A tennis state intelligent grading method and system for a tennis match

Abstract

This invention provides a method and system for intelligent grading of tennis ball condition in tennis matches. The method includes: acquiring the weight and geometric shape data of the tennis ball to be tested; acquiring the surface morphology data of the tennis ball to determine its wear index; acquiring the vibration spectrum of the tennis ball when it rotates at a first preset speed to analyze its dynamic imbalance; acquiring the velocity before and after the collision and the pressure change at the moment of collision when the tennis ball is released from a preset height with no initial velocity to determine its elasticity characteristics; determining a weighted scoring model matching the current tennis match based on the actual conditions of the match; using the weighted scoring model to weight and score the weight, geometric shape, wear index, dynamic imbalance, and elasticity characteristics data; and grading the tennis ball condition based on the scoring results, thereby achieving rapid, accurate, and multi-dimensional physical state analysis and grading of the match ball.

Description

Technical Field

[0001] This invention relates to the field of tennis quality testing technology, and in particular to a method and system for intelligent grading of tennis condition in tennis matches. Background Technology

[0002] In the realm of professional tennis tournaments, existing technologies for ensuring the quality of match balls primarily rely on procedural rules enforced by the tournament and quality control standards at the manufacturing stage. However, both of these aspects have significant limitations and are insufficient to address the ball quality issues inherent in tournament settings.

[0003] 1. Procedural rules of the competition and their invalidity; Professional tennis tournaments generally employ the "7-9 game ball change rule," meaning that the first seven games are played with a batch of new balls, and then new balls are used every nine games thereafter. This rule aims to maintain relatively stable ball performance through regular changes, but as an experience-based macro-management approach, its effectiveness is facing serious challenges. First, it lacks precision and adaptability: the rate of ball performance degradation is affected by various variables such as court type (clay, hard court), weather conditions, and player playing style. A fixed ball change cycle is a "one-size-fits-all" solution that cannot accurately adapt to the actual conditions of different matches. Second, it cannot cope with the increasingly serious trend of declining product quality: the biggest problem with this rule is that it is based on the assumption that "the ball's performance gradually deteriorates under normal use." However, in recent years, professional players have generally reported a significant decline in the quality of match balls. Modern balls quickly become "soft," shed excessively, and lose internal pressure after only a few games of use; even balls from the same batch show significant differences. For example, some professional tennis players have complained that their balls are "ruined" after five games, while others have reported that their balls frequently leak air after seven games. In this situation, the fixed ball-changing cycle is practically meaningless and can hardly guarantee the basic performance of the match ball.

[0004] 2. Production-side quality inspection standards and their limitations; The production of tennis balls must comply with a series of standards, including those of the International Tennis Federation (ITF) and the Chinese National Standard (GB / T 22754-2008), which specify key parameters such as ball size, weight, and bounce height. Manufacturers use calipers, precision electronic scales, and specialized bounce testing equipment during production, employing methods such as sampling inspection to control batch quality. However, these production-side quality control methods cannot meet the precise demands of a match. First, they are not on-site or real-time: the aforementioned tests are conducted offline in production line or laboratory environments, aiming to control the macro-quality of batches rather than providing 100% performance certification for every ball to be used in a specific match. Second, they suffer from insufficient testing dimensions and efficiency issues: traditional quality inspection methods are relatively time-consuming and typically focus only on a few static physical indicators. They cannot provide a rapid and comprehensive assessment of deeper performance indicators such as ball roundness, surface integrity, dynamic balance, and elastic decay curves before or during matches.

[0005] The inadequacy of current technology has directly led to two consequences. First, the inconsistency and rapid decline in ball performance force players to increase their swing power to compensate for the performance deficiency, significantly increasing their risk of shoulder, elbow, and especially wrist injuries. Second, the differences in ball performance between different tournaments and even different stages of the same tournament undermine the foundation of fair competition and reduce the overall quality of the matches. Summary of the Invention

[0006] In view of the above problems, the present invention is proposed to provide a method and system for intelligent grading of tennis status in tennis matches that solves or at least partially solves the above technical problems.

[0007] One aspect of the present invention provides a method for intelligent grading of tennis performance in tennis matches, the method comprising: Obtain the weight data of the tennis ball to be tested; A three-dimensional point cloud model of the tennis ball to be tested is established, and the geometric shape data of the tennis ball to be tested is calculated based on the three-dimensional point cloud model of the ball. Acquire the surface morphology data of the tennis ball to be tested, and determine the wear index of the tennis ball to be tested based on the surface morphology data; Obtain the vibration spectrum of the tennis ball to be tested when it is rotating at a first preset speed, and analyze the dynamic imbalance of the tennis ball to be tested based on the vibration spectrum. Acquire the velocity and pressure change data of the tennis ball before and after the collision when it is released from a preset height with no initial velocity and collides with the ball; determine the elastic characterization data of the tennis ball based on the velocity and / or pressure change data before and after the collision. Based on the actual working conditions of the current tennis tournament, a weighted scoring model matching the current tennis tournament is determined. The weight data, geometric data, wear index, dynamic imbalance, and elasticity characterization data of the tennis ball to be tested are weighted and scored using the weighted scoring model. The tennis ball condition is graded based on the scoring results.

[0008] Furthermore, a three-dimensional point cloud model of the tennis ball to be tested is established, including: The three-dimensional shape contour data of the tennis ball to be inspected is obtained by scanning it with a three-dimensional contour scanner, and a three-dimensional point cloud model of the tennis ball to be inspected is established based on the three-dimensional shape contour data.

[0009] Furthermore, a three-dimensional point cloud model of the tennis ball to be tested is established, including: Two-dimensional static images of the tennis ball to be detected from different angles are acquired simultaneously by multiple cameras arranged at different angles. A three-dimensional point cloud model of the tennis ball to be detected is reconstructed based on the acquired two-dimensional static images using a stereo vision algorithm.

[0010] Further, the geometric shape data of the tennis ball to be detected is calculated based on the 3D point cloud model of the sphere, including: Calculate the diameter of the tennis ball in at least three orthogonal directions based on the 3D point cloud model of the sphere, and evaluate the sphericity of the tennis ball to be tested based on each tennis ball diameter.

[0011] Further, the step of acquiring the surface morphology data of the tennis ball to be tested, and determining the wear index of the tennis ball to be tested based on the surface morphology data, includes: An image data sequence of a tennis ball to be tested is acquired. The image data sequence is an image data sequence of the tennis ball to be tested being rotated at a second preset speed, acquired using a line scan camera. A 360° surface unfolded map of the tennis ball to be tested is generated based on the image data sequence. Texture features, contrast features, and edge blurring features are extracted from the surface unfolded map. The felt coverage, surface cleanliness and contrast, and / or wear area ratio of the tennis ball surface are calculated based on the feature extraction results. The wear index of the tennis ball to be tested is determined based on the felt coverage, surface cleanliness and contrast, and / or wear area ratio.

[0012] Further, the step of acquiring the surface morphology data of the tennis ball to be tested, and determining the wear index of the tennis ball to be tested based on the surface morphology data, includes: The spectral data of the tennis ball to be tested is obtained. The spectral data of the ball surface is the reflectance information of each pixel on the ball surface in different spectral bands, which is collected by a hyperspectral camera. The material distribution information of the tennis ball surface is determined based on the reflectance information. The felt coverage, surface cleanliness and contrast and / or wear area ratio of the tennis ball surface are calculated based on the material distribution information of the tennis ball surface. The wear index of the tennis ball to be tested is determined based on the felt coverage, surface cleanliness and contrast and / or wear area ratio.

[0013] Further, the step of acquiring the surface morphology data of the tennis ball to be tested, and determining the wear index of the tennis ball to be tested based on the surface morphology data, includes: Multiple deformed images are obtained after a grating with a preset mode is projected onto the surface of a tennis ball to be tested from different angles. The material distribution information of the surface of the tennis ball to be tested is analyzed based on the multiple deformed images. The felt coverage, surface cleanliness and contrast and / or wear area ratio of the tennis ball surface are calculated based on the material distribution information of the tennis ball surface. The wear index of the tennis ball to be tested is determined based on the felt coverage, surface cleanliness and contrast and / or wear area ratio.

[0014] Another aspect of the present invention provides a tennis condition intelligent grading system for tennis matches, the system comprising a detection chamber, a static characteristic analysis unit, a dynamic and surface characteristic analysis unit and a dynamic rebound characteristic measurement unit placed inside the detection chamber, and a central control and data processing unit placed outside the detection chamber, wherein the static characteristic analysis unit, the dynamic and surface characteristic analysis unit, the dynamic rebound characteristic measurement unit and the central control and data processing unit are electrically connected. The static characteristic analysis unit includes a weight detection device and a three-dimensional contour scanner located directly above the weight detection device, which is used to measure the weight data and three-dimensional shape contour data of the tennis ball to be detected and upload them to the central control and data processing unit. The dynamic and surface characteristic analysis unit includes a rotary table for supporting and rotating the tennis ball to be tested, a high-speed line scan camera and a dome-shaped shadowless light source fixedly installed above the rotary table, and a vibration sensor installed on the support structure of the rotary table. The central control and data processing unit is used to control the rotary table to rotate at a first preset speed and a second preset speed respectively. The high-speed line scan camera is used to acquire image data sequences of the tennis ball to be tested when rotating at the second preset speed and upload them to the central control and data processing unit. The vibration sensor is used to acquire the vibration spectrum of the tennis ball to be tested when rotating at the first preset speed and upload it to the central control and data processing unit. The dynamic rebound characteristic measurement unit includes an electromagnetic release device fixed in the detection chamber, a mechanical sensing platform located directly below the electromagnetic release device, and a high-speed sensing device located to the side of the electromagnetic release device. The central control and data processing unit is used to control the electromagnetic release device to release the tennis ball to be tested without initial velocity. The high-speed sensing device is used to collect the instantaneous velocity of the tennis ball released by the electromagnetic release device before and after colliding with the mechanical sensing platform and upload it to the central control and data processing unit. The mechanical sensing platform is used to collect the pressure change data of the tennis ball at the moment of collision and upload it to the central control and data processing unit. The central control and data processing unit is used to execute the intelligent tennis status classification method for tennis events as described above.

[0015] Furthermore, the system also includes one or two robotic arms installed in the detection chamber. The central control and data processing unit controls the robotic arms to move the tennis ball to be tested in the static characteristic analysis unit, the dynamic and surface characteristic analysis unit, and the dynamic rebound characteristic measurement unit.

[0016] Furthermore, the system also includes a display unit that is communicatively connected to the central control and data processing unit, the display unit being used to display the scoring results and tennis ball condition grading results of the tennis ball to be tested.

[0017] The intelligent tennis performance grading method and system for tennis matches provided in this invention have the following beneficial effects: 1. This invention represents a leap from "rules of thumb" to "data-driven" approaches, ensuring the objectivity and accuracy of intelligent assessment and grading of tennis ball condition. By introducing high-precision sensors, this invention enables direct quantitative measurement of key physical parameters of the tennis ball, such as its elasticity, weight, geometric shape, and dynamic balance. This transforms the judgment of ball quality from subjective feelings and fixed-period replacements into a scientific assessment based on objective, quantifiable data, fundamentally solving the problem of insufficient accuracy in existing rules.

[0018] 2. This invention achieves a deeper analysis from "single-dimensional evaluation" to "multi-dimensional profiling," providing comprehensive performance characterization. It significantly expands the detection dimensions, for the first time incorporating key performance indicators such as dynamic balance (affecting high-speed flight stability), geometric morphology data (affecting bounce consistency), and surface morphology analysis (for detailed assessment of felt condition) into the on-site evaluation system. This creates a comprehensive "performance profile" for each tennis ball, rather than simply judging whether it is "qualified," thus achieving an unprecedented depth of understanding of the ball's condition.

[0019] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0020] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. In the drawings: Figure 1 This is a schematic diagram of the intelligent tennis status grading system for tennis events according to an embodiment of the present invention; Figure 2 This is a flowchart of a method for intelligently classifying tennis status in tennis matches, according to an embodiment of the present invention. Detailed Implementation

[0021] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0022] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art and should not be interpreted in an idealized or overly formal sense unless specifically defined.

[0023] This invention provides an intelligent tennis performance grading system for tennis matches, such as... Figure 1 As shown, the intelligent tennis condition grading system for tennis matches proposed in this invention includes a detection chamber 100, a static characteristic analysis unit 200, a dynamic and surface characteristic analysis unit 300, and a dynamic rebound characteristic measurement unit 400 placed inside the detection chamber 100, and a central control and data processing unit 500 placed outside the detection chamber 100. The static characteristic analysis unit 200, the dynamic and surface characteristic analysis unit 300, and the dynamic rebound characteristic measurement unit 400 are electrically connected to the central control and data processing unit 500. The detection chamber is a precision detection chamber designed to isolate external environmental interference.

[0024] In this embodiment, the static characteristic analysis unit 200 includes a weight detection device 210 and a three-dimensional contour scanner 220 located directly above the weight detection device. Both devices have data output ports connected to the central control and data processing unit 500, used to measure the weight and three-dimensional contour data of the tennis ball to be tested and upload them to the central control and data processing unit 500. Specifically, the weight detection device and the three-dimensional contour scanner are located in the initial testing area of ​​the testing chamber. The weight detection device can be implemented using a high-precision electronic balance, with a measurement accuracy down to 0.01 grams of static weight. The three-dimensional contour scanner uses a structured light or laser scanner, enabling non-contact acquisition of three-dimensional contour data for subsequent creation of a three-dimensional point cloud model of the sphere to calculate its average diameter and sphericity (roundness). Further, a weight score for the tennis ball to be tested can be calculated based on the difference between its weight data and that of a standard tennis ball. A geometric score for the tennis ball to be tested can be calculated based on the difference between its sphericity and that of a standard tennis ball.

[0025] In this embodiment, the dynamic and surface characteristic analysis unit 300 includes a rotary table 310 for supporting and rotating the tennis ball to be tested, a high-speed line scan camera 320 and a dome-shaped shadowless light source 330 fixedly mounted above the rotary table 310, and a vibration sensor 340 disposed on the support structure of the rotary table 310. The central control and data processing unit 500 controls the rotary table 310 to rotate at a first preset speed and a second preset speed, respectively. The high-speed line scan camera 320 collects image data sequences of the tennis ball to be tested rotating at the second preset speed and uploads them to the central control and data processing unit 500. The vibration sensor 340 collects the vibration spectrum of the tennis ball to be tested rotating at the first preset speed and uploads it to the central control and data processing unit 500. The rotary table motor, high-speed line scan camera, dome-shaped shadowless light source, and vibration sensor are all connected to and controlled by the central control and data processing unit 500. Specifically, the rotary table is a precision rotary table driven by a servo motor, and grooves can be provided on the surface of the rotary table for placing the tennis ball. The vibration sensor 340 integrated into the support structure of the rotating stage can be implemented using an accelerometer. The rotating stage 310 rotates the ball at a precise and stable angular velocity. The dome-shaped shadowless light source 330 provides uniform and shadowless illumination to the ball. The high-speed linear array camera 320 scans line by line as the ball rotates at a second preset speed to acquire a sequence of image data of the tennis ball to be tested, which is then stitched together to form a complete 360° surface unfolded map. The second preset speed can be selected from 15 RPM to 90 RPM (revolutions per minute). Texture features, contrast features, and edge blurring features are extracted from the surface unfolded map. Based on the feature extraction results, the felt coverage, surface cleanliness and contrast, and / or wear area ratio of the tennis ball surface are calculated, and the wear index of the tennis ball to be tested is determined accordingly. The vibration sensor 340 monitors vibration data when the ball rotates at high speed to evaluate the uniformity of its mass distribution, and then analyzes the dynamic imbalance of the tennis ball to be tested.

[0026] The calculation process for the wear index of the tennis ball to be tested is as follows: M1: Felt Integrity Score, calculated based on felt coverage; a brand new ball with a uniform, dense felt texture. With use, the texture will become fluffy, pilled, or even sparse. The higher this score, the better the original felt texture is preserved.

[0027] M2: Surface Cleanliness & Contrast Score is calculated based on surface cleanliness and contrast characteristics. New spheres have vibrant colors, clear logos and seams, and high contrast. Dirt and wear will darken the sphere's color and reduce its contrast. A higher score indicates that the sphere's surface is closer to its original color and contrast.

[0028] M3: The Structural Wear Resistance Score is calculated based on the percentage of wear area. It is derived through edge ambiguity features and specific region identification, directly quantifying the most severe wear conditions. Specifically, it calculates the percentage of the area worn down to the rubber layer (A_exposed). The formula for this score is M3 = (1 - A_exposed) * 100. For example, if 1% of the area is worn down to the rubber layer, then M3 = 99.

[0029] Step 1: Weights Assignment. Based on experience from professional tournaments, different types of wear have varying impacts on the ball's flight and bounce performance. Structural wear has the greatest impact, followed by felt integrity, and finally surface cleanliness. Therefore, in this embodiment, different weights (w1, w2, w3) are assigned to these three scores, and w1 + w2 + w3 = 1.

[0030] Optionally, the following weights can be set: w1 = 0.4 (felt integrity weight), w2 = 0.1 (cleanliness weight), w3 = 0.5 (structural wear weight).

[0031] Step 2: Calculate the scores of a ball to be tested. Assuming the system analyzes a used match ball, the following quantitative results are obtained: The felt exhibits partial pilling and fluffing; M1 (felt integrity) = 75 points.

[0032] The surface of the sphere is contaminated with a small amount of red soil and dust, M2 (cleanliness) = 85 points.

[0033] The system identified a very small area of ​​severe wear in 0.5% of the region, close to the rubber layer, i.e., A_exposed = 0.005. Therefore, M3 (structural wear resistance) = (1 - 0.005) * 100 = 99.5 points.

[0034] Step 3: Calculate the final "Wear Index" (I_wear) using a weighted fusion method. The final wear index is obtained by weighted summation. I_wear = (M1 × w1) + (M2 × w2) + (M3 × w3); finally, I_wear = 88.25; After calculation, the final wear index of the tennis ball under test was 88.25. This wear index calculation method reflects the true condition of the ball more scientifically and comprehensively than a single indicator.

[0035] The dynamic imbalance is quantified through precise analysis of the vibration spectrum collected during high-speed rotation. The underlying physics is as follows: when a sphere with uneven mass distribution rotates at high speed, its center of mass deviates from the axis of rotation, generating a periodic centrifugal force. This force acts like a hammer, striking the entire system at the rotational frequency, inducing measurable vibrations at a specific frequency. The quantification process of the dynamic imbalance mainly includes the following four core steps: Step 1: Define the "first preset speed" (ω1) for high-speed rotation; Objective: To generate a sufficiently strong unbalanced vibration signal with a sufficiently high signal-to-noise ratio. The centrifugal force is proportional to the square of the angular velocity (F = mω). 2 Therefore, a speed much higher than that required for surface scanning is needed.

[0036] A reasonable range is 600 RPM to 1800 RPM (i.e., 10 Hz to 30 Hz). This speed range is sufficient to generate noticeable vibration signals while remaining within the safe operating range of most precision servo motors and mechanical structures. In this embodiment, the rotational frequency is denoted as f_rot = ω_1 / 60.

[0037] Step 2: Vibration signal acquisition and frequency domain transformation (FFT); Process: When the tennis ball spins at a constant high speed ω_1, the vibration sensor (accelerometer) 340 will collect the raw vibration signal for a period of time (e.g., 1-2 seconds), which is a complex time-domain waveform a(t).

[0038] Core processing: The central control and data processing unit 500 immediately performs a Fast Fourier Transform (FFT) on the acquired signal a(t). The function of FFT is to decompose this seemingly chaotic time signal into a set of vibration intensities (amplitudes) at different frequencies, resulting in a vibration spectrum A(f).

[0039] Step 3: Feature Extraction – Locating the “imbalance amplitude peak” (P_imbalance); On the obtained vibration spectrum diagram A(f), the vibration caused by dynamic imbalance will appear very precisely at the rotational frequency f_rot, forming a sharp energy peak. Vibrations at other frequencies may be the inherent vibration of the motor, environmental noise, or other random disturbances.

[0040] In this embodiment of the invention, the position of frequency f_rot is accurately located in the spectrum, and the amplitude value P_imbalance at that point is extracted. This amplitude value directly and physically reflects the vibration intensity caused by dynamic imbalance. A perfectly balanced tennis ball will have a P_imbalance value very close to the background noise level of the system; while a severely unbalanced ball will have a very high P_imbalance value.

[0041] Step 4: Normalization and Indexation – Calculate the “Dynamic Imbalance Index” (I_dyn); The amplitude value P_imbalance in physical units is converted into a standardized, easily understood percentage index (e.g., 0-100, with 100 representing perfect balance). Specifically, a normalized model is built through a calibration process.

[0042] Determine the baseline (P_baseline): First, rotate a precisely calibrated, perfectly balanced "standard ball" or empty rotary table at a speed of ω_1 and measure its amplitude at the f_rot frequency. This value is defined as the system's "reference noise / vibration level" P_baseline.

[0043] Determine the disqualification threshold (P_reject): Next, through experimentation or by introducing a "bad ball" with a known eccentric mass, determine the "maximum acceptable amplitude" P_reject that vibrates violently enough to affect the fairness of the game.

[0044] Calculation of the index: For a tennis ball to be tested, its final "dynamic imbalance index" I_dyn can be calculated using the following formula: I_dyn=100×[1-((P_imbalance-P_baseline) / (P_reject-P_baseline))]; If the ball's vibration is close to the baseline (P_imbalance≈P_baseline), then the exponent I_dyn approaches 100, representing perfect balance.

[0045] If the ball's vibration reaches the disqualification threshold (P_imbalance=P_reject), then the exponent I_dyn is 0, representing a severe imbalance.

[0046] In a specific example, assume that after system calibration, the baseline vibration P_baseline = 0.05 m / s 2 The disqualification threshold P_reject = 1.05 m / s 2 .

[0047] Now, a new ball is tested, rotating at 1200 RPM (20 Hz). After FFT analysis, its peak amplitude at 20 Hz, P_imbalance = 0.25 m / s², is measured. 2 .

[0048] Its dynamic imbalance index I_dyn = 100 × [1 - ((0.25 - 0.05) / (1.05 - 0.05))]; That is, I_dyn=100×[1-(0.20 / 1.00)]= 80; Conclusion: The dynamic imbalance index of this ball is 80 points.

[0049] In this embodiment, the dynamic rebound characteristic measurement unit 400 includes an electromagnetic release device 410 fixed in the detection chamber 100, a mechanical sensing platform 430 located directly below the electromagnetic release device 410, and a high-speed sensing device 420 located to the side of the electromagnetic release device 410. The central control and data processing unit 500 is used to control the electromagnetic release device 410 to release the tennis ball to be tested without initial velocity. The high-speed sensing device 420 is used to collect the instantaneous velocity of the tennis ball released by the electromagnetic release device 410 before and after colliding with the mechanical sensing platform 430 and upload it to the central control and data processing unit 500. The mechanical sensing platform 430 is used to collect the pressure change data of the tennis ball at the moment of collision and upload it to the central control and data processing unit 500. Specifically, the electromagnetic release device 410 can be fixed in the detection chamber 100 at the height required for the collision operation, and is used to release the ball at a standard height (such as 254cm as specified by ITF) without initial velocity. Directly below it is the mechanical sensing platform 430, and a high-speed sensing device 420 is installed on the side or below. The high-speed sensing device is implemented by a high-speed camera or a laser speed measuring gate.

[0050] The central control and data processing unit 500 is used to execute the following intelligent tennis status classification method for tennis matches: The process involves: acquiring the weight data of the tennis ball to be tested; establishing a three-dimensional point cloud model of the tennis ball and calculating its geometric shape data based on the model; acquiring the surface morphology data of the tennis ball and determining its wear index based on the surface morphology data; acquiring the vibration spectrum of the tennis ball when it rotates at a first preset speed and analyzing its dynamic imbalance based on the vibration spectrum; acquiring the velocity before and after the collision and the pressure change data at the moment of collision when the tennis ball is released from a preset height with no initial velocity and collides with the object, and determining the elastic characterization data of the tennis ball based on the velocity before and after the collision and / or the pressure change data at the moment of collision; determining a weighted scoring model matching the current tennis tournament based on the actual conditions of the tournament, and using the weighted scoring model to weight and score the weight data, geometric shape data, wear index, dynamic imbalance, and elastic characterization data of the tennis ball, and classifying the tennis ball's condition based on the scoring results.

[0051] Specifically, establishing a three-dimensional point cloud model of the tennis ball to be detected can be achieved by establishing a three-dimensional point cloud model of the tennis ball to be detected based on the three-dimensional shape contour data.

[0052] Specifically, the surface morphology data of the tennis ball to be tested is obtained, and the wear index of the tennis ball to be tested is determined based on the surface morphology data. Specifically, the wear index of the tennis ball to be tested can be determined based on the image data sequence of the tennis ball to be tested.

[0053] Specifically, the elasticity characterization data of the tennis ball under test is determined based on the velocity changes before and after the collision and / or the pressure changes at the instant of the collision. This can be achieved by calculating the coefficient of restitution (COR) based on precisely captured instantaneous velocities before and after the collision, generating a force-time curve of the ball at the moment of collision based on pressure change data, analyzing peak force and contact time to calculate the dynamic response factor, and then determining the elasticity characterization data of the tennis ball under test based on the COR and dynamic response factor. The elasticity characterization data is a standardized index, the "Elasticity Index" (I_elas), calculated using a comprehensive evaluation model to fully reflect the elasticity and internal structural health of the tennis ball. The quantification process of this index involves a weighted fusion of macroscopic elastic performance (COR) and microscopic dynamic response (force-time curve characteristics). The quantification process mainly consists of the following three steps: Step 1: Calculate and normalize the macroeconomic elasticity index – “collision recovery coefficient score” (S_cor); 1. Calculate COR: The system uses a high-speed camera or laser rangefinder to accurately measure the instantaneous velocity v_impact of the sphere before it hits the ground and the instantaneous velocity v_rebound after it bounces off the ground. COR = v_rebound / v_impact; A brand new, ITF-compliant tennis ball typically has a COR value within a specific ideal range; for example, when dropped from a height of 254cm, its COR value is approximately between 0.73 and 0.78.

[0054] 2. Normalize to a score S_cor: Maps the measured COR value to a standard score of 0-100. A "ideal value" COR_ideal is preset, for example, 0.78, which is counted as 100 points, and a "retirement threshold" COR_retired is preset, for example, 0.65, which is counted as 0 points.

[0055] S_cor=100×((COR_measured-COR_retired) / (COR_ideal-COR_retired)); Where COR_measured is the COR measurement value.

[0056] In a specific example: if the COR_measured of a ball is measured to be 0.75, then its S_cor = 100 × ((0.75 - 0.65) / (0.78 - 0.65)) ≈ 76.9 points.

[0057] Step 2: Calculate and normalize the micro-dynamic response index "dynamic response score" (S_dyn); 1. Extracting Force-Time Curve Features: A high-frequency force sensor records the complete force-time curve at the moment of impact. From this curve, two key features are extracted: Peak Force (F_peak): The maximum force generated when the sphere is compressed to its maximum extent. A sphere with sufficient air pressure and a well-formed structure will have a higher F_peak. Contact Time (T_contact): The complete duration from the onset of force to its dissipation. A "dynamic" sphere, with very rapid energy transfer and recovery, will have a shorter T_contact. These two features together depict the "mass" of the impact. An ideal impact is "short and powerful."

[0058] 2. Calculate the dynamic response factor and normalize it to a fraction S_dyn: Define a "dynamic response factor" R_dyn, R_dyn = F_peak / T_contact. This factor physically represents the average rate of change of force and can effectively characterize the "speed" of the impact. A higher R_dyn value indicates a better dynamic response of the ball.

[0059] Similar to S_cor, an ideal value R_ideal and a retirement threshold R_retired are obtained through calibration, and then the measured R_dyn is normalized to S_dyn with a score of 0-100.

[0060] S_dyn =100×((R_measured-R_retired) / (R_ideal - R_retired)); In a specific example: assuming F_peak = 350 N and T_contact = 5 milliseconds, then R_measured = 350 / 0.005 = 70000 N / s. If the ideal calibration value is 80000 and the decommissioning value is 40000, then S_dyn = 100 × ((70000-40000) / (80000-40000)) = 75 minutes.

[0061] Step 3: Calculate the final "elasticity index" (I_elas) using weighted fusion. The effect of fusion: A high COR alone does not necessarily mean the ball is good. For example, a ball may still bounce high due to an abnormal surface material, but its impact mechanics have deteriorated, resulting in a poor feel when hitting the ball. Fusion of S_cor and S_dyn provides a more comprehensive and robust evaluation.

[0062] Weighting: The Coefficient of Resilience (COR), as a core indicator of macroeconomic resilience, should receive the primary weight. The Dynamic Response Score, as a more refined indicator reflecting internal structure and "sense of the ball," should be given secondary weight. Optionally, the COR weight w_cor = 0.7 and the Dynamic Response Score weight w_dyn = 0.3 can be set.

[0063] Calculate the final exponent: I_elas = (S_cor × w_cor) + (S_dyn × w_dyn); In a specific example: For the tennis ball to be tested: if S_cor=76.9 points, S_dyn=75.0 points, w_cor=0.7, w_dyn=0.3, then its final elasticity index I_elas=(76.9×0.7)+ (75.0×0.3); that is, I_elas=53.83+22.5 = 76.33.

[0064] The final "elasticity characterization data" of this tennis ball under test is an elasticity index with a quantitative value of 76.33. It integrates the ball's macroscopic bouncing performance and microscopic impact dynamics characteristics in free fall testing, and can more accurately and comprehensively describe its elasticity and internal state.

[0065] Furthermore, such as Figure 1As shown, the intelligent tennis status grading system for tennis matches also includes a display unit 600 communicatively connected to the central control and data processing unit 500. The display unit 600 displays the scoring results and tennis status grading results of the tennis ball being tested. Specifically, the central control and data processing unit 500, which can be composed of a high-performance industrial computer or an embedded system motherboard, is the brain of the entire system. It communicates and controls all detection units and the external display unit 600, primarily responsible for controlling the collaborative work of all detection units, receiving and processing all sensor data, running data analysis and intelligent grading algorithms, and outputting the final results to the display interface.

[0066] Furthermore, the system also includes one or two robotic arms installed in the testing chamber. The central control and data processing unit controls the robotic arms to move the tennis ball under test within the static characteristic analysis unit, the dynamic and surface characteristic analysis unit, and the dynamic rebound characteristic measurement unit. Specifically, the placement and number of robotic arms can be determined based on the positional distribution of the static characteristic analysis unit, the dynamic and surface characteristic analysis unit, and the dynamic rebound characteristic measurement unit within the testing chamber.

[0067] The execution flow of the intelligent tennis ball condition grading system for tennis matches provided in this embodiment of the invention is as follows: After a tennis ball to be tested is placed into the system automatically or manually, the central control and data processing unit 500 initiates the automated process: First, the static characteristic analysis unit 200 completes the weight and three-dimensional dimension measurement; then, the ball is transferred by a robotic arm to a rotary table 310 for surface morphology and dynamic balance analysis; finally, the ball is sent to the dynamic rebound characteristic measurement unit 400 for drop testing. Data collected by all detection units is aggregated in real time to the central control and data processing unit 500, which can complete data processing, comprehensive weighted scoring, and grading in a very short time, and present the results on the display unit 600.

[0068] The detailed steps of the unified execution process are given below: Step 1: Initialization and Sphere Placement Content: The system starts a self-test, and the operator selects the testing mode (such as "pre-match authentication" or "in-match analysis") through the interface. The tennis ball to be tested is placed into the designated entry point of the system.

[0069] Objective: To ensure the equipment is in normal working order and to initiate the automated testing process.

[0070] Step 2: Static and 3D Geometric Property Measurement Content: The system automatically acquires the static weight data of the sphere with an accuracy requirement of ±0.01 grams. Simultaneously, a 3D contour scanner scans the sphere, establishes a 3D model, calculates the diameter in at least three orthogonal directions, and evaluates its sphericity accordingly.

[0071] Objective: To accurately quantify the basic physical parameters of the sphere and screen for defects in the product or weight abnormalities caused by moisture absorption.

[0072] Step 3: Surface morphology and dynamic equilibrium analysis Content: A sphere rotates on a rotary table at preset low speeds (for surface scanning) and high speeds (for dynamic balancing tests). A linear scan camera generates a 360° surface unfolding map, and an AI algorithm analyzes and calculates indicators such as "felt coverage" and "wear area percentage." Simultaneously, a vibration sensor records the vibration spectrum during high-speed rotation and analyzes its imbalance.

[0073] Objective: To comprehensively and quantitatively evaluate the wear degree and mass distribution uniformity of the sphere surface.

[0074] Step 4: Dynamic rebound characteristic test Content: A sphere is precisely released from 254cm (ITF standard height). High-speed sensors measure its velocity before and after the impact and calculate the coefficient of restitution (COR). A force-time curve of the impact is recorded by a mechanical sensing platform.

[0075] Objective: To obtain the most fundamental and authoritative physical quantity (COR) characterizing the elasticity of a sphere, and to acquire more dynamic response data.

[0076] Purpose of specific parameters: Choosing 254cm as the release height is to maintain consistency with international standards and ensure the comparability and authority of test results. In different levels of competition, the height can be adjusted proportionally downwards, for example, to 63.5cm. Step 5: Data Fusion and Intelligent Hierarchy Content: The central controller aggregates all the aforementioned quantitative indicators (weight, sphericity, wear index, dynamic imbalance, COR, etc.). Based on the weighted scoring model preset in the system (which can be adjusted according to different competitions or venue types), it calculates the overall performance score of the sphere. Finally, based on the score range, the sphere is automatically rated as "Prime (P grade)," "Grade A," "Grade B," or "Retired (R grade)," etc.

[0077] Objective: To transform complex, multi-dimensional data into intuitive and actionable quality levels, providing a final, objective basis for decision-making regarding the balls used in competitions.

[0078] In an optional embodiment of the present invention, a multi-camera stereo vision system can be used to replace a 3D contour scanner. Specifically, multiple (e.g., 3-6) high-resolution industrial cameras are arranged at different angles within the inspection chamber to simultaneously capture images of a stationary sphere. Using stereo vision algorithms, a 3D point cloud model of the sphere is reconstructed from the multiple 2D images, and its geometric morphology data is then calculated. Compared to a 3D laser scanner, this embodiment has lower hardware costs.

[0079] In an optional embodiment of the present invention, a hyperspectral camera can be used instead of a conventional linear or area array camera. The hyperspectral camera can acquire reflectance information for each pixel on the surface of a sphere across hundreds of different spectral bands, forming a "data cube." The reflectance information provides material distribution information far exceeding that of the human eye and conventional cameras, and can be used to accurately identify material aging of the felt, intrusion of specific chemicals (such as sweat and dirt), and early fiber breakage invisible to the naked eye. The analytical dimensions are extremely rich. Based on the material distribution information on the tennis ball's surface, the felt coverage, surface cleanliness and contrast, and / or wear area percentage of the tennis ball's surface can be calculated, and the wear index of the tennis ball under test can be determined based on the felt coverage, surface cleanliness and contrast, and / or wear area percentage.

[0080] In an optional embodiment of the present invention, a grating (structured light) of a specific pattern can be projected onto the surface of a sphere, and multiple deformed images of the grating caused by the undulations of the sphere's surface can be captured by a camera to calculate the three-dimensional morphology. The phase deflection method detects extremely fine surface irregularities by analyzing changes in specular reflection fringes. Specifically, the material distribution information of the tennis ball surface to be tested is analyzed based on the multiple deformed images, and the felt coverage, surface cleanliness and contrast, and / or wear area ratio of the tennis ball surface are calculated based on the material distribution information. The wear index of the tennis ball to be tested is determined based on the felt coverage, surface cleanliness and contrast, and / or wear area ratio. This embodiment can reconstruct the microscopic three-dimensional texture of the felt surface with extremely high resolution, which can be used to quantify the degree of "fluffiness" or "compaction" of the felt.

[0081] The intelligent tennis performance grading system for tennis matches provided in this invention has the following beneficial effects: 1. This invention represents a leap from "rules of thumb" to "data-driven" approaches, ensuring the objectivity and accuracy of intelligent assessment and grading of tennis ball condition. By introducing high-precision sensors, this invention enables direct quantitative measurement of key physical parameters of the tennis ball, such as its elasticity, weight, geometric shape, and dynamic balance. This transforms the judgment of ball quality from subjective feelings and fixed-period replacements into a scientific assessment based on objective, quantifiable data, fundamentally solving the problem of insufficient accuracy in existing rules.

[0082] 2. This invention achieves a deeper analysis from "single-dimensional evaluation" to "multi-dimensional profiling," providing comprehensive performance characterization. It significantly expands the detection dimensions, for the first time incorporating key performance indicators such as dynamic balance (affecting high-speed flight stability), geometric morphology data (affecting bounce consistency), and surface morphology analysis (for detailed assessment of felt condition) into the on-site evaluation system. This creates a comprehensive "performance profile" for each tennis ball, rather than simply judging whether it is "qualified," thus achieving an unprecedented depth of understanding of the ball's condition.

[0083] 3. This invention represents a paradigm shift from "offline spot checks" to "on-site full inspection," ensuring the initial consistency of the balls used. By conducting 100% full inspection of all balls used before the competition, this invention effectively eliminates individuals with manufacturing tolerances or initial defects, ensuring that the "matching set" used at the start of the competition is highly consistent in performance, providing an unprecedentedly fair starting point for the competition.

[0084] 4. This invention enables dynamic quality monitoring of the entire competition process, ensuring that the balls used in each game meet competition standards. During the match, the technical solution of this invention performs a rapid inspection on each ball used by the server, ensuring that balls with abnormal performance due to accidental stepping, violent impact, or dampness are immediately detected and removed. This directly solves the problem of players receiving "bad balls" during the match, guaranteeing that every point and every game is played on balls that meet strict standards, thereby elevating the fairness of the competition to a new level.

[0085] 5. This invention represents a conceptual upgrade from "passive response" to "proactive protection," enhancing the fairness of competition and the health of athletes. Existing technologies operate on a passive model, assuming the ball's quality is acceptable and replacing it passively after a period of use. This invention, however, is a proactive protection model, certifying the ball's quality before the competition, thus eliminating unfair competition caused by inconsistent or rapidly deteriorating ball performance. Furthermore, by ensuring stable ball performance, it avoids players being forced to exert excessive force when dealing with "soft balls," directly addressing professional athletes' concerns about increased risk of wrist, elbow, and shoulder injuries from inferior balls, significantly improving the health protection for athletes.

[0086] Another embodiment of the present invention also provides a method for intelligent grading of tennis performance in tennis matches, such as... Figure 2 As shown, the intelligent tennis performance classification method for tennis matches proposed in this invention includes the following steps: S1. Obtain the weight data of the tennis ball to be tested; S2. Establish a three-dimensional point cloud model of the tennis ball to be detected, and calculate the geometric shape data of the tennis ball to be detected based on the three-dimensional point cloud model of the ball. S3. Obtain the surface morphology data of the tennis ball to be tested, and determine the wear index of the tennis ball to be tested based on the surface morphology data. S4. Obtain the vibration spectrum of the tennis ball to be tested when it rotates at a first preset speed, and analyze the dynamic imbalance of the tennis ball to be tested based on the vibration spectrum. S5. Acquire the velocity before and after the collision and the pressure change data at the instant of the collision when the tennis ball to be tested is released from a preset height with no initial velocity. Determine the elastic characterization data of the tennis ball to be tested based on the velocity before and after the collision and / or the pressure change data at the instant of the collision. S6. Determine a weighted scoring model that matches the current tennis tournament based on the actual working conditions of the current tennis tournament. Use the weighted scoring model to score the weight data, geometric data, wear index, dynamic imbalance, and elasticity characteristics of the tennis ball to be tested. Classify the tennis ball's condition based on the scoring results.

[0087] The intelligent tennis condition classification method for tennis events provided in this embodiment can be used in the aforementioned intelligent tennis condition classification system for tennis events.

[0088] In one embodiment of the present invention, step S2, establishing a three-dimensional point cloud model of the tennis ball to be detected, includes: acquiring three-dimensional shape contour data obtained by scanning the tennis ball to be detected using a three-dimensional contour scanner, and establishing a three-dimensional point cloud model of the tennis ball to be detected based on the three-dimensional shape contour data.

[0089] In another embodiment of the present invention, step S2, establishing a three-dimensional point cloud model of the tennis ball to be detected, includes: acquiring two-dimensional static images of the tennis ball to be detected from different angles, which are simultaneously acquired by multiple cameras arranged at different angles, and reconstructing a three-dimensional point cloud model of the tennis ball to be detected based on the acquired two-dimensional static images using a stereo vision algorithm.

[0090] In this embodiment of the invention, step S2, which calculates the geometric shape data of the tennis ball to be detected based on the three-dimensional point cloud model of the sphere, includes: calculating the diameter of the tennis ball in at least three orthogonal directions based on the three-dimensional point cloud model of the sphere, and evaluating the sphericity of the tennis ball to be detected based on each tennis ball diameter.

[0091] In one embodiment of the present invention, step S3, acquiring the surface morphology data of the tennis ball to be tested and determining the wear index of the tennis ball to be tested based on the surface morphology data, includes: acquiring an image data sequence of the tennis ball to be tested, wherein the image data sequence is an image data sequence of the tennis ball to be tested being rotated at a second preset speed using a line scan camera; generating a 360° surface unfolded map of the tennis ball to be tested based on the image data sequence; extracting texture features, contrast features, and edge blurring features from the surface unfolded map; calculating the felt coverage rate, surface cleanliness and contrast, and / or wear area ratio of the tennis ball surface based on the feature extraction results; and determining the wear index of the tennis ball to be tested based on the felt coverage rate, surface cleanliness and contrast, and / or wear area ratio.

[0092] In another embodiment of the present invention, step S3, acquiring the surface morphology data of the tennis ball to be tested and determining the wear index of the tennis ball to be tested based on the surface morphology data, includes: acquiring the spectral data of the surface of the tennis ball to be tested, wherein the spectral data of the surface of the tennis ball is the reflectance information of each pixel point on the surface of the ball in different spectral bands acquired by a hyperspectral camera; determining the material distribution information of the tennis ball surface based on the reflectance information; calculating the felt coverage, surface cleanliness and contrast and / or wear area ratio of the tennis ball surface based on the material distribution information of the tennis ball surface; and determining the wear index of the tennis ball to be tested based on the felt coverage, surface cleanliness and contrast and / or wear area ratio.

[0093] In another embodiment of the present invention, step S3, acquiring the surface morphology data of the tennis ball to be tested and determining the wear index of the tennis ball to be tested based on the surface morphology data, includes: acquiring multiple deformed images obtained by projecting a grating of a preset mode onto the surface of the tennis ball to be tested from different angles; analyzing the material distribution information of the surface of the tennis ball to be tested based on the multiple deformed images; calculating the felt coverage, surface cleanliness and contrast and / or wear area ratio of the tennis ball surface based on the material distribution information of the tennis ball surface; and determining the wear index of the tennis ball to be tested based on the felt coverage, surface cleanliness and contrast and / or wear area ratio.

[0094] The intelligent tennis ball condition classification method and system provided in this invention are an automated system and method specifically designed for professional tennis events. This system and method enables on-site, rapid, multi-dimensional, and high-precision analysis and intelligent classification of the physical characteristics of a single ball, and has the following advantages: 1. A method for quality certification and traceability of competition balls based on on-site, full-scale testing. This method overturns the existing model that relies on macro-level rules and offline sampling, and for the first time proposes a concept and method for 100% full-scale testing of every ball to be used or that has been used at the competition site. This includes not only "matching set" certification of new balls before the competition, but also real-time status monitoring of balls in circulation during the competition. This "full lifecycle" on-site quality traceability method is a fundamental innovation of this invention.

[0095] 2. A Multi-Dimensional Comprehensive Evaluation Model Integrating Dynamic and Static Characteristics. This invention establishes a more comprehensive sphere performance evaluation model that far surpasses existing ITF standards. This model creatively integrates deep-seated dynamic indicators such as dynamic equilibrium (through rotational vibration analysis), sphericity (through 3D contour scanning), and collision recovery coefficient (COR) (through high-speed sensing) with static indicators such as weight, size, and surface morphology. This multi-dimensional, comprehensive performance "portrait" method can characterize the state of a sphere with unprecedented depth and breadth.

[0096] 3. High-precision, multi-sensor fusion integrated detection system architecture. In terms of physical structure, this invention designs a highly integrated system architecture. Its uniqueness lies in organically integrating multiple high-precision sensors based on different principles (such as 3D scanners, line scan cameras, vibration sensors, high-speed cameras, and mechanical sensing platforms) into a compact workstation, with a central controller enabling their complex collaborative operation and synchronous data acquisition. This multi-sensor system integration scheme, customized for a specific target (precise tennis ball analysis), is a key engineering engineering solution for achieving rapid, accurate, and automated detection.

[0097] 4. Precise Quantification Technology of Surface Topography Based on Panoramic Images and AI. Targeting tennis ball wear as a key indicator, this invention employs a technique using a high-speed linear array camera and a servo rotary table to acquire a 360° seamless surface unfolded image, combined with AI image analysis algorithms for defect identification and quantification. Compared to traditional subjective observation or ordinary camera photography, this panoramic, blind-spot-free precise quantification method of surface topography can objectively and accurately assess the wear, pilling, and staining of the felt, representing a significant technological breakthrough in assessing ball performance degradation.

[0098] For the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0099] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, any of the claimed embodiments can be used in any combination.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligently classifying tennis performance in tennis matches, characterized in that, The method includes: Obtain the weight data of the tennis ball to be tested; A three-dimensional point cloud model of the tennis ball to be tested is established, and the geometric shape data of the tennis ball to be tested is calculated based on the three-dimensional point cloud model of the ball. Acquire the surface morphology data of the tennis ball to be tested, and determine the wear index of the tennis ball to be tested based on the surface morphology data; Obtain the vibration spectrum of the tennis ball to be tested when it rotates at a first preset speed, and analyze the dynamic imbalance of the tennis ball to be tested based on the vibration spectrum. Acquire the velocity and pressure change data of the tennis ball before and after the collision when it is released from a preset height with no initial velocity and collides with the ball; determine the elastic characterization data of the tennis ball based on the velocity and / or pressure change data before and after the collision. Based on the actual working conditions of the current tennis tournament, a weighted scoring model matching the current tennis tournament is determined. The weighted scoring model is used to score the weight data, geometric data, wear index, dynamic imbalance, and elasticity characterization data of the tennis ball to be tested. The tennis ball condition is graded based on the scoring results.

2. The method according to claim 1, characterized in that, Establish a 3D point cloud model of the tennis ball to be tested, including: The three-dimensional shape contour data of the tennis ball to be inspected is obtained by scanning it with a three-dimensional contour scanner, and a three-dimensional point cloud model of the tennis ball to be inspected is established based on the three-dimensional shape contour data.

3. The method according to claim 1, characterized in that, Establish a 3D point cloud model of the tennis ball to be tested, including: Two-dimensional static images of the tennis ball to be detected from different angles are acquired simultaneously by multiple cameras arranged at different angles. A three-dimensional point cloud model of the tennis ball to be detected is reconstructed based on the acquired two-dimensional static images using a stereo vision algorithm.

4. The method according to claim 1, characterized in that, The geometric shape data of the tennis ball to be detected are calculated based on the three-dimensional point cloud model of the sphere, including: Calculate the diameter of the tennis ball in at least three orthogonal directions based on the 3D point cloud model of the sphere, and evaluate the sphericity of the tennis ball to be tested based on each tennis ball diameter.

5. The method according to claim 1, characterized in that, The process of acquiring the surface morphology data of the tennis ball to be tested and determining the wear index of the tennis ball to be tested based on the surface morphology data includes: An image data sequence of a tennis ball to be tested is acquired. The image data sequence is an image data sequence of the tennis ball to be tested being rotated at a second preset speed, acquired using a line scan camera. A 360° surface unfolded map of the tennis ball to be tested is generated based on the image data sequence. Texture features, contrast features, and edge blurring features are extracted from the surface unfolded map. The felt coverage, surface cleanliness and contrast, and / or wear area ratio of the tennis ball surface are calculated based on the feature extraction results. The wear index of the tennis ball to be tested is determined based on the felt coverage, surface cleanliness and contrast, and / or wear area ratio.

6. The method according to claim 1, characterized in that, The process of acquiring the surface morphology data of the tennis ball to be tested and determining the wear index of the tennis ball to be tested based on the surface morphology data includes: The spectral data of the tennis ball to be tested is obtained. The spectral data of the ball surface is the reflectance information of each pixel on the ball surface in different spectral bands, which is collected by a hyperspectral camera. The material distribution information of the tennis ball surface is determined based on the reflectance information. The felt coverage, surface cleanliness and contrast and / or wear area ratio of the tennis ball surface are calculated based on the material distribution information of the tennis ball surface. The wear index of the tennis ball to be tested is determined based on the felt coverage, surface cleanliness and contrast and / or wear area ratio.

7. The method according to claim 1, characterized in that, The process of acquiring the surface morphology data of the tennis ball to be tested and determining the wear index of the tennis ball to be tested based on the surface morphology data includes: Multiple deformed images are obtained after a grating with a preset mode is projected onto the surface of a tennis ball to be tested from different angles. The material distribution information of the surface of the tennis ball to be tested is analyzed based on the multiple deformed images. The felt coverage, surface cleanliness and contrast and / or wear area ratio of the tennis ball surface are calculated based on the material distribution information of the tennis ball surface. The wear index of the tennis ball to be tested is determined based on the felt coverage, surface cleanliness and contrast and / or wear area ratio.

8. A smart grading system for tennis performance in tennis matches, characterized in that, The system includes a detection chamber, a static characteristic analysis unit, a dynamic and surface characteristic analysis unit, and a dynamic rebound characteristic measurement unit placed inside the detection chamber, as well as a central control and data processing unit placed outside the detection chamber. The static characteristic analysis unit, the dynamic and surface characteristic analysis unit, and the dynamic rebound characteristic measurement unit are electrically connected to the central control and data processing unit. The static characteristic analysis unit includes a weight detection device and a three-dimensional contour scanner located directly above the weight detection device, which is used to measure the weight data and three-dimensional shape contour data of the tennis ball to be detected and upload them to the central control and data processing unit. The dynamic and surface characteristic analysis unit includes a rotary table for supporting and rotating the tennis ball to be tested, a high-speed line scan camera and a dome-shaped shadowless light source fixedly installed above the rotary table, and a vibration sensor installed on the support structure of the rotary table. The central control and data processing unit is used to control the rotary table to rotate at a first preset speed and a second preset speed respectively. The high-speed line scan camera is used to acquire image data sequences of the tennis ball to be tested when rotating at the second preset speed and upload them to the central control and data processing unit. The vibration sensor is used to acquire the vibration spectrum of the tennis ball to be tested when rotating at the first preset speed and upload it to the central control and data processing unit. The dynamic rebound characteristic measurement unit includes an electromagnetic release device fixed in the detection chamber, a mechanical sensing platform located directly below the electromagnetic release device, and a high-speed sensing device located to the side of the electromagnetic release device. The central control and data processing unit is used to control the electromagnetic release device to release the tennis ball to be tested without initial velocity. The high-speed sensing device is used to collect the instantaneous velocity of the tennis ball released by the electromagnetic release device before and after colliding with the mechanical sensing platform and upload it to the central control and data processing unit. The mechanical sensing platform is used to collect the pressure change data of the tennis ball at the moment of collision and upload it to the central control and data processing unit. The central control and data processing unit is used to execute the intelligent tennis status classification method for tennis events as described in any one of claims 1-5.

9. The intelligent ranking system for tennis tournaments according to claim 8, characterized in that, The system also includes one or two robotic arms installed in the testing chamber. The central control and data processing unit controls the robotic arms to move the tennis ball to be tested in the static characteristic analysis unit, the dynamic and surface characteristic analysis unit, and the dynamic rebound characteristic measurement unit.

10. The intelligent ranking system for tennis tournaments according to claim 8, characterized in that, The system also includes a display unit that is communicatively connected to the central control and data processing unit. The display unit is used to display the scoring results and tennis ball condition rating results of the tennis ball to be tested.

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