Motion data evidence storage method and device, equipment and storage medium

Abstract

The invention relates to a motion data storage method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting multiple types of motion data of a user in real time, verifying the multiple types of motion data, generating a hash value corresponding to the motion data and a timestamp of the hash value according to the verified motion data, and storing the generated hash value and the timestamp into a database; and performing signature operation on the hash value and the timestamp to obtain signed data, and writing the signed data into the block chain based on the smart contract for evidence storage. According to the method, the authenticity and the reliability of the motion data can be improved, the time sequence and the traceability of the motion data are ensured, the motion data are prevented from being forged or tampered, and the motion data passing verification can serve as verifiable assets to be circulated and used among different platforms.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a method and device for storing evidence of motion data, an electronic device and a storage medium. BACKGROUND

[0002] At present, in the current fitness and exercise recording field, there are a large number of systems based on intelligent hardware, but most of them belong to closed platforms, so that the motion data of users lacks verifiability and asset properties, and phenomena such as fake data and data cheating are common, especially in virtual sports or competition scenarios, the authenticity of the motion data plays a key role in the user's honor, rewards and on-chain interest allocation. For example, in a virtual running competition, some users may tamper with the motion data through improper means to obtain a higher ranking, which destroys the fairness of the competition and damages the interests of other users. Such motion data without authentication is unreliable and cannot be circulated and used between different platforms, restricting the development of the "virtual reality + sports" industry.

[0003] Therefore, there is an urgent need for an effective mechanism to authenticate motion data so that it can be circulated and used between different platforms as verifiable assets. It should be noted that the information disclosed in the above background section is only used to strengthen the understanding of the background of the present disclosure, and therefore can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0004] In view of the above, the present application provides a method and device for storing evidence of motion data, an electronic device and a storage medium, which aims to solve the above technical problems.

[0005] In a first aspect, the present application provides a method for storing evidence of motion data, the method comprising:

[0006] real-time collection of a plurality of types of motion data of a user, verification of the plurality of types of motion data;

[0007] for the motion data that passes the verification, generating a hash value corresponding to the motion data and a timestamp of the hash value;

[0008] performing a signature operation on the hash value and the timestamp to obtain signed data;

[0009] writing the signed data into a blockchain based on a smart contract for storage.

[0010] In a second aspect, the present application provides a device for storing evidence of motion data, the device comprising:

[0011] a verification module for real-time collection of a plurality of types of motion data of a user, verification of the plurality of types of motion data;

[0012] generating module: for generating, for the motion data passed verification, a hash value corresponding to the motion data and a timestamp of the hash value;

[0013] signature module: for performing a signature operation on the hash value and the timestamp to obtain signed data;

[0014] evidence storage module: for writing the signed data into a blockchain based on a smart contract to store evidence.

[0015] In a third aspect, the present application provides an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus;

[0016] The memory is used to store a computer program.

[0017] The processor is used to execute the program stored on the memory, and realize the motion data evidence storage method of any one of the embodiments of the first aspect.

[0018] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to realize the motion data evidence storage method of any one of the embodiments of the first aspect.

[0019] Compared with the prior art, the above technical solution provided by the embodiments of the present application has the following advantages:

[0020] The present application improves the authenticity and reliability of the motion data by collecting multiple types of motion data of the user in real time, verifying the multiple types of motion data, generating a hash value corresponding to the motion data and a timestamp of the hash value for the motion data passed verification, ensuring the time sequence and traceability of the data, performing a signature operation on the hash value and the timestamp to obtain signed data, ensuring the traceability of the source of the data, preventing the motion data from being forged or tampered, writing the signed data into a blockchain through a smart contract to complete the right protection and evidence storage of the data, and ensuring the security and credibility of the motion data due to the decentralization and tamper-proofing characteristics of the blockchain, so that the motion data passed verification can be circulated and used between different platforms as verifiable assets. BRIEF DESCRIPTION OF DRAWINGS

[0021] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present application and, together with the specification, serve to explain the principles of the application.

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without any creative effort.

[0023] Figure 1 A flowchart of an embodiment of the motion data evidence storage method of the present application;

[0024] Figure 2 A module diagram of an embodiment of the motion data evidence storage device of the present application;

[0025] Figure 3 A schematic diagram of an embodiment of the electronic device of the present application;

[0026] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0027] In order to make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without any creative effort fall within the scope of protection of the present application.

[0028] The following disclosure provides many different embodiments, or examples, for implementing different structures of the present application. For the purpose of simplicity and clarity, the description in the following text describes the components and settings of specific examples. Of course, they are only examples, and the purpose is not to limit the present application. In addition, the present application can repeatedly refer to numbers and / or letters in different examples. Such repetition is for the purpose of simplification and clarity, and does not indicate the relationship between the various embodiments and / or settings discussed.

[0029] The present application provides a motion data evidence storage method. Referring to Figure 1 The present application provides a motion data evidence storage method. Referring to

[0030] Step S10: Real-time collection of multiple types of motion data of a user, and verification of the multiple types of motion data;

[0031] Step S20: For the motion data that passes the verification, generating a hash value corresponding to the motion data and a timestamp of the hash value;

[0032] Step S30: performing a signing operation on the hash value and the timestamp to obtain signed data;

[0033] Step S40: Writing the signed data into the blockchain for evidence storage based on the smart contract.

[0034] The motion data evidence storage system includes hardware layer, data layer, blockchain layer and application layer. Each layer collaborates with each other to complete data collection, verification, evidence storage and application.

[0035] The hardware layer includes various types of sports equipment, such as treadmills, wristbands, and cameras. It is responsible for collecting user motion data, including speed, cadence, resistance level, heart rate, time, distance, and other parameters. The data acquisition module supports 5.0 / BLE protocol device access, ensuring compatibility with mainstream sports equipment. The minimum requirement for motion sensors is a sampling frequency of 1Hz or higher to ensure the timeliness and accuracy of the collected data.

[0036] The data layer is used to verify multiple types of motion data. The multi-source data cross-validation module of the data layer verifies the consistency of the collected motion data from three dimensions: data comparison between running devices, physical laws, and behavioral characteristic curves. Only data that passes the consistency verification of the three dimensions will enter the next processing flow.

[0037] The blockchain layer is used for the on-chain storage of signed data and the execution of smart contracts. Smart contracts automatically execute data on-chain operations based on preset rules, ensuring that once data is on-chain, it cannot be tampered with, ensuring data security and traceability. The data storage function stores verified data on the blockchain, and timestamps can add a precise time stamp to each data entry, further enhancing the data's credibility.

[0038] The application layer applies authenticated and authenticated athletic data to multiple scenarios. The NFT (Non-Fungible Token) system transforms users' athletic data into unique and scarce NFT assets, capitalizing on this data. For example, a competition platform can use trusted athletic data to conduct fair and impartial rankings, ensuring the authenticity of results. The fitness incentive module rewards users based on their athletic data, encouraging them to actively participate in exercise.

[0039] Specifically, the user starts the sports equipment into a sports state, the system starts to prepare to collect sports data, the sports equipment and sensors of the hardware layer start to work, and multiple types of sports data of the user are collected in real time. During the user's movement, various movement behaviors and changes in physiological indicators such as speed, step frequency, heart rate, distance, etc. may be involved. By collecting multiple types of sports data in real time, the user's sports state can be comprehensively and accurately reflected, providing rich and accurate information for subsequent data verification and evidence storage, and ensuring the authenticity and credibility of the sports data. The hardware layer includes various sports equipment, such as treadmills, wristbands, smart bikes, cameras, etc. These devices are equipped with corresponding sensors for collecting sports data of the user. For example, a treadmill can collect data such as speed, step frequency, resistance level, time, distance, etc.; a wristband can collect data such as heart rate, movement trajectory, etc.; a smart bike can collect data such as resistance, speed, power, etc.; a camera can collect data such as the user's action posture, etc.

[0040] Since the sports data is collected by various sports equipment, the data sources are diverse and may have differences and errors, and even inaccurate data caused by equipment failure or human tampering may occur. In order to ensure the authenticity and reliability of the sports data, prevent inaccurate or tampered data from entering the subsequent evidence storage process, it is necessary to strictly verify the multiple types of sports data collected. Ensure the credibility of the user's sports data. Specifically, the verification of the multiple types of sports data includes:

[0041] The matching degree of sports data of the same type and different sources is verified to obtain a first verification result;

[0042] The rationality of sports data of each type is verified based on a pre-configured verification rule to obtain a second verification result;

[0043] Based on a pre-determined user movement behavior reference curve, it is verified whether the sports data of each type conforms to the historical movement behavior characteristics of the user to obtain a third verification result;

[0044] If the first verification result, the second verification result and the third verification result all represent that the sports data of the type passes the verification, it is determined that the sports data of the type passes the verification;

[0045] If any of the first verification result, the second verification result and the third verification result represents that the sports data of the type fails the verification, it is determined that the sports data of the type fails the verification.

[0046] In the process of motion data collection, the same type of motion data can be collected by multiple devices from different sources. For example, the user's motion speed data can be collected by a treadmill or calculated by the positioning and motion algorithm of a bracelet. Due to the differences in accuracy, sampling frequency, sensor performance, etc. of different devices, the same type of data collected may have certain deviations. In order to ensure the authenticity and reliability of the data, the matching degree of the same type of motion data from different sources needs to be verified to determine whether these data are consistent within a reasonable range, so as to determine whether the type of data is reliable. For example, in a running scenario, both the treadmill and the bracelet can collect speed data, and the data collected by the two devices need to be verified for matching degree, and the verification result is recorded as the first verification result.

[0047] The motion data not only needs to have matching between different sources, but also needs to comply with the basic laws of kinematics and physiology. For example, under normal circumstances, the motion speed, heart rate and other indicators of the human body are within a certain range to comply with the principles of physiology. If the motion data exceeds this reasonable range, it is likely to be caused by device failure, data transmission error or human tampering. Therefore, the reasonableness of each type of motion data needs to be verified based on the pre-configured verification rules to ensure the logicality and authenticity of the data. For example, in a running scenario, if the speed data collected by the treadmill is 50 km / h, and the pre-configured verification rule specifies that the maximum reasonable value of the speed is 40 km / h, then the speed data exceeds the range of the verification rule, and the second verification result indicates that the speed data fails the verification. Verifying the reasonableness of the motion data based on the pre-configured verification rules can timely find and exclude abnormal data that does not comply with the laws of kinematics and physiology, further improving the authenticity and reliability of the motion data.

[0048] Each user will form motion behavior characteristics with certain regularity and stability in the long-term motion process. For example, a user who often runs will have relatively stable trends in characteristics such as heart rate-speed curve and step frequency stability in multiple motions. By analyzing the user's historical motion data, a user motion behavior benchmark curve can be established to provide personalized reference for the verification of current motion data. This can more accurately determine whether the current motion data complies with the user's actual motion ability and habit, thereby further improving the accuracy and reliability of data verification. Assuming that a user's historical average cadence is 70±5 rpm, and the cadence data collected in this spinning motion is 100 rpm. Comparing the cadence data with the user's historical average cadence data, the deviation is large and exceeds the set threshold range. At this time, the third verification result indicates that the cadence data fails the verification.

[0049] By multi-dimensional verification of the same type of motion data, including matching degree verification of different source data, reasonableness verification of the data, and comparison verification with historical motion behavior characteristics of the user, the authenticity and reliability of the type of motion data can be comprehensively and comprehensively evaluated. Only when the verification results of the three aspects are all indicative of the type of motion data verification passing, can it be ensured that the data meets the requirements in terms of different sources, reasonableness, and user personalized characteristics, and has a high degree of credibility, so that it can be used as reliable data for subsequent right confirmation and evidence storage operations.

[0050] The data layer of the present application verifies the consistency of the collected data from three dimensions of inter-device data comparison, physical laws, and behavior characteristic curves through the multi-source data cross-verification module. The inter-device comparison verifies whether the relevant data collected by different devices match, the physical law verifies whether the data conforms to the principles of kinematics and physiology, and the behavior characteristic curve verifies whether the current motion data is consistent with the historical motion behavior characteristics of the user. The authenticity and reliability of the type of motion data can be comprehensively and comprehensively evaluated.

[0051] Further, the matching degree of the same type and different source motion data is verified to obtain a first verification result, including:

[0052] determining whether the matching degree of the same type and different source motion data is greater than a preset threshold;

[0053] If yes, the first verification result indicates that the type of motion data verification passes;

[0054] If no, the first verification result indicates that the type of motion data verification does not pass.

[0055] In the process of motion data collection, the same type of motion data can be collected by multiple devices from different sources. Due to the differences in accuracy, sampling frequency, sensor performance, etc. of different devices, the same type of data collected may have certain deviations. In order to ensure the authenticity and reliability of the data, the matching degree of the same type and different source motion data needs to be verified to determine whether these data are consistent within a reasonable range, so as to determine whether the type of data is reliable. Determine the different source devices of the same type of motion data that need to be verified. For example, in the running scene, the speed data collected by the treadmill and the bracelet are determined as the matching objects that need to be verified. For example, in the embodiment of the single cycle competition data authentication, the resistance and speed data collected by the smart bicycle are used to calculate the bicycle power, and the heart rate data collected by the bracelet has a certain correlation with the bicycle power. According to the formula of bicycle power = resistance x speed, the bicycle power is calculated, and then the matching degree is analyzed with the heart rate change curve. Assuming that the matching degree threshold is set to 85%, after calculation, if the matching degree is above 85%, the first verification result indicates that the type of motion data passes the verification; if the matching degree is below 85%, the first verification result indicates that the type of motion data fails the verification.

[0056] Further, the rationality of each type of motion data is verified based on the preconfigured verification rule to obtain a second verification result, including:

[0057] Determine the corresponding associated data of each type of motion data based on the preconfigured verification rule;

[0058] Determine whether the relationship between the type of motion data and its corresponding associated data conforms to the verification rule;

[0059] If it conforms, the second verification result indicates that the type of motion data passes the verification;

[0060] If it does not conform, the second verification result indicates that the type of motion data fails the verification.

[0061] The motion data needs to not only be matched between different sources, but also comply with basic laws of kinematics and physiology. For example, under normal circumstances, the motion speed and heart rate of a human body are within a certain range to comply with physiological principles. If the motion data exceeds the reasonable range, it is likely caused by device failure, data transmission error or human tampering. Therefore, the reasonableness of each type of motion data needs to be verified based on pre-configured verification rules to ensure the logic and authenticity of the data. For example, in a running scenario, the pre-configured verification rule stipulates that when the running speed exceeds 30 km / h, the heart rate should be at least 110 times per minute. If the collected running speed is 35 km / h, but the heart rate is only 70 times per minute, the relationship between the heart rate data and the speed data does not comply with the verification rule, and the second verification result indicates that the heart rate data fails the verification.

[0062] Further, the third verification result is obtained by verifying whether each type of motion data complies with historical motion behavior characteristics of the user based on a pre-determined user motion behavior reference curve, and includes:

[0063] The user motion behavior reference curve is updated in real time based on a pre-trained lightweight neural network;

[0064] Each type of motion data is verified based on the updated user motion behavior reference curve to determine whether it complies with historical motion behavior characteristics of the user;

[0065] If the verification is passed, the third verification result indicates that the type of motion data passes the verification;

[0066] If the verification is not passed, the third verification result indicates that the type of motion data fails the verification.

[0067] Each user will form a certain regularity and stability of the movement behavior characteristics in the long-term movement process. By analyzing the user's historical movement data, the user movement behavior benchmark curve can be established, which can provide personalized reference for the verification of current movement data. A large amount of historical movement data of the user can be collected, including movement data under different time periods and different movement types, and a lightweight neural network and other machine learning algorithms are used to analyze and model the user's historical movement data, and the user movement behavior benchmark curve is constructed. The lightweight neural network has the characteristics of small amount of calculation and fast training speed, and is suitable for running on devices with limited resources, and can update the user's movement behavior benchmark curve in real time to adapt to the changes of the user's movement ability. Based on the pre-trained lightweight neural network, the user movement behavior benchmark curve is updated in real time. For example, as the user's movement ability improves, the baseline value of the heart rate-speed curve will change accordingly, and the system can timely capture this change and update the benchmark curve. After collecting the current movement data, each type of movement data is compared and verified with the updated user movement behavior benchmark curve. If the deviation of the current data from the benchmark curve is within the set threshold range (such as 30%), the third verification result indicates that the type of movement data is verified; otherwise, the third verification result indicates that the type of movement data is not verified. Based on the user movement behavior benchmark curve verification, the user's personalized movement characteristics and habits are fully considered, making the data verification more accurate and in line with the user's actual situation.

[0068] Although the verified movement data has a certain authenticity and accuracy in content, in the process of data storage, transmission and sharing, a safe and efficient way is still needed to ensure the integrity and non-tamperability of the data, while accurately recording the time of data generation, so that the source and historical state of the data can be traced in subsequent use. Specifically, the generation of the hash value corresponding to the movement data and the timestamp of the hash value includes:

[0069] Hashing the movement data using a preset hash algorithm to generate a hash value corresponding to the movement data;

[0070] Adding a timestamp to the hash value.

[0071] For the verified motion data, a SHA256 algorithm is used to calculate its hash value, generating a fixed length hash value. SHA256 algorithm has high security and irreversibility, even if the input data has a slight change, the generated hash value will have a huge difference, so it can uniquely identify the original data and ensure the integrity of the data. At the same time of generating the hash value, a precise timestamp is added to the hash value. The timestamp is recorded by the system according to the accurate time of data generation, usually with millisecond level accuracy to ensure the accuracy of time, and the format can use international standard time format (such as ISO8601) to better share and compare data. The generation of hash value ensures the uniqueness and tamper resistance of motion data, and any change to the original data will cause the change of hash value, so that it can quickly find out whether the data is tampered. The addition of timestamp provides accurate time record for data, enhances the credibility and traceability of data, and provides important basis for subsequent data evidence and use.

[0072] In the process of storing motion data, it is necessary to ensure that the source of data can be accurately identified and traced, and to prevent data from being forged or tampered during transmission and storage. By signing the hash value and timestamp, digital signature technology can be used to provide identity authentication and integrity protection for data, ensuring that the source of data is reliable, and that the data can be effectively prevented from being tampered or forged in subsequent use. Specifically, the signing operation on the hash value and the timestamp obtains signed data, comprising:

[0073] Obtaining the private key of the device used to collect the user's motion data;

[0074] Signing the data containing the hash value and the timestamp based on the private key to obtain the signed data.

[0075] Each motion device has its own private key and public key pair. After generating the hash value and timestamp, the system uses the private key of the device to perform the encryption signature operation on the hash value and timestamp. The private key is usually stored in the security chip or encryption module of the device to ensure that only the device itself can access and use it. The signature algorithm can use SM4 national encryption algorithm and other encryption algorithms that meet the security standards to encrypt the data containing the hash value and the timestamp, generating signed data. The signature data has uniqueness and unforgeability, only the device with the corresponding private key can generate a valid signature. The signature operation provides reliable identity authentication and integrity protection for motion data, ensures the traceability of data source, enhances the security and credibility of data, and provides security guarantee for subsequent data on-chain storage and use.

[0076] Blockchain, with its decentralized, tamper-proof, and highly transparent nature, provides a secure and reliable platform for the verification and storage of sports data. Automating data on-chain transactions through smart contracts ensures that once written to the blockchain, data cannot be tampered with. It also enables automated data verification and sharing, improving the efficiency and credibility of data storage. Smart contracts are pre-deployed on the blockchain platform, defining the rules and logic for data on-chain. Smart contracts include verification logic for signature data, ensuring that only verified signatures are written to the blockchain.

[0077] The signed data is sent to the smart contract, which automatically verifies the data according to pre-set rules. Verification includes the legitimacy of the signature and the correctness of the data format. If verification passes, the smart contract writes the data to a designated storage location on the blockchain, completing the data ownership confirmation and evidence storage.

[0078] Blockchains can choose between public chains (such as BSC) or private chains, depending on the specific application requirements. Public chains are decentralized, open, and transparent, making them suitable for scenarios requiring broad verifiability. Private chains, on the other hand, offer greater data privacy and access control, making them suitable for data storage and management within specific organizations. For example, critical data can be stored on public chains (such as BSC). Public chains are decentralized, open, and transparent, allowing anyone to query and verify on-chain data. Critical information such as user competition results and sports data summaries can be stored on public chains, ensuring the data's immutability and broad verifiability, thus safeguarding its credibility.

[0079] Full data is stored on a private blockchain for internal auditing and data analysis. Access to the private blockchain is controlled by specific organizations or institutions, effectively protecting user privacy and business secrets. For example, gyms or event organizers can use the private blockchain to conduct in-depth analysis of users' full exercise data and provide personalized fitness recommendations or event organization optimization solutions.

[0080] This application stores the collected raw motion data locally and uploads only the data summary to the blockchain. This strategy protects user privacy and allows for data integrity verification via the on-chain summary. 10MB of raw motion data can be compressed into a mere 32-byte on-chain summary, significantly reducing the blockchain's storage burden and improving data transmission and processing efficiency.

[0081] This application effectively improves the data credibility of the VR sports platform, enables the ownership and traceability of sports behaviors, and provides strong support for multiple application scenarios:

[0082] In the scenario of virtual competition ranking, the competition ranking is based on reliable sports data, which eliminates the phenomenon of score cheating and ensures the fairness of the competition. Users can participate in the competition in a fair environment, improving their participation experience and competitive enthusiasm, and promoting the healthy development of virtual competition activities.

[0083] In the scenario of user sports data assetization and NFT, the user's sports data is converted into NFT assets, realizing the assetization of sports data. Users can trade and collect their own sports data NFT, providing a new value realization way for users and promoting the development of sports data asset market.

[0084] In the scenario of blockchain fitness incentive model (Move-to-Earn), rewards are given according to the user's sports data, encouraging users to actively participate in sports, promoting the formation of a healthy lifestyle for users, and promoting the widespread application of blockchain technology in the fitness field, bringing new development opportunities to the fitness industry.

[0085] In the scenario of data external certification and authorization of gym or event organizing institutions, the application provides a reliable data certification service for gym and event organizing institutions, authenticates and authorizes the user's sports data, facilitates data sharing and cooperation with external partners, expands the business scope, and improves the competitiveness of the institution.

[0086] Reference Figure 2 As shown in FIG. 1, it is a functional module schematic diagram of the sports data evidence storage device 100 of the application.

[0087] The sports data evidence storage device 100 of the application can be installed in an electronic device. According to the implemented functions, the sports data evidence storage device 100 can include a verification module 110, a generation module 120, a signature module 130, and an evidence storage module 140. The modules of the application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, stored in the memory of the electronic device.

[0088] In this embodiment, the functions of each module / unit are as follows:

[0089] Verification module 110: used for real-time collection of multiple types of user sports data, and verification of the multiple types of sports data;

[0090] Generation module 120: used for generating a hash value corresponding to the verified sports data and a timestamp of the hash value for the sports data;

[0091] Signature module 130: used for signature operation on the hash value and the timestamp, to obtain signed data;

[0092] The storage module 140 is configured to write the signed data into the blockchain based on the smart contract for storage.

[0093] In an embodiment, the verification of the plurality of types of motion data comprises:

[0094] verifying matching degrees of motion data of the same type and different sources to obtain a first verification result;

[0095] verifying rationality of each type of motion data based on a pre-configured verification rule to obtain a second verification result;

[0096] verifying whether each type of motion data conforms to historical motion behavior characteristics of the user based on a pre-determined user motion behavior reference curve to obtain a third verification result;

[0097] if the first verification result, the second verification result and the third verification result all indicate that the type of motion data passes the verification, it is determined that the type of motion data passes the verification;

[0098] if any of the first verification result, the second verification result and the third verification result indicates that the type of motion data fails the verification, it is determined that the type of motion data fails the verification.

[0099] In an embodiment, the verification of the matching degrees of motion data of the same type and different sources to obtain a first verification result comprises:

[0100] determining whether the matching degrees of motion data of the same type and different sources are greater than a preset threshold;

[0101] if yes, the first verification result indicates that the type of motion data passes the verification;

[0102] if no, the first verification result indicates that the type of motion data fails the verification.

[0103] In an embodiment, the verification of the rationality of each type of motion data based on a pre-configured verification rule to obtain a second verification result comprises:

[0104] determining associated data corresponding to each type of motion data based on the pre-configured verification rule;

[0105] determining whether a relationship between the type of motion data and the associated data conforms to the verification rule;

[0106] if yes, the second verification result indicates that the type of motion data passes the verification;

[0107] if no, the second verification result indicates that the type of motion data fails the verification.

[0108] In one embodiment, the third verification result is obtained by verifying whether each type of motion data conforms to the historical motion behavior characteristics of the user based on the predetermined user motion behavior reference curve, including:

[0109] updating the user motion behavior reference curve in real time based on the pre-trained lightweight neural network;

[0110] verifying whether each type of motion data conforms to the historical motion behavior characteristics of the user based on the updated user motion behavior reference curve;

[0111] If it conforms, the third verification result indicates that the type of motion data passes the verification;

[0112] If it does not conform, the third verification result indicates that the type of motion data fails the verification.

[0113] In one embodiment, the generation of the hash value corresponding to the motion data and the timestamp of the hash value includes:

[0114] hashing the motion data by using a preset hash algorithm to generate a hash value corresponding to the motion data;

[0115] adding a timestamp to the hash value.

[0116] In one embodiment, the signature operation on the hash value and the timestamp to obtain the signed data includes:

[0117] obtaining a private key of a device used to collect the motion data of the user;

[0118] signing the data containing the hash value and the timestamp based on the private key to obtain the signed data.

[0119] Referring to Figure 3 FIG. 1 is a schematic diagram of a preferred embodiment of an electronic device of the present application.

[0120] The electronic device includes a processor 111, a communication interface 112, a memory 113, and a communication bus 114, wherein the processor 111, the communication interface 112, and the memory 113 complete mutual communication through the communication bus 114;

[0121] The memory 113 is used to store a computer program, for example, a motion data evidence storage program;

[0122] The processor 111 may, in some embodiments, be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 111 is generally used to control the overall operation of the electronic device, such as performing control and processing related to data interaction or communication, etc. In the present embodiment, the processor 111 is used to run program codes or process data stored in the memory 113, such as running program codes of the evidence storage program of the motion data, etc.

[0123] The communication interface 112 may, optionally, include a standard wired interface, a wireless interface (such as a WI-FI interface), and be used to establish a communication connection between the electronic device and other electronic devices.

[0124] The memory 113 includes at least one type of readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 113 may be an internal storage unit of the electronic device, such as a hard disk or a memory of the electronic device. In other embodiments, the memory 113 may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. of the electronic device. Of course, the memory 113 may also include both the internal storage unit and the external storage device of the electronic device. In the present embodiment, the memory 113 is generally used to store an operating system and various computer programs installed in the electronic device, such as program codes of the evidence storage program of the motion data, etc. In addition, the memory 113 may also be used to temporarily store various data that have been output or will be output.

[0125] Figure 3 Only an electronic device with components 111-114 is shown, but it should be understood that not all of the shown components are required to be implemented, and more or fewer components can be alternatively implemented.

[0126] In an embodiment of the present application, the processor 111 is used to implement the motion data evidence storage method provided by any one of the method embodiments when running the program stored in the memory 113, including:

[0127] Real-time collection of multiple types of user motion data, verification of the multiple types of motion data;

[0128] For the verified motion data, generate the hash value corresponding to the motion data and the timestamp of the hash value;

[0129] Sign the hash value and the timestamp to obtain signed data;

[0130] Based on the smart contract, write the signed data into the blockchain for notarization.

[0131] For details of the above steps, please refer to the above Figure 1 The flowchart of the motion data notarization method embodiment.

[0132] In addition, the embodiments of the present application also propose a computer readable storage medium, which can be non-volatile or volatile. The computer readable storage medium includes a storage data area and a storage program area, and the storage program area stores a motion data notarization program. When the motion data notarization program is executed by a processor, the following operations are realized:

[0133] Real-time collection of multiple types of user motion data, verification of the multiple types of motion data;

[0134] For the verified motion data, generate the hash value corresponding to the motion data and the timestamp of the hash value;

[0135] Sign the hash value and the timestamp to obtain signed data;

[0136] Based on the smart contract, write the signed data into the blockchain for notarization.

[0137] The specific implementation of the computer readable storage medium of the present application is substantially the same as that of the motion data notarization method described above, and will not be repeated here.

[0138] The device embodiments described above are only schematic, wherein the units illustrated as separate components can or can not be physically separated, and the components illustrated as units can or can not be physical units, i.e. they can be located in one place or distributed on multiple network units. According to actual needs, part or all of the modules can be selected to achieve the purpose of the present embodiment.

[0139] Those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a general hardware platform, and of course can be implemented by hardware. Based on such an understanding, the technical solutions described above can be embodied in the form of a software product, and the computer software product can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a plurality of instructions to cause a computer device (such as a personal computer, a server, or a network device) to execute the methods described in each embodiment or some parts of the embodiments.

[0140] It should be noted that the terms "first", "second", and the like in the present application are only used for the purpose of description, and should not be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined as "first" and "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions of various embodiments can be combined with each other, but it must be based on the fact that a person skilled in the art can realize it, and when the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist, and is not within the protection scope required by the present application.

[0141] It should be understood that the terms used herein are for the purpose of describing particular example embodiments only and are not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises", "comprising", "includes", "including" and "has" are inclusive and therefore specify the presence of stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order in which they are described, unless otherwise specifically identified as an order dependency. It is also to be understood that additional or alternative steps can be employed.

[0142] The above description is merely one specific implementation of the present application, which enables those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for storing motion data, characterized in that: The method comprises: Collect multiple types of motion data of the user in real time and verify the multiple types of motion data; For the verified motion data, generate a hash value corresponding to the motion data and a timestamp of the hash value; Performing a signing operation on the hash value and the timestamp to obtain signed data; Based on the smart contract, the signed data is written into the blockchain for evidence storage.

2. The method for storing motion data according to claim 1, wherein: The verifying of the multiple types of motion data includes: Verifying the matching degree of motion data of the same type but from different sources to obtain a first verification result; Verifying the rationality of each type of motion data based on pre-configured verification rules to obtain a second verification result; Verifying, based on a predetermined user motion behavior benchmark curve, whether each type of motion data conforms to the user's historical motion behavior characteristics, to obtain a third verification result; If the first verification result, the second verification result, and the third verification result all indicate that the motion data of this type has passed verification, then it is determined that the motion data of this type has passed verification; If any one of the first verification result, the second verification result, and the third verification result indicates that the motion data of this type fails verification, it is determined that the motion data of this type fails verification.

3. The method for storing sports data as claimed in claim 2, characterized in that: The verification of the matching degree of the motion data of the same type but from different sources to obtain a first verification result includes: Determine whether the matching degree of motion data of the same type but from different sources is greater than a preset threshold; If so, the first verification result indicates that the type of motion data verification has passed; If not, the first verification result indicates that the verification of this type of motion data fails.

4. The method for storing sports data as claimed in claim 2, wherein: The rationality of each type of motion data is verified based on the pre-configured verification rules to obtain a second verification result, including: Determine the associated data corresponding to each type of motion data based on pre-configured verification rules; Determining whether the relationship between the type of motion data and its corresponding associated data complies with the verification rule; If yes, the second verification result indicates that the type of motion data verification has passed; If not, the second verification result indicates that the verification of this type of motion data fails.

5. The method for storing sports data as claimed in claim 2, wherein: The step of verifying whether each type of exercise data conforms to the user's historical exercise behavior characteristics based on a predetermined user exercise behavior benchmark curve to obtain a third verification result includes: Real-time update of user motion behavior baseline curve based on pre-trained lightweight neural network; Based on the updated user motion behavior benchmark curve, verify whether each type of motion data conforms to the user's historical motion behavior characteristics; If the verification is in compliance, the third verification result indicates that the verification of the motion data of this type has passed; If not, the third verification result indicates that the verification of this type of motion data fails.

6. The method for storing sports data as claimed in claim 1, wherein: The generating of the hash value corresponding to the motion data and the timestamp of the hash value includes: Performing a hash operation on the motion data using a preset hash algorithm to generate a hash value corresponding to the motion data; A timestamp is added to the hash value.

7. The method for storing sports data as claimed in claim 1, wherein: The signing operation is performed on the hash value and the timestamp to obtain signed data, including: Obtain the private key of the device used to collect the user's motion data; The data including the hash value and the timestamp is signed based on the private key to obtain signed data.

8. A motion data evidence storage device, characterized in that: The device comprises: Verification module: used to collect multiple types of motion data of the user in real time and verify the multiple types of motion data; A generation module: configured to generate a hash value corresponding to the verified motion data and a timestamp of the hash value; Signature module: used to perform a signature operation on the hash value and the timestamp to obtain signed data; Evidence storage module: used to write the signed data into the blockchain for evidence storage based on the smart contract.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory for storing computer programs; A processor is used to implement the motion data storage method described in any one of claims 1 to 7 when executing a program stored in a memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for storing motion data as described in any one of claims 1 to 7 is implemented.

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