Athlete influence assessment method, electronic device, and storage medium

Abstract

The embodiment of the application provides a kind of athlete influence evaluation method, electronic equipment and storage medium, belong to sports analysis technical field.The method comprises: obtaining the motion trajectory data of each athlete in target space region;Target space region contains multiple sub-regions;Based on the personal influence evaluation of each athlete corresponding motion trajectory data, the personal control field intensity of corresponding athlete at target time is obtained;Based on the personal control field intensity of each athlete, superimposed processing is carried out, and attack party total field intensity and defense party total field intensity are obtained;For each sub-region, field intensity difference comparison is carried out based on attack party total field intensity and defense party total field intensity, and the space net field intensity corresponding to the sub-region is obtained;Based on space net field intensity, region attribute division is carried out, and region division result is obtained, influence evaluation is carried out based on region division result, and influence evaluation index is obtained.The embodiment of the application can more accurately evaluate the influence of athletes when competing against each other.

Description

Technical Field

[0001] This application relates to the field of sports analysis technology, and in particular to a method for assessing athlete influence, an electronic device, and a storage medium. Background Technology

[0002] In highly competitive team sports such as basketball and football, athletes often use their physical attributes and defensive positions to create an effective deterrent and interception range in specific areas, whether on the defensive or in a static state. Alternatively, athletes can break the balance of power through tactical coordination and movement to create an advantageous area where their team has the upper hand.

[0003] It is evident that athletes can exert influence through actions in team sports, but this influence is difficult to measure. Current technology primarily assesses this influence based on the absolute physical distance between players. However, in actual competition, each player's influence differs, making an assessment based solely on absolute physical distance inaccurate. Furthermore, team sports are dynamic processes involving high-frequency confrontation between offensive and defensive teams within the same physical space. During this process, the actions of multiple players within the team simultaneously generate influence, and current technology lacks a computational model that can integrate the spatial control forces of all individuals on both sides within the same coordinate system.

[0004] Therefore, how to quantify and evaluate the impact of players' action strategies or tactical coordination on the field has become a technical problem that urgently needs to be solved. Summary of the Invention

[0005] The main objective of this application is to propose a method, electronic device, and storage medium for evaluating athlete influence, aiming to more accurately assess the influence of athletes during competitive matches.

[0006] To achieve the above objectives, a first aspect of this application proposes a method for evaluating athlete influence, the method comprising: Acquire the motion trajectory data of each athlete within a target spatial region; wherein, the target spatial region comprises multiple sub-regions; Based on the motion trajectory data corresponding to each athlete, an individual influence assessment is performed to obtain the individual control field strength of the corresponding athlete at the target time. The total field strength of the attacking side and the total field strength of the defending side are obtained by superimposing the individual field strengths of each athlete. For each sub-region, the field strength difference between the total field strength of the attacking side and the total field strength of the defending side is compared to obtain the spatial net field strength corresponding to the sub-region. Based on the spatial net field strength, regional attributes are divided to obtain regional division results, and based on the regional division results, influence assessment is performed to obtain influence assessment indicators.

[0007] In some embodiments, the motion trajectory data includes the athlete's real-time physical position and real-time velocity vector; The step of evaluating individual influence based on the motion trajectory data corresponding to each athlete to obtain the individual control field strength of the corresponding athlete at the target time includes: Obtain the static attribute data of each athlete; For each athlete, the static attribute data is weighted and converted to obtain the basic influence and the influence space radius; wherein, the basic influence is used to characterize the influence generated by the athlete based on its own ability, and the influence space radius is used to characterize the characteristic radius of the rate at which the influence decays with distance; Based on the real-time physical location, the real-time velocity vector, the basic influence, and the influence spatial radius, the individual field strength is evaluated to obtain the athlete's individual control field strength at the target location.

[0008] In some embodiments, after performing weighted calculations on the static attribute data for each athlete to obtain the basic influence and influence spatial radius, the method further includes: Obtain the athlete's real-time physiological energy consumption data; Based on the real-time physiological energy consumption data, fatigue quantification is performed to obtain the physical fitness fine-tuning coefficient. The basic influence or the influence space radius is adjusted based on the physical fitness fine-tuning coefficient to obtain the adjusted basic influence or the adjusted influence space radius.

[0009] In some embodiments, the process of dividing the region based on the spatial net field strength to obtain a region division result, and then performing an influence assessment based on the region division result to obtain an influence assessment index, includes: In response to the fact that the value of the net field strength of the space in the sub-region is greater than a preset positive threshold, the corresponding sub-region is divided into an offensive space advantage zone; In response to the fact that the value of the net spatial field strength of the sub-region is less than a preset negative threshold, the corresponding sub-region is divided into a strong defensive control zone; An influence assessment index is obtained by evaluating the area of ​​offensive space advantage or the area of ​​strong defensive control.

[0010] In some embodiments, the influence assessment based on the offensive space advantage zone or the defensive control strength zone to obtain influence assessment indicators includes: Based on the area statistical processing of the offensive space advantage zone, the area data of the advantage zone is obtained. The average advantage field strength data is obtained by averaging the net field strength of each coordinate point in the offensive space advantage zone. By combining the area data of the advantageous region with the average field strength data, the spatial advantage index of the attacking side at the target time is determined.

[0011] In some embodiments, the influence assessment based on the offensive space advantage zone or the defensive control strength zone to obtain influence assessment indicators specifically includes: Obtain the real-time physical positions of each athlete on the attacking side at the target time; Based on the matching of each of the real-time physical locations with the defensive control strong zone, the defensive advantage zone coverage rate and the defensive matching zone are obtained; wherein, the defensive matching zone represents the area in the defensive control strong zone that matches the real-time physical location; Based on the aforementioned defensive matching area, statistical analysis and surface accumulation processing are performed to obtain the defensive advantage index of the defending side.

[0012] In some embodiments, the process of dividing the region based on the spatial net field strength to obtain a region division result, and then performing an influence assessment based on the region division result to obtain an influence assessment index, includes: Obtain the real-time physical positions of each of the attacking players; Based on each of the real-time physical locations, numerical extraction and processing are performed on the corresponding spatial net field strength to obtain the offensive advantage value. Obtain the individual goal data for each of the aforementioned athletes; Based on the mapping between the individual goal data and the real-time physical location, the expected score data is obtained; Based on the offensive advantage value and the expected score data, an influence assessment is performed to obtain the opportunity generation index of each athlete at the real-time physical location, which is determined as the influence assessment index.

[0013] In some embodiments, the method further includes: Based on a preset time sampling period, the intensity changes of the net field strength of the offensive space advantage zone or the net field strength of the defensive control strong zone at multiple time points are monitored to obtain an influence time series change dataset. Acquire the action events of each athlete within the time sampling period; Based on the action events of each athlete, an influence weight mapping is performed with the influence time-series change dataset to obtain the influence index corresponding to each action event.

[0014] To achieve the above objectives, a second aspect of this application proposes an athlete influence assessment system, the system comprising: The motion data acquisition module is used to acquire the motion trajectory data of each athlete within a target spatial area; the target spatial area contains multiple sub-regions. The Personal Influence Acquisition Module is used to evaluate the personal influence of each athlete based on their corresponding movement trajectory data, and to obtain the athlete's personal control field strength at the target time. The Faction Influence Acquisition Module is used to superimpose the individual control field strength of each athlete to obtain the total field strength of the attacking side and the total field strength of the defending side. The influence processing module is used to compare the field strength difference between the total field strength of the attacker and the total field strength of the defender for each sub-region, and obtain the spatial net field strength of the corresponding sub-region. The influence assessment module is used to divide regional attributes based on spatial net field strength, obtain regional division results, and conduct influence assessment based on regional division results to obtain influence assessment indicators.

[0015] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the athlete influence assessment method described in the first aspect.

[0016] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the athlete influence assessment method described in the first aspect.

[0017] The athlete influence assessment method, electronic device, and storage medium proposed in this application acquire the motion trajectory data of each athlete within a target spatial region, wherein the target spatial region comprises multiple sub-regions; based on the motion trajectory data corresponding to each athlete, an individual influence assessment is performed to obtain the individual control field strength of the corresponding athlete at the target time; the individual control field strengths of each athlete are superimposed to obtain the total field strength of the attacking side and the total field strength of the defending side; for each sub-region, the field strength difference is compared based on the total field strength of the attacking side and the total field strength of the defending side to obtain the spatial net field strength of the corresponding sub-region; based on the spatial net field strength, regional attributes are divided to obtain the regional division results, and influence assessment is performed based on the regional division results to obtain influence assessment indicators. Therefore, this application evaluates the individual influence of the movement trajectory data of each athlete within the target spatial area, transforming it into a personal control field strength that dynamically evolves with the movement state at the target time. This field strength is used to characterize the athlete's influence. The individual control field strengths of each athlete are superimposed according to their respective camps, resulting in the total field strength of the attacking side representing the group's influence and the total field strength of the defending side representing the group's interception power. Then, by comparing the field strength difference within the gridded sub-regions, a spatial net field strength is generated. Finally, an influence evaluation index is obtained based on the regional attribute division of this spatial net field strength. This optimizes the influence quantification mechanism for complex tactical cooperation and enables a more accurate assessment of the athletes' influence during competitive confrontations. Attached Figure Description

[0018] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a schematic flowchart of an athlete influence assessment method according to an embodiment of this application; Figure 2 This is a schematic diagram of another process for the athlete influence assessment method in this application. Figure 3 This is a schematic diagram of another process for the athlete influence assessment method in this application. Figure 4 This is a schematic diagram of another process for the athlete influence assessment method in this application. Figure 5 This is a schematic diagram of another process for the athlete influence assessment method in this application. Figure 6 This is a schematic diagram of another process for the athlete influence assessment method in this application. Figure 7 This is a schematic diagram of another process for the athlete influence assessment method in this application. Figure 8 This is a schematic diagram of another process for the athlete influence assessment method in this application. Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0020] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0022] In highly competitive team sports such as basketball and football, athletes often use their physical attributes and defensive positions to create an effective deterrent and interception range in specific areas, whether on the defensive or in a static state. Alternatively, athletes can break the balance of power through tactical coordination and movement to create an advantageous area where their team has the upper hand.

[0023] It is evident that athletes can exert influence through actions in team sports, but this influence is difficult to measure. Current technology primarily assesses this influence based on the absolute physical distance between players. However, in actual competition, each player's influence differs, and assessing all players based on absolute physical distance is inaccurate. It fails to evaluate the effective spatial range of an athlete's actual deterrence or influence at a specific location, and the dynamic changes in that range as the athlete moves.

[0024] Furthermore, team sports are dynamic processes involving high-frequency confrontation between the offensive and defensive sides in the same physical space. During this process, the actions of multiple players in the team will have an impact simultaneously. However, existing technologies lack a computational model that can integrate the spatial control forces of all individuals on both sides in the same coordinate system for confrontation.

[0025] Therefore, how to quantify and evaluate the impact of players' action strategies or tactical coordination on the field has become a technical problem that urgently needs to be solved.

[0026] Based on this, embodiments of this application provide a method, electronic device, and storage medium for evaluating athlete influence, aiming to more accurately assess the influence of athletes during competitive matches.

[0027] The athlete influence assessment method, electronic device, and storage medium provided in this application are specifically described through the following embodiments. First, the athlete influence assessment method in this application is described.

[0028] Figure 1 This is an optional flowchart illustrating the athlete influence assessment method provided in this application embodiment. Figure 1 The method may include, but is not limited to, steps S101 to S105.

[0029] Step S101: Obtain the motion trajectory data of each athlete within the target spatial region; wherein, the target spatial region contains multiple sub-regions; Step S102: Based on the motion trajectory data of each athlete, perform an individual influence assessment to obtain the individual control field strength of the corresponding athlete at the target time. Step S103: Based on the individual field strength controlled by each athlete, the field strength is superimposed to obtain the total field strength of the attacking side and the total field strength of the defending side. Step S104: For each sub-region, compare the field strength difference based on the total field strength of the attacker and the total field strength of the defender to obtain the spatial net field strength corresponding to the sub-region. Step S105: Based on the spatial net field strength, the region attributes are divided to obtain the region division results, and the influence is assessed based on the region division results to obtain the influence assessment indicators.

[0030] In step S101 of some embodiments, motion trajectory data of each athlete within the target space area is acquired. A sports floor tile screen is deployed within the target area to acquire raw data. The sports floor tile screen contains an LED display array, an optical sensor, and a pressure sensor. The optical sensor emits detection light through the sports floor tile screen towards the target area. During the movement of multiple athletes, the athletes modulate the detection light to generate feedback light reflected back to the optical sensor, forming an optical sensing signal. During each athlete's movement, the pressure generated by the sports floor tile screen is transmitted to the pressure sensor, forming a pressure sensing signal. Based on the optical sensing signal and the pressure sensing signal, the posture of each athlete is identified to determine the real-time motion trajectory data corresponding to each athlete. The LED display array can be used to provide athletes with feedback motion data or motion instruction information.

[0031] Optical sensors can be densely packed arrays of infrared photocells with centimeter-level spacing, acquiring precise two-dimensional coordinates, velocity vectors, and movement trajectories of the athlete's feet, the ball, and opponents. Pressure sensors can also be arranged in arrays to acquire mechanical characteristic data such as the athlete's plantar pressure distribution, pressure center trajectory, and impact force timing. Ultimately, this allows for the acquisition of movement trajectory data for each athlete within a target spatial area. This movement trajectory data typically includes core parameters such as each athlete's three-dimensional or two-dimensional physical coordinates, instantaneous velocity vectors, and acceleration over a continuous time series.

[0032] It's important to note that the optical sensor is a component inside the sports floor tile screen used to generate light signals. The optical sensor emits probe light towards the target area, and this light penetrates the surface material of the sports floor tile screen to enter the playing space. Its function is to establish an active optical detection mechanism, outputting light signals from the field itself to provide a stable lighting or structured light foundation for subsequent acquisition of athlete position information. Using an internal light source, rather than relying on ambient light, helps reduce the interference of changes in external lighting conditions on data acquisition quality.

[0033] When athletes move on the court surface, their footsteps exert pressure on the sports tile screen. This pressure is transmitted through the screen structure to pressure sensors deployed inside, generating pressure sensing signals. These pressure sensors detect the direct contact between the athlete and the court surface. This allows for the acquisition of physical interaction information between the athlete and the court, recording mechanical characteristics such as foot placement and movement rhythm. The design is based on the fact that optical signals can be interfered with by body posture or occlusion, while pressure signals directly reflect the athlete's contact with the court. These two sensing modes complement each other, improving the reliability of position determination.

[0034] In team sports, traditional external camera acquisition mechanisms are easily affected by fluctuations in ambient lighting, overlapping players, and visual obstruction caused by tight formations, making it impossible to accurately locate the positions of each athlete and consequently, to accurately assess the range and intensity of each athlete's influence. However, the sports floor tile screen provided in this application, through its hardware configuration mechanism that integrates active light sensing and mechanical perception, not only overcomes the limitations of traditional external visual capture technologies in terms of perception distortion and coordinate fragmentation under high-density personnel obstruction and variable lighting conditions, but also effectively eliminates perception blind spots and measurement errors by transforming raw heterogeneous sensor signals into structured, highly expressive motion trajectory data. This provides highly accurate and stable underlying data for subsequent calculations of individual control field strength, the superposition of the total field strength of offensive and defensive groups, and the derivation of the spatial advantage index.

[0035] Pose recognition refers to the process of comprehensively determining an athlete's spatial position and body posture on a playing field. The steps in this embodiment fuse optical and pressure sensor signals to perform pose recognition on each athlete, thereby determining the real-time motion trajectory data of each athlete.

[0036] The purpose of this application is to transform raw sensor signals into personnel state data with clear semantics, enabling subsequent analysis to be based on structured athlete role and movement position information. The concept is that single sensors have perception blind spots or errors; through the collaborative analysis of optical and pressure signals from different sources, mutual verification and compensation can be achieved, thereby improving the accuracy and stability of athlete posture recognition and providing a reliable data foundation for assessing the impact of the entire athlete's tactical coordination.

[0037] To achieve more microscopic and refined spatiotemporal data processing, a three-dimensional or two-dimensional coordinate system can be established based on the target spatial region. The complete target spatial region can be pre-divided into a gridded spatial structure composed of multiple sub-regions. Each sub-region represents an independent spatial unit on the field, so as to facilitate subsequent quantitative assessment of the spatial influence of different locations.

[0038] In step S102 of some embodiments, the acquired motion trajectory data is used to model each athlete as a dynamic influence source capable of generating deterrence and interception forces on the surrounding space. Based on the motion trajectory data corresponding to each athlete, an individual influence assessment is performed to obtain the individual control field strength of the corresponding athlete at the target time. The individual control field strength can be used to obtain the non-uniform, dynamically changing spatial deterrence and interception force value generated by an athlete on any coordinate point within the field at a specific time.

[0039] Please see Figure 2 In some embodiments, the motion trajectory data includes the athlete's real-time physical position and real-time velocity vector, and step S102 may include, but is not limited to, steps S201 to S203: Step S201: Obtain the static attribute data of each athlete; Step S202: Perform weighted conversion processing on the static attribute data of each athlete to obtain the basic influence and the influence space radius; wherein, the basic influence is used to characterize the influence generated by the athlete based on his own ability, and the influence space radius is used to characterize the characteristic radius of the rate at which the influence decays with distance. Step S203: Based on real-time physical position, real-time velocity vector, basic influence and influence spatial radius, evaluate the individual field strength to obtain the athlete's individual control field strength at the target position.

[0040] In step S201 of some embodiments, in order to more comprehensively and accurately characterize the influence of different athletes, richer individual characteristic differences can be introduced. This can be achieved by accessing a pre-stored athlete database or reading the registration information of each athlete to obtain their static attribute data. These static attribute data may specifically include, but are not limited to, each athlete's height data, wingspan data, and tactical position label in the team, such as center, guard, or offensive core.

[0041] In step S202 of some embodiments, the static attribute data of each athlete is weighted and processed to obtain the basic influence and the influence spatial radius. Specifically, through a multi-factor weight matrix or a benchmark numerical mapping relationship, the athlete's physical scale parameters such as height and wingspan are processed with dimensionless gain. The basic influence is used to characterize the influence generated by the athlete based on his / her own ability. Its value directly reflects the benchmark deterrent strength formed by the athlete in the surrounding space when in a static position. The influence spatial radius is used to characterize the characteristic radius of the rate attenuation of influence with distance. It determines the boundary and diffusion speed of the athlete's influence in spatial geometric extension.

[0042] In step S203 of some embodiments, an individual field strength assessment is performed based on real-time physical location, real-time velocity vector, basic influence, and influence spatial radius to obtain the athlete's individual control field strength at the target location point.

[0043] Specifically, any discrete target location point in the target space region, i.e., on the court, can be used as the calculation object. First, the Euclidean distance from the athlete to the target location point is determined based on the real-time physical position. Then, this Euclidean distance is used to perform a decay function operation with the influence space radius to construct the basic radial decay space. Then, the real-time velocity vector can be extracted and a unit direction vector from the athlete to the target location point can be constructed. Through the dot product operation between the two, a Doppler stretching and enhancement effect is introduced in front of the movement using a preset motion enhancement coefficient. Finally, a personal control field strength that dynamically stretches and deforms with the movement of the athlete and can accurately reflect the individual athlete's control field strength in the target space region can be output. For details, please refer to the following expression (1): (1) in, This represents the personal control field strength generated by athlete i with respect to the target location point (x, y) within the target spatial region at the target time t. Let represent the Euclidean distance between athlete i at the target time t and the target position (x, y). It represents the basic influence, which can be preset based on the static attributes of athlete i, such as role, position, height, and wingspan, and can be dynamically adjusted in combination with athlete i's real-time physical condition data. Indicates the spatial radius of influence. This represents the velocity vector of athlete i at the target time t. This represents the unit direction vector from athlete i's current position to the target position point (x, y). This represents the motion enhancement coefficient, used to simulate the impact force when an athlete moves in a specific direction. When the athlete moves towards the target position, the dot product is greater than 0, indicating that the field strength in front is enhanced; conversely, the field strength behind is weakened.

[0044] The field strength controlled by an individual exhibits a non-uniform, asymmetrical cloud-like distribution in space. The field strength value reaches its maximum at the athlete's current physical position and gradually decreases with increasing spatial distance. At the same time, it generates dynamic stretching and enhancement in the direction of the athlete's movement, accurately quantifying the substantial range of influence of different individuals on the surrounding space at a specific instant.

[0045] Through steps S201 to S203, by introducing two static attribute parameters—the athlete's basic influence and influence spatial radius—and calculating them in conjunction with real-time physical position and real-time velocity vector, the individual control field strength of each athlete, resulting from differences in individual physical conditions and real-time motion state, can be obtained. This influence assessment mechanism, which integrates static attributes with dynamic physical trajectories, effectively transforms the athlete's deterrent capability within the target space into a quantifiable characteristic quantity.

[0046] Please see Figure 3 In some embodiments, step S202 may be followed by steps S301 to S303, including but not limited to: Step S301: Obtain the athlete's real-time physiological energy consumption data; Step S302: Perform fatigue quantification based on real-time physiological energy consumption data to obtain physical fitness fine-tuning coefficients; Step S303: Adjust the basic influence or influence space radius based on the physical fitness fine-tuning coefficient to obtain the adjusted basic influence or the adjusted influence space radius.

[0047] In step S301 of some embodiments, in order to capture the spatial influence decay caused by the athlete's physical exertion during prolonged high-intensity competition, real-time physiological energy consumption data of the athlete is acquired through a wireless sensor network deployed at the sidelines or a smart wearable mobile data acquisition terminal worn by the player. This real-time physiological energy consumption data includes deep physiological indicators that can directly or indirectly reflect the intensity of human metabolism and the state of fatigue accumulation, specifically including but not limited to the athlete's real-time heart rate data, cumulative heart rate load, instantaneous respiratory rate, changes in blood oxygen saturation, and instantaneous high-frequency acceleration peaks and movement change frequencies collected based on accelerometers.

[0048] In step S302 of some embodiments, fatigue quantification is performed based on real-time physiological energy consumption data to obtain a physical fitness fine-tuning coefficient. Specifically, real-time heart rate data can be integrated over time to confirm the cumulative heart rate load, and high-frequency acceleration peaks can be converted into exercise energy consumption estimates through a preset energy consumption mapping function. Then, the cumulative heart rate load and exercise energy consumption estimates can be input into a preset fatigue assessment matrix to quantitatively assess the athlete's current physical fatigue state and the degree of decline in muscle function, thereby outputting a physical fitness fine-tuning coefficient for dynamically correcting the athlete's personal control field strength.

[0049] In step S303 of some embodiments, the basic influence or influence space radius is adjusted based on the physical fitness fine-tuning coefficient to obtain the adjusted basic influence or adjusted influence space radius. Since athletes inevitably experience a decline in physical strength and defensive agility due to fatigue in the latter half of a race or after a long sprint, the adjusted basic influence or adjusted influence space radius can accurately reflect the athlete's actual deterrent influence under current fatigue conditions by correcting and updating the physical fitness fine-tuning coefficient and the statically calculated basic influence or influence space radius. This ensures that the subsequently constructed personal control field strength has realistic fit and accuracy.

[0050] Through steps S301 to S303, by introducing athletes' real-time physiological energy consumption data and converting it into a physical fitness fine-tuning coefficient that can dynamically adjust the athletes' personal control field strength, the reduction effect of fatigue on the athletes' spatial control range and deterrence intensity is scientifically quantified. This allows the final generated personal control field strength to dynamically and adaptively evolve with the progress of the game and fluctuations in physical fitness, improving the accuracy of subsequent multi-person field strength superposition and offensive and defensive spatial confrontation effectiveness assessment.

[0051] In step S103 of some embodiments, the individual control field strengths of each athlete are superimposed to obtain the total field strength of the attacking team and the total field strength of the defending team. Since the tactical execution of team sports relies on the coordinated cooperation of multiple players, the individual control field strength of a single athlete cannot represent this reality; therefore, it is not appropriate to focus solely on the individual's influence field. Instead, the individual control field strengths of each athlete can be aggregated according to their team affiliation tags.

[0052] Within the target space area, the linear summation of the field strengths of all individuals controlled by the attacking side can yield the total field strength representing the attacking side's overall advancement and offensive momentum. Similarly, the linear summation of the field strengths of all individuals controlled by the defending side can yield the total field strength representing the defending side's overall interception and coverage barrier.

[0053] In step S104 of some embodiments, for each sub-region, the field strength difference is compared based on the total field strength of the attacking side and the total field strength of the defending side to obtain the spatial net field strength corresponding to the sub-region. Specifically, the grid coordinate set within the court is traversed, and for each small sub-region, the total field strength values ​​of the attacking side and the defending side at that coordinate point are extracted. Subsequently, using a difference offset algorithm, the total field strength value of the attacking side at that sub-region is subtracted from the total field strength value of the defending side, thereby calculating the spatial net field strength corresponding to that specific sub-region. The two opposing group influences compete in the same spatial coordinate system, and the difference is mapped using a spatial matrix to obtain the spatial net field strength. The positive or negative sign and absolute value of the spatial net field strength directly reflect the game result of the attacking and defending sides at each position on the court.

[0054] In step S105 of some embodiments, the spatial net field strength is subjected to feature extraction and index conversion, the regional attributes are divided based on the spatial net field strength to obtain the regional division results, and the influence is evaluated based on the regional division results to obtain the influence evaluation index.

[0055] Please see Figure 4 In some embodiments, step S105 may include, but is not limited to, steps S401 to S403: Step S401: In response to the fact that the value of the spatial net field strength of the sub-region is greater than the preset positive threshold, the corresponding sub-region is divided into an offensive space advantage zone. Step S402: In response to the fact that the value of the spatial net field strength of the sub-region is less than the preset negative threshold, the corresponding sub-region is divided into a strong defense control zone. Step S403: Conduct an impact assessment based on the offensive space advantage zone or the defensive control strong zone to obtain the impact assessment index.

[0056] In step S401 of some embodiments, in response to the value of the net spatial field strength of a sub-region being greater than a preset positive threshold, the corresponding sub-region is classified as an offensive spatial advantage zone. Specifically, the calculated net spatial field strength of each sub-region is compared with the preset positive threshold. When the net spatial field strength of a sub-region exceeds the positive threshold, it indicates that at this location point, the attacking group has formed an overwhelming spatial dominance through the influence gain generated by tactical maneuvering, running, or sprinting. The deterrent force of the defending side cannot be offset equally here. Therefore, these sub-regions that meet the conditions are uniformly classified and marked as offensive spatial advantage zones.

[0057] In step S402 of some embodiments, in response to the fact that the value of the spatial net field strength of a sub-region is less than a preset negative threshold, the corresponding sub-region is classified as a defensive control strong zone. Specifically, the spatial net field strength of each sub-region is compared with the preset negative threshold. When the spatial net field strength of a sub-region is negative and numerically lower than the negative threshold, it indicates that the joint interception barrier and approach deterrence constructed by the defending group have an absolute dominant position here, and the offensive posture of the attacking side has been completely suppressed. This sub-region is then classified as a defensive control strong zone.

[0058] In step S403 of some embodiments, an influence assessment is performed based on the offensive space advantage area or the defensive control strong area to obtain an influence assessment index. Specifically, this can be based on the total geometric area of ​​the offensive space advantage area or the defensive control strong area, or by analyzing the distribution of the offensive space advantage area or the defensive control strong area, or by other quantitative analysis methods based on the offensive space advantage area or the defensive control strong area, to characterize the spatial control effectiveness index of the attacker or defender, i.e., the influence assessment index.

[0059] See Figure 5 In some embodiments, step S403 may include, but is not limited to, steps S501 to S503: Step S501: Perform area statistical processing based on the advantageous area of ​​the offensive space to obtain the area data of the advantageous area; Step S502: The average value of the net field strength at each coordinate point within the offensive space advantage zone is processed to obtain the average advantage field strength data. Step S503: Combine the area data of the advantageous region with the average field strength data to determine the spatial advantage index of the attacking side at the target time.

[0060] In step S501 of some embodiments, area statistics are performed based on the advantageous area of ​​the attack space to obtain the area data of the advantageous area. Specifically, by traversing the grid areas contained within the advantageous area of ​​the attack space, the total number of sub-regions falling into the specific attribute area is counted. Since each sub-region has a constant physical area reference value in the preset spatial grid structure, the area data of the advantageous area corresponding to the current moment can be obtained by multiplying the total number of sub-regions by the physical area reference value of a single sub-region.

[0061] In step S502 of some embodiments, the average dominant field strength data is obtained by averaging the net spatial field strength of each coordinate point within the dominant area of ​​attack space. Specifically, using grid coordinate positioning, the net spatial field strength value corresponding to each coordinate point falling inside the boundary of the dominant area of ​​attack space is retrieved and extracted. Subsequently, all extracted net spatial field strength values ​​are summed to obtain the total dominant field strength, and the average dominant field strength data is calculated by dividing the total dominant field strength by the total number of coordinate points within the dominant area of ​​attack space.

[0062] In step S503 of some embodiments, the area data of the advantageous region and the average advantageous field strength data are fused and calculated to determine the spatial advantage index of the attacking side at the target time. In actual competitive combat, possessing only a vast but weakly controlled influence space, or only an extremely high but unextendable field strength within a very small area, is insufficient to construct an efficient overall tactical advantage. Therefore, by multiplying and integrating the area data of the advantageous region and the average advantageous field strength data, a spatial advantage index that takes into account both spatial breadth and control depth can be output. The spatial advantage index can be directly used as a quantitative benchmark for the overall spatial control effectiveness of the attacking side at the current target time.

[0063] Through steps S501 to S503, the area data of the advantageous region in the offensive space advantage zone is extracted, and the average field strength data of each coordinate point in the region is calculated. Finally, the spatial advantage index of the attacking side at the target time is determined by the product of the area data of the advantageous region and the average field strength data. Through this multi-dimensional index quantification mechanism, the macroscopic tactical confrontation situation can be successfully converged into a highly standardized spatiotemporal effectiveness index with continuous dynamic tracking value by outputting the spatial advantage index.

[0064] Please see Figure 6 In some embodiments, step S403 may include, but is not limited to, steps S601 to S603: Step S601: Obtain the real-time physical positions of each athlete on the attacking side at the target time; Step S602: Based on the matching of each real-time physical location with the strong defensive control zone, the coverage rate of the strong defensive control zone and the matching defensive zone are obtained; wherein, the matching defensive zone represents the area in the strong defensive control zone that matches the real-time physical location. Step S603: Based on the defensive matching area, perform quantity statistics and area accumulation processing to obtain the defensive advantage index of the defending side.

[0065] In step S601 of some embodiments, the real-time physical position of each athlete on the attacking side at the target time can be obtained through the moving tile screen in the target space area. The moving tile screen is equipped with optical sensors and pressure sensors. The optical sensors can be an array of infrared photocell sensors densely arranged at centimeter-level intervals, which can obtain position information such as the precise two-dimensional coordinates, velocity vectors and movement trajectories of the athlete's feet, the ball and the opponent. The pressure sensors can also be arranged in an array to obtain mechanical characteristic data such as the athlete's foot pressure distribution, pressure center trajectory and impact force timing, thereby obtaining the real-time physical position of each athlete on the attacking side in the target space area at the target time.

[0066] Alternatively, high-frequency optical motion capture equipment, court image sensors, or ultra-wideband positioning tags attached to athletes can be used to collect and analyze the real-time coordinates of all athletes belonging to the attacking team within the court, thereby obtaining the real-time physical position of each athlete at the target moment. These real-time physical positions are stored in the data acquisition buffer in the form of a two-dimensional pixel coordinate matrix or three-dimensional physical space coordinates. The coordinate data represented by each real-time physical position is linked to the corresponding athlete's identity identifier to determine the instantaneous geometric position of each athlete in the target space area.

[0067] In step S602 of some embodiments, the defensive advantage area coverage rate and the defensive matching area are obtained by matching each real-time physical location with the defensive control strong zone. Specifically, the real-time physical location of each attacking player is mapped to the area division result, and the attributes of the area where the real-time physical location is located are retrieved to determine whether these real-time physical locations fall within the range of the defensive control strong zone, thus obtaining the defensive matching area. Subsequently, in order to evaluate the overall coverage of the attacking points by the defensive system, the total number of sub-regions contained in the defensive matching area and the number of sub-regions contained in the defensive control strong zone can be counted, and the total number of sub-regions contained in the defensive matching area can be divided by the number of sub-regions contained in the defensive control strong zone to obtain the defensive advantage area coverage rate. The defensive advantage area coverage rate accurately reflects what proportion of sub-regions in the defensive team's strong control area are actually used to directly block the attacking players.

[0068] In step S603 of some embodiments, a geometric topology analysis is performed on the extracted defensive matching area. This involves statistical analysis and surface accumulation processing based on the defensive matching area to obtain the defender's defensive advantage index. Specifically, starting from each location point within the defensive matching area, a connected region gradient search is performed outwards. Sub-regions that are geometrically adjacent to each other in space are identified as independent defensive overlapping blocks, and the number of these overlapping blocks is counted to obtain the number of matching blocks. Next, the number of discrete grid cells contained in each defensive overlapping block is extracted, and the number of grid cells is multiplied by the base area of ​​a single grid cell. Then, the physical areas of all defensive overlapping blocks are summed to obtain the total defensive coverage area. Finally, the number of matching blocks and the total defensive coverage area are fused using multi-dimensional indicators to obtain the defender's defensive advantage index. This defensive advantage index can be directly used as standardized data to evaluate the compactness of the defensive formation, the elasticity of interception coverage, and the probability of defensive gaps. It can also be sent to the coach's display terminal for tactical effect demonstration.

[0069] Through steps S401 to S403, by introducing preset positive and negative thresholds, the spatial net field strength is divided into regional attributes to determine the offensive spatial advantage zone and the defensive control strong zone. In subsequent processes, the influence of either the offensive spatial advantage zone or the defensive control strong zone is quantitatively assessed to obtain the offensive side's spatial advantage index, which evaluates whether the offensive side's tactical coordination has formed an effective offensive influence; or to obtain the defensive side's defensive advantage index, which evaluates whether the defensive formation is compact, whether there are defensive gaps, and whether it covers the offensive side's offensive posture. This allows for an effective assessment of the influence generated by the tactical coordination of the offensive or defensive sides during competitive combat.

[0070] Please see Figure 7 In some embodiments, step S105 may also include, but is not limited to, steps S701 to S705: Step S701: Obtain the real-time physical positions of each athlete on the attacking side. Step S702: Based on each real-time physical location, perform numerical extraction processing on the corresponding spatial net field strength to obtain the offensive advantage value. Step S703: Obtain the individual goal data for each athlete; Step S704: Based on the individual goal data and real-time physical location, perform mapping processing to obtain the expected score data; Step S705: Based on the offensive advantage value and expected score data, conduct an impact assessment to obtain the opportunity generation index of each athlete in the real-time physical position, which is determined as the impact assessment index.

[0071] In step S701 of some embodiments, the real-time physical position of each athlete on the attacking side at the target time can be obtained through the moving tile screen in the target space area. The moving tile screen is equipped with optical sensors and pressure sensors. The optical sensors can be an array of infrared photocell sensors densely arranged at centimeter-level intervals, which can obtain position information such as the precise two-dimensional coordinates, velocity vectors and movement trajectories of the athlete's feet, the ball and the opponent. The pressure sensors can also be arranged in an array to obtain mechanical characteristic data such as the athlete's foot pressure distribution, pressure center trajectory and impact force timing, thereby obtaining the real-time physical position of each athlete on the attacking side in the target space area at the target time.

[0072] Alternatively, high-frequency optical motion capture equipment, court image sensors, or ultra-wideband positioning tags fixed to athletes can be used to collect and analyze the real-time coordinates of all individuals on the attacking side at the current moment, thereby obtaining the real-time physical position of each athlete on the attacking side.

[0073] In step S702 of some embodiments, based on each real-time physical position, numerical extraction processing is performed on the corresponding spatial net field strength to obtain the offensive advantage value. Specifically, the real-time physical position of each attacking athlete can be used for positioning to determine the specific sub-region in which the real-time physical position falls. Subsequently, the corresponding spatial net field strength value within the specific sub-region is retrieved and extracted, and the extracted spatial net field strength value is confirmed as the offensive advantage value of the athlete at that real-time physical position. The offensive advantage value directly reflects the offensive situation of the athlete's position after tactical movement at the target time. The offensive advantage value can be positive or negative. If it is a large positive number, it indicates that the attacking athlete is currently undefended and is in a very safe position to receive or shoot the ball, with a large gap in his / her position. If the offensive advantage value is negative, it indicates that the athlete is being closely guarded by a defensive player, with intense physical contact and a strong defensive pressure.

[0074] In step S703 of some embodiments, individual goal data for each athlete is acquired. This individual goal data covers the athlete's scoring statistics in previous historical matches or in the previous period of the current match, and may include, but is not limited to, total number of goals, shooting percentage in different areas, historical hot zone distribution, and scoring conversion rate under specific defensive pressure.

[0075] In step S704 of some embodiments, the expected score data is obtained by mapping the individual goal data with the real-time physical position. Specifically, the Euclidean distance and relative angle from the real-time physical position to the center point of the goal or basket are calculated, and a weighted mapping calculation is performed based on the athlete's historical shooting percentage corresponding to the distance and angle interval in the individual goal data, thereby obtaining the expected score data. The expected score data quantifies the potential probability of the athlete converting a shot from the real-time physical position into a real score.

[0076] In step S705 of some embodiments, an influence assessment is performed based on the offensive advantage value and expected score data to obtain the opportunity generation index of each athlete at their real-time physical position, which is then determined as the influence assessment index. Specifically, the offensive advantage value of each athlete at their real-time physical position is multiplied and integrated with the corresponding expected score data to calculate the athlete's opportunity generation index at that instantaneous coordinate point. The opportunity generation index takes into account both spatial safety and positional scoring expectation, and can be directly used as a quantitative assessment basis for the tactical value of the athlete's running position or receiving point at the current moment, serving as an influence assessment index.

[0077] Through steps S701 to S705, by introducing an opportunity generation index, a correlation between the quality of the spatial environment and the technical characteristics of players is established. This allows for the accurate identification of the quality of low-defensive-pressure zones created by offensive players through movement, and the calculation of expected scores is combined with shooting heat zone data. This breaks the limitation of traditional statistics that attribute opportunities entirely to the ball-handler, enabling a scientific and quantitative assessment of the implicit contributions of off-ball players in tactical execution, and clarifying the true causes of tactical opportunity generation.

[0078] In current tactical assessments, the generation of a scoring opportunity is often mechanically attributed to the ball handler's passing technique or the final shooting motion. However, in high-quality tactical plays, the essence of the opportunity often lies in the low-pressure zone created by offensive players actively moving and drawing defenders through the defensive formation. Due to the lack of dynamic quantitative indicators of spatial environment quality, current technology cannot accurately distinguish whose movement disrupted the defensive court in a tactical play, nor can it objectively and quantitatively assess the implicit contributions of athletes in tactical execution.

[0079] For this, please refer to Figure 8 In some embodiments, the embodiments of this application may include, but are not limited to, steps S801 to S803: Step S801: Based on a preset time sampling period, monitor the intensity changes of the net field strength of the offensive space advantage zone or the net field strength of the defensive control strong zone at multiple time points to obtain an influence time series change dataset. Step S802: Obtain the action events of each athlete within the time sampling period; Step S803: Based on the action events of each athlete, perform influence weight mapping with the influence time series change dataset to obtain the influence index of each action event.

[0080] In step S801 of some embodiments, in order to capture the evolution trend of offensive and defensive forces over time, intensity changes in the net spatial field strength of the offensive spatial advantage zone or the defensive control strong zone can be monitored at multiple time points based on a preset time sampling period, resulting in an influence time-series change dataset. Specifically, at each consecutive sampling node, the net spatial field strength values ​​of the entire sub-region belonging to the offensive spatial advantage zone or the defensive control strong zone are extracted at high frequency. As the time series progresses, these net spatial field strength values ​​calculated at different times are concatenated into a one-dimensional time-series data stream in chronological order, thereby generating an influence time-series change dataset. The influence time-series change dataset, from the perspective of time-series evolution, shows the fluctuations and deformation waveforms of the overall offensive and defensive control forces as the game progresses, providing a low-level timeline reference for subsequent identification of the influence of action events.

[0081] In step S802 of some embodiments, action events of each athlete within the time sampling period are acquired. These action events include technical actions with clear tactical intentions performed by athletes during the game, specifically including but not limited to passing events, breakthrough events, and shooting events performed by ball-handling athletes, and screen events and back-cut pulling events performed by non-ball-handling athletes. Each action event, when acquired, is assigned a precise start timestamp, end timestamp, and player identity tag of the executor, and is stored as an event feature element in the event cache queue.

[0082] In step S803 of some embodiments, based on the action events of each athlete, an influence weight mapping is performed with the influence time-series change dataset to obtain the influence index corresponding to each action event. Specifically, the start and end timestamps of each action event are extracted, and the field strength deformation waveform within the time window is extracted from the time-series data stream corresponding to the influence time-series change dataset. Subsequently, by calculating the integral first-order difference or peak slope change of the spatial net field strength within the time window, the net gain of the overall offensive and defensive situation before and after the action event is quantified, thereby outputting the influence index corresponding to each action event. The influence index can be directly used as a quantitative indicator to evaluate the contribution of a specific technical action to the overall offensive and defensive situation.

[0083] Through steps S801 to S803, the intensity change of the net field strength of the offensive space advantage zone or the defensive control strong zone is monitored based on a preset time sampling period to obtain the influence time series change dataset. The action events of each athlete within the time sampling period are obtained. Finally, the time series features are aligned based on the action events of each athlete and the influence time series change dataset to obtain the influence index of each action event. This allows for accurate differentiation of which action event has the greatest influence on the offensive and defensive situation in a tactical combination, and enables objective and quantitative evaluation of each athlete's contribution to the tactical execution.

[0084] Especially during the process, the generation of a scoring opportunity is often attributed to the passing skills of the ball handler or the final shooting action, but the movement of players who do not have the ball also has a significant impact. Through the embodiments of this application, it is possible to objectively and quantitatively evaluate the implicit contribution of off-ball offensive players in tactical execution.

[0085] Steps S101 to S105 as shown in the embodiments of this application involve acquiring the motion trajectory data of each athlete within a target spatial region; wherein the target spatial region includes multiple sub-regions; performing a personal influence assessment based on the motion trajectory data corresponding to each athlete to obtain the personal control field strength of the corresponding athlete at the target time; superimposing the personal control field strengths of each athlete to obtain the total field strength of the attacking side and the total field strength of the defending side; for each sub-region, comparing the field strength difference based on the total field strength of the attacking side and the total field strength of the defending side to obtain the spatial net field strength of the corresponding sub-region; dividing the region attributes based on the spatial net field strength to obtain the region division result, and performing an influence assessment based on the region division result to obtain the influence assessment index. Therefore, this application evaluates the individual influence of the movement trajectory data of each athlete within the target spatial area, transforming it into a personal control field strength that dynamically evolves with the movement state at the target time. This field strength is used to characterize the athlete's influence. The individual control field strengths of each athlete are superimposed according to their respective camps, resulting in the total field strength of the attacking side representing the group's influence and the total field strength of the defending side representing the group's interception power. Then, by comparing the field strength difference within the gridded sub-regions, a spatial net field strength is generated. Finally, an influence evaluation index is obtained based on the regional attribute division of this spatial net field strength. This optimizes the influence quantification mechanism for complex tactical cooperation and enables a more accurate assessment of the athletes' influence during competitive confrontations.

[0086] This application also provides an athlete influence assessment system that can implement the above-described athlete influence assessment method. The system includes: The motion data acquisition module is used to acquire the motion trajectory data of each athlete within a target spatial area; the target spatial area contains multiple sub-regions. The Personal Influence Acquisition Module is used to evaluate the personal influence of each athlete based on their corresponding movement trajectory data, and to obtain the athlete's personal control field strength at the target time. The Faction Influence Acquisition Module is used to superimpose the individual field strength of each athlete to obtain the total field strength of the attacking side and the total field strength of the defending side. The influence processing module is used to compare the field strength difference between the total field strength of the attacker and the total field strength of the defender for each sub-region, and obtain the spatial net field strength of the corresponding sub-region. The influence assessment module is used to divide regional attributes based on spatial net field strength, obtain regional division results, and conduct influence assessment based on regional division results to obtain influence assessment indicators.

[0087] The specific implementation method of this athlete influence assessment system is basically the same as the specific implementation method of the athlete influence assessment method described above, and will not be repeated here.

[0088] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned athlete influence assessment method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0089] Please see Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 902 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 using the athlete influence assessment method of the embodiments of this application. The input / output interface 903 is used to implement information input and output; The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904); The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0090] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described athlete influence assessment method.

[0091] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0092] The athlete influence assessment method, electronic device, and storage medium provided in this application embodiment acquire the motion trajectory data of each athlete within a target spatial region. The target spatial region includes multiple sub-regions. Based on the motion trajectory data corresponding to each athlete, an individual influence assessment is performed to obtain the individual control field strength of the corresponding athlete at the target time. The individual control field strengths of each athlete are superimposed to obtain the total field strength of the attacking side and the total field strength of the defending side. For each sub-region, the field strength difference is compared based on the total field strength of the attacking side and the total field strength of the defending side to obtain the spatial net field strength corresponding to the sub-region. Based on the spatial net field strength, regional attributes are divided to obtain regional division results, and influence assessment is performed based on the regional division results to obtain influence assessment indicators. Therefore, this application evaluates the individual influence of the movement trajectory data of each athlete within the target spatial area, transforming it into a personal control field strength that dynamically evolves with the movement state at the target time. This field strength is used to characterize the athlete's influence. The individual control field strengths of each athlete are superimposed according to their respective camps, resulting in the total field strength of the attacking side representing the group's influence and the total field strength of the defending side representing the group's interception power. Then, by comparing the field strength difference within the gridded sub-regions, a spatial net field strength is generated. Finally, an influence evaluation index is obtained based on the regional attribute division of this spatial net field strength. This optimizes the influence quantification mechanism for complex tactical cooperation and enables a more accurate assessment of the athletes' influence during competitive confrontations.

[0093] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0094] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0095] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0096] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0097] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0098] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, or indirect coupling or communication connection between the apparatus or units, and may be electrical, mechanical, or other forms.

[0100] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0101] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0102] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0103] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for evaluating athlete influence, characterized in that, The method includes: Acquire the motion trajectory data of each athlete within a target spatial region; wherein, the target spatial region comprises multiple sub-regions; Based on the motion trajectory data corresponding to each athlete, an individual influence assessment is performed to obtain the individual control field strength of the corresponding athlete at the target time. The total field strength of the attacking side and the total field strength of the defending side are obtained by superimposing the individual field strengths of each athlete. For each sub-region, the field strength difference between the total field strength of the attacking side and the total field strength of the defending side is compared to obtain the spatial net field strength corresponding to the sub-region. Based on the spatial net field strength, regional attributes are divided to obtain regional division results, and based on the regional division results, influence assessment is performed to obtain influence assessment indicators.

2. The method according to claim 1, characterized in that, The motion trajectory data includes the athlete's real-time physical position and real-time velocity vector; The step of evaluating individual influence based on the motion trajectory data corresponding to each athlete to obtain the individual control field strength of the corresponding athlete at the target time includes: Obtain the static attribute data of each athlete; For each athlete's static attribute data, a weighted calculation is performed to obtain the basic influence and the influence space radius; wherein, the basic influence is used to characterize the influence generated by the athlete based on their own ability, and the influence space radius is used to characterize the characteristic radius of the rate at which the influence decays with distance; Based on the real-time physical location, the real-time velocity vector, the basic influence, and the influence spatial radius, the individual field strength is evaluated to obtain the athlete's individual control field strength at the target location.

3. The method according to claim 2, characterized in that, After performing weighted calculations on the static attribute data for each athlete to obtain the basic influence and influence spatial radius, the method further includes: Obtain the athlete's real-time physiological energy consumption data; Based on the real-time physiological energy consumption data, fatigue quantification is performed to obtain the physical fitness fine-tuning coefficient. The basic influence or the influence space radius is adjusted based on the physical fitness fine-tuning coefficient to obtain the adjusted basic influence or the adjusted influence space radius.

4. The method according to claim 1, characterized in that, The process involves dividing the region based on the spatial net field strength to obtain the region division result, and then conducting an influence assessment based on the region division result to obtain influence assessment indicators, including: In response to the fact that the value of the net field strength of the space in the sub-region is greater than a preset positive threshold, the corresponding sub-region is divided into an offensive space advantage zone; In response to the fact that the value of the net spatial field strength of the sub-region is less than a preset negative threshold, the corresponding sub-region is divided into a strong defensive control zone; An influence assessment index is obtained by evaluating the area of ​​offensive space advantage or the area of ​​strong defensive control.

5. The method according to claim 4, characterized in that, The influence assessment based on the offensive space advantage zone or the defensive control strong zone yields influence assessment indicators, including: Based on the area statistical processing of the offensive space advantage zone, the area data of the advantage zone is obtained. The average advantage field strength data is obtained by averaging the net field strength of each coordinate point in the offensive space advantage zone. By combining the area data of the advantageous region with the average field strength data, the spatial advantage index of the attacking side at the target time is determined.

6. The method according to claim 4, characterized in that, The influence assessment based on the offensive space advantage zone or the defensive control strong zone yields influence assessment indicators, specifically including: Obtain the real-time physical positions of each athlete on the attacking side at the target time; Based on the matching of each of the real-time physical locations with the defensive control strong zone, the defensive advantage zone coverage rate and the defensive matching zone are obtained; wherein, the defensive matching zone represents the area in the defensive control strong zone that matches the real-time physical location; Based on the aforementioned defensive matching area, statistical analysis and surface accumulation processing are performed to obtain the defensive advantage index of the defending side.

7. The method according to claim 4, characterized in that, The process involves dividing the region based on the spatial net field strength to obtain the region division result, and then conducting an influence assessment based on the region division result to obtain influence assessment indicators, including: Obtain the real-time physical positions of each of the attacking players; Based on each of the real-time physical locations, numerical extraction and processing are performed on the corresponding spatial net field strength to obtain the offensive advantage value. Obtain the individual goal data for each of the aforementioned athletes; Based on the mapping between the individual goal data and the real-time physical location, the expected score data is obtained; Based on the offensive advantage value and the expected score data, an influence assessment is performed to obtain the opportunity generation index of each athlete at the real-time physical location, which is determined as the influence assessment index.

8. The method according to any one of claims 4 to 7, characterized in that, The method further includes: Based on a preset time sampling period, the intensity changes of the net field strength of the offensive space advantage zone or the net field strength of the defensive control strong zone at multiple time points are monitored to obtain an influence time series change dataset. Acquire the action events of each athlete within the time sampling period; Based on the action events of each athlete, an influence weight mapping is performed with the influence time-series change dataset to obtain the influence index corresponding to each action event.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the athlete influence assessment method according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the athlete influence assessment method according to any one of claims 1 to 8.

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