A verification method for athletes' coordination consistency in team events

Abstract

The present invention belongs to the technical field of group sports coordination assessment and discloses a method for verifying the coordination consistency of athletes in group projects, comprising: collecting various types of sports data generated by each athlete in the group project through a sensor; the sensor encrypts the collected sports data using a homomorphic encryption method to obtain ciphertext data of the various types of sports data, and uploads the ciphertext data to a server; the server calculates encrypted coordination indicators of the various types of sports data using the ciphertext data, applies a disturbance factor to the corresponding encrypted coordination indicator, and sends the disturbed encrypted coordination indicators of the various types of sports data to a terminal device; the terminal device decrypts the received disturbed encrypted coordination indicators of the various types of sports data to obtain plaintext values ​​of the disturbed coordination indicators of the various types of sports data; the server performs zero-knowledge verification on the plaintext values ​​of the disturbed coordination indicators according to a set verification interval, and feeds back the verification results to the terminal device.

Description

Technical Field

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

[0002] Team sports such as dragon boat racing, team rowing, and group dance require extremely high levels of collaborative consistency among athletes. Existing technologies primarily rely on manual observation or centralized analysis after data collection, which poses challenges such as insufficient data privacy protection, poor real-time performance, and an inability to accurately quantify group coordination. Furthermore, most methods for analyzing sports data fail to address privacy concerns during data transmission and delivery, making it difficult to verify collaborative consistency while protecting athlete privacy. Athletes' technical and tactical parameters are crucial confidential data. Leaking this data allows potential opponents to conduct targeted analysis of their technical and tactical characteristics and adjust their strategies, severely impacting their competitive strength and ranking. Summary of the Invention

[0003] The purpose of the present invention is to overcome the defects of the existing technology and provide a verification method for the coordination consistency of athletes in team events, which can keep the original motion data and coordination indicators encrypted throughout the entire process and evaluate the coordination consistency of team sports while ensuring data privacy.

[0004] The technical solution provided by the present invention is:

[0005] A method for verifying the coordination consistency of athletes in a team event, comprising:

[0006] Collect various sports data generated by each athlete in team events through sensors;

[0007] The sensor uses homomorphic encryption to encrypt the collected motion data, obtains ciphertext data of various motion data, and uploads the ciphertext data to the server;

[0008] The server calculates encryption coordination indexes of various types of motion data using the ciphertext data, and generates disturbance factors for the encryption coordination indexes of the various types of motion data respectively;

[0009] Applying the perturbation factor to the corresponding encryption coordination index to obtain the perturbation encryption coordination index of each type of motion data, and sending the perturbation encryption coordination index of each type of motion data to the terminal device;

[0010] The terminal device decrypts the received encrypted disturbance coordination index of each type of motion data to obtain the plaintext value of the disturbance coordination index of each type of motion data;

[0011] The server performs zero-knowledge verification on the plaintext value of the disturbance 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.

[0012] Preferably, before performing zero-knowledge verification, the following steps are further included:

[0013] The terminal device converts the disturbance coordination index plaintext value to obtain a converted disturbance coordination index plaintext value, and the formula is as follows:

[0014] ;

[0015] The server converts the verification interval to obtain the converted verification interval. The formula is as follows:

[0016] ;

[0017] And zero-knowledge verification is performed based on the converted verification interval and the converted perturbation coordination index plaintext value;

[0018] in, is the plaintext value of the disturbance coordination index, is the preset precision control parameter, represents a positive integer, Indicates rounding up. represents the plaintext value of the perturbation coordination index of the conversion, , To verify the lower and upper limits of the interval, , are the lower and upper bounds of the conversion validation interval, respectively. Represents the motion data category.

[0019] Preferably, , ;

[0020] in, represents the disturbance factor, Indicates the lower limit of the coordination index of motion data, Indicates the upper limit of the coordination index of motion data, Represents the motion data category.

[0021] Preferably, the types of data collected by the sensor include: acceleration of the action, angle of the action, triggering time of a specific action, and period of the specific action.

[0022] Preferably, the calculation formula for the encryption coordination index of various types of motion data is:

[0023] ;

[0024] in, For the Athlete's The relinearized value of the square of the deviation of the motion-like data, is the number of athletes in the team event.

[0025] Preferably, before performing zero-knowledge verification, the following steps are further included:

[0026] The terminal device constructs a commitment based on the converted perturbation coordination indicator plaintext value, generates a proof using a zero-knowledge verification range protocol, and sends the commitment and proof together to the server;

[0027] The commitments stated are: ;

[0028] The proof is: ;

[0029] in, and is the system's preset generator, A hidden factor randomly generated by the terminal device, is a finite field of integer rings, , is a prime number.

[0030] Preferably, the zero-knowledge verification range protocol adopts Bulletproofs or Sigma Protocol.

[0031] The beneficial effects of the present invention are:

[0032] The method for verifying the coordination consistency of athletes in team events provided by the present invention utilizes distributed sensors to collect athletes' motion data, and through homomorphic encryption calculation and zero-knowledge verification technology, evaluates the coordination consistency of team sports while ensuring data privacy; the original motion data and coordination indicators remain encrypted throughout the entire process; the verification process conforms to the standard zero-knowledge definition, and the results are credible. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a schematic diagram of the structure of the system for verifying the coordination and consistency of athletes in team events according to the present invention.

[0034] Figure 2 The present invention provides a flow chart of a method for verifying the coordination and consistency of athletes in a team event. DETAILED DESCRIPTION

[0035] The present invention will be described in further detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0036] like Figure 1-2 The present invention provides a method for verifying the coordination and consistency of athletes in team events, which is implemented by 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 is composed of three types of core nodes: sensor nodes, server nodes, and terminal devices.

[0037] The verification system for the coordination and consistency of athletes in the team event adopts two core encryption and decryption mechanisms and verification methods: one is the homomorphic encryption mechanism, which includes the encryption function With the decryption function , which is used to encrypt the motion data collected by the sensor and support the calculation of coordination indicators in an encrypted state; the second is a range verification mechanism based on commitment and zero-knowledge proof. The terminal device completes the range verification without exposing the specific value of the indicator by constructing the commitment value and generating zero-knowledge proof. Among them, the sensor node has Encryption capability, used to encrypt collected data; server nodes have Encryption capability and responsible for the calculation of coordination indicators within the encryption domain, and also has public verification capability, used to receive the commitment and zero-knowledge proof sent by the terminal and complete the verification; the terminal device has Decryption capability, and can construct commitments based on the decrypted coordination index value, generate zero-knowledge proofs, and collaborate with the server to complete the verification process.

[0038] Homomorphic encryption allows addition, scalar multiplication, and other computational operations to be performed in an encrypted state, ensuring data privacy during the coordination indicator calculation process. By combining homomorphic encryption with zero-knowledge verification technology, motion data is protected from decryption throughout the entire process, ensuring that coordination verification results are trustworthy and cannot be forged.

[0039] The present invention provides a method for verifying the coordination consistency of athletes in team events, and the specific implementation process is as follows.

[0040] 1. Motion data collection

[0041] Deploy sensor nodes on the surface or near the body of athletes to collect multiple types of sports data generated by each athlete during team sports. Athlete's The motion data is ,in: Represents different types of motion data (such as acceleration, angle, specific action trigger time, cycle, etc.), is the sampling time or event occurrence time, It is a time series data or a sequence of discrete event data that changes over time.

[0042] Sports data can be divided into: time series data, which are the sports parameters of athletes that change over time; event data, which are the timestamps of key moments during the sports process.

[0043] 2. Motion data encryption and upload

[0044] In order to protect the privacy of athlete data, the sensor node uses fully homomorphic encryption to encrypt the collected data, so that the server can perform calculations without decryption during the calculation process. Suppose the motion data collected by the sensor node is , the encryption process is as follows:

[0045] ;

[0046] in, is the encryption public key, The encrypted motion data is uploaded to the server via wireless or wired network.

[0047] The server calculates motion coordination index

[0048] Without decrypting the data, the server uses homomorphic encryption to calculate team coordination metrics, measuring the coordination of different athletes during exercise. These metrics include synchronization deviation, dynamic consistency deviation, and overall movement rhythm tolerance. Synchronization deviation measures the degree of synchronization among team members at key moments; dynamic consistency deviation measures the consistency of amplitude, rhythm, and intensity among team members throughout the entire movement; and overall movement rhythm tolerance measures the stability of the team's movement cycle.

[0049] The server encrypts motion data for each type of data Perform encryption domain calculations in an encrypted state. First, calculate the team sports data mean:

[0050] ;

[0051] in, is the number of athletes in the team event. Then, calculate the data deviation of each athlete:

[0052] ;

[0053] Furthermore, we calculate the square of the deviation and perform a relinearization:

[0054] ;

[0055] in, Indicates that a relinearization operation is performed on the ciphertext multiplication result to reduce the ciphertext order and maintain encryption compatibility.

[0056] Finally, calculate the overall deviation index for this type of data:

[0057] ;

[0058] in, It is an encrypted coordination indicator for motion data.

[0059] The calculated overall deviation indicator is homomorphically encrypted data, making the server unable to determine its true value. To verify the indicator and prevent the true value of the indicator data from being leaked during data transmission and processing, the server splits the indicator and collaborates with multiple sensors to obtain subsequent indicator data based on zero-knowledge verification.

[0060] 4. Coordination Index Perturbation and Verification Value Construction

[0061] After the server completes the calculation of the coordination index in 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 indicator range requirements, the server generates a disturbance factor for each coordination indicator. , Represents a positive real number and is known only to the server.

[0062] The server performs a scalar multiplication operation in an encrypted state, and the perturbation factor Applied to the coordination index ciphertext, the perturbed ciphertext value is obtained:

[0063] ;

[0064] The perturbed indicator value remains in ciphertext, which is then sent to the terminal device.

[0065] 5. Verification Value Decryption and Zero-Knowledge Range Verification Construction

[0066] Encryption coordination index after the terminal device receives the disturbance Then, use your own private key to decrypt it and obtain the disturbed plaintext value:

[0067] ;

[0068] in, is the plaintext value of the disturbance coordination index, is the plaintext value of the coordination indicator.

[0069] because Only the server knows, and the terminal cannot calculate the original coordination index plaintext Specific value of .

[0070] In order to enable this value to participate in the zero-knowledge range verification protocol, the terminal converts the real number Perform scaling and rounding operations:

[0071] ;

[0072] in, is the plaintext value of the disturbance coordination index, represents the plaintext value of the perturbation coordination index of the conversion, is the preset precision control parameter, represents a positive integer, Indicates rounding up, for example express Preserves 4 decimal places of precision. Mapping to the integer domain , which facilitates subsequent range verification.

[0073] At the same time, the server will also preset the verification interval Synchronous multiplication And round it to get the integer interval:

[0074] ;

[0075] in, , ; , To verify the lower and upper limits of the interval, , are the lower and upper bounds of the validation interval for the conversion, respectively; represents the disturbance factor, Indicates the lower limit of the coordination index of the motion data (plain text), Indicates the upper limit of the coordination index of the motion data (plain text), and It represents the category of motion data according to the actual allowable coordination deviation setting.

[0076] The server only knows the verification interval, and the terminal only knows , and then the two conduct zero-knowledge range verification to determine whether they meet:

[0077] .

[0078] 6. Zero-knowledge range verification process

[0079] The terminal device selects a 256-bit prime number , construct a finite field integer ring , and in a stage Cyclic group of Select generators on 、 . Further, according to the converted perturbation coordination index plaintext value Construct a commitment and generate a proof using the zero-knowledge verification range protocol to construct the following commitment:

[0080] ;

[0081] in, A hidden factor randomly generated for the terminal device.

[0082] The terminal then generates a proof using a zero-knowledge range verification protocol such as Bulletproofs or Sigma Protocol:

[0083] ;

[0084] and will The server verifies the certificate using the verification algorithm:

[0085] .

[0086] If the verification is successful, it means that the coordination index after the disturbance meets the range requirements, and the server cannot obtain or Any plaintext information can be obtained through the blockchain, thus achieving zero-knowledge verification.

[0087] 7. Verification result feedback and training optimization

[0088] After the server completes the verification, it returns a verification result to the terminal device. The terminal device can use this result to generate real-time prompts or training suggestions, helping athletes adjust their movements and improve overall teamwork.

[0089] The present invention realizes the effective verification of group sports coordination indicators on the basis of protecting the privacy of sports data. By combining homomorphic encryption and zero-knowledge verification technology, it ensures that the sports data is not decrypted in the whole process, and the coordination verification results are credible and cannot be forged. This method has the advantages of high security, strong adaptability, and good computing efficiency. It is suitable for a variety of group sports that require high consistency and support real-time feedback of results, which helps to realize intelligent and refined management of sports training. Compared with the existing technology, the present invention significantly improves the reliability and security of verification, and is particularly suitable for scenarios with high requirements for collaborative consistency, such as dragon boat racing, team rowing, and group dancing.

[0090] The verification method provided by the present invention of this application will be further described below with reference to specific embodiments.

[0091] Example 1

[0092] This implementation uses dragon boat racing as an example to illustrate how to implement team sports coordination consistency verification based on homomorphic encryption and zero-knowledge verification.

[0093] In dragon boat racing, team members must paddle in highly synchronized fashion to maximize propulsion efficiency and minimize energy loss. This method uses distributed sensors to collect each athlete's motion data and, through collaborative processing on a server, verifies team coordination consistency, ensuring data privacy while also verifying team coordination.

[0094] S1. Motion data collection

[0095] Two types of sensors are installed on each athlete’s body surface: an inertial measurement unit (IMU) and a contact switch sensor. The IMU is used to measure the athlete’s acceleration changes during the paddling process, forming continuous motion data; the contact switch sensor is used to record the athlete’s entry and exit moments of paddling, forming discrete sequence data. The paddle acceleration of each athlete (as a function of time) is , the athlete's paddle blade entering the water sequence is .

[0096] S2. Data encryption upload

[0097] After collecting motion data, the sensor node uses the CKKS fully homomorphic encryption scheme to encrypt the data.

[0098] The continuous acceleration data collected Encrypt to get ciphertext data:

[0099] ;

[0100] in, is the encryption public key, is the encrypted acceleration data.

[0101] The collected discrete water entry and exit time data are encrypted to obtain ciphertext data:

[0102] , ;

[0103] in, is the encrypted water entry moment data, The encrypted water discharge time data; Indicates the number of times.

[0104] Afterwards, the sensor sends the encrypted data to the server.

[0105] S3, server computing coordination indicators

[0106] The server calculates team coordination metrics in an encrypted state, including time synchronization deviation, dynamic consistency deviation, and overall movement rhythm tolerance. These calculations are performed within the encrypted domain, preventing the server from directly obtaining the athletes' actual movement data.

[0107] S3-1. Calculate time synchronization deviation (compare the same entry or exit time)

[0108] Time synchronization measures whether different athletes’ entry and exit times are consistent. The mean of the entry times is calculated as:

[0109] ;

[0110] in, is the number of athletes, is the encrypted mean of the water entry time data.

[0111] Calculate the water entry time deviation for each athlete:

[0112] ;

[0113] Then square the deviation and relinearize it to get:

[0114] ;

[0115] This value reflects the deviation of each athlete from the team's average water entry time. Then calculate the average of the water entry time synchronization deviation of all athletes:

[0116] ;

[0117] Calculated That is the encryption coordination index at the time of entry into the water (corresponding to the The calculation method for the encryption coordination index at the time of exiting the water is the same as that at the time of entering the water, and will not be repeated here.

[0118] S3-2, Calculate Dynamic Consistency Deviation

[0119] Dynamic consistency measures whether the acceleration changes of different athletes are consistent. The mean acceleration is calculated as:

[0120] ;

[0121] Then calculate the deviation ciphertext between each athlete's acceleration data and the mean:

[0122] ;

[0123] Then perform the square and relinearization operations:

[0124] ;

[0125] Finally, the dynamic consistency deviation index is obtained:

[0126] ;

[0127] in, Encrypted coordination index for the athlete's paddling acceleration, The encrypted mean acceleration of the athlete paddling.

[0128] S3-3. Calculate the tolerance of the overall movement rhythm

[0129] Cadence measures the stability of the overall stroke cycle and is calculated for a single athlete:

[0130] ;

[0131] Then calculate the encrypted mean of all athlete cycles:

[0132] ;

[0133] The deviation of each athlete's period from the mean is then calculated:

[0134] ;

[0135] After relinearizing the square of the deviation, we get:

[0136] ;

[0137] Finally, the overall motion rhythm tolerance index is obtained:

[0138] ;

[0139] in, After encryption Athlete No. Data on the moment of paddling into the water, After encryption Athlete No. Data on the moment of entry of the paddle into the water; is the total number of strokes; is the average value of the paddling cycle after encryption, Provides an encrypted coordination indicator for the athlete's paddling cycle.

[0140] S4. The server generates a disturbance index and sends it to the terminal

[0141] After completing the three types of coordination indicators (time synchronization deviation , dynamic consistency deviation , overall movement rhythm tolerance ), the server introduces a random positive real number perturbation factor for each type of indicator (in ) to hide its true value and perform the multiplication operation in the encrypted domain:

[0142] ;

[0143] The ciphertext result after perturbation Sent to the terminal device for subsequent verification processing.

[0144] S5. The terminal decrypts the disturbance indicator and generates zero-knowledge verification data

[0145] The terminal device receives the indicator ciphertext after the disturbance After that, use the private key to perform decryption and obtain the corresponding perturbed index value:

[0146] ;

[0147] Since standard zero-knowledge verification protocols (such as Bulletproofs and Sigma Protocol) are usually executed in the integer domain, the terminal needs to Scale to integer domain. Set scaling precision parameters ,Will Convert to integer form:

[0148] ;

[0149] The terminal then generates a Pedersen commitment to the integer value:

[0150] ;

[0151] in, A random hidden factor generated for the terminal, and The generator preset by the system.

[0152] Based on the above commitment value, the terminal uses the zero-knowledge range proof protocol to generate the following proof:

[0153] ;

[0154] in, is the validation interval after the same scaling process, satisfying:

[0155] ;

[0156] Where, Indicates the lower limit of the coordination index of the motion data (plain text), Indicates the upper limit of the coordination index of the motion data (plain text), and Set according to the actual allowable coordination deviation.

[0157] The terminal device will Send to the server together.

[0158] S6. Server performs zero-knowledge verification

[0159] After receiving the commitment value and zero-knowledge proof from the terminal, the server performs the standard verification process using the public verification algorithm:

[0160] ;

[0161] The verification is passed, indicating the coordination index value after the disturbance Falling into the preset disturbance range , thus indirectly indicating that the original indicator The coordination requirements are met, and neither the server nor the terminal obtains any information about The plaintext information ensures the complete privacy protection of indicator data.

[0162] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and implementation methods. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A method for verifying the coordination and consistency of athletes in a team event, characterized in that: include: Collect various sports data generated by each athlete in team events through sensors; The sensor uses homomorphic encryption to encrypt the collected motion data, obtains ciphertext data of various motion data, and uploads the ciphertext data to the server; The server calculates encryption coordination indexes of various types of motion data using the ciphertext data, and generates disturbance factors for the encryption coordination indexes of the various types of motion data respectively; Applying the perturbation factor to the corresponding encryption coordination index to obtain the perturbation encryption coordination index of each type of motion data, and sending the perturbation encryption coordination index of each type of motion data to the terminal device; The terminal device decrypts the received encrypted disturbance coordination index of each type of motion data to obtain the plaintext value of the disturbance coordination index of each type of motion data; The server performs zero-knowledge verification on the plaintext value of the disturbance 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 method for verifying the coordination and consistency of athletes in a team event according to claim 1, wherein: Before zero-knowledge verification, it also includes: The terminal device converts the disturbance coordination index plaintext value to obtain a converted disturbance coordination index plaintext value, and the formula is as follows: ； The server converts the verification interval to obtain the converted verification interval. The formula is as follows: ； And zero-knowledge verification is performed based on the converted verification interval and the converted perturbation coordination index plaintext value; in, is the plaintext value of the disturbance coordination index, is the preset precision control parameter, represents a positive integer, Indicates rounding up. represents the plaintext value of the perturbation coordination index of the conversion, , To verify the lower and upper limits of the interval, , are the lower and upper bounds of the conversion validation interval, respectively. Represents the motion data category.

3. The method for verifying the coordination consistency of athletes in a team event according to claim 2, characterized in that: ， ； in, represents the disturbance factor, Indicates the lower limit of the coordination index of motion data, Indicates the upper limit of the coordination index of motion data, Represents the motion data category.

4. The method for verifying the coordination and consistency of athletes in a team event according to claim 3, wherein: The types of data collected by sensors include: acceleration of the movement, angle of the movement, trigger time of a specific movement, and period of a specific movement.

5. The method for verifying the coordination consistency of athletes in a team event according to claim 3 or 4, characterized in that: The calculation formula for the encryption coordination index of various types of motion data is: ； in, For the Athlete's The relinearized value of the square of the deviation of the motion-like data, is the number of athletes in the team event.

6. The method for verifying the coordination and consistency of athletes in a team event according to claim 5, characterized in that: Before zero-knowledge verification, it also includes: The terminal device constructs a commitment based on the converted perturbation coordination indicator plaintext value, generates a proof using a zero-knowledge verification range protocol, and sends the commitment and proof together to the server; The commitments stated are: ; The proof is: ; in, and is the system's preset generator, A hidden factor randomly generated by the terminal device, is a finite field of integer rings, , is a prime number.

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

Images