A virtual identity generation method based on a meta universe

Abstract

This invention provides a method for generating virtual identities based on the metaverse, aiming to achieve trusted identity generation and verification of consistency between virtual and real identities. The method collects user public and private keys, iris features, and semantic description information, and achieves multi-party trusted binding through an additive homomorphic threshold mechanism. It employs a structured perturbation-lookup table hybrid hash mechanism, fusing Griffin power mapping and Reinforced Concrete lookup table compression, to generate tamper-resistant digests for user public keys. It combines iris features and proxy signature parameters to generate physical identity information. Furthermore, it uses an identity-driven consistency twin network to achieve behavioral consistency verification and driver type discrimination between virtual and original identities. The method is applicable to Web3.0, decentralized identity authentication, and metaverse identity management, possessing high security, strong verifiability, and cross-platform interoperability.

Description

Technical Field

[0001] This invention relates to the field of digital identity generation technology, and in particular to a method for generating virtual identities based on a metaverse. Background Technology

[0002] With the development of metaverse technology and the integration of related technologies such as virtual reality, augmented reality, and human-computer interaction, identity recognition and trusted authentication in virtual space have increasingly become core research and application focuses. Traditional identity authentication systems mainly rely on static credentials (such as usernames and passwords), single-factor biometrics (such as fingerprints, faces, and irises), or public-private key signature mechanisms, which have been widely deployed in real-world scenarios. However, in highly open and anonymous digital environments such as the metaverse, they face a series of challenges such as identity forgery, key tampering, identity drift, and untraceable proxy identities. In existing technologies, the generation of virtual identities is mostly based on user-defined digital information (such as nicknames and avatars) for registration and binding, lacking a deep binding mechanism for the user's real physical identity or multi-factor characteristics, making them easy to be counterfeited or abused. At the same time, some virtual identity systems based on single-point centralized authentication lack effective support when facing requirements such as cross-platform migration of on-chain identities, proxy interactions, and auditable behavior. In addition, conventional digital signature mechanisms are difficult to simultaneously meet the opposing requirements of "virtual expressive ability" and "real identity traceability" in metaverse scenarios. Summary of the Invention

[0003] This invention provides a method for generating virtual identities suitable for the metaverse environment, aiming to solve problems such as high risk of identity forgery, weak binding ability of real identities, untraceable proxy identities, and difficulties in cross-platform authentication in existing virtual identity systems. This method integrates multi-source information such as user public and private keys, biometric features (e.g., iris scans), and semantic descriptions. Through a multi-party collaborative identity binding mechanism, a structural perturbation-table lookup hybrid hash mechanism, and an identity-driven consistency twin network, it constructs an Avatar digital identity with verifiability, traceability, and consistency between the virtual and real worlds. The method introduces an additive homomorphic threshold mechanism to achieve multi-party trusted binding of semantic features and user identities, improving anti-forgery capabilities. Furthermore, it integrates Griffin power mapping perturbation and Reinforced... The Concrete lookup table compression mechanism performs nonlinear structural perturbation and digest generation on the user's public key, effectively preventing key substitution attacks. A SharedCNN embedding network and a driving discriminant network are constructed, and a joint contrastive loss function combining class center alignment, discriminant boundary, and information entropy constraints is introduced to perform consistency verification and drive type inference for Avatar identities. This method can be widely applied to Web3.0, decentralized identity authentication (DID), virtual reality interaction, and metaverse proxy identity management scenarios, overcoming the technical bottlenecks of existing virtual identities being untraceable, unverifiable, and lacking secure binding mechanisms.

[0004] This invention provides a method for generating virtual identities based on a metaverse, the method comprising the following steps:

[0005] Step S1: Collect user identity identifier, user iris feature, user virtual description, user private key and user public key, and submit them to the identity provider. The identity provider generates a metaverse identity token based on this information.

[0006] Step S2: Bind the user's virtual description and public key to form virtual identity information through a multi-party collaborative virtual identity binding method, which includes the virtual identity hash value;

[0007] Step S3: Combining the user's iris features, user's private key, and virtual identity hash value, a proxy signature mechanism is used to generate signature parameters. Specifically, the virtual identity hash value is divided by the digest value of the user's iris features, and the ratio is then raised to the power of the user's private key to obtain the signature parameters. The user's iris features are then combined with the signature parameters to form physical identity information.

[0008] Step S4: Combine the metaverse identity token, virtual identity information, and physical identity information to generate an Avatar digital identity, which serves as the metaverse-virtual identity;

[0009] Step S5: Establish a shared feature extraction network, optimize the feature discrimination capability of the shared feature extraction network, construct an identity-driven consistency twin network, and use the identity-driven consistency twin network to verify and infer the identity of the Avatar digital identity, ensuring that the identity in the metaverse has consistency between the virtual and real worlds and is traceable; the identity-driven consistency twin network includes Shared CNN1, Shared CNN2 and the driving discriminant network.

[0010] Furthermore, step S2 specifically includes the following steps:

[0011] Step S21: Extract the description summary of the user's virtual description using a standard hash function to generate a semantic feature summary, which will be used as the semantic representative of the binding message in the subsequent identity binding process;

[0012] Step S22: Define the public key set and threshold conditions of the participants. Select the participants that meet the threshold conditions from the public key set to ensure that the credibility of identity binding is completed by multiple entities in collaboration, thereby improving the credibility of the binding structure and preventing single point of forgery. For each participant in the participant set, use its public key and semantic feature digest to perform chameleon hash binding to generate identity binding fragments and form the basic data of identity mapping.

[0013] Step S23: Based on the identity mapping basic data, an additive homomorphic threshold mechanism is adopted to perform threshold addition aggregation on all participants to generate a unified aggregated public key, aggregated random factor and global hash value, and construct an intermediate identity binding result; the obtained intermediate identity binding result realizes the unique hash binding of semantic feature digest under multi-party trusted verification, effectively enhancing the tamper resistance of identity mapping and the traceability in the metaverse environment;

[0014] Step S24: Utilizing the structural perturbation mechanism and Reinforced Concrete lookup and reorganization mechanism of the Griffin hash function, and integrating power mapping, MDS diffusion, and subdomain lookup compression paths, a hybrid hash mechanism of structural perturbation and lookup is constructed. This mechanism is used to perform digest calculation on the user's public key to obtain a public key digest, which is used to prevent key substitution attacks. The public key digest is combined with the global hash value and the aggregated random factor to construct a ternary binding structure. Then, combined with the semantic feature digest, a quaternary binding structure is constructed to obtain the virtual identity information.

[0015] Furthermore, the process of authenticating and reasoning about the Avatar digital identity through an identity-driven consistency twin network specifically includes the following steps:

[0016] Step S51: Collect original identity samples as comparison benchmarks, input the original identity samples and Avatar digital identities into Shared CNN1 and Shared CNN2 for embedding and mapping, and generate feature vector pairs;

[0017] Step S52: Construct a center alignment-entropy constraint joint contrastive loss function through class center alignment, discrimination boundary and information entropy constraints, and calculate the intra-class consistency and inter-class separability index of feature vector pairs through the center alignment-entropy constraint joint contrastive loss function for identity verification;

[0018] Step S53: After identity verification, extract the micro-feature data of the Avatar digital identity, input it into the driving discriminative network for inference and judgment, and generate the judgment result; the judgment result includes human-driven and AI agent-driven; the micro-feature data includes iris texture data, facial expression dynamic change parameters, speech audio domain fluctuation data, and interaction rhythm stability data.

[0019] By adopting the above solution, the beneficial effects achieved by the present invention are as follows:

[0020] This invention establishes a collaborative binding relationship between user virtual descriptions and multi-party public keys by introducing an additive homomorphic threshold mechanism, realizing the identity mapping process of semantic feature digests with the joint participation of multiple trusted entities. Compared with traditional single-signature or static binding methods, this mechanism enhances the anti-forgery capability of user virtual identities, ensuring that even if some participants are attacked, the overall binding result still has integrity and non-repudiation. This mechanism is particularly suitable for the distributed identity management needs in the metaverse, enabling the generated avatar digital identity to have a multi-party collaborative verification basis, significantly improving the credibility and robustness of the virtual identity system in an open environment.

[0021] In the public key digest generation process, this invention integrates the Griffin structural perturbation mechanism and the ReinforcedConcrete lookup and reorganization mechanism to construct a structural perturbation-lookup hybrid hashing mechanism adapted to zero-knowledge proof scenarios. This mechanism combines power mapping perturbation, MDS diffusion, and subdomain-level lookup mapping paths to effectively improve the randomness of the structural distribution of user public keys and the digest compression efficiency during digest processing. It solves the problems of insufficient structural perturbation, limited digest security, and vulnerability to key substitution attacks in traditional hash algorithms. The digests generated by this mechanism have high nonlinearity, high collision resistance, and low constraint complexity, providing an efficient and secure digest foundation for the unique authentication of Avatar digital identities, and greatly enhancing the structural security and computational feasibility of the identity system.

[0022] In the authentication and driving inference stages, this invention constructs an identity-driven consistency twin network, introduces a Shared CNN structure and a driving discriminant network, and combines a joint contrastive loss function of class center alignment, discriminant boundary enhancement, and information entropy constraint to achieve high-precision consistency verification and driving attribute inference judgment between the avatar digital identity and the real identity. This mechanism not only solves the problem of the lack of behavioral-level verification path for virtual identities in existing systems, but also improves the ability to discriminate complex identity behaviors (such as AI agent-driven and human-driven), significantly enhancing the system's interpretability, fine-grained control capabilities, and traceability. This design is particularly suitable for authentication needs in high-interaction and high-security scenarios such as Web3.0, metaverse social networking, and AI agent interaction, ensuring a robust connection between the virtual identity system and real-world mapping and behavioral control. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a virtual identity generation method based on the metaverse proposed in this invention.

[0024] Figure 2 This is a schematic diagram of the fusion of the four-element binding structure proposed in Example 2. Detailed Implementation

[0025] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0026] Example 1, according to Figure 1 This invention provides a method for generating virtual identities based on the metaverse, the method comprising the following steps:

[0027] Step S1: Collect identity: Collect user identity identifier, user iris feature, user virtual description, user private key and user public key, and submit them to the identity provider. The identity provider generates a metaverse identity token based on this information.

[0028] Step S2: Binding Virtual Identity: Bind the user's virtual description and public key to form virtual identity information through a multi-party collaborative virtual identity binding method. The virtual identity information includes the virtual identity hash value.

[0029] Step S3: Construct physical identity: Combine user iris features, user private key, and virtual identity hash value, and use a proxy signature mechanism to generate signature parameters. Specifically, divide the virtual identity hash value by the digest value of the user iris feature, and then perform a private key exponentiation operation on the ratio to obtain the signature parameters; combine the user iris features with the signature parameters to form physical identity information.

[0030] Step S4: Generate Avatar Identity: Combine the metaverse identity token, virtual identity information, and physical identity information to generate an Avatar digital identity, which serves as the metaverse-virtual identity. The Avatar digital identity possesses both virtual behavior expression capabilities and traceable authentication capabilities, supporting subsequent login authentication, proxy delegation authentication, and two-way mutual authentication application scenarios.

[0031] Step S5: Verification and Inference: Establish a shared feature extraction network, optimize the feature discrimination capability of the shared feature extraction network, construct an identity-driven consistency twin network, and use the identity-driven consistency twin network to verify and infer the identity of the Avatar digital identity, ensuring that the identity in the metaverse has consistency between virtual and real and traceability; the identity-driven consistency twin network includes Shared CNN1, Shared CNN2 and the driving discriminant network.

[0032] Example 2, according to Figure 2 This embodiment is based on Embodiment 1. In this embodiment, step S2 specifically includes the following steps:

[0033] Step S21: Extract the description summary of the user's virtual description using a standard hash function to generate a semantic feature summary, which will be used as the semantic representative of the binding message in the subsequent identity binding process;

[0034] Step S22: Define the public key set and threshold conditions of the participants. Select the participants that meet the threshold conditions from the public key set to ensure that the credibility of identity binding is completed by multiple entities in collaboration, thereby improving the credibility of the binding structure and preventing single point of forgery. For each participant in the participant set, use its public key and semantic feature digest to perform chameleon hash binding to generate identity binding fragments and form the basic data of identity mapping.

[0035] Step S23: Each participant includes a local hash fragment, a local public key, and a local random factor. Based on the identity mapping foundation data, an additive homomorphic threshold mechanism is used to aggregate the local hash fragments, local public keys, and local random factors of all participants using threshold addition, generating a unified aggregated public key, aggregated random factor, and global hash value to construct an intermediate identity binding result. The obtained intermediate identity binding result achieves a unique hash binding of semantic feature digests under multi-party trusted verification, effectively enhancing the tamper resistance and traceability of identity mapping in the metaverse environment. The formula used is as follows:

[0036] The formula used in the additive homomorphic threshold mechanism is as follows:

[0037]

[0038] Where T represents the set of participant indices that satisfy the threshold, and i represents the participant index. pk represents the homomorphic aggregation operator for addition. i Let pk represent the local public key of the i-th participant, and pk represent the public keys of all participants in the homomorphic addition aggregation T. i The obtained aggregate public key;

[0039]

[0040] Where, r i Let r represent the local binding random factor of the i-th participant, and let r represent the random factors of all participants. i The aggregated random factor obtained from the homomorphic addition result;

[0041]

[0042] Where h represents the local hash value of all participants. i Performing additive homomorphic aggregation yields the global hash value, h. i CH represents the local chameleon hash fragment calculated by the i-th participant; pk(m,r) represents the aggregate chameleon hash operation performed on the semantic feature digest m using the aggregate public key pk and the aggregate random factor r;

[0043] Step S24: Construct a hybrid hash mechanism of structural perturbation and table lookup. Perform digest calculation on the user's public key using the hybrid hash mechanism of structural perturbation and table lookup to obtain the public key digest. Combine the public key digest with the global hash value and the aggregated random factor, and then combine it with the semantic feature digest to construct a four-element binding structure to obtain the virtual identity information.

[0044] Example 3, based on Example 2, specifically includes step S24: utilizing the structural perturbation mechanism and Reinforced Concrete lookup and reorganization mechanism of the Griffin hash function, integrating power mapping, MDS diffusion, and subdomain lookup compression paths to construct a structural perturbation-lookup hybrid hash mechanism; using this mechanism to perform digest calculation on the user's public key to obtain a public key digest for preventing key substitution attacks; combining the public key digest with the global hash value and aggregated random factors to construct a ternary binding structure, and then combining it with a semantic feature digest to construct a quaternary binding structure to obtain virtual identity information;

[0045] The specific steps of the structure perturbation-table lookup hybrid hash mechanism to perform digest calculation on the user's public key are as follows:

[0046] The vector of the user's public key is

[0047] Step 1: Power Mapping Perturbation Layer (Griffin Style):

[0048] Applying a Griffin power mapping plus a polynomial perturbation function to the vector of the user's public key yields the vector after power mapping perturbation.

[0049] Step 2: MDS diffusion

[0050] The vector perturbed by the power mapping is diffused using the MDS matrix and a constant vector to obtain the diffused state vector.

[0051] Step 3: Domain table lookup and compression path:

[0052] Each element in the diffused state vector is decomposed, table-lookup permutation, and recombination to obtain a compressed state vector;

[0053] Step 4: Attach and compress the output:

[0054] The compressed state vector is compressed using a sponge function to generate a public key digest;

[0055] Ternary binding structure:

[0056] VID core=(d,h,r);

[0057] Where d represents the public key digest, h represents the global hash value, r represents the aggregate random factor, and VID core This indicates a ternary binding structure;

[0058] Quad binding structure:

[0059] VID = (m, VID) core );

[0060] Where m represents the semantic feature summary and VID represents the four-element binding structure;

[0061] The Griffin hash function employs a structural perturbation mechanism based on "positional difference nonlinear mapping". Its core is to apply different power mapping transformations to different components of the input vector and introduce a perturbation strategy that couples between states to enhance nonlinear diffusion capability.

[0062] Reinforced Concrete lookup table reconstruction mechanism: It realizes the structural compression transformation of state elements through its Bars lookup table module; its mechanism includes three steps: (1) decompose the input domain elements into several subdomain chunks with smaller bit widths; (2) each chunk is mapped through a predefined nonlinear lookup table to complete nonlinear perturbation; (3) finally reconstruct all perturbed chunks into complete finite domain elements; since the lookup table is essentially a constant-time operation, this process has extremely low constraint complexity in the zero-knowledge proof (ZK) scenario and can be used for state compression or digest generation, which is an efficient structural encoding scheme;

[0063] Mechanism Fusion: A hybrid hash mechanism combining structural perturbation of the Griffin hash function and lookup and reconstruction of Reinforced Concrete is constructed. In this mechanism, a Griffin-style power mapping perturbation and state coupling operation is first performed on the input user public key vector. This involves applying heterogeneous power function transformations to the components and introducing global structural deformation through a cross-perturbation function. Subsequently, the perturbed state vector is input into the Bars module of Reinforced Concrete to complete subdomain-level lookup mapping and reconstruction compression, thereby efficiently generating a low-constraint digest value. This combination of pre-perturbation and post-compression achieves a dual improvement in structural randomness and constraint efficiency, providing a robust, verifiable, and high-performance digest foundation for subsequent virtual identity binding.

[0064] Example 4, based on Example 2, specifically includes step S24: performing a regular hash digest calculation on the user's public key to generate a corresponding public key digest value; combining the public key digest with the generated global hash value and the aggregated random factor to construct a ternary binding structure; and then combining the semantic feature digest with the ternary binding structure to construct a quaternary binding structure, ultimately generating virtual identity information.

[0065] Example 5, based on Example 3, describes the process of authenticating and reasoning about the Avatar digital identity through an identity-driven consistency twin network. The specific steps include:

[0066] Step S51: Collect original identity samples as comparison benchmarks, input the original identity samples and Avatar digital identities into Shared CNN1 and Shared CNN2 for embedding and mapping, and generate feature vector pairs;

[0067] Step S52: Construct a center alignment-entropy constraint joint contrastive loss function through class center alignment, discrimination boundary, and information entropy constraints. Calculate the intra-class consistency and inter-class separability indices of feature vector pairs using the center alignment-entropy constraint joint contrastive loss function for identity verification. The formulas used are as follows:

[0068]

[0069] Among them, L info The information entropy contrastive loss is represented by η, where η is the information entropy loss weight coefficient, N represents the sample batch of the Avatar digital identity, and each sample corresponds to a feature vector pair; i represents the sample index, v i Let cy represent the embedding representation of the Avatar digital identity in the i-th feature vector pair. i The target identity category center corresponds to the i-th sample, which is the original identity sample; K represents the total number of categories, j represents the category index, and c represents the target identity category center. j Let τ represent the j-th category center, τ represent the temperature coefficient, sim() represent the similarity function, and exp() represent the exponential function.

[0070]

[0071] Where L represents the center alignment-entropy constraint joint contrastive loss, L con Let λ represent the basic contrast loss term, and let λ represent the weight coefficient of the center boundary discriminant term. The embedding of an Avatar digital identity is represented by the squared Euclidean distance to the center of the target identity category. The embedding of the Avatar digital identity is represented by the squared Euclidean distance to the center of the non-target identity category, Δ is the discrimination boundary margin, and []+ denotes the ReLU truncation function;

[0072] Center alignment:

[0073] Determine the boundary:

[0074] Information entropy constraint:

[0075] Step S53: After identity verification, extract the micro-feature data of the Avatar digital identity, input it into the driving discriminative network for inference and judgment, and generate the judgment result; the judgment result includes human-driven and AI agent-driven; the micro-feature data includes iris texture data, facial expression dynamic change parameters, speech audio domain fluctuation data, and interaction rhythm stability data.

[0076] Example 6, based on Example 3, describes the process of authenticating and reasoning about the Avatar digital identity, specifically including the following steps:

[0077] Step S51: Collect original identity samples as comparison benchmarks, input the original identity samples and Avatar digital identities into Shared CNN1 and Shared CNN2 for embedding and mapping, and generate feature vector pairs;

[0078] Step S52: Input the feature vector pairs into the traditional contrastive loss function, calculate the similarity score between the vectors, and determine the consistency between the Avatar digital identity and the original identity sample based on the similarity level to complete the identity verification;

[0079] Step S53: After identity verification, extract the micro-feature data of the Avatar digital identity, input it into the driving discriminative network for inference and judgment, and generate the judgment result; the judgment result includes human-driven and AI agent-driven; the micro-feature data includes iris texture data, facial expression dynamic change parameters, speech audio domain fluctuation data, and interaction rhythm stability data.

[0080] Example 7, based on Example 6, in this example, step S4: combine the metaverse identity token, virtual identity information and physical identity information to generate an Avatar digital identity;

[0081] Avatar Digital Identity:

[0082]

[0083]

[0084] Step S5: Establish a shared feature extraction network, optimize the feature discrimination capability of the shared feature extraction network, construct an identity-driven consistency twin network, and use the identity-driven consistency twin network to perform identity verification and reasoning judgment on the Avatar digital identity;

[0085] Authentication: The threshold is set to 0.1, and the joint contrastive loss of center alignment and entropy constraint is 0.04, which is less than the threshold, so the authentication passes.

[0086] Reasoning and judgment:

[0087] The micro-feature data analysis is shown in Table 1:

[0088] Table 1

[0089]

[0090] It was determined to be driven by human intervention.

[0091] The present invention and its embodiments have been described above. This description is not restrictive. The accompanying drawings are only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this description and designs a similar structure and embodiment without departing from the spirit of the present invention, such design should fall within the protection scope of the present invention.

Claims

1. A method for generating a virtual identity based on a meta universe, characterized in that: The method comprises the following steps: Step S1: Collecting user identity, user iris feature, user virtual description, user private key and user public key, generating a meta-universe identity token; Step S2: binding the user virtual description and the user public key through a multi-party collaborative virtual identity binding method to form virtual identity information, wherein the virtual identity information comprises a virtual identity hash value; Step S3: combining the user iris feature, the user private key and the virtual identity hash value to generate a signature parameter, and forming physical identity information; Step S4: combining the meta-universe identity token, the virtual identity information and the physical identity information to generate an Avatar digital identity as a meta-universe-virtual identity; Step S5: constructing an identity-driven consistency twin network, and performing identity authentication and reasoning judgment on the Avatar digital identity through the identity-driven consistency twin network.

2. The method of claim 1, wherein: The identity-driven consistency twin network comprises a feature mapping unit, a verification and discrimination unit and a reasoning and discrimination unit, the feature mapping unit is internally provided with Shared CNN1 and Shared CNN2, and the reasoning and discrimination unit is internally provided with a driving discrimination network.

3. The method of claim 1, wherein: Step S2 specifically comprises the following steps: Step S21: extracting a description abstract of the user virtual description to generate a semantic feature abstract; Step S22: defining a public key set of participants and a threshold condition, selecting a participant set satisfying the threshold condition from the public key set of participants; using the public key of each participant in the participant set to perform chameleon hash binding with the semantic feature abstract to form identity mapping basic data; Step S23: based on the identity mapping basic data, using an additive homomorphism threshold mechanism, performing threshold addition aggregation on all participants respectively to generate an aggregated random factor and a global hash value; Step S24: constructing a structure disturbance-lookup table hybrid hash mechanism, performing abstract calculation on the user public key using the structure disturbance-lookup table hybrid hash mechanism to obtain a public key abstract; combining the public key abstract, the global hash value, the aggregated random factor and the semantic feature abstract to obtain virtual identity information.

4. The method of claim 3, wherein: The structure disturbance-lookup table hybrid hash mechanism is constructed by using the structure disturbance mechanism of the Griffin hash function and the reinforced concrete lookup table reorganization mechanism.

5. The method of claim 2, wherein: The feature mapping unit collects original identity samples, inputs the original identity samples and the Avatar digital identity into Shared CNN1 and Shared CNN2 for embedding mapping to generate a feature vector pair.

6. The method of claim 5, wherein: The verification and discrimination unit constructs a center alignment-entropy constraint joint contrast loss function, calculates an index of the feature vector pair through the center alignment-entropy constraint joint contrast loss function, and performs identity verification.

7. The method of claim 6, wherein: After identity verification is confirmed, the reasoning and discrimination unit extracts micro-feature data of the Avatar digital identity, inputs the micro-feature data into the driving discrimination network for reasoning judgment to generate a judgment result.

8. The method of claim 7, wherein: The micro-feature data comprises iris texture data, facial expression dynamic change parameters, speech frequency domain fluctuation data and interactive rhythm stability data.

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