Intelligent motion data processing method and system based on block chain

Abstract

The invention discloses an intelligent exercise data processing method and system based on a block chain. The method comprises the following steps: collecting a related exercise data set when a user uses intelligent exercise equipment to exercise; dividing the related motion data set into a plurality of original data blocks based on data types, and determining the type of noise to be injected according to the data type of each original data block and the current privacy budget; respectively injecting noise corresponding to the noise type into the corresponding original data blocks to generate fuzzy data blocks and metadata thereof; performing multi-level encryption on each fuzzy data block and metadata thereof to generate an intelligent encryption cluster; and allocating each intelligent encryption cluster to a plurality of block chain nodes for storage, and performing redundancy storage at the block chain nodes at different geographic positions. By utilizing the embodiment of the invention, the problems of privacy protection and data security in the prior art can be solved through a multi-level encryption and refined noise injection mechanism in combination with a block chain technology, and a more reliable and safer motion data processing solution is provided.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent motion technology, and in particular to an intelligent motion data processing method and system based on blockchain. Background Art

[0002] With the increasing popularity of smart sports equipment and the increasing awareness of fitness and health management, more and more users are beginning to use various smart sports devices (such as smart bracelets, smart watches, fitness trackers, etc.) to monitor and record their sports data. These data not only include basic sports information, such as steps, heart rate, calories burned, etc., but may also include users' personal health information, exercise habits and preferences. However, how to safely and effectively process, store and share these sports data has become a technical challenge that needs to be solved. Summary of the invention

[0003] The purpose of the present invention is to provide a blockchain-based intelligent motion data processing method and system to address the deficiencies in the prior art. It can solve the privacy protection and data security problems in the prior art through multi-level encryption and refined noise injection mechanism, combined with the advantages of blockchain technology, and provide users with a more reliable and secure motion data processing solution.

[0004] An embodiment of the present application provides a blockchain-based intelligent sports data processing method, the method comprising: Collect relevant sports data sets when users use smart sports equipment for exercise; Dividing the relevant motion data set into a plurality of original data blocks based on data type, and determining the type of noise to be injected according to the data type of each original data block and a current privacy budget, wherein the privacy budget is used to quantify an acceptable degree of information sharing; Injecting noise corresponding to the noise type into the corresponding original data block respectively to generate a fuzzy data block and metadata thereof, wherein the metadata at least includes: mapping information between the original data block and the injected noise; Each fuzzy data block and its metadata are encrypted at multiple levels to generate an intelligent encryption cluster, where each intelligent encryption cluster contains multiple encryption layers, and each layer uses a different encryption algorithm; Each smart encryption cluster is assigned to multiple blockchain nodes for storage, and redundant storage is performed on blockchain nodes in different geographical locations.

[0005] Optionally, the data type of the data block includes: continuous data and discrete data; The method of determining the type of noise to be injected according to the data type of each data block and the current privacy budget includes: Assess the information sensitivity of each data type and obtain a current privacy budget for each data type, wherein the privacy budget may be dynamically updated regularly; For the continuous data and the discrete data, determining the type of noise to be injected is Gaussian noise and Laplace noise; According to the information sensitivity and the current privacy budget, noise magnitudes of the Gaussian noise and the Laplace noise are calculated respectively.

[0006] Optionally, each fuzzy data block and its metadata are encrypted at multiple levels to generate an intelligent encryption cluster, wherein each intelligent encryption cluster contains multiple encryption layers, and each layer uses a different encryption algorithm, including: For each fuzzy data block and its metadata, the fuzzy data block is encrypted using a symmetric encryption algorithm to obtain an encrypted data block; Encrypt the corresponding metadata using an asymmetric encryption algorithm to obtain encrypted metadata; Combining the encrypted data block with the encrypted metadata to obtain a preliminary intelligent encryption cluster, and generating a first verification code using a dedicated key to verify the integrity and consistency of the encrypted data block and the encrypted metadata; The first check code, the encrypted data block, and the encrypted metadata form a leaf node of a binary tree, and a root hash is calculated as a second check code to verify the integrity of the preliminary intelligent encryption cluster; The preliminary intelligent encryption cluster and the second verification code are packaged to obtain a final intelligent encryption cluster.

[0007] Optionally, combining the encrypted data block with the encrypted metadata to obtain a preliminary intelligent encryption cluster, and generating a first verification code using a dedicated key, includes: concatenating the encrypted data block and the encrypted metadata into a single input string through a connector; Generate a private key based on a user password and a random salt using a password generator; Determine whether the current value of a preset internal flag is true, and if so, perform byte concatenation of the private key and the input string to obtain a first hash input, wherein the preset internal flag is a Boolean value; Calculating a first hash value of the first hash input using a preset hash function; Generating a random factor by using a pseudo-random number generator, and performing byte concatenation of the random factor, the private key and the first hash value to obtain a second hash input; A second hash value of the second hash input is calculated using the preset hash function as a first verification code.

[0008] Optionally, the method further includes: If the current value of the preset internal flag is false, the first hash value is directly used as the first verification code.

[0009] Optionally, the step of forming the first check code, the encrypted data block, and the encrypted metadata into leaf nodes of a binary tree, and calculating a root hash as a second check code, includes: Using the first check code, the encrypted data block, and the encrypted metadata as data items for constructing a binary tree, and generating a hash value for each data item; Take each hash value as the leaf node of the binary tree, and perform hash calculations on each pair of adjacent leaf nodes to generate the corresponding parent node; Hash calculations are performed on adjacent parent nodes to generate a final root node hash value as the second check code.

[0010] Optionally, each smart encryption cluster is allocated to multiple blockchain nodes for storage, and redundant storage is performed on blockchain nodes in different geographical locations, including: Generate a corresponding redundant copy for each smart encryption cluster on the blockchain side chain, and randomly distribute the hash value of each smart encryption cluster and the corresponding redundant copy to the main chain node of the blockchain for storage; The redundant copies are randomly distributed to the side chain nodes of the blockchain for storage, wherein the side chain nodes in different geographical locations store multiple redundant copies of the same smart encryption cluster.

[0011] Another embodiment of the present application provides a blockchain-based intelligent sports data processing system, the system comprising: A collection module, used to collect relevant sports data sets when users exercise using smart sports equipment; a determination module, configured to divide the relevant motion data set into a plurality of original data blocks based on data types, and determine the type of noise to be injected respectively according to the data type of each original data block and a current privacy budget, wherein the privacy budget is used to quantify an acceptable degree of information sharing; An injection module, used for injecting noise corresponding to the noise type into the corresponding original data block, generating a fuzzy data block and metadata thereof, wherein the metadata at least includes: mapping information between the original data block and the injected noise; An encryption module is used to perform multi-level encryption on each fuzzy data block and its metadata to generate an intelligent encryption cluster, wherein each intelligent encryption cluster contains multiple encryption layers, and each layer uses a different encryption algorithm; The storage module is used to distribute each smart encryption cluster to multiple blockchain nodes for storage and perform redundant storage in blockchain nodes in different geographical locations.

[0012] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0013] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0014] Compared with the prior art, the present invention provides a blockchain-based intelligent sports data processing method, which collects relevant sports data sets when users exercise with intelligent sports equipment; divides the relevant sports data sets into multiple original data blocks based on data type, and determines the type of noise to be injected according to the data type and the current privacy budget of each original data block; injects noise corresponding to the noise type into the corresponding original data block to generate fuzzy data blocks and their metadata, and the metadata at least includes: mapping information between the original data block and the injected noise; performs multi-level encryption on each fuzzy data block and its metadata to generate an intelligent encryption cluster; allocates each intelligent encryption cluster to multiple blockchain nodes for storage, and performs redundant storage on blockchain nodes in different geographical locations, so that through multi-level encryption and refined noise injection mechanism, combined with the advantages of blockchain technology, the privacy protection and data security problems in the prior art can be solved, and a more reliable and secure sports data processing solution can be provided for users. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A hardware structure block diagram of a computer terminal for a blockchain-based intelligent sports data processing method provided by an embodiment of the present invention; Figure 2 A flowchart of a blockchain-based intelligent sports data processing method provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a blockchain-based intelligent sports data processing system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0017] The embodiment of the present invention first provides a blockchain-based intelligent motion data processing method, which can be applied to electronic devices such as computer terminals, specifically ordinary computers, etc.

[0018] The following describes it in detail by taking running on a computer terminal as an example. Figure 1The hardware structure block diagram of a computer terminal for a blockchain-based intelligent motion data processing method provided by an embodiment of the present invention. Figure 1 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0019] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, which, when executed, can cause the processor to execute any blockchain-based intelligent motion data processing method.

[0020] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0021] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any blockchain-based intelligent motion data processing method.

[0022] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0023] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0024] See also Figure 2 , an embodiment of the present invention provides a method for processing intelligent sports data based on blockchain, which may include the following steps: S201, collecting relevant sports data sets when the user uses the smart sports equipment for exercise; In the present invention, collecting relevant sports data sets when users use smart sports equipment (such as smart bracelets, smart watches, fitness trackers, etc.) to exercise is the first step in the entire data processing process. The key to this process is how to efficiently and accurately obtain various sports data to ensure the integrity and authenticity of the data. The data collected by smart sports equipment can reflect the user's exercise status in real time, with high time accuracy and spatial accuracy, making subsequent data processing more reliable; the relevant data generated when the user exercises includes heart rate, number of steps, exercise duration, calorie consumption, GPS location information, etc. These multi-dimensional data provide rich materials for subsequent data analysis and processing, and help to form a comprehensive user exercise profile; accurate data collection provides a basis for subsequent privacy budget quantification and noise injection, so that data protection and information sharing can be better balanced during the processing process.

[0025] Specifically, the unique identifier of smart sports equipment (such as MAC address or serial number) can be used for identification. Whenever a user wears sports equipment, the system will establish a connection through Bluetooth or Wi-Fi to ensure the effectiveness of the equipment. Before the user starts exercising, the interface provided by the application (App) allows the user to select the type of exercise (such as running, cycling, swimming, etc.) and enter relevant parameters (such as weight, height). This information will help the system set the accuracy and type of data collection. During exercise, sports equipment will collect different types of data in real time through built-in sensors (such as accelerometers, heart rate sensors, GPS modules, etc.). For example: continuous data: such as heart rate, speed, displacement, etc.; discrete data: such as exercise start / end mark, exercise status (such as walking, running, stopping, etc.).

[0026] The system will monitor and record data in real time, use preset thresholds (such as heart rate not exceeding the maximum heart rate) to verify the validity of the data, and notify users in time to make adjustments (such as stopping exercise or taking a rest) when abnormal data appears. After the exercise, the system will initially cache all collected data and automatically synchronize the data to the cloud server when the network connection is available. At the same time, the application will regularly upload the locally cached data to ensure the integrity of the data.

[0027] S202, dividing the relevant motion data set into a plurality of original data blocks based on data type, and determining the type of noise to be injected according to the data type of each original data block and a current privacy budget, wherein the privacy budget is used to quantify an acceptable degree of information sharing; In this step, the various data generated by the user during exercise (such as heart rate, number of steps, calories, etc.) are first classified according to their data types. Data types can be divided into continuous data (such as heart rate, speed, displacement, etc.) and discrete data (such as exercise status, start / end time, etc.). Then, by analyzing the specific type of each data block and the corresponding current privacy budget, the system determines the type of noise that needs to be injected. The privacy budget is a quantitative parameter set by the user to protect the privacy of personal data, reflecting the degree to which the user is willing to share.

[0028] By dividing the relevant motion data into multiple raw data blocks, the system can manage and organize the data more effectively, facilitating subsequent processing and analysis; by injecting appropriate noise into each data block according to the different data types and privacy budget settings, the user's data can still be privately protected when shared, reducing the risk of being attacked or abused; by identifying different types of data, the system can maximize user privacy while maintaining data availability, thereby achieving a balance between data utilization and privacy protection.

[0029] First, the system stores the collected user motion data in a temporary data structure (such as a list or dictionary). The data includes information in multiple dimensions such as heart rate, number of steps, and distance. By analyzing the type field of the data, the data is divided into continuous data and discrete data. For each record, the system determines its data type. For example, heart rate, speed, and distance are continuous data; exercise start / end flags, exercise status, etc. are discrete data. Then, data of the same type can be further grouped into different raw data blocks. Each raw data block can contain all the data of its category. For example, one raw data block may include all heart rate records, and another raw data block may include all exercise status flags.

[0030] Specifically, the data type of the data block may include: continuous data, discrete data. According to the data type of each data block and the current privacy budget, the type of noise to be injected is determined respectively, the information sensitivity of each data type can be evaluated, and the current privacy budget of each data type can be obtained, wherein the privacy budget can be updated regularly and dynamically; In this step, the system needs to decide what type of noise to inject based on the type and privacy budget of different motion data blocks. First, the system evaluates the sensitivity of each data type to determine its potential impact on user privacy. Next, the system reads the currently set privacy budget, which reflects the user's privacy protection requirements for different data blocks and may be adjusted based on the user's choice or the system's dynamic evaluation.

[0031] Based on changes in data sensitivity and user privacy budget, the system can flexibly determine the type and degree of noise injected to ensure that the privacy of user data is protected at any point in time; by evaluating data sensitivity, the system can take appropriate protection measures to prevent user data leakage or abuse; and allow users to adjust their privacy budget to improve their sense of control over their personal data.

[0032] Data sensitivity can be obtained based on historical data analysis, user feedback, etc. For example, heart rate data is generally considered to be highly sensitive, while step count or calorie consumption data is relatively less sensitive. Access the privacy budget set by the user in the application and obtain the budget value corresponding to each data type. The budget can be a fixed value or a dynamic value based on time and scenario. Users can adjust their privacy budget at any time in the application. For example, at the beginning or end of an exercise, the system will ask the user whether the budget needs to be adjusted to ensure that the user's data protection needs in different scenarios are met. Based on the data sensitivity and privacy budget, the system decides what type of noise to inject (such as Gaussian noise or Laplace noise) and determines the amplitude of the noise to ensure that the availability of the data is retained as much as possible while protecting privacy.

[0033] For the continuous data and the discrete data, determining the type of noise to be injected is Gaussian noise and Laplace noise; In this step, the system determines the corresponding noise injection method based on the data type (continuous or discrete) determined in the previous step. Continuous data is usually processed with Gaussian noise, which is suitable for data that can be smoothed, such as heart rate and speed. Discrete data uses Laplace noise, which is suitable for perturbing classified data such as motion status to reduce the accuracy of information.

[0034] By selecting appropriate noise types for different types of data, the system can ensure the effectiveness and applicability of privacy protection measures, thereby improving the overall quality of data processing; by adding noise to the data, it can effectively prevent attackers from extracting accurate user information from it, reducing the risk of data abuse.

[0035] According to the information sensitivity and the current privacy budget, noise magnitudes of the Gaussian noise and the Laplace noise are calculated respectively.

[0036] In this step, the system will calculate the specific amount of Gaussian noise and Laplace noise that needs to be injected based on the sensitivity of different data types and the privacy budget set by the user. This process involves dynamic evaluation of the privacy budget and quantification of data sensitivity, allowing the system to inject an appropriate amount of noise into each data block to ensure that the validity of the data is preserved as much as possible while protecting user privacy.

[0037] There is an inverse relationship between the privacy budget (usually expressed as ε) and the noise level: the higher the privacy budget, the smaller the allowed noise: this means that the user has a lower requirement for privacy protection, and the system can use real data in a relatively small range to improve the effectiveness of the data; the lower the privacy budget, the larger the allowed noise: this means that the user wants to protect privacy more strictly, and the system needs to add more noise to ensure the ambiguity of the data and reduce the risk of leakage.

[0038] Exemplarily, by evaluating the sensitivity of the data type, for example, the sensitivity of continuous data such as heart rate can be set to 5, and discrete data such as emotional state can be set to 1. The user or the system can set the privacy budget (ε) according to the needs. For example, assuming that the privacy budget of heart rate data is set to 0.1, and the privacy budget of emotional state is set to 0.5.

[0039] For continuous data (such as heart rate), the standard deviation of the noise (σ) can be calculated by the following formula:

[0040] For example, if the heart rate sensitivity is 5 and the privacy budget ε is 0.1, the standard deviation is 50, which means that a large range of Gaussian noise needs to be added to blur the actual data.

[0041] For discrete data (such as emotional states), a similar calculation is used, usually using Laplace noise, whose scale parameter (b) is calculated by a similar formula:

[0042] For example, if the sensitivity of the emotional state is 1 and the privacy budget ε is 0.5, the scale is 2, which means that in this case, moderate noise can be added to ensure that the data maintains a certain degree of privacy.

[0043] By accurately calculating the noise size based on information sensitivity and privacy budget, the system can effectively balance data privacy and data quality, allowing users to feel more at ease when sharing data; the system can dynamically adjust the amplitude of noise injection based on the user's privacy needs and the actual situation of the data, ensuring more effective protection of sensitive data; by reasonably setting the noise size, users can feel the system's emphasis on data privacy, thereby enhancing their trust in the system.

[0044] S203, injecting noise corresponding to the noise type into the corresponding original data block respectively to generate a fuzzy data block and metadata thereof, wherein the metadata at least includes: mapping information between the original data block and the injected noise; In this step, the system uses a predefined noise type to inject the original data blocks that have been identified and classified. Depending on the nature of different data blocks, the system will inject Gaussian noise or Laplace noise respectively. The core of this process is how to effectively inject noise into the data blocks and ensure that the generated fuzzy data strikes a balance between privacy protection and data availability. The generation of metadata contains the mapping information between the original data blocks and the injected noise, which is used for the subsequent tracing and verification of the fuzzy data and the reverse recovery of the original data.

[0045] By injecting noise and blurring the original data, the privacy protection of user information is enhanced and the risk of attack is reduced; ensuring that the injected fuzzy data still retains its validity for subsequent data analysis, statistics and mining; through the generated metadata, the system can track the relationship between the original data and the fuzzy data to achieve better data management and security control.

[0046] Specifically, first, according to the calculation result of the previous step, the type of injected noise (Gaussian noise or Laplace noise) is determined, and specific parameters are set for each noise, including the standard deviation (for Gaussian noise) or scale (for Laplace noise) of the noise. A pseudo-random number generator can be used to generate a set of random noises according to the set noise parameters (such as standard deviation or scale). Preferably, a high-quality random number generation algorithm (such as Mersenne Twister) can be used to ensure that the generated noise has good uniform distribution characteristics, so as to reduce the risk of potential attackers obtaining sensitive information through pattern recognition.

[0047] For each original data block, the system will generate random noise and perform weighted injection of the original data element by element. For example, for continuous data blocks, the weighted injection is performed as follows: fuzzy data [i] = original data [i] + noise [i], where i represents the sequence number; for discrete data blocks, the original data value can be randomly replaced with noise within a discrete range to ensure the same fuzzification effect.

[0048] Generate corresponding metadata for each fuzzy data block, which may include: original data block identification: a unique identifier pointing to the original data block; noise type: recording the type of injected noise (Gaussian or Laplace); noise parameters: including the numerical characteristics of the injected noise (such as standard deviation or scale); mapping information between the original data block and the injected noise: including the injection method, injection range, injection specific value, etc. of the original data block and the corresponding injected noise, to ensure that the original data block can be traced back and restored through the injected fuzzy data block and the corresponding injected noise information.

[0049] S204, encrypting each fuzzy data block and its metadata in multiple layers to generate an intelligent encryption cluster, wherein each intelligent encryption cluster includes multiple encryption layers, and each layer uses a different encryption algorithm; In this step, the system performs multi-level encryption processing on each generated fuzzy data block and its corresponding metadata. The purpose of encryption is to further enhance the security of the data to prevent unauthorized access and ensure the protection of user privacy. By adopting multiple encryption algorithms, the system can effectively improve security. Even if one encryption algorithm is cracked, other levels of encryption can still protect the confidentiality of the data. Multi-level encryption means that even if a certain layer of encryption is cracked, the data is still protected by other levels, reducing the risk of data leakage; different encryption algorithms have their own characteristics. By using them in combination, it is difficult for attackers to grasp all attack vectors, which increases the complexity and difficulty of the attack; protecting user rights and preventing personal data from being abused or illegally obtained will help enhance users' trust in smart sports equipment.

[0050] Specifically, for each fuzzy data block and its metadata, the fuzzy data block can be encrypted using a symmetric encryption algorithm to obtain an encrypted data block; the corresponding metadata can be encrypted using an asymmetric encryption algorithm to obtain encrypted metadata; Two different types of encryption algorithms are applied to the obfuscated data blocks and the corresponding metadata to ensure confidentiality and security of the information. Symmetric encryption is used to encrypt the obfuscated data blocks because it is relatively efficient and can quickly process large-scale data; while asymmetric encryption is used to encrypt the metadata because it can provide stronger security and ensure that only authorized users can access the metadata.

[0051] Symmetric encryption algorithms are more efficient when processing large-scale data, making them suitable for encrypting fuzzy data blocks; while asymmetric encryption provides higher security protection and is suitable for protecting key metadata; through asymmetric encryption of metadata, it is ensured that only users with the corresponding private key can access it, thereby effectively controlling the use and access rights of data; the combined use of encryption algorithms helps to improve the security of data exchange and reduce the risk of data being stolen during network transmission.

[0052] You can select an appropriate symmetric encryption algorithm (such as AES) to generate a random key to encrypt the fuzzy data block. During the encryption process, the system divides the fuzzy data block into multiple small blocks, encrypts each block by the encryption algorithm, and generates an encrypted fuzzy data block.

[0053] A pair of asymmetric keys (public key and private key) can be generated, and the original metadata can be encrypted with the public key to ensure the security of the metadata. After the metadata is encrypted, only the user holding the corresponding private key can decrypt it, so that even if the metadata is intercepted during transmission, it cannot be obtained.

[0054] Combining the encrypted data block with the encrypted metadata to obtain a preliminary intelligent encryption cluster, and generating a first verification code using a dedicated key to verify the integrity and consistency of the encrypted data block and the encrypted metadata; The encrypted fuzzy data block is combined with the encrypted metadata to form a preliminary intelligent encryption cluster. At the same time, a specific private key is used to generate a checksum to ensure the integrity and consistency of the data. The generated checksum is used to confirm that the encrypted data block and the encrypted metadata have not been tampered with during storage and transmission; to ensure that the encrypted data block corresponds to its metadata one by one, avoiding data errors caused by mismatches during the decryption process; by introducing a checksum mechanism, the system can detect data integrity issues and enhance users' trust in data security.

[0055] Specifically, the encrypted data block and the encrypted metadata may be concatenated into a single input string through a connector; The two parts of information can be concatenated together using connectors (such as "|", "#", or ";", etc.) to form a single input string for subsequent processing and operation. The operation of concatenating the input string combines the data and metadata, which not only makes its structure neat, but also provides a complete input for subsequent hash calculations. The uniqueness of this string and its description of data integrity help with subsequent verification work. For example, assuming the encrypted data block is "abc123" and the encrypted metadata is "2023-10-17", the generated input string is "abc123|2023-10-17".

[0056] Generate a private key based on a user password and a random salt using a password generator; The password generator algorithm generates a private key using the password provided by the user and a randomly generated salt as input. The role of salt is to increase the complexity and security of the generated key. Generating a private key is a key step in ensuring the security of data encryption and decryption. Using salt can prevent common password attacks such as dictionary attacks and brute force cracking, and improve the strength and resistance of user passwords.

[0057] You can use a password hashing algorithm such as PBKDF2, bcrypt or scrypt. When executing it, first generate a random salt (for example, a 12-byte random string), then input the password entered by the user together with the salt into PBKDF2, set the number of iterations (for example, 10,000), and finally generate a 256-bit private key.

[0058] Determine whether the current value of a preset internal flag is true, and if so, perform byte concatenation of the private key and the input string to obtain a first hash input, wherein the preset internal flag is a Boolean value; This step is to check a preset internal flag. The flag is a Boolean value that determines whether the subsequent operation is performed. If the flag is true, the operation of concatenating the private key with the input string is performed to form a new data block as the first hash input. By setting the internal flag, the execution flow of the program can be flexibly controlled. According to different flag values, you can choose whether to apply the private key to the input string, thereby affecting the final generated hash value and enhancing the flexibility and security strategy of the system.

[0059] The current value of the internal flag can be checked. If its value is "true", the private key (assuming it is "key123") is concatenated with the previously generated input string (such as "abc123|2023-10-17") in a byte-by-byte manner to form a new byte array as the first hash input (for example: key123 +abc123|2023-10-17).

[0060] Calculating a first hash value of the first hash input using a preset hash function; In this step, the first hash input is hashed using a preset hash function (such as SHA-256, SHA-512, etc.) to generate a hash value, which is the unique "fingerprint" of the input data. The hash value is used to verify the integrity and security of the data. The hash function ensures that even if the input data changes slightly, the output hash value will change dramatically, effectively preventing the data from being tampered with.

[0061] The first hash input can be passed into the selected hash function (such as SHA-256) and then its hash value can be calculated. For example, if the input is "key123abc123|2023-10-17", a 256-bit hash value (such as "a3f5...b8c9") will be generated after running SHA-256 as the first hash value.

[0062] Generating a random factor by using a pseudo-random number generator, and performing byte concatenation of the random factor, the private key and the first hash value to obtain a second hash input; In this step, a pseudo-random number generator (PRNG) is used to generate a random factor. The random factor is a randomly generated value whose main function is to increase the complexity and unpredictability of the key input. This random factor, the private key and the first hash value are concatenated together to form a new string, thereby providing new input for the next step of security processing. By introducing a random factor, it is ensured that even if the same input data is processed multiple times, the second hash input generated each time may be different, reducing the risk of replay attacks. This additional randomness can effectively resist some common attack methods, such as hash collisions and password speculation.

[0063] A mature pseudo-random number generator (such as Mersenne Twister, AES-CTR mode, etc.) can be used to generate a random factor, for example, a 16-byte random string "randomFactor123". Then, this random factor, a private key (such as "key123"), and the first hash value (such as "a3f5...b8c9") are byte-concatenated to form a new byte input (such as: randomFactor123 + key123 + a3f5...b8c9), which is used as the second hash input.

[0064] A second hash value of the second hash input is calculated using the preset hash function as a first verification code.

[0065] Finally, the second hash input is calculated using a preset hash function to obtain a new hash value, which is called the first checksum. It is obtained by performing a final hash on the complex data output by all previous steps, which helps to ensure that the data maintains integrity in subsequent operations. As an important part of data integrity verification, the first checksum can effectively resist data tampering and damage. When data is transmitted or stored, this checksum can be used to ensure that the data has not been changed. If any part of the data changes, the corresponding checksum will also change, thereby alerting the user.

[0066] The second hash input (for example, randomFactor123 + key123 + a3f5...b8c9) can be input into the selected hash function (such as SHA-256) to calculate the second hash value (for example, "5c6f...f3d4"), which is the first check code. This value can be used in subsequent data transmission or storage to ensure data security.

[0067] Specifically, if the current value of the preset internal flag is false, the first Hash value may be directly used as the first verification code.

[0068] In this step, the system checks the status of an internal flag. If the flag indicates that a complex verification process is not required, the first hash value is directly used as the verification code without further security enhancement operations. This flexibility improves the operating efficiency of the system, especially in cases where security requirements are not high, it can reduce processing steps and improve the response speed of the system.

[0069] It can be judged according to the state of the internal flag. If the flag is false, the subsequent hash generation process is skipped, and the first hash value is directly used as the verification code for subsequent data processing and storage.

[0070] The first check code, the encrypted data block, and the encrypted metadata form a leaf node of a binary tree, and a root hash is calculated as a second check code to verify the integrity of the preliminary intelligent encryption cluster; The generated first check code, encrypted data block and encrypted metadata are combined into leaf nodes of a binary tree, and the root hash value is calculated through hash operation as the second check code. This process provides an additional integrity verification mechanism so that any tampering with the data can be detected in time.

[0071] Through the calculation of the binary tree structure and its root hash, the overall integrity of the encrypted data block and metadata can be effectively verified, providing a secondary verification method; the hash algorithm is highly efficient when processing and can quickly verify the integrity of the data during transmission and storage; the root hash value is stored together with the data, and any attempt to change the data will result in a change in the root hash value, which will be quickly identified and blocked.

[0072] Specifically, the first check code, the encrypted data block, and the encrypted metadata may be used as data items for constructing a binary tree to generate a hash value for each data item; The binary tree construction method effectively improves the efficiency of data organization and management, and can provide higher security during data verification and integrity checking. Any data change in a leaf node will cause a change in the root hash value, so that it can quickly identify whether the data has been tampered with.

[0073] The first checksum, encrypted data block, and encrypted metadata are treated as independent data items, and their respective hash values ​​are calculated through a preset hash function. These hash values ​​will serve as the basic nodes of the binary tree and become leaf nodes for building a tree structure. The hash value of each data item provides a concise and unique representation, making subsequent hash calculations more efficient and reducing the complexity of storage and calculation. At the same time, these hash values ​​also lay the foundation for the construction of the tree structure, ensuring that the root hash value can be effectively generated in the end.

[0074] Each data item (first check code, encrypted data block, encrypted metadata) can be calculated using a preset hash function (such as SHA-256). For example, the hash value of the first check code is "hash1", the hash value of the encrypted data block is "hash2", and the hash value of the encrypted metadata is "hash3". The hash values ​​of these three will be used as subsequent binary tree nodes.

[0075] Take each hash value as the leaf node of the binary tree, and perform hash calculations on each pair of adjacent leaf nodes to generate the corresponding parent node; The leaf nodes of the binary tree are hashed in pairs according to the adjacent order to form new parent nodes. The hash values ​​of each pair of leaf nodes are combined through a preset hash function to generate new hash values, which will enter the tree structure as parent nodes. By continuously combining the hash values ​​of leaf nodes into parent nodes, a multi-level security and integrity check mechanism is provided. This structure can effectively detect local changes in data and provide global integrity checks at the same time, ensuring that changes to any data node can affect the final root hash value.

[0076] Assume that there are three hash values ​​"hash1", "hash2" and "hash3". First, hash "hash1" and "hash2" are hashed to generate the parent node hash value "parentHash1". Then, hash "hash2" and "hash3" are hashed to generate the parent node hash value "parentHash2". These two steps ensure the layer-by-layer generation of the tree structure.

[0077] Hash calculations are performed on adjacent parent nodes to generate a final root node hash value as the second check code.

[0078] The parent node generated by hash calculation is hashed again. By combining the adjacent parent nodes, a root node hash value is finally generated, and this root node hash value is the second check code. The root node hash value, as the top-level hash of the binary tree, summarizes the hash information of all leaf nodes (i.e. data items) and provides a global integrity flag. Any change in any data will cause a change in the root node hash value, thereby achieving the purpose of quickly detecting data integrity.

[0079] Based on the existing parent node hash values ​​(such as "parentHash1" and "parentHash2"), the two parent nodes are hashed, and the generated new hash value (such as "finalRootHash") is the final root node hash value, which can be used as the second verification code for subsequent verification and storage of data.

[0080] The preliminary intelligent encryption cluster and the second verification code are packaged to obtain a final intelligent encryption cluster.

[0081] Combine the preliminary smart encryption cluster with the calculated root hash value and package them into the final smart encryption cluster. This final smart encryption cluster will have complete encrypted data, metadata, and integrity verification information to ensure secure storage and effective verification of data. By centrally encapsulating all key data and verification information, effectively managing and storing fuzzy data blocks and their metadata, the overall efficiency and security of the system are improved; the final smart encryption cluster facilitates subsequent storage, verification, and retrieval operations, and improves the flexibility of data access; by providing complete and secure data packaging, users' trust in system security can be improved, encouraging active user participation.

[0082] S205, allocating each smart encryption cluster to multiple blockchain nodes for storage, and performing redundant storage in blockchain nodes at different geographical locations.

[0083] In this step, the system distributes the generated smart encryption clusters to multiple blockchain nodes for storage. This distributed storage strategy not only improves data availability and access speed, but also provides higher security for smart encryption clusters. Blockchain nodes in different geographical locations mean that even if a node fails or is attacked, other nodes can continue to ensure data availability. By redundantly storing smart encryption clusters on multiple blockchain nodes, the system can prevent data loss and tampering, while also enhancing data security and reliability. In addition, distributed storage makes data recovery and access more efficient, which helps achieve better system performance and user experience.

[0084] Specifically, a corresponding redundant copy can be generated for each smart encryption cluster on the blockchain side chain, and the hash value of each smart encryption cluster and the corresponding redundant copy can be randomly assigned to the main chain node of the blockchain for storage; This step involves creating redundant copies of each smart crypto cluster on the blockchain's sidechain. A sidechain is an independent chain connected to the main chain that can host additional data processing. These redundant copies are stored in the main chain node along with the hash value of the original smart crypto cluster to ensure data integrity and security.

[0085] By generating redundant copies on the side chain, the burden on the main chain can be effectively reduced while improving storage flexibility and scalability. This approach not only reduces the risk of data loss, but also ensures that when the main chain node fails, the redundant copy can still provide data access.

[0086] After each smart encryption cluster is generated, the system performs hash calculation on its data content to obtain a hash value. Then, the system randomly selects multiple nodes of the main chain to store the smart encryption cluster and its corresponding hash value to ensure the distributed nature and security of the data.

[0087] The redundant copies are randomly distributed to the side chain nodes of the blockchain for storage, wherein the side chain nodes in different geographical locations store multiple redundant copies of the same smart encryption cluster.

[0088] In this step, redundant copies are randomly assigned to sidechain nodes in different geographical locations for storage. This distributed storage strategy ensures that even if a sidechain node in a certain area fails, copies in other geographical locations can still maintain data availability. This step enhances data security and risk resistance by storing redundant copies in multiple locations, preventing data loss caused by single point failures. Ensuring the geographical dispersion of data can improve the system's disaster recovery capabilities and provide users with greater confidence.

[0089] All available side chain nodes can be identified first, and then the redundant copies of each smart encryption cluster can be assigned to different side chain nodes using a random algorithm. In this way, multiple redundant copies of each smart encryption cluster will be securely distributed in different geographical locations to form redundant storage and ensure high availability of the system.

[0090] It can be seen that relevant sports data sets are collected when users use smart sports equipment for exercise; the relevant sports data sets are divided into multiple original data blocks based on data type, and the type of noise to be injected is determined according to the data type and the current privacy budget of each original data block; the noise corresponding to the noise type is injected into the corresponding original data block to generate fuzzy data blocks and their metadata, and the metadata at least includes: mapping information between the original data block and the injected noise; each fuzzy data block and its metadata are encrypted at multiple levels to generate an intelligent encryption cluster; each intelligent encryption cluster is allocated to multiple blockchain nodes for storage, and redundantly stored in blockchain nodes at different geographical locations, so that through multi-level encryption and refined noise injection mechanism, combined with the advantages of blockchain technology, the privacy protection and data security problems in the existing technology can be solved, and users can be provided with a more reliable and secure sports data processing solution.

[0091] Another embodiment of the present invention provides a blockchain-based intelligent sports data processing system, see Figure 3 , the system may include: The collection module 301 is used to collect relevant sports data sets when the user uses the intelligent sports equipment for exercise; A determination module 302 is used to divide the relevant motion data set into a plurality of original data blocks based on data types, and determine the type of noise to be injected according to the data type of each original data block and a current privacy budget, wherein the privacy budget is used to quantify an acceptable degree of information sharing; The injection module 303 is used to inject the noise corresponding to the noise type into the corresponding original data block to generate a fuzzy data block and its metadata, wherein the metadata at least includes: mapping information between the original data block and the injected noise; The encryption module 304 is used to perform multi-level encryption on each fuzzy data block and its metadata to generate a smart encryption cluster, wherein each smart encryption cluster contains multiple encryption layers, and each layer uses a different encryption algorithm; The storage module 305 is used to distribute each smart encryption cluster to multiple blockchain nodes for storage, and perform redundant storage in blockchain nodes at different geographical locations.

[0092] It can be seen that relevant sports data sets are collected when users use smart sports equipment for exercise; the relevant sports data sets are divided into multiple original data blocks based on data type, and the type of noise to be injected is determined according to the data type and the current privacy budget of each original data block; the noise corresponding to the noise type is injected into the corresponding original data block to generate fuzzy data blocks and their metadata, and the metadata at least includes: mapping information between the original data block and the injected noise; each fuzzy data block and its metadata are encrypted at multiple levels to generate an intelligent encryption cluster; each intelligent encryption cluster is allocated to multiple blockchain nodes for storage, and redundantly stored in blockchain nodes at different geographical locations, so that through multi-level encryption and refined noise injection mechanism, combined with the advantages of blockchain technology, the privacy protection and data security problems in the existing technology can be solved, and users can be provided with a more reliable and secure sports data processing solution.

[0093] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0094] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps: S201, collecting relevant sports data sets when the user uses the smart sports equipment for exercise; S202, dividing the relevant motion data set into a plurality of original data blocks based on data type, and determining the type of noise to be injected according to the data type of each original data block and a current privacy budget, wherein the privacy budget is used to quantify an acceptable degree of information sharing; S203, injecting noise corresponding to the noise type into the corresponding original data block respectively to generate a fuzzy data block and metadata thereof, wherein the metadata at least includes: mapping information between the original data block and the injected noise; S204, encrypting each fuzzy data block and its metadata in multiple layers to generate an intelligent encryption cluster, wherein each intelligent encryption cluster includes multiple encryption layers, and each layer uses a different encryption algorithm; S205, allocating each smart encryption cluster to multiple blockchain nodes for storage, and performing redundant storage in blockchain nodes at different geographical locations.

[0095] It can be seen that relevant sports data sets are collected when users use smart sports equipment for exercise; the relevant sports data sets are divided into multiple original data blocks based on data type, and the type of noise to be injected is determined according to the data type and the current privacy budget of each original data block; the noise corresponding to the noise type is injected into the corresponding original data block to generate fuzzy data blocks and their metadata, and the metadata at least includes: mapping information between the original data block and the injected noise; each fuzzy data block and its metadata are encrypted at multiple levels to generate an intelligent encryption cluster; each intelligent encryption cluster is allocated to multiple blockchain nodes for storage, and redundantly stored in blockchain nodes at different geographical locations, so that through multi-level encryption and refined noise injection mechanism, combined with the advantages of blockchain technology, the privacy protection and data security problems in the existing technology can be solved, and users can be provided with a more reliable and secure sports data processing solution.

[0096] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0097] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0098] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program: S201, collecting relevant sports data sets when the user uses the smart sports equipment for exercise; S202, dividing the relevant motion data set into a plurality of original data blocks based on data type, and determining the type of noise to be injected according to the data type of each original data block and a current privacy budget, wherein the privacy budget is used to quantify an acceptable degree of information sharing; S203, injecting noise corresponding to the noise type into the corresponding original data block respectively to generate a fuzzy data block and metadata thereof, wherein the metadata at least includes: mapping information between the original data block and the injected noise; S204, encrypting each fuzzy data block and its metadata in multiple layers to generate an intelligent encryption cluster, wherein each intelligent encryption cluster includes multiple encryption layers, and each layer uses a different encryption algorithm; S205, allocating each smart encryption cluster to multiple blockchain nodes for storage, and performing redundant storage in blockchain nodes at different geographical locations.

[0099] It can be seen that relevant sports data sets are collected when users use smart sports equipment for exercise; the relevant sports data sets are divided into multiple original data blocks based on data type, and the type of noise to be injected is determined according to the data type and the current privacy budget of each original data block; the noise corresponding to the noise type is injected into the corresponding original data block to generate fuzzy data blocks and their metadata, and the metadata at least includes: mapping information between the original data block and the injected noise; each fuzzy data block and its metadata are encrypted at multiple levels to generate an intelligent encryption cluster; each intelligent encryption cluster is allocated to multiple blockchain nodes for storage, and redundantly stored in blockchain nodes at different geographical locations, so that through multi-level encryption and refined noise injection mechanism, combined with the advantages of blockchain technology, the privacy protection and data security problems in the existing technology can be solved, and users can be provided with a more reliable and secure sports data processing solution.

[0100] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A blockchain-based intelligent sports data processing method, characterized in that: The method comprises: Collect relevant sports data sets when users use smart sports equipment for exercise; Dividing the relevant motion data set into a plurality of original data blocks based on data type, and determining the type of noise to be injected according to the data type of each original data block and a current privacy budget, wherein the privacy budget is used to quantify an acceptable degree of information sharing; Injecting noise corresponding to the noise type into the corresponding original data block respectively to generate a fuzzy data block and metadata thereof, wherein the metadata at least includes: mapping information between the original data block and the injected noise; Each fuzzy data block and its metadata are encrypted at multiple levels to generate an intelligent encryption cluster, where each intelligent encryption cluster contains multiple encryption layers, and each layer uses a different encryption algorithm; Each smart encryption cluster is assigned to multiple blockchain nodes for storage, and redundant storage is performed on blockchain nodes in different geographical locations.

2. The method according to claim 1, characterized in that The data types of the data blocks include: continuous data and discrete data; The method of determining the type of noise to be injected according to the data type of each data block and the current privacy budget includes: Assess the information sensitivity of each data type and obtain a current privacy budget for each data type, wherein the privacy budget may be dynamically updated regularly; For the continuous data and the discrete data, determining the type of noise to be injected is Gaussian noise and Laplace noise; According to the information sensitivity and the current privacy budget, noise magnitudes of the Gaussian noise and the Laplace noise are calculated respectively.

3. The method according to claim 2, characterized in that Each fuzzy data block and its metadata are encrypted at multiple levels to generate an intelligent encryption cluster, wherein each intelligent encryption cluster contains multiple encryption layers, each layer uses a different encryption algorithm, including: For each fuzzy data block and its metadata, the fuzzy data block is encrypted using a symmetric encryption algorithm to obtain an encrypted data block; Encrypt the corresponding metadata using an asymmetric encryption algorithm to obtain encrypted metadata; Combining the encrypted data block with the encrypted metadata to obtain a preliminary intelligent encryption cluster, and generating a first verification code using a dedicated key to verify the integrity and consistency of the encrypted data block and the encrypted metadata; The first check code, the encrypted data block, and the encrypted metadata form a leaf node of a binary tree, and a root hash is calculated as a second check code to verify the integrity of the preliminary intelligent encryption cluster; The preliminary intelligent encryption cluster and the second verification code are packaged to obtain a final intelligent encryption cluster.

4. The method according to claim 3, characterized in that The step of combining the encrypted data block with the encrypted metadata to obtain a preliminary intelligent encryption cluster and generating a first verification code using a dedicated key includes: concatenating the encrypted data block and the encrypted metadata into a single input string through a connector; Generate a private key based on a user password and a random salt using a password generator; Determine whether the current value of a preset internal flag is true, and if so, perform byte concatenation of the private key and the input string to obtain a first hash input, wherein the preset internal flag is a Boolean value; Calculating a first hash value of the first hash input using a preset hash function; Generating a random factor by using a pseudo-random number generator, and performing byte concatenation of the random factor, the private key and the first hash value to obtain a second hash input; A second hash value of the second hash input is calculated using the preset hash function as a first verification code.

5. The method according to claim 4, characterized in that The method further comprises: If the current value of the preset internal flag is false, the first Hash value is directly used as the first verification code.

6. The method according to claim 5, characterized in that The step of forming a leaf node of a binary tree with the first check code, the encrypted data block, and the encrypted metadata, and calculating a root hash as a second check code, includes: Using the first check code, the encrypted data block, and the encrypted metadata as data items for constructing a binary tree, and generating a hash value for each data item; Take each hash value as the leaf node of the binary tree, and perform hash calculations on each pair of adjacent leaf nodes to generate the corresponding parent node; Hash calculations are performed on adjacent parent nodes to generate a final root node hash value as the second check code.

7. The method according to claim 6, characterized in that The method of allocating each smart encryption cluster to multiple blockchain nodes for storage and performing redundant storage on blockchain nodes in different geographical locations includes: Generate a corresponding redundant copy for each smart encryption cluster on the blockchain side chain, and randomly distribute the hash value of each smart encryption cluster and the corresponding redundant copy to the main chain node of the blockchain for storage; The redundant copies are randomly distributed to the side chain nodes of the blockchain for storage, wherein the side chain nodes in different geographical locations store multiple redundant copies of the same smart encryption cluster.

8. A blockchain-based intelligent sports data processing system, characterized in that: The system comprises: A collection module, used to collect relevant sports data sets when users exercise using smart sports equipment; a determination module, configured to divide the relevant motion data set into a plurality of original data blocks based on data types, and determine the type of noise to be injected respectively according to the data type of each original data block and a current privacy budget, wherein the privacy budget is used to quantify an acceptable degree of information sharing; An injection module, used for injecting noise corresponding to the noise type into the corresponding original data block, generating a fuzzy data block and metadata thereof, wherein the metadata at least includes: mapping information between the original data block and the injected noise; An encryption module is used to perform multi-level encryption on each fuzzy data block and its metadata to generate an intelligent encryption cluster, wherein each intelligent encryption cluster contains multiple encryption layers, and each layer uses a different encryption algorithm; The storage module is used to distribute each smart encryption cluster to multiple blockchain nodes for storage and perform redundant storage in blockchain nodes in different geographical locations.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 7 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 7.

Images