Method for verifying coordination consistency of athletes in group project

Abstract

The invention belongs to the technical field of group sports coordination evaluation, and discloses a method for verifying the coordination consistency of athletes in a group project, and the method comprises the steps: collecting all kinds of sports data generated by each athlete in the group project through a sensor; the sensor encrypts the collected motion data by adopting a homomorphic encryption method to obtain ciphertext data of each type of motion data, and uploads the ciphertext data to the server; the server calculates an encryption coordination index of each type of motion data by using the ciphertext data, applies the disturbance factor to the corresponding encryption coordination index, and sends the disturbance encryption coordination index of each type of motion data to the terminal equipment; the terminal device decrypts the received disturbance encryption coordination index of each type of motion data to obtain a disturbance coordination index plaintext value of each type of motion data; and the server performs zero-knowledge verification on the plaintext value of the disturbance coordination index according to a set verification interval, and feeds back a verification result to the terminal equipment.

Description

Technical Field

[0001] The present invention belongs to the technical field of group sports coordination evaluation, and particularly relates to a method for verifying the coordination and consistency of athletes in a group event. Background Art

[0002] Group sports events such as dragon boat racing, team rowing, group dancing, etc. have extremely high requirements for the collaboration and consistency of athletes. Existing technologies mainly rely on manual observation or centralized analysis after data collection, and there are problems such as insufficient data privacy protection, poor real-time performance, and inability to accurately quantify the coordination of group movements. At the same time, for the analysis of sports data, most methods fail to solve the privacy protection problem during data transmission and transfer, and it is difficult to verify the coordination and consistency while protecting the privacy of athletes. And the technical and tactical parameters of athletes are important confidential data. Once the data is leaked, potential opponents can analyze the technical and tactical characteristics and adjust the battle strategies accordingly, which will have a serious impact on the competitive strength and ranking of athletes. Summary of the Invention

[0003] The purpose of the present invention is to overcome the defects of the prior art and provide a method for verifying the coordination and consistency of athletes in a group event, which can keep the original sports data and coordination indexes encrypted throughout the process and evaluate the coordination and consistency of team sports on the premise of ensuring data privacy.

[0004] The technical solution provided by the present invention is as follows: A method for verifying the coordination and consistency of athletes in a group event, comprising: Collecting various sports data generated by each athlete in the group event through sensors; The sensors encrypt the collected sports data by using a homomorphic encryption method to obtain ciphertext data of various sports data, and upload the ciphertext data to the server; The server calculates the encrypted coordination indexes of various sports data by using the ciphertext data, and generates perturbation factors for the encrypted coordination indexes of various sports data respectively; Applying the perturbation factors to the corresponding encrypted coordination indexes to obtain the perturbed encrypted coordination indexes of various sports data, and sending the perturbed encrypted coordination indexes of various sports data to the terminal device; The terminal device decrypts the received perturbed encrypted coordination indexes of various sports data to obtain the plaintext values of the perturbed coordination indexes of various sports data; The server performs zero-knowledge verification on the plaintext values of the perturbed coordination indexes according to the set verification interval, determines whether the coordination indexes meet the requirements, and feeds back the verification result to the terminal device.

[0005] Preferably, before performing zero-knowledge verification, it further includes: The terminal device converts the plaintext value of the disturbance coordination index to obtain a converted plaintext value of the disturbance coordination index. The formula is as follows: ; And the server converts the verification interval to obtain a converted verification interval. The formula is as follows: ; And perform zero-knowledge verification based on the converted verification interval and the converted plaintext value of the disturbance coordination index; Wherein, is the plaintext value of the disturbance coordination index, is a preset precision control parameter, represents a positive integer, represents rounding up, represents the converted plaintext value of the disturbance coordination index, , are the lower and upper limits of the verification interval, , are respectively the lower and upper limits of the converted verification interval, represents the type of motion data.

[0006] Preferably, , ; Wherein, represents the disturbance factor, represents the lower limit value of the coordination index of the motion data, represents the upper limit value of the coordination index of the motion data, represents the type of motion data.

[0007] Preferably, the types of data collected by the sensor include: the acceleration of the action, the angle of the action, the trigger time of a specific action, and the period of a specific action.

[0008] Preferably, the calculation formula for the encryption coordination index of various types of motion data is: ; Wherein, is the re-linearization value of the square of the deviation of the th athlete's th type of motion data, is the number of athletes in the team event.

[0009] Preferably, before performing zero-knowledge verification, it further includes: The terminal device constructs a commitment based on the converted plaintext value of the disturbance coordination index, generates a proof using the zero-knowledge verification range protocol, and sends the commitment and the proof to the server together; The commitment is: ; The proof is: ; Wherein, and are system-predefined generators, is a hidden factor randomly generated by the terminal device, is a finite field integer ring, , is a prime number.

[0010] Preferably, the zero-knowledge verification range protocol adopts Bulletproofs or Sigma Protocol.

[0011] The beneficial effects of the present invention are: The verification method for the coordination and consistency of athletes in team events provided by the present invention uses distributed sensors to collect the motion data of athletes, and through homomorphic encryption calculation and zero-knowledge verification technology, evaluates the coordination and consistency of team sports on the premise of ensuring data privacy; the original motion data and coordination index remain encrypted throughout the process; the verification process conforms to the standard zero-knowledge definition and the results are credible. Brief Description of the Drawings

[0012] Figure 1 is a schematic structural diagram of the verification system for the coordination and consistency of athletes in team events described in the present invention.

[0013] Figure 2 is a flowchart of the verification method for the coordination and consistency of athletes in team events described in the present invention. Detailed Embodiments

[0014] The following further describes the present invention in detail with reference to the drawings, so that those skilled in the art can implement it according to the description in the specification.

[0015] As Figure 1-2 shown, the present invention provides a verification method for the coordination and consistency of athletes in team events, and this verification method is implemented through a verification system for the coordination and consistency of athletes in team events. The verification system for the coordination and consistency of athletes in team events consists of three types of core nodes: sensor nodes, server nodes and terminal devices.

[0016] The verification system for the coordination and consistency of athletes in team events adopts two types of core encryption / decryption mechanisms and verification means: one is the homomorphic encryption mechanism, including the encryption function and the decryption function which is used to encrypt the motion data collected by the sensors and support the calculation of coordination metrics in the encrypted state; second, a range verification mechanism based on commitment and zero-knowledge proof. The terminal device constructs a commitment value and generates a zero-knowledge proof to complete the range verification without exposing the specific values of the metrics. Among them, the sensor node has encryption capabilities for encrypting the collected data; the server node has encryption capabilities and is responsible for calculating the coordination metrics within the encrypted domain. At the same time, it has public verification capabilities for receiving the commitment and zero-knowledge proof sent by the terminal and completing the verification; the terminal device has decryption capabilities and can construct a commitment based on the decrypted coordination metric value, generate a zero-knowledge proof, and cooperate with the server to complete the verification process.

[0017] The homomorphic encryption method supports performing addition, scalar multiplication, and other calculation operations in the encrypted state to ensure the data privacy during the calculation process of the coordination metrics. By combining homomorphic encryption and zero-knowledge verification technologies, it is ensured that the motion data is not decrypted throughout the entire process, and the coordination verification result is credible and cannot be forged.

[0018] The verification method for the coordination and consistency of athletes in team events provided by the present invention is specifically implemented as follows.

[0019] 1. Motion data collection Sensor nodes are deployed on the surface or near the body of the athletes to collect various types of motion data generated by each athlete during the team movement. Define the th athlete's nd type of motion data as , where: represents different types of motion data (such as acceleration, angle, specific action trigger time, period, etc.), is the sampling time or the event occurrence time, is the time-series data or the discrete event data sequence that changes over time.

[0020] The motion data can be divided into: time-series data, the motion parameters of the athlete changing over time; event data, the timestamps of critical moments during the movement process.

[0021] 2. Motion data encryption and upload To protect the privacy of the athletes' data, the sensor nodes use fully homomorphic encryption to encrypt the collected data, enabling the server to perform calculations without decrypting during the calculation process. Let the motion data collected by the sensor node be , and its encryption process is as follows: ; where, is the encryption public key, is the encrypted motion data. The encrypted motion data is uploaded to the server through a wireless or wired network.

[0022] The server calculates the motion coordination index Without decrypting the data, the server calculates the team motion coordination index through homomorphic encryption to measure the coordination of different athletes during the movement. The index includes: synchronization deviation, dynamic consistency deviation, and overall motion rhythm tolerance. The synchronization deviation is used to measure the synchronization degree of team members at key time points; the dynamic consistency deviation is used to measure the amplitude, rhythm, and intensity consistency of team members during the entire movement process; the overall motion rhythm tolerance is used to measure the stability of the team motion cycle.

[0023] The server encrypts each type of data encrypted motion data Under the encrypted state, perform encrypted domain calculation. First, calculate the mean value of the team motion data of the motion data: ; where, is the number of athletes in the team event. Then, calculate the data deviation of each athlete: ; Furthermore, calculate the square of the deviation and perform relinearization: ; where, represents performing a relinearization operation on the result of the ciphertext multiplication to reduce the ciphertext order and maintain encryption compatibility.

[0024] Finally, calculate the overall deviation index of this type of data: ; where, is the encrypted coordination index of the motion data.

[0025] For the overall deviation index obtained by homomorphic encryption, the server cannot know the true value of the index. In order to verify the index and not disclose the true value of the index data during data transmission and processing, the server splits the index and collaborates with multiple sensors to obtain subsequent index data based on zero-knowledge verification.

[0026] 4. Coordination Index Perturbation and Verification Value Construction After the server completes the calculation of the coordination index within the homomorphic encryption domain, it obtains the encrypted coordination index of a certain type of motion data , to further verify whether it meets the preset index range requirements, the server generates a perturbation factor for each coordination index , represents a positive real number and is known only to the server.

[0027] The server performs a scalar multiplication operation in the encrypted state, applying the perturbation factor to the ciphertext of the coordination index to obtain the perturbed ciphertext value: ; Among them, the perturbed index value remains in the ciphertext state. This encrypted value is then sent to the terminal device.

[0028] 5. Verification Value Decryption and Zero-Knowledge Range Verification Construction After receiving the perturbed encrypted coordination index , the terminal device decrypts it using its own private key to obtain the perturbed plaintext value: ; Among them, is the plaintext value of the perturbed coordination index, is the plaintext value of the coordination index.

[0029] Since is only known to the server, the terminal cannot deduce the specific value of the original coordination index plaintext .

[0030] To enable this value to participate in the zero-knowledge range verification protocol, the terminal performs scaling and rounding operations on the real number form of : ; Among them, is the plaintext value of the perturbed coordination index, represents the converted plaintext value of the perturbed coordination index, is a preset precision control parameter, represents a positive integer, represents rounding up, for example represents retaining 4 decimal places of precision. This conversion maps the real value to the integer domain , facilitating subsequent range verification.

[0031] At the same time, the server also synchronously multiplies the preset verification interval by and rounds it up to obtain the integer interval: ; Among them, , ; , are the lower and upper limits of the verification interval, , are respectively the lower and upper limits of the verification interval for the conversion; represents the perturbation factor, represents the lower limit value (plaintext) of the coordination index of the motion data, represents the upper limit value (plaintext) of the coordination index of the motion data, and is set according to the actually allowable coordination deviation and represents the motion data category.

[0032] The server only knows this verification interval, and the terminal only knows , and then they perform zero-knowledge range verification to determine whether the following is satisfied: .

[0033] 6. Zero-Knowledge Range Verification Process The terminal device selects a 256-bit large prime number , constructs a finite field integer ring , and selects a generator on a cyclic group of order , . Further, according to the plaintext value of the converted perturbation coordination index, a commitment is constructed, and a proof is generated using the zero-knowledge verification range protocol. The following commitment is constructed: ; where is a hidden factor randomly generated by the terminal device.

[0034] Subsequently, the terminal uses the zero-knowledge range verification protocol (such as Bulletproofs or Sigma Protocol) to generate a proof: ; and sends to the server together. The server verifies this proof through a verification algorithm: .

[0035] If the verification passes, it means that the coordination index after perturbation meets the range requirements, and the server cannot obtain any plaintext information of or during the whole process, thus realizing zero-knowledge verification.

[0036] 7. Verification Result Feedback and Training Optimization After the server completes the verification, it returns the result of whether the verification passes to the terminal device. The terminal device can use this result to generate real-time prompts or training suggestions to assist athletes in adjusting their cooperation actions and improving the overall teamwork level of the team.

[0037] On the basis of ensuring the privacy of sports data, the present invention realizes the effective verification of the coordination index of group sports. By combining homomorphic encryption and zero-knowledge verification technologies, it is ensured that the sports data is not decrypted throughout the process, and the coordination verification result is credible and cannot be forged. This method has the advantages of high security, strong adaptability, good computing efficiency, etc., is applicable to a variety of group sports events that require high consistency in cooperation, and supports real-time feedback of results, helping to realize intelligent and refined management of sports training. Compared with the prior art, the present invention significantly improves the reliability and security of verification, and is especially applicable to scenarios with high requirements for cooperation consistency such as dragon boat racing, team rowing, group dance, etc.

[0038] The verification method provided by the present invention of the present application will be further described below in conjunction with specific embodiments.

[0039] Embodiment 1 Taking the dragon boat sport as an example, this embodiment illustrates how to achieve the verification of group sports coordination based on homomorphic encryption and zero-knowledge verification.

[0040] In dragon boat racing, team members need to row the oars highly synchronously to ensure the maximum propulsion efficiency and minimum energy loss. This method collects the motion data of each athlete through distributed sensors and realizes the verification of group sports coordination under the collaborative processing of the server, verifying the team coordination while ensuring data privacy.

[0041] S1. Motion data collection Install two types of sensors on the body surface of each athlete, namely inertial measurement units (IMUs) and contact switch sensors. Among them, the IMU is used to measure the acceleration change during the athlete's rowing process to form continuous motion data; the contact switch sensor is used to record the entry and exit times of the athlete's oar into the water to form discrete sequence data. Specifically, the oar acceleration (changing with time) of the th athlete is , and the sequence of the oar blade entry times of the athlete is .

[0042] S2. Data encryption and upload After the sensor node collects the motion data, it uses the CKKS fully homomorphic encryption scheme to encrypt the data.

[0043] Encrypt the collected continuous acceleration data to obtain the ciphertext data: ; Among them, is the encryption public key, is the encrypted acceleration data.

[0044] Encrypt the collected discrete water entry and water exit time data respectively to obtain ciphertext data: , ; Among them, is the encrypted water entry time data, is the encrypted water exit time data; represents the number of times.

[0045] After that, the sensor sends the encrypted data to the server.

[0046] S3. The server calculates the coordination index The server calculates the team coordination index in the encrypted state, specifically including time synchronization deviation, dynamic consistency deviation, and overall movement rhythm tolerance range. These calculations are all carried out in the encrypted domain, so that the server cannot directly obtain the actual movement data of the athletes.

[0047] S3-1. Calculate the time synchronization deviation (compare the times of the same water entry or water exit) Time synchronization measures whether the water entry and water exit times of different athletes are consistent. Calculate the mean value of the water entry time: ; Among them, is the number of athletes, is the mean value of the encrypted water entry time data.

[0048] Calculate the water entry time deviation of each athlete: ; Then square and re-linearize this deviation to get: ; This value reflects the deviation of each athlete from the team's average water entry time. Then calculate the mean value of the water entry time synchronization deviation of all athletes: ; The calculated is the encrypted coordination index at the water entry time (corresponding to the th water entry). The calculation method of the encrypted coordination index at the water exit time is the same as that at the water entry time, which will not be elaborated here.

[0049] S3-2. Calculate the dynamic consistency deviation Dynamic consistency measures whether the acceleration changes of different athletes' paddling are consistent, and calculates the average acceleration: ; Then calculate the ciphertext of the deviation between each athlete's acceleration data and the average value: ; Then perform the sum of squares and re-linearization operation: ; Finally, obtain the dynamic consistency deviation index: ; Among them, is the encrypted coordination index of the athlete's paddling acceleration, is the average value of the encrypted acceleration of the athlete's paddling.

[0050] S3-3. Calculate the tolerance of the overall movement rhythm The movement rhythm measures the stability of the overall paddling cycle. Calculate the paddling cycle of a single athlete: ; Then calculate the encrypted average value of the cycles of all athletes: ; Subsequently, calculate the deviation between each athlete's cycle and the average value: ; After re-linearizing the square of this deviation, we get: ; Finally, obtain the overall movement rhythm tolerance index: ; Among them, is the data of the th athlete's th paddling entry time encrypted, is the data of the th athlete's th paddling entry time encrypted; is the total number of paddles; is the average value of the encrypted paddling cycle, is the encrypted coordination index of the athlete's paddling cycle.

[0051] S4. The server generates a perturbation index and sends it to the terminal After completing the three types of coordination indexes (time synchronization deviation , dynamic consistency deviation , overall movement rhythm tolerance After the homomorphic calculation of (), the server introduces a random positive real number perturbation factor for each type of metric (where ) to hide its true value and perform a multiplication operation in the encrypted domain: ; The perturbed ciphertext result is sent to the terminal device for subsequent verification processing.

[0052] S5. The terminal decrypts the perturbed metric and generates zero - knowledge verification data After receiving the perturbed metric ciphertext by the terminal device, it performs a decryption operation using the private key to obtain the corresponding perturbed metric value: ; Since standard zero - knowledge verification protocols (such as Bulletproofs, Sigma Protocol) usually operate in the integer domain, the terminal needs to scale to the integer domain. Set the scaling precision parameter , and convert to an integer form: ; Subsequently, the terminal generates a Pedersen commitment for this integer value: ; where is the random hiding factor generated by the terminal, and are the system - preset generators.

[0053] Based on the above commitment value, the terminal uses the zero - knowledge range proof protocol to generate the following proof: ; where is the verification interval after the same scaling process, satisfying: ; In the formula, represents the lower limit value (plaintext) of the coordination index of the motion data, represents the upper limit value (plaintext) of the coordination index of the motion data, and are set according to the actually allowable coordination deviation.

[0054] The terminal device sends to the server together.

[0055] S6. The server performs zero - knowledge verification After the server receives the commitment value and zero-knowledge proof from the terminal, it executes the standard verification process using the public verification algorithm: ; If the verification passes, it indicates that the perturbed coordination index value falls within the preset perturbation range , thereby indirectly indicating that the original index meets the coordination requirements, and neither the server nor the terminal obtains any plaintext information about during this process, ensuring the complete privacy protection of the index data.

[0056] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. It can be fully applied to various fields suitable for the present invention. For those familiar with the field, additional modifications can be easily made. Therefore, without departing from the general concept defined by the claims and the equivalent scope, the present invention is not limited to the specific details and the illustrated and described examples here.

Claims

1. A method for verifying the coordination and consistency of athletes in a team event, characterized in that Including: Collecting various types of motion data generated by each athlete in a group event through sensors; The sensors encrypt the collected motion data using the homomorphic encryption method to obtain ciphertext data of various types of motion data, and upload the ciphertext data to the server; The server calculates the encrypted coordination index of various types of motion data using the ciphertext data, and generates a perturbation factor for the encrypted coordination index of each type of motion data respectively; Applying the perturbation factor to the corresponding encrypted coordination index to obtain the perturbed encrypted coordination index of various types of motion data, and sending the perturbed encrypted coordination index of various types of motion data to the terminal device; The terminal device decrypts the perturbed encrypted coordination index of various types of motion data received to obtain the plaintext value of the perturbed coordination index of various types of motion data; The server performs zero-knowledge verification on the plaintext value of the perturbed coordination index according to the set verification interval, determines whether the coordination index meets the requirements, and feeds back the verification result to the terminal device.

2. The verification method for the coordination and consistency of athletes in team events according to claim 1, characterized in that, Before performing zero-knowledge verification, it further includes: The terminal device converts the plaintext value of the perturbed coordination index to obtain the converted plaintext value of the perturbed coordination index, and the formula is as follows: ； And the server converts the verification interval to obtain the converted verification interval, and the formula is as follows: ； And performs zero-knowledge verification based on the converted verification interval and the converted plaintext value of the perturbed coordination index; Among them, is the plaintext value of the disturbance coordination index, is the preset precision control parameter, represents a positive integer, represents rounding up, represents the converted plaintext value of the disturbance coordination index, , are the lower and upper limits of the verification interval, , are respectively the lower and upper limits of the converted verification interval, represents the motion data category.

3. The verification method for the coordination and consistency of athletes in team events according to claim 2, characterized in that, ， ； Among them, represents the perturbation factor, represents the lower limit value of the coordination index of the motion data, represents the upper limit value of the coordination index of the motion data, represents the motion data category.

4. The verification method for the coordination and consistency of athletes in team events according to claim 3, characterized in that, The types of data collected by the sensors include: the acceleration of the action, the angle of the action, the trigger time of a specific action, and the period of a specific action.

5. The verification method for the coordination and consistency of athletes in team events according to claim 3 or 4, characterized in that The calculation formula for the encrypted coordination index of various types of motion data is: ； Among them, is the re-linearized value of the square of the th sports data deviation of the th athlete, and is the number of athletes in the team event.

6. The verification method for the coordination and consistency of athletes in team events according to claim 5, characterized in that, Before performing zero-knowledge verification, it further includes: The terminal device constructs a commitment based on the converted plaintext value of the perturbed coordination index, and generates a proof using the zero-knowledge verification range protocol, and sends the commitment and the proof to the server together; The commitment is as follows: ; The proof is as follows: ; Among them, and are system-predefined generators, is a hidden factor randomly generated by the terminal device, is a finite field integer ring, , is a prime number.

7. The verification method for the coordination and consistency of athletes in team events according to claim 6, characterized in that, The zero-knowledge verification range protocol uses Bulletproofs or Sigma Protocol.

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