Mechanical gait training and motion trajectory big data acquisition device

Abstract

The application discloses a mechanical gait training and motion trajectory big data acquisition device and relates to the technical field of data acquisition. The mechanical gait training and motion trajectory big data acquisition device comprises an acquisition unit, a binary coding unit, a complement unit, a first division unit, a first statistical unit, a first calculation unit, a second calculation unit, a key generation unit, a second statistical unit, a third calculation unit, a chaotic sequence generation unit, a second division unit, an exclusive or operation unit and a splicing and uploading unit. The mechanical gait training and motion trajectory big data acquisition device disclosed by the application can significantly reduce the risk of patient gait training data leakage and realize the protection of patient privacy data.

Description

Technical Field

[0001] This invention belongs to the field of data acquisition technology, specifically relating to a mechanical gait training and motion trajectory big data acquisition device. Background Technology

[0002] Mechanical gait training is an important rehabilitation method for patients with lower limb motor dysfunction. It involves using devices such as lower limb exoskeletons to assist patients in repetitive gait training to restore or improve their walking ability. During training, various sensors on the lower limb exoskeleton robot, including angle sensors, pressure sensors, and inertial measurement units, continuously collect a large amount of gait data, such as joint angles, plantar pressure, and gait frequency. Based on this data, the robot can simulate the patient's movement trajectory. This data not only contains information about the patient's rehabilitation progress but also constitutes the patient's personal privacy data.

[0003] Currently, existing mechanical gait training data collection solutions generally adopt a plaintext collection and upload mode, transmitting patients' gait data and movement trajectory data to the cloud or medical institution's data platform without sufficient encryption processing. Sensors on lower limb exoskeleton robots can collect patients' gait data and simulate their movement trajectory based on the collected gait data. Once this data is maliciously mined, attackers can not only identify individuals but also analyze the data to know the user's movement trajectory and achieve tracking, posing a serious risk of privacy leakage.

[0004] Therefore, how to provide an effective solution to protect the data collected during mechanical gait training has become a pressing problem in the existing technology. Summary of the Invention

[0005] The purpose of this invention is to provide a mechanical gait training and motion trajectory big data collection device to solve the above-mentioned problems existing in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a mechanical gait training and motion trajectory big data acquisition device, comprising: The acquisition unit is used to acquire gait data collected by various sensors on the lower limb exoskeleton robot worn by the user during mechanical gait training, as well as motion trajectory data simulated based on the gait data collected by various sensors. A binary encoding unit is used to concatenate the gait data and the motion trajectory data and convert them into binary code to obtain binary encoded data. The padding unit is used to pad the binary encoded data to a specified length to obtain the padded binary encoded data. The first partitioning unit is used to divide the padded binary encoded data into multiple binary encoded sub-data of the same length; The first statistical unit is used to count the maximum and minimum consecutive number of bits with a value of 1 in each binary encoded sub-data. The first calculation unit is used to calculate the block standard value of each binary coded sub-data based on the sorting position of each binary coded sub-data in the padded binary coded data, the data length corresponding to the padded binary coded data, and the data compression coefficient of each binary coded sub-data. The data compression coefficient of any binary coded sub-data is the ratio of the length of the non-padded data in the binary coded sub-data to the length of the binary coded sub-data. The second calculation unit is used to calculate the intermediate digest corresponding to the first binary coded sub-data based on the preset standard reference length, preset number of iterations, block standard value of the first binary coded sub-data, and the maximum and minimum consecutive bits corresponding to the first binary coded sub-data. It then uses the intermediate digest corresponding to the previous binary coded sub-data as the standard reference length to calculate the intermediate digest corresponding to the next binary coded sub-data, thus obtaining the intermediate digest corresponding to each binary coded sub-data. A key generation unit is used to generate key pairs using the RSA algorithm; The second statistical unit is used to count the total number of bits with a value of 1 in the binary encoded data to obtain the plaintext encoding features, and to count the total number of bits with a value of 0 in the binary encoded sequence corresponding to the intermediate digest of each binary encoded sub-data to obtain the ciphertext encoding features. The third calculation unit is used to calculate the RSA symbol vector based on the key pair, the plaintext encoding feature, and the ciphertext encoding feature; The chaotic sequence generation unit is used to initialize the initial parameters of the dual Logistic chaotic subsystem based on the RSA symbol vector, and to perform coupled iteration on the dual Logistic chaotic subsystem to continuously generate random scrambled characters until the total length of the generated scrambled characters is equal to the length of the binary code number, thus obtaining a chaotic sequence. The second partitioning unit is used to divide the chaotic sequence into multiple chaotic subsequences of the same length, each corresponding to a multiple binary encoded sub-data. The XOR operation unit is used to perform a bitwise XOR operation between each chaotic subsequence and the binary sequence converted from the corresponding intermediate digest to obtain the ciphertext fragment corresponding to each binary encoded subdata. The splicing and uploading unit is used to sequentially splice the ciphertext fragments corresponding to each binary encoded sub-data to obtain ciphertext data, and upload the ciphertext data to the data storage terminal.

[0007] In one possible design, the padding unit, when used to pad the binary encoded data to a specified length, is specifically used for: After the binary encoded data, add one bit with a value of 1, and then add multiple bits with a value of 0 continuously until the length of the binary encoded data reaches the specified length.

[0008] In one possible design, the first computing unit, when calculating the block standard value of each binary coded sub-data based on the sorting position of each binary coded sub-data in the padded binary coded data, the data length corresponding to the padded binary coded data, and the data compression coefficient of each binary coded sub-data, is specifically used for: The block standard value of each binary coded sub-data is calculated according to the following formula based on the sorting position of each binary coded sub-data in the padded binary coded data, the data length corresponding to the padded binary coded data, and the data compression coefficient of each binary coded sub-data.

[0009] Where Y represents the block standard value of the binary encoded sub-data, R represents the data length corresponding to the padded binary encoded data, r represents the sorting position of the binary encoded sub-data in the padded binary encoded data, and φ represents the data compression coefficient of the binary encoded sub-data.

[0010] In one possible design, the second computing unit, when calculating the intermediate digest corresponding to the first binary encoded sub-data based on a pre-set standard reference length, a pre-set number of iterations, the block standard value of the first binary encoded sub-data, and the maximum and minimum consecutive bit lengths corresponding to the first binary encoded sub-data, is specifically used for: Based on the pre-set standard reference length, preset number of iterations, block standard value of the first binary encoded sub-data, and the maximum and minimum consecutive bits corresponding to the first binary encoded sub-data, the intermediate digest corresponding to the first binary encoded sub-data is calculated according to the following formula;

[0011] Where T represents the intermediate digest corresponding to the first binary encoded subdata, u represents the standard reference length, and e represents the preset number of iterations. This indicates the maximum number of consecutive bits corresponding to the first binary encoded sub-data. It represents the minimum number of consecutive bits corresponding to the first binary encoded subdata.

[0012] In one possible design, the third computing unit, when calculating the RSA symbol vector based on the key pair, the plaintext encoding features, and the ciphertext encoding features, specifically performs the following tasks: Based on the key pair, the plaintext encoding feature, and the ciphertext encoding feature, the RSA symbol vector is calculated according to the following formula;

[0013] Where J represents the RSA symbol vector, A represents the RSA encryption modulus of the key pair, K represents the system constant, G represents the plaintext encoding feature, H represents the ciphertext encoding feature, λ represents the preset asymmetric definition parameter, d represents the RSA public key exponent of the key pair, and g represents the RSA private key exponent of the key pair.

[0014] In one possible design, the binary encoding unit, when used to convert the gait data and the motion trajectory data after concatenation into binary code, is specifically used for: After concatenating the gait data and the motion trajectory data, the concatenated data is converted into binary code using ASCII encoding or UTF-8 encoding.

[0015] In one possible design, the gait data includes joint angle data, plantar pressure data, and / or cadence data.

[0016] Secondly, the present invention provides a mechanical gait training and motion trajectory big data collection method, including: Acquire gait data collected by various sensors on the lower limb exoskeleton robot worn by the user during mechanical gait training, as well as motion trajectory data simulated based on the gait data collected by each sensor; The gait data and the motion trajectory data are concatenated and then converted into binary code to obtain binary encoded data. The binary encoded data is padded to a specified length to obtain padded binary encoded data; The padded binary encoded data is divided into multiple binary encoded sub-data of the same length; Calculate the maximum and minimum consecutive number of bits with a value of 1 in each binary encoded sub-data. Based on the sorting position of each binary coded sub-data in the padded binary coded data, the data length corresponding to the padded binary coded data, and the data compression coefficient of each binary coded sub-data, the block standard value of each binary coded sub-data is calculated. Among them, the data compression coefficient of any binary coded sub-data is the ratio of the non-padded data in the binary coded sub-data to the length of the binary coded sub-data. Based on the pre-set standard reference length, preset number of iterations, block standard value of the first binary coded sub-data, and the maximum and minimum consecutive bits corresponding to the first binary coded sub-data, the intermediate digest corresponding to the first binary coded sub-data is calculated. The intermediate digest corresponding to the previous binary coded sub-data is used as the standard reference length to calculate the intermediate digest corresponding to the next binary coded sub-data, thus obtaining the intermediate digest corresponding to each binary coded sub-data. Generate a key pair using the RSA algorithm; The plaintext encoding features are obtained by counting the total number of bits with a value of 1 in the binary encoded data, and the ciphertext encoding features are obtained by counting the total number of bits with a value of 0 in the binary encoded sequence corresponding to the intermediate digest of each binary encoded sub-data. Based on the key pair, the plaintext encoding features, and the ciphertext encoding features, the RSA symbol vector is calculated; The initial parameters of the dual-Logistic chaotic subsystem are initialized based on the RSA symbol vector, and the dual-Logistic chaotic subsystem is coupled and iterated to continuously generate random scrambled characters until the total length of the generated scrambled characters is equal to the length of the binary code number, thus obtaining a chaotic sequence. The chaotic sequence is divided into multiple chaotic subsequences of the same length, each corresponding one-to-one with multiple binary encoded sub-data. Perform a bitwise XOR operation between each chaotic subsequence and the corresponding binary sequence converted from the intermediate digest to obtain the ciphertext fragment corresponding to each binary encoded subdata. The ciphertext fragments corresponding to each binary encoded sub-data are concatenated in sequence to obtain ciphertext data, and the ciphertext data is uploaded to the data storage terminal.

[0017] Thirdly, the present invention provides an electronic device comprising a memory, a processor, and a transceiver connected in sequence, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the mechanical gait training and motion trajectory big data collection method as described in the second aspect above.

[0018] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the mechanical gait training and motion trajectory big data collection method described in the first or second aspect.

[0019] Fifthly, the present invention provides a computer program product containing instructions that, when the instructions are executed on a computer, cause the computer to perform the mechanical gait training and motion trajectory big data collection method as described in the second aspect.

[0020] Beneficial effects: This invention discloses a mechanical gait training and motion trajectory big data collection scheme, which can significantly reduce the risk of leakage of patient gait training data and protect patient privacy data.Specifically, it includes: an acquisition unit, used to acquire gait data collected by various sensors on the lower limb exoskeleton robot worn by the user during mechanical gait training, and motion trajectory data simulated based on the gait data collected by each sensor; a binary encoding unit, used to concatenate the gait data and the motion trajectory data and convert them into binary encoding to obtain binary encoded data; a padding unit, used to pad the binary encoded data to a specified length to obtain padded binary encoded data; a first partitioning unit, used to divide the padded binary encoded data into multiple binary encoded sub-data of the same length; and a statistics unit, used to count the maximum and minimum consecutive number of bits with a value of 1 in each binary encoded sub-data; and a second... A first calculation unit is used to calculate the block standard value of each binary coded sub-data based on the sorting position of each binary coded sub-data in the padded binary coded data, the data length corresponding to the padded binary coded data, and the data compression coefficient of each binary coded sub-data. The data compression coefficient of any binary coded sub-data is the ratio of the length of the non-padded data to the length of the binary coded sub-data. A second calculation unit is used to calculate the intermediate digest corresponding to the first binary coded sub-data based on a pre-set standard reference length, a preset number of iterations, the block standard value of the first binary coded sub-data, and the maximum and minimum consecutive bit lengths corresponding to the first binary coded sub-data. The intermediate digest corresponding to the previous binary encoded sub-data is used as the standard reference length to calculate the intermediate digest corresponding to the next binary encoded sub-data, thus obtaining the intermediate digests corresponding to each binary encoded sub-data; a key generation unit is used to generate key pairs using the RSA algorithm; a statistics unit is used to count the total number of bits with a value of 1 in the binary encoded data to obtain the plaintext encoding feature, and to count the total number of bits with a value of 0 in the binary encoded sequence corresponding to the intermediate digest of each binary encoded sub-data to obtain the ciphertext encoding feature; a third calculation unit is used to calculate the RSA symbol vector based on the key pair, the plaintext encoding feature, and the ciphertext encoding feature; a chaotic sequence generation unit is used to initialize the dual key pairs based on the RSA symbol vector. The system generates initial parameters for the Logistic chaotic subsystem and performs coupled iterations on the dual Logistic chaotic subsystem to continuously generate random scrambled characters until the total length of the generated scrambled characters equals the length of the binary code number, thus obtaining a chaotic sequence. A second partitioning unit divides the chaotic sequence into multiple chaotic subsequences of equal length, each corresponding one-to-one with a plurality of binary code sub-data. An XOR operation unit performs a bitwise XOR operation between each chaotic subsequence and the corresponding binary sequence converted from the intermediate digest to obtain the ciphertext fragment corresponding to each binary code sub-data. A splicing and uploading unit splices the ciphertext fragments corresponding to each binary code sub-data in sequence to obtain ciphertext data and uploads the ciphertext data to the data storage terminal.Thus, by employing a triple-fusion encryption mechanism of hash compression, RSA asymmetric encryption, and a dual-Logistic chaotic system, data from patient gait training can be encrypted. Even if an attacker obtains the ciphertext, they cannot recover any gait training data. Compared to existing technologies that transmit data directly in plaintext or rely solely on single encryption, this significantly reduces the risk of malicious mining of patient gait training data during transmission and storage, effectively fulfilling the privacy protection requirements for medical and health data. Simultaneously, by padding the binary encoded data to a specified length, the encrypted data storage length remains highly consistent across different data batches. This solves the problem of significant variations in the unencrypted data storage length in traditional encryption schemes, allowing for the reservation of fixed space for the ciphertext during storage. This avoids storage fragmentation and space waste caused by length fluctuations, making it particularly suitable for scenarios requiring long-term storage of massive amounts of patient gait training data, significantly reducing data storage costs. Furthermore, this scheme utilizes the RSA algorithm to generate asymmetric key pairs and, combined with the extreme sensitivity of the dual-Logistic chaotic system to initial values, uses the RSA symbol vector as the "trigger" of the chaotic system. The random scrambled data generated by chaotic systems is non-periodic and unpredictable, and its key space far exceeds the computational capacity limit of brute-force attacks. Even if an attacker intercepts the chaotic sequence, they cannot deduce the RSA private key, and therefore cannot predict the initial chaotic state of the next encryption, effectively resisting brute-force attacks and known-plaintext attacks. Attached Figure Description

[0021] Figure 1 This is a block diagram of the mechanical gait training and motion trajectory big data acquisition device provided in the embodiments of this application. Detailed Implementation

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0023] It should be understood that although the terms first, second, etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, without departing from the scope of the exemplary embodiments of the invention.

[0024] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0025] It should be understood that specific details are provided in the following description to facilitate a complete understanding of the exemplary embodiments. However, those skilled in the art will understand that the exemplary embodiments can be implemented without these specific details. For example, the system may be shown in block diagrams to avoid obscuring the example with unnecessary details. In other instances, well-known processes, structures, and techniques may be shown without unnecessary details to avoid obscuring the exemplary embodiments.

[0026] To protect patient privacy data, this application provides a mechanical gait training and motion trajectory big data collection device, which can significantly reduce the risk of leakage of patient gait training data and protect patient privacy data.

[0027] like Figure 1 As shown in the figure, this application provides a mechanical gait training and motion trajectory big data acquisition device. The mechanical gait training and motion trajectory big data acquisition device includes an acquisition unit, a binary encoding unit, a padding unit, a first division unit, a first statistics unit, a first calculation unit, a second calculation unit, a key generation unit, a second statistics unit, a third calculation unit, a chaotic sequence generation unit, a second division unit, an XOR operation unit, and a splicing and uploading unit.

[0028] The acquisition unit is used to acquire gait data collected by various sensors on the lower limb exoskeleton robot worn by the user during mechanical gait training, as well as motion trajectory data simulated based on the gait data collected by each sensor.

[0029] The gait data may include, but is not limited to, joint angle data, plantar pressure data, and / or cadence data.

[0030] A binary encoding unit is used to concatenate the gait data and the motion trajectory data and convert them into binary encoding to obtain binary encoded data.

[0031] In one or more embodiments, after gait data and motion trajectory data are concatenated, the concatenated data can be converted into binary code using ASCII encoding or UTF-8 encoding.

[0032] The padding unit is used to pad binary encoded data to a specified length to obtain padded binary encoded data.

[0033] The specified length may be, but is not limited to, 512 bits or 1024 bits, or a power of 2.

[0034] Specifically, when padding binary encoded data to a specified length, one bit with a value of 1 can be added after the binary encoded data, followed by multiple bits with a value of 0, until the length of the binary encoded data reaches the specified length. The padded binary encoded data can then be divided into its content and the padded content based on the last bit with a value of 1.

[0035] The first partitioning unit is used to divide the padded binary encoded data into multiple binary encoded sub-data of the same length.

[0036] Multiple binary encoded sub-data have the same length. The length of the binary encoded sub-data can be 256 bits, 512 bits, etc., and can be set according to the actual situation.

[0037] The statistical unit is used to count the maximum and minimum consecutive number of bits with a value of 1 in each binary encoded subdata.

[0038] The first calculation unit is used to calculate the block standard value of each binary coded sub-data based on the sorting position of each binary coded sub-data in the padded binary coded data, the data length corresponding to the padded binary coded data, and the data compression coefficient of each binary coded sub-data.

[0039] The data compression factor of any binary encoded sub-data is the ratio of the length of the non-padded data to the length of the binary encoded sub-data.

[0040] For example, if the length of a certain binary encoded subdata is 256 bits, of which 16 bits are padding data, that is, the non-padding data is 240 bits, then the data compression coefficient of this binary encoded subdata can be expressed as 240 / 256=0.9375.

[0041] In one or more embodiments, the first computing unit may calculate the block standard value of each binary coded sub-data according to the following formula based on the sorting position of each binary coded sub-data in the padded binary coded data, the data length corresponding to the padded binary coded data, and the data compression coefficient of each binary coded sub-data.

[0042] Where Y represents the block standard value of the binary encoded sub-data, R represents the data length corresponding to the padded binary encoded data, r represents the sorting position of the binary encoded sub-data in the padded binary encoded data, and φ represents the data compression coefficient of the binary encoded sub-data. For example, if the padded binary encoded data is divided into 4 binary encoded sub-data, then the sorting positions of these 4 binary encoded sub-data in the padded binary encoded data can be 1, 2, 3, and 4, respectively.

[0043] The second calculation unit is used to calculate the intermediate digest corresponding to the first binary coded sub-data based on a pre-set standard reference length, a preset number of iterations, the block standard value of the first binary coded sub-data, and the maximum and minimum consecutive bits corresponding to the first binary coded sub-data. It then uses the intermediate digest corresponding to the previous binary coded sub-data as the standard reference length to calculate the intermediate digest corresponding to the next binary coded sub-data, thus obtaining the intermediate digest corresponding to each binary coded sub-data.

[0044] In one or more embodiments, the second computing unit may calculate the intermediate digest corresponding to the first binary encoded sub-data according to the following formula based on a preset standard reference length, a preset number of iterations, the block standard value of the first binary encoded sub-data, and the maximum and minimum consecutive bits corresponding to the first binary encoded sub-data.

[0045] Where T represents the intermediate digest corresponding to the first binary encoded subdata, u represents the standard reference length, and e represents the preset number of iterations. This indicates the maximum number of consecutive bits corresponding to the first binary encoded sub-data. It represents the minimum number of consecutive bits corresponding to the first binary encoded subdata.

[0046] By using the intermediate digest corresponding to the previous binary encoded sub-data as the standard reference length to calculate the intermediate digest corresponding to the next binary encoded sub-data, the association between binary encoded sub-data can be realized, preventing independent cracking.

[0047] The key generation unit is used to generate key pairs using the RSA algorithm.

[0048] The statistical unit is used to count the total number of bits with a value of 1 in the binary encoded data to obtain the plaintext encoding features, and to count the total number of bits with a value of 0 in the binary encoded sequence corresponding to the intermediate digest of each binary encoded sub-data to obtain the ciphertext encoding features.

[0049] The third calculation unit is used to calculate the RSA symbol vector based on the key pair, the plaintext encoding feature, and the ciphertext encoding feature.

[0050] In one or more embodiments, the third computing unit may calculate the RSA symbol vector based on the key pair, the plaintext encoding feature, and the ciphertext encoding feature according to the following formula;

[0051] Where J represents the RSA symbol vector, A represents the RSA encryption modulus of the key pair, K represents the system constant, G represents the plaintext encoding feature, H represents the ciphertext encoding feature, λ represents the preset asymmetric definition parameter, d represents the RSA public key exponent of the key pair, and g represents the RSA private key exponent of the key pair.

[0052] The chaotic sequence generation unit is used to initialize the initial parameters of the dual Logistic chaotic subsystem based on the RSA symbol vector, and to perform coupled iteration on the dual Logistic chaotic subsystem to continuously generate random scrambled characters until the total length of the generated scrambled characters is equal to the length of the binary code number, thus obtaining a chaotic sequence.

[0053] Specifically, the RSA symbol vector can be split into two segments and used as the initial parameters of two Logistic chaotic subsystems. The two Logistic chaotic subsystems are then coupled and iterated to continuously generate random scrambled characters until the total length of the generated scrambled characters is equal to the length of the binary code number. Finally, all the generated scrambled characters are combined in sequence to obtain a chaotic sequence.

[0054] The second partitioning unit is used to divide the chaotic sequence into multiple chaotic subsequences of the same length, each corresponding one-to-one with multiple binary encoded sub-data.

[0055] The XOR operation unit is used to perform a bitwise XOR operation between each chaotic subsequence and the corresponding binary sequence converted from the intermediate digest to obtain the ciphertext fragment corresponding to each binary encoded subdata.

[0056] Specifically, each chaotic subsequence can be XORed bit by bit with the corresponding binary sequence (of the same length as the chaotic subsequence) converted from the intermediate digest (corresponding to the binary encoded sub-data) to obtain the ciphertext fragment corresponding to each binary encoded sub-data.

[0057] The splicing and uploading unit is used to sequentially splice the ciphertext fragments corresponding to each binary encoded sub-data to obtain ciphertext data, and upload the ciphertext data to the data storage terminal.

[0058] In summary, the mechanical gait training and motion trajectory big data acquisition device provided by this invention includes: an acquisition unit for acquiring gait data collected by various sensors on the lower limb exoskeleton robot worn by the user during mechanical gait training, and motion trajectory data simulated based on the gait data collected by each sensor; a binary encoding unit for concatenating the gait data and the motion trajectory data and converting them into binary encoding to obtain binary encoded data; a padding unit for padding the binary encoded data to a specified length to obtain padded binary encoded data; a first partitioning unit for dividing the padded binary encoded data into multiple binary encoded sub-data of the same length; and a statistics unit for... The system calculates the maximum and minimum consecutive number of consecutive bits with a value of 1 in each binary coded sub-data. A first calculation unit calculates the block standard value for each binary coded sub-data based on its sorting position in the padded binary coded data, the data length of the padded binary coded data, and the data compression coefficient of each binary coded sub-data. The data compression coefficient of any binary coded sub-data is the ratio of the length of the non-padded data to the length of the binary coded sub-data. A second calculation unit calculates the block standard value based on a pre-set standard reference length, a preset number of iterations, the block standard value of the first binary coded sub-data, and the first binary coded sub-data. The system calculates the intermediate digest corresponding to the first binary encoded sub-data based on the maximum and minimum consecutive bit lengths, and uses the intermediate digest corresponding to the previous binary encoded sub-data as a standard reference length to calculate the intermediate digest corresponding to the next binary encoded sub-data, thus obtaining the intermediate digests corresponding to each binary encoded sub-data; a key generation unit is used to generate key pairs using the RSA algorithm; a statistics unit is used to count the total number of bits with a value of 1 in the binary encoded data to obtain the plaintext encoding features, and to count the total number of bits with a value of 0 in the binary encoded sequence corresponding to the intermediate digest of each binary encoded sub-data to obtain the ciphertext encoding features; a third calculation unit is used to calculate the intermediate digests based on the key pairs and the plaintext encoding. The system uses the features and the ciphertext encoding features to calculate the RSA symbol vector; a chaotic sequence generation unit is used to initialize the initial parameters of the dual Logistic chaotic subsystem based on the RSA symbol vector, and to perform coupled iteration on the dual Logistic chaotic subsystem to continuously generate random scrambled characters until the total length of the generated scrambled characters is equal to the length of the binary code number, thus obtaining a chaotic sequence; a second partitioning unit is used to divide the chaotic sequence into multiple chaotic subsequences of the same length, each corresponding one-to-one with multiple binary coded sub-data; and an XOR operation unit is used to perform a bitwise XOR operation between each chaotic subsequence and the binary sequence converted from the corresponding intermediate digest to obtain the ciphertext fragment corresponding to each binary coded sub-data.The splicing and uploading unit is used to sequentially splice the ciphertext fragments corresponding to each binary encoded sub-data to obtain ciphertext data, and then upload the ciphertext data to the data storage terminal. Thus, by employing a triple-fusion encryption mechanism of hash compression + RSA asymmetric encryption + dual Logistic chaotic system, data during patient gait training can be encrypted. Even if an attacker obtains the ciphertext, they cannot recover any gait training data from it. Compared to existing technologies that directly transmit plaintext or rely solely on single encryption, this significantly reduces the risk of malicious mining of patient gait training data during transmission and storage, effectively fulfilling the privacy protection requirements for medical and health data. Simultaneously, by padding the binary encoded data to a specified length, the length of the encrypted data storage remains highly consistent across different data batches, solving the problem of large differences in the length of unencrypted data storage in traditional encryption schemes. This allows for the reservation of fixed space for the ciphertext during storage, avoiding storage fragmentation and space waste caused by length fluctuations. This is particularly suitable for scenarios requiring long-term storage of massive amounts of patient gait training data, significantly reducing data storage costs. Furthermore, this scheme utilizes the RSA algorithm to generate asymmetric key pairs and leverages the extreme sensitivity of dual-logistic chaotic systems to initial values, using the RSA symbol vector as the "detonator" of the chaotic system. The random scrambling generated by the chaotic system is aperiodic and unpredictable, and the key space far exceeds the computational limit of brute-force attacks. Even if an attacker intercepts the chaotic sequence, they cannot deduce the RSA private key, and therefore cannot predict the initial chaotic state of the next encryption, effectively resisting brute-force attacks and known-plaintext attacks.

[0059] The second aspect of this application provides a mechanical gait training and motion trajectory big data collection method, which may include, but is not limited to, the following steps S201-S214.

[0060] Step S201. Acquire gait data collected by various sensors on the lower limb exoskeleton robot worn by the user during mechanical gait training, as well as motion trajectory data simulated based on the gait data collected by each sensor.

[0061] Step S202. The gait data and the motion trajectory data are concatenated and converted into binary code to obtain binary encoded data.

[0062] Step S203. Pad the binary encoded data to a specified length to obtain padded binary encoded data.

[0063] Step S204. Divide the padded binary encoded data into multiple binary encoded sub-data of the same length.

[0064] Step S205. Count the maximum and minimum consecutive number of bits with a value of 1 in each binary encoded sub-data.

[0065] Step S206. Based on the sorting position of each binary coded sub-data in the padded binary coded data, the data length corresponding to the padded binary coded data, and the data compression coefficient of each binary coded sub-data, calculate the block standard value of each binary coded sub-data.

[0066] The data compression factor of any binary encoded sub-data is the ratio of the length of the non-padded data to the length of the binary encoded sub-data.

[0067] Step S207. Based on the preset standard reference length, preset number of iterations, block standard value of the first binary coded sub-data, and the maximum and minimum consecutive bits corresponding to the first binary coded sub-data, calculate the intermediate digest corresponding to the first binary coded sub-data, and use the intermediate digest corresponding to the previous binary coded sub-data as the standard reference length to calculate the intermediate digest corresponding to the next binary coded sub-data, thus obtaining the intermediate digest corresponding to each binary coded sub-data.

[0068] Step S208. Generate a key pair using the RSA algorithm.

[0069] Step S209. Count the total number of bits with a value of 1 in the binary encoded data to obtain the plaintext encoding features. Count the total number of bits with a value of 0 in the binary encoded sequence corresponding to the intermediate digest of each binary encoded sub-data to obtain the ciphertext encoding features.

[0070] Step S210. Calculate the RSA symbol vector based on the key pair, the plaintext encoding feature, and the ciphertext encoding feature.

[0071] Step S211. Initialize the initial parameters of the dual Logistic chaotic subsystem based on the RSA symbol vector, and perform coupled iteration on the dual Logistic chaotic subsystem to continuously generate random scrambled characters until the total length of the generated scrambled characters is equal to the length of the binary code number, thus obtaining a chaotic sequence.

[0072] Step S212. Divide the chaotic sequence into multiple chaotic subsequences of the same length, each corresponding one-to-one with multiple binary encoded sub-data.

[0073] Step S213. Perform a bitwise XOR operation between each chaotic subsequence and the corresponding binary sequence converted from the intermediate digest to obtain the ciphertext fragment corresponding to each binary encoded subdata.

[0074] Step S214. Concatenate the ciphertext fragments corresponding to each binary encoded sub-data in sequence to obtain ciphertext data, and upload the ciphertext data to the data storage terminal.

[0075] The third aspect of this application provides an electronic device, including a memory, a processor, and a transceiver that are sequentially and communicatively connected. The memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the mechanical gait training and motion trajectory big data collection method as described in the second aspect of the application.

[0076] Specifically, the memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or last-in-first-out (FILO) memory, etc.; the processor may not be limited to microprocessors of the STM32F105 series, ARM (Advanced RISC Machines), x86 architecture processors, or processors with integrated NPU (neural-network processing units); the transceiver may be, but is not limited to, WiFi (Wireless Fidelity) wireless transceivers, Bluetooth wireless transceivers, General Packet Radio Service (GPRS) wireless transceivers, ZigBee (a low-power LAN protocol based on the IEEE 802.15.4 standard), 3G transceivers, 4G transceivers, and / or 5G transceivers, etc.

[0077] This fourth aspect of the embodiment provides a computer-readable storage medium storing instructions containing the mechanical gait training and motion trajectory big data acquisition method described in the second aspect of the embodiment. Specifically, the computer-readable storage medium stores instructions that, when executed on a computer, perform the mechanical gait training and motion trajectory big data acquisition method as described in the second aspect. The computer-readable storage medium refers to a data storage medium, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or memory sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0078] The fifth aspect of this embodiment provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the mechanical gait training and motion trajectory big data collection method as described in the second aspect of this embodiment. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0079] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A mechanical gait training and motion trajectory big data acquisition device, characterized in that, include: The acquisition unit is used to acquire gait data collected by various sensors on the lower limb exoskeleton robot worn by the user during mechanical gait training, as well as motion trajectory data simulated based on the gait data collected by various sensors. A binary encoding unit is used to concatenate the gait data and the motion trajectory data and convert them into binary code to obtain binary encoded data. The padding unit is used to pad the binary encoded data to a specified length to obtain the padded binary encoded data. The first partitioning unit is used to divide the padded binary encoded data into multiple binary encoded sub-data of the same length; The first statistical unit is used to count the maximum and minimum consecutive number of bits with a value of 1 in each binary encoded sub-data. The first calculation unit is used to calculate the block standard value of each binary coded sub-data based on the sorting position of each binary coded sub-data in the padded binary coded data, the data length corresponding to the padded binary coded data, and the data compression coefficient of each binary coded sub-data. The data compression coefficient of any binary coded sub-data is the ratio of the length of the non-padded data in the binary coded sub-data to the length of the binary coded sub-data. The second calculation unit is used to calculate the intermediate digest corresponding to the first binary coded sub-data based on the preset standard reference length, preset number of iterations, block standard value of the first binary coded sub-data, and the maximum and minimum consecutive bits corresponding to the first binary coded sub-data. It then uses the intermediate digest corresponding to the previous binary coded sub-data as the standard reference length to calculate the intermediate digest corresponding to the next binary coded sub-data, thus obtaining the intermediate digest corresponding to each binary coded sub-data. A key generation unit is used to generate key pairs using the RSA algorithm; The second statistical unit is used to count the total number of bits with a value of 1 in the binary encoded data to obtain the plaintext encoding features, and to count the total number of bits with a value of 0 in the binary encoded sequence corresponding to the intermediate digest of each binary encoded sub-data to obtain the ciphertext encoding features. The third calculation unit is used to calculate the RSA symbol vector based on the key pair, the plaintext encoding feature, and the ciphertext encoding feature; The chaotic sequence generation unit is used to initialize the initial parameters of the dual Logistic chaotic subsystem based on the RSA symbol vector, and to perform coupled iteration on the dual Logistic chaotic subsystem to continuously generate random scrambled characters until the total length of the generated scrambled characters is equal to the length of the binary code number, thus obtaining a chaotic sequence. The second partitioning unit is used to divide the chaotic sequence into multiple chaotic subsequences of the same length, each corresponding one-to-one with multiple binary encoded sub-data. The XOR operation unit is used to perform a bitwise XOR operation between each chaotic subsequence and the corresponding binary sequence converted from the intermediate digest to obtain the ciphertext fragment corresponding to each binary encoded subdata. The splicing and uploading unit is used to sequentially splice the ciphertext fragments corresponding to each binary encoded sub-data to obtain ciphertext data, and upload the ciphertext data to the data storage terminal.

2. The mechanical gait training and motion trajectory big data acquisition device according to claim 1, characterized in that, When the padding unit is used to pad the binary encoded data to a specified length, it is specifically used for: After the binary encoded data, add one bit with a value of 1, and then add multiple bits with a value of 0 continuously until the length of the binary encoded data reaches the specified length.

3. The mechanical gait training and motion trajectory big data acquisition device according to claim 1, characterized in that, The first calculation unit, when calculating the block standard value of each binary coded sub-data based on the sorting position of each binary coded sub-data in the padded binary coded data, the data length corresponding to the padded binary coded data, and the data compression coefficient of each binary coded sub-data, is specifically used for: The block standard value of each binary coded sub-data is calculated according to the following formula based on the sorting position of each binary coded sub-data in the padded binary coded data, the data length corresponding to the padded binary coded data, and the data compression coefficient of each binary coded sub-data. Where Y represents the block standard value of the binary encoded sub-data, R represents the data length corresponding to the padded binary encoded data, r represents the sorting position of the binary encoded sub-data in the padded binary encoded data, and φ represents the data compression coefficient of the binary encoded sub-data.

4. The mechanical gait training and motion trajectory big data acquisition device according to claim 3, characterized in that, The second computing unit, when calculating the intermediate digest corresponding to the first binary encoded sub-data based on a pre-set standard reference length, a pre-set number of iterations, the block standard value of the first binary encoded sub-data, and the maximum and minimum consecutive bit lengths corresponding to the first binary encoded sub-data, is specifically used for: Based on the pre-set standard reference length, preset number of iterations, block standard value of the first binary encoded sub-data, and the maximum and minimum consecutive bits corresponding to the first binary encoded sub-data, the intermediate digest corresponding to the first binary encoded sub-data is calculated according to the following formula; Where T represents the intermediate digest corresponding to the first binary encoded subdata, u represents the standard reference length, and e represents the preset number of iterations. This indicates the maximum number of consecutive bits corresponding to the first binary encoded sub-data. It represents the minimum number of consecutive bits corresponding to the first binary encoded subdata.

5. The mechanical gait training and motion trajectory big data acquisition device according to claim 1, characterized in that, The third calculation unit, when calculating the RSA symbol vector based on the key pair, the plaintext encoding features, and the ciphertext encoding features, specifically performs the following: Based on the key pair, the plaintext encoding feature, and the ciphertext encoding feature, the RSA symbol vector is calculated according to the following formula; Where J represents the RSA symbol vector, A represents the RSA encryption modulus of the key pair, K represents the system constant, G represents the plaintext encoding feature, H represents the ciphertext encoding feature, λ represents the preset asymmetric definition parameter, d represents the RSA public key exponent of the key pair, and g represents the RSA private key exponent of the key pair.

6. The mechanical gait training and motion trajectory big data acquisition device according to claim 1, characterized in that, When the binary encoding unit is used to concatenate the gait data and the motion trajectory data and convert them into binary code, it is specifically used for: After concatenating the gait data and the motion trajectory data, the concatenated data is converted into binary code using ASCII encoding or UTF-8 encoding.

7. The mechanical gait training and motion trajectory big data acquisition device according to claim 1, characterized in that, The gait data includes joint angle data, plantar pressure data, and / or cadence data.

8. A mechanical gait training and motion trajectory big data collection method, characterized in that, include: Acquire gait data collected by various sensors on the lower limb exoskeleton robot worn by the user during mechanical gait training, as well as motion trajectory data simulated based on the gait data collected by each sensor; The gait data and the motion trajectory data are concatenated and then converted into binary code to obtain binary encoded data. The binary encoded data is padded to a specified length to obtain padded binary encoded data; The padded binary encoded data is divided into multiple binary encoded sub-data of the same length; Calculate the maximum and minimum consecutive number of bits with a value of 1 in each binary encoded sub-data. Based on the sorting position of each binary coded sub-data in the padded binary coded data, the data length corresponding to the padded binary coded data, and the data compression coefficient of each binary coded sub-data, the block standard value of each binary coded sub-data is calculated. Among them, the data compression coefficient of any binary coded sub-data is the ratio of the non-padded data in the binary coded sub-data to the length of the binary coded sub-data. Based on the pre-set standard reference length, preset number of iterations, block standard value of the first binary coded sub-data, and the maximum and minimum consecutive bits corresponding to the first binary coded sub-data, the intermediate digest corresponding to the first binary coded sub-data is calculated. The intermediate digest corresponding to the previous binary coded sub-data is used as the standard reference length to calculate the intermediate digest corresponding to the next binary coded sub-data, thus obtaining the intermediate digest corresponding to each binary coded sub-data. Generate a key pair using the RSA algorithm; The plaintext encoding features are obtained by counting the total number of bits with a value of 1 in the binary encoded data, and the ciphertext encoding features are obtained by counting the total number of bits with a value of 0 in the binary encoded sequence corresponding to the intermediate digest of each binary encoded sub-data. Based on the key pair, the plaintext encoding features, and the ciphertext encoding features, the RSA symbol vector is calculated; The initial parameters of the dual-Logistic chaotic subsystem are initialized based on the RSA symbol vector, and the dual-Logistic chaotic subsystem is coupled and iterated to continuously generate random scrambled characters until the total length of the generated scrambled characters is equal to the length of the binary code number, thus obtaining a chaotic sequence. The chaotic sequence is divided into multiple chaotic subsequences of the same length, each corresponding one-to-one with multiple binary encoded sub-data. Perform a bitwise XOR operation between each chaotic subsequence and the corresponding binary sequence converted from the intermediate digest to obtain the ciphertext fragment corresponding to each binary encoded subdata. The ciphertext fragments corresponding to each binary encoded sub-data are concatenated in sequence to obtain ciphertext data, and the ciphertext data is uploaded to the data storage terminal.

9. An electronic device, characterized in that, The device includes a memory, a processor, and a transceiver that are connected in sequence. The memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the mechanical gait training and motion trajectory big data acquisition method as described in claim 8.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or the instructions are executed by the computer, they implement the mechanical gait training and motion trajectory big data collection method as described in claim 8.

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