A blockchain-based metal door and window anti-counterfeiting identity authentication system and method

Abstract

The present application relates to the field of identity authentication, in particular to a metal door and window anti-fake identity authentication system and method based on a block chain. A broadband acoustic pulse signal is emitted to a metal door and window frame body, and a micro acoustic resonance frequency spectrum response signal is collected; the response signal is quantization encoded to generate a binary physically unclonable sequence, a public and private key pair is generated through elliptic curve password operation based on the sequence; the public key and attribute data are encapsulated as a digital identity certificate and written into a block chain distributed ledger; when verifying, the current physical feature hash value is extracted again, and the corresponding relationship is confirmed through zero-knowledge proof protocol interaction based on the current physical feature hash value and the on-chain certificate. The effect is that the cryptographic hard binding of physical entities and digital identities is realized, the identity fraud caused by identity transfer is blocked, the defect that physical entities and digital identities are easy to separate is overcome, and the robustness of identity authentication is improved.

Description

Technical Field

[0001] This invention relates to the field of identity verification, specifically to a blockchain-based anti-counterfeiting identity authentication system and method for metal doors and windows. Background Technology

[0002] Anti-counterfeiting authentication for metal doors and windows typically employs radio frequency identification (RFID) tags or QR codes. RFID tags contain a chip and antenna, exchanging data with a reader via radio signals and storing a unique identifier in the chip's storage area. QR codes, on the other hand, store information through a specific dot matrix pattern printed on the tag surface, which is then scanned by a terminal device. These identifiers are linked to the specifications of the metal doors and windows at the factory, and this binding data is recorded in a centralized database or distributed ledger as the basis for subsequent authenticity verification. In this method, authentication heavily relies on the completeness of the attached identifiers.

[0003] Existing anti-counterfeiting authentication schemes based on distributed ledgers hash data such as the serial number and specifications of metal doors and windows before writing it into the blockchain, leveraging the blockchain's tamper-proof nature to ensure the authenticity of data storage. During verification, users scan the identifier with their terminals to obtain plaintext identifier information, calculate the hash value, and compare it with the hash value stored on the blockchain. If the comparison matches, the identifier is deemed genuine. However, the identity verification process relies entirely on the physical existence of the identifier and data retrieval; the identifier and the blockchain only form a one-way data read / write relationship, lacking a deep interlock between physical and digital identity.

[0004] Existing technologies suffer from the core technical problem of the easy separation between physical entities and digital identities. RFID tags, QR codes, and other identifiers are merely physically attached to metal doors and windows; the identifier itself can be completely peeled off and transferred to counterfeit metal doors and windows. Blockchain can only guarantee that the digital identity data on the chain is not tampered with; it cannot detect whether the front-end physical entity corresponds to the on-chain identity. Since the identifier and the door / window itself lack an intrinsic physical connection, once the identifier is transferred, counterfeit doors and windows can still pass blockchain verification using the transferred identifier, causing the physical entity to become detached from the digital identity, rendering anti-counterfeiting measures ineffective. Summary of the Invention

[0005] The purpose of this invention is to provide a blockchain-based anti-counterfeiting identity authentication system and method for metal doors and windows, which can solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A blockchain-based anti-counterfeiting identity authentication system for metal doors and windows includes: an acoustic excitation component for transmitting broadband acoustic pulse signals of a preset frequency band to the metal door and window frame; an acoustic signal acquisition component for acquiring the microscopic acoustic resonance spectrum response signal generated by the metal door and window frame under the excitation of the broadband acoustic pulse signal; a feature extraction and key generation component for quantizing and encoding the microscopic acoustic resonance spectrum response signal to generate a binary physically unclonable function sequence, and generating a public-private key pair bound to the metal door and window based on the binary physically unclonable function sequence through elliptic curve cryptography; a blockchain node network for encapsulating the public key in the public-private key pair and the attribute data of the metal door and window into a digital identity credential and writing it into a distributed ledger; and an identity verification component for re-transmitting broadband acoustic excitation to the metal door and window upon receiving an verification request, extracting the current physical feature hash value, and performing a zero-knowledge proof protocol interaction based on the current physical feature hash value and the digital identity credential in the distributed ledger to confirm the correspondence between the physical entity and the on-chain state.

[0007] Preferably, the acoustic excitation component includes a linear frequency modulated (LFM) signal generator and an acoustic transducer. The LFM signal generator generates a LFM pulse signal with controlled duration and frequency offset. The acoustic transducer converts the LFM pulse signal into acoustic waves and applies them to the metal door and window frame. When the acoustic signal acquisition component acquires the resonance signal, it sets a time window based on the transmission timestamp of the LFM pulse signal, intercepts the acoustic echo signal reflected by the metal door and window frame within the time window, performs pulse compression processing on the acoustic echo signal to extract time delay and Doppler frequency shift information, inputs the time delay and Doppler frequency shift information into a Kalman filter to filter out environmental noise interference, and uses the filtered acoustic echo signal as the microscopic acoustic resonance spectral response signal.

[0008] Preferably, the feature extraction and key generation component performs a fast Fourier transform on the microacoustic resonance spectral response signal to obtain the frequency domain amplitude spectrum, performs multi-level wavelet packet decomposition on the frequency domain amplitude spectrum to extract the energy feature vector of a specified frequency band, performs fuzzy extraction processing on the energy feature vector, uses error correction codes to encode auxiliary data and stores the auxiliary data in the blockchain node network, quantizes the energy feature vector after error correction code correction into the binary physically non-cloning function sequence, uses the binary physically non-cloning function sequence as the seed input of a deterministic random bit generator, outputs a random large integer that conforms to the requirements of elliptic curve cryptography as the private key through the deterministic random bit generator, and calculates and generates a public key based on the private key and the elliptic curve basis points.

[0009] Preferably, the blockchain node network receives the public key and the attribute data of the metal door and window, concatenates the manufacturing batch, material model, and installation coordinates in the attribute data into a plaintext attribute string, performs a joint hash calculation on the plaintext attribute string and the public key using a hash operation to generate an attribute digest value, and writes the attribute digest value, the public key, and the installation coordinates in the plaintext attribute string into the storage slot of the smart contract. The smart contract is deployed in the genesis block of the blockchain node network. The smart contract includes identity state transition logic, which receives the verification result of the identity verification component and updates the identity state flag bit in the storage slot.

[0010] Preferably, when the authentication component interacts with the digital identity credential using the current physical feature hash value, the authentication component, acting as a prover, generates a random blinding factor, calculates a commitment value based on the random blinding factor and the current physical feature hash value, and broadcasts the commitment value to the blockchain node network. The verification nodes in the blockchain node network generate a random challenge number based on the commitment value and return it to the authentication component. The authentication component uses the random challenge number, the random blinding factor, and the private key to calculate a response value, submits the response value to the verification node, and the verification node performs bilinear mapping verification based on the response value, the commitment value, the random challenge number, and the public key stored in the distributed ledger.

[0011] Preferably, when the authentication component receives the verification request, it extracts the timestamp parameter and the verification initiator network address parameter carried in the verification request, compares the timestamp parameter with the consensus time of the blockchain node network, and if the difference between the timestamp parameter and the consensus time exceeds a preset threshold, it refuses to execute the zero-knowledge proof protocol interaction. If it does not exceed the preset threshold, it combines the timestamp parameter, the verification initiator network address parameter, and the current physical feature hash value to generate a composite hash input value to be verified, and executes subsequent zero-knowledge proof protocol interactions based on the composite hash input value to be verified.

[0012] Preferably, the acoustic excitation component is also connected to a temperature sensor. The temperature sensor collects the current surface temperature value of the metal door and window frame and the ambient temperature value of the environment where the acoustic transducer is located. The acoustic signal acquisition component looks up the corresponding sound velocity compensation coefficient and medium refractive index offset in a preset temperature-sound velocity mapping table based on the difference between the current surface temperature value and the ambient temperature value. The sound velocity compensation coefficient is used to perform phase compensation correction on the pulse compression result of the linear frequency modulated pulse signal, and the medium refractive index offset is used to dynamically correct the state transition matrix parameters of the Kalman filter. The pulse compression result after phase compensation correction is input into the dynamically corrected Kalman filter to perform temperature drift compensation on the microscopic acoustic resonance spectrum response signal.

[0013] Preferably, when the feature extraction and key generation component performs multi-level wavelet packet decomposition on the frequency domain amplitude spectrum, it calculates the information entropy value of each wavelet packet node, filters out the frequency bands corresponding to nodes with information entropy values ​​lower than a preset entropy threshold, retains the frequency bands corresponding to nodes with information entropy values ​​higher than the preset entropy threshold as target frequency bands, sets the components in the target frequency band whose absolute value of wavelet packet decomposition coefficients is greater than a preset coefficient threshold to zero, reconstructs the zeroed target frequency band to obtain a denoised frequency domain amplitude spectrum, and extracts the energy feature vector of the denoised frequency domain amplitude spectrum into the fuzzy extraction process.

[0014] Preferably, when updating the identity status flag in the storage slot, the identity state transition logic of the smart contract obtains the verification voting weight of the response value submitted by each consensus node in the blockchain node network to the identity verification component. The verification voting weight is determined based on the product of the equity share held by each consensus node and the node network latency parameter. The verification result in which the sum of the verification voting weights exceeds 2 / 3 of the total voting weight of the entire network is taken as the final verification consensus. Based on the final verification consensus, the smart contract is triggered to switch the identity status flag in the storage slot from the unverified state to the verified state.

[0015] A blockchain-based anti-counterfeiting identity authentication method for metal doors and windows includes: transmitting a broadband acoustic pulse signal in a preset frequency band to the metal door and window frame; acquiring the microscopic acoustic resonance spectrum response signal generated by the metal door and window frame under the excitation of the broadband acoustic pulse signal; quantizing and encoding the microscopic acoustic resonance spectrum response signal to generate a binary physically unclonable function sequence, and generating a public-private key pair bound to the metal door and window based on the binary physically unclonable function sequence through elliptic curve cryptography; encapsulating the public key in the public-private key pair and the attribute data of the metal door and window into a digital identity credential and writing it into a distributed ledger; upon receiving a verification request, re-transmitting the broadband acoustic excitation to the metal door and window, extracting the current physical feature hash value, and performing a zero-knowledge proof protocol interaction based on the current physical feature hash value and the digital identity credential in the distributed ledger to confirm the correspondence between the physical entity and the on-chain state.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Extract the microscopic acoustic resonance spectrum response of metal door and window frames under acoustic excitation, quantize and encode it into a physically non-clonable function sequence, and generate a public-private key pair based on this. Encapsulate the public key and attribute data onto the blockchain. The microscopic acoustic characteristics of metal materials are determined by their internal crystal structure and stress distribution, and cannot be physically copied or extracted. Transforming physical characteristics into cryptographic key pairs allows the physical entity to directly participate in the key generation process, establishing a cryptographic hard binding between the physical entity and its on-chain digital identity. This blocks the path of identity misuse after the identifier is stripped and transferred, eliminating the risk of the physical entity and digital identity becoming detached.

[0017] 2. During the verification phase, acoustic stimuli are re-emitted to the metal doors and windows, the current physical feature hash value is extracted, and zero-knowledge proof protocol interaction is performed with the on-chain identity credential. The verification process directly probes the physical features of the metal doors and windows on-site, without relying on external identifiers. The verification basis is reconstructed based on the current physical features, ensuring that each verification is linked to the entity's current state in real time. The use of zero-knowledge proof protocol interaction completes identity verification without exposing the original physical feature data, preventing the acoustic feature data from being intercepted and analyzed during transmission, and ensuring the anti-eavesdropping capability of the identity authentication protocol.

[0018] 3. When acquiring acoustic resonance signals, linear frequency modulated pulse signals and pulse compression processing are used, and Kalman filtering is introduced to suppress environmental noise. Based on the temperature difference between the metal door / window surface and the ambient temperature, the sound velocity compensation coefficient and the refractive index offset of the medium are dynamically queried. Phase compensation is performed on the pulse compression results, and the Kalman filter state transition matrix is ​​corrected. This processing method overcomes the physical phenomenon of acoustic resonance feature drift caused by temperature changes, ensuring the stability and consistency of physical feature extraction under different temperature environments, and improving the robustness of the identity authentication system in complex physical environments. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the overall registration and verification process of the blockchain-based anti-counterfeiting identity authentication system for metal doors and windows according to the present invention. Figure 2 This is a flowchart of the acoustic excitation signal generation, acquisition, and temperature drift compensation processing of the present invention. Figure 3 This is a flowchart of the microscopic acoustic feature extraction and elliptic curve public / private key generation process of the present invention. Figure 4 This is a flowchart illustrating the digital identity credential encapsulation and blockchain distributed ledger storage process of the present invention. Figure 5 This is a flowchart of the zero-knowledge proof protocol interactive authentication process of the present invention; Figure 6 This is a flowchart of the verification request parameter validation and composite hash generation process of the present invention. Detailed Implementation

[0020] refer to Figure 1 In one embodiment, the blockchain-based anti-counterfeiting identity authentication system for metal doors and windows includes an acoustic excitation component, an acoustic signal acquisition component, a feature extraction and key generation component, a blockchain node network, and an identity verification component. The acoustic excitation component transmits a broadband acoustic pulse signal in a preset frequency band to the metal door and window frame. This preset frequency band is determined based on the material characteristics of the metal door and window frame, typically selected within the ultrasonic frequency range of 20kHz to 100kHz to avoid interference from common low-frequency noise in the environment. The acoustic signal acquisition component acquires the microscopic acoustic resonance spectrum response signal generated by the metal door and window frame under the excitation of the broadband acoustic pulse signal. This response signal reflects the inherent physical characteristics of the metal door and window frame, such as its internal lattice structure, stress distribution, welding point characteristics, and microscopic defects formed during processing. The feature extraction and key generation component quantizes and encodes the microscopic acoustic resonance spectrum response signal to generate a binary physically unclonable function sequence, and generates a public-private key pair bound to the metal door and window using elliptic curve cryptography based on this binary physically unclonable function sequence. The blockchain node network encapsulates the public key in the public-private key pair with the attribute data of the metal doors and windows into a digital identity credential and writes it into a distributed ledger. This distributed ledger uses a chain-like data structure for storage, with each block containing the hash value of the previous block, ensuring data immutability. When the authentication component receives an authentication request, it re-emits broadband acoustic stimuli to the metal doors and windows, extracts the current physical feature hash value, and performs a zero-knowledge proof protocol interaction based on the current physical feature hash value and the digital identity credential in the distributed ledger to confirm the correspondence between the physical entity and the on-chain state.

[0021] In this embodiment, the overall workflow of the system is divided into a registration phase and a verification phase. In the registration phase, the acoustic excitation component emits a broadband acoustic pulse signal to the metal door / window frame to be registered. The acoustic signal acquisition component acquires the generated microscopic acoustic resonance spectrum response signal and transmits it to the feature extraction and key generation component. The feature extraction and key generation component preprocesses, extracts features, and quantizes the signal to generate a unique binary physically unclonable function sequence. This sequence is unclonable and unique; even metal doors and windows from the same batch and model will have significantly different binary physically unclonable function sequences due to unavoidable microscopic differences during manufacturing. Using this binary physically unclonable function sequence as a seed, the feature extraction and key generation component generates a public-private key pair using an elliptic curve cryptography algorithm. The private key is uniquely determined by the physical characteristics of the metal door / window and does not need to be stored in any external device; it is only generated when needed by re-measuring the physical characteristics and performing the same calculation process. The blockchain node network receives the public key and the attribute data of the metal door / window, encapsulates them into a digital identity credential, and writes it into the distributed ledger, completing the identity registration of the metal door / window.

[0022] During the verification phase, upon receiving a verification request from the verification initiator, the identity verification component controls the acoustic excitation component to re-emit broadband acoustic pulse signals to the metal door / window frame to be verified. The acoustic signal acquisition component acquires the current microscopic acoustic resonance spectrum response signal and transmits it to the identity verification component. The identity verification component performs the same preprocessing and feature extraction operations on this signal as in the registration phase, generating a current physical feature hash value. As a prover, the identity verification component interacts with the digital identity credentials stored in the distributed ledger based on the current physical feature hash value, performing a zero-knowledge proof protocol interaction to prove to the verification nodes in the blockchain node network that it possesses the private key corresponding to the on-chain public key, without exposing the private key or the original physical feature data. The verification nodes confirm the prover's legitimacy by executing the verification algorithm of the zero-knowledge proof protocol and record the verification result in the distributed ledger.

[0023] Specifically, the microscopic acoustic resonance characteristics of metal door and window frames are determined by factors such as the elastic modulus, density, Poisson's ratio of the material, as well as the geometry and boundary conditions of the internal structure. When a broadband acoustic pulse signal is incident on a metal door and window frame, it excites various vibration modes within the frame, including longitudinal waves, transverse waves, and surface waves. These vibration modes propagate and interfere with each other within the frame, forming a complex resonance spectrum response. Due to unavoidable microscopic defects generated during the manufacturing process of metal materials, such as dislocations, grain boundaries, and inclusions, as well as differences in residual stress distribution introduced by processing techniques such as welding and stamping, these microscopic features modulate the propagation of sound waves, making the resonance spectrum response of each metal door and window frame unique. This uniqueness is determined by physical laws and cannot be replicated or forged artificially; therefore, it can serve as a natural physical fingerprint of metal doors and windows.

[0024] In this embodiment, the generation process of the physically unclonable function sequence is based on the microscopic acoustic resonance spectrum response of the metal door and window frame. This sequence has the following characteristics: unclonability, meaning it is impossible to physically replicate another metal door and window frame that can generate the same sequence; unpredictability, meaning it is impossible to predict the sequence of other metal door and window frames based on the existing sequence; and repeatability, meaning that under the same test conditions, multiple measurements on the same metal door and window frame can generate the same or highly similar sequences. These characteristics make the physically unclonable function sequence an ideal seed for generating cryptographic keys, ensuring that each metal door and window has a unique and unforgeable digital identity.

[0025] Elliptic curve cryptography is based on the elliptic curve discrete logarithm problem over a finite field. This problem is computationally difficult, meaning that given two points P and Q on an elliptic curve, where Q = kP, it is computationally infeasible to find the integer k given P and Q. In this embodiment, the secp256k1 elliptic curve is chosen as the cryptographic basis. The equation of this curve is:

[0026] The curve is defined on the finite field GF(p), where p is a large prime number with the following specific value:

[0027] The base point G of the elliptic curve is a fixed point with the following coordinates:

[0028]

[0029] The private key d is a random large integer that satisfies Where n is the order of the base point G, and its specific value is:

[0030] The public key Q is the product of the private key d and the base point G, that is:

[0031] The public key Q consists of two coordinates (x, y), which are typically compressed into a 33-byte string for storage and transmission. In this embodiment, a binary physically non-cloning function sequence is used as the seed input of a deterministic random bit generator. The deterministic random bit generator outputs a random large integer that meets the above requirements as the private key d, and then the public key Q is calculated based on the private key d.

[0032] The blockchain node network employs a proof-of-stake consensus mechanism. Each node in the network holds a certain number of stake tokens, and a node's voting weight is proportional to the number of stake tokens it holds. When a new digital identity credential needs to be written to the distributed ledger, the nodes in the network verify the validity of the credential. Once verified, the credential is packaged into a block and added to the end of the blockchain. Each block contains a block header and a block body. The block header contains information such as the hash value, timestamp, and difficulty target of the previous block, while the block body contains multiple transaction records, each corresponding to a write or update operation of a digital identity credential.

[0033] Zero-knowledge proof protocols allow a prover to demonstrate possession of secret information to a verifier without revealing any secret information. In this embodiment, the Schnorr zero-knowledge proof protocol is used. This protocol is based on the discrete logarithm problem and has advantages such as high computational efficiency and low communication volume. The Schnorr zero-knowledge proof protocol's interaction process includes three steps: commitment, challenge, and response. The prover generates a random number as a blinding factor, calculates the commitment value, and sends it to the verifier; the verifier generates a random challenge number and sends it to the prover; the prover uses the challenge number, blinding factor, and private key to calculate the response value and sends it to the verifier; the verifier verifies the prover's identity and legitimacy based on the response value, commitment value, challenge number, and public key.

[0034] In this embodiment, Table 1 shows the functional division and data flow relationship of each component of the system.

[0035] Table 1 System Component Function Division and Data Flow Relationship ; In this embodiment, the acoustic excitation component and the acoustic signal acquisition component are integrated into the same portable device, which transmits data with the feature extraction and key generation component and the authentication component via wireless communication. The feature extraction and key generation component and the authentication component can be deployed on the same computing device or on different computing devices. The blockchain node network consists of multiple distributed nodes, which can be servers, personal computers, or IoT devices. They are interconnected via the Internet to jointly maintain the consistency of the distributed ledger.

[0036] refer to Figure 2 In one embodiment, the acoustic excitation component includes a linear frequency modulated (LFM) signal generator and an acoustic transducer. The LFM signal generator generates a LFM pulse signal with controlled duration and frequency offset, the instantaneous frequency of which varies linearly with time, and its mathematical expression is:

[0037] Where A is the amplitude of the signal. Let B be the carrier frequency, B be the signal frequency offset, and T be the signal duration. Linear frequency modulated (LFM) pulse signals have a large time-bandwidth product, which can increase the energy of the transmitted signal and enhance the signal-to-noise ratio of the echo signal without reducing distance resolution. The acoustic transducer converts the LFM pulse signal into acoustic waves that are applied to the metal door and window frame. The acoustic transducer is made of piezoelectric ceramic material, enabling efficient conversion between electrical and mechanical energy.

[0038] When the acoustic signal acquisition component acquires the resonance signal, it sets a time window based on the transmission timestamp of the linear frequency modulated (LFM) pulse signal, and captures the acoustic echo signal reflected by the metal door / window frame within this time window. The start time of the time window is set to the moment after the LFM pulse signal transmission is completed, and the length of the time window is determined based on the size of the metal door / window frame and the speed of sound propagation in the metal to ensure that all resonance echo signals can be completely acquired. The acoustic signal acquisition component performs pulse compression processing on the captured acoustic echo signal to extract time delay and Doppler frequency shift information. Pulse compression processing is achieved by cross-correlation between the received echo signal and the transmitted LFM pulse signal, and its mathematical expression is:

[0039] in, for The complex conjugate, For time delay. The result of the cross-correlation operation is in A sharp peak appears when the actual time delay of the echo signal is equal to the actual time delay. By detecting the location of this peak, the time delay of the echo signal can be accurately measured. Doppler frequency shift information is obtained by analyzing the phase change of the cross-correlation results.

[0040] The acoustic signal acquisition component inputs the extracted time delay and Doppler shift information into a Kalman filter to filter out environmental noise interference. The Kalman filter is an optimal recursive estimation algorithm capable of estimating the system state from noisy measurement data. In this embodiment, the state vector of the Kalman filter includes the echo signal's time delay, Doppler shift, rate of change of time delay, and rate of change of Doppler shift. The prediction and update steps of the Kalman filter are as follows: Prediction steps:

[0041]

[0042] Update steps:

[0043]

[0044]

[0045]

[0046]

[0047] in, This is the predicted state value. Here is the state transition matrix. To control the input matrix, To control the input vector, To predict the covariance matrix, The process noise covariance matrix is... To measure the residual, For measured values, For the measurement matrix, The residual covariance matrix is... To measure the noise covariance matrix, For Kalman gain, Update the state value. To update the covariance matrix, It is an identity matrix.

[0048] The acoustic signal acquisition component outputs the filtered acoustic echo signal as a microscopic acoustic resonance spectral response signal to the feature extraction and key generation component.

[0049] In a preferred embodiment, the acoustic excitation component is also connected to a temperature sensor, which collects the current surface temperature of the metal door / window frame and the ambient temperature of the environment where the acoustic transducer is located. Temperature changes cause changes in the elastic modulus and density of the metal material, affecting the propagation speed and resonant frequency of sound waves in the metal, resulting in a drift in the microscopic acoustic resonance spectrum response signal. To overcome the effect of temperature drift, the acoustic signal acquisition component looks up the corresponding sound velocity compensation coefficient and medium refractive index offset in a preset temperature-sound velocity mapping table based on the difference between the current surface temperature and the ambient temperature.

[0050] The temperature-velocity of sound mapping table was pre-determined experimentally. For metal door and window frames of different materials, the propagation speed of sound within them was measured under different temperature conditions to establish the correlation between temperature and sound speed. Sound speed compensation coefficient. Defined as the ratio of the actual speed of sound to the reference speed of sound, i.e.:

[0051] in, Let T be the speed of sound at temperature T. Reference temperature The speed of sound at that time. The refractive index shift of the medium. Defined as the difference between the actual refractive index and the reference refractive index, i.e.:

[0052] in, Let T be the refractive index of the medium at temperature T. Reference temperature The refractive index of the medium at that time.

[0053] The acoustic signal acquisition component uses a sound velocity compensation coefficient to perform phase compensation correction on the pulse compression result of the linear frequency modulated pulse signal. Since changes in sound velocity cause changes in the phase of the echo signal, phase compensation correction is achieved by multiplying the pulse compression result by a phase factor. The mathematical expression for this phase factor is:

[0054] in, For carrier frequency, The time delay is considered. The pulse compression result after phase compensation correction is as follows:

[0055] The acoustic signal acquisition component dynamically corrects the state transition matrix parameters of the Kalman filter using the refractive index offset of the medium. (State transition matrix) The elements in the matrix are related to the refractive index of the medium. When the refractive index changes, the state transition matrix needs to be corrected accordingly to ensure the estimation accuracy of the Kalman filter. The corrected state transition matrix... for:

[0056] in, The correction coefficient was determined experimentally.

[0057] The acoustic signal acquisition component inputs the phase-compensated pulse compression result into a dynamically corrected Kalman filter to compensate for temperature drift in the microscopic acoustic resonance spectral response signal. The temperature-drift-compensated microscopic acoustic resonance spectral response signal can more accurately reflect the inherent physical characteristics of the metal door and window frame, improving the stability and consistency of physical feature extraction.

[0058] In this embodiment, Table 2 shows the experimentally measured values ​​of the sound velocity compensation coefficient and the refractive index offset of the medium for aluminum alloy door and window frames under different temperature conditions.

[0059] Table 2 Temperature-Sound Velocity Mapping Table for Aluminum Alloy Door and Window Frames (Partial) ; In this embodiment, a contact-type platinum resistance temperature sensor is used, which has advantages such as high measurement accuracy and good stability. The temperature sensors are installed on the surface of the acoustic transducer and the surface of the metal door / window frame, respectively, to simultaneously measure the ambient temperature and the surface temperature of the metal door / window frame. The temperature measurement data is converted into digital signals by an analog-to-digital converter and then transmitted to the microcontroller of the acoustic signal acquisition component. The microcontroller queries the temperature-sound velocity mapping table based on the temperature difference to obtain the corresponding sound velocity compensation coefficient and the medium refractive index offset, and performs the corresponding compensation and correction operations.

[0060] refer to Figure 3 In one embodiment, the feature extraction and key generation component performs a Fast Fourier Transform (FFT) on the microscopic acoustic resonance spectral response signal to obtain the frequency domain amplitude spectrum. The Fast Fourier Transform is an efficient Discrete Fourier Transform algorithm that can convert time-domain signals into frequency-domain signals; its mathematical expression is:

[0061] in, It is a discrete-time signal. For a discrete-time signal in the frequency domain, N is the length of the signal. Frequency domain amplitude spectrum. The magnitude of the frequency domain signal reflects the energy distribution of the signal at different frequency components.

[0062] The feature extraction and key generation components perform multi-level wavelet packet decomposition on the frequency domain amplitude spectrum to extract energy feature vectors for a specified frequency band. Wavelet packet decomposition is an extension of wavelet transform, capable of simultaneously decomposing both high-frequency and low-frequency components of a signal, providing finer frequency resolution. The wavelet packet decomposition process can be represented as:

[0063] in, The number of layers in the decomposition. These are the wavelet packet decomposition coefficients. The wavelet envelope basis function is used. In this embodiment, the Daubechies4 wavelet is selected as the wavelet basis function. This wavelet has compact support, orthogonality, and good vanishing moment characteristics, making it suitable for analyzing non-stationary signals.

[0064] The feature extraction and key generation components perform fuzzy extraction processing on the energy feature vector, encode auxiliary data using error-correcting codes, and store the auxiliary data in the blockchain node network. Fuzzy extraction processing can extract a stable binary sequence from noisy input data, and its process includes a generation phase and a reconstruction phase. In the generation phase, the input energy feature vector w is encoded to generate a secret value s and auxiliary data p; in the reconstruction phase, based on the noisy input vector w' and auxiliary data p, the same secret value s as in the generation phase is reconstructed.

[0065] In this embodiment, a fuzzy extractor based on linear error-correcting codes is used. Let C be an [n,k,d] linear error-correcting code, where n is the code length, k is the information bit length, and d is the minimum Hamming distance. The generation stage process is as follows: randomly select a codeword c. C, calculate the secret value s = Hash(c), calculate the auxiliary data p = w c, where This is a bitwise XOR operation. The reconstruction phase involves calculating... ,right Perform error-correcting decoding to obtain the codeword c, and calculate the secret value s = Hash(c). As long as w and... The Hamming distance between them does not exceed Then the secret value s can be correctly reconstructed.

[0066] The feature extraction and key generation components quantize the energy feature vector, after error correction, into a binary sequence of physically non-cloning functions. The quantization process employs threshold quantization, comparing each element in the energy feature vector to a preset threshold. Elements greater than the threshold are quantized as 1, and elements less than or equal to the threshold are quantized as 0. The threshold is chosen based on the statistical properties of the energy feature vector, typically the average value of the energy feature vector, to ensure that the number of 0s and 1s in the generated binary sequence is approximately equal.

[0067] The feature extraction and key generation component uses a sequence of binary physically unclonable functions as the seed input to a deterministic random bit generator. The generator outputs a large random integer conforming to elliptic curve cryptography requirements as the private key. The deterministic random bit generator employs an iterative structure of the SHA-256 hash function. Its operation is as follows: the seed is input into the SHA-256 hash function to generate a 256-bit hash value; this hash value is then used as the input for the next hash operation to generate a new hash value; this process is repeated until a sufficiently long random bit sequence is generated. The output of the deterministic random bit generator exhibits good randomness and unpredictability, meeting the requirements of cryptographic applications.

[0068] The feature extraction and key generation components calculate and generate a public key based on the private key and elliptic curve basis points. The calculation process for the public key is as described above, i.e., Q = dG, where d is the private key and G is the elliptic curve basis point.

[0069] In a preferred embodiment, when the feature extraction and key generation component performs multi-level wavelet packet decomposition on the frequency domain amplitude spectrum, it calculates the information entropy value of each wavelet packet node. Information entropy is an indicator of signal uncertainty, and its mathematical expression is:

[0070] Where M is the length of the signal, Let be the probability of the i-th value appearing in the signal. The higher the information entropy value, the greater the uncertainty of the signal and the more information it contains; the lower the information entropy value, the stronger the regularity of the signal and the less information it contains.

[0071] The feature extraction and key generation components filter out the frequency bands corresponding to nodes with information entropy values ​​below a preset entropy threshold, retaining the frequency bands corresponding to nodes with information entropy values ​​above the preset threshold as the target frequency bands. The preset entropy threshold is determined experimentally, typically chosen as 0.5. By filtering out low-entropy frequency bands, redundant information and noise in the signal can be removed, improving the efficiency and accuracy of feature extraction.

[0072] The feature extraction and key generation components set the components in the target frequency band whose absolute values ​​of wavelet packet decomposition coefficients are greater than a preset coefficient threshold to zero. The preset coefficient threshold is determined based on the statistical characteristics of the wavelet packet decomposition coefficients, typically chosen as the median of the absolute values ​​of the coefficients. This operation removes spike noise and outliers from the signal, further improving signal quality.

[0073] The feature extraction and key generation components reconstruct the denoised frequency domain amplitude spectrum of the target frequency band after zeroing. The reconstruction process is the inverse of wavelet packet decomposition; by combining the wavelet packet decomposition coefficients of the target frequency band, the frequency domain signal is recovered. The energy feature vector of the denoised frequency domain amplitude spectrum is then input into the fuzzy extraction process.

[0074] In this embodiment, Table 3 shows the calculation results of the information entropy values ​​of each node after performing a 3-level wavelet packet decomposition on the microscopic acoustic resonance spectrum response signal of an aluminum alloy door and window frame.

[0075] Table 33: Information Entropy Values ​​of Each Node in the Level 33 Wavelet Packet Decomposition ; In this embodiment, the wavelet packet decomposition is performed at 3 levels, dividing the frequency range of 20kHz to 100kHz into 8 equal sub-bands, each with a width of 10kHz. As shown in Table 3, the information entropy values ​​of nodes (3,3), (3,5), and (3,7) are below the preset threshold of 0.5 and are therefore filtered out; the information entropy values ​​of the remaining 5 nodes are above the preset threshold and are retained as target frequency bands. The wavelet packet decomposition coefficients of these 5 target frequency bands are zeroed out and reconstructed to obtain the denoised frequency domain amplitude spectrum, and then their energy feature vectors are extracted for subsequent fuzzy extraction processing.

[0076] refer to Figure 4 In one embodiment, the blockchain node network receives a public key and attribute data of the metal doors and windows, and concatenates the manufacturing batch, material model, and installation coordinates from the attribute data into a plaintext attribute string. The manufacturing batch consists of the manufacturer code, production date, and batch number; the material model indicates the type and specifications of material used in the metal door and window frame; and the installation coordinates indicate the specific location of the metal doors and windows within the building, consisting of the building number, floor number, and room number. The format of the plaintext attribute string is: Manufacturing Batch: XXX; Material Model: XXX; Installation Coordinates: XXX.

[0077] The blockchain node network uses hash operations to perform a joint hash calculation on the plaintext attribute string and the public key to generate an attribute digest value. The hash operation uses the SHA-256 hash function, whose mathematical expression is:

[0078] Where m is the input message and h is the output 256-bit hash value. The joint hash calculation process is as follows: the plaintext attribute string is concatenated with the compressed representation of the public key to form a new message, and then the SHA-256 hash operation is performed on the message to generate the attribute digest value.

[0079] The blockchain node network writes the attribute digest value, public key, and installation coordinates from the plaintext attribute string into the smart contract's storage slots. A smart contract is a computer program that runs on the blockchain and can automatically execute predefined logic. A smart contract's storage slot is an area within the smart contract used for persistently storing data; each slot can store 256 bits of data. In this embodiment, the smart contract includes multiple storage slots, used to store the attribute digest value, public key, installation coordinates, and identity status flags, respectively.

[0080] Smart contracts are deployed in the genesis block of the blockchain node network. The genesis block is the first block of the blockchain and contains the initial configuration information of the blockchain. The smart contract includes identity state transition logic, which receives the verification result from the identity verification component and updates the identity state flag in the storage slot. The identity state flag is a binary bit, where 0 indicates an unverified state and 1 indicates a verified state.

[0081] In a preferred embodiment, when updating the identity state flag in the storage slot, the smart contract's identity state transition logic obtains the verification voting weights of each consensus node in the blockchain node network for the response values ​​submitted by the identity verification component. The verification voting weights are determined based on the product of the stake held by each consensus node and the node network latency parameter, and their mathematical expression is:

[0082] in, Let i be the verification voting weight of the i-th consensus node. For the equity share held by the i-th consensus node, Let i be the network latency of the i-th consensus node. The latency penalty coefficient was determined experimentally. This formula shows that the larger the stake a node holds, the lower the network latency, and the greater its verification voting weight.

[0083] The identity state transition logic uses a statistical verification vote weighting system where the sum of the vote weights exceeds two-thirds of the total network vote weight as the final verification consensus. The total network vote weight is the sum of the verification vote weights of all consensus nodes. If the sum of the verified vote weights exceeds two-thirds of the total network vote weight, the verification result is considered to have achieved network consensus; otherwise, the verification result is considered invalid.

[0084] The identity state transition logic is triggered by the final verification consensus, which changes the identity state flag in the storage slot from unverified to verified. Simultaneously, the smart contract records the verification result in the blockchain's transaction log, including verification time, verification initiator address, and verification result, for subsequent querying and auditing.

[0085] refer to Figure 5 In this embodiment, when the authentication component interacts with the digital identity credential based on the current physical feature hash value, the authentication component, acting as the prover, generates a random blinding factor r, where r is a random large integer that satisfies... , where n is the order of the elliptic curve base point G. The authentication component calculates the commitment value C based on the random blinding factor r and the current physical feature hash value. The mathematical expression for the commitment value C is:

[0086] Where h is the current physical feature hash value, and P is a fixed point on the elliptic curve whose coordinates are predetermined and publicly available.

[0087] The authentication component broadcasts the commitment value C to the blockchain node network. The validating nodes in the blockchain node network generate a random challenge number c based on the commitment value C and return it to the authentication component. The random challenge number c is a random integer, typically 128 bits or 256 bits.

[0088] The authentication component uses a random challenge number c, a random blinding factor r, and a private key d to calculate the response value s. The mathematical expression for the response value s is:

[0089] The authentication component submits the response value s to the verification node. The verification node performs a bilinear mapping verification based on the response value s, the commitment value C, the random challenge number c, and the public key Q stored in the distributed ledger. The mathematical expression for bilinear mapping verification is:

[0090] Where e is a bilinear mapping function. If the equation holds true, the verification passes; otherwise, the verification fails.

[0091] refer to Figure 6 In a preferred embodiment, when the authentication component receives an authentication request, it extracts the timestamp parameter and the authentication initiator's network address parameter carried in the authentication request. The timestamp parameter indicates the time the authentication request was sent, and the authentication initiator's network address parameter indicates the IP address or MAC address of the authentication initiator.

[0092] The authentication component compares the timestamp parameter with the consensus time of the blockchain node network. The consensus time of the blockchain node network is a time mutually agreed upon by all nodes in the network and synchronized through a consensus mechanism. If the difference between the timestamp parameter and the consensus time exceeds a preset threshold, the zero-knowledge proof protocol interaction is rejected. The preset threshold is typically set to 5 minutes to prevent replay attacks.

[0093] If the difference between the timestamp parameter and the consensus time does not exceed a preset threshold, the authentication component will combine the timestamp parameter, the verification initiator's network address parameter, and the current physical feature hash value to generate a composite hash input value to be verified. The combination method is as follows: the timestamp parameter is converted into a string and concatenated with the string representations of the verification initiator's network address parameter and the current physical feature hash value to form a new string.

[0094] The authentication component performs subsequent zero-knowledge proof protocol interactions based on the composite hash input value to be verified. Specifically, the composite hash input value to be verified is substituted as the current physical feature hash value h into the calculation process of the aforementioned zero-knowledge proof protocol to generate a commitment value C and a response value s. This ensures that the input for each zero-knowledge proof interaction is unique, preventing attackers from using previously intercepted interaction data for replay attacks.

[0095] In this embodiment, Table 4 shows an example of the structure and data content of the smart contract storage slot.

[0096] Table 4. Examples of Smart Contract Storage Slot Structure and Data Content ; In this embodiment, storage slot 0 of the smart contract stores the attribute digest value, generated by hashing the plaintext attribute string and the public key; storage slot 1 stores the compressed representation of the public key; storage slot 2 stores the installation coordinates of the metal doors and windows; storage slot 3 stores the identity status flag, with 0x00 indicating an unverified state and 0x01 indicating a verified state; storage slot 4 stores the timestamp of the last verification; and storage slot 5 stores the network address of the initiator of the last verification. The data in these storage slots can be queried and updated through the interface provided by the smart contract, but only update operations that conform to the identity status transition logic will be executed.

[0097] In this embodiment, the blockchain node network uses the Ethereum blockchain platform, and the smart contract is written in the Solidity programming language. The smart contract code includes data structure definitions, constructors, identity registration functions, authentication functions, and identity status query functions. The constructor is executed during smart contract deployment and is used to initialize the smart contract's state variables. The identity registration function receives the public key and the attribute data of the metal doors and windows, calculates the attribute digest value, and writes it to the storage slot. The authentication function receives the zero-knowledge proof interaction data, executes the verification algorithm, and updates the identity status flag. The identity status query function allows users to query the identity status and related attribute information of the metal doors and windows.

Claims

1. A blockchain-based anti-counterfeiting identity authentication system for metal doors and windows, characterized in that, include: Acoustic excitation component, used to transmit broadband acoustic pulse signals of a preset frequency band to metal door and window frames; An acoustic signal acquisition component is used to acquire the microscopic acoustic resonance spectrum response signal generated by the metal door and window frame under the excitation of the broadband acoustic pulse signal. The feature extraction and key generation component is used to quantize and encode the microscopic acoustic resonance spectrum response signal to generate a binary physically unclonable function sequence, and to generate a public-private key pair bound to the metal door and window based on the binary physically unclonable function sequence through elliptic curve cryptography. A blockchain node network is used to encapsulate the public key in the public-private key pair and the attribute data of the metal doors and windows into a digital identity credential and write it into a distributed ledger. An authentication component is used to re-emit broadband acoustic stimuli to the metal door and window when an authentication request is received, extract the current physical feature hash value, and perform a zero-knowledge proof protocol interaction with the digital identity credential in the distributed ledger based on the current physical feature hash value to confirm the correspondence between the physical entity and the on-chain state.

2. The blockchain-based anti-counterfeiting identity authentication system for metal doors and windows according to claim 1, characterized in that, The acoustic excitation component includes a linear frequency modulated signal generator and an acoustic transducer. The linear frequency modulated signal generator generates a linear frequency modulated pulse signal with controlled duration and frequency deviation. The acoustic transducer converts the linear frequency modulated pulse signal into acoustic waves and applies them to the metal door and window frame. When the acoustic signal acquisition component acquires the resonance signal, it sets a time window based on the transmission timestamp of the linear frequency modulated pulse signal, intercepts the acoustic echo signal reflected by the metal door and window frame within the time window, performs pulse compression processing on the acoustic echo signal to extract time delay and Doppler frequency shift information, inputs the time delay and Doppler frequency shift information into a Kalman filter to filter out environmental noise interference, and uses the filtered acoustic echo signal as the microscopic acoustic resonance spectral response signal.

3. The blockchain-based anti-counterfeiting identity authentication system for metal doors and windows according to claim 1, characterized in that, The feature extraction and key generation component performs a fast Fourier transform on the microacoustic resonance spectral response signal to obtain the frequency domain amplitude spectrum, performs multi-level wavelet packet decomposition on the frequency domain amplitude spectrum to extract the energy feature vector of a specified frequency band, performs fuzzy extraction processing on the energy feature vector, uses error correction codes to encode and calculate auxiliary data and stores the auxiliary data in the blockchain node network, quantizes the energy feature vector after error correction code correction into the binary physically non-cloning function sequence, uses the binary physically non-cloning function sequence as the seed input of a deterministic random bit generator, outputs a random large integer that conforms to the requirements of elliptic curve cryptography as the private key through the deterministic random bit generator, and calculates and generates a public key based on the private key and the elliptic curve basis points.

4. The blockchain-based anti-counterfeiting identity authentication system for metal doors and windows according to claim 3, characterized in that, The blockchain node network receives the public key and the attribute data of the metal door and window. It concatenates the manufacturing batch, material model, and installation coordinates from the attribute data into a plaintext attribute string. It then uses a hash operation to perform a joint hash calculation on the plaintext attribute string and the public key to generate an attribute digest value. The attribute digest value, the public key, and the installation coordinates from the plaintext attribute string are written into the storage slot of the smart contract. The smart contract is deployed in the genesis block of the blockchain node network. The smart contract includes identity state transition logic, which receives the verification result from the identity verification component and updates the identity state flag bit in the storage slot.

5. The blockchain-based anti-counterfeiting identity authentication system for metal doors and windows according to claim 4, characterized in that, When the authentication component interacts with the digital identity credential using the current physical feature hash value, the authentication component, acting as a prover, generates a random blinding factor, calculates a commitment value based on the random blinding factor and the current physical feature hash value, and broadcasts the commitment value to the blockchain node network. The verification nodes in the blockchain node network generate a random challenge number based on the commitment value and return it to the authentication component. The authentication component uses the random challenge number, the random blinding factor, and the private key to calculate a response value, and submits the response value to the verification node. The verification node performs bilinear mapping verification based on the response value, the commitment value, the random challenge number, and the public key stored in the distributed ledger.

6. The blockchain-based anti-counterfeiting identity authentication system for metal doors and windows according to claim 1, characterized in that, When the authentication component receives the authentication request, it extracts the timestamp parameter and the network address parameter of the authentication initiator carried in the authentication request, compares the timestamp parameter with the consensus time of the blockchain node network, and if the difference between the timestamp parameter and the consensus time exceeds a preset threshold, it refuses to execute the zero-knowledge proof protocol interaction. If it does not exceed the preset threshold, it combines the timestamp parameter, the network address parameter of the authentication initiator, and the current physical feature hash value to generate a composite hash input value to be verified, and executes subsequent zero-knowledge proof protocol interactions based on the composite hash input value to be verified.

7. The blockchain-based anti-counterfeiting identity authentication system for metal doors and windows according to claim 2, characterized in that, The acoustic excitation component is also connected to a temperature sensor, which collects the current surface temperature of the metal door and window frame and the ambient temperature of the environment where the acoustic transducer is located. The acoustic signal acquisition component looks up the corresponding sound velocity compensation coefficient and medium refractive index offset in a preset temperature-sound velocity mapping table based on the difference between the current surface temperature and the ambient temperature. The sound velocity compensation coefficient is used to perform phase compensation correction on the pulse compression result of the linear frequency modulated pulse signal, and the medium refractive index offset is used to dynamically correct the state transition matrix parameters of the Kalman filter. The pulse compression result after phase compensation correction is input into the dynamically corrected Kalman filter to perform temperature drift compensation on the microscopic acoustic resonance spectrum response signal.

8. The blockchain-based anti-counterfeiting identity authentication system for metal doors and windows according to claim 4, characterized in that, When the feature extraction and key generation component performs multi-level wavelet packet decomposition on the frequency domain amplitude spectrum, it calculates the information entropy value of each wavelet packet node, filters out the frequency bands corresponding to nodes with information entropy values ​​lower than a preset entropy threshold, retains the frequency bands corresponding to nodes with information entropy values ​​higher than the preset entropy threshold as target frequency bands, sets the components in the target frequency band whose absolute value of wavelet packet decomposition coefficients is greater than a preset coefficient threshold to zero, reconstructs the zeroed target frequency band to obtain the denoised frequency domain amplitude spectrum, and extracts the energy feature vector of the denoised frequency domain amplitude spectrum into the fuzzy extraction process.

9. The blockchain-based anti-counterfeiting identity authentication system for metal doors and windows according to claim 4, characterized in that, When updating the identity status flag in the storage slot, the identity state transition logic of the smart contract obtains the verification voting weight of the response value submitted by each consensus node in the blockchain node network to the identity verification component. The verification voting weight is determined based on the product of the equity share held by each consensus node and the node network latency parameter. The verification result where the sum of the verification voting weights exceeds 2 / 3 of the total voting weight of the entire network is taken as the final verification consensus. Based on the final verification consensus, the smart contract is triggered to switch the identity status flag in the storage slot from the unverified state to the verified state.

10. A blockchain-based anti-counterfeiting identity authentication method for metal doors and windows, characterized in that, include: Broadband acoustic pulse signals of a preset frequency band are emitted into the metal door and window frames; The microscopic acoustic resonance spectrum response signal generated by the metal door and window frame under the excitation of the broadband acoustic pulse signal was collected. The microscopic acoustic resonance spectrum response signal is quantized and encoded to generate a binary physically unclonable function sequence, and the public-private key pair bound to the metal door and window is generated by elliptic curve cryptography based on the binary physically unclonable function sequence. The public key in the public-private key pair and the attribute data of the metal doors and windows are encapsulated into a digital identity credential and written into a distributed ledger; Upon receiving a verification request, broadband acoustic stimuli are re-emitted to the metal doors and windows, the current physical feature hash value is extracted, and a zero-knowledge proof protocol is executed based on the current physical feature hash value and the digital identity credential in the distributed ledger to confirm the correspondence between the physical entity and the on-chain state.

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