A combat digital attack and defense training intelligent detection and analysis system and method

Abstract

The application discloses a combat digital attack and defense training intelligent detection and analysis system and method, through multi-site sensing collection hardware, real-time acquisition of hitting strength, identity and hitting position data, after wireless data transmission, by a data processing terminal, invalid hitting is determined and counted according to a preset strength threshold, at the same time, the number of missed defenses, the defense rate and the limb score rate are calculated, and by means of heart rate monitoring and video collection playback, physical state tracking and key attack and defense image accurate archiving are realized, then AI intelligent analysis is used for fusion analysis of hitting strength, defense data, heart rate change and image and other multi-source information, the problems of strong subjectivity of traditional manual judgment, confusion of invalid hitting and qualified hitting, insufficient quantification of missed defense data, difficulty in real-time tracking of physical changes, lack of image retention in key attack and defense moments and difficulty in deep mining of training data are solved, and accurate quantification of attack and defense data, digital recording of the training process and intelligent technical and tactical research and judgment are realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent detection technology for combat training, and in particular to an intelligent detection and analysis system and method for digital offensive and defensive combat training. Background Technology

[0002] Combat sports include stand-up fighting styles such as Sanda, kickboxing, Muay Thai, and Taekwondo. Daily training and specialized improvement heavily rely on offensive and defensive sparring. For a long time, training assessments and tactical guidance have mainly relied on coaches' manual observation and subjective judgment. Core indicators such as striking power, striking position, number of defensive lapses, and offensive / defensive scoring rates lack precise data support, and the assessment results are greatly influenced by personal experience and subjective preferences, making errors unavoidable.

[0003] The shortcomings of traditional training models in practical application are quite prominent. Ineffective strikes due to insufficient force or inadequate power cannot be accurately distinguished from effective strikes; a large number of ineffective strikes are simply counted, failing to truly reflect an athlete's effective offensive capabilities. The frequency of missed defenses, the locations of missed defenses, and defensive weaknesses lack quantifiable methods, thus limiting the targeted nature of specialized offensive and defensive training. Changes in heart rate, fatigue levels, and post-combat recovery during combat cannot be tracked in real time, making it difficult to accurately assess the impact of declining physical fitness on the quality of offensive and defensive movements. There is a lack of precise mechanisms for retaining key offensive and defensive moments, and review training relies on manual memory, resulting in limited effectiveness. Furthermore, technical shortcomings, limb imbalances, and tactical loopholes hidden within massive amounts of training data are difficult to uncover using intelligent methods, leaving training optimization without data-driven support.

[0004] The few existing combat testing devices can only achieve simple strike counting or force display, and their functions are relatively limited. They are still lacking in areas such as specialized defensive data statistics, detailed scoring statistics by limb and body part, physical fitness analysis, and intelligent technical and tactical judgment, making it difficult to meet the core needs of professional sports teams for digital, standardized, and intelligent training.

[0005] Therefore, there is an urgent need to provide a technical solution to address the above problems. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides an intelligent detection and analysis system and method for digital combat offensive and defensive training.

[0007] Firstly, the present invention provides an intelligent detection and analysis system for digital combat offense and defense training, the technical solution of which is as follows: Multi-part sensor data acquisition hardware module, used to collect data on the striking force, identity, and striking position of combat athletes; A wireless data transmission module is used to receive and transmit the impact force data, the identity data, and the impact location data. The data processing terminal is used to receive and, based on the striking force data, the identity data, and the striking position data, determine and count invalid strikes according to a preset force threshold, calculate the number of missed defenses and the defense rate based on the total number of strikes and the number of effective defenses, and calculate the limb scoring rate based on the number of effective hits and the total number of attacks. Heart rate monitoring module, used to collect the heart rate data of the combat athlete; The video capture and playback module is used to record training process videos and synchronize them with the data processing terminal in time. It also captures and archives the corresponding images based on the valid scoring actions, missed defense actions, and invalid hitting actions determined by the data processing terminal. The AI ​​intelligent analysis module is used to load the striking force data, the identity data, the striking location data, the number of missed defenses, the defense rate, the limb scoring rate, the invalid striking statistics, the heart rate data, and the images, and generate technical diagnosis, physical fitness assessment, and tactical analysis reports through multi-source data fusion analysis.

[0008] The beneficial effects of the intelligent detection and analysis system for digital combat training of the present invention are as follows: The system of this invention acquires real-time data on striking force, identity, and striking location through multi-site sensing hardware. After wireless data transmission, the data processing terminal judges and counts invalid strikes according to a preset force threshold. At the same time, it calculates the number of missed defenses, defense rate, and limb score rate. With the help of heart rate monitoring and video acquisition and playback, it realizes the tracking of physical condition and the accurate archiving of key offensive and defensive images. Then, AI intelligent analysis integrates and analyzes multi-source information such as striking force, defense data, heart rate changes, and images. This solves the problems of traditional manual judgment being highly subjective, confusion between invalid strikes and qualified strikes, insufficient quantification of missed defense data, difficulty in real-time tracking of physical condition changes, lack of image retention for key offensive and defensive moments, and difficulty in in-depth mining of training data. It realizes the accurate quantitative acquisition of offensive and defensive data, digital recording of the training process, and intelligent technical and tactical analysis.

[0009] Based on the above solution, the intelligent detection and analysis system for digital combat offense and defense training of the present invention can be further improved as follows.

[0010] In one alternative embodiment, the multi-site sensing and acquisition hardware module includes an electronic chest protector mounted on the torso of the combat athlete, an electronic helmet mounted on the head of the combat athlete, electronic arm guards mounted on the upper limbs of the combat athlete, electronic boxing gloves mounted on the hands of the combat athlete, and electronic foot covers mounted on the feet of the athlete. The electronic chest protector, electronic helmet, and electronic arm protector are all equipped with built-in piezoelectric pressure sensors, RFID induction antennas, and wireless transmitters. The electronic boxing gloves and electronic foot covers are equipped with built-in passive RFID identification chips.

[0011] The beneficial effects of adopting the above-mentioned optional methods are as follows: Electronic chest protectors, electronic helmets, electronic arm guards, electronic boxing gloves, and electronic foot covers are further installed on the torso, head, upper limbs, hands, and feet, respectively, covering the main offensive and defensive parts of the body. The chest protectors, helmets, and arm guards have built-in piezoelectric force sensors, RFID induction antennas, and wireless transmitters, while the boxing gloves and foot covers have built-in passive RFID identification chips, enabling simultaneous acquisition of striking force, attacker identity, and striking location.

[0012] In one alternative embodiment, the electronic foot cover is divided into eight independently coded zones: the left foot is divided into the first zone of the left instep, the second zone of the left instep, the third zone of the left sole, and the fourth zone of the left heel; the right foot is divided into the fifth zone of the right instep, the sixth zone of the right instep, the seventh zone of the right sole, and the eighth zone of the right heel.

[0013] The beneficial effects of adopting the above optional method are as follows: the electronic foot cover is further divided into eight independent coded zones. The left foot is divided into the first and second zones of the instep, the third zone of the sole, and the fourth zone of the heel. The right foot is divided into the fifth and sixth zones of the instep, the seventh zone of the sole, and the eighth zone of the heel. This allows the landing point of the leg technique to be accurately distinguished and recorded, providing data support for the refined training of leg techniques.

[0014] In one alternative approach, the identity data includes an attacker's identity identifier, an attacker's limb identifier, and a striking zone location identifier; the passive RFID identification chip built into the electronic boxing gloves is used to identify the punching attacker, and the passive RFID identification chip built into each zone of the electronic foot gloves is used to identify the leg attacking attacker and the leg striking location.

[0015] The beneficial effects of adopting the above-mentioned optional method are as follows: further identifying the punching attacker by the chip built into the glove, and identifying the kicking attacker and the striking position by the chips in each section of the foot glove, so that the identity data covers the attacker, the attacking limb and the striking section identification, and realizes the automatic differentiation of the punching and kicking attacker and the landing point.

[0016] In one optional approach, the preset force threshold includes an electronic chest protector force threshold, an electronic helmet force threshold, and an electronic arm protector force threshold; the data processing terminal is specifically used for: Hit force data that is less than the corresponding protective gear force threshold is determined as invalid hit, and hit force data that is not less than the corresponding protective gear force threshold is determined as valid scoring hit.

[0017] The beneficial effects of adopting the above-mentioned optional methods are as follows: Further force thresholds are set for chest protectors, helmets and arm guards respectively. The data processing terminal will judge the blows below the threshold as unqualified blows and the blows above the threshold as scoring blows, so that the judgment standards for different parts are adapted to the differences in human tolerance and avoid attacks with insufficient force being miscounted.

[0018] In one alternative approach, the formula for calculating the number of leak prevention attempts is: in, Number of times to prevent leaks The total number of hits by the opponent. The effective defensive count of the electronic arm guard; The formula for calculating the defense rate is: in, For defense rate; The formula for calculating the limb score rate is as follows: in, Indicates the point of impact. For the first Number of effective hits to the target area For the first Total number of attacks on each part.

[0019] The beneficial effects of adopting the above optional methods are as follows: further calculate the difference between the opponent's total number of strikes and the number of times the arm guard defends by using the formula for the number of missed defenses, calculate the percentage of defensive strikes by using the formula for the defense rate, and calculate the percentage of hits by each body part by using the formula for the scoring rate of each limb, so that defensive loopholes and the success rate of limb attacks can be quantitatively presented.

[0020] In one alternative approach, the AI ​​intelligent analysis module is specifically used for: The impact force data, impact location data, number of missed defenses, and invalid impact statistics are extracted using a sensor time-series data analysis model to obtain the impact force change trend, impact location distribution characteristics, and invalid impact frequency characteristics. The human skeleton visual recognition model is used to perform motion skeleton recognition on the image to obtain striking motion trajectory data and defensive blocking posture data. The striking motion trajectory data and striking force data are time-aligned and verified, and the defensive blocking posture data are matched and verified with the number of effective defensive blocks by the electronic arm guard to obtain motion standardization score and defensive effectiveness verification result. The decision-making model of combat experts loads the trend of striking force changes, the distribution characteristics of striking positions, the frequency characteristics of invalid strikes, the standardization score of actions, the verification results of defensive effectiveness, and the heart rate data. It then performs a comprehensive evaluation according to the preset offensive efficiency judgment rules, defensive ability judgment rules, and physical condition judgment rules in the combat-specific rule library to obtain the technical diagnosis, the physical assessment, and the tactical analysis report.

[0021] The beneficial effects of adopting the above-mentioned optional methods are as follows: further extract the trend of striking force changes, position distribution and frequency of non-standard strikes through time series data analysis model, identify striking trajectory and defensive posture through skeletal visual recognition model and verify with sensor data, and then generate technical diagnosis, physical fitness assessment and tactical analysis reports through comprehensive scoring by combat expert decision model, supporting multi-source data fusion and technical and tactical judgment.

[0022] Secondly, this invention provides an intelligent detection and analysis method for digital combat offense and defense training. The technical solution of this method is as follows: The multi-part sensor data acquisition hardware module collects data on the striking force, identity, and striking position of the combat athlete. The wireless data transmission module receives and transmits the impact force data, the identity data, and the impact location data. The data processing terminal receives and, based on the striking force data, the identity data, and the striking location data, determines and counts invalid strikes according to a preset force threshold, calculates the number of missed defenses and the defense rate based on the total number of strikes and the number of effective defenses, and calculates the limb scoring rate based on the number of effective hits and the total number of attacks. The heart rate monitoring module collects the heart rate data of the combat athletes; The video capture and playback module records the training process video and synchronizes it with the data processing terminal in time. Based on the valid scoring actions, missed defense actions, and invalid hitting actions determined by the data processing terminal, the corresponding images are extracted and archived. The AI ​​intelligent analysis module loads the striking force data, the identity data, the striking location data, the number of missed defenses, the defense rate, the limb scoring rate, the invalid strike statistics, the heart rate data, and the images, and generates technical diagnostic, physical fitness assessment, and tactical analysis reports through multi-source data fusion analysis.

[0023] The beneficial effects of the intelligent detection and analysis method for digital combat offense and defense training of the present invention are as follows: The method of this invention acquires real-time data on striking force, identity, and striking location through multi-site sensing hardware. After wireless data transmission, the data processing terminal judges and counts invalid strikes according to a preset force threshold. At the same time, it calculates the number of missed defenses, defense rate, and limb score rate. With the help of heart rate monitoring and video acquisition and playback, it realizes the tracking of physical condition and the accurate archiving of key offensive and defensive images. Then, AI intelligent analysis integrates and analyzes multi-source information such as striking force, defense data, heart rate changes, and images. This solves the problems of traditional manual judgment being highly subjective, confusion between invalid strikes and qualified strikes, insufficient quantification of missed defense data, difficulty in real-time tracking of physical changes, lack of image retention for key offensive and defensive moments, and difficulty in in-depth mining of training data. It realizes the accurate quantitative acquisition of offensive and defensive data, digital recording of the training process, and intelligent technical and tactical analysis.

[0024] Thirdly, the technical solution of an electronic device according to the present invention is as follows: It includes a memory, a processor, and a program stored in the memory and running on the processor, wherein the processor executes the program to implement the steps of the intelligent detection and analysis method for digital combat attack and defense training as described in this invention.

[0025] Fourthly, the technical solution of a computer-readable storage medium provided by the present invention is as follows: The computer-readable storage medium stores instructions that, when read, cause the computer-readable storage medium to perform the steps of the intelligent detection and analysis method for digital combat attack and defense training as described in this invention.

[0026] 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, it can be implemented according to the contents of the specification. In order to make the above and other objects, features and advantages of the present invention more obvious and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0027] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a schematic diagram of an embodiment of the intelligent detection and analysis system for digital combat offensive and defensive training according to the present invention; Figure 2 This is a schematic diagram of another embodiment of the intelligent detection and analysis system for digital combat offensive and defensive training according to the present invention; Figure 3 A schematic diagram of the hardware components of the sensor-equipped protective gear; Figure 4 A schematic diagram of the eight-zone coding positions for the electronic foot cover; Figure 5This is a flowchart illustrating the intelligent detection and analysis method. Figure 6 This is a flowchart illustrating an embodiment of the intelligent detection and analysis method for digital combat offensive and defensive training according to the present invention. Figure 7 This is a schematic diagram of an embodiment of an electronic device according to the present invention. Detailed Implementation

[0028] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0029] Figure 1 This diagram illustrates the structure of an embodiment of an intelligent detection and analysis system for digital combat training provided by the present invention. Figure 1 As shown, this intelligent detection and analysis system for digital combat training includes: Multi-part sensor acquisition hardware module 1 is used to collect data on the striking force, identity, and striking position of combat athletes.

[0030] Among them, combat athletes refer to: personnel who participate in combat training; for example, red team athlete A and blue team athlete B conduct a Sanda (Chinese kickboxing) combat training session, and both A and B are combat athletes.

[0031] The impact force data refers to the instantaneous pressure value collected by a piezoelectric pressure sensor, used to quantify the intensity of each impact. For example, if A punches B's electronic chest protector with a straight punch, the piezoelectric pressure sensor collects a pressure value of 350 N, which is the impact force data. The identity data refers to the set of information used to uniquely identify the source of each impact action. For example, when A punches B, the passive RFID chip in A's electronic glove emits an identity code signal. This code corresponds to A's identity information. Simultaneously, the chip code on the electronic glove indicates that the impact originated from a punch, and the electronic foot glove did not participate in the impact. This identity code, together with the limb code, constitutes the identity data. The impact location data refers to information used to record the specific part of the opponent's body where each impact action lands. For example, if A kicks B's electronic chest protector in the left side with a right leg sweep, the RFID antenna built into B's electronic chest protector reads the encoding signal from the passive RFID chip built into the fifth zone of A's right foot. This fifth zone code is recorded as the impact location data.

[0032] The wireless data transmission module 2 is used to receive and transmit the impact force data, the identity data, and the impact location data.

[0033] The data processing terminal 3 is used to receive and, based on the striking force data, the identity data, and the striking position data, determine and count invalid strikes according to a preset force threshold, calculate the number of missed defenses and the defense rate based on the total number of strikes and the number of effective defenses, and calculate the limb scoring rate based on the number of effective hits and the total number of attacks.

[0034] The preset force threshold refers to the striking force standard set for different protective gears before training begins. Strikes below this standard are not considered valid and score points. For example, the coach sets a force threshold of 300 N for the electronic chest protector, 280 N for the electronic helmet, and 250 N for the electronic arm protector in the data processing terminal 3. An invalid strike is a strike whose force does not reach the preset force threshold of the corresponding protective gear; such a strike is not counted as a valid score. For example, if A hits B's electronic chest protector with a jab, the piezoelectric force sensor collects a force value of 150 N, which is lower than the preset force threshold of 300 N for the electronic chest protector. This strike is judged as invalid and is only recorded statistically without being included in the score.

[0035] The total number of strikes refers to the cumulative number of all striking actions initiated by a fighter against their opponent during a training session, including invalid strikes and valid scoring strikes. For example, in the entire training session, A launched a total of 68 attacks against B, including 42 punches and 26 kicks; 68 strikes constitute A's total number of strikes. The number of valid defenses refers to the cumulative number of times a fighter successfully blocked an opponent's striking actions using electronic arm guards worn on their upper limbs. The electronic arm guards have built-in piezoelectric pressure sensors and RFID antennas that simultaneously record each blocking event. For example, A launched a total of 68 attacks against B, of which B successfully blocked 26 using the electronic arm guards on both arms. The piezoelectric pressure sensors and RFID antennas of the electronic arm guards simultaneously record each blocking event; 26 of these blocking events constitute B's number of valid defenses.

[0036] The number of missed defenses refers to the cumulative number of times a fighter fails to successfully defend against an opponent's strikes. It is calculated by subtracting the number of effective defenses by the fighter from the total number of strikes by the opponent. For example, if A launches 68 strikes at B and B has 26 effective defenses, then B's number of missed defenses is... The defense rate refers to the percentage of times a fighter successfully defends against an opponent's strikes out of the opponent's total strikes. For example, if B has 26 effective defenses and A has 68 total strikes, B's defense rate is... .

[0037] Valid hits refer to the cumulative number of strikes by a combat athlete that reach or exceed the preset threshold of the corresponding protective gear and are judged as valid scores. For example, if A launches 68 attacks against B, a total of 24 strikes reach the threshold of the corresponding protective gear, including 12 hits to the electronic chest protector, 6 hits to the electronic helmet, and 6 hits to the electronic arm protector. These 24 hits constitute A's valid hit count. Total attacks refer to the cumulative number of attacks initiated by a combat athlete during training, counted separately for punches and kicks. For example, if the coach presets A's total punch attacks to 42 and kick attacks to 26 before training, the total of 68 punch attacks constitutes A's total attack count.

[0038] The limb score rate refers to the percentage of effective hits to a specific striking area out of the total number of attacks to that area, used to evaluate the offensive efficiency of different limbs. For example, if A's left leg technique has a total of 12 attacks, and the left leg effectively hits B's electronic chest protector 4 times, then A's left leg chest hit score rate is... .

[0039] Heart rate monitoring module 5 is used to collect the heart rate data of the combat athlete.

[0040] Heart rate data refers to the numerical sequence of heart rate beats of a combat athlete during the entire training process, which is collected in real time by a heart rate monitor. For example, the heart rate monitor collects A's heart rate data at a frequency of 1 beat / second. A's resting heart rate before training is 72 beats / min, the peak heart rate at the end of the first round is 178 beats / min, the heart rate at the end of the halftime break is 105 beats / min, and the heart rate recovers to 94 beats / min after the halftime break. These heart rate numerical sequences constitute the heart rate data.

[0041] The video acquisition and playback module 4 is used to record training process videos and synchronize them with the data processing terminal 3 in time. Based on the valid scoring actions, missed defense actions, and invalid hitting actions determined by the data processing terminal 3, the corresponding images are extracted and archived.

[0042] A valid scoring action is defined as a strike whose force reaches the preset force threshold of the corresponding protective gear and is judged as a scoring action. For example, if A strikes B's electronic helmet with a rear straight punch, the piezoelectric pressure sensor collects a force value of 320 N, reaching the preset force threshold of 280 N for the electronic helmet, and this strike is judged as a valid scoring action. A missed defense action is defined as a strike initiated by one combat athlete and which the other athlete fails to block with their electronic arm guard. For example, if A strikes B's electronic chest guard with a hook punch, and B's electronic arm guard does not record this blocking event, this strike is considered a missed defense action by B. An invalid striking action is defined as a strike whose force does not reach the preset force threshold of the corresponding protective gear. For example, if A strikes B's electronic chest guard with a forehand jab punch, the piezoelectric pressure sensor collects a force value of 180 N, which is lower than the preset force threshold of 300 N for the electronic chest guard, and this strike is judged as an invalid striking action.

[0043] AI intelligent analysis module 6 is used to load the striking force data, the identity data, the striking location data, the number of missed defenses, the defense rate, the limb scoring rate, the invalid striking statistics, the heart rate data, and the images, and generate technical diagnosis, physical fitness assessment, and tactical analysis reports through multi-source data fusion analysis.

[0044] Technical diagnosis refers to assessment conclusions generated based on the results of multi-source data fusion analysis regarding the offensive techniques, defensive capabilities, power control, limb balance, and movement standardization of combat athletes. For example, the technical diagnosis output by AI intelligent analysis indicates that A's left leg attack score is high but the right leg score is low, indicating a limb imbalance. Additionally, A's straight punch movement standardization score is 85 points, suggesting strengthening training in right leg sweeping kick power. Physical fitness assessment refers to evaluation conclusions generated based on heart rate data and training load data regarding the cardiopulmonary function level and physical fitness reserves of combat athletes. For example, the physical fitness assessment output by AI intelligent analysis indicates that A's peak heart rate reached 192 beats / min during training, and recovered to 110 beats / min after a 1-minute break, with a heart rate recovery rate of 65%. This indicates that A's physical fitness recovery ability after high-intensity combat is at a moderate level, suggesting strengthening intermittent high-intensity endurance training. A tactical analysis report refers to a comprehensive analytical conclusion on the tactical game performance of combat athletes, generated based on full data. For example, a tactical analysis report may indicate that A has more offensive attacks in the first half of training but a lower hit rate, while the counter-attack scoring rate increases significantly when A is on passive defense in the second half of training. The report recommends that A adopt a defensive counter-attack tactic in the early stages of the match rather than taking the initiative to attack.

[0045] The technical solution of this embodiment acquires data on striking force, identity, and striking location in real time through multi-site sensing hardware. After wireless data transmission, the data processing terminal 3 judges and counts invalid strikes according to a preset force threshold. At the same time, it calculates the number of missed defenses, defense rate, and limb score rate. With the help of heart rate monitoring and video acquisition and playback, it realizes the tracking of physical condition and the accurate archiving of key offensive and defensive images. Then, AI intelligent analysis integrates and analyzes multi-source information such as striking force, defense data, heart rate changes, and images. This solves the problems of traditional manual judgment being highly subjective, confusion between invalid strikes and qualified strikes, insufficient quantification of missed defense data, difficulty in real-time tracking of physical condition changes, lack of image retention for key offensive and defensive moments, and difficulty in in-depth mining of training data. It realizes the accurate quantitative acquisition of offensive and defensive data, digital recording of the training process, and intelligent technical and tactical analysis.

[0046] In one alternative embodiment, the multi-site sensing and acquisition hardware module 1 includes an electronic chest protector mounted on the torso of the combat athlete, an electronic helmet mounted on the head of the combat athlete, electronic arm guards mounted on the upper limbs of the combat athlete, electronic boxing gloves mounted on the hands of the combat athlete, and electronic foot covers mounted on the feet of the athlete.

[0047] The electronic chest protector refers to a sensor-equipped protective gear worn on the front and sides of a combat athlete's torso. It incorporates a piezoelectric force sensor, an RFID antenna, and a wireless transmitter to collect data on the force and location of strikes to the torso. For example, if A wears the electronic chest protector throughout training, covering the chest and rib area, and B strikes A's left rib area with a right sweeping kick, the piezoelectric force sensor in the electronic chest protector collects the force value, the RFID antenna reads the identification code of the chip on the instep of B's ​​right foot, and the wireless transmitter transmits the data. Similarly, the electronic helmet refers to a sensor-equipped protective gear worn on a combat athlete's head, incorporating a piezoelectric force sensor, an RFID antenna, and a wireless transmitter to collect data on the force and location of strikes to the head area. For example, if B wears the electronic helmet throughout training, and A strikes B's forehead with a rear straight punch, the piezoelectric force sensor in the electronic helmet collects the force value of the strike, the RFID antenna reads the identification code of the chip in A's electronic boxing gloves, and the wireless transmitter transmits the data.

[0048] Among them, electronic arm guards refer to: sensor-equipped protective gear worn on the forearm of combat athletes, which has built-in piezoelectric pressure sensors, RFID induction antennas and wireless transmitters to collect pressure data and attacker identification data during defensive blocking; for example, B wears electronic arm guards on both arms during training. When A throws a hook punch at B's head, B raises his left arm to block. A's glove hits B's left electronic arm guard. The built-in piezoelectric pressure sensor in the arm guard collects the blocking pressure value, and the RFID induction antenna reads the identification code of A's glove chip. This block is recorded as a valid defensive attempt.

[0049] Electronic gloves refer to sensor-equipped protective gear worn on the hands of combat athletes, containing a passive RFID chip to identify the attacker and the striker in a punch. For example, A wears electronic gloves on both hands during training, each glove containing a passive RFID chip with A's athlete identification code written into it. When A lands a straight punch on B, the RFID antenna in B's protective gear reads the chip code, thus identifying that the punch originated from A. Electronic foot gloves refer to sensor-equipped protective gear worn on the feet of combat athletes, containing a passive RFID chip divided into eight independently coded zones to identify the attacker and the specific location of a leg strike. For example, A wears electronic foot gloves on both feet during training. The first zone on the instep of the left foot contains a passive RFID chip, with the chip code corresponding to the left instep area. When A strikes B with the instep of the left foot, the RFID antenna in B's protective gear reads the chip code in that zone, thus identifying that the leg strike originated from A's left instep.

[0050] The electronic chest protector, electronic helmet, and electronic arm protector are all equipped with built-in piezoelectric pressure sensors, RFID induction antennas, and wireless transmitters. The electronic boxing gloves and electronic foot covers are equipped with built-in passive RFID identification chips.

[0051] The piezoelectric force sensor refers to a sensing element that converts the mechanical pressure generated by a strike into an electrical signal, used to quantify the force of each strike or block. For example, if A hits B's electronic chest protector with a hook punch, the piezoelectric force sensor built into the chest protector generates an electrical signal proportional to the pressure after being mechanically compressed. The data processing terminal 3 converts this electrical signal into a force value of 350 N. The RFID inductive antenna refers to an antenna assembly installed inside the electronic chest protector, electronic helmet, and electronic arm protector, used to read the identification code signal emitted by the passive RFID identification chip built into the electronic gloves and electronic foot gloves. For example, when A hits B's electronic chest protector with his right foot, the RFID inductive antenna built into B's chest protector receives the radio frequency signal emitted by the partition chip in A's right foot glove. The data processing terminal 3 parses this signal into the corresponding partition code and athlete identification code. A wireless transmitter refers to a wireless signal transmitting device installed inside an electronic chest protector, electronic helmet, and electronic arm protector. It is used to transmit force data collected by a piezoelectric force sensor and identification data read by an RFID induction antenna wirelessly. For example, after A hits B's electronic chest protector, the wireless transmitter built into the chest protector packages the impact force value of 350 N, A's identification code, and the impact location code into a data packet and sends it to the wireless data transmitter in real time.

[0052] Among them, the passive RFID identification chip refers to an RFID chip that does not require a built-in power supply, obtains energy from the radio frequency field emitted by the RFID induction antenna and returns an encoded signal, and is used to uniquely identify the wearer's identity and the striking part; for example, the passive RFID identification chip in the electronic boxing glove worn by A does not have a battery. When the chip enters the working range of the RFID induction antenna built into B's chest protector, the chip obtains energy from the radio frequency field emitted by the induction antenna and returns A's identity encoding signal, thereby completing the identity identification.

[0053] In the aforementioned optional methods, electronic chest protectors, electronic helmets, electronic arm guards, electronic boxing gloves, and electronic foot covers are further configured on the torso, head, upper limbs, hands, and feet, respectively, covering the main offensive and defensive parts of the body. The chest protectors, helmets, and arm guards have built-in piezoelectric force sensors, RFID induction antennas, and wireless transmitters, while the boxing gloves and foot covers have built-in passive RFID identification chips, enabling simultaneous acquisition of striking force, attacker identity, and striking location.

[0054] In one alternative embodiment, the electronic foot cover is divided into eight independently coded zones: the left foot is divided into the first zone of the left instep, the second zone of the left instep, the third zone of the left sole, and the fourth zone of the left heel; the right foot is divided into the fifth zone of the right instep, the sixth zone of the right instep, the seventh zone of the right sole, and the eighth zone of the right heel.

[0055] The first zone of the left instep refers to an independent coded sensing area covering the front side of the left instep near the toes within the left foot region of the electronic foot cover. This area is used to identify leg strikes occurring in the front of the left foot. For example, if A kicks B's electronic chest protector with the front of their left foot, the RFID antenna inside B's chest protector reads the chip code corresponding to the first zone of the left instep in A's left foot electronic foot cover. Based on this, the data processing terminal 3 determines that the leg strike location is the first zone of the left foot. The second zone of the left instep refers to an independent coded sensing area covering the back side of the left foot near the ankle within the left foot region of the electronic foot cover. This area is used to identify leg strikes occurring in the back of the left foot. For example, if A kicks B's electronic arm guard with the back of their left foot, the RFID antenna inside B's arm guard reads the chip code corresponding to the second zone of the left foot in A's left foot electronic foot cover. Based on this, the data processing terminal 3 determines that the leg strike location is the second zone of the left foot. The third zone on the left sole refers to an independently coded sensing area covering the sole of the left foot within the electronic foot cover, used to identify leg strikes at the left sole location. For example, if A steps on or kicks B's electronic chest protector with the sole of their left foot, the RFID antenna inside B's chest protector reads the chip code corresponding to the third zone on the left sole of A's electronic foot cover, and the data processing terminal 3 determines the leg strike location to be the third zone on the left sole. The fourth zone on the left heel refers to an independently coded sensing area covering the left heel within the electronic foot cover, used to identify leg strikes at the left heel location. For example, if A turns and kicks B's electronic chest protector with their left heel, the RFID antenna inside B's chest protector reads the chip code corresponding to the fourth zone on the left heel of A's electronic foot cover, and the data processing terminal 3 determines the leg strike location to be the fourth zone on the left heel.

[0056] The fifth zone of the right instep refers to an independent coded sensing area covering the front side of the right instep near the toes within the right foot region of the electronic foot cover. This area is used to identify leg strikes occurring in the front of the right foot. For example, if A strikes the left side of B's ​​electronic chest protector with a sweeping kick from the front of the right foot, the RFID antenna inside B's chest protector reads the chip code corresponding to the fifth zone of the right instep in A's right foot electronic foot cover. Based on this, the data processing terminal 3 determines that the leg strike location is the fifth zone of the right instep. The sixth zone of the right instep refers to an independent coded sensing area covering the back side of the right foot near the ankle within the right foot region of the electronic foot cover. This area is used to identify leg strikes occurring in the back of the right foot. For example, if A kicks B's electronic arm guard with the back of the right foot, the RFID antenna inside B's arm guard reads the chip code corresponding to the sixth zone of the right instep in A's right foot electronic foot cover. Based on this, the data processing terminal 3 determines that the leg strike location is the sixth zone of the right instep. The seventh zone on the right sole refers to an independently coded sensing area covering the sole of the right foot within the electronic foot cover, used to identify leg strikes at the location of the right sole. For example, if A kicks forward with the sole of their right foot against B's electronic chest protector, the RFID antenna inside B's chest protector reads the chip code corresponding to the seventh zone on the right sole of A's electronic foot cover, and the data processing terminal 3 determines that the leg strike location is the seventh zone on the right sole. The eighth zone on the right heel refers to an independently coded sensing area covering the heel area within the electronic foot cover, used to identify leg strikes at the location of the right heel. For example, if A turns and swings their right heel to strike B's electronic helmet, the RFID antenna inside B's helmet reads the chip code corresponding to the eighth zone on the right heel of A's electronic foot cover, and the data processing terminal 3 determines that the leg strike location is the eighth zone on the right heel.

[0057] In the above-mentioned optional methods, the electronic foot sleeve is further divided into eight independent coded zones. The left foot is divided into the first and second zones of the instep, the third zone of the sole, and the fourth zone of the heel. The right foot is divided into the fifth and sixth zones of the instep, the seventh zone of the sole, and the eighth zone of the heel. This allows the landing point of the leg technique to be accurately distinguished and recorded, providing data support for the refined training of leg techniques.

[0058] In one alternative approach, the identity data includes an attacker's identity identifier, an attacker's limb identifier, and a striking zone location identifier; the passive RFID identification chip built into the electronic boxing gloves is used to identify the punching attacker, and the passive RFID identification chip built into each zone of the electronic foot gloves is used to identify the leg attacking attacker and the leg striking location.

[0059] The attacker's identification refers to the unique identity information of the striking initiator, determined by a passive RFID chip. For example, the passive RFID chips in A's electronic boxing gloves and electronic boots contain A's athlete number. When A strikes B, the RFID antenna in B's protective gear reads this number and uploads it to the data processing terminal 3. The data processing terminal 3 confirms A as the attacker based on this number. The attacking limb identification refers to the limb type of the strike, determined by the type of protective gear containing the passive RFID chip, including both punching and kicking attacks. For example, when A strikes B with a straight punch, the RFID antenna in B's protective gear reads the code from the chip in A's electronic boxing gloves, and the data processing terminal 3 identifies the attack as a punching attack based on the chip's origin. When A strikes B with a sweeping kick, the code from the chip in A's electronic boots is read, and the data processing terminal 3 identifies the attack as a kicking attack. The striking zone location identifier refers to the specific striking point location information determined by the independent coding of each zone of the electronic foot cover. For example, when A strikes B's electronic chest protector with a sweeping kick with the instep of his right foot, the RFID sensing antenna inside B's chest protector reads the chip code of the fifth zone of the right foot cover in A's right foot cover. The data processing terminal 3 determines the striking point location as the fifth zone of the right foot cover based on this code.

[0060] Among them, the subject of the punch attack refers to the identity of the punch attack initiator determined by the passive RFID identification chip built into the electronic boxing glove; for example, A punches B's electronic helmet with a left straight punch, and the RFID sensing antenna inside B's helmet reads the identity code of the chip inside A's left fist electronic boxing glove. The data processing terminal 3 determines that the subject of the punch attack is A based on the code.

[0061] The "leg attack subject" refers to the identity of the attacker, determined by the passive RFID identification chip embedded in each section of the electronic ankle monitor. For example, if A strikes B's electronic chest protector with a right sweeping kick, the RFID antenna inside B's chest protector reads the identification code from the chip inside A's right ankle monitor. The data processing terminal 3 then determines that A is the attacker based on this code. The "leg strike location" refers to the specific point of impact of the kick, determined by the independent codes in each section of the electronic ankle monitor. For example, if A strikes B's electronic chest protector with a right instep sweeping kick, the RFID antenna inside B's chest protector reads the chip code in the fifth section of A's right instep within the ankle monitor. The data processing terminal 3 then determines the strike location as the fifth section of A's right instep based on this code.

[0062] In the above-mentioned optional methods, the attacker of punches is further identified by the chip built into the gloves, and the attacker of kicks and the striking position are identified by the chips in each section of the foot gloves. This makes the identity data cover the attacker, the attacking limb, and the striking section identification, so as to achieve automatic differentiation between the attacker of punches and kicks and the landing point.

[0063] In one optional embodiment, the preset force threshold includes an electronic chest protector force threshold, an electronic helmet force threshold, and an electronic arm protector force threshold; the data processing terminal 3 is specifically used for: Hit force data that is less than the corresponding protective gear force threshold is determined as invalid hit, and hit force data that is not less than the corresponding protective gear force threshold is determined as valid scoring hit.

[0064] The electronic chest protector force threshold refers to the minimum force required to determine whether a strike to the electronic chest protector area constitutes a valid score. This threshold is preset by the coach before training. For example, the coach sets the electronic chest protector force threshold to 300 N on the data processing terminal 3. If A strikes the electronic chest protector B with a force of 350 N, it is considered a valid score. If A strikes the electronic chest protector B with a force of 250 N, which is below the threshold, it is considered an invalid strike. The electronic helmet force threshold refers to the minimum force required to determine whether a strike to the electronic helmet area constitutes a valid score. This threshold is preset by the coach before training. For example, the coach sets the electronic helmet force threshold to 280 N on the data processing terminal 3. If A strikes the electronic helmet B with a force of 320 N, it is considered a valid score. If A strikes the electronic helmet B with a force of 260 N, which is below the threshold, it is considered an invalid strike. The electronic arm guard force threshold refers to the minimum force standard required to determine whether a strike to the electronic arm guard area constitutes a valid score. It is preset by the coach before training. For example, the coach sets the electronic arm guard force threshold to 250N on the data processing terminal 3. If A hits the electronic arm guard B with a force of 270N, it is considered a valid score if the force reaches the threshold. If A hits the electronic arm guard B with a force of 220N, which is below the threshold, it is considered an invalid strike.

[0065] In the above-mentioned optional methods, force thresholds are further set for chest protectors, helmets and arm guards respectively. The data processing terminal 3 will judge the blows below the threshold as unqualified blows and the blows above the threshold as scoring blows, so that the judgment standards for different parts are adapted to the differences in human tolerance and avoid attacks with insufficient force being miscounted.

[0066] In one alternative approach, the formula for calculating the number of leak prevention attempts is: in, Number of times to prevent leaks The total number of hits by the opponent. The effective defensive count for the electronic arm guard.

[0067] The formula for calculating the defense rate is: in, For defense rate.

[0068] The formula for calculating the limb score rate is as follows: in, Indicates the point of impact. For the first Number of effective hits to the target area For the first Total number of attacks on each part.

[0069] Among the above-mentioned optional methods, the difference between the opponent's total number of strikes and the number of times the arm guard defends is further calculated using the formula for the number of missed defenses, the percentage of defensive strikes is calculated using the formula for the defense rate, and the percentage of hits to each body part is calculated using the formula for the scoring rate of each limb, so that defensive loopholes and the success rate of limb attacks can be quantitatively presented.

[0070] In one alternative embodiment, the AI ​​intelligent analysis module 6 is specifically used for: By using a sensor time-series data analysis model, time-series features are extracted from the impact force data, impact location data, number of missed defenses, and invalid impact statistics to obtain the impact force change trend, impact location distribution characteristics, and invalid impact frequency characteristics.

[0071] The sensor time series data analysis model refers to an algorithmic model used for mathematical modeling and feature extraction of the impact force time series, impact position time series, and invalid impact frequency series collected by the piezoelectric pressure sensor and RFID inductive antenna. For example, the sensor time series data analysis model loads the impact force data series of A throughout the training process, extracts the impact force change trend of A, which is 320 N in the first round, decreases to 280 N in the second round, and decreases to 240 N in the third round. At the same time, it extracts the distribution characteristics of A's impact position concentrated in the electronic chest protector area and the frequency characteristics of the gradually increasing invalid impact frequency.

[0072] The striking force trend refers to the change in striking force data over the entire training timeline, used to assess the stability of a combat athlete's power exertion. For example, the sensor time-series data analysis model statistically analyzes A's striking force data round by round. In the first round, A's average striking force is 320 N, in the second round it is 290 N, and in the third round it is 260 N, showing a decreasing trend in striking force round by round. The striking position distribution characteristics refer to the proportion of striking actions on different parts of the opponent's body, used to identify the combat athlete's offensive habits and attack preferences. For example, the sensor time-series data analysis model statistically analyzes A's striking position data. A hit B's electronic chest protector a total of 28 times, accounting for 70% of all effective hits; hit the electronic helmet 8 times, accounting for 20%; and hit the electronic arm protector 4 times, accounting for 10%. This distribution ratio represents A's striking position distribution characteristics. Invalid hit frequency characteristics refer to the frequency of invalid hits throughout the training process and their distribution over time, used to assess the power endurance of combat athletes. For example, the sensor time series data analysis model statistically analyzes the invalid hit data of A. A has 3 invalid hits in the first round, 5 invalid hits in the second round, and 10 invalid hits in the third round. The invalid hit frequency shows an increasing trend in each round, and this trend is the invalid hit frequency characteristic of A.

[0073] Specifically, a sensor time-series data analysis model is used to mathematically model and extract features from the distribution of striking force data, striking location data, missed defense counts, and invalid strike statistics along the training timeline. Striking force data is collected by a piezoelectric pressure sensor, striking location data is identified by an RFID inductive antenna, and missed defense counts and invalid strike statistics are calculated by a data processing terminal. Using time-series data from the entire training process as input, the sensor time-series data analysis model extracts the trend of striking force changes along the timeline and outputs the striking force change trend; it extracts the distribution proportion of striking actions on different parts of the opponent's body and outputs the striking location distribution characteristics; and it extracts the frequency and temporal distribution patterns of invalid strikes throughout the training process and outputs the invalid strike frequency characteristics.

[0074] The human skeleton visual recognition model is used to perform motion skeleton recognition on the image to obtain striking motion trajectory data and defensive blocking posture data. The striking motion trajectory data and striking force data are time-aligned and verified, and the defensive blocking posture data are matched and verified with the number of effective defensive blocks by the electronic arm guard to obtain the motion standardization score and defensive effectiveness verification result.

[0075] Among them, the human skeleton visual recognition model refers to an image processing model based on convolutional neural networks, which is used to identify skeletal key points and classify actions of human postures and movements in training videos. For example, the human skeleton visual recognition model processes the image segment of A hitting B's electronic chest protector. By detecting the movement trajectory of the skeletal key points of A's shoulder, elbow and wrist, it identifies A's action as a rear straight punch. At the same time, it detects the position of the skeletal key points of B's ​​arms and determines that B did not make an effective blocking action. The recognition result is the striking action trajectory data and the defensive blocking posture data.

[0076] The striking motion trajectory data refers to the positional changes of key skeletal points of the striking party's limbs extracted from images using a human skeletal visual recognition model over a continuous time sequence. This data is used to identify the type of striking motion and assess its standardization. For example, the human skeletal visual recognition model processes an image segment of A hitting B's electronic chest protector, extracting the continuous motion trajectory of A's right fist from its initial position to the point of impact. This trajectory shows that A's elbow did not significantly turn outward during the punch, and his wrist remained straight, conforming to the standard straight punch motion. The defensive blocking posture data refers to the positional information of key skeletal points of the defending party's limbs at the moment of impact, extracted from images using a human skeletal visual recognition model. This data is used to determine whether the defending party has made an effective defensive action. For example, the human skeletal visual recognition model performs single-frame analysis on the image of A hitting B's electronic chest protector. It detects that the key skeletal points of B's ​​arms are both below the shoulders, and B's arms are not in front of the electronic chest protector area. The defensive blocking posture data determines that B did not make an effective block at that moment.

[0077] The action standardization score refers to a quantitative evaluation score generated by comparing striking action trajectory data with a standard action template, used to measure the standardization of a combat athlete's striking actions. For example, the human skeleton visual recognition model compares A's straight punch trajectory data with a standard straight punch trajectory template. A's elbow outward angle during the punch is 12 degrees, exceeding the standard value by 5 degrees, resulting in an action standardization score of 78 points. The defensive effectiveness verification result refers to the accuracy assessment conclusion of the defensive action generated by cross-referencing the defensive blocking posture data determined by the human skeleton visual recognition model with the number of effective defenses recorded by the electronic arm protector sensor. For example, the human skeleton visual recognition model determines that B's arms were in the effective blocking area at the moment A hits, but B's electronic arm protector sensor did not record this blocking event. The two are inconsistent, and the defensive effectiveness verification result indicates that B's defensive action was inadequate, prompting the coach to further verify whether this defense should be counted as a valid defense.

[0078] Specifically, a human skeleton visual recognition model, based on a convolutional neural network, identifies key skeletal points and classifies movements in training process footage recorded by the video capture and playback module. It extracts positional changes of key skeletal points on the striking limbs over a continuous time series, outputting striking trajectory data. Simultaneously, it extracts the position and posture of key skeletal points on the defending limbs at the moment of impact, outputting defensive blocking posture data. The model then performs time alignment verification between the striking trajectory data and striking force data using a globally unified timestamp. The synchronization accuracy is verified by comparing the timing of the impact indicated by the trajectory data with the peak striking pressure recorded by the force data. Furthermore, the model matches the defensive blocking posture data with the number of effective defensive blocks recorded by the electronic arm guard sensor. The accuracy of the defensive data is verified by comparing the valid blocking actions determined by the posture data with the number of effective defensive blocks recorded by the sensor. The human skeleton visual recognition model generates a motion standardization score based on the comparison between the striking motion trajectory data and the standard motion template, and generates a defense effectiveness verification result based on the matching and verification results between the defensive blocking posture data and the data recorded by the electronic arm protector sensor.

[0079] The decision-making model of combat experts loads the trend of striking force changes, the distribution characteristics of striking positions, the frequency characteristics of invalid strikes, the standardization score of actions, the verification results of defensive effectiveness, and the heart rate data. It then performs a comprehensive evaluation according to the preset offensive efficiency judgment rules, defensive ability judgment rules, and physical condition judgment rules in the combat-specific rule library to obtain the technical diagnosis, the physical assessment, and the tactical analysis report.

[0080] Among them, the combat expert decision model refers to a fusion decision model that integrates the output features of the sensor time-series data analysis model and the output results of the human skeleton visual recognition model, and performs a comprehensive score based on combat-specific knowledge rules. For example, the combat expert decision model loads A's striking force change trend, striking position distribution characteristics, invalid striking frequency characteristics, action standardization score, defensive effectiveness verification results, and heart rate data, performs weighted synthesis according to the scoring rules in the combat-specific rule base, and outputs A's technical diagnosis, physical fitness assessment, and tactical analysis report.

[0081] The combat-specific rule base refers to a knowledge base that stores specific evaluation rules for combat sports training and competition, used to support the comprehensive scoring logic of the combat expert decision-making model. For example, the combat-specific rule base stores rules for judging offensive efficiency, defensive ability, and physical condition. The offensive efficiency rule stipulates that hitting the torso earns 1 point and hitting the head earns 2 points. The defensive ability rule stipulates that a defensive rate higher than 60% is excellent. The physical condition rule stipulates that a heart rate recovery rate lower than 50% is insufficient. The combat expert decision-making model calls the above rules to perform comprehensive scoring.

[0082] The offensive efficiency judgment rule refers to the judgment logic that evaluates a combat athlete's offensive ability by calculating the score rate based on the number of effective hits and the total number of attacks, combined with the weighting of the striking positions. For example, the offensive efficiency judgment rule in the combat-specific rule base stipulates that the weighting coefficient for hitting the head area is 1.5, and the weighting coefficient for hitting the torso area is 1.0. If A's effective hits include 6 head hits and 18 torso hits, then A's effective hits, after weighting, are... Divide this by the total number of attacks by A (68) to obtain A's weighted attack efficiency value. The defensive ability assessment rules refer to the logic for evaluating a combat athlete's defensive ability based on the ratio of missed defenses to total strikes, combined with the results of defensive effectiveness verification. For example, the defensive ability assessment rules in the combat-specific rule base stipulate that a defensive rate above 60% is excellent, 40% to 60% is good, and below 40% is needing improvement. B's defensive rate is 38.2%. Considering that some defensive actions were not recorded by sensors in the defensive effectiveness verification results, the combat expert decision-making model, based on the defensive ability assessment rules, comprehensively evaluates B's defensive ability as needing improvement. The physical fitness assessment rules refer to the logic for evaluating a combat athlete's physical reserves and recovery ability based on peak heart rate, mid-break recovery heart rate, and heart rate recovery rate. For example, the physical fitness assessment rules in the combat-specific rule base stipulate that a heart rate recovery rate above 80% is excellent, 60% to 80% is good, and below 60% is needing improvement. A's heart rate recovery rate is 65%, and the physical fitness assessment rules evaluate his physical fitness recovery ability as good.

[0083] Specifically, the combat expert decision-making model incorporates striking force trends, striking position distribution characteristics, invalid striking frequency characteristics, movement standardization scores, defensive effectiveness verification results, and heart rate data. A comprehensive score is then calculated based on pre-set offensive efficiency, defensive ability, and physical condition assessment rules within the combat-specific rule base. The offensive efficiency assessment rule calculates the score ratio based on the number of valid hits to the total number of attacks, combined with striking position weights to evaluate offensive ability. The defensive ability assessment rule evaluates defensive ability based on the ratio of missed defenses to the total number of hits, combined with defensive effectiveness verification results. The physical condition assessment rule evaluates physical reserves and recovery ability based on peak heart rate, mid-break recovery heart rate, and heart rate recovery rate from the heart rate data. The combat expert decision-making model outputs technical diagnostic, physical fitness assessment, and tactical analysis reports. The technical diagnostic includes offensive technique analysis results, defensive ability analysis results, force control analysis results, limb balance analysis results, and movement standardization analysis results. The physical fitness assessment includes cardiopulmonary function analysis results, and the tactical analysis report includes tactical game analysis results.

[0084] It should be noted that the combat expert decision-making model adopts a weighted fusion strategy. The first weight set is assigned to the striking force change trend, striking position distribution characteristics, and invalid striking frequency characteristics output by the sensor time series data analysis model. The second weight set is assigned to the action standardization score and defense effectiveness verification results output by the human skeleton visual recognition model. The third weight set is assigned to the heart rate data. Each weight set is dynamically adjusted according to the three categories of judgment rules of offense, defense, and physical fitness preset in the combat-specific rule base. Finally, the comprehensive score of each dimension is obtained by weighted summation.

[0085] Among the above-mentioned optional methods, the trend of striking force changes, position distribution and frequency of non-compliant strikes are further extracted through time-series data analysis models, the striking trajectory and defensive posture are identified through skeletal visual recognition models and verified with sensor data, and then comprehensive scoring is performed through combat expert decision-making models to generate technical diagnosis, physical fitness assessment and tactical analysis reports, supporting multi-source data fusion and technical and tactical judgment.

[0086] In another embodiment of the intelligent detection and analysis system for digital combat offense and defense training of the present invention, the following contents are included: like Figure 2As shown, the intelligent detection and analysis system for digital combat training includes a multi-body sensor acquisition hardware module 1, a wireless data transmission module 2, a data processing terminal 3, a video acquisition and playback module 4, a heart rate monitoring module 5, and an AI intelligent analysis module 6. The multi-body sensor acquisition hardware module 1 is located at the multi-source sensing acquisition hardware layer, the wireless data transmission module 2 is located at the 2.4G wireless synchronous transmission layer, the data processing terminal 3 is located at the core data processing and computation layer, the video acquisition and playback module 4 is located at the video visualization and review layer, and the AI ​​intelligent analysis module 6 is located at the AI ​​intelligent analysis application layer. Each module operates in a hierarchical manner. The multi-body sensor acquisition hardware module 1 collects raw offensive and defensive and physical fitness data, which is then uploaded in real-time to the data processing terminal 3 via the wireless data transmission module 2. The video acquisition and playback module 4 and the heart rate monitoring module 5 synchronously collect data, and finally, the AI ​​intelligent analysis module 6 completes multi-dimensional data analysis and outputs results.

[0087] The multi-part sensor acquisition hardware module 1 is used to collect data on the striking force, identity, and striking location of the combat athlete. The multi-part sensor acquisition hardware module 1 adopts a dual-sensor architecture combining piezoelectric force sensing and RFID identification positioning. For example... Figure 3 As shown, the core hardware includes an electronic chest protector 7, an electronic helmet 8, electronic arm guards 9, electronic boxing gloves 10, electronic foot covers 11, a heart rate monitor 12, and a wireless receiver 13. The electronic chest protector 7, electronic helmet 8, and electronic arm guards 9 all have built-in piezoelectric pressure sensors 14, RFID antennas 15, and wireless transmitters 16, enabling simultaneous collection of striking pressure data and attack / defense identity data. The electronic boxing gloves 10 and electronic foot covers 11 have built-in passive RFID identification chips 17, enabling precise identification of the attacker and the striking position. The heart rate monitor 12 collects heart rate data, and the wireless receiver 13 receives data transmitted by the wireless transmitters 16. The electronic foot covers 11 employ an eight-segment coding structure to accurately distinguish between different striking points on the left and right feet, achieving refined traceability of leg strike locations. Figure 4As shown, the left foot of the electronic foot glove 11 is divided into four independent detection areas: left instep area 1, left instep area 2, left sole area 3, and left heel area 4. The right foot of the electronic foot glove 11 is divided into four independent detection areas: right instep area 5, right instep area 6, right sole area 7, and right heel area 8. Each of the eight areas is independently bound to an RFID identification code, accurately locating the specific position of each leg strike, enabling refined strike traceability statistics. A piezoelectric pressure sensor 14 is used to collect the pressure value at the moment of impact, converting mechanical pressure into an electrical signal and outputting strike force data. An RFID induction antenna 15 is used to read the identification code signal emitted by the passive RFID identification chip 17 built into the electronic glove 10 and electronic foot glove 11, outputting the attacker's identity, the attacker's limb identification, and the strike zone location identification. The passive RFID identification chip 17 built into the electronic glove 10 is used to identify the punching attacker. The passive RFID identification chip 17 built into each zone of the electronic foot glove 11 is used to identify the leg attacking attacker and the leg strike location. The wireless transmitter 16 is used to wirelessly transmit the impact force data collected by the piezoelectric pressure sensor 14 and the identification data read by the RFID sensing antenna 15.

[0088] The wireless data transmission module 2 receives impact force data, identity data, and impact location data sent by the multi-part sensor acquisition hardware module 1, and transmits these data to the data processing terminal 3. The wireless data transmission module 2 uses a dedicated 2.4GHz communication frequency band, exhibiting strong anti-signal interference capabilities. It supports concurrent synchronous reception and transmission of data from the multi-part sensor acquisition hardware module 1, with a data transmission delay of no more than 50 milliseconds, ensuring data upload without loss or delay during training.

[0089] Data processing terminal 3 receives and analyzes striking force data, identity data, and striking location data. It determines and counts invalid strikes according to preset force thresholds, calculates missed defenses and defense rate based on the total number of strikes and effective defenses, and calculates the limb scoring rate based on the number of effective hits and the total number of attacks. Data processing terminal 3 has built-in dedicated data processing logic, supports custom striking force thresholds, and accurately identifies invalid strikes with insufficient force. Preset force thresholds include electronic chest protector force threshold, electronic helmet force threshold, and electronic arm protector force threshold. Data processing terminal 3 determines strikes with force less than the corresponding protective gear force threshold as invalid strikes and strikes with force not less than the corresponding protective gear force threshold as effective scoring strikes. Data processing terminal 3 establishes a missed defense statistical model based on the defense data of electronic arm protector 9, accurately calculating the number of missed defenses and defense rate. Data processing terminal 3 also solidifies multi-dimensional scoring rate calculation formulas, enabling refined statistics of scoring rates for left leg, right leg, punches, and different striking parts. Data processing terminal 3 is used to input information on athletes from both the red and blue teams and preset attack and defense counts, and to complete independent calculation and comparative analysis of data from both sides.

[0090] The formula for calculating the number of leak prevention attempts is: in, Number of times to prevent leaks The total number of hits by the opponent. The effective defensive count for the electronic arm guard.

[0091] The formula for calculating defensive percentage is: in, For defense rate.

[0092] The formula for calculating the score rate by limb is: in, Indicates the point of impact. For the first Number of effective hits to the target area For the first Total number of attacks on each part.

[0093] Heart rate monitoring module 5 is used to collect the heart rate data of the combat athletes. The heart rate monitoring module 5 collects data once per second, recording the heart rate data of the combat athletes throughout their training in real time, plotting dynamic heart rate change curves, statistically analyzing peak and real-time heart rates during combat, and calculating the heart rate recovery rate during the one-minute intermission. The formula for calculating the heart rate recovery rate is: ,in Heart rate recovery rate Resting heart rate before training Heart rate at the end of halftime. To achieve the lowest possible heart rate throughout training, quantitative assessments of combat athletes' physical reserves, fatigue resistance, and recovery capabilities are conducted.

[0094] The video capture and playback module 4 records training process videos and synchronizes them with the data processing terminal 3 using millisecond-level timestamps. The video capture and playback module 4 is precisely synchronized with the system data timeline, binding each attack, defense, and missed shot action at the millisecond level. The video capture and playback module 4 is bound to a globally unified timestamp, which is synchronized with the processing timestamp of the data processing terminal 3. Based on the valid scoring actions, missed shots, and invalid attack actions determined by the data processing terminal 3, the video capture and playback module 4 extracts and archives the corresponding images. These images include pictures and short videos. The video capture and playback module 4 archives and stores the images according to the classification tags of valid scoring actions, missed shots, and invalid attack actions, as well as by action type, valid status, and attack / defense side. It supports retrieval, playback, and export operations, enabling full traceability of the training process.

[0095] The AI-powered intelligent analysis module 6 loads data on striking force, identity, striking location, number of missed defenses, defense rate, limb score rate, invalid strike statistics, heart rate, and video footage. Through multi-source data fusion analysis, it generates technical diagnostics, physical fitness assessments, and tactical analysis reports for the combat athletes. The AI-powered intelligent analysis module 6 employs a triple-fusion algorithm architecture. This architecture includes a sensor time-series data analysis model, a human skeleton visual recognition model, and a combat expert decision-making model. The sensor time-series data analysis model processes striking force, identity, striking location, number of missed defenses, defense rate, limb score rate, and invalid strike statistics. The human skeleton visual recognition model processes video footage. The combat expert decision-making model fuses the outputs of the sensor time-series data analysis model and the human skeleton visual recognition model, and generates technical diagnostics, physical fitness assessments, and tactical analysis reports based on combat-specific rules.

[0096] The sensor time-series data analysis model is used to mathematically model and extract features from the time series of striking force, striking position, and invalid striking frequency collected by the piezoelectric pressure sensor 14 and the RFID induction antenna 15, outputting the striking force change trend, striking position distribution characteristics, and invalid striking frequency characteristics. The human skeleton visual recognition model, based on a convolutional neural network, is used to identify skeletal key points and classify movements in training videos, outputting striking motion trajectory data and defensive blocking posture data. The human skeleton visual recognition model performs time alignment verification between the striking motion trajectory data and the striking force data, and matches the defensive blocking posture data with the number of effective defenses by the electronic arm guard, outputting a motion standardization score and a defense effectiveness verification result. The combat expert decision-making model loads the striking force change trend, striking position distribution characteristics, invalid striking frequency characteristics, motion standardization score, defense effectiveness verification result, and heart rate data, and performs a comprehensive score according to the preset attack efficiency judgment rules, defensive ability judgment rules, and physical condition judgment rules in the combat-specific rule base, outputting a technical diagnosis, physical fitness assessment, and tactical analysis report. The AI ​​intelligent analysis module 6 performs intelligent analysis from seven dimensions: offensive techniques, defensive capabilities, force control, limb balance, physical fitness and cardiopulmonary function, tactical game, and movement standardization.

[0097] Technical diagnostics include analysis results of offensive techniques, defensive capabilities, force control, limb balance, and movement standardization. Physical fitness assessment includes cardiopulmonary function analysis results. Tactical analysis reports include tactical game analysis results. The AI ​​intelligent analysis module 6 outputs standardized text diagnostic reports, visualization charts, and training optimization suggestions.

[0098] like Figure 5 As shown, the intelligent detection and analysis method of the intelligent detection and analysis system for digital combat offense and defense training includes six core steps: parameter configuration, device wearing and startup, multi-source data synchronous acquisition, real-time data calculation and judgment, video material archiving and extraction, and AI intelligent analysis output. This forms a closed-loop intelligent process for combat training data acquisition, calculation, review, and analysis.

[0099] In the parameter configuration step, the names, groups and levels of the red and blue team athletes are entered, the total number of punches and kicks for this training session is preset, and the electronic chest protection force threshold, electronic helmet force threshold and electronic arm protection force threshold are set respectively.

[0100] During the device donning and activation process, the combat athletes don a full set of sensor protective gear and heart rate monitoring equipment, and start synchronous video recording and wireless data transmission.

[0101] In the multi-source data synchronous acquisition step, during the confrontation training, the piezoelectric pressure sensor 14 collects the impact pressure data of each part in real time, the RFID induction antenna 15 identifies the attacker's identity, the limbs hit and the location of the impact zone, the wireless data transmission module 2 uploads the impact force data, identity data and impact location data in real time, the heart rate monitor 12 collects heart rate data at a frequency of once per second, and the video recording device records the entire process synchronously and binds the timestamp.

[0102] In the real-time data processing and judgment step, the data processing terminal 3 judges and counts invalid hits according to the preset force threshold, counts the total number of hits and the number of effective defenses by the red and blue sides in real time, calculates the number of missed defenses and the defense rate, counts the number of effective scores for chest strikes and head strikes separately for left leg, right leg and punch, calculates the score rate of each limb in each dimension, classifies and counts the number and proportion of all invalid hits, and generates a heart rate change curve simultaneously.

[0103] In the video footage archiving and extraction step, after training, the video acquisition and playback module 4 automatically identifies all valid scoring actions, missed defense actions, and invalid hitting actions, and automatically extracts the corresponding images and short videos, classifying and archiving them according to red and blue sides, action type, and valid status.

[0104] In the AI ​​intelligent analysis output step, the AI ​​intelligent analysis module 6 loads all sensor data, heart rate data, and image data to complete intelligent analysis in seven dimensions: offensive techniques, defensive capabilities, strength control, limb balance, physical fitness and cardiopulmonary function, tactical game, and movement standardization. It outputs technical diagnosis, physical fitness assessment, and tactical analysis reports, and generates visual charts and training optimization suggestions.

[0105] The following detailed description of this embodiment, with reference to specific examples, provides a digital combat offense and defense training intelligent detection and analysis system. The specific implementation process is as follows.

[0106] During the equipment deployment and debugging phase, the installation and debugging of the electronic chest protector 7, electronic helmet 8, electronic arm protector 9, electronic boxing gloves 10, eight-zone electronic foot protector 11, heart rate monitor 12, wireless receiver 13, and high-definition video recording equipment were completed. The communication frequency band was unified, the force accuracy of the piezoelectric pressure sensor 14 was calibrated, the coding of the eight zones of the electronic foot protector 11 was solidified, and the identity binding of the red and blue sides of the equipment was completed.

[0107] In the basic parameter configuration stage, the names, groups and levels of the red and blue athletes are entered into the data processing terminal 3. The total number of punches and kicks for this training session are preset, and the electronic chest protection force threshold, electronic helmet force threshold and electronic arm protection force threshold are set respectively. Hit below the threshold is automatically judged as invalid hit.

[0108] During the combat training data collection phase, combat athletes wear a full set of equipment to begin combat training. Throughout the training, the piezoelectric pressure sensor 14 collects real-time data on the striking pressure of various parts of the body, the RFID induction antenna 15 identifies the attacker's identity, the striking limbs, and the striking zone, the wireless data transmission module 2 uploads all data in real time, the heart rate monitor 12 collects heart rate data at a frequency of once per second, and the video recording device records the entire process synchronously and binds a timestamp.

[0109] In the real-time data processing stage, the data processing terminal 3 counts the total number of hits and the number of effective defenses by both the red and blue sides in real time, calculates the number of missed defenses and the defense rate, counts the number of effective scores for chest strikes and head strikes separately for left leg, right leg and punch, calculates the score rate of each limb in each dimension, classifies and counts the number and percentage of all invalid hits, and generates a real-time heart rate change curve simultaneously.

[0110] In the post-match video review and processing phase, after training, the video acquisition and playback module 4 automatically identifies all valid scoring actions, missed defensive actions, and invalid hitting actions, automatically extracts corresponding images and short videos, and archives them according to red and blue sides, action type, and valid status, supporting coaches to review and export materials with one click.

[0111] In the physical fitness data calculation process, the data processing terminal 3 extracts peak heart rate during training, heart rate change data throughout the training session, and heart rate data during the 1-minute break. It automatically calculates the heart rate recovery rate and assesses the physical fatigue level and recovery ability of the combat athletes.

[0112] In the AI ​​intelligent analysis and judgment stage, the AI ​​intelligent analysis module 6 loads all sensor data, heart rate data, and video image data to complete intelligent analysis in seven dimensions: offensive techniques, defensive capabilities, force control, limb balance, physical fitness and cardiopulmonary function, tactical game, and movement standardization. It outputs offensive and defensive data comparison reports, technical weakness diagnosis, physical fitness status assessment, tactical problem analysis, and targeted training optimization plans, and generates visual radar charts, comparison charts, change curves, and standardized analysis reports.

[0113] The technical solution of this embodiment achieves a striking force determination accuracy of 99%, an RFID location and identity recognition accuracy of 98.5%, an AI tactical analysis accuracy of 93.8%, and a data transmission latency of no more than 50 milliseconds. It meets the needs of professional combat sports teams for daily digital training, specialized defensive assessment, physical fitness testing, and tactical optimization, effectively solving various technical pain points of traditional training models.

[0114] Figure 6 This diagram illustrates a flowchart of an embodiment of an intelligent detection and analysis method for digital combat training provided by the present invention. This method employs the intelligent detection and analysis system for digital combat training provided by the present invention. Figure 6 As shown, the method includes the following steps: S1. The multi-part sensor acquisition hardware module collects data on the striking force, identity, and striking position of the combat athlete. S2. The wireless data transmission module receives and transmits the impact force data, the identity data, and the impact location data. S3. The data processing terminal receives and, based on the striking force data, the identity data, and the striking position data, determines and counts invalid strikes according to a preset force threshold, calculates the number of missed defenses and the defense rate based on the total number of strikes and the number of effective defenses, and calculates the limb scoring rate based on the number of effective hits and the total number of attacks. S4. The heart rate monitoring module collects the heart rate data of the combat athlete; S5. The video acquisition and playback module records the training process video and synchronizes it with the data processing terminal in time. Based on the valid scoring action, missed defense action and invalid hitting action determined by the data processing terminal, the corresponding images are extracted and archived. S6. The AI ​​intelligent analysis module loads the striking force data, the identity data, the striking location data, the number of missed defenses, the defense rate, the limb scoring rate, the invalid striking statistics, the heart rate data, and the images, and generates technical diagnosis, physical fitness assessment, and tactical analysis reports through multi-source data fusion analysis.

[0115] It should be noted that the beneficial effects of the intelligent detection and analysis method for digital combat offense and defense training provided in the above embodiments are the same as those of the intelligent detection and analysis system for digital combat offense and defense training described above, and will not be repeated here. Furthermore, the method and system embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the system embodiments, and will not be repeated here.

[0116] The intelligent detection and analysis system for digital combat training of the present invention can be a computer program (including program code) running on a computer device. For example, the intelligent detection and analysis system for digital combat training of the present invention is an application software that can be used to execute the corresponding steps in the intelligent detection and analysis method for digital combat training of the present invention.

[0117] In some embodiments, the intelligent detection and analysis system for digital combat training of the present invention can be implemented in a combination of hardware and software. As an example, the intelligent detection and analysis system for digital combat training of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the intelligent detection and analysis method for digital combat training of the present invention. For example, the processor in the form of a hardware decoding processor can be one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0118] The modules described in the embodiments of this invention can be implemented in software or hardware. The names of the modules are not, in some cases, limiting the scope of the module itself.

[0119] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned intelligent detection and analysis methods for digital combat training. That is, an electronic device according to an embodiment of the present invention may include, but is not limited to: a processor and a memory; the memory is used to store the computer program; the processor is used to execute the intelligent detection and analysis method for digital combat training according to any embodiment of the present invention by calling the computer program.

[0120] In one alternative embodiment, an electronic device is provided, such as Figure 7 As shown, Figure 7 The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0121] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0122] Bus 4002 may include a path for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 7 The bus 4002 is represented by only one thick line, but this does not mean that there is only one bus or one type of bus.

[0123] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0124] The memory 4003 stores application code (computer program) for executing the present invention, and its execution is controlled by the processor 4001. The processor 4001 executes the application code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0125] Among them, electronic devices can also be terminal devices. A terminal device can be any terminal device that can install applications and access web pages through applications, including at least one of smartphones, tablets, laptops, desktop computers, smart speakers, smartwatches, smart TVs, and smart in-vehicle devices.

[0126] It should be noted that, Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0127] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-mentioned intelligent detection and analysis methods for digital combat offense and defense training.

[0128] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.

[0129] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. The processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the aforementioned intelligent detection and analysis method for digital combat offense and defense training.

[0130] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0131] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0132] The computer-readable storage medium provided in this invention can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0133] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0134] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0135] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0136] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this invention can be specifically implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0137] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A digital combat offense and defense training intelligent detection and analysis system, characterized in that, include: Multi-part sensor data acquisition hardware module, used to collect data on the striking force, identity, and striking position of combat athletes; A wireless data transmission module is used to receive and transmit the impact force data, the identity data, and the impact location data. The data processing terminal is used to receive and, based on the striking force data, the identity data, and the striking position data, determine and count invalid strikes according to a preset force threshold, calculate the number of missed defenses and the defense rate based on the total number of strikes and the number of effective defenses, and calculate the limb scoring rate based on the number of effective hits and the total number of attacks. Heart rate monitoring module, used to collect the heart rate data of the combat athlete; The video capture and playback module is used to record training process videos and synchronize them with the data processing terminal in time. It also captures and archives the corresponding images based on the valid scoring actions, missed defense actions, and invalid hitting actions determined by the data processing terminal. The AI ​​intelligent analysis module is used to load the striking force data, the identity data, the striking location data, the number of missed defenses, the defense rate, the limb scoring rate, the invalid striking statistics, the heart rate data, and the images, and generate a technical diagnosis, physical fitness assessment, and tactical analysis report for the combat athlete through multi-source data fusion analysis.

2. The intelligent detection and analysis system for digital combat offense and defense training according to claim 1, characterized in that, The multi-site sensing and acquisition hardware module includes an electronic chest protector installed on the combat athlete's torso, an electronic helmet installed on the combat athlete's head, electronic arm guards installed on the combat athlete's upper limbs, electronic boxing gloves installed on the combat athlete's hands, and electronic foot covers installed on the athlete's feet. The electronic chest protector, electronic helmet, and electronic arm protector are all equipped with built-in piezoelectric pressure sensors, RFID induction antennas, and wireless transmitters. The electronic boxing gloves and electronic foot covers are equipped with built-in passive RFID identification chips.

3. The intelligent detection and analysis system for digital combat offense and defense training according to claim 2, characterized in that, The electronic foot sleeve is divided into eight independent coded zones. The left foot is divided into the first zone of the left instep, the second zone of the left instep, the third zone of the left sole, and the fourth zone of the left heel. The right foot is divided into the fifth zone of the right instep, the sixth zone of the right instep, the seventh zone of the right sole, and the eighth zone of the right heel.

4. The intelligent detection and analysis system for digital combat offense and defense training according to claim 3, characterized in that, The identity data includes the attacker's identity identifier, the attacker's limb identifier, and the striking zone location identifier; the passive RFID identification chip built into the electronic boxing gloves is used to identify the punching attacker, and the passive RFID identification chip built into each zone of the electronic foot gloves is used to identify the leg attacking attacker and the leg striking location.

5. The intelligent detection and analysis system for digital combat offense and defense training according to claim 1, characterized in that, The preset force thresholds include electronic chest protector force thresholds, electronic helmet force thresholds, and electronic arm protector force thresholds; the data processing terminal is specifically used for: Hit force data that is less than the corresponding protective gear force threshold is determined as invalid hit, and hit force data that is not less than the corresponding protective gear force threshold is determined as valid scoring hit.

6. The intelligent detection and analysis system for digital combat offense and defense training according to claim 5, characterized in that, The formula for calculating the number of leakage prevention attempts is: in, Number of times to prevent leaks The total number of hits by the opponent. The effective defensive count of the electronic arm guard; The formula for calculating the defense rate is: in, For defense rate; The formula for calculating the limb score rate is as follows: in, Indicates the point of impact. For the first Number of effective hits to the target area For the first Total number of attacks on each part.

7. The intelligent detection and analysis system for digital combat offense and defense training according to claim 1, characterized in that, The AI ​​intelligent analysis module is specifically used for: The impact force data, impact location data, number of missed defenses, and invalid impact statistics are extracted using a sensor time-series data analysis model to obtain the impact force change trend, impact location distribution characteristics, and invalid impact frequency characteristics. The human skeleton visual recognition model is used to perform motion skeleton recognition on the image to obtain striking motion trajectory data and defensive blocking posture data. The striking motion trajectory data and striking force data are time-aligned and verified, and the defensive blocking posture data are matched and verified with the number of effective defensive blocks by the electronic arm guard to obtain motion standardization score and defensive effectiveness verification result. The decision-making model of combat experts loads the trend of striking force changes, the distribution characteristics of striking positions, the frequency characteristics of invalid strikes, the standardization score of actions, the verification results of defensive effectiveness, and the heart rate data. It then performs a comprehensive evaluation according to the preset offensive efficiency judgment rules, defensive ability judgment rules, and physical condition judgment rules in the combat-specific rule library to obtain the technical diagnosis, the physical assessment, and the tactical analysis report.

8. A method for intelligent detection and analysis of digital combat offensive and defensive training, characterized in that, The method employs the intelligent detection and analysis system for digital combat offense and defense training as described in any one of claims 1 to 7, comprising: The multi-part sensor data acquisition hardware module collects data on the striking force, identity, and striking position of the combat athlete. The wireless data transmission module receives and transmits the impact force data, the identity data, and the impact location data. The data processing terminal receives and, based on the striking force data, the identity data, and the striking location data, determines and counts invalid strikes according to a preset force threshold, calculates the number of missed defenses and the defense rate based on the total number of strikes and the number of effective defenses, and calculates the limb scoring rate based on the number of effective hits and the total number of attacks. The heart rate monitoring module collects the heart rate data of the combat athletes; The video capture and playback module records the training process video and synchronizes it with the data processing terminal in time. Based on the valid scoring actions, missed defense actions, and invalid hitting actions determined by the data processing terminal, the corresponding images are extracted and archived. The AI ​​intelligent analysis module loads the striking force data, the identity data, the striking location data, the number of missed defenses, the defense rate, the limb scoring rate, the invalid strike statistics, the heart rate data, and the images, and generates technical diagnostic, physical fitness assessment, and tactical analysis reports through multi-source data fusion analysis.

9. An electronic device, characterized in that, The electronic device includes a processor coupled to a memory, the memory storing at least one computer program, which is loaded and executed by the processor to enable the electronic device to implement the intelligent detection and analysis method for digital combat offense and defense training as described in claim 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program, which, when executed by a processor, implements the intelligent detection and analysis method for digital combat offense and defense training as described in claim 8.

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