A self-adaptive mode switching and force control method of a massage instrument

Abstract

This invention discloses an adaptive mode switching and intensity control method for a massager, relating to the field of intelligent massage equipment technology. It generates an initial personalized data set for each user and constructs a protective layer for each data set. All real data sets are centrally stored in a dedicated real database in a central manager. When the user uses the massager again, the system quickly matches the detected user biometric features or input identity identifier with the unique features stored in the protective layers of each data set to identify and retrieve the corresponding target user data set. The system then performs multi-source fusion of the historical personalized data stored in the target user data set. Based on the fused comprehensive data, it generates adaptive massage mode switching commands and dynamic intensity adjustment commands to control the massage actuator to provide personalized services to the user. This achieves the function of adaptive adjustment of massage mode and intensity according to the user's overall condition.

Description

Technical Field

[0001] This invention relates to the field of intelligent massage equipment technology, specifically to a method for adaptive mode switching and intensity control of a massager. Background Technology

[0002] Currently, smart massagers with multi-user memory capabilities are becoming increasingly popular. These devices typically provide personalized services by collecting data such as users' body parameters and usage preferences. However, existing technology suffers from two major drawbacks: Firstly, regarding data security, existing solutions mostly employ simple local encrypted storage or cloud account isolation. The former has limited security strength and is easily cracked through physical extraction; the latter relies on the network and vendor servers, posing risks of data leakage and server compromise, and lacks proactive defense and recovery mechanisms after data theft. The consequences of leaking a user's sensitive health data are severe.

[0003] Secondly, in terms of personalized service logic, most existing methods are based on simple calls to historical preferences or only make minor adjustments based on a single real-time signal. They lack multi-dimensional and dynamic fusion analysis of users' historical status, real-time physiological signals and usage environment, resulting in low adaptability, rigid service models, and an inability to accurately match users' ever-changing needs.

[0004] Therefore, in order to address the above problems, there is an urgent need for an adaptive mode switching and intensity control method for massage devices. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an adaptive mode switching and intensity control method for a massager. It not only constructs a multi-layered, proactive user privacy data protection system to ensure data security throughout its entire lifecycle, but also designs a personalized service generation logic based on deep fusion of multi-source data, achieving truly intelligent adaptive service.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an adaptive mode switching and intensity control method for a massager, comprising the following steps: Step S1, for each user, collecting their personal privacy data to form an initial personalized data cluster, and constructing a protective layer for each data cluster. The protective layer stores unique features corresponding to the user's privacy data and has a monitoring mechanism to monitor the security of the data within the data cluster and the integrity of the protective layer itself in real time. All real data clusters are centrally stored in a dedicated real database in a central manager; Step S2, when the user uses the massager again, the detected user biometric features or input identity identifier are quickly matched with the unique features stored in the protective layers of each data cluster to identify and call the corresponding target user data cluster; Step S3, the historical personalized data stored in the target user data cluster is fused with the user's current state data and environmental data of the massage area collected in real time by the massager. Based on the fused comprehensive data, an adaptive massage mode switching command and a dynamic intensity adjustment command are generated to control the massage actuator to provide personalized services to the user.

[0007] Furthermore, the monitoring mechanism described in step S1 monitors the integrity of the protection layer in real time. Specifically, it quantifies and assesses the degree to which the protection layer has been attacked or accessed abnormally. When the degree reaches a preset threshold, it automatically triggers the hardening process. The hardening process includes: S11, the central manager initiates a data authorization request to the user corresponding to the data cluster to obtain the user's latest data update authorization; S12, based on the obtained data update authorization, it verifies the integrity of the data within the data cluster and compares it with the user's possible latest data, supplementing and repairing incomplete or outdated data within the data cluster; S13, after verification and repair are completed, it calls a preset encryption algorithm and verification information to strengthen the structure and reset the integrity of the protection layer of the data cluster, thus completing the hardening.

[0008] Furthermore, in addition to a dedicated real database storing real data clusters, the central manager also includes at least one virtual database. Each virtual database stores multiple obfuscated data clusters, which are filled with randomly generated, meaningless obfuscated data. When an external attack attempts to access the database, the virtual database exists in parallel with the real database and has similar external interface characteristics, causing the attacker to mistakenly enter the virtual database and retrieve the obfuscated data clusters, thereby obtaining erroneous and chaotic data, thus achieving proactive obfuscation protection for the real data clusters.

[0009] Furthermore, the protection layer is also equipped with a self-destruct port, which is linked to the data archive port of the central manager. When the protection layer is breached and the attacker begins to identify the real data inside the data cluster, the following defense and recovery process is triggered: Before the attacker parses the data, the data cluster automatically copies its internal data and transmits the copied content to the data archive port in real time through the link; after receiving the copied content, the data archive port immediately starts the data regeneration process to create a copy of the data cluster; while transmitting the copied content, the original data cluster under attack activates a self-destruct mechanism through the self-destruct port, destroying all its internal real data and applying a hacker marking method to the attack source. The hacker marking method is used to record and extract the attacker's personalized characteristics to form an attacker characteristic mark, which is used by the central manager for comparison and investigation in subsequent access requests.

[0010] Furthermore, the defense and recovery process also includes a data recovery step: retransmitting and storing a copy of the data cluster regenerated from the data archive port into the real database, replacing the original data cluster that has been destroyed, to ensure the continuity of user services; uploading the attacker feature markers generated by the hacker marking methods to the security audit module of the central manager, so that when the attacker initiates any access request again, the security audit module performs feature comparison and investigation at the central manager level before the request reaches the database, and immediately intercepts and alarms when a match is found.

[0011] Further, the specific analysis of the multi-source fusion is as follows: S31, the historical personalized data includes the user's past physiological parameters, preferred massage modes, and historical intensity feedback; S32, the user's current state data is collected in real time by the built-in sensors of the massager, including electromyography signals, skin impedance, and real-time body position information; S33, the environmental data of the massage area includes the surface temperature of the massage area, ambient humidity, and the user's preset fatigue level; S34, using the fusion algorithm of the central manager, the three types of data in S31, S32, and S33 are used as input, and weighted fusion is performed through a pre-trained dynamic weight allocation model to output a comprehensive evaluation vector that represents the user's current immediate needs and tolerance level. The massage mode switching command and the dynamic intensity adjustment command are generated based on the mapping of this comprehensive evaluation vector.

[0012] Furthermore, the user's biometric features include, but are not limited to, fingerprint features, voiceprint features, or subcutaneous vein distribution features in specific locations; the input identity identifier is an encrypted identification code uniquely bound to the user; when the massager identifies the user, the process of calling the target user's data set is completed within a local encrypted security zone, and any temporary data generated during the call process is immediately cleared after the service session ends.

[0013] The present invention has the following beneficial effects: This massager's adaptive mode switching and intensity control method employs real-time monitoring and reinforcement of the protection layer, virtual database deception attacks, and a self-destruction mechanism to destroy data and mark attackers. This forms a full-chain protection system encompassing prevention, interference during attacks, and recovery and traceability afterward, significantly enhancing data security. Through multi-source fusion algorithms, it comprehensively considers users' long-term preferences, immediate physiological responses, and environmental conditions, making massage modes and intensity adjustments more scientific and better suited to users' current needs, significantly improving user experience and relaxation effects. In the event of an attack, the system can proactively transfer data, destroy the source, mark attackers, and quickly restore service, ensuring service continuity and system resilience. The security mechanism and service logic are not separate but deeply integrated. The security mechanism ensures the trustworthiness and usability of personalized data, while high-quality service incentivizes users to authorize data updates, creating a virtuous cycle where security and experience mutually reinforce each other.

[0014] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0015] Figure 1 This is a flowchart of an adaptive mode switching and intensity control method for a massager according to the present invention. Detailed Implementation

[0016] This application embodiment provides an adaptive mode switching and intensity control method for a massager, which enables the massage mode and intensity to be adaptively adjusted according to the user's overall condition.

[0017] The overall concept of this application's embodiments is as follows: Each user's privacy data is encapsulated into an independent data cluster and equipped with a protective layer that has monitoring, self-healing, deception, and self-destruction capabilities. At the same time, through an innovative data fusion engine, the user's historical records, real-time physiological status, and environmental context information are comprehensively analyzed to dynamically generate the optimal massage control strategy.

[0018] Please see Figure 1This invention provides a technical solution: an adaptive mode switching and intensity control method for a massager, comprising the following steps: Step S1, for each user, collect their personal privacy data to form an initial personalized data cluster, and construct a protective layer for each data cluster. The protective layer stores the unique characteristics of the corresponding user's privacy data and has a monitoring mechanism to monitor the security of the data within the data cluster and the integrity of the protective layer itself in real time. All real data clusters are centrally stored in a dedicated real database in a central manager; Step S2, when the user uses the massager again, the detected user biometrics or input identity identifier are quickly matched with the unique characteristics stored in the protective layer of each data cluster to identify and call the corresponding target user data cluster; Step S3, the historical personalized data stored in the target user data cluster is fused with the user's current status data and environmental data of the massage area collected in real time by the massager. Based on the fused comprehensive data, an adaptive massage mode switching command and a dynamic intensity adjustment command are generated to control the massage execution mechanism to provide personalized services to the user.

[0019] In this implementation plan, step S1 specifically involves data cluster construction and secure storage initialization, which is analyzed in detail as follows: When a new user uses the massager for the first time, the system guides the user through registration and initial data collection.

[0020] Personal privacy data collection: Through a questionnaire via the massager's built-in interactive interface, the system collects information such as the user's age, gender, frequently experiencing discomfort areas, and subjective preferences for pressure. Additionally, during the initial massage experience, the system guides the user through a short, standard massage pattern, during which biosensors such as electromyography (EMG) sensors and pressure sensors collect basic physiological response data to create an initial user profile.

[0021] Creating personalized data clusters: After structuring all the data collected above, it is packaged into a single data packet called a personalized data cluster. Each data cluster has a globally unique identifier.

[0022] Build a protection layer: Generate a dedicated protection layer for each data cluster. This protection layer is essentially an encrypted encapsulation shell containing metadata.

[0023] Storing Unique Features: In the metadata area of ​​the protection layer, unique features used for quick user matching are stored. These features can be a hashed unique username set during user registration, or summary information extracted from the user's biometrics, such as the fingerprint template feature code initially entered. For example, if fingerprints are used, the protection layer does not store the complete fingerprint image, but rather the encrypted hash value of the fingerprint feature points for subsequent comparison.

[0024] Integrated monitoring mechanism: The protection layer incorporates lightweight monitoring code. It continuously records the number of accesses to the data cluster, the access source, and access behavior patterns, and calculates an integrity score. The initial score is perfect. Any unusual access attempts, such as trying to bypass matching to read directly or high-frequency probing, will result in a lower score. The monitoring mechanism runs periodically under the scheduling of a security daemon in the central manager.

[0025] Centralized storage: All users' real data is stored in a dedicated, highly isolated storage area within a central manager—the real database. Access to this database is subject to the strictest access control.

[0026] Step S2 specifically involves user identity matching and data retrieval, which can be analyzed as follows: When the user uses the massager again: Users can press the fingerprint recognition module, input commands via voice, or enter their personal identification code.

[0027] Fast matching: The central manager quickly compares the collected features or input identification codes with the unique features stored in all data cluster protection layers. This comparison is performed within the secure area using a single hash comparison, making it extremely fast.

[0028] Data Cluster Retrieval: Upon successful matching, the central manager decrypts the protection layer of the corresponding target data cluster, loading historical personalized data such as past preferences and basic physiological profiles into the secure working area of ​​memory, ready for use in this service decision. For example: If user Zhang San's fingerprint verification is successful, the system immediately retrieves Zhang San's previously set preferences for neck massage and medium intensity, based on historical data.

[0029] Step S3 specifically involves multi-source data fusion and adaptive control, which is analyzed in detail as follows: Real-time data acquisition: User's current status data: The electromyography (EMG) sensor on the massager monitors the tension of the muscles in the massage area in real time, such as the amplitude and frequency of the EMG signal; the skin conductivity sensor monitors changes in skin impedance, reflecting the excitation level of the sympathetic nervous system, i.e., the degree of relaxation; the gyroscope and accelerometer determine the user's real-time body position, such as whether they are sitting, lying down, or on their side.

[0030] Environmental data: An infrared temperature sensor measures the surface temperature of the massaged area; a humidity sensor monitors the ambient humidity; in addition, users can self-assess their fatigue level, such as mild fatigue or severe soreness, through the app before the massage begins.

[0031] Data fusion and decision-making: The fusion algorithm of the central manager begins to work.

[0032] Inputs: Historical personalized data, real-time status data, and environmental data, which are used as parallel input streams.

[0033] Fusion Process: The algorithm internally employs a pre-trained dynamic weight allocation model. This model dynamically determines the reference weights for various data types based on the current service stage and data type. For example, historical preferences have a higher weight at the start of the massage; during the massage, the weight of real-time electromyography (EMG) signals gradually increases to respond to the user's immediate muscle responses. Specific logic explanation: If the system detects strong current EMG signals from the user, even if the historical preference is for moderate intensity, the fusion algorithm may, after integrating high fatigue levels and low body surface temperature data, determine that the user is in a state of cold tension. This results in a decision to initially use gentle techniques to warm up the user, then gradually increase the intensity to a moderate to high level to alleviate deep stiffness.

[0034] Output comprehensive evaluation vector: The fusion result is not a single instruction, but a comprehensive evaluation vector. This vector is a multi-dimensional array, such as [massage pattern preference score, suggested intensity value, suggested rhythm value, and key area markers].

[0035] Generate and execute control commands: Massage mode switching command: Based on the mode preference score in the vector (e.g., tapping mode 0.7, kneading mode 0.3), the system may decide that this service will primarily use "tapping mode," intermittently interspersed with "kneading mode." The controller will send the corresponding mode switching timing command to the motor assembly of the massage head.

[0036] Dynamic intensity adjustment command: Based on the suggested intensity value (65) from the vector and combined with feedback from the real-time pressure sensor, a closed-loop control is formed. The controller drives the motor to operate at 65% of its maximum torque and fine-tunes it according to the pressure feedback to ensure that the actual intensity applied to the body remains stable near the target value. For example, when the massage head moves to a softer area near the shoulder blade, the pressure feedback increases, and the intensity command automatically drops slightly to 60% to prevent discomfort.

[0037] Specifically, the protection layer is not simply an encrypted shell, but an intelligent security container that integrates state awareness, access control, and self-healing capabilities. It adopts a three-layer structure: the outermost layer is the interface authentication layer, which is responsible for handling matching requests and verifying access tokens; the middle layer is the integrity monitoring layer, which runs a lightweight intrusion detection algorithm and continuously calculates integrity scores; and the innermost layer is the data encryption layer, which uses a key bound to user characteristics to encrypt core data.

[0038] Specifically, in step S1, the monitoring mechanism monitors the integrity of the protection layer in real time. Specifically, it quantifies and assesses the extent to which the protection layer has been attacked or accessed abnormally. When the extent reaches a preset threshold, the hardening process is automatically triggered. The hardening process includes: S11, the central manager initiates a data authorization request to the user corresponding to the data cluster to obtain the user's latest data update authorization; S12, based on the obtained data update authorization, it verifies the integrity of the data within the data cluster and compares it with the user's possible latest data, supplementing and repairing any incomplete or outdated data within the data cluster; S13, after verification and repair are completed, it calls a preset encryption algorithm and verification information to strengthen the structure and reset the integrity of the protection layer of the data cluster, thus completing the hardening.

[0039] In this implementation scheme, the monitoring mechanism of the protection layer is continuously running. Suppose the system detects abnormal port scanning behavior on Zhang San's data group, and its integrity score drops from 100 to 60, falling below the preset threshold of 75.

[0040] Triggering the hardening process: The security module in the central manager responds immediately.

[0041] Initiating an authorization request: The massager sends a request to the user Zhang San's linked mobile phone via the network-connected app: "Your personal profile needs to be updated and verified. Please confirm to optimize subsequent services." The user clicks to confirm, completing a proactive data update authorization. This serves as both a security verification and an opportunity for the user to participate in data maintenance.

[0042] Data Verification and Repair: Once authorized, the system can securely connect to the cloud or prompt the user to manually enter the latest information. For example, if the system discovers that Zhang San's frequently experiencing discomfort only recorded the neck in the data cluster, but the user recently added the lower back to the app logs, the system will add the lower back information to the data cluster. Simultaneously, the system checks the structural integrity of the data cluster and repairs minor errors caused by potential damage.

[0043] Enhanced Protection Layer: After content repair, the system uses a stronger encryption algorithm to re-encrypt the entire data packet. Simultaneously, the digital signature and access token within the protection layer are updated, and the integrity score is reset to 100. This is equivalent to replacing the lock on a safe with a more complex lock and a new sealing label.

[0044] Specifically, in the central manager, in addition to a dedicated real database that stores real data clusters, there is at least one virtual database. Each virtual database stores multiple obfuscated data clusters, which are filled with randomly generated, meaningless obfuscated data. When an external attack attempts to access the database, the virtual database and the real database exist in parallel and have similar external interface characteristics, causing the attacker to mistakenly enter the virtual database and retrieve the obfuscated data clusters, thereby obtaining incorrect and chaotic data and achieving proactive obfuscation protection for the real data clusters.

[0045] In this implementation scheme, the system periodically, or each time a real data cluster is created, synchronously generates a batch of obfuscated data clusters in the virtual database. These data clusters have the exact same file format and size characteristics as the real data clusters. Their internal data is filled by a random number generator; for example, the user's age field might be 253 years old, and the preference strength might be -5.

[0046] The access APIs of real and virtual databases are highly similar in design. Attackers discover database service ports through vulnerability scanning, which are ultimately connected to a database routing layer. This routing layer has a certain probability of redirecting access requests to the virtual database.

[0047] Once attackers breach the system and successfully connect to the database, what they painstakingly crack is likely to be a jumbled mess of data filled with gibberish.

[0048] Specifically, the protection layer also includes a self-destruct port, which is linked to the data archive port of the central manager. When the protection layer is breached and the attacker begins to identify the real data within the data cluster, the following defense and recovery process is triggered: Before the attacker parses the data, the data cluster automatically copies its internal data and transmits the copied content to the data archive port in real time via the link; after receiving the copied content, the data archive port immediately starts the data regeneration process to create a copy of the data cluster; while transmitting the copied content, the original data cluster under attack activates its self-destruct mechanism through the self-destruct port, destroying all its internal real data and applying hacker marking techniques to the attack source. These hacker marking techniques are used to record and extract the attacker's personalized characteristics, forming an attacker feature tag, which is used by the central manager for comparison and investigation in subsequent access requests.

[0049] The defense and recovery process also includes a data recovery step: re-transmitting and storing a copy of the data cluster regenerated from the data archive port into the real database to replace the original data cluster that has been destroyed, ensuring the continuity of user services; uploading the attacker's feature tags generated by hacker marking methods to the security audit module of the central manager. When the attacker initiates any access request again, the security audit module performs feature comparison and investigation at the central manager level before the request reaches the database. If a match is found, it will be immediately intercepted and an alarm will be triggered.

[0050] In this implementation plan, it is assumed that the attacker bypasses all front-end protections through an unknown vulnerability, directly locates and successfully decrypts the protection layer of Li Si's real data group, and is about to read his plaintext health data.

[0051] The moment the protection layer is breached, the trigger on the self-locking port is activated. This trigger may be the final verification procedure for the decrypted state of the protection layer. The trigger first, with the highest priority, instantly copies the plaintext data in memory.

[0052] The copied data is immediately transmitted to a highly secure data archive port within the central manager via a pre-defined high-priority secure channel, also known as the data archive port. Upon receiving the data, the archive port, like a cloning factory, immediately uses this data to regenerate a completely new, encrypted data set and assigns it a new layer of protection. This process can be completed in milliseconds.

[0053] While the data is being copied and transmitted, the self-destruct mechanism of the original data cluster is activated.

[0054] Destroying data is not simply deleting the file pointer; rather, it involves performing multiple overwrite operations on the physical flash memory sectors or memory regions where the data is stored to ensure that the original data is unrecoverable.

[0055] The auto-logging program captures characteristics of attack sessions. For example, it records process path fragments left by the attack connection source in the system kernel, special malformed fields in request packets, or timestamp patterns of attack behavior. These characteristics are encrypted and packaged to form an attacker signature.

[0056] The newly generated data cluster is immediately written to an empty space in the real database, updating the database index. User Li Si's next use is completely unaffected, because his data has been completely removed from the database.

[0057] The generated attacker signatures are uploaded to the security audit module. Thereafter, all network requests and internal process calls entering the central manager will first undergo rapid signature filtering by this module.

[0058] Specifically, the multi-source fusion analysis is as follows: S31, historical personalized data includes the user's past physiological parameters, preferred massage modes, and historical intensity feedback; S32, the user's current state data is collected in real time by the massager's built-in sensors, including electromyography signals, skin impedance, and real-time body position information; S33, environmental data of the massage area includes the surface temperature of the massage area, ambient humidity, and the user's preset fatigue level; S34, using the central manager's fusion algorithm, the three types of data in S31, S32, and S33 are used as input, and weighted fusion is performed through a pre-trained dynamic weight allocation model to output a comprehensive evaluation vector that represents the user's current immediate needs and tolerance level. Massage mode switching commands and dynamic intensity adjustment commands are generated based on this comprehensive evaluation vector.

[0059] In this implementation plan, the specific logical flow of the fusion algorithm is as follows: Unify data from different sources to a comparable scale. For example, normalize the amplitude of electromyographic signals to a tension index of 0-1; quantify the historical preference for moderate intensity to a value of 0.6; and quantify the fatigue level of severe soreness to 0.9.

[0060] Dynamic weight allocation model working logic: This model is a state machine based on rules and lightweight machine learning.

[0061] Example of phase rule: In the first 30 seconds of the massage, historical preferences are given a higher weight, while real-time status is given a lower weight, because the muscles need time to adapt. After 30 seconds, the weight of real-time status increases to 0.6.

[0062] Data credibility rule: If data from a certain sensor is abnormal, the model will automatically reduce its weight and increase the weight of other reliable data sources.

[0063] Association rule: When the ambient temperature data is low, the model knows that the user's muscles may be stiffer, so it will suggest a gentler initial force increase slope for the same tension index.

[0064] The algorithm performs a weighted summation calculation and finally packages it into a comprehensive evaluation vector.

[0065] Specifically, user biometrics include, but are not limited to, fingerprint features, voiceprint features, or subcutaneous vein distribution features in specific locations; the input identity is an encrypted identification code uniquely bound to the user; when the massager identifies the user, the process of calling the target user's data set is completed in a local encrypted secure area, and any temporary data generated during the call is immediately cleared after the service session ends.

[0066] In this implementation plan, the verification of user biometrics and data processing are both completed in a trusted execution environment or an equivalent encrypted security zone on the device.

[0067] For example, the fingerprint sensor chip itself has a built-in security element. The fingerprint comparison is completed within the chip, and only a pass or fail token is output to the main system. The original fingerprint data is never transmitted.

[0068] After a successful match, the decryption and retrieval of the target data block are performed within an encrypted secure area in memory. This area is isolated from the rest of the system.

[0069] After the massage service ends, all plaintext historical data and intermediate data in real-time processing within the secure zone will be immediately and securely erased. This ensures that even if the device is lost for a short period, no sensitive information will remain in memory.

[0070] In summary, this application has at least the following effects: In terms of data security, measures such as building a protective layer, setting up a virtual database, and establishing self-closing ports comprehensively protect user privacy and data security, preventing data leaks and malicious attacks. Regarding service personalization, collecting personalized user data and performing multi-source fusion analysis enables precise generation of adaptive massage mode switching and intensity adjustment commands, providing users with highly personalized massage services and enhancing user experience. Furthermore, in the event of an attack, a comprehensive defense and recovery process is in place, promptly destroying original data, generating copies, and marking attackers to ensure user service continuity and maintain the system's secure and stable operation. In addition, locally encrypted secure zones promptly clear access data and temporary data, further strengthening security during data usage.

[0071] Those skilled in the art will understand that embodiments of the present invention can be provided as methods. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] This invention is described with reference to a flowchart of a method according to embodiments of the invention. It should be understood that the combination of each step in the flowchart can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the process. Figure 1 A device for a function specified in one or more processes.

[0073] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 The function specified in one or more processes.

[0074] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 Steps of a specified function in one or more processes.

[0075] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0076] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for adaptive mode switching and intensity control of a massager, characterized in that, Includes the following steps: Step S1: For each user, collect their personal privacy data to form an initial personalized data cluster, and build a protection layer for each data cluster. The protection layer stores the unique characteristics of the corresponding user's privacy data and has a monitoring mechanism to monitor the security of the data inside the data cluster and the integrity of the protection layer itself in real time. All real data clusters are centrally stored in a dedicated real database of the central manager. Step S2: When the user uses the massager again, the detected user biometrics or input identity identifier are quickly matched with the unique features stored in the protection layer of each data cluster to identify and call the corresponding target user data cluster. Step S3: The historical personalized data stored in the target user data group is fused with the user's current status data and the environmental data of the massage area collected in real time by the massager. Based on the fused comprehensive data, an adaptive massage mode switching command and a dynamic intensity adjustment command are generated to control the massage execution mechanism to provide personalized services to the user.

2. The adaptive mode switching and intensity control method for a massager according to claim 1, characterized in that, The monitoring mechanism described in step S1 monitors the integrity of the protection layer in real time. Specifically, it quantitatively assesses the extent to which the protection layer has been attacked or accessed abnormally. When the extent reaches a preset threshold, it automatically triggers the hardening process. The reinforcement process includes: S11, The central manager sends a data authorization request to the user corresponding to the data cluster in order to obtain the user's latest data update authorization; S12, based on the obtained data update authorization, verify the integrity of the data within the data group, compare it with the latest data that the user may have, and supplement and repair any incomplete or outdated data within the data group; S13, After the verification and repair are completed, the preset encryption algorithm and verification information are invoked to strengthen the structure and reset the integrity of the protection layer of the data group, thus completing the reinforcement.

3. The adaptive mode switching and intensity control method for a massager according to claim 1, characterized in that, In the central manager, in addition to a dedicated real database that stores real data clusters, there is at least one virtual database. Each virtual database stores multiple obfuscated data clusters, and the obfuscated data clusters are filled with randomly generated obfuscated data that has no practical meaning. When an external attack attempts to access the database, the virtual database exists in parallel with the real database and has similar external interface characteristics, causing the attacker to mistakenly enter the virtual database and retrieve obfuscated data blocks, thereby obtaining erroneous and chaotic data, thus achieving proactive obfuscation protection against the real data blocks.

4. The adaptive mode switching and intensity control method for a massager according to claim 1, characterized in that, The protective layer is also provided with a self-locking port, which is linked to the data archive port of the central manager. When the protection layer is breached and attackers begin to identify the actual data within the data cluster, the following defense and recovery process is triggered: Before an attacker can parse the data, the data group automatically copies its internal data and transmits the copied content to the data archive port in real time via the link. After receiving the copied content, the data archiving port immediately starts the data regeneration process to create a data cluster copy; While transmitting the copied content, the attacked original data group activates a self-destruct mechanism through a self-destruct port, destroying all real data within it and applying a hacker marking method to the attack source. This hacker marking method is used to record and extract the attacker's personalized characteristics to form an attacker feature mark, which is then used by the central manager for comparison and investigation in subsequent access requests.

5. The adaptive mode switching and intensity control method for a massager according to claim 4, characterized in that, The defense and recovery process also includes a data recovery step: The data archived copy is re-transmitted and stored in the real database, replacing the original data archive that has been destroyed, to ensure the continuity of user services. The attacker's signature generated by the hacker marking method is uploaded to the security audit module of the central manager. When the attacker initiates any access request again, the security audit module performs signature comparison and investigation at the central manager level before the request reaches the database. If a match is found, the system will immediately intercept the request and issue an alarm.

6. The adaptive mode switching and intensity control method for a massager according to claim 1, characterized in that, The specific analysis of the multi-source fusion is as follows: S31, the historical personalized data includes the user's past physiological parameters, preferred massage modes, and historical intensity feedback; S32, the user's current status data is collected in real time by the built-in sensors of the massager, including electromyography signals, skin impedance, and real-time body position information; S33, the environmental data of the massage area includes the surface temperature of the massage area, the ambient humidity, and the user-preset fatigue level; S34: Using the fusion algorithm of the central manager, the three types of data S31, S32 and S33 are taken as input, and weighted fusion is performed through a pre-trained dynamic weight allocation model to output a comprehensive evaluation vector that represents the current user's immediate needs and tolerance level. The massage mode switching command and dynamic intensity adjustment command are generated based on the mapping of this comprehensive evaluation vector.

7. The adaptive mode switching and intensity control method for a massager according to claim 1, characterized in that, The user's biometric features include, but are not limited to, fingerprint features, voiceprint features, or subcutaneous vein distribution features in specific locations; The input identity identifier is an encrypted identification code uniquely bound to the user; Once the massager identifies the user, the process of accessing the target user's data set is completed within a local encrypted secure zone, and any temporary data generated during the access process is immediately cleared after the service session ends.

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