Payment method and system based on smart glasses

Abstract

The application relates to the technical field of intelligent glasses payment, and discloses a payment method and system based on intelligent glasses. The method collects iris biological feature data of a user to generate an iris feature sequence through an intelligent glasses eye pupil recognition module; feature point extraction and mode analysis are performed on the iris feature sequence to obtain an identity verification result; when the identity verification result reaches a predetermined confidence threshold, a near-field communication module is activated to establish a payment channel; transaction terminal payment request data is obtained through the near-field communication module to generate a transaction information package; a block chain encryption algorithm is used to perform layered encryption on the transaction information package to generate an encrypted transaction block; a distributed verification chain is constructed based on the digital signature feature and the time sequence feature of the encrypted transaction block; a transaction verification state is obtained through consensus calculation of verification results of all nodes in the distributed verification chain; the blockchain network parameters are dynamically adjusted according to the transaction verification state confirmation level and the network load; and the payment instruction is finally executed.

Description

Technical Field

[0001] This invention relates to the field of smart glasses payment technology, specifically to a payment method and system based on smart glasses. Background Technology

[0002] With the rapid development of mobile payment technology, various payment methods are constantly emerging, from early password and fingerprint payments to today's facial recognition payments. Convenience and security have always been core directions for the industry's development. With the popularization of wearable devices, smart glasses, as terminals with image acquisition, data processing, and wireless communication capabilities, are gradually being explored for application in payment scenarios. However, current payment-related technologies based on smart glasses still have many shortcomings.

[0003] Traditional smart device payments mostly rely on passwords or simple biometrics, such as fingerprints, which have significant limitations. Password payments are prone to problems such as forgotten or leaked passwords, and if a password is obtained by someone else, it may lead to security risks to account funds. Although fingerprint payments improve convenience, fingerprints are easy to copy, and the success rate of recognition drops significantly when fingers are wet or damaged, making it impossible to reliably meet the identity verification requirements during the payment process.

[0004] In the payment channel establishment phase, existing technologies often employ conventional wireless communication methods, lacking dedicated security mechanisms for payment scenarios. Data transmission is susceptible to interception and tampering, leading to the leakage of payment request data and causing losses to both parties in the transaction. Furthermore, in terms of transaction information processing, traditional encryption methods are mostly single-level encryption with limited encryption strength, making them vulnerable to complex network attacks and failing to adequately guarantee the integrity and security of transaction information.

[0005] Current payment technologies largely rely on centralized servers for transaction verification. If a server malfunctions or is attacked, the entire payment system may be paralyzed, unable to complete transaction verification, and disrupting the normal progress of the payment process. Furthermore, existing technologies struggle to adjust system parameters according to fluctuating network loads, easily leading to transaction delays and stuttering, thus degrading the user payment experience. In the final stage of payment instruction execution, the lack of effective integration with distributed ledgers results in weak transaction record storage and traceability capabilities, failing to achieve tamper-proof and fully traceable transaction information, which is detrimental to resolving subsequent transaction disputes. These issues collectively restrict the widespread application and development of payment technologies based on smart glasses. Summary of the Invention

[0006] The purpose of this invention is to provide a payment method and system based on smart glasses to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a payment method based on smart glasses, the method comprising:

[0008] The eye pupil recognition module of the smart glasses collects the user's iris biometric data and generates an iris feature sequence;

[0009] Feature point extraction and pattern analysis are performed on the iris feature sequence to obtain the identity verification result;

[0010] When the authentication result reaches a predetermined confidence threshold, the near-field communication module is activated to establish a payment channel;

[0011] The near-field communication module acquires payment request data sent by the transaction terminal and generates a transaction information packet.

[0012] The transaction information packet is encrypted in layers using a blockchain encryption algorithm to generate an encrypted transaction block.

[0013] A distributed verification chain is constructed based on the digital signature and timing characteristics of the encrypted transaction blocks.

[0014] The distributed verification chain is used to perform consensus calculations on the verification results of each node to obtain the transaction verification status.

[0015] The blockchain network parameters are dynamically adjusted based on the confirmation level of the transaction verification status and the network load.

[0016] Based on the adjusted blockchain network parameters and transaction verification status, the final execution of the payment instruction is completed.

[0017] Preferably, the method for processing the iris feature sequence includes:

[0018] Multi-scale filtering and noise suppression are performed on the iris feature sequence to extract the iris texture feature vector;

[0019] Calculate the similarity matrix between the iris texture feature vector and the pre-stored registered template to obtain the feature matching degree;

[0020] Based on the distribution and statistical characteristics of the feature matching degree, an identity verification confidence score is generated;

[0021] The authentication confidence score is compared with a preset threshold, and the authentication result is output.

[0022] Preferably, the method for establishing the payment channel includes:

[0023] When the authentication result is confirmed to be valid, the initialization protocol of the near-field communication module is triggered;

[0024] The near-field communication module is used to send device authentication requests and payment capability statements.

[0025] Receive communication parameters and security policies returned by the transaction terminal, and negotiate the encryption key exchange mechanism;

[0026] A secure payment data transmission channel is established based on a negotiated encryption key exchange mechanism.

[0027] Preferably, the method for generating the transaction information package includes:

[0028] The payment request data from the transaction terminal is received through the secure payment data transmission channel;

[0029] Parse the format and content of the payment request data to extract the transaction amount and merchant information;

[0030] The transaction amount, merchant information, device identifier, and timestamp are combined to generate the original transaction information package;

[0031] The original transaction information package is standardized in data and length, and a standard transaction information package is generated.

[0032] Preferably, the method for generating encrypted transaction blocks by performing layered encryption processing on the transaction information packet using a blockchain encryption algorithm includes:

[0033] The standard transaction information packet is first-layer encrypted using an asymmetric encryption algorithm to generate an encrypted data payload.

[0034] A hash function is used to calculate a digital digest of the encrypted data payload, and a data integrity verification code is generated.

[0035] The data integrity verification code is bound to the device digital certificate to generate a digital signature block;

[0036] The encrypted data payload and digital signature block are combined to generate the final encrypted transaction block.

[0037] Preferably, the method for constructing the distributed verification chain includes:

[0038] The encrypted transaction block is broadcast to the set of verification nodes in the blockchain network;

[0039] Collect the verification response data returned by each verification node, including signature validity and time consistency;

[0040] Calculate the consensus credibility index based on the statistical distribution of the verification response data;

[0041] Based on the consensus credibility index and network topology, a dynamic verification chain is constructed.

[0042] Preferably, the method for obtaining the transaction verification status includes:

[0043] Monitor the changes in the verification status of each node in the dynamic verification chain;

[0044] The initial verification level is obtained by calculating the ratio of the number of verified nodes to the total number of nodes.

[0045] The preliminary verification level is weighted and adjusted based on network latency and node reputation weight;

[0046] Output the final transaction verification status and its corresponding security level identifier.

[0047] Preferably, the method for dynamically adjusting blockchain network parameters includes:

[0048] Analyze the risk coefficient corresponding to the security level identifier of the transaction verification status;

[0049] Monitor the current load status and transaction throughput metrics of the blockchain network;

[0050] Calculate the parameter adjustment amount based on the degree of matching between the risk coefficient and the network load status;

[0051] Dynamically adjust block generation interval and number of verification nodes.

[0052] Preferably, the method for completing the final execution of the payment instruction includes:

[0053] Generate transaction confirmation instructions based on the adjusted blockchain network parameters;

[0054] A payment authorization signal is sent to the transaction terminal through the secure payment data transmission channel;

[0055] Receive transaction completion confirmation information returned by the transaction terminal and update the local transaction record;

[0056] The transaction details are written into the blockchain distributed ledger to complete the payment process.

[0057] Preferably, the present invention also includes a payment system based on smart glasses, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the payment method based on smart glasses described above.

[0058] Compared with the prior art, the beneficial effects of the present invention are:

[0059] This payment method based on smart glasses has demonstrated several significant advantages in practical applications. The eye recognition module of the smart glasses collects the user's iris biometric data to generate an iris feature sequence. Iris biometrics are unique and stable; each person's iris texture is significantly different and does not change with age or external environmental changes. Using this method for identity verification can avoid problems such as password leakage and forgetting in traditional password payments, and the ease of fingerprint copying and unstable recognition in fingerprint payments. This makes the identity verification process more reliable and strengthens the initial security barrier of the payment process.

[0060] After identity verification is successful, the near-field communication module is activated to establish a payment channel. Near-field communication technology itself has the characteristics of short-range communication, which can reduce the spread of data during transmission and reduce the probability of being intercepted. At the same time, the process does not require any additional complicated operations. Users only need to complete identity verification through smart glasses to automatically trigger the establishment of the channel, which simplifies the operation steps before payment and improves the convenience of payment initiation. This allows users to quickly enter the transaction process in daily shopping and other payment scenarios without cumbersome operations.

[0061] After acquiring payment request data and generating transaction information packets through the near-field communication module, the blockchain encryption algorithm is used for layered encryption processing to generate encrypted transaction blocks. The layered encryption design ensures that transaction information is securely protected at different levels. The application of technologies such as asymmetric encryption algorithms and hash functions ensures the security of encrypted data payloads and the validity of data integrity verification codes. Combined with the generation of digital signature blocks by binding the device's digital certificate, the immutability of transaction information is further strengthened. Even in complex network environments, it can effectively resist attacks and ensure the security of transaction information during transmission and processing.

[0062] By constructing a distributed verification chain based on encrypted transaction blocks, transaction verification tasks are distributed across multiple nodes, eliminating reliance on centralized servers. Each node can participate in transaction verification, and even if some nodes fail, others can continue to complete the verification work, ensuring that the payment system will not be paralyzed due to a single node's failure. This improves the stability and resilience of the entire payment system. Furthermore, by using the distributed verification chain to perform consensus calculations on the verification results of each node to obtain the transaction verification status, the judgments of multiple nodes can be integrated, reducing the errors that may occur in single-node verification and making the transaction verification results more credible.

[0063] Dynamically adjusting blockchain network parameters based on the confirmation level of transaction verification status and network load enables the payment system to better adapt to different network environments and transaction needs. When network load is high, adjusting the block generation interval and the number of verification nodes can avoid transaction congestion and reduce transaction latency. When transaction verification statuses have different security levels, parameter adjustments allow the system to operate more efficiently while ensuring security, balancing system security and operational efficiency.

[0064] The final execution of the payment instruction is completed based on the adjusted parameters and transaction verification status. The entire process is seamless, forming a complete closed loop from identity verification to transaction completion. The transaction details are ultimately written into the blockchain distributed ledger, achieving permanent storage and traceability of transaction records. Both parties to the transaction can query transaction information at any time. In the event of a transaction dispute, responsibility can be quickly clarified based on the records in the ledger, providing strong support for the protection of rights and interests after the transaction, and further improving the integrity and reliability of the payment process. Attached Figure Description

[0065] Figure 1 This is a schematic diagram illustrating the working principle of the payment method based on smart glasses described in this invention.

[0066] Figure 2 A schematic diagram illustrating the working principle of iris feature sequence processing;

[0067] Figure 3 A diagram illustrating the working principle of the payment channel establishment method. Detailed Implementation

[0068] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] Please see Figure 1This invention provides a payment method and system based on smart glasses. The method includes biometric recognition, near-field communication (NFC), and blockchain technology to achieve a secure and efficient payment process. The smart glasses are equipped with an iris recognition module to collect the user's iris biometric data. The iris recognition module captures the user's iris image through an infrared camera or similar sensor, generating an iris feature sequence. This sequence undergoes feature point extraction and pattern analysis to obtain an identity verification result. The identity verification result is compared with a predetermined confidence threshold. When the threshold is reached, the NFC module is activated, establishing a payment channel with the transaction terminal. The NFC module receives payment request data sent by the transaction terminal and generates a transaction information packet. The transaction information packet undergoes layered encryption using a blockchain encryption algorithm to generate an encrypted transaction block. The encrypted transaction block constructs a distributed verification chain based on digital signature features and time sequence features. The distributed verification chain calculates the verification results of each node through consensus to obtain the transaction verification status. The blockchain network parameters are dynamically adjusted according to the confirmation level of the transaction verification status and network load. Finally, based on the adjusted parameters and the transaction verification status, the payment instruction is executed.

[0070] Example 1: See Figure 2 The processing of iris feature sequences begins with multi-scale filtering of the raw acquired data. This multi-scale filtering is achieved by constructing a Gaussian pyramid of the image, which consists of a series of image layers with different resolutions. Each layer is obtained through downsampling. This process effectively captures the spatial frequency features of iris texture at different scales. At each scale, Gabor filter banks are applied for feature enhancement. Due to their frequency and direction selectivity, Gabor filters can effectively simulate the receptive field characteristics of the human visual system, thereby highlighting the unique details of the iris, such as folds and crypts. The filtered feature maps at each scale are then upsampled to the original resolution through interpolation and weighted fusion to generate an enhanced iris image containing multi-scale information. Noise suppression follows immediately after multi-scale filtering. Its main goal is to eliminate interference factors introduced during the acquisition process, such as uneven ambient lighting, eyelid occlusion, eyelash projection, and camera noise. An anisotropic diffusion model based on partial differential equations was employed. This model adaptively smooths uniform regions while preserving edge details based on local gradient information of the image. For salt-and-pepper noise, an improved median filtering algorithm was combined, distinguishing noise points from real edge points by judging the statistical characteristics within the pixel neighborhood. After noise suppression processing, the iris image showed a significantly improved signal-to-noise ratio, creating conditions for subsequent accurate feature extraction.

[0071] Extracting iris texture feature vectors is a precise quantization process. The preprocessed iris region is normalized, transforming it from a Cartesian coordinate system to a rectangular band in a polar coordinate system to eliminate the geometric distortion caused by pupil dilation and contraction. On the normalized iris image, two-dimensional discrete wavelet transform is used for texture analysis, extracting the energy statistical features in different sub-bands as primary descriptors. Simultaneously, local binary pattern coding is applied to characterize the local contrast information of the micro-texture. All these feature components are concatenated into a high-dimensional feature vector, which constitutes a mathematical expression of the uniqueness of an individual iris.

[0072] The calculation of the similarity matrix is ​​a core step in identity verification. It compares the iris texture feature vector extracted in real time with the registration template pre-stored within the device's secure area. The comparison algorithm is typically based on a modified Hamming distance metric, which tolerates minor differences in certain bits between feature vectors, potentially stemming from unavoidable pose variations during each acquisition. The calculated distance value is mapped to a similarity score between 0 and 1. All comparison scores form a similarity matrix that reflects the degree of matching between the feature to be verified and the registration template. The evaluation of feature matching relies not only on a single similarity score threshold but also on a comprehensive assessment considering its distribution and statistical characteristics. The system analyzes the position of the current comparison score within the historical distribution, calculates its Z-score to evaluate the statistical significance of the comparison, and checks the spatial distribution consistency of matching feature points to avoid high-score matches caused by local feature forgery. These analytical results are summarized into a comprehensive feature matching index, which is more robust than a single score.

[0073] The generation of the authentication confidence score is a process of fusing multi-source information for decision-making. This scoring model integrates factors such as feature matching degree, image quality assessment score, liveness detection results, and historical verification behavior patterns. A classifier based on support vector machines or deep neural networks is employed, trained on a large dataset containing both genuine and fake verification attempts, enabling it to learn the complex boundaries distinguishing legitimate users from imposters. The posterior probability output by the classifier, after calibration, serves as the final authentication confidence score, quantifying the probability estimate of the current verification attempt's authenticity. The authentication result is generated by comparing the confidence score with a dynamically adjusted preset threshold. This threshold is not fixed but adaptively adjusted based on factors such as the risk level of the current transaction and the security status of the network environment in which the device is located. For high-risk transactions, the system automatically increases the threshold requirement to ensure a higher level of security. The comparison result produces a binary decision (pass / reject), or in some application scenarios, directly outputs continuous confidence scores for upper-layer applications to make further risk control decisions. The entire process ensures security while also considering the system's ease of use and flexibility.

[0074] Example 2: See Figure 3 The establishment of a payment channel begins the moment the identity verification result is confirmed as valid. The near-field communication controller within the smart glasses is then awakened from low-power standby mode and executes a strict initialization protocol. This initialization protocol strictly adheres to the near-field communication interface protocol stack defined by international standards organizations, starting with the activation of the radio frequency field at the physical layer, followed by the exchange of activation sequences at the link layer, negotiation of protocol parameters at the transport layer, and finally, the processing of selection commands at the application layer. During initialization, the smart glasses function as a card emulation device, with its internal security elements configured to respond to commands from the external card reader. Simultaneously, the near-field communication controller continuously monitors the strength and stability of the radio frequency field. Sending the device authentication request and payment capability declaration is the first step in establishing a trust relationship. The smart glasses proactively send a structured request data packet via the near-field communication link. This data packet contains a device digital certificate issued by a certification authority, a randomly generated session identifier, and a list of security algorithms supported by the device. The payment capability declaration details key parameters such as the payment application identifiers supported by the smart glasses, the transaction currency type, and the single transaction limit. This information is encoded and arranged according to the data element format of payment industry standards, ensuring that the transaction terminal can correctly parse and understand the device's payment capability range, setting clear boundaries for subsequent interactions.

[0075] Upon receiving the authentication request from the smart glasses, the transaction terminal initiates its terminal behavior analysis process. This analysis includes checking the validity of the device certificate, verifying the integrity of the certificate chain, and assessing the match between the device's claimed payment capabilities and the terminal's supported scope. After passing these checks, the transaction terminal returns a set of communication parameters and a security policy. The communication parameters specify the baud rate, frame format, and timeout for data transmission, while the security policy specifies the encryption suite to be used in this session, the authentication method to be performed, and the type of key to be exchanged. These parameters and policies constitute the basic rules for subsequent communication between the two parties, providing the technical framework for establishing a secure channel. Negotiating the encryption key exchange mechanism is the core step in establishing a secure channel. Both parties select a mutually agreed-upon key exchange algorithm based on their respective supported security capabilities. If both parties support elliptic curve cryptography, the Diffie-Hellman key exchange algorithm based on elliptic curves will be preferentially chosen. This algorithm can securely generate a shared secret known only to the communicating parties over a public channel. The negotiation process is completed by exchanging a series of messages containing public parameters and temporary public keys. Each message is protected by a message authentication code to prevent tampering. The final generated session key will be used for the encryption and decryption of subsequent payment data. This key only exists in the memory of this session and is cleared when the session ends.

[0076] The establishment of a secure payment data transmission channel marks the realization of link-layer security. This channel constructs an end-to-end encrypted tunnel over the physical radio frequency link. The channel employs a proven transport layer security protocol as its security framework. Immediately after key exchange, a switch between symmetric encryption algorithms and authentication modes is performed. All application-layer data transmitted through this channel is protected by Advanced Encryption Standard (AES) algorithms, and each data packet is appended with a calculated value based on a hash message authentication code. This design ensures the confidentiality and integrity of payment data during transmission; even if the radio frequency signal is intercepted, attackers cannot obtain or tamper with valid transaction information. Payment request data is received through the established secure channel. The transaction terminal sends the constructed payment request instruction to the smart glasses according to the application protocol data unit format. Payment request data typically includes a clear transaction type identifier, a precise transaction amount, a merchant identifier, and a random number to prevent replay attacks. After receiving this data, the smart glasses' near-field communication module verifies the correctness of the message authentication code, confirming that the data has not been tampered with during transmission, before submitting the decrypted plaintext data to the payment application for processing.

[0077] Parsing payment request data is a process of extracting data elements and performing semantic analysis according to predefined rules. The payment application processor in the smart glasses unpacks the received byte stream according to the basic specifications of the payment system. The parsing process requires identifying the meaning of each data element tag, converting the transaction amount from binary encoding to decimal value, parsing the merchant category code and country code from the merchant identifier, and checking that all necessary data elements are complete. The accuracy and robustness of this parsing process directly affect the correctness of subsequent transaction information packet construction; any parsing error will lead to transaction termination. Generating the original transaction information packet is the process of combining the parsed discrete data elements into a structured data object. This combination process follows a standard template that defines the order and encoding rules of fields such as transaction amount, merchant information, device identifier, and transaction timestamp. The device identifier uses a unique serial number stored in the smart glasses' secure element, and the timestamp is taken from the device's internal secure clock. All these fields are concatenated according to the template to form a complete original transaction information packet, which fully describes the basic elements of this transaction.

[0078] Data standardization and length normalization are performed to meet the input data format requirements of subsequent encryption algorithms and transmission protocols. Standardization involves converting all character data to UTF-8 encoding, all numeric data to fixed-length binary representation, and all date and time data to Coordinated Universal Time (UTC) format. Length normalization ensures, through padding algorithms, that the length of the final standard transaction packet conforms to the cryptographic algorithm's requirement of an integer multiple of the data block size. This normalization process employs a standard padding scheme, enabling the receiver to clearly distinguish between the original data and the padding data, thereby accurately reconstructing the valid transaction information.

[0079] Example 3: The initial stage of layered encryption processing involves applying an asymmetric encryption algorithm to the standard transaction packet. This process uses an encryption scheme based on the large integer factorization problem or the elliptic curve discrete logarithm problem to generate the encrypted data payload. The encryption operation uses the recipient's public key, which typically comes from the digital certificate provided by the transaction terminal when establishing a secure channel. The encryption process follows standard public-key cryptography padding schemes to enhance security and prevent chosen-plaintext attacks. The generated encrypted data payload is a data block with a length related to the original packet but whose content is completely obfuscated. Its security depends on the secrecy of the private key; only the legitimate recipient holding the corresponding private key can perform the decryption operation.

[0080] The generation of data integrity verification codes relies on the one-way and collision-resistant properties of cryptographic hash functions. A hash function maps an encrypted data payload of arbitrary length to a fixed-length digital digest. This calculation process can be represented as:

[0081]

[0082] Where: symbol Represents the generated data integrity verification code, symbol Represents the selected cryptographic hash function (such as the SHA-256 algorithm), symbol Represents cryptographic operations performed using the recipient's public key, symbol This represents the standard transaction message packet to be encrypted. This digital digest serves as a fingerprint of data integrity; any slight modification to the encrypted data payload will cause a drastic change in the CAPTCHA, making it detectable.

[0083] The construction of a digital signature block involves binding a data integrity verification code to the device's digital certificate. This binding is achieved by digitally signing the verification code using the private key stored within the smart glasses' secure element. The signature algorithm employs a public-key cryptography-based scheme, such as an elliptic curve digital signature algorithm. This process not only signs the verification code itself but also embeds a high-precision timestamp and an incrementing sequence number to prevent replay attacks. The generated digital signature block contains structured information such as the signature value, timestamp, sequence number, and signature algorithm identifier, forming a complete authentication and non-repudiation credential. The final combination of the encrypted transaction blocks involves serializing and packaging the encrypted data payload and the digital signature block according to a predefined format. The serialization format uses type-length-value encoding or a concise binary structure to ensure unambiguous parsing. The combined data block constitutes a complete encrypted transaction block, containing the encrypted transaction content, a verification code guaranteeing content integrity, and a digital signature proving the legitimacy of the source, preparing for subsequent broadcasting and verification.

[0084] The broadcasting process of encrypted transaction blocks employs a controlled flooding mechanism to propagate the data across the set of validator nodes in the blockchain network. The smart glasses, acting as the initiating node, send the block data to several directly connected neighboring nodes. Each validator node receiving the block performs basic syntax and signature verification. After successful verification, it forwards the block to its own neighboring nodes. This propagation mechanism enables rapid diffusion throughout the network topology, and network congestion is avoided by setting a time-to-live (TTL) threshold. The broadcast protocol includes a duplicate data detection mechanism to prevent nodes from processing the same block multiple times, improving network propagation efficiency. The collection of verification response data is the starting point of the consensus process. After receiving the broadcast encrypted transaction block, each validator node independently performs a series of verification checks. These checks include verifying the validity of digital signatures, checking the freshness of timestamps, reviewing the compliance of transaction formats, and detecting double-spending attacks. Each node generates a local verification conclusion based on the check results. This conclusion, along with the node's own identity and time information, is encapsulated into a verification response data unit and sent back to the designated collection node or broadcast throughout the network, providing raw data for consensus computation.

[0085] The consensus credibility index is calculated through statistical analysis and aggregation of numerous collected verification response data. This statistical analysis considers not only the simple proportion of pass / fail responses but also introduces a weighted mechanism based on node reputation. The allocation of node reputation weights is based on the node's historical behavior records. Initially, the reputation weight of newly joined nodes may be set to a moderate value. Subsequently, it is dynamically adjusted based on the node's performance in historical verification: whenever a node's verification opinion on a transaction block aligns with the network's final consensus, its reputation weight increases appropriately; conversely, if its opinion contradicts the consensus, its weight decreases. Furthermore, the node's online time and computational contribution rate can also be used as adjustment factors. The specific value of the reputation weight can be determined through an iterative update formula, for example: Here, α is the learning rate, and the consensus metric is 1 when the node's opinion aligns with the consensus, and 0 otherwise. This mechanism gives greater weight to the opinions of long-term reliable nodes in the consensus process. Each validator node's historical accuracy, online stability, and its weight in the network are all considered, and a weighted voting algorithm is used to calculate the overall credibility score of the entire network for the validity of the transaction block. This score quantifies the network's acceptance of the transaction and is a crucial basis for constructing the verification chain. The dynamic verification chain is built based on the consensus credibility metric and the actual network topology. The construction process selects nodes with fast response times and high reputation scores as the initial nodes of the verification chain. Based on the network topology's connectivity, these high-quality nodes are linked in a logical order (e.g., geographical location, network latency) to form an initial verification path. As more verification response data flows in, the system dynamically evaluates the real-time performance of each node on the chain, replacing nodes with increased response latency or strained computing resources, and optimizing the verification path in real time to ensure the efficiency and reliability of the verification process. This dynamic adjustment mechanism allows the verification chain to adapt to changes in network conditions and maintain high consensus efficiency.

[0086] Example 4: Continuous monitoring of the dynamic verification chain and comprehensive judgment of transaction verification status, the core of which lies in real-time assessment of the strength and quality of network consensus. The monitoring process is not a simple query of the binary responses of nodes, but rather a complex heartbeat detection and event listening mechanism to track the behavioral integrity of each verification node. Heartbeat signals are transmitted between verification chain nodes at fixed intervals, and each node needs to respond with its current state summary within a specified time window, including operational metrics such as CPU load, memory usage, and the length of the pending transaction queue. Event listening focuses on capturing specific behaviors, such as a node's verification response to a new block, abnormal fluctuations in communication latency with other nodes, or sudden reversals of verification conclusions. This real-time data provides raw material for judging the health of the entire verification chain. The initial verification level is calculated based on a basic but important ratio: the ratio of the number of nodes that have passed verification to the total number of nodes. In a verification network consisting of dozens of nodes, assuming that 8 nodes are observed to return a "verification passed" signature validity confirmation signal, and the total number of currently active nodes in the entire dynamic verification chain is 10, then the initial verification level is calculated to be 80%. This ratio is a coarse-grained metric that reflects the breadth of consensus, but it does not take into account differences in network quality and the credibility of the nodes themselves.

[0087] Network latency measurement and integration is the first dimension for correcting the initial verification level. The system records the time difference between each verification node sending a request and receiving its response. This latency data is categorized and organized; for example, node A might have a latency of 120 milliseconds, node B 280 milliseconds, and node C as high as 550 milliseconds. Higher latency not only affects user experience but may also indicate that the node's network connection is unstable or under high load, compromising the timeliness and reliability of its response. Therefore, in the correction calculation, the voting weight of high-latency nodes is appropriately reduced, and a latency-based weighting function maps the latency value to a weight coefficient between 0 and 1. The introduction of node reputation weight is another key input to the correction model. Each participating node maintains a dynamically updated reputation profile in the system. This reputation value is calculated by comprehensively considering historical verification accuracy, long-term online duration, and past consensus participation behavior. A node that is consistently stable and makes accurate judgments has a higher reputation weight, and its "pass" or "reject" vote has a far greater impact on the final result than a newly joined node or one with a poor track record. The existence of reputation weights enables the consensus mechanism to resist interference from a few malicious nodes.

[0088] The weighted correction process combines the initial verification level, network latency weight, and node reputation weight. It's not a simple multiplication relationship, but a more complex aggregation function that ensures a valid final verification state can still be formed even when high-latency nodes have a high proportion, as long as high-reputation nodes reach a consensus. The corrected result is a more accurate and representative security level value, which is ultimately mapped to several discrete security level labels, such as "low," "medium," "high," or "critical." The correspondence between security level labels and risk coefficients is determined by the system's preset risk control strategy. For example, a "low" security level might correspond to an acceptable low-risk coefficient, allowing transactions to proceed quickly; while a "high" or "critical" level might trigger stricter risk control procedures, or even suspend transactions. The calculation of the risk coefficient takes into account the characteristics of the current transaction, historical fraud pattern databases, and real-time threat intelligence data streams.

[0089] Monitoring the current blockchain network load is a parallel process. The system continuously collects global performance metrics, including the overall transaction throughput (transactions per second), the depth of the unconfirmed transaction pool, the bandwidth utilization of the broadcast network, and the system resource consumption of major validator nodes. These metrics collectively depict the real-time health and processing capacity of the network. The calculation of parameter adjustments is a function of the degree of matching between the risk coefficient and the network load status. When the system identifies a high security level for the current verification status (meaning low risk) but the network load is heavy and transactions are congested, parameters may be dynamically adjusted to improve efficiency. For example, the block generation interval may be appropriately increased to reduce network pressure caused by frequent block production, or the set of validator nodes may be temporarily expanded, incorporating some backup nodes into the consensus group to share the verification workload. Conversely, if the security level indicates increased risk, the block interval may be shortened and verification requirements strengthened, even if this temporarily reduces throughput, prioritizing security. Refer to Table 1, which shows an intermediate data segment of monitoring and weighted evaluation of the status of ten validator nodes in a simplified scenario, to help illustrate the derivation process from raw data to the weighted correction result.

[0090] Table 1: Evaluation Table of Verification Node Status and Weight

[0091]

[0092] The final transaction verification status and its security level identifier are the culmination of all the above processes, generating a structured verification result report. This report clearly indicates the final status of the transaction verification (e.g., "Verification Successful," "Verification Failed," or "Manual Review Required") and includes a clear security level identifier. Furthermore, the report typically includes a summary of the key evidence used to reach this conclusion, such as opinions from critical high-reputation nodes and observed abnormal network conditions. This final output provides authoritative and risk-assessed decision-making basis for the final execution of payment instructions. The entire process demonstrates the system's ability to achieve a fine balance between security, efficiency, and stability in the face of complex and dynamic network environments.

[0093] Example 5: The final execution of a payment instruction begins with the generation of a transaction confirmation instruction, which strictly depends on dynamically adjusted blockchain network parameters. Assume that in a specific transaction scenario, the system has just completed an assessment of network load and risk factors, deciding to temporarily adjust the block generation interval from the default 2 seconds to 3 seconds, and increase the number of active verification nodes from 15 to 20 to cope with higher transaction throughput demands. Based on these new parameters, the system constructs a structured transaction confirmation instruction. This instruction includes a globally unique transaction sequence number, a consensus-based final transaction verification status (e.g., "highly trusted"), an adjusted parameter digest (such as the new block interval and node set version number), and an authorization token signed with the smart glasses' private key. The generation of this instruction signifies that the system has reached an internal consensus on the validity of the transaction and is ready for final interaction with the external world. The payment authorization signal is sent to the transaction terminal through a previously established secure payment data transmission channel, with the signal format conforming to message standards such as ISO 8583 commonly used in the financial industry. The message type identifier is set to "authorization request," and the data field is filled with the transaction amount, merchant code, smart glasses device identifier, and most importantly, a digital signature verified by the blockchain network. Before being sent, the entire message is encrypted using the session key negotiated during the channel establishment phase, and a message authentication code is attached to ensure the integrity and authenticity of the transmission process. After receiving this encrypted message, the payment processing system of the transaction terminal will perform decryption and verification operations to confirm the legitimacy of the signal source.

[0094] The processing of payment authorization signals by the transaction terminal is a multi-step verification and execution process. The terminal verifies the message authentication code, decrypts the message, checks if the transaction amount matches the initial request, and compares the smart glasses' device certificate with a blacklist database. After all checks pass, the transaction terminal connects to its acquiring bank or payment gateway and initiates a fund authorization request. During this process, the transaction terminal itself does not directly manipulate funds but acts as a bridge between the merchant system and the payment network, transforming the verification results provided by the blockchain network into an authorization request that the traditional payment network can understand and process. The generation and return of transaction completion confirmation information occurs after the payment gateway or issuing bank approves the authorization request. The approval operation generates a bank system authorization code, a transaction reference number, and a precise transaction completion timestamp. The transaction terminal encapsulates this information into a standard response message, also encrypted and protected for integrity, and sends it back to the smart glasses via a near-field communication link. This confirmation information is a crucial credential proving that the transaction has been formally accepted by the financial network; it signifies that the fund deduction process has been initiated, and the merchant can confidently deliver goods or services to the user.

[0095] Upon receiving confirmation of a completed transaction, the smart glasses immediately initiate a local transaction record update process. This update operation is characterized by high priority and atomicity. The update process combines the original payment request data, various hash values ​​and signatures generated during blockchain verification, and the recently received bank authorization code into a complete local transaction log entry. This entry is written to a protected flash memory area within the smart glasses' secure element. The write operation is subject to access control policy checks to ensure that only legitimate payment applications can modify the transaction history. The system adds a local, high-precision timestamp to this record and synchronizes it with network time, creating an immutable local audit trail. Writing transaction details to the blockchain distributed ledger is the final step in achieving transaction non-repudiation and public verifiability. This write operation is not directly performed by the smart glasses but is delegated to a verification node or a dedicated ledger access service. The smart glasses hash the complete transaction data packet stored locally (containing all details from the payment request to the bank authorization code) to generate a digest value that uniquely represents the transaction. This digest value, the smart glasses' digital signature, and the write fee are then packaged into a special "ledger write transaction." This transaction is broadcast to the blockchain network, where miners or validators include it in the next new block.

[0096] The acceptance of a new block by network consensus and its addition to the longest chain signifies that the payment details have become a permanent part of the distributed ledger. The ledger's data structure ensures that transaction records are arranged chronologically and linked together via cryptographic hash pointers. Any attempt to modify historical records will disrupt the chain's continuity and be detected by the network. At this point, any participant with the appropriate permissions (such as a merchant, regulatory body, or the user themselves) can query the blockchain and use index information such as the transaction sequence number to verify the existence and details of the transaction without relying on a centralized database. The final completion of the payment process involves state synchronization and resource cleanup. The smart glasses' user interface displays a clear transaction success message, which may include the merchant's name, transaction amount, and completion time. After confirming the complete data exchange, the near-field communication module sends a session termination command to the transaction terminal according to the protocol specifications and then automatically switches back to low-power standby mode. The smart glasses' payment application process also releases its occupied computing resources, awaiting the user's next interaction command. From the user's perspective, the entire experience involves staring at the glasses to confirm identity, then approaching the terminal device, and hearing a prompt or seeing screen feedback to complete the process. The complex process behind it is completely encapsulated in the efficient collaboration between the device and the network.

[0097] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0098] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A payment method based on smart glasses, characterized in that, The method includes the following steps: The eye pupil recognition module of the smart glasses collects the user's iris biometric data and generates an iris feature sequence; Feature point extraction and pattern analysis are performed on the iris feature sequence to obtain the identity verification result; When the authentication result reaches a predetermined confidence threshold, the near-field communication module is activated to establish a payment channel; The near-field communication module acquires payment request data sent by the transaction terminal and generates a transaction information packet. The transaction information packet is encrypted in layers using a blockchain encryption algorithm to generate an encrypted transaction block. A distributed verification chain is constructed based on the digital signature and timing characteristics of the encrypted transaction blocks. The distributed verification chain is used to perform consensus calculations on the verification results of each node to obtain the transaction verification status. The blockchain network parameters are dynamically adjusted based on the confirmation level of the transaction verification status and the network load. Based on the adjusted blockchain network parameters and transaction verification status, the final execution of the payment instruction is completed; The methods for obtaining the transaction verification status include: Monitor the changes in the verification status of each node in the dynamic verification chain; The initial verification level is obtained by calculating the ratio of the number of verified nodes to the total number of nodes. The preliminary verification level is weighted and adjusted based on network latency and node reputation weight; Output the final transaction verification status and its corresponding security level identifier; The method for dynamically adjusting blockchain network parameters includes: Analyze the risk coefficient corresponding to the security level identifier of the transaction verification status; Monitor the current load status and transaction throughput metrics of the blockchain network; Calculate the parameter adjustment amount based on the degree of matching between the risk coefficient and the network load status; Dynamically adjust block generation interval and number of verification nodes.

2. The payment method based on smart glasses as described in claim 1, characterized in that, The processing method for the iris feature sequence includes: Multi-scale filtering and noise suppression are performed on the iris feature sequence to extract the iris texture feature vector; Calculate the similarity matrix between the iris texture feature vector and the pre-stored registered template to obtain the feature matching degree; Based on the distribution and statistical characteristics of the feature matching degree, an identity verification confidence score is generated; The authentication confidence score is compared with a preset threshold, and the authentication result is output.

3. The payment method based on smart glasses as described in claim 2, characterized in that, The method for establishing the payment channel includes: When the authentication result is confirmed to be valid, the initialization protocol of the near-field communication module is triggered; The near-field communication module is used to send device authentication requests and payment capability statements. Receive communication parameters and security policies returned by the transaction terminal, and negotiate the encryption key exchange mechanism; A secure payment data transmission channel is established based on a negotiated encryption key exchange mechanism.

4. A payment method based on smart glasses as described in claim 3, characterized in that, The method for generating the transaction information package includes: The payment request data from the transaction terminal is received through the secure payment data transmission channel; Parse the format and content of the payment request data to extract the transaction amount and merchant information; The transaction amount, merchant information, device identifier, and timestamp are combined to generate the original transaction information package; The original transaction information package is standardized in data and length, and a standard transaction information package is generated.

5. A payment method based on smart glasses as described in claim 4, characterized in that, The method for generating encrypted transaction blocks by performing layered encryption processing on the transaction information packet using a blockchain encryption algorithm includes: The standard transaction information packet is first-layer encrypted using an asymmetric encryption algorithm to generate an encrypted data payload. A hash function is used to calculate a digital digest of the encrypted data payload, and a data integrity verification code is generated. The data integrity verification code is bound to the device digital certificate to generate a digital signature block; The encrypted data payload and digital signature block are combined to generate the final encrypted transaction block.

6. A payment method based on smart glasses as described in claim 5, characterized in that, The method for constructing the distributed verification chain includes: The encrypted transaction block is broadcast to the set of verification nodes in the blockchain network; Collect the verification response data returned by each verification node, including signature validity and time consistency; Calculate the consensus credibility index based on the statistical distribution of the verification response data; Based on the consensus credibility index and network topology, a dynamic verification chain is constructed.

7. A payment method based on smart glasses as described in claim 6, characterized in that, The method for completing the final execution of the payment instruction includes: Generate transaction confirmation instructions based on the adjusted blockchain network parameters; A payment authorization signal is sent to the transaction terminal through the secure payment data transmission channel; Receive transaction completion confirmation information returned by the transaction terminal and update the local transaction record; The transaction details are written into the blockchain distributed ledger to complete the payment process.

8. A payment system based on smart glasses, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of a payment method based on smart glasses as described in any one of claims 1-7.

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