A payment method based on NFC electronic tag

Abstract

The present invention relates to the field of payment method management technology, and in particular to a payment method based on NFC electronic tags. The method comprises the following steps: collecting NFC electronic tag payment signals and mobile terminal data, performing signal synchronization correction and integrating payment features, generating integrated payment feature data, mixing and processing the data with random salt values, generating mixed semi-random data, and then generating a one-time key through one-time encryption coding, extracting the feature data of the mobile terminal and using the one-time key to build an encrypted communication channel, after the one-time key is processed by random position replacement, using the encrypted channel for multi-path distribution, and simultaneously collecting the operation data of the payer's terminal, performing payment authorization verification according to the collected data, ensuring the legitimacy of the payment operation, and finally executing the payment action to complete the intelligent payment of the NFC electronic tag. The present invention realizes a safer and more efficient payment method based on NFC electronic tags.

Description

Technical Field

[0001] The present invention relates to the technical field of payment method management, and in particular to a payment method based on NFC electronic tags. Background Art

[0002] NFC technology uses wireless communication to quickly transmit and verify payment information, greatly improving the convenience and security of transactions. However, the existing NFC payment system still faces many challenges, one of which is the synchronization and security of payment signals. Especially in an environment where multiple payment devices are used at the same time, NFC electronic tag payment signals are easily interfered with, resulting in delays or failures in the payment process. In addition, due to the openness of NFC payment signals, they are vulnerable to malicious attacks or data tampering, increasing the security risks in the payment process. In existing payment systems, the fusion and encryption processing of payment signals are often relatively simple, and it is difficult to fully guarantee the privacy and security of data. The payment process involves the collaborative work of multiple data sources, such as NFC payment signals, mobile terminal data, etc. Their fusion processing is difficult to ensure the synchronization and security of different signals. Although encryption technology can provide certain protection, the frequent generation of keys and information exchange during the payment process will increase the attacker's chance of obtaining information. Summary of the invention

[0003] Based on this, it is necessary to provide a payment method based on NFC electronic tags to solve at least one of the above technical problems.

[0004] To achieve the above object, a payment method based on NFC electronic tag includes the following steps:

[0005] Step S1: Collect NFC electronic tag payment signals and mobile terminal data; perform signal synchronization correction on the NFC electronic tag payment signals to obtain synchronous payment signals; perform payment feature fusion processing on the synchronous payment signals to generate fused payment feature data;

[0006] Step S2: Mix the fused payment feature data with a random salt value to obtain mixed semi-random data; perform one-time encryption encoding on the mixed semi-random data to generate a one-time key;

[0007] Step S3: extract terminal features from the mobile terminal data to obtain terminal feature data; establish an encryption channel for the terminal feature data based on a one-time key to generate a one-time communication channel;

[0008] Step S4: Perform random position replacement processing on the one-time key to obtain a position replacement key; use the one-time communication channel to perform multi-path distribution on the position replacement key, and collect the payer's terminal operation data;

[0009] Step S5: Based on the payer's terminal operation, the NFC electronic tag payment signal is subjected to payment authorization verification to obtain payment authorization data; payment actions are performed according to the payment authorization data to execute the smart payment method based on the NFC electronic tag.

[0010] The present invention collects NFC electronic tag payment signals and mobile terminal data and performs signal synchronization correction, which can ensure that the signals collected during the payment process have a high degree of synchronization, reduce payment errors or delays caused by signal lag or interference, and effectively integrate payment signals from different sources through payment feature fusion processing to obtain fused payment feature data, thereby improving the accuracy and security of the payment process and reducing transaction failures caused by data inconsistency or signal interference. After the fused payment feature data is mixed with a random salt value, the generated mixed semi-random data is processed using a one-time key during encryption encoding. Through this process, the payment data is encrypted, which improves the security of the data transmission process and prevents malicious attackers from obtaining payment information by intercepting and cracking data streams. At the same time, the generated one-time key can effectively prevent key reuse attacks, improve the protection capability of the payment system, and reduce the risk of being attacked. By extracting terminal feature data from mobile terminal data and building an encrypted communication channel based on the one-time key, highly secure encrypted communication can be performed on the terminal device during the payment process to ensure that all The confidentiality of user information prevents data from being stolen or tampered with by illegal devices during the payment process. The generated encryption channel can effectively prevent unauthorized access and ensure the security of data exchange during the payment process. The one-time key is randomly replaced to obtain a position replacement key, and the key is distributed in multiple paths using a one-time communication channel, further improving the security of the payment signal. The position replacement key makes it difficult for attackers to predict the key distribution path, and the multi-path processing during the distribution process increases the difficulty of key leakage. By collecting the operation data of the payer's terminal, the legality and correctness of the user's operation can be monitored and verified in real time, enhancing the security of identity authentication and payment authorization in the payment process. Payment authorization verification is performed based on the payer's terminal operation to ensure that the user's identity is verified before payment. Through an accurate payment authorization verification mechanism, illegal payment requests can be effectively blocked to ensure that payment can only be continued after the user confirms the operation. The payment action is accurately controlled through the generated payment authorization data, which can achieve a fast, secure and seamless payment experience, improving the user-friendliness of the payment process and the stability of the payment system.

[0011] Preferably, step S1 comprises the following steps:

[0012] Step S11: monitoring NFC electronic tag payment behavior and mobile terminal data; capturing payment signals of NFC electronic tag payment behavior to obtain NFC electronic tag payment signals; collecting mobile terminal data;

[0013] Step S12: Perform time alignment processing on the NFC electronic tag payment signal to obtain a time-aligned payment signal; perform amplitude change analysis on the event-aligned payment signal to generate signal amplitude change data;

[0014] Step S13: Correcting the amplitude of the time-aligned payment signal based on the signal amplitude variation data to generate a synchronous payment signal;

[0015] Step S14: extracting the timestamp of the synchronous payment signal to obtain payment timestamp data; identifying the payment amount of the synchronous payment signal to generate payment amount data;

[0016] Step S15: Perform feature fusion processing on the payment timestamp data and the payment amount data to generate fused payment feature data.

[0017] The present invention ensures that the complete payment signal and related data are captured by monitoring the payment behavior of the NFC electronic tag and the data of the mobile terminal, and can accurately obtain the NFC electronic tag payment signal and the real-time mobile terminal data, provide high-quality original data for subsequent processing, improve the integrity and reliability of the payment signal collection, ensure the effective tracking and analysis of the payment process, and perform time alignment processing on the NFC electronic tag payment signal to ensure the synchronization of multiple payment signals on the time axis, thereby providing an accurate time reference for the subsequent amplitude change analysis, and further obtain the signal amplitude change data through the amplitude change analysis, thereby improving the accuracy of the signal analysis, reducing the error caused by time asynchrony or signal noise, and changing the time-aligned payment signal Amplitude correction can eliminate signal fluctuations caused by external interference or equipment differences during signal transmission, generate synchronous payment signals, ensure the stability and accuracy of payment signals, optimize the quality of payment signals, and improve the security and accuracy of the payment process. Timestamp extraction of synchronous payment signals can accurately record the time when payment occurs. At the same time, through payment amount recognition and processing, payment amount data can be accurately extracted to ensure the correctness of the payment amount, thereby improving the transparency and credibility of data in the payment process. By performing feature fusion processing on payment timestamp data and payment amount data to generate fused payment feature data, multiple key payment features can be integrated, thereby improving the intelligence and comprehensiveness of payment information processing and ensuring the accuracy and security of payment operations.

[0018] Preferably, step S2 comprises the following steps:

[0019] Step S21: performing a timestamp modulo operation on the fused payment feature data to obtain a time code; performing a pseudo-random abstract mapping on the time code to generate a basic random number;

[0020] Step S22: Mixing the basic random number with a salt value based on the fused payment feature data to generate mixed semi-random data;

[0021] Step S23: performing group connection processing on the mixed semi-random data to obtain link data; performing permutation matrix construction on the link data to generate a permutation matrix;

[0022] Step S24: performing block encryption operation on the permutation matrix to obtain encrypted data; performing key expansion on the encrypted data to generate key expansion data;

[0023] Step S25: Encapsulate the key extension data in a format to generate a one-time key.

[0024] The present invention performs a timestamp modulo operation on the fused payment feature data to obtain a time code, thereby injecting time features into the payment data, and generates a basic random number through a pseudo-random abstract mapping, thereby enhancing the unpredictability of the data, improving the security of the payment data, and providing a stronger protection measure for subsequent encryption and identity authentication, effectively preventing time synchronization attacks and data forgery, and performing salt value mixing on the basic random number based on the fused payment feature data to generate mixed semi-random data, thereby ensuring that the generated data is enhanced in terms of randomness and uniqueness, and further increasing the complexity of the data through the salt value mixing method, thereby avoiding the predictability brought by simple random number generation, and improving the security of the encryption process, and performing group connection processing on the mixed semi-random data to obtain linked data, thereby being able to effectively connect and integrate multiple data sources, thereby ensuring the safety of the data processing process. Information completeness and consistency. By constructing a permutation matrix for linked data and generating a permutation matrix, a high degree of data obfuscation and complexity is achieved, which increases the difficulty for attackers to analyze data. Block encryption operations are performed on the permutation matrix to obtain encrypted data, ensuring that the data is in an encrypted state during transmission and storage, and preventing the leakage of sensitive payment information. Block encryption operations are used to further enhance the strength of the encryption process, effectively protecting the confidentiality of payment information and ensuring the security of data throughout the payment process. Key expansion is performed on encrypted data to generate key expansion data, avoiding the risks brought by the use of static keys. Through format encapsulation, a one-time key is generated to ensure that the key used in each payment process is unique and cannot be reused, thereby improving the security and anti-cracking capabilities of the key and strengthening the protection measures for each link in the payment process.

[0025] Preferably, step S22 comprises the following steps:

[0026] Perform bitwise operations on the basic random numbers to obtain a random salt value sequence; perform XOR operations on the fused payment feature data and the random salt value sequence to generate initial mixed data;

[0027] Divide the initial mixed data into blocks to obtain a data block set;

[0028] Performing cyclic shift processing on the data block set to obtain a cyclic shift data set; performing enhanced expansion on the cyclic shift data set to generate an expanded data set;

[0029] The extended data set is subjected to nonlinear transformation to obtain a transformed data set; the transformed data set is subjected to round diffusion processing to generate mixed semi-random data.

[0030] The present invention performs bit operation processing on basic random numbers to obtain a random salt value sequence, thereby ensuring that the random number generation process is more complicated and unpredictable, enhancing the security of the generated random sequence, avoiding the predictability of conventional random number generation methods, thereby improving the protection capability of the entire encryption process, and preventing potential attack risks. The present invention performs an XOR operation on the fused payment feature data and the random salt value sequence to generate initial mixed data, thereby ensuring the strong coupling between the payment feature data and the random salt value. The XOR operation effectively increases the degree of confusion of the data, and improves the complexity and security of data mixing. The initial mixed data is divided into blocks to obtain a data block set. The block processing helps to improve the parallelism and efficiency of data processing, and at the same time enhances the operability of the data structure, avoiding the impact of data redundancy or single-point attack on the security of the entire system. The data block set is subjected to cyclic shift processing to obtain a cyclic shift data set. The cyclic shift operation enhances the pseudo-randomness of the data, and prevents attackers from attacking through common pattern analysis technology. Find the data patterns and ensure that the information in the data processing process is effectively dispersed and encrypted, thereby improving the unpredictability of the data. Strengthen and expand the cyclic shift data set to generate an extended data set. The strengthening and expansion operation effectively expands the processing range of the data, improves the data diversity and strength, and enhances the randomness of the data, making it difficult for any external attack to predict the data pattern, further increasing the strength of the encryption. Perform nonlinear transformation on the extended data set to obtain a transformed data set. A higher order of complexity is introduced through nonlinear transformation, ensuring that the data cannot be reversed by linear methods in each round of transformation, thereby enhancing the encryption degree of the data and preventing linear analysis attacks and other common cryptographic attack methods. Perform round diffusion processing on the transformed data set to generate mixed semi-random data. Round diffusion effectively increases the range of data diffusion, ensuring that each information bit in the encryption process can be fully encrypted and mixed, preventing simple reverse calculations and cryptanalysis attacks, and improving the anti-cracking ability of payment data.

[0031] Preferably, step S3 comprises the following steps:

[0032] Step S31: extracting the hardware identification of the mobile terminal data to obtain a hardware identification code; performing network feature recognition on the hardware identification code to generate a network feature code;

[0033] Step S32: performing feature fusion on the hardware identification code and the network feature code to obtain terminal feature data;

[0034] Step S33: segmenting the one-time key to obtain a key segment set; performing channel parameter mapping on the key segment set to obtain a channel parameter set;

[0035] Step S34: Building an encrypted channel for the terminal feature data based on the channel parameter set to generate a one-time communication channel.

[0036] The present invention extracts hardware identification from mobile terminal data to obtain a hardware identification code, thereby ensuring that each terminal device has unique identification information that is difficult to forge. The hardware identification code can effectively distinguish different terminals, avoid malicious counterfeiting and disguise of the device, and make the terminal identity authentication in the payment process more secure. The hardware identification code provides a solid foundation for subsequent network feature identification. Network feature identification is performed on the hardware identification code to generate a network feature code. Combining the hardware identification code with the network feature code can form more accurate terminal feature data, thereby improving the ability to accurately identify and verify the terminal identity. By identifying network features, attacks that modify or forge terminal network behaviors can be effectively prevented, and the authenticity of the terminal in the payment process is enhanced. Feature fusion is performed on the hardware identification code and the network feature code to obtain terminal feature data. Fusion of data from different sources can comprehensively reflect the multi-dimensional features of the terminal, ensuring a more comprehensive security analysis in the terminal identity authentication process, and making attacks identified through a single feature difficult to detect. The one-time key is successfully segmented and the fused data is more comprehensive and anti-interference. The key segment set is obtained by segmentation. The segmentation process effectively improves the management and security of the key, avoids the potential safety hazard caused by a single key, and ensures that the key is used more flexibly and safely. The segmented key is more difficult to be intercepted or cracked during transmission. The channel parameter mapping is performed on the key segment set to obtain a channel parameter set. The channel parameter mapping introduces a multi-level encryption method to ensure that the key segment has higher flexibility and adaptability during transmission and use, increases the complexity of the cracking process, and further improves the encryption security. An encrypted channel is established for the terminal feature data based on the channel parameter set to generate a one-time communication channel. By establishing an encrypted channel, it is ensured that the terminal feature data is encrypted and protected during transmission, and data leakage or tampering during network transmission is prevented. The encrypted channel provides an efficient and secure data exchange channel for the entire payment system, which improves the information security in the payment process.

[0037] Preferably, step S34 includes the following steps:

[0038] Step S341: Connect and bind the channel parameter set and the terminal feature data to obtain a bound data set; perform channel encryption processing on the bound data set to obtain an encrypted channel set;

[0039] Step S342: performing channel quality assessment on the encrypted channel set according to preset quality assessment data to obtain a channel assessment data set;

[0040] Step S343: performing optimal channel selection on the encrypted channel set based on the channel assessment data set to generate a preferred channel set;

[0041] Step S344: Perform handshake verification on the preferred channel set to obtain a verification data set; and activate the preferred channel set based on the verification data set to generate a one-time communication channel.

[0042] The present invention connects and binds the channel parameter set and the terminal characteristic data to obtain a binding data set, thereby ensuring the effective combination of the terminal characteristic data and the communication channel, thereby ensuring the security of information circulation. Through channel encryption processing, an encrypted channel set is generated, which further enhances the confidentiality and integrity of the data transmission process and avoids the risk of middleman attack or data leakage. Channel encryption processing is performed on the binding data set to obtain an encrypted channel set, thereby ensuring the encryption protection of the communication channel during the transmission process and avoiding any unauthorized access and data tampering. At the same time, the data security and privacy protection during the transmission process are improved, and the confidentiality and security of the user's payment information are ensured. Channel quality evaluation is performed on the encrypted channel set according to preset quality assessment data to obtain a channel evaluation data set, which evaluates the transmission quality and stability of the encrypted channel, and timely identifies potential unstable or inefficient communication through the channel quality data. By selecting higher-quality communication channels, the smoothness and timeliness of payment operations are guaranteed. Based on the channel evaluation data set, the encrypted channel set is selected to generate a preferred channel set, which avoids delays or failures in the payment process due to unstable signals or poor-quality communication channels. By selecting channels with high signal quality, the transmission efficiency of payment data is improved, and the failure rate caused by network problems in the payment process is reduced. The preferred channel set is handshake verified to obtain a verification data set, which avoids the impact of untrusted or unstable channels on the payment process. By verifying whether the channel meets the standards for secure transmission, it is effectively prevented The use of false channels and potential security threats, the preferred channel set is activated based on the verification data set to generate a one-time communication channel. The activated channel ensures the stability and efficiency of the data flow, and improves the security and accuracy of information transmission in the payment process.

[0043] Preferably, step S4 comprises the following steps:

[0044] Step S41: constructing a position index for the one-time key to obtain an index key set; performing position mapping on the index key set to generate a position mapping key set;

[0045] Step S42: performing random sequence mapping on the position mapping key set to obtain a random key sequence; performing position permutation on the random key sequence to obtain a position permutation key;

[0046] Step S43: performing path analysis on the one-time communication channel to obtain a path data set; performing bandwidth evaluation on the path data set to generate a bandwidth evaluation data set;

[0047] Step S44: based on the bandwidth evaluation data set, the location replacement key is optimally selected for path sub-packaging to obtain a path-sub-packaging data set; the path-sub-packaging data set is transmitted in parallel, and the payer's terminal operation data is collected at the same time.

[0048] The present invention constructs a position index for a one-time key to obtain an index key set, which can ensure the positioning accuracy and manageability of the key during the communication process; generates a position mapping key set through position mapping, which ensures that the key distribution and mapping rules are effectively applied, improves the flexibility and effectiveness of key management, and prevents errors or failures in the key management process; performs random sequence mapping on the position mapping key set to obtain a random key sequence; increases the randomness and unpredictability of the key through random key sequence mapping, and further enhances the security and anti-attack capability of data transmission; obtains a position replacement key by position replacement of the random key sequence, ensures the randomization and distributed transmission of the key, and effectively avoids the risk of cracking the key through a fixed pattern; performs path analysis on the one-time communication channel to obtain a path data set, which can analyze the effective path of the communication channel and optimize the path selection, thereby ensuring the efficiency of data transmission; evaluates the quality of each communication path through the path data set, ensures the reliability of data transmission, avoids unstable communication paths or communication paths with insufficient bandwidth when selecting paths, and The path data set is used to perform bandwidth evaluation and generate a bandwidth evaluation data set. This can evaluate the bandwidth of each communication path in real time, ensuring that a suitable path is selected for data transmission in an environment with large bandwidth restrictions, thereby improving the efficiency and stability of data transmission. The bandwidth evaluation data set ensures the high efficiency of transmission and avoids the impact of network bottlenecks on the timely processing of data in the payment process. Based on the bandwidth evaluation data set, the position replacement key is selected for optimal path subpackaging to obtain a path-subpackaging data set. This can dynamically select a suitable transmission path and subpackaging strategy based on the real-time bandwidth situation and path quality, thereby improving the flexibility of data transmission, optimizing the transmission strategy, and avoiding the impact of path congestion or transmission delay. The path-subpackaging data set is transmitted in parallel, and the payer's terminal operation data is collected at the same time. The speed and efficiency of data transmission are improved through parallel transmission, ensuring synchronous data transmission on multiple paths, avoiding payment delays caused by congestion of a single path, and collecting the payer's terminal operation data in real time, ensuring real-time monitoring and recording of payment behavior, and effectively protecting the integrity and security of the payment process.

[0049] Preferably, step S42 includes the following steps:

[0050] Step S421: performing random seed mapping on the position mapping key set to obtain seed data; performing sequence linear expansion processing on the seed data to generate linear sequence data;

[0051] Step S422: performing period extension on the linear sequence data to obtain periodic sequence data; performing sequence completion on the periodic sequence data to generate expanded sequence data;

[0052] Step S423: constructing mapping rules for the expanded sequence data to obtain rule mapping data; performing sequence rearrangement processing on the rule mapping data to generate a random key sequence;

[0053] Step S424: Perform double helix interleaving permutation on the random key sequence to obtain a position permutation key.

[0054] The present invention performs random seed mapping on the position mapping key set to obtain seed data, which can effectively enhance the randomness and unpredictability of key generation. By performing sequence linear expansion processing on the seed data to generate linear sequence data, the seed data can be expanded in a linear manner, further enriching the key generation rule, and improving the complexity and security of the key. The linear sequence data is periodically extended to obtain periodic sequence data, which can extend the periodicity of the sequence and ensure that the generated key has a strong periodic feature. By performing sequence completion on the periodic sequence data to generate expanded sequence data, the completed sequence helps to ensure that there is no vacancy or repetition in the key generation during the encryption process, thereby enhancing the stability and integrity of the key generation. The expanded sequence data is mapped By constructing rules and obtaining rule mapping data, the expanded sequence data can be reasonably configured according to certain mapping rules, ensuring that the key generation and mapping process has strong structure and systematicity. By performing sequence rearrangement processing on the rule mapping data, a random key sequence is generated, ensuring the randomness of key generation, while avoiding regular repetitive patterns and improving the security of the encryption process. The random key sequence is subjected to double-helix interleaving permutation to obtain a position permutation key. The double-helix interleaving permutation method makes the structure of the key sequence more complex, further improving the security of the key, avoiding the reverse deduction of the key through a simple mathematical model or rule, enhancing the key's anti-cracking ability, and ensuring that the key distribution during the encryption process is more uniform and complex.

[0055] Preferably, step S424 includes the following steps:

[0056] The random key sequence is segmented by sequence length to obtain a segmented key sequence; a spiral permutation matrix is ​​constructed on the segmented key sequence to generate a spiral matrix;

[0057] Performing vacancy filling and inversion on the spiral matrix to obtain an interleaving matrix; performing mirror image flipping processing on the interleaving matrix to generate spiral permutation matrix data;

[0058] Performing a bidirectional linked list structure component on the spiral permutation matrix data to generate linked list data; performing an alternate jump traversal on the linked list data to obtain jump traversal data;

[0059] Perform multi-layer index tree structure construction on jump traversal data to generate reconstructed index data;

[0060] Perform local replacement operation on the reconstructed index data to generate local replacement data; perform key combination processing on the local replacement data to generate a position replacement key.

[0061] The present invention obtains a segmented key sequence by segmenting the length of the random key sequence, which can effectively divide the key sequence into different length segments, improve the organization and manageability of the key, ensure that the key in the encryption process is optimized according to different lengths and structures, and avoid the easy cracking caused by a single-length key. By constructing a spiral permutation matrix for the segmented key sequence to generate a spiral matrix, the complexity of the key sequence is further increased, so that each part of the key has a unique arrangement, which increases the difficulty of cracking. The spiral matrix is ​​reversed by filling the vacancies to obtain an interleaved matrix, which can disrupt the original arrangement and increase the filled elements, thereby enhancing the complexity of the key. By performing a mirror-flipping process on the interleaved matrix to generate spiral permutation matrix data, the arrangement direction of the key is changed, which further increases the unpredictability of the key and ensures that the key is more random and unique. The spiral permutation matrix data is constructed into a bidirectional linked list structure. The invention relates to a method for generating a key storage device, generating linked list data, and utilizing the linked list structure to enhance the dynamic storage and retrieval capability of the key. The linked list data is subjected to alternate jump traversal to obtain jump traversal data, which disrupts the arrangement order of the key, makes the arrangement of the key sequence more unpredictable, and improves the security of encryption. The jump traversal data is subjected to a multi-layer index tree structure component to generate reconstructed index data. The multi-level tree structure makes the data storage of the key more efficient and flexible, and can provide a fast access path. The reconstructed index data is subjected to a local permutation operation to generate local permutation data. The local permutation further disrupts the key structure, increases the diversity and complexity of the key, and ensures the validity and protection capability of the key. The local permutation data is subjected to key combination processing to generate a position permutation key. The complexity and uniqueness of the key are further improved through key combination, and the predictability brought by the simple combination is avoided, so that the final key combination is more secure and anti-cracking.

[0062] Preferably, step S5 comprises the following steps:

[0063] Step S51: extracting behavior features from the payer's terminal operation data to obtain a behavior feature set; performing operation sequence analysis on the behavior feature set to generate a sequence data set;

[0064] Step S52: performing security rule matching on the sequence data set to obtain security score data; performing threshold determination processing on the security score data to generate verification result data;

[0065] Step S53: Perform authorization policy mapping on the verification result data to obtain authorization policy data; construct payment instructions on the authorization policy data to generate a payment instruction set;

[0066] Step S54: assigning a communication channel to the payment instruction set to obtain payment channel data; performing a transaction on the payment channel data to generate transaction result data;

[0067] Step S55: Sign and encapsulate the transaction result data to obtain payment credential data; synchronize the status of the payment credential data to generate payment completion data to execute the smart payment method based on the NFC electronic tag.

[0068] The present invention extracts and processes the behavior characteristics of the payer's terminal operation data to obtain a behavior characteristic set, which can efficiently analyze the payer's behavior characteristics, capture the user's operation habits, and improve the accuracy of payment verification. Then, the behavior characteristic set is subjected to operation sequence analysis to generate a sequence data set, which further ensures detailed analysis of user behavior from multiple dimensions, thereby providing more accurate operation mode recognition, and performs security rule matching on the sequence data set to obtain security score data. By setting rules, it can automatically identify and match corresponding security risks for different payment scenarios, and generate highly targeted security scores. Then, the security score data is subjected to threshold determination processing to generate verification result data, which ensures that the payment operation will continue only when the security standards are met, thereby reducing potential risks, and performs authorization strategy mapping on the verification result data. , obtain authorization policy data, and be able to generate appropriate authorization policies according to different payment scenarios and risk levels to ensure sufficient security control in the payment process. Then, construct payment instructions based on the authorization policy data and generate payment instruction sets to effectively manage the payment process. Allocate communication channels for the payment instruction sets to obtain payment channel data, optimize the selection of payment channels, and avoid payment risks caused by unstable or insecure channels. Perform transactions on the payment channel data to generate transaction result data to ensure that transactions are completed efficiently and securely in the appropriate channel. Sign and encapsulate the transaction result data to obtain payment voucher data, generate authenticated transaction vouchers, ensure the legality and validity of the transaction, synchronize the status of the payment voucher data, generate payment completion data, and improve the traceability and transparency of the payment process. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 A schematic diagram of the steps of a payment method based on NFC electronic tags;

[0070] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0071] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0072] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0073] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0074] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0075] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0076] To achieve this, please refer to Figures 1 to 3 , a payment method based on NFC electronic tag, comprising the following steps:

[0077] Step S1: Collect NFC electronic tag payment signals and mobile terminal data; perform signal synchronization correction on the NFC electronic tag payment signals to obtain synchronous payment signals; perform payment feature fusion processing on the synchronous payment signals to generate fused payment feature data;

[0078] Step S2: Mix the fused payment feature data with a random salt value to obtain mixed semi-random data; perform one-time encryption encoding on the mixed semi-random data to generate a one-time key;

[0079] Step S3: extract terminal features from the mobile terminal data to obtain terminal feature data; establish an encryption channel for the terminal feature data based on a one-time key to generate a one-time communication channel;

[0080] Step S4: Perform random position replacement processing on the one-time key to obtain a position replacement key; use the one-time communication channel to perform multi-path distribution on the position replacement key, and collect the payer's terminal operation data;

[0081] Step S5: Based on the payer's terminal operation, the NFC electronic tag payment signal is subjected to payment authorization verification to obtain payment authorization data; payment actions are performed according to the payment authorization data to execute the smart payment method based on the NFC electronic tag.

[0082] The present invention collects NFC electronic tag payment signals and mobile terminal data and performs signal synchronization correction, which can ensure that the signals collected during the payment process have a high degree of synchronization, reduce payment errors or delays caused by signal lag or interference, and effectively integrate payment signals from different sources through payment feature fusion processing to obtain fused payment feature data, thereby improving the accuracy and security of the payment process and reducing transaction failures caused by data inconsistency or signal interference. After the fused payment feature data is mixed with a random salt value, the generated mixed semi-random data is processed using a one-time key during encryption encoding. Through this process, the payment data is encrypted, which improves the security of the data transmission process and prevents malicious attackers from obtaining payment information by intercepting and cracking data streams. At the same time, the generated one-time key can effectively prevent key reuse attacks, improve the protection capability of the payment system, and reduce the risk of being attacked. By extracting terminal feature data from mobile terminal data and building an encrypted communication channel based on the one-time key, highly secure encrypted communication can be performed on the terminal device during the payment process to ensure that all The confidentiality of user information prevents data from being stolen or tampered with by illegal devices during the payment process. The generated encryption channel can effectively prevent unauthorized access and ensure the security of data exchange during the payment process. The one-time key is randomly replaced to obtain a position replacement key, and the key is distributed in multiple paths using a one-time communication channel, further improving the security of the payment signal. The position replacement key makes it difficult for attackers to predict the key distribution path, and the multi-path processing during the distribution process increases the difficulty of key leakage. By collecting the operation data of the payer's terminal, the legality and correctness of the user's operation can be monitored and verified in real time, enhancing the security of identity authentication and payment authorization in the payment process. Payment authorization verification is performed based on the payer's terminal operation to ensure that the user's identity is verified before payment. Through an accurate payment authorization verification mechanism, illegal payment requests can be effectively blocked to ensure that payment can only be continued after the user confirms the operation. The payment action is accurately controlled through the generated payment authorization data, which can achieve a fast, secure and seamless payment experience, improving the user-friendliness of the payment process and the stability of the payment system.

[0083] In the embodiment of the present invention, refer to Figure 1 , is a schematic diagram of the steps of a payment method based on NFC electronic tags of the present invention. In this example, the payment method based on NFC electronic tags includes the following steps:

[0084] Step S1: Collect NFC electronic tag payment signals and mobile terminal data; perform signal synchronization correction on the NFC electronic tag payment signals to obtain synchronous payment signals; perform payment feature fusion processing on the synchronous payment signals to generate fused payment feature data;

[0085] In this embodiment, the NFC electronic tag payment signal is collected by a dedicated NFC reading device. The device can identify and capture the payment signal sent by the NFC electronic tag. It usually uses the 13.56 MHz frequency band for communication. The signal includes payment request information, tag ID and timing data. Then, the terminal data is collected by using the built-in sensors of the mobile terminal, including acceleration sensors, gyroscopes, GPS information, etc., and the data is transmitted to the data processing unit through a wireless network (such as Wi-Fi or Bluetooth). During the collection process, the synchronization of the NFC signal and the terminal data is ensured, and the collected data is timestamped by the timing alignment module to ensure the consistency and timeliness of the data in the subsequent processing process. Next, the signal synchronization correction algorithm is used to adjust the NFC electronic tag payment signal to eliminate the deviation caused by signal delay or interference, and generate an accurate synchronous payment signal. Finally, the synchronous payment signal is subjected to feature fusion processing by a feature extraction-based algorithm to extract the effective payment features (such as payment amount, payment ID, timestamp, etc.) in the signal to obtain fused payment feature data.

[0086] Step S2: Mix the fused payment feature data with a random salt value to obtain mixed semi-random data; perform one-time encryption encoding on the mixed semi-random data to generate a one-time key;

[0087] In this embodiment, the fused payment feature data is subjected to preliminary numerical processing, the feature values ​​in the data (such as payment amount, timestamp, etc.) are standardized, and random salt values ​​are mixed. The salt value is a pseudo-random number generated by a dedicated random number generation algorithm (such as the Mersenne Twister algorithm). The random salt value sequence and the payment feature data are subjected to bitwise XOR operation (XOR operation) to obtain mixed semi-random data. Subsequently, a one-way hash encryption algorithm (such as SHA-256) is used to perform one-time encryption encoding on the mixed semi-random data to generate a one-time key. This process ensures that the encrypted data has high security, and each generated key is unique and irreversible. This encryption process not only improves the privacy of the data, but also prevents tampering and forgery in the middle. The generated key is used for communication encryption in subsequent steps.

[0088] Step S3: extract terminal features from the mobile terminal data to obtain terminal feature data; establish an encryption channel for the terminal feature data based on a one-time key to generate a one-time communication channel;

[0089] In this embodiment, terminal feature data is extracted based on the built-in hardware features of the mobile terminal (such as CPU model, operating system version, device unique identifier IMEI, etc.), and the original terminal data is processed using a feature extraction algorithm (such as principal component analysis PCA) to reduce redundant data and highlight the multi-dimensional representation of terminal features. Terminal feature data includes but is not limited to hardware identification, network connection status, operating system characteristics, etc. Next, the terminal feature data is encrypted using a one-time key, and an encrypted communication channel is established through an encryption algorithm (such as AES-256) to ensure the secure transmission of terminal data. The encrypted channel encrypts the terminal feature data and ensures that only valid key holders can decrypt and access the data, thereby constructing a one-time communication channel, which will be used for data interaction in subsequent payment processes.

[0090] Step S4: Perform random position replacement processing on the one-time key to obtain a position replacement key; use the one-time communication channel to perform multi-path distribution on the position replacement key, and collect the payer's terminal operation data;

[0091] In this embodiment, the one-time key is randomly permuted, and the one-time key is indexed using an algorithm based on a permutation network. The value of each bit in the key is exchanged through the position index to obtain a position permuted key, thereby ensuring the unpredictability and security of the key. Then, the position permuted key is distributed through multiple paths using a one-time communication channel. During the distribution process, the key is transmitted through multiple communication paths, thereby reducing the impact of a single path failure and improving the stability of data transmission. In this process, channel separation technology is used to ensure that the data of each path is not affected by other paths, and at the same time, the operation data of the payer's terminal (such as touch events, key input, etc.) is collected. These data are used as a reference for subsequent payment authorization verification to ensure the legitimacy and consistency of the payer's operation.

[0092] Step S5: Based on the payer's terminal operation, the NFC electronic tag payment signal is subjected to payment authorization verification to obtain payment authorization data; payment actions are performed according to the payment authorization data to execute the smart payment method based on the NFC electronic tag.

[0093] In this embodiment, behavioral analysis is performed on the payer's terminal operation data, and pattern recognition is performed on the terminal operation data through a behavioral recognition model (such as operation pattern learning based on time series analysis) to generate the payer's operation behavior characteristics, and then these behavior characteristics are matched with the payment signal data to verify whether the payer is the payment initiator. During the verification process, a security algorithm (such as dynamic threshold judgment based on risk assessment) is used to verify the payment authorization of the payment signal. By comparing the data in the payment signal (such as payment amount, timestamp, etc.) with the payer's behavior characteristics, the payment authorization data is obtained to further determine whether the payment is allowed to be executed. Based on the authorization data, a payment action instruction is generated, and the payment instruction is sent to the payment network through a communication channel, and finally the smart payment operation based on the NFC electronic tag is completed. In this process, the synchronous verification of the payment signal and the operation data provides a strong security guarantee to prevent abnormal or fraudulent behavior during the payment process.

[0094] Preferably, step S1 comprises the following steps:

[0095] Step S11: monitoring NFC electronic tag payment behavior and mobile terminal data; capturing payment signals of NFC electronic tag payment behavior to obtain NFC electronic tag payment signals; collecting mobile terminal data;

[0096] Step S12: Perform time alignment processing on the NFC electronic tag payment signal to obtain a time-aligned payment signal; perform amplitude change analysis on the event-aligned payment signal to generate signal amplitude change data;

[0097] Step S13: Correcting the amplitude of the time-aligned payment signal based on the signal amplitude variation data to generate a synchronous payment signal;

[0098] Step S14: extracting the timestamp of the synchronous payment signal to obtain payment timestamp data; identifying the payment amount of the synchronous payment signal to generate payment amount data;

[0099] Step S15: Perform feature fusion processing on the payment timestamp data and the payment amount data to generate fused payment feature data.

[0100] In this embodiment, the payment behavior of NFC (near field communication) electronic tags is monitored in real time through embedded systems or dedicated hardware devices, and the payment signals of electronic tags are collected using high-frequency NFC signal capturers. These devices receive NFC signals through a radio frequency (RF) signal receiving and processing mechanism, and record parameters such as the initiation time and frequency characteristics of the payment signals. At the same time, the payer's mobile terminal data is collected, mainly including operation data (such as touch screen input, sensor data, etc.) and device status data (such as device model, operating system version, etc.). The device data, including sensor information such as accelerometer, gyroscope, magnetometer, etc., is obtained from the payer's terminal in real time through the integrated mobile device SDK (software development kit) interface, and the timing relationship between these terminal data and NFC payment signals is further analyzed. The collected NFC electronic tag payment signals are time-aligned. First, the timestamp information of the payment signal is extracted, and the system time (such as the time of the mobile terminal or payment server) is compared with the time deviation of the NFC electronic tag payment signal through an algorithm, and the timing alignment algorithm (such as dynamic time warping (DTW) or linear interpolation algorithm) is used to correct it to ensure that all captured payment signals are accurately aligned with the system time and generate a unified time standard signal. Perform amplitude change analysis on the time-aligned payment signal, use frequency domain analysis tools such as Fourier transform (FFT) or wavelet transform to extract the amplitude change characteristics in the signal, analyze the signal intensity fluctuation, and generate the corresponding signal amplitude change data. Use numerical optimization algorithms (such as least squares method or gradient descent method) to identify the amplitude error in the signal based on the amplitude change data and make corrections. In this step, by establishing a mathematical model between the signal amplitude and the ideal state (for example, through a pre-defined signal amplitude standard), adjust the amplitude of the original payment signal, eliminate the signal amplitude fluctuation caused by equipment or environmental interference, and generate a synchronous payment signal.To this end, the signal amplitude is first smoothed to remove high-frequency noise, and then the signal amplitude is weighted corrected using the weighted average method to make the signal amplitude conform to the expected normal range. The synchronous payment signal is timestamped and extracted. The extraction algorithm is used to extract accurate payment timestamp data from the time-aligned payment signal. The time marking algorithm (such as the timestamp extraction method based on timing marking) is used to parse the key information in the payment signal (such as the frequency pulse or time mark in the signal). By analyzing the time feature points in the signal, the specific time when the payment transaction is initiated is accurately obtained. The payment amount of the synchronous payment signal is identified, and the signal is extracted through digital signal processing (DSP) technology. The payment amount information in the NFC payment signal is usually encoded in a specific data field of the NFC payment signal. By parsing these fields, pattern recognition technology (such as support vector machine (SVM) or convolutional neural network (CNN)) is used to automatically identify and extract the payment amount data, normalize the payment timestamp data, convert the timestamp into a standard format, and ensure the uniformity of time data between different payment requests. Then the payment amount data is standardized so that different payment amounts are processed under a unified scale. Then, a feature fusion algorithm (such as principal component analysis PCA or weighted average method) is used to combine the payment timestamp data with the payment amount data to generate fused payment feature data. This dataset contains both the time features and amount features of the payment, providing a complete payment feature set for subsequent payment verification to ensure the integrity and legitimacy of the payment request.

[0101] Preferably, step S2 comprises the following steps:

[0102] Step S21: performing a timestamp modulo operation on the fused payment feature data to obtain a time code; performing a pseudo-random abstract mapping on the time code to generate a basic random number;

[0103] Step S22: Mixing the basic random number with a salt value based on the fused payment feature data to generate mixed semi-random data;

[0104] Step S23: performing group connection processing on the mixed semi-random data to obtain link data; performing permutation matrix construction on the link data to generate a permutation matrix;

[0105] Step S24: performing block encryption operation on the permutation matrix to obtain encrypted data; performing key expansion on the encrypted data to generate key expansion data;

[0106] Step S25: Encapsulate the key extension data in a format to generate a one-time key.

[0107] As an example of the present invention, see Figure 2 In this example, step S2 includes:

[0108] Step S21: performing a timestamp modulo operation on the fused payment feature data to obtain a time code; performing a pseudo-random abstract mapping on the time code to generate a basic random number;

[0109] In this embodiment, the timestamp part of the extracted fusion payment feature data is mapped to a predetermined numerical range using a modular operation. For example, the timestamp is modulo a specific base (such as 60 seconds) using a modular operation to obtain a new time code data. The specific operation includes: dividing the timestamp value by a set base (such as 60) to obtain a remainder, which is the new time code data. The time code represents the relative time information of the payment request and can provide a basis for subsequent pseudo-random mapping. In this step, the modular operation method used can ensure that the timestamp is mapped within a specific range, which is convenient for subsequent processing and analysis, and ensures the consistency and availability of the data. Next, the time code data is subjected to pseudo-random abstract mapping, and the time code data is mapped into a basic random number through a pseudo-random number generation algorithm (such as a linear congruential method) for the subsequent data encryption process.

[0110] Step S22: Mixing the basic random number with a salt value based on the fused payment feature data to generate mixed semi-random data;

[0111] In this embodiment, the basic random number obtained is mixed with other elements in the data (such as payment amount, device ID, etc.) with a salt value to generate mixed semi-random data. The salt value mixing process includes: extracting a randomly generated salt value from the fused payment feature data, which can be generated based on data such as timestamp, payment amount or device identification, and then performing an XOR operation (XOR) with the basic random number. Through this operation, the basic random number is mixed with the salt value to obtain new mixed semi-random data. This mixing process increases the complexity of the data and ensures the uniqueness of each encryption process, avoiding the patterned characteristics brought by simple random number generation methods. The mixed data will then be used for subsequent encryption operations, increasing the security of the payment process.

[0112] Step S23: performing group connection processing on the mixed semi-random data to obtain link data; performing permutation matrix construction on the link data to generate a permutation matrix;

[0113] In this embodiment, the mixed semi-random data is divided into a certain size (e.g., 128 bits or 256 bits), each group of data contains multiple bytes, and then these data are connected according to specific rules. For example, the data can be divided into several groups by sorting by block size, and the end of each group is connected to the beginning of the next group to form a continuous data block. The connected data will form a data link structure for subsequent processing. During the grouping process, each data block can be processed by byte exchange or other forms of data rearrangement to ensure that there is no obvious correlation between each data block. Afterwards, the data is passed to the permutation matrix construction stage, and the data is further encrypted and adjusted in a matrixed manner.

[0114] Step S24: performing block encryption operation on the permutation matrix to obtain encrypted data; performing key expansion on the encrypted data to generate key expansion data;

[0115] In this embodiment, according to the structure of the data block, a matrix of a fixed size is created, such as a matrix of 8x8 or 16x16, and the data blocks are filled into the matrix one by one. The constructed matrix is ​​encrypted by a permutation algorithm, and commonly used encryption methods include AES (Advanced Encryption Standard) or DES (Data Encryption Standard) algorithms. The encrypted data is generated by permuting the data in the matrix, changing the order of the original data and performing byte-by-byte encryption processing. During the encryption process, each element in the matrix will be operated with the key, and the contents in the matrix will be encrypted one by one using a block encryption algorithm to obtain encrypted data. After the matrix encryption is completed, the output encrypted data is the ciphertext obtained by the block encryption operation, which has high security and prevents data leakage or tampering. The initial key is extracted from the encrypted data, and the key is processed by a specific key expansion algorithm (such as the key expansion algorithm of AES) to generate multiple subkeys. The key expansion process includes: multiple rounds of processing of the initial key (such as through cyclic shift, XOR operation, substitution operation, etc.) to generate a set of key subsets that match the length of the encrypted data. Each key subset can be used for different encryption operations, which increases the complexity and security of the encryption process. During the key expansion process, the algorithm used ensures that each generated subkey is independent and unpredictable, thereby improving the anti-attack ability of the encryption process. The expanded key will be used to generate the final encryption key, ensuring the irreversibility and high security of the encryption operation.

[0116] Step S25: Encapsulate the key extension data in a format to generate a one-time key.

[0117] In this embodiment, the encapsulation format of the key (such as PKCS7, ASN.1, etc.) is determined according to the type and usage requirements of the key. Then, the expanded key data is encoded and packaged according to a predetermined format to ensure that the key data will not be tampered with during transmission and storage. The encapsulation process includes encoding, compressing and appending verification information to the key data to ensure the integrity of the data. In this step, the security of the key during transmission can be ensured through the encapsulation standard of the encryption algorithm to prevent it from being stolen or tampered with by a middleman during the process. The encapsulated key eventually generates a one-time key for use in subsequent operations during the payment process to ensure data security during the payment process.

[0118] Preferably, step S22 comprises the following steps:

[0119] Perform bitwise operations on the basic random numbers to obtain a random salt value sequence; perform XOR operations on the fused payment feature data and the random salt value sequence to generate initial mixed data;

[0120] Divide the initial mixed data into blocks to obtain a data block set;

[0121] Performing cyclic shift processing on the data block set to obtain a cyclic shift data set; performing enhanced expansion on the cyclic shift data set to generate an expanded data set;

[0122] The extended data set is subjected to nonlinear transformation to obtain a transformed data set; the transformed data set is subjected to round diffusion processing to generate mixed semi-random data.

[0123] In this embodiment, the salt value sequence is generated by bit operation, and the specific operation is to perform bitwise AND, bitwise OR, or bitwise XOR operation on each bit of the basic random number and a specific constant. The constant used in the operation can be a preset fixed value, or it can be dynamically generated according to the current transaction time, device information, or payment amount to increase the randomness and complexity of the salt value sequence. For example, by performing an XOR operation on each bit of the basic random number and combining it with a set of dynamically generated constants, a new random salt value sequence will be obtained, which will be used for subsequent mixing processing after generation. In this step, the bit operation ensures the unpredictability and uniqueness of the salt value sequence generated each time, thereby enhancing the security and anti-tampering ability of the data, extracting key information in the fused payment feature data, such as the payment amount, payment time, device information, etc., which will be XORed with the random salt value sequence bit by bit. In actual operation, each bit of the payment feature data and the salt value sequence is compared bit by bit. If the two bits are the same, the result is 0, and if they are different, the result is 1. In this way, each bit of the payment feature data and the salt value sequence is encrypted and mixed to obtain a set of unpredictable initial mixed data. The initial mixed data is divided into several equal-length data blocks according to the size of the data and the required block size. For example, assuming that the initial mixed data is 512 bits, it can be divided into 4 128-bit data blocks. The data block process can be performed by simply dividing the initial mixed data by bits or cutting it by a sliding window. Each data block is operated independently in the subsequent processing process to ensure the privacy and independence of the data. A fixed bit number (such as 8 bits or 16 bits) is selected to perform a circular shift operation on the data in each data block. The circular shift operation refers to moving the bits in the data block to the left or right one by one, and relocating the excess part to the other end of the data block. For example, if the data block length is 128 bits, and a left shift of 8 bits is selected, the first 8 bits in the original data block will be moved to the end of the data block, and the following data will be moved forward. Through cyclic shift, the information in the data block is disrupted to form a new cyclic shift data set, and a fixed expansion factor is applied to each data block, such as using a hash algorithm (such as SHA-256) to hash the data block, and then splicing the hash result with the original data block to generate an extended data set. The expansion factor can be dynamically generated according to the different stages of the payment process, which increases the diversity and complexity of data processing, using non-linear encryption algorithms (such as S-box transformation) or other non-linear mathematical transformation methods.The purpose of nonlinear transformation is to make the relationship between input data and output data unpredictable through complex mathematical operations, thereby increasing the complexity of the data. The S-box (substitution box) in the AES encryption algorithm is used for transformation, and each data block in the extended data set is mapped to the predefined S-box to generate a transformed data set. Each data block in the transformed data set is iteratively processed according to a specific number of rounds. The round diffusion method includes multiple rounds of encryption, data substitution and complex arithmetic operations. In each round of processing, the data block is mathematically transformed, such as XOR operation, addition operation, rotation shift, etc., until the predetermined round is reached. The output of each round of operation is used as the input of the next round of operation, and the final result is mixed semi-random data.

[0124] Preferably, step S3 comprises the following steps:

[0125] Step S31: extracting the hardware identification of the mobile terminal data to obtain a hardware identification code; performing network feature recognition on the hardware identification code to generate a network feature code;

[0126] Step S32: performing feature fusion on the hardware identification code and the network feature code to obtain terminal feature data;

[0127] Step S33: segmenting the one-time key to obtain a key segment set; performing channel parameter mapping on the key segment set to obtain a channel parameter set;

[0128] Step S34: Building an encrypted channel for the terminal feature data based on the channel parameter set to generate a one-time communication channel.

[0129] As an example of the present invention, see Figure 3 In this example, step S3 includes:

[0130] Step S31: extracting the hardware identification of the mobile terminal data to obtain a hardware identification code; performing network feature recognition on the hardware identification code to generate a network feature code;

[0131] In this embodiment, the unique identifier (such as device ID, serial number, etc.) of the mobile terminal is read through the hardware interface (such as CPU, motherboard or NFC module), and the identifier is used as the hardware identification code. The hardware identification code is usually stored in the firmware or embedded memory of the mobile terminal, using a fixed encoding format (such as UUID). In the extraction process, the unique identifier of the device is extracted by scanning and reading the hardware interface to ensure that each terminal has a unique hardware identification code. The identification code is used to distinguish different devices in the subsequent data processing process. Assuming that the extracted hardware identification code is "1234567890ABCDEF", the identification code will be used as the identifier of the device. The network feature recognition processing is performed on the identification code. By analyzing the network behavior, connection mode, accessed network service or protocol of the device, the network feature code is generated. By capturing the IP address, MAC address, frequency of network connection, protocol type and other information when the device is connected to the network, the network feature code is generated in combination with the hardware identification code. For example, if the device is connected via Wi-Fi and uses a specific router or VPN, the network feature code can be generated based on these network connection details.

[0132] Step S32: performing feature fusion on the hardware identification code and the network feature code to obtain terminal feature data;

[0133] In this embodiment, the hardware identification code and the network feature code are fused to obtain terminal feature data, the hardware identification code and the network feature code are spliced ​​into a large feature vector, and processed using a hash algorithm or an encryption algorithm to generate final terminal feature data, and a hash algorithm (such as SHA-256) is used to merge the hardware identification code and the network feature code into a long string, and then the string is hashed to obtain terminal feature data of a fixed length. For example, assuming that the hardware identification code is "1234567890ABCDEF" and the network feature code is "192.168.1.1_XXWiFi", these two codes are processed by the hash algorithm to generate a unique terminal feature data, such as "abcdef1234567890", which will be used as the basis for device identification in subsequent security operations.

[0134] Step S33: segmenting the one-time key to obtain a key segment set; performing channel parameter mapping on the key segment set to obtain a channel parameter set;

[0135] In this embodiment, the one-time key is cut into multiple smaller key segments according to a predetermined length (e.g., 128 bits or 256 bits), and each key segment can be used independently. This operation is performed through a fixed segmentation algorithm, for example, the one-time key is divided into multiple parts according to a fixed length to generate a key segment set. For example, assuming that the one-time key is "0123456789ABCDEF0123456789ABCDEF", it is segmented into four 128-bit key segments, such as "0123456789ABCDEF", "0123456789ABCDEF", "0123456789ABCDEF", "0123456789ABCDEF", and each key segment is parameterized and mapped according to the channel used in the communication process. For example, each part of the key segment set is associated with the channel characteristics of the payment communication (such as transmission speed, delay, bandwidth, etc.) through a specific mapping function to generate a corresponding channel parameter set. These channel parameters are used in the subsequent secure encryption and transmission process to ensure the security and effectiveness of the data. For example, assuming the key segment is "0123456789ABCDEF", its mapped channel parameter set includes transmission delay time "100ms" and transmission rate "1Mbps". These channel characteristics are bound to the key segment to form an unreplicable security chain.

[0136] Step S34: Building an encrypted channel for the terminal feature data based on the channel parameter set to generate a one-time communication channel.

[0137] In this embodiment, according to the characteristics of the channel parameter set (such as delay, bandwidth, etc.), a suitable encryption algorithm (such as AES or RSA) is selected and encryption parameters are configured, and the terminal feature data is encrypted through the selected encryption channel to form a one-time communication channel. This channel has a high degree of security and uniqueness, and ensures that encrypted data can only be transmitted and decrypted through this channel. For example, if the bandwidth in the channel parameter set is "2Mbps" and the delay is "50ms", the encryption process will take these network characteristics into account, dynamically adjust the processing method of the encryption algorithm, ensure that the encryption process matches the network environment, thereby optimizing the security and efficiency of data transmission, and finally generating a one-time communication channel. This communication channel will be used for subsequent data transmission and payment processes to ensure that each payment request is securely transmitted through an independent and unique encryption channel.

[0138] Preferably, step S34 includes the following steps:

[0139] Step S341: Connect and bind the channel parameter set and the terminal feature data to obtain a bound data set; perform channel encryption processing on the bound data set to obtain an encrypted channel set;

[0140] Step S342: performing channel quality assessment on the encrypted channel set according to preset quality assessment data to obtain a channel assessment data set;

[0141] Step S343: performing optimal channel selection on the encrypted channel set based on the channel assessment data set to generate a preferred channel set;

[0142] Step S344: Perform handshake verification on the preferred channel set to obtain a verification data set; and activate the preferred channel set based on the verification data set to generate a one-time communication channel.

[0143] In this embodiment, according to the network characteristics of each channel, such as bandwidth, delay, packet loss rate, etc., a suitable encryption protocol (such as AES or ChaCha20) is selected to encrypt the terminal feature data. Then, the encrypted terminal feature data is associated with each item in the channel parameter set to form a bound data set. This process ensures that the encrypted data is closely integrated with the channel characteristics through methods such as hash operations, encrypted links or digital signatures, so that the final generated data set contains not only channel information, but also encrypted terminal feature data. For example, if the channel parameter set includes bandwidth of 50Mbps, latency of 15ms, channel type of Wi-Fi, and terminal feature data of "6a098bcdc8d33b890612f47b10404597", the bound data set is "Wi-Fi-50Mbps-15ms-6a098bcdc8d33b890612f47b10404597". Each channel in the encrypted channel set is tested through network measurement tools (such as iperf, Wireshark, etc.) to obtain quality indicators such as throughput and latency. Then, the test results are compared with the preset standards to generate a channel evaluation data set, which contains the quality evaluation results of each channel. For example, if the throughput of a channel is 50Mbps, the delay is 10ms, and the packet loss rate is 2%, then the channel evaluation data of the channel is "throughput 50Mbps, delay 10ms, packet loss rate 2%". The channel evaluation data set is the evaluation result set of all test channels. According to the indicators in the channel evaluation data set (such as throughput, delay, packet loss rate, etc.), the sorting algorithm (such as quick sort, heap sort, etc.) is used to sort each channel and select the channel with the best quality. The preferred criteria can be the maximum throughput, the minimum delay, or the lowest packet loss rate. For example, if the two channels in the evaluation data set are "throughput 40Mbps, delay 20ms, packet loss rate 1%" and "throughput 50Mbps, delay 15ms, packet loss rate 2%", the second channel is selected as the preferred channel. The preferred channel set contains all encrypted channels that meet the quality standards. The sender initiates a handshake request to the receiver. The receiver responds after verifying the sender's identity and the security of the channel to establish an encrypted channel. A verification data set will be generated during the handshake process. The verification data set includes the response code of the handshake protocol, the encryption certificate, the identity verification result, etc. For example, if the preferred channel is a Wi-Fi network, the TLS protocol is used for identity authentication during the handshake verification process, and a verification data set is finally generated, such as "protocol TLS, identity authentication successful, channel security authentication passed". According to the verification data set generated during the handshake verification process, it is confirmed that all verification steps have passed and the channel security is guaranteed. Then, the encryption channel is started by activating the protocol (such as starting the encryption key exchange, establishing the data transmission protocol, etc.) to ensure that the data can be transmitted in a secure environment.Once activated, the channel becomes a one-time communication channel, which is used to encrypt the transmission of payment data during the payment process. For example, if the verification data set indicates that the handshake verification of the Wi-Fi network is successful, the channel will be activated through the key exchange protocol, and finally a one-time communication channel will be generated.

[0144] Preferably, step S4 comprises the following steps:

[0145] Step S41: constructing a position index for the one-time key to obtain an index key set; performing position mapping on the index key set to generate a position mapping key set;

[0146] Step S42: performing random sequence mapping on the position mapping key set to obtain a random key sequence; performing position permutation on the random key sequence to obtain a position permutation key;

[0147] Step S43: performing path analysis on the one-time communication channel to obtain a path data set; performing bandwidth evaluation on the path data set to generate a bandwidth evaluation data set;

[0148] Step S44: based on the bandwidth evaluation data set, the location replacement key is optimally selected for path sub-packaging to obtain a path-sub-packaging data set; the path-sub-packaging data set is transmitted in parallel, and the payer's terminal operation data is collected at the same time.

[0149] In this embodiment, the one-time key is input into the position index building module, and the one-time key is indexed and assigned using a hash algorithm (such as SHA-256) to generate an index key set, which contains a set of key values ​​divided by position, and each key value is associated with specific position information. Then, a position mapping algorithm (such as linear mapping or nonlinear mapping) is used to perform position mapping on each key in the index key set. The mapping process associates the key with the specific position that needs to be accessed during the encrypted transmission process to generate a position mapping key set. Each key in the position mapping key set carries the corresponding mapping position information. The position mapping key set is input into the random sequence mapping module, and a pseudo-random number generator (such as the Mersenne Twister algorithm) is used to generate a random sequence. This sequence corresponds one-to-one to the keys in the position mapping key set, ensuring that the order of the keys is adjusted in a random manner. Subsequently, a permutation algorithm (such as permutation and combination method or finite field operation) is used to permute the position of the generated random key sequence. The permutation process adjusts the keys in the sequence according to the preset rules to generate position permutation keys. Each position permutation key is mapped to its corresponding position and the position of the random sequence. When performing path analysis on a one-time communication channel, a network analysis tool (such as Wireshark or a custom path analysis algorithm) is used to gradually evaluate the data transmission path in the channel to determine the network characteristics of each data transmission path, including key parameters such as delay, bandwidth, and packet loss rate. Through these analyses, a path data set is generated, which contains detailed information on each data path. Then, the bandwidth of each path is evaluated based on the parameters in the path data set, and the maximum available bandwidth of each path is calculated using a bandwidth evaluation algorithm (such as TCP throughput measurement, transmission rate estimation, etc.), and a bandwidth evaluation data set is generated based on the bandwidth information. The bandwidth evaluation data set provides a quantitative analysis of the bandwidth status of each path. Based on the bandwidth evaluation data set, a bandwidth optimization algorithm (such as the shortest path algorithm and the Dijkstra algorithm) is used to optimize each communication path and select the path with the best bandwidth as the main channel for data transmission. Subsequently, the position replacement key is divided into multiple data packets, and each data packet is processed by path according to the preferred path to generate a path-subpacket data set, which contains each data packet and its corresponding transmission path information. In this process, parallel transmission technology (such as multi-threaded or multi-channel transmission) is used to transmit multiple data packets at the same time, and the transmission delay is reduced through parallel processing to ensure efficient data transmission. While the data is being transmitted, the operation data of the terminal device is collected (such as monitoring the payer's operation status, clicks, verification, etc. through sensors or NFC modules), and the operation data is associated with the transmission process to ensure the integrity and security of the payment process.

[0150] Preferably, step S42 includes the following steps:

[0151] Step S421: performing random seed mapping on the position mapping key set to obtain seed data; performing sequence linear expansion processing on the seed data to generate linear sequence data;

[0152] Step S422: performing period extension on the linear sequence data to obtain periodic sequence data; performing sequence completion on the periodic sequence data to generate expanded sequence data;

[0153] Step S423: constructing mapping rules for the expanded sequence data to obtain rule mapping data; performing sequence rearrangement processing on the rule mapping data to generate a random key sequence;

[0154] Step S424: Perform double helix interleaving permutation on the random key sequence to obtain a position permutation key.

[0155] In this embodiment, a preset random number generator (such as Mersenne Twister or linear congruential generator) is used to perform random seed mapping on each key value in the position mapping key set, and the mapping process generates unique seed data according to the hash value of the key. The seed data is a set of randomly generated numbers, representing the initial random seed corresponding to each key in the position mapping key set. Next, the generated seed data is subjected to sequence linear expansion processing. This process uses a linear function (such as f(x) = ax + b) to convert the seed data into a set of equally spaced values ​​to generate linear sequence data. Each linear sequence data element is calculated by the relationship between its previous element and a fixed increment to form a continuously increasing sequence, and the linear sequence data is periodically extended. The extension process uses a preset periodic rule (such as a period of N) to repeatedly extend the linear sequence data to generate a periodic data sequence. After the periodic extension, each part of the data sequence will be repeated in the entire sequence according to the same rule, so that the data pattern of the sequence is extended within the periodic range. Next, based on the generated periodic sequence data, it is expanded by sequence completion technology. The completion process uses a difference algorithm (such as Lagrange interpolation or linear interpolation) to fill in the missing parts to ensure that the generated expanded sequence data is continuous and without omissions. In the process of generating the expanded sequence data, the integrity of the sequence is maintained by filling in the missing data points so that the entire sequence can be evenly distributed. Based on the expanded sequence data, the mapping rules are designed and established. The mapping rules are based on each data point of the expanded sequence and its corresponding position. A new position is assigned to each data point through a specific mapping algorithm (such as modular operation or permutation operation), thereby constructing rule mapping data. This rule mapping data is used to indicate the position or replacement value of each data point in the expanded sequence in the final sequence. Then, based on the rule mapping data, the expanded sequence data is sequence rearranged, and the permutation rearrangement algorithm (such as bubble sort, quick sort, etc.) is used to rearrange the expanded sequence according to the indication of the mapping rule, and finally a random key sequence is generated. During the rearrangement process, each element of the sequence will be rearranged to a specified position, so that the generated random key sequence is highly random in order and position. The random key sequence is divided into two parts, each forming two spiral sequences, where the first part is arranged in order and the second part is arranged according to a specific interleaving rule, so that the two parts of the key are interleaved alternately. The interleaving process sets spiral parameters (such as step size, initial position, etc.) and alternately exchanges the data elements in each key sequence according to predetermined rules to form a double helix interleaved structure. This structure increases the complexity and unpredictability of the key sequence by alternating operations on the two sequences. Finally, the obtained double helix interleaved permutation key, as a position permutation key, has high security and can effectively resist common attack methods such as brute force cracking or key prediction.

[0156] Preferably, step S424 includes the following steps:

[0157] The random key sequence is segmented by sequence length to obtain a segmented key sequence; a spiral permutation matrix is ​​constructed on the segmented key sequence to generate a spiral matrix;

[0158] Performing vacancy filling and inversion on the spiral matrix to obtain an interleaving matrix; performing mirror image flipping processing on the interleaving matrix to generate spiral permutation matrix data;

[0159] Performing a bidirectional linked list structure component on the spiral permutation matrix data to generate linked list data; performing an alternate jump traversal on the linked list data to obtain jump traversal data;

[0160] Perform multi-layer index tree structure construction on jump traversal data to generate reconstructed index data;

[0161] Perform local replacement operation on the reconstructed index data to generate local replacement data; perform key combination processing on the local replacement data to generate a position replacement key.

[0162] In this embodiment, the random key sequence is segmented by the length of the sequence, and the length of each segment can be set according to the specific encryption requirements, such as 8 data units per segment. The entire key sequence is divided according to the set length to obtain several segmented key sequences of the same length, which will be used for subsequent permutation and rearrangement operations. Next, a spiral permutation matrix construction method is adopted to arrange each segmented key sequence into a matrix in a spiral manner. The starting point of the spiral is usually located at the center of the matrix, and then it is gradually expanded in a clockwise or counterclockwise direction to fill each position of the matrix, and finally a complete spiral matrix is ​​formed. The size of the matrix is ​​determined according to the number of segments and the requirements of the key length. Check whether there are vacancies in the spiral matrix. These vacancies are generated because the size of the matrix does not completely match the length of the key sequence. Use a filling algorithm (such as nearest neighbor filling, zero filling, etc.) to fill these vacancies. The filling content can choose the same value as other data in the matrix, or choose a random number that does not repeat other data to ensure that there are no vacancies in the matrix. Then, the filled matrix is ​​inverted. During the inversion process, row inversion or column inversion can be selected to ensure that the position of the data elements in the matrix is ​​flipped, thereby disrupting the original arrangement order. The interleaved matrix is ​​mirror-flipped. Mirror flipping is to reverse the interleaved matrix along the central symmetry axis. The flipping method can be selected to flip horizontally, vertically or along the diagonal. After flipping, each data element of the matrix will exchange its relative position to generate a new matrix. The matrix has a different data order compared to the original interleaved matrix. The spiral permutation matrix data is converted into a bidirectional linked list structure. The bidirectional linked list is a data structure in which each node contains two pointers, which point to the previous node and the next node of the current node respectively. In this way, each data element in the matrix is ​​constructed as a node in the linked list, and the linked list nodes are linked in the order of the rows or columns of the matrix. The generated linked list data is traversed by alternating jumps. Alternating jump traversal means that when traversing the linked list, a fixed number of nodes are skipped each time, for example, 2 nodes are skipped each time, and the third node is directly accessed. By alternating jump traversal, not only can the traversal order be changed, but also the data obtained during the traversal process can be made more random and dispersed, thereby increasing the security of the key. The data after jump traversal is jump traversal data, which is further converted into a multi-layer index tree structure. The multi-layer index tree is a tree data structure, in which each layer contains several nodes, each node points to the node of the lower layer, and finally all data elements are organized on the leaf nodes of the tree.During the construction process, the height of the tree is first determined according to the number of jump traversal data and the hierarchical relationship, and the data is evenly distributed in each node. The data is added to the tree structure layer by layer in a recursive or iterative manner, and finally a multi-layer index tree is formed. The generated reconstructed index data reflects the multi-level relationship of the jump traversal data. The generated reconstructed index data is subjected to local permutation operation. Local permutation refers to the local arrangement or exchange of data according to certain rules within each layer or each node of the tree structure. The rules adopted by the local permutation operation can be numerical comparison of data, position exchange, or operation according to the mapping rules generated by the random algorithm. Through this local permutation, the order of data in the tree structure changes, but the accessibility and consistency of the overall structure are still maintained. Finally, local permutation data is generated, and the generated local permutation data is subjected to key combination processing. The process of key combination processing is to merge different parts of the local permutation data through specified combination rules (such as addition, XOR, splicing, etc.) to form the final key data. During the combination processing, each data element is combined multiple times, so that the number of bits and content of each key undergo multiple changes, ensuring that the generated key is complex and has high security. The final result is the position permutation key.

[0163] Preferably, step S5 comprises the following steps:

[0164] Step S51: extracting behavior features from the payer's terminal operation data to obtain a behavior feature set; performing operation sequence analysis on the behavior feature set to generate a sequence data set;

[0165] Step S52: performing security rule matching on the sequence data set to obtain security score data; performing threshold determination processing on the security score data to generate verification result data;

[0166] Step S53: Perform authorization policy mapping on the verification result data to obtain authorization policy data; construct payment instructions on the authorization policy data to generate a payment instruction set;

[0167] Step S54: assigning a communication channel to the payment instruction set to obtain payment channel data; performing a transaction on the payment channel data to generate transaction result data;

[0168] Step S55: Sign and encapsulate the transaction result data to obtain payment credential data; synchronize the status of the payment credential data to generate payment completion data to execute the smart payment method based on the NFC electronic tag.

[0169] In this embodiment, operation data is collected from the payee terminal, and the operation data includes the password entered by the user, the card swiping time, the transaction amount, the contact position and time of the NFC electronic tag, etc. These data are collected and transmitted to the server through the NFC module at the terminal. In the behavior feature extraction and processing stage, the time series analysis method is used to process the data, and key behavior features such as operation frequency, duration, and the rhythm of user input are extracted, and these features are standardized to form a feature set. Next, the behavior feature set is subjected to operation sequence analysis, and the continuous user behavior data is divided into multiple subsequences using the sliding window technology, and the subsequences are classified using the clustering algorithm to obtain a sequence data set, where each data subset represents a series of continuous payee operation behaviors, such as the time interval for entering the password, whether there are abnormal operations during the transaction, etc. Each operation subset in the sequence data set will be matched with the preset security rules. The security rules can be constructed based on the historical transaction behavior model, password input mode, transaction amount and other features. The rule engine technology is used to compare the behavior data with the preset parameters in the security rules to obtain a security score, and the score is weighted based on the number of rule matches, the accuracy of the match and other factors. After the security score is calculated, a threshold determination process is performed to set a threshold for the security score. For example, a score above 80 is safe, a score below 50 is abnormal, and a score in the middle requires manual review or secondary verification. The determination result is "verification passed" or "verification failed". The verification result data is first mapped to the authorization strategy according to different results (such as "verification passed" or "verification failed"). The mapping rules are adjusted according to different payment security levels. For example, if the verification is passed and the transaction amount is small, a quick payment method can be used. If the amount is large, additional verification or multiple identity authentication is required. By looking up the mapping table or rule engine, the verification result is matched with the corresponding authorization policy to generate authorization policy data. The authorization policy data specifically includes information such as payment method, verification requirements, and payment limit. Subsequently, based on the authorization policy data, a payment instruction set is constructed. The payment instruction set contains specific payment instructions and execution logic, such as payment amount, target account information, payment method, etc. The payment instruction set will be transmitted to the payment network or processing center to start the payment process. The data in the payment instruction set first needs to be allocated to a communication channel. The selection of the communication channel is determined based on factors such as the real-time load of the network and the urgency of the payment instruction. The most appropriate channel is selected for data transmission to ensure that the data during the communication process is not interfered with and is safe and reliable. The allocated communication channel is used to send the payment instruction to the transaction platform or banking system. In this process, data exchange between the NFC tag and the payment terminal is involved, or instruction transmission is carried out through a dedicated encrypted channel.Next, the payment channel data will interact with the processing system of the transaction platform to perform payment operations and generate transaction result data, including payment success or failure status, transaction amount, payment time and other information. If the payment is successful, the transaction result data will record the detailed information of the payment; if the payment fails, the result data will indicate the reason for the failure. The transaction result data will first be processed by signature encapsulation, and the transaction result data will be digitally signed using an asymmetric encryption algorithm to ensure the integrity and tamper-proofness of the transaction data, and generate payment voucher data, which contains all necessary information of the transaction, such as payment amount, payment time, transaction status, etc., and is accompanied by an encrypted signature. The signed payment voucher data will be synchronized with the payer's terminal through the NFC electronic tag to ensure that the payer can view the complete transaction voucher. Next, the payment voucher data will be synchronized through the network or NFC technology, and the status will be updated with the bank system, merchant system and payment platform to ensure that the information of all relevant parties is consistent, and the payment completion data will be generated. The payment completion data records the final transaction status and marks the completion of the entire payment process. All transaction information will be saved in the transaction log to ensure the traceability and compliance of the payment process.

Claims

1. A payment method based on NFC electronic tags, characterized in that: The following steps are involved: Step S1: Collect NFC electronic tag payment signals and mobile terminal data; perform signal synchronization correction on the NFC electronic tag payment signals to obtain synchronous payment signals; perform payment feature fusion processing on the synchronous payment signals to generate fused payment feature data; Step S2: Mix the fused payment feature data with a random salt value to obtain mixed semi-random data; perform one-time encryption encoding on the mixed semi-random data to generate a one-time key; Step S3: extracting terminal features from the mobile terminal data to obtain terminal feature data; An encrypted channel is established for the terminal feature data based on the one-time key to generate a one-time communication channel; wherein step S3 includes the following steps: Step S31: extracting the hardware identification of the mobile terminal data to obtain a hardware identification code; performing network feature recognition on the hardware identification code to generate a network feature code; Step S32: performing feature fusion on the hardware identification code and the network feature code to obtain terminal feature data; Step S33: segmenting the one-time key to obtain a key segment set; performing channel parameter mapping on the key segment set to obtain a channel parameter set; Step S34: Building an encrypted channel for the terminal feature data based on the channel parameter set to generate a one-time communication channel, wherein step S34 includes the following steps: Step S341: Connect and bind the channel parameter set and the terminal feature data to obtain a bound data set; perform channel encryption processing on the bound data set to obtain an encrypted channel set; Step S342: performing channel quality assessment on the encrypted channel set according to preset quality assessment data to obtain a channel assessment data set; Step S343: performing optimal channel selection on the encrypted channel set based on the channel assessment data set to generate a preferred channel set; Step S344: performing handshake verification on the preferred channel set to obtain a verification data set; performing channel activation on the preferred channel set based on the verification data set to generate a one-time communication channel; Step S4: Perform random position replacement processing on the one-time key to obtain a position replacement key; use the one-time communication channel to perform multi-path distribution on the position replacement key, and collect the payer's terminal operation data; Step S5: Based on the payer's terminal operation, the NFC electronic tag payment signal is subjected to payment authorization verification to obtain payment authorization data; payment actions are performed according to the payment authorization data to execute the smart payment method based on the NFC electronic tag.

2. The payment method based on NFC electronic tag according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: monitoring NFC electronic tag payment behavior and mobile terminal data; capturing payment signals of NFC electronic tag payment behavior to obtain NFC electronic tag payment signals; collecting mobile terminal data; Step S12: Perform time alignment processing on the NFC electronic tag payment signal to obtain a time-aligned payment signal; perform amplitude change analysis on the event-aligned payment signal to generate signal amplitude change data; Step S13: Correcting the amplitude of the time-aligned payment signal based on the signal amplitude variation data to generate a synchronous payment signal; Step S14: extracting the timestamp of the synchronous payment signal to obtain payment timestamp data; identifying the payment amount of the synchronous payment signal to generate payment amount data; Step S15: Perform feature fusion processing on the payment timestamp data and the payment amount data to generate fused payment feature data.

3. The payment method based on NFC electronic tag according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing a timestamp modulo operation on the fused payment feature data to obtain a time code; performing a pseudo-random abstract mapping on the time code to generate a basic random number; Step S22: Mixing the basic random number with a salt value based on the fused payment feature data to generate mixed semi-random data; Step S23: performing group connection processing on the mixed semi-random data to obtain link data; performing permutation matrix construction on the link data to generate a permutation matrix; Step S24: performing block encryption operation on the permutation matrix to obtain encrypted data; performing key expansion on the encrypted data to generate key expansion data; Step S25: format-encapsulate the key extension data and generate a one-time key.

4. The payment method based on NFC electronic tag according to claim 3 is characterized in that: Step S22 includes the following steps: Perform bitwise operations on the basic random number to obtain a random salt value sequence; perform XOR operations on the fused payment feature data and the random salt value sequence to generate initial mixed data; Divide the initial mixed data into blocks to obtain a data block set; Performing cyclic shift processing on the data block set to obtain a cyclic shift data set; performing enhanced expansion on the cyclic shift data set to generate an expanded data set; The extended data set is subjected to nonlinear transformation to obtain a transformed data set; the transformed data set is subjected to round diffusion processing to generate mixed semi-random data.

5. The payment method based on NFC electronic tag according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: constructing a position index for the one-time key to obtain an index key set; performing position mapping on the index key set to generate a position mapping key set; Step S42: performing random sequence mapping on the position mapping key set to obtain a random key sequence; performing position permutation on the random key sequence to obtain a position permutation key; Step S43: performing path analysis on the one-time communication channel to obtain a path data set; performing bandwidth evaluation on the path data set to generate a bandwidth evaluation data set; Step S44: based on the bandwidth evaluation data set, the location replacement key is optimally selected for path sub-packaging to obtain a path-sub-packaging data set; the path-sub-packaging data set is transmitted in parallel, and the payer's terminal operation data is collected at the same time.

6. The payment method based on NFC electronic tag according to claim 5, characterized in that: Step S42 includes the following steps: Step S421: performing random seed mapping on the position mapping key set to obtain seed data; performing sequence linear expansion processing on the seed data to generate linear sequence data; Step S422: performing period extension on the linear sequence data to obtain periodic sequence data; performing sequence completion on the periodic sequence data to generate expanded sequence data; Step S423: constructing mapping rules for the expanded sequence data to obtain rule mapping data; performing sequence rearrangement processing on the rule mapping data to generate a random key sequence; Step S424: Perform double helix interleaving permutation on the random key sequence to obtain a position permutation key.

7. The payment method based on NFC electronic tag according to claim 6, characterized in that: Step S424 includes the following steps: The random key sequence is segmented by sequence length to obtain a segmented key sequence; a spiral permutation matrix is ​​constructed on the segmented key sequence to generate a spiral matrix; Performing vacancy filling and inversion on the spiral matrix to obtain an interleaving matrix; performing mirror image flipping processing on the interleaving matrix to generate spiral permutation matrix data; Performing a bidirectional linked list structure component on the spiral permutation matrix data to generate linked list data; performing an alternate jump traversal on the linked list data to obtain jump traversal data; Perform multi-layer index tree structure construction on jump traversal data to generate reconstructed index data; Perform local replacement operation on the reconstructed index data to generate local replacement data; perform key combination processing on the local replacement data to generate a position replacement key.

8. The payment method based on NFC electronic tag according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: extracting behavior features from the payer's terminal operation data to obtain a behavior feature set; performing operation sequence analysis on the behavior feature set to generate a sequence data set; Step S52: performing security rule matching on the sequence data set to obtain security score data; performing threshold determination processing on the security score data to generate verification result data; Step S53: Perform authorization policy mapping on the verification result data to obtain authorization policy data; construct payment instructions on the authorization policy data to generate a payment instruction set; Step S54: assigning a communication channel to the payment instruction set to obtain payment channel data; performing a transaction on the payment channel data to generate transaction result data; Step S55: Sign and encapsulate the transaction result data to obtain payment credential data; synchronize the status of the payment credential data to generate payment completion data to execute the smart payment method based on the NFC electronic tag.

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