Identity verification method and device based on iris and block chain, and storage medium

Abstract

The invention discloses an identity verification method and device based on an iris and a block chain, and a medium, and the method comprises the steps: collecting the iris phase information of a user, and enabling the iris phase information to be collected by an iris collection device; the iris acquisition equipment integrates a quantum random light source and a dynamic and dynamic light field modulator; performing multi-layer vector space mapping on iris phase information through an asymmetric topological decomposition algorithm, generating a dynamic weight matrix in combination with a quantum random number, and generating an iris feature hash value which is valid in single authentication; dividing the iris data into N ciphertext fragments by adopting threshold lattice-based encryption, and storing the N ciphertext fragments in a plurality of physical isolation nodes of a block chain network through a distributed storage protocol; zero-knowledge proof is generated in the authentication stage, and multi-party collaborative identity authentication is completed through an on-chain consensus mechanism constrained by a verifiable delay function. The security of identity verification is improved, and the leakage of verification information of a user is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of blockchain, and specifically provides an identity authentication method based on iris and blockchain. Background Art

[0002] Currently, mainstream identity authentication systems mostly rely on centralized databases to store user biometric features, such as iris and fingerprint, which pose a risk of data leakage. Although some solutions introduce blockchain technology to store feature hash values, there are still obvious defects: First, directly writing the biometric feature hash into the blockchain may lead to privacy exposure, and attackers can reverse-correlate user identities through the data on the chain; Second, existing blockchain solutions mostly use static encryption and cannot resist quantum computing attacks. Once quantum computers are practical, traditional encryption algorithms may be quickly cracked.

[0003] Existing blockchain identity authentication systems usually adopt fixed verification nodes and a single-chain structure, and the verification process lacks dynamics. For example, users use the same key to generate verification proofs each time they authenticate, which is prone to being analyzed by attackers in the long term; at the same time, once the biometric template is registered, it is permanently stored, lacking a regular update mechanism. Once the initial data is leaked, it will lead to permanent security risks, restricting the application of blockchain in identity authentication in high-security scenarios such as finance and government affairs. Summary of the Invention

[0004] The purpose of the present invention is to provide an identity authentication method, device, and storage medium based on iris and blockchain, improve the security of identity authentication, and avoid the leakage of user verification information.

[0005] The present invention provides an identity authentication method based on iris and blockchain, including:

[0006] Collect the iris phase information of the user, which is collected by an iris collection device; the iris collection device integrates a quantum random light source and a dynamic light field modulator;

[0007] Perform multi-layer vector space mapping on the iris phase information through an asymmetric topological decomposition algorithm, combine quantum random numbers to generate a dynamic weight matrix, and generate an iris feature hash value that is valid for a single authentication;

[0008] Use threshold lattice-based encryption to divide the iris data into N ciphertext shards, and store them in multiple physically isolated nodes of the blockchain network through a distributed storage protocol;

[0009] Generate a zero-knowledge proof in the authentication stage, and complete multi-party collaborative identity authentication through a consensus mechanism on the chain constrained by a verifiable delay function.

[0010] In the above technical solution, it further includes:

[0011] Dynamically update the iris matrix and encryption keys based on the chaotic system and blockchain events.

[0012] In the above technical solution, the iris phase information includes an iris phase map; the iris phase information is subjected to multi-layer vector space mapping through an asymmetric topological decomposition algorithm, and a dynamic weight matrix is generated in combination with quantum random numbers to generate an iris feature hash value that is valid for single authentication, including:

[0013] Decompose the iris phase map into 128 layers of directional gradient histograms, and map each layer of directional gradient histogram to a 256-dimensional vector space;

[0014] Apply the Logistic chaotic mapping to perform a non-linear transformation on the matrix initialized by quantum random numbers to generate a dynamic weight matrix;

[0015] Calculate the feature hash value based on the chaotic perturbation function and the feature hash value generation formula.

[0016] In the above technical solution, multi-party collaborative identity authentication is completed through a consensus mechanism on the chain constrained by a verifiable delay function, including:

[0017] Dynamically select 5 nodes from the blockchain verification node pool through a verifiable random function to form a verification group;

[0018] Each of the nodes partially decrypts the stored ciphertext shards through a threshold decryption private key to generate a zero-knowledge proof;

[0019] The master node aggregates the decryption results, calculates the normalized similarity between the real-time feature and the registered feature, and if the similarity is greater than the threshold, the identity authentication is passed.

[0020] In the above technical solution, based on the chaotic system and blockchain events, the iris matrix and encryption keys are dynamically updated, including:

[0021] When the triggering condition is satisfied, generate a rotation matrix based on the Lorenz chaotic system to update the dynamic weight matrix; the triggering condition includes that the time period is greater than a preset period or the on-chain block height of the blockchain is greater than a preset height;

[0022] Encapsulate the temporary key with the current key, calculate the chaotic evolution factor, and generate an encryption key according to the temporary key and the chaotic evolution factor.

[0023] In the above technical solution, generating a rotation matrix based on the Lorenz chaotic system to update the dynamic weight matrix includes:

[0024] Obtain the latest block header hash B from the blockchain h, extract the 256-bit entropy value as the initial condition of the chaotic system, solve the Lorenz equations, and input the Lorenz chaotic system to construct a rotation matrix \(R\in SO(128)\);

[0025] Update the dynamic weight matrix according to the rotation matrix:

[0026] W new =W old ×R + ΔW qrng

[0027] Wnew is the updated dynamic weight matrix, Wold is the dynamic weight matrix before update, R is the rotation matrix, and ΔW qrng is the perturbation matrix driven by quantum random numbers.

[0028] In the above technical solution, the hash value generation formula includes:

[0029]

[0030] where Δ(·) is a chaotic perturbation function based on the timestamp, and Chaos(QRNG t ) is a non-linear function based on the Lorenz system, is the tensor product operation, and W i is the dynamic weight matrix.

[0031] The present invention also provides a computer device, which is characterized by including: a processor and a memory; wherein, the memory stores a computer program, and the computer program is adapted to be loaded and executed by the processor to perform the steps of an iris and blockchain-based identity authentication method as described above.

[0032] The present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of an iris and blockchain-based identity authentication method as described above.

[0033] Compared with the prior art, the beneficial effects of the present invention are:

[0034] The present invention provides an iris and blockchain-based identity authentication method, device, and storage medium. Through zero-knowledge proof technology, the reliability verification of biometric matching can be completed without transmitting or exposing the plaintext of iris features during the identity authentication process, realizing privacy protection of "data can be used but not seen" at the protocol level; combined with generating a single-use valid hash and blockchain threshold sharding storage with a dynamic weight matrix, a dynamic defense mechanism of "collect and burn" is constructed to ensure that iris data cannot be associated and reused even if intercepted; at the same time, based on the consensus constraint of the verifiable delay function, multi-party collaborative verification is completed, taking into account both high security and high efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic flowchart of an embodiment of the present invention;

[0036] Figure 2 It is a schematic flowchart of step S2 in an embodiment of the present invention;

[0037] Figure 3 It is a schematic flowchart of step S4 in an embodiment of the present invention;

[0038] Figure 4 It is a schematic flowchart of step S5 in an embodiment of the present invention. Detailed implementation manners

[0039] For better understanding and implementation, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] The terms "including" and "having" in the embodiments of the present invention and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or modules does not necessarily limit to those clearly listed steps or modules, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or devices.

[0041] The embodiments of the present invention disclose an iris and blockchain-based identity authentication method, which improves the security of identity authentication and avoids the leakage of users' authentication information.

[0042] As Figure 1 shown, the method includes the following steps:

[0043] Step S1, collect the iris phase information of the user, and the iris phase information is collected by an iris collection device; the iris collection device integrates a quantum random light source and a dynamic light field modulator.

[0044] When the user uses the identity authentication service for the first time, dynamic registration of iris biometric features needs to be completed. In this application, an iris collection device is used to collect iris phase information, and the iris collection device integrates a quantum random light source and a dynamic light field modulator. Among them, the iris collection device emits near-infrared light with a wavelength of 850±5nm to penetrate the cornea, and captures the corresponding iris phase map by adjusting the phase response of the metasurface structure in real time. The dynamic light field modulator generates a unique light scattering fingerprint parameter, and then the fingerprint of the quantum organic light source is naturally formed by the nanoscale tolerance in the manufacturing process, meeting the randomness requirements of the physical unclonable function (PUF).

[0045] Specifically, the modulation function of the iris acquisition device is

[0046] Φ(x, y) = Asin(2πx / Λ x ) + Bcos(2πy / Λ y )

[0047] where A and B are amplitude parameters generated by quantum random numbers, and Λ x , Λ y are the structural periods of the metasurface, generally taking 200 nm. In this embodiment, the iris phase diagram is 512x512 pixels, and the signal-to-noise ratio is stable above 45 dB.

[0048] Step S2: Perform multi-layer vector space mapping on the iris phase information through the asymmetric topological decomposition algorithm, combine quantum random numbers to generate a dynamic weight matrix, and generate an iris feature hash value that is valid for single authentication;

[0049] Perform multi-layer vector space mapping on the iris phase information through the asymmetric topological decomposition algorithm, combine quantum random numbers to generate a dynamic weight matrix, and generate an iris feature hash value that is valid for single authentication, as Figure 2 shown, including:

[0050] Step S21: Decompose the iris phase diagram into 128 layers of directional gradient histograms, and map each layer of directional gradient histogram to a 256-dimensional vector space;

[0051] Perform directional gradient decomposition on the iris phase diagram, that is, decompose the iris texture into 128 layers of directional gradient histograms (HOG), and each layer corresponds to an angle θ i = 1.4°×(i - 1) (i = 1, 2,..., 128), generating a feature vector HOG i ∈R 256 .

[0052] Step S22: Apply the Logistic chaotic mapping to perform a non-linear transformation on the matrix initialized by quantum random numbers to generate a dynamic weight matrix;

[0053] The quantum random number generator QRNG generates an initialized matrix, which is iteratively updated through the Logistic chaotic mapping. Specifically, the recurrence formula is W {k+1} = μW k (1 - W k ), μ = 3.99, the number of iterations k = 1000), and the dynamic weight matrix W is output.

[0054] Step S23: Calculate the feature hash value based on the chaotic perturbation function and the feature hash value generation formula.

[0055] The formula for generating the feature hash value is

[0056]

[0057] where Δ(·) is a chaotic perturbation function based on the timestamp, and Chaos(QRNG t ) is a non - linear function based on the Lorenz system, is the tensor product operation, and W i is the dynamic weight matrix.

[0058] Step S3: Use threshold lattice - based encryption to split the iris data into N ciphertext shards, and store them in multiple physically isolated nodes of the blockchain network through a distributed storage protocol.

[0059] The iris data is split into 21 ciphertext shards through the Shamir threshold secret sharing protocol (parameters n = 21, k = 5). Each ciphertext shard is encrypted by the LWE lattice - based encryption and stored in physically isolated nodes in different geographical regions. At the same time, the hash values of the PUF fingerprints of the iris acquisition device, including the optical scattering parameter α i and the silicon - based threshold voltage parameter V th are written into the blockchain smart contract to complete the dual binding of biometric features and physical devices.

[0060] Step S4: Generate a zero - knowledge proof in the authentication stage and complete multi - party collaborative identity verification through a consensus mechanism on the chain constrained by a verifiable delay function.

[0061] When the user initiates an identity authentication request, the system performs multi - stage collaborative verification. First, the iris acquisition device performs dynamic feature acquisition, reconfigures the optical field distribution according to the quantum random number seed, acquires a new iris phase map, and generates a temporary feature vector. The difference degree of the optical field distribution environment between the two iris acquisitions needs to be greater than 99% to improve the security of verification.

[0062] Generate zero-knowledge proofs, whose constraints include that the device PUF hash value meets the matching requirements, the dynamic matrix time is lower than the preset threshold, and the feature similarity is greater than the threshold. Through zero-knowledge proofs, users can perform privacy-protected identity verification in the blockchain. For example, when users authenticate their identities, they verify their identities through iris recognition technology and generate corresponding zero-knowledge proofs. The zero-knowledge proofs are then stored on the blockchain as part of verifying the identity, ensuring that only the correct users can access their services or participate in activities on the blockchain. Each node in the decentralized network based on the blockchain can verify the validity of the zero-knowledge proofs without knowing the actual content of the iris features. This not only ensures privacy protection but also guarantees the transparency and security of identity verification. The tamper-proof feature of the blockchain ensures the credibility of each identity authentication and data verification process. Even if the blockchain data is copied or tampered with, it is impossible to change the stored zero-knowledge proofs and authentication information.

[0063] As Figure 3 shown, the multi-party collaborative identity verification is completed through the on-chain consensus mechanism constrained by the verifiable delay function, including:

[0064] Step S41: Dynamically select 5 nodes from the blockchain verification node pool through the verifiable random function to form a verification group;

[0065] Step S42: Each of the nodes partially decrypts the stored ciphertext shards through the threshold decryption private key to generate zero-knowledge proofs;

[0066] Step S43: The master node aggregates the decryption results, calculates the normalized similarity between the real-time features and the registered features. If the similarity is greater than the threshold, the identity verification is passed.

[0067] The smart contract triggers the verifiable delay function. In this application, VDF is adopted, with parameter τ = 50ms and iteration number N = 2 20 , forcing the verification process to be completed within a 300ms ± 5% time window. Randomly select 5 verification nodes from 21 nodes through the verifiable random function to form a task group. The verifiable random function can also be VRF, based on elliptic curve Curve25519, etc. Each node uses the threshold decryption private key sk i to decrypt the stored shard data m i , and generate a local proof π i to prove the correctness of the decryption. After the master node aggregates 5 valid shards, the Lagrange interpolation formula is applied:

[0068]

[0069] Restore the original feature vector S and calculate the real-time feature similarity. If the real-time feature similarity Sim ≥ 0.982, generate a BLS-12-381 aggregate signature and write it into the blockchain. To improve the authentication timeliness, the total process time is controlled within 800 ms, including 300 ms of block confirmation time.

[0070] As Figure 4 shown, to improve the security of authentication, a dynamic update mechanism is also set up, including:

[0071] Step S5: Dynamically update the iris matrix and encryption key based on the chaotic system and blockchain events, including:

[0072] Step S51: When the trigger condition is met, generate a rotation matrix based on the Lorenz chaotic system and update the dynamic weight matrix; the trigger conditions include that the time period is greater than the preset period or the on-chain block height of the blockchain is greater than the preset height;

[0073] Step S52: Encapsulate the temporary key with the current key, calculate the chaotic evolution factor, and generate the encryption key according to the temporary key and the chaotic evolution factor.

[0074] In this embodiment, the preset period is 72 hours, that is, the biometric template is automatically updated every 72 hours, and an update request signal is sent to the blockchain network. Or, when the on-chain block height of the blockchain is greater than the preset height, the full network update task is automatically triggered. Through the update of the period and the blockchain height, it is ensured that the update period can still be maintained when the device is offline or the blockchain forks.

[0075] Specifically, obtain the latest block header hash B h from the blockchain, extract 256-bit entropy value as the initial condition of the chaotic system, solve the Lorenz equations, adopt the fourth-order Runge-Kutta method, with a step size h = 0.01 and iterate 1000 times, and intercept the chaotic orbit data between t = 10 and t = 20. Sample 128 groups of three-dimensional coordinates from the chaotic orbit, input them into the Lorenz chaotic system, and generate the initial rotation matrix. Orthogonalize the initial rotation matrix:

[0076] R T R = I 128 and det(R) = 1

[0077] to obtain the rotation matrix R ∈ SO(128) and update the dynamic weight matrix:

[0078] W new = W old ×R + ΔW qrng

[0079] where ΔW qrngIt is a perturbation matrix driven by quantum random numbers, and each element in the perturbation matrix follows a Gaussian distribution with a mean of 0 and a variance of 0.01.

[0080] Encapsulate the temporary key using the current key K:

[0081] CT = Kyber-1024.Enc(K t , p knode )

[0082] where pk node is the node public key, and the Kyber parameters are that the ring dimension n = 256 and the modulus q = 3329.

[0083] The hash of the latest block header B h Calculate the chaos evolution factor Chaos_Seed:

[0084] Chaos_Seed = SHA3-512(B h ||R)

[0085] Generate a new encryption key K t+1 is

[0086] K t+1 = Argon2id(CT, Chaos_Seed, iter = 5, mem = 8MB)

[0087] Time overhead = 5 iterations, memory overhead = 8MB, number of threads = 1.

[0088] The old matrix data is securely erased at the physical level. It can be completely destroyed after overwriting with 0xFF 35 times. At the same time, the key is iteratively updated using the Kyber-1024 post-quantum algorithm (ring polynomial dimension n = 1024, modulus q = 3329), combined with the block hash to generate a new key, ensuring forward security of more than 10 years. Even if an attacker obtains the old key, since the old key has been physically destroyed and ΔW is irreversible, the key cannot be deduced. At the same time, a perturbation matrix driven by quantum random numbers is added to the new key, exponentially decaying the mathematical correlation of the old biometric template. Through the above dynamic update mechanism, while maintaining high security, seamless and efficient iris template and key rotation are achieved, providing sustainable security for long-term running IoT devices and high-value digital identities.

[0089] In the cross-border payment scenario, users scan their irises through iris collection devices, and the system conducts verification. For example, when user A initiates a large cross-border transfer in Tokyo, 5 nodes located in Tokyo, Singapore, Frankfurt, etc. are dynamically selected based on the VRF algorithm. The original iris features are restored through threshold decryption and the similarity is calculated. Zero-knowledge proof ensures that the nodes only verify whether the similarity meets the standard (≥0.982) without obtaining the clear text of the user's biometric features. At the same time, the dynamic update mechanism automatically refreshes the feature template every 72 hours, so that even if an attacker intercepts historical data, it cannot be reused. In practical applications, the identity verification time for cross-border payments is shortened from 2 minutes in the traditional solution to 800 milliseconds, and the false recognition rate is lower than one in a billion, meeting the high-frequency trading requirements of financial networks such as SWIFT.

[0090] In the medical data application scenario, patients log in to the health platform through iris authentication. The system dynamically generates an irreversible iris feature hash to replace the traditional password to ensure live identity verification. When a doctor initiates a data request, the patient uses zero-knowledge proof to authorize access to specific medical records. The medical AI model can analyze the de-identified data without decrypting the patient's identity. Sensitive information such as the patient's genome is stored in encrypted shards on the blockchain nodes of multiple hospitals through threshold encryption. The leakage of a single node only exposes meaningless fragments. After each data use, the system automatically updates the de-identification strategy and resets the access key to prevent long-term data tracking. This solution shortens the cross-institutional data access time from 3 days to 5 minutes, and at the same time supports the AI training of 200,000 anonymous cases, achieving a double breakthrough in improving the efficiency of medical research and zeroing the risk of privacy leakage.

[0091] Furthermore, in the industrial Internet of Things scenario, each intelligent device is embedded with an iris collection module, which dynamically binds the unique PUF fingerprint of the device to the iris features of the administrator, replacing the traditional digital certificate. The device performs an iris liveness verification every 10 minutes and submits a "legitimate device" proof to the central control system through zero-knowledge proof without transmitting the clear text of the biometric features. The communication between devices uses a quantum-resistant encryption algorithm, and the data interaction records are stored on the blockchain in real time, supporting second-level traceability of the historical operations of 2,000 devices. After implementation, the interception rate of illegal device access attacks in the factory reaches 100%, the labor cost of device identity management is reduced by 65%, and the production interruption events caused by identity forgery throughout the year are zero, providing an all-weather trusted device identity foundation for intelligent manufacturing.

[0092] In summary, the proposed method uses an iris acquisition device that integrates a quantum random light source and a dynamic light field modulator to physically collect the true randomness of the living iris phase information. Each authentication generates an irreproducible optical fingerprint, eliminating replay attacks and 3D spoofing risks at the source. Based on the dynamic weight matrix generation mechanism of the asymmetric topological decomposition algorithm (ATD), combined with quantum random numbers for non-linear spatial mapping of iris features, the iris feature hash value generated each time has the characteristics of "single-use and self-destruct upon authentication". Even if it encounters brute-force cracking by quantum computing, it is impossible to associate historical or future data. The sharded storage architecture using threshold lattice-based encryption distributes the ciphertext of iris information to physically isolated blockchain nodes, achieving the protection ability of "zero single-point leakage risk". If any node is compromised, only fragmented information that cannot be restored is exposed. Through the on-chain collaborative verification mechanism of zero-knowledge proof and verifiable delay function (VDF), multi-party secure computing and consensus confirmation are completed within 800 ms. This not only ensures that the verification process does not disclose the biometric plaintext but also enforces a time lock to constrain the attack window period, exponentially increasing the implementation cost of traditional threat models such as man-in-the-middle attacks and Sybil attacks. This solution realizes financial-level real-time authentication with a false acceptance rate ≤ 10 -9 under the security strength, while meeting the "Privacy by Design" requirements of privacy regulations such as GDPR, providing a quantum-secure dynamic defense system for the full life cycle management of digital identities.

[0093] Based on the same inventive concept, the present invention also provides a computer device, including: a processor and a memory; wherein, the memory stores a computer program, and the computer program is adapted to be loaded and executed by the processor to perform the steps of an iris and blockchain-based identity authentication method as described above.

[0094] The processing method of the computer device can refer to the description of the above method and will not be elaborated here.

[0095] The embodiment of the present application also provides a non-transitory machine-readable storage medium, on which an executable program is stored. When the executable program runs on a microprocessor, it causes the processor to execute an iris and blockchain-based identity authentication method provided in the above embodiment.

[0096] The embodiment of the present invention discloses a computer-readable storage medium that stores a computer program for electronic data exchange, wherein the computer program causes the computer to execute an iris and blockchain-based identity authentication method as described.

[0097] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute a described authentication method based on iris and blockchain.

[0098] The above-described embodiments are merely illustrative. The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative labor.

[0099] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium. The storage medium includes read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memories, magnetic disk memories, tape memories, or any other computer-readable medium capable of carrying or storing data.

[0100] Finally, it should be noted that: What is disclosed in the embodiments of the present invention is only the preferred embodiments of the present invention, which are only used to illustrate the technical solutions of the present invention, rather than to limit it; Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: They can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An iris and blockchain-based identity authentication method, characterized in that Including: Collecting the iris phase information of the user, which is collected by an iris collection device; The iris collection device integrates a quantum random light source and a dynamic light field modulator; Performing multi-layer vector space mapping on the iris phase information through an asymmetric topological decomposition algorithm, combining quantum random numbers to generate a dynamic weight matrix, and generating an iris feature hash value that is valid for a single authentication; Using threshold lattice-based encryption to divide the iris data into N ciphertext shards, and storing them in multiple physically isolated nodes of the blockchain network through a distributed storage protocol; Generating a zero-knowledge proof in the authentication stage, and completing multi-party collaborative identity verification through a chain consensus mechanism constrained by a verifiable delay function.

2. The authentication method based on iris and blockchain according to claim 1, wherein It also includes: Dynamically updating the iris matrix and encryption key based on a chaotic system and blockchain events.

3. The authentication method based on iris and blockchain according to claim 2, characterized in that, The iris phase information includes an iris phase map; performing multi-layer vector space mapping on the iris phase information through an asymmetric topological decomposition algorithm, combining quantum random numbers to generate a dynamic weight matrix, and generating an iris feature hash value that is valid for a single authentication, including: Decomposing the iris phase map into 128 layers of directional gradient histograms, and mapping each layer of directional gradient histogram to a 256-dimensional vector space; Applying a Logistic chaotic map to perform a non-linear transformation on the matrix initialized with quantum random numbers to generate a dynamic weight matrix; Calculating the feature hash value based on a chaotic perturbation function and a feature hash value generation formula.

4. The authentication method based on iris and blockchain according to claim 3, characterized in that, Completing multi-party collaborative identity verification through a chain consensus mechanism constrained by a verifiable delay function, including: Dynamically selecting 5 nodes from the blockchain verification node pool through a verifiable random function to form a verification group; Each of the nodes partially decrypts the stored ciphertext shard through a threshold decryption private key to generate a zero-knowledge proof; The main node aggregates the decryption results, calculates the normalized similarity between the real-time feature and the registered feature, and if the similarity is greater than the threshold, the identity verification is passed.

5. The authentication method based on iris and blockchain according to claim 2, characterized in that, Dynamically updating the iris matrix and encryption key based on a chaotic system and blockchain events, including: When the triggering condition is met, generating a rotation matrix based on the Lorenz chaotic system to update the dynamic weight matrix; the triggering condition includes that the time period is greater than a preset period or the on-chain block height of the blockchain is greater than a preset height; Encapsulating the temporary key with the current key, calculating a chaotic evolution factor, and generating an encryption key according to the temporary key and the chaotic evolution factor.

6. The authentication method based on iris and blockchain according to claim 5, wherein, Generating a rotation matrix based on the Lorenz chaotic system to update the dynamic weight matrix, including: Obtain the latest block header hash B from the blockchain h , extract the 256-bit entropy value as the initial condition of the chaotic system, solve the Lorenz equations, and input the Lorenz chaotic system to construct a rotation matrix R ∈ SO(128); Updating the dynamic weight matrix according to the rotation matrix: W new = W old × R + ΔW qrng $W_{new}$ is the updated dynamic weight matrix, $W_{old}$ is the dynamic weight matrix before update, $R$ is the rotation matrix, and $\Delta W$ qrng is the perturbation matrix driven by quantum random numbers.

7. The authentication method based on iris and blockchain according to claim 3, wherein, The hash value generation formula includes: Among them, Δ(·) is a chaotic perturbation function based on timestamps, and Chaos(QRNG t ) is a non-linear function based on the Lorenz system, is a tensor product operation, and W i is a dynamic weight matrix.

8. A computer device, characterized in that, Including: A processor and a memory; wherein, the memory stores a computer program, and the computer program is adapted to be loaded and executed by the processor to perform the steps of an iris and blockchain-based identity verification method according to any one of claims 1-7.

9. A computer storage medium, characterized in that, Stored thereon is a computer program, and when the computer program is executed by the processor, it implements the steps of an iris and blockchain-based identity verification method according to any one of claims 1-7.

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