptive enhancement scheme. Specifically, the process includes: designing reconstruction exercises for basic tactical units to address the tactical chain disrupted by the critical tactical vulnerability. These exercises focus on restoring the standard execution flow of the basic tactical units under no-confrontation or low-confrontation conditions. After the basic tactical units stabilize, a single adversarial disturbance factor is introduced, and adaptive fine-tuning exercises are designed to enable trainees to learn how to cope with the single adversarial disturbance factor while maintaining core tactical maneuvers. The introduction of a single adversarial disturbance factor and the design of adaptive fine-tuning exercises specifically involve: selecting a speed or spin variation pattern from a virtual ball interference library as the initial disturbance factor; designing a ball feeding program to keep the ball stable in 80% of the normal ball trajectories and introducing the initial disturbance factor at 20% of preset points; requiring trainees to maintain standard movements for normal ball trajectories while responding to the disturbed ball with pre-trained, fine-tuned specific techniques; real-time data collection of trainees' success rate and return quality when responding to disturbed balls, and dynamically adjusting the frequency and location of disturbances accordingly; when the difference between the trainee's success rate under the initial disturbance factor and the success rate on normal ball trajectories is less than a set threshold, the adaptive fine-tuning exercise is considered complete, and the trainee proceeds to the next complexity level. Subsequently, the number, intensity, or unpredictability of adversarial disturbance factors are gradually increased, and tactical execution exercises under complex adversarial scenarios are designed to improve the robustness of tactics in complex environments. Based on the predicted tactical errors, frequently occurring error scenarios are extracted, and tactical decision-making and execution reinforcement exercises under specific pressure situations are designed. The reconstruction exercises, adaptive fine-tuning exercises, composite adversarial situation exercises, and stress situation reinforcement exercises are arranged in order of increasing adversarial intensity and increasing adversarial complexity to form a tactical reconstruction scheme with progressive logic. Simultaneously, based on the description of the aforementioned technological compensatory behavior, an adversarial adaptive enhancement scheme is designed to establish new and more efficient body kinetic chains and decision-making patterns. This scheme works in tandem with the tactical reconstruction scheme during the training phase.

[0098] In practical implementation, based on the aforementioned key tactical vulnerabilities, a corrective task with progressive counter-strategy logic is designed. This corrective task includes a tactical reconstruction scheme and a counter-adaptability enhancement scheme. This process is unfolded through a specific example scenario. In the example scenario, addressing the key tactical vulnerability of "the vulnerability of easily predictable tactical intent caused by the simplification of landing point control," the system first designs reconstruction exercises for basic tactical units. These exercises focus on restoring the standard execution flow of the basic tactical unit "forehand continuous topspin attack combined with landing point variation" under no-confrontation or low-confrontation conditions. The exercises require trainees to hit the ball in a preset sequence of "straight line-cross-straight line" or "cross-straight line-cross-cross" during fixed-point multi-ball training. Emphasis is placed on proper footwork, complete body weight transfer, and stable swing trajectory to ensure accurate hitting of the ball into a designated half-table area under no pressure.

[0099] In some embodiments, after the reconstruction practice of the basic tactical unit has stabilized, the system introduces a single adversarial disturbance factor to design adaptive fine-tuning exercises. A speed change pattern, "a sudden 20% increase in ball speed," is selected from a virtual ball interference library as the initial disturbance factor, and a ball feeding program is designed for the serving machine or coach. The feeding program maintains a stable speed and spin for the ball in 80% of the normal ball trajectories, and at a preset 20% point, such as when the student is preparing to connect to the next shot after completing a cross-court shot, the initial disturbance factor, i.e., a ball with a sudden increase in speed, is introduced. Students are required to maintain standard movements for balls with normal speed when executing the preset tactic of "continuous topspin forehand attack combined with varying landing points," and to respond to balls with a sudden increase in speed disturbance using pre-trained, finely tuned specific technical movements. These finely tuned specific technical movements include earlier backswing preparation and a more compact arm recovery movement. The system collects real-time data on the trainee's success rate and return quality when dealing with balls that suddenly increase in speed. Success rate is defined as the return successfully clearing the net and landing in the valid area, while return quality is assessed through ball speed and spin intensity. Based on the collected success rate and return quality data, the system dynamically adjusts the frequency and location of sudden speed increases. When the trainee's success rate under sudden speed increases reaches 85%, and the difference between this success rate and the 90% success rate for regular shots is less than the system's set 5% threshold, the adaptive fine-tuning practice targeting the single disturbance factor of "sudden speed increase" is considered complete, and the trainee moves on to the next level of complexity.

[0100] In practice, after completing adaptive practice with a single disturbance factor, the system gradually increases the number, intensity, and unpredictability of the disturbance factors, designing tactical execution exercises under complex adversarial scenarios. The system increases the number of disturbance factors, for example, by simultaneously introducing two disturbances: a sudden increase in speed and a sudden change in spin from backspin to topspin. The system also increases the intensity of the disturbance factors, for example, by increasing the speed increase from 20% to 35%. Furthermore, the system increases the unpredictability of the disturbance factors, for example, by changing the preset pattern of disturbance occurrence, allowing it to randomly occur at any point in the tactical chain. These tactical execution exercises under complex adversarial scenarios aim to improve the robustness of the "forehand continuous topspin attack combined with varying landing points" tactic in complex environments. Based on frequently occurring error scenarios in tactical error prediction, such as "at a crucial point of 9-9 tie, a third consecutive attacking shot is misjudged due to the obvious intention being anticipated," the system designs tactical decision-making and execution reinforcement exercises under specific pressure situations. These exercises simulate a crucial 9-9 tie, requiring trainees to randomly change the landing point of the third shot during continuous attacks, while simultaneously monitoring their heart rate and the success rate of their movements.

[0101] It is understandable that reconstruction exercises, adaptive fine-tuning exercises, complex adversarial situation exercises, and stress-based reinforcement exercises are systematically arranged, following a sequence from low to high adversarial intensity and from simple to complex adversarial complexity. A complete tactical reconstruction plan is illustrated in Table 1.

[0102] Table 1: Progressive Logic Table of Tactical Reconstruction Scheme Targeting the "Single-Point Control" Vulnerability

[0103] Training phase Practice type Confrontational strength Disturbance factors Preset success rate target Advanced Standards Phase 1 Basic Reconstruction Exercises Low none Landing accuracy > 95% Three consecutive groups met the standard Phase 2 Adaptive fine-tuning exercises medium to low Single velocity increases disturbance The success rate of the deflected shot is >85%, and the difference between the success rate and the regular shot is <5%. Achieve the preset goal Phase 3 Complex adversarial scenario practice Medium and high Dual random perturbations of velocity and rotation Overall success rate >80% Two consecutive groups met the standard Phase 4 Stressful situation reinforcement exercises high Key score, psychological pressure, and random disturbances Tactical execution consistency rate under pressure >75% Achieve the preset goal

[0104] Simultaneously, focusing on the description of compensatory shoulder thrust and rapid contraction patterns induced by delayed anticipation, the system design aims to establish a new and more efficient body kinetic chain and decision-making pattern through a counter-adaptive enhancement program. This program includes using assistive devices that limit shoulder forward movement for swing practice to build new muscle memory, and shortening the judgment time for rotation through high-speed image feedback training. The counter-adaptive enhancement program is synchronized with the aforementioned tactical reconstruction program during training phases. For example, during adaptive fine-tuning exercises in Phase 2 of tactical reconstruction, training on decision-making patterns targeting anticipatory responses is conducted concurrently.

[0105] Optionally, the mechanism for dynamically adjusting the frequency of disturbances during adaptive fine-tuning exercises follows a feedback control logic. The disturbance frequency adjustment value... The calculation is based on the difference between the current success rate and the target success rate. The formula is as follows:

[0106]

[0107] in: This indicates the frequency of the current disturbance. This is an adjustment factor (which can be set to 0.05). Indicates the success rate of the target. This represents the average success rate measured within the current period. When the measured success rate is lower than the target, the perturbation frequency... The frequency will be appropriately reduced to help trainees adapt; when the actual success rate is higher than the target, the perturbation frequency will be adjusted. The difficulty level will be appropriately increased to enhance the challenge.

[0108] See Figure 4 This is a bar chart showing the success rate and return quality of different perturbation combinations in students' table tennis practice. As the complexity of the perturbation factors increases, both the students' success rate and return quality show a gradual downward trend. Priority should be given to specific reinforcement of double perturbation scenarios such as "speed + 35% + sudden spin change" to help students build muscle memory and decision-making patterns to cope with complex perturbations. After adaptive fine-tuning practice, a step-by-step progression from "single perturbation → double perturbation → random perturbation" can more effectively improve students' technical robustness in complex matches. When a student's success rate under a certain perturbation combination falls below a preset threshold, the perturbation frequency can be dynamically reduced to help them gradually adapt; the challenge difficulty can be increased after stability is improved.

[0109] In one embodiment of the present invention, after a teaching cycle, full-process technical performance data of trainees in new practical combat is collected. From this full-process technical performance data, the evolution of the original technical weakness map and whether any new technical weakness areas have emerged are identified. The evolution and information on newly emerging weakness areas are compared with previously generated tactical error predictions to assess the accuracy of the predictions. Based on the comparison results, the parameter sensitivity of the adversarial response model used to generate the technical weakness map is adjusted in reverse, and the amplification coefficient in the stress response model is corrected. Using the adjusted model and coefficients, a new round of analysis and prediction is conducted on the subsequent combat performance of the same trainee, forming a closed loop of teaching evaluation.

[0110] In practice, after a teaching cycle, data on the trainees' full-process technical performance in new real-world matches is collected. This process is illustrated through a specific example scenario. In this scenario, the trainees complete a full corrective training cycle targeting "the vulnerability of easily predictable tactical intentions caused by the simplistic control of landing points" and "the compensatory forward thrust and abrupt contraction of the shoulder induced by delayed prediction." Subsequently, they play an 11-point teaching match against an opponent of similar skill level. The system then collects data on the trainees' full-process technical performance again in the new real-world matches through sensor arrays deployed on the table and on the trainees. This full-process technical performance data includes new ball trajectories, body response timing, and multi-point tactile feedback.

[0111] In some embodiments, the system identifies the evolution of the original technical weakness map from new full-process technical performance data, and retrieves the technical weakness map generated before training as a comparison benchmark. For the technical scenario of "responding to topspin combined with left side spin," the system analysis of new data reveals that the average initial reaction delay of trainees has improved from 150 milliseconds to 125 milliseconds, and the standard deviation of the arm swing trajectory angular velocity has improved from 8 degrees / second to 5.5 degrees / second. The high-delay and high-instability areas marked in the original technical weakness map have significantly reduced in volume in the three-dimensional competitive ability assessment space, indicating that the weakness has been improved. Simultaneously, the system detects whether new technical weakness areas have emerged. Analysis reveals that when trainees respond to the opponent's newly frequently used "backhand long backspin ball," the predicted body posture sequence in the receiving phase shows new inconsistencies, with a tendency for insufficient backward lean in the center of gravity preparation position. The system identifies this as a newly emerging technical weakness area.

[0112] In its implementation, the system compares the evolution of the existing technical weakness map and information on newly emerging weak areas with the tactical error predictions generated before the start of the training cycle to assess the accuracy of the predictions. The tactical error predictions previously included a sequence of events such as "the ball going out of bounds due to excessive shoulder thrust during a third consecutive attack." By comparing new real-world data, the system statistically analyzed the actual causes of errors in the third-ball attack under similar pressure scenarios. It found that errors caused by excessive shoulder thrust decreased from a frequently predicted event to a less frequent one, while direct point loss due to insufficient preparation for "rapid backhand underspin balls" became one of the new main error types. Based on this, the system assessed that the previous tactical error predictions targeting shoulder compensation were highly accurate, but failed to predict new vulnerabilities caused by changes in the opponent's tactics.

[0113] Understandably, based on the comparison results, the system adjusts the parameter sensitivity of the adversarial response model used to generate the technical weakness map, and corrects the amplification factor in the stress model. For the adversarial response model, the system reduces the sensitivity weight of the parameter "shoulder forward angle," as its improvement indicates that this parameter no longer constitutes a major problem; simultaneously, the system increases the sensitivity weight of parameters related to "stability of the center of gravity in the receiving phase," to more accurately detect emerging weak areas. For the stress model, the system corrects the amplification factor in the adversarial intensity dimension. Amplification factor for the dimension of match duration The correlation weights are adjusted based on the deviation between the trainees' stability data and predictions in later stages of the competition. The adjustment of the amplification factor follows an adaptive rule, with the adjustment amount... Deviation from prediction and the current value of the coefficient The relevant calculation formula is as follows:

[0114]

[0115] in: The learning rate is a positive decimal less than 1. This is the normalized prediction bias value. The formula ensures that the correction process is stable and controlled, given the current value of the amplification factor to be corrected.

[0116] Optionally, the system utilizes the adjusted parameters of the adversarial response model and the amplification factor of the stress model to conduct a new round of analysis and prediction of the same trainee's subsequent adversarial performance. The system inputs the trainee's full-process technical performance data in the new teaching match into the adjusted model, re-derives the technical weakness map, and generates updated tactical error predictions based on the revised stress model. The updated tactical error predictions will focus more on the stability risks in the serve reception stage and the risk of reduced effectiveness in dealing with new tactics under fatigue, thus forming a closed loop of teaching evaluation and providing input for the design of corrective tasks in the next training cycle.

[0117] See Figure 5 This is a bar chart showing the sensitivity adjustments of parameters in a table tennis teaching and competitive response model, reflecting the changes in the focus of the teaching system on different technical indicators after closed-loop optimization. In subsequent training, specific exercises on "weight transfer in multi-ball reception" can be added to improve students' initiation efficiency during the reception phase. Combining high-speed video feedback helps students establish a more stable forehand swing kinetic chain, reducing motion distortion in complex competitive situations. Although the model's focus on the forward shoulder angle has decreased, low-intensity shoulder control exercises should still be retained to prevent recurrence of compensatory behaviors. The chart visually demonstrates the teaching system's self-iteration after completing one cycle: by comparing the parameter weights before and after adjustment, it clearly proves that the system can dynamically adjust the evaluation model based on the students' actual performance, verifying the effectiveness of the teaching evaluation closed loop.

[0118] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A college table tennis teaching method based on practical modules, characterized in that, include: In a real competitive scenario, the complete hitting process data of the trainee is obtained. The complete hitting process data includes the trajectory of the ball, the timing of the body response, and multi-point tactile feedback. Based on the complete ball-hitting process data, the technical performance in the simulated competitive situation is used to deduce the technical weaknesses of the students under the current competitive intensity. The map of technological weaknesses is compared with the adversarial fitness of an ideal adversarial model to generate a description of technological compensation behavior and key tactical vulnerabilities. The description of the technical compensation behavior is subjected to stress amplification calculation under stress environment to form a tactical error prediction that includes stress state parameters; Based on the aforementioned key tactical vulnerabilities, a corrective task with progressive adversarial logic is designed, which includes a tactical reconstruction scheme and an adversarial adaptive enhancement scheme.

2. The college table tennis teaching method based on practical modules according to claim 1, characterized in that, Based on the complete ball-hitting process data, the technical performance in simulated competitive situations is used to deduce the technical weaknesses of the student under the current competitive intensity, including: Identify the preparation phase, initiation phase, and ball-hitting completion phase in the body response time sequence, and establish an adversarial response model for each phase; In the preparation phase, visual capture data and predicted body posture are extracted, and an initial reaction capability profile is generated by simulating decision delays under different combinations of incoming ball speed and spin. During the initiation phase, the muscle activation sequence and force perception in the multi-point tactile feedback are combined to analyze the step initiation efficiency and arm swing trajectory stability under counter-pressure, and generate a motion execution efficiency profile. During the ball-hitting completion phase, based on the antagonistic deviation between the incoming ball trajectory and the outgoing ball trajectory, as well as the body's return speed, the ball-hitting quality maintenance capability and continuous antagonistic capability are calculated, and a technical maintenance capability profile is generated. By integrating the initial reaction capability profile, the motion execution efficiency profile, and the technology maintenance capability profile, a three-dimensional adversarial capability assessment space is constructed. In the adversarial capability assessment space, regions where various capabilities are below a preset adversarial threshold are identified. These regions are then connected and mapped to form a map of technical weaknesses with spatial distribution characteristics under a specific adversarial intensity.

3. The college table tennis teaching method based on practical modules according to claim 2, characterized in that, In the preparation phase, visual capture data and predicted body posture are extracted. An initial reaction capability profile is generated by simulating decision delays under different combinations of incoming ball speed and spin, including: The ball image sequence and the trajectory of the student's gaze point change are separated from the complete ball-hitting process data to form a visual capture data stream; Simultaneously acquire the student's torso angle, center of gravity preparation position, and racket face orientation at each time point of the incoming ball image sequence to form a predictive body posture sequence; A virtual ball interference library containing speed mutations and rotation combinations was established. The visual capture data stream and the predicted body posture sequence were input sequentially to test the student's decision-making mechanism. Calculate the time increment between the appearance of the stimulus and the body's effective response action when facing each type of virtual ball interference, and obtain a set of decision delay spectra; The distribution patterns and abrupt change points of the decision delay spectrum under different interference types were analyzed, and an initial reaction capability profile was drawn with interference type as the horizontal axis and delay time as the vertical axis. The initial reaction capability profile revealed the vulnerability of trainees to different incoming balls in the confrontation.

4. The college table tennis teaching method based on practical modules according to claim 1, characterized in that, The step of comparing the map of technological weaknesses with an ideal adversarial model to determine their adversarial fitness, and generating descriptions of technological compensation behaviors and key tactical vulnerabilities, includes: Obtain a preset ideal adversarial model, which defines the optimal range and dynamic balance relationship of various technical indicators under standard adversarial pressure; The map of technical weaknesses is superimposed on the ideal adversarial model, and the deviation between the actual data of the trainees and the ideal interval is compared point by point to generate a local fitness difference field. In the local fitness difference field, regions where the deviation exceeds the tolerance threshold are identified, and the correlation and temporal induced relationship between the regions where the deviation exceeds the tolerance threshold are analyzed. For deviations that have a triggering relationship, trace their original performance in the complete ball-hitting process data, describe the unconventional action or decision-making patterns that students spontaneously form to compensate for a certain technical deficiency and that may trigger a chain of problems, and form a description of technical compensation behavior. For isolated deviations or those that serve as the starting point of a triggering chain, their destructive impact on overall tactical coherence, tempo control, and scoring efficiency is analyzed and defined as critical tactical vulnerabilities.

5. The college table tennis teaching method based on practical modules according to claim 4, characterized in that, The description of the technical compensation behavior is subjected to stress amplification calculations under pressure conditions to form a tactical error prediction that includes stress state parameters, including: The description of the technical compensation behavior is analyzed to extract non-standard action features, unconventional force exertion patterns, and redundant decision-making processes. A stress response model was established, which defined the amplification factors of confrontation intensity, game duration, and key score situations on trainees' physiological tension and cognitive load. The non-standard movement characteristics are input into the stress model to calculate the possible technical deformation range and movement failure probability of the non-standard movement characteristics under simulated high-pressure situations, and to generate movement error risk parameters. The unconventional power generation mode and redundant decision-making process are input into the stress response model to calculate the additional energy consumption rate and decision delay growth curve under simulated continuous confrontation and fatigue accumulation, thereby generating performance decay risk parameters. By combining the aforementioned action error risk parameters and the aforementioned effectiveness decay risk parameters, a tactical error sequence with temporal order and developmental pattern is deduced under specific pressure scenarios, which is directly or indirectly caused by technical compensation behavior. This is known as tactical error prediction.

6. The college table tennis teaching method based on practical modules according to claim 5, characterized in that, The aforementioned stress model defines the amplification factors of confrontation intensity, game duration, and key score situations on trainees' physiological tension and cognitive load, including: Set up basic resistance intensity units and define the mapping relationship between resistance intensity levels and trainees' heart rate increase and muscle tension increase; Set a basic competition duration unit and define a correlation function between the cumulative effect of time and the student's attention fluctuation cycle and reaction speed baseline offset; Typical key score scenarios are set up, including leading, stalemate and trailing, and the impact weight of each scenario on the participants' risk preference and decision conservatism coefficient is defined; Based on the mapping relationship, the correlation function, and the influence weight, a multi-dimensional stress response surface is constructed. On the stress response surface, a corresponding amplification factor is configured for each dimension, so that the input original technical compensation features can be mapped into a quantified risk value with stress state parameters according to the real-time simulated stress state.

7. The college table tennis teaching method based on practical modules according to claim 1, characterized in that, Based on the aforementioned key tactical vulnerabilities, a corrective task with progressive adversarial logic is designed. This corrective task includes a tactical reconstruction scheme and an adversarial adaptive enhancement scheme, comprising: To address the tactical chain disrupted by the aforementioned critical tactical vulnerabilities, a reconstruction exercise for basic tactical units is designed. This reconstruction exercise focuses on restoring the standard execution flow of the basic tactical units under no-confrontation or low-confrontation conditions. After the basic tactical units are stabilized, a single counter-disruption factor is introduced, and adaptive fine-tuning exercises are designed to enable trainees to learn how to deal with the single counter-disruption factor while maintaining core tactical movements. Gradually increase the number, intensity, or unpredictability of the counter-disruptive factors, design tactical execution exercises under complex counter-environment scenarios, and improve the robustness of tactics in complex environments. Based on the aforementioned tactical error predictions, high-incidence error scenarios are extracted, and tactical decision-making and execution reinforcement exercises are designed under specific pressure situations. The reconstruction exercises, adaptive fine-tuning exercises, tactical execution exercises under complex adversarial situations, and tactical decision-making and execution enhancement exercises under pressure situations are arranged in order of increasing adversarial intensity and increasing adversarial complexity to form a tactical reconstruction scheme with progressive logic. Simultaneously, based on the description of the aforementioned technical compensatory behavior, an adversarial adaptive enhancement scheme is designed to establish a new and more efficient body kinetic chain and decision-making mode. This adversarial adaptive enhancement scheme works in sync with the tactical reconstruction scheme during the training phase.

8. The college table tennis teaching method based on practical modules according to claim 7, characterized in that, The introduction of a single adversarial perturbation factor and the design of adaptive fine-tuning exercises include: Select a specific velocity or spin change pattern from the virtual incoming ball interference library as the initial disturbance factor; The ball feeding program is designed to keep the incoming ball stable in 80% of the normal ball paths, and to introduce the initial disturbance factor at 20% of the preset nodes. Trainees are required to maintain standard movements for regular ball trajectories when executing pre-set tactics, and to respond to balls that introduce disturbances using specific, finely tuned, pre-trained techniques. The system collects real-time data on trainees' success rate and return quality when dealing with disturbed balls, and dynamically adjusts the frequency and location of disturbances accordingly. When the difference between the student's success rate under the initial disturbance factors and the success rate of the regular ball path is less than the set threshold, the adaptive fine-tuning practice is deemed complete, and the student proceeds to the next complexity level of practice.

9. The college table tennis teaching method based on practical modules according to claim 5, characterized in that, Also includes: After a teaching cycle is completed, collect data on the trainees' technical performance throughout the new real-world combat scenarios. From the full-process technical performance data, identify the evolution of the original technical weakness map and whether any new technical weakness areas have emerged; The evolution and newly emerging weak areas are compared with previously generated tactical error predictions to assess the accuracy of the predictions. Based on the comparison results, the parameter sensitivity of the adversarial response model used to generate a map of technical weaknesses is adjusted in reverse, and the amplification factor in the stress model is corrected. By using the adjusted model and coefficients, a new round of analysis and prediction is conducted on the subsequent performance of the same student in adversarial situations, forming a closed loop for teaching evaluation.

10. A table tennis teaching system based on actual combat, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the college table tennis teaching method based on the practical module as described in any one of claims 1 to 9.

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