A Method and System for Dynamic Electronic Information Authentication Based on Mobile Terminal Payment

Abstract

This application discloses a method and system for dynamic electronic information authentication based on mobile terminal payment, relating to the field of information security technology. The disclosed method and system include: collecting and processing ambient sound wave signals; extracting sound entropy feature values ​​representing the randomness of instantaneous information; concatenating the sound entropy feature values ​​with a local user identification code to generate an encrypted hash, producing a first intermediate hash value; generating a one-time dynamic session key using a key algorithm; using this key to encrypt transaction information containing payment elements to generate encrypted transaction data; sending the encrypted transaction data and payment request time information to a payment server for synchronous reconstruction of the sound entropy feature values ​​and generation of a verification key to complete decryption authentication. By dynamically generating the session key through real-time capture of sound waves and extraction of sound entropy feature values, the uniqueness and unpredictability of the transaction source are ensured, thereby improving transaction security.

Description

Technical Field

[0001] This application relates to the field of information security technology, and in particular to a method and system for dynamic electronic information authentication based on mobile terminal payment. Background Technology

[0002] In mobile payment applications, the secure authentication of electronic information is directly related to the reliability of transactions and the protection of user assets. Current mainstream authentication mechanisms generally employ static or semi-static strategies, including fixed passwords, time-based one-time tokens, and biometric template matching. While these schemes offer some protection in typical scenarios, their core flaw lies in the fact that the generation and transmission of authentication factors are completely detached from the dynamic changes in the physical environment at the time of the transaction. For example, passwords or tokens remain unchanged within a preset period, and biometric data relies on a fixed sample library collected historically, resulting in a lack of real-time randomness in the entire authentication chain. This static characteristic is highly vulnerable to exploitation by attackers: they can obtain authentication data through eavesdropping on network communications and then launch replay attacks, or intercept and tamper with transmitted content at intermediate nodes to conduct man-in-the-middle attacks, or even predict future authentication values ​​based on historical data patterns to launch pre-computation attacks. Especially in the typical high-concurrency, low-latency scenarios of mobile payments, user operations are intensive and the network environment is highly variable. Existing mechanisms cannot maintain operational convenience while ensuring the uniqueness and unpredictability of the source of electronic information for each transaction.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide a method and system for dynamic electronic information authentication based on mobile terminal payment, which aims to improve the security of transactions.

[0005] To achieve the above objectives, this application proposes a method for dynamic electronic information authentication based on mobile terminal payment, the method comprising: In response to a payment request initiated by a mobile terminal, the microphone of the mobile terminal is activated to acquire real-time ambient sound wave signals, and the ambient sound wave signals are processed to extract sound entropy feature values ​​that characterize the randomness of their instantaneous information. The acoustic entropy feature value is deterministically concatenated with the user identification code pre-stored locally on the mobile terminal to generate the first concatenated data; Perform a cryptographic hash operation on the first concatenated data to generate a first intermediate hash value; Based on the first intermediate hash value, a one-time dynamic session key is generated by performing calculations using a preset key generation algorithm. The transaction information containing payment elements is encrypted using the one-time dynamic session key to generate encrypted transaction data; The encrypted transaction data and the payment request initiation time information are sent to the payment server together, so that the payment server can synchronously reconstruct the acoustic entropy feature value based on the payment request initiation time information, and generate a corresponding verification key to complete the decryption and authentication of the encrypted transaction data.

[0006] In one embodiment, the step of processing the ambient sound wave signal to extract acoustic entropy feature values ​​characterizing the instantaneous randomness of its information includes: The ambient sound wave signal is converted into a digital signal sequence, and the digital signal sequence is divided into multiple consecutive signal frames; For at least one of the signal frames, it is converted from the time domain to the frequency domain to obtain the spectral data of the signal frame; Calculate the frame-level entropy value of the signal frame based on the spectrum data; The acoustic entropy feature value is obtained by combining the frame-level entropy values ​​of multiple signal frames.

[0007] In one embodiment, the step of converting at least one of the signal frames from the time domain to the frequency domain to obtain the spectral data of the signal frame includes: A preset window function is added to the signal frame to reduce spectral leakage; Perform a Fast Fourier Transform or an improved Discrete Cosine Transform on the windowed signal frame to obtain a preliminary spectrum; The preliminary spectrum is logarithmically compressed to simulate the characteristics of human hearing, thereby obtaining the spectrum data.

[0008] In one embodiment, the step of combining frame-level entropy values ​​of multiple signal frames to obtain the acoustic entropy feature value includes: The frame-level entropy value of each signal frame is checked for temporal continuity. If the difference between the frame-level entropy values ​​of adjacent frames exceeds a preset threshold, it is determined that the current signal frame is affected by sudden noise interference. For the frame-level entropy values ​​that pass the continuity check, a weighted average is calculated based on the signal-to-noise ratio (SNR) estimates of each signal frame. The higher the SNR estimate, the greater the weight of the signal frame, in order to suppress interference from signals with an SNR lower than a preset SNR threshold, and thus obtain the final acoustic entropy feature value.

[0009] In one embodiment, the step of generating a one-time dynamic session key based on the first intermediate hash value using a preset key generation algorithm includes: The first intermediate hash value is mapped to a scalar value under the elliptic curve cryptography system to serve as a private key seed; The private key seed is multiplied by a preset base point of the elliptic curve cryptosystem to generate elliptic curve points. Data of a preset length is extracted from the coordinate data of the elliptic curve points to serve as the one-time dynamic session key.

[0010] In one embodiment, the step of mapping the first intermediate hash value to a scalar value under elliptic curve cryptography as a private key seed includes: The first intermediate hash value is used as the initial input, and a bit-by-bit cyclic XOR operation is performed with the perturbation vector derived from the acoustic entropy feature value to obtain the initial seed. The initial seed is input into an extension function based on a nonlinear feedback shift register to generate a pseudo-random sequence with extended length. The first N bits of the pseudo-random sequence are extracted and interpreted as an integer value represented by the first N bits that falls within the range of private key values ​​required by the elliptic curve cryptosystem, and used as the final private key seed; wherein N is greater than the length of the initial seed so that the entropy of the private key seed meets the entropy security requirements of the elliptic curve cryptosystem for the private key.

[0011] In one embodiment, the step of extracting data of a preset length from the coordinate data of the elliptic curve points to serve as the one-time dynamic session key includes: Obtain the binary representation of the x-coordinate and y-coordinate of the points on the elliptic curve; Extract the lower M bits of the horizontal axis and the higher M bits of the vertical axis; where M is a preset value; The two extracted bit sequences are cross-mixed and arranged. The mixed bit sequence is converted into a hexadecimal string, and the first L characters are taken as the one-time dynamic session key; where L is determined according to the key length required by the target encryption algorithm.

[0012] In one embodiment, the payment server synchronously reconstructs the acoustic entropy feature value based on the payment request initiation time information, and generates a corresponding verification key accordingly to complete the decryption and authentication of the encrypted transaction data, including: The payment server, based on the received payment request initiation time information, queries a reference acoustic entropy feature that matches the time window of the time from a pre-established acoustic feature benchmark library associated with the mobile terminal. Based on the retrieved reference acoustic entropy features, the reconstructed acoustic entropy feature values ​​are determined; The reconstructed acoustic entropy feature value is concatenated with the user identification code corresponding to the mobile terminal stored on the payment server to generate the second concatenated data; Perform a cryptographic hash operation on the second concatenated data to generate a second intermediate hash value; The second intermediate hash value is input into the same key generation algorithm as the mobile terminal for calculation to generate the verification key, and the verification key is used to decrypt and authenticate the encrypted transaction data.

[0013] In one embodiment, the step of the payment server querying a reference acoustic entropy feature matching the time window of the received payment request initiation time information from a pre-established acoustic feature reference library associated with the mobile terminal includes: Based on the payment request initiation time information and combined with the preset sliding window strategy, a query time window with a time length greater than the real-time acquisition time window is determined to adapt to the clock deviation and network transmission delay between the mobile terminal and the server. Extract all historical acoustic entropy feature records within the query time window from the acoustic feature benchmark library, and calculate their statistical distribution characteristics; Using a pre-trained anomaly detection model, all extracted historical records are filtered to remove abnormal records that significantly deviate from the statistical distribution characteristics. For the remaining records after filtering, the median of their entropy values ​​or the weighted average based on time decay is taken as the final reference acoustic entropy feature to enhance the stability and anti-interference of the matching.

[0014] Furthermore, to achieve the above objectives, this application also proposes an electronic information dynamic authentication system based on mobile terminal payment. The electronic information dynamic authentication system based on mobile terminal payment includes: a memory, a processor, and an electronic information dynamic authentication program based on mobile terminal payment stored in the memory and executable on the processor. The electronic information dynamic authentication program based on mobile terminal payment is configured to implement the steps of the electronic information dynamic authentication method based on mobile terminal payment.

[0015] The proposed method and system for dynamic electronic information authentication based on mobile terminal payment includes: responding to a payment request initiated by the mobile terminal by activating the mobile terminal's microphone to acquire real-time ambient sound wave signals and processing the ambient sound wave signals to extract acoustic entropy feature values ​​characterizing the instantaneous randomness of the information; concatenating the acoustic entropy feature values ​​with a user identity code pre-stored locally on the mobile terminal to generate first concatenated data; performing an encrypted hash operation on the first concatenated data to generate a first intermediate hash value; generating a one-time dynamic session key based on the first intermediate hash value using a preset key generation algorithm; encrypting transaction information containing payment elements using the one-time dynamic session key to generate encrypted transaction data; and sending the encrypted transaction data and payment request initiation time information together to a payment server, so that the payment server can synchronously reconstruct the acoustic entropy feature values ​​based on the payment request initiation time information and generate a corresponding verification key to complete the decryption and authentication of the encrypted transaction data. By capturing ambient sound wave signals in real time to extract acoustic entropy feature values ​​and dynamically generating one-time session keys, the system ensures the uniqueness and unpredictability of the transaction source, thereby improving transaction security. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating an embodiment of the electronic information dynamic authentication method based on mobile terminal payment in this application; Figure 2 This is a schematic diagram of a structure provided for an embodiment of the electronic information dynamic authentication system based on mobile terminal payment in this application.

[0019] Explanation of icon numbers: 10. Memory; 20. Processor.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of this application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] It should be understood that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0023] In existing technologies, mobile payment authentication mechanisms lack a dynamic link between their authentication keys or tokens and the real-time physical environment, making them vulnerable to security threats such as replay attacks, man-in-the-middle attacks, or pre-computation attacks. In a massive, high-concurrency mobile payment environment, existing solutions struggle to ensure both convenience and the unpredictability and uniqueness of each transaction from the information source, resulting in inherent security challenges.

[0024] Based on this, the embodiments of this application provide a method for dynamic electronic information authentication based on mobile terminal payment, referring to... Figure 1 The electronic information dynamic authentication method based on mobile terminal payment includes steps S100 to S600, wherein: Step S100: In response to the payment request initiated by the mobile terminal, the microphone of the mobile terminal is activated to acquire the real-time collected ambient sound wave signal, and the ambient sound wave signal is processed to extract the sound entropy feature value that characterizes the randomness of its instantaneous information. Step S200: The acoustic entropy feature value is deterministically concatenated with the user identification code pre-stored locally on the mobile terminal to generate the first concatenated data; Step S300: Perform an encrypted hash operation on the first concatenated data to generate a first intermediate hash value; Step S400: Based on the first intermediate hash value, a one-time dynamic session key is generated by performing calculations using a preset key generation algorithm. Step S500: Use the one-time dynamic session key to encrypt the transaction information containing payment elements to generate encrypted transaction data; Step S600: The encrypted transaction data and the payment request initiation time information are sent to the payment server together, so that the payment server can synchronously reconstruct the acoustic entropy feature value based on the payment request initiation time information, and generate a corresponding verification key to complete the decryption and authentication of the encrypted transaction data.

[0025] In this embodiment, a mobile terminal refers to a portable electronic device, such as a smartphone, tablet, or wearable device, used by the user for mobile payment operations. This mobile terminal typically includes a microphone, processor, memory, and network communication module, enabling it to execute applications and interact with external servers. The ambient sound wave signal is the sound waveform data of the surrounding environment collected in real-time by the mobile terminal's microphone within a specific time period. This signal contains various acoustic information from the environment, such as human voices, background noise, music, or other ambient sounds. The sound entropy feature value is a numerical value extracted after processing the ambient sound wave signal; this value quantifies the randomness or unpredictability of the ambient sound wave signal in instantaneous information. The higher the sound entropy feature value, the stronger the randomness of the ambient sound wave signal. The user identification code refers to a string of data pre-stored locally on the mobile terminal to uniquely identify the user. This identification code can be the user's account ID, device ID, or other unique code associated with the user's identity.

[0026] In this embodiment, cryptographic hashing is a one-way cryptographic operation that converts input data of arbitrary length into a fixed-length output value using a hash function. This operation features collision resistance and preimage irreversibility, and is commonly used for data integrity verification and cryptographic key derivation. A key generation algorithm is a predefined mathematical algorithm whose input is specific seed data and output is a key used for encryption or decryption. This algorithm aims to securely derive a key with sufficient randomness and strength from the input data. A one-time dynamic session key is an encryption key temporarily generated and used only once during each payment request. The dynamic and one-time use characteristics of this key enhance transaction security; even if intercepted, it cannot be used for subsequent transactions. A payment server is a backend service system responsible for processing payment requests, verifying transaction information, and completing fund settlement. This server typically possesses powerful computing capabilities, storage capacity, and security mechanisms, and can communicate with multiple mobile terminals. Payment request initiation time information refers to the timestamp data when the mobile terminal initiates a payment request. This information is used by the payment server to synchronize its internal clock and, based on this, reconstruct an acoustic entropy characteristic value consistent with the mobile terminal on the server side.

[0027] In this embodiment, the electronic information dynamic authentication method based on mobile terminal payment first responds to a payment request initiated by the mobile terminal. When a user triggers a payment operation on the mobile terminal, the application of the mobile terminal detects this request. Subsequently, the method activates the built-in microphone of the mobile terminal to acquire real-time ambient sound wave signals. For example, the microphone can continuously acquire ambient sound data at a preset sampling frequency and bit depth, forming a raw sound wave data stream. After acquiring the ambient sound wave signal, it needs to be processed to extract the sound entropy feature value that characterizes the randomness of its instantaneous information. For example, the acquired raw sound wave data can be segmented into frames, and the energy or spectral centroid of each frame can be calculated. Then, these simple statistics are accumulated or averaged to obtain a rough sound entropy feature value.

[0028] Furthermore, the acoustic entropy feature value is deterministically concatenated with the user identification code pre-stored locally on the mobile terminal to generate the first concatenated data. For example, the binary representation of the acoustic entropy feature value can be directly appended to the binary representation of the user identification code to form a longer data string. The user identification code can be a unique numerical sequence assigned to the user during registration, or a unique identifier bound to the device hardware.

[0029] In this embodiment, the first concatenated data is then subjected to an encrypted hash operation to generate a first intermediate hash value. For example, the SHA-256 hash algorithm can be used to calculate the hash value on the first concatenated data, resulting in a fixed-length hash output. This hash operation aims to convert the concatenated data into a digest value with one-way and collision-resistant properties.

[0030] Based on this, a one-time dynamic session key is generated using a preset key generation algorithm based on the first intermediate hash value. For example, the first intermediate hash value can be used as input, and a relatively long pseudo-random sequence can be generated using a simple pseudo-random number generator algorithm, such as the linear congruential method. A fixed-length portion of this sequence can then be extracted as the one-time dynamic session key. This one-time dynamic session key is used to encrypt transaction information containing payment elements to generate encrypted transaction data. For example, a symmetric encryption algorithm, such as DES or 3DES, can be used, with payment elements such as the transaction amount, payee information, and product details as plaintext input, and then encrypted using the generated one-time dynamic session key to obtain encrypted transaction data in ciphertext form.

[0031] In this embodiment, the encrypted transaction data, along with the payment request initiation time information, is finally sent to the payment server. For example, the mobile terminal packages and sends the encrypted transaction data and its local system timestamp via a network connection. Upon receiving this information, the payment server synchronously reconstructs the acoustic entropy feature value based on the payment request initiation time information. For instance, the payment server can search its locally stored historical acoustic entropy feature database for the closest acoustic entropy feature record uploaded by the mobile terminal, using this record as the reconstructed acoustic entropy feature value. Accordingly, the payment server generates a corresponding verification key and uses this key to decrypt and authenticate the encrypted transaction data. For example, the payment server concatenates the reconstructed acoustic entropy feature value with a stored user identification code, performs a hash operation to obtain a verification key, and attempts to decrypt the received encrypted transaction data using this key. If decryption is successful and the transaction information is complete and valid, authentication is successful.

[0032] In this embodiment, by introducing the randomness of real-time ambient sound signals into the electronic information authentication process, the problem of insufficient dynamic correlation between traditional authentication mechanisms and the physical environment is effectively solved. This method utilizes the ambient sound entropy characteristic value and the user's identity code to generate a one-time dynamic session key, making the authentication key for each transaction unique and unpredictable. Therefore, it can effectively resist replay attacks, man-in-the-middle attacks, and pre-computation attacks, significantly improving the inherent security of electronic information and the uniqueness of transactions in mobile payment scenarios.

[0033] In one feasible implementation, the step of processing the ambient sound wave signal to extract acoustic entropy feature values ​​characterizing its instantaneous information randomness includes: converting the ambient sound wave signal into a digital signal sequence and dividing the digital signal sequence into multiple consecutive signal frames; converting at least one of the signal frames from the time domain to the frequency domain to obtain the spectral data of the signal frame; calculating the frame-level entropy value of the signal frame based on the spectral data; and combining the frame-level entropy values ​​of multiple signal frames to obtain the acoustic entropy feature value.

[0034] In this embodiment, the ambient sound signal is essentially a continuous analog signal. To facilitate processing and analysis by the digital system, it needs to be converted into a discrete digital signal sequence using an analog-to-digital converter (ADC). This conversion process typically involves sampling and quantization, for example, sampling at a sampling rate of 44.1 kHz or 48 kHz and quantizing with 16-bit or 24-bit precision. Subsequently, due to the non-stationary nature of acoustic signals, to perform quasi-stationary analysis within a local range, the continuous digital signal sequence needs to be divided into multiple time-continuous, short signal frames. The length of each signal frame is typically between 20 and 40 milliseconds, and there can be some overlap between frames (e.g., 50% or 75%) to ensure the continuity and smoothness of the analysis and reduce frame boundary effects.

[0035] In this embodiment, after obtaining the signal frame, the time-domain representation of the acoustic signal often fails to directly reveal its inherent frequency distribution and energy composition, while frequency-domain analysis can effectively characterize the signal's spectral characteristics. Therefore, for at least one segmented signal frame, it is necessary to transform it from the time domain to the frequency domain. This transformation can be achieved through various signal processing techniques, such as Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT), or Modified Discrete Cosine Transform (MDCT). Through these transformations, the energy distribution of the signal frame can be mapped from the time axis to the frequency axis, thereby obtaining the spectral data of the signal frame, which contains the intensity information of the signal at different frequency components.

[0036] In this embodiment, after obtaining the spectral data of the signal frame, the frame-level entropy value of the signal frame can be calculated based on this data. Entropy is an important indicator for measuring the randomness or uncertainty of information. In the frequency domain, the more uniform and wider the distribution of spectral data, the higher its entropy value, indicating stronger randomness of the signal. Methods for calculating the frame-level entropy value may include, but are not limited to, normalizing the spectral data so that its sum is 1, and then applying the Shannon entropy formula for calculation. In this way, the degree of randomness of information in a single signal frame in the frequency domain can be quantified.

[0037] In this embodiment, the frame-level entropy value of a single signal frame may be affected by instantaneous noise or local signal fluctuations, resulting in insufficient stability. To obtain a more representative and robust acoustic entropy feature value, it is necessary to comprehensively process the frame-level entropy values ​​of multiple consecutive signal frames. The comprehensive processing methods may include, but are not limited to, simple averaging, weighted averaging, median taking, or statistical analysis (such as calculating variance and standard deviation). Through comprehensive processing, instantaneous fluctuations can be effectively smoothed, and the impact of noise on the entropy value of a single frame can be reduced, thereby obtaining a more stable acoustic entropy feature value that better characterizes the randomness of the instantaneous information of the overall environmental acoustic signal, providing reliable input for the subsequent authentication process.

[0038] In this embodiment, by digitizing and framing continuous ambient sound signals and converting them from the time domain to the frequency domain, redundant information in the signals can be effectively removed, focusing on their spectral characteristics. Calculating frame-level entropy values ​​based on the spectral data allows for a more accurate quantification of the instantaneous randomness of ambient sound information within each time window. Furthermore, by integrating the frame-level entropy values ​​of multiple signal frames, the effects of instantaneous noise and local signal fluctuations can be effectively smoothed, resulting in a more stable, robust, and accurate acoustic entropy feature value that characterizes the randomness of the ambient sound signal. This processing method significantly improves the extraction accuracy and stability of the acoustic entropy feature value, providing high-quality input for subsequent deterministic data concatenation, encrypted hash operations, and the generation of one-time dynamic session keys, thereby enhancing the security and reliability of the entire electronic information dynamic authentication method.

[0039] In one feasible implementation, the step of converting at least one of the signal frames from the time domain to the frequency domain to obtain the spectral data of the signal frame includes: adding a preset window function to the signal frame to reduce spectral leakage; performing a fast Fourier transform or an improved discrete cosine transform on the windowed signal frame to obtain a preliminary spectrum; and performing logarithmic compression on the preliminary spectrum to simulate the characteristics of human hearing, thereby obtaining the spectral data.

[0040] In this embodiment, before converting the signal frame from the time domain to the frequency domain, a preset window function is first applied to the signal frame. The function smooths the two ends of the signal frame before performing a Fourier transform, causing it to gradually decay to zero in the time domain. This effectively suppresses spectral leakage caused by signal truncation. Spectral leakage refers to the phenomenon where, during a Fourier transform of a finite-length signal, the spectral energy diffuses to adjacent frequency components due to signal truncation, leading to reduced frequency resolution and spectral distortion. By applying a window function, the signal at the truncation point can be smoothed, thereby reducing sidelobe effects and improving the accuracy of spectral analysis. Common window functions include the Hamming window, Hanning window, and Blackman window; the specific window function chosen depends on the requirements for main lobe width and sidelobe attenuation.

[0041] In this embodiment, the windowed signal frame is then subjected to a Fast Fourier Transform (FFT) or a Modified Discrete Cosine Transform (MDCT) to obtain a preliminary spectrum. The Fast Fourier Transform (FFT) is an efficient algorithm for calculating the Discrete Fourier Transform (DFT), capable of converting a time-domain signal into a frequency-domain signal, revealing the frequency components and their intensities. The Modified Discrete Cosine Transform (MDCT) is another commonly used frequency-domain transformation method, particularly advantageous in audio coding due to its ability to achieve perfect reconstruction and good energy concentration characteristics. The purpose of these transformations is to convert the windowed time-domain signal into its corresponding frequency-domain representation, i.e., the preliminary spectrum. Through the frequency-domain representation, the energy distribution of the signal at different frequencies can be clearly observed. The calculation can be performed using standard FFT or MDCT algorithm libraries. FFT is typically used for general spectrum analysis, while MDCT is more suitable for scenarios requiring overlapping and high compression efficiency.

[0042] Based on this, the preliminary spectrum is logarithmically compressed to simulate the characteristics of human hearing, thereby obtaining the spectral data. Logarithmic compression refers to performing a nonlinear transformation on the amplitude values ​​of the preliminary spectrum, typically converting linear amplitude values ​​to a logarithmic scale (e.g., decibels). The human ear's perception of sound intensity is nonlinear, closer to a logarithmic relationship. By performing logarithmic compression on the preliminary spectrum, the characteristics of human hearing can be better simulated, making the spectral data more representative in perception. This processing amplifies differences in the low-energy components while compressing differences in the high-energy components, thus allowing subsequent sound entropy calculations to better reflect the actual perception of the randomness of environmental sound waves by the human ear. This is usually achieved by taking the common logarithm or natural logarithm of the spectral amplitude values, for example, "log10(magnitude + epsilon)", where "epsilon" is a small positive number used to avoid taking the logarithm of zero.

[0043] In this embodiment, by adding a preset window function to the signal frame, spectral leakage caused by signal truncation is effectively suppressed, ensuring the accuracy of frequency domain conversion. Subsequently, by performing a Fast Fourier Transform or a modified Discrete Cosine Transform, the windowed time-domain signal is efficiently converted into a preliminary spectrum, providing a foundation for subsequent analysis. Based on this, the preliminary spectrum is logarithmically compressed to better simulate the auditory characteristics of the human ear, resulting in spectral data that better matches human sound perception patterns. These steps work synergistically to make the spectral data extracted from the ambient sound wave signal more accurate, robust, and perceptually relevant, laying a solid foundation for the accurate calculation of subsequent sound entropy feature values, thereby improving the reliability and security of dynamic authentication based on ambient sound wave signals.

[0044] In one feasible implementation, the step of combining the frame-level entropy values ​​of multiple signal frames to obtain the acoustic entropy feature value includes: performing a time continuity check on the frame-level entropy values ​​of each signal frame; if the difference between the frame-level entropy values ​​of adjacent frames exceeds a preset threshold, it is determined that the current signal frame is subject to sudden noise interference; for the frame-level entropy values ​​that pass the continuity check, a weighted average is calculated based on the signal-to-noise ratio (SNR) estimates of each signal frame, with the signal frame having a higher SNR estimate having a larger weight, in order to suppress interference from signals with an SNR lower than a preset SNR threshold, thereby obtaining the final acoustic entropy feature value.

[0045] In this embodiment, the frame-level entropy values ​​of each signal frame are checked for temporal continuity to evaluate the stationarity or consistency of the frame-level entropy values ​​over time. This check can be achieved by comparing the frame-level entropy values ​​of adjacent signal frames, for example, by calculating the absolute or relative difference between the frame-level entropy value of the current frame and the frame-level entropy value of the previous or next frame. If this difference exceeds a preset threshold, it indicates that the entropy value of the current signal frame has undergone a significant and unexpected change, which is usually introduced by sudden noise (such as sudden knocking sounds, human voice interference, etc.). The preset threshold can be determined experimentally or through machine learning methods based on the actual application scenario and the statistical characteristics of the ambient sound wave signal to balance the sensitivity to noise and the tolerance to normal signal changes.

[0046] In this embodiment, when the current signal frame is determined to be affected by sudden noise interference, the frame-level entropy value of that signal frame may be considered an outlier and should be specially processed or its influence reduced in subsequent comprehensive calculations. For frame-level entropy values ​​that pass the continuity check, i.e., those signal frames not determined to be affected by sudden noise interference, a weighted average calculation will be performed based on their signal-to-noise ratio (SNR) estimates. The SNR estimate is an important indicator of signal quality; a higher SNR indicates a larger proportion of effective information in the signal and less noise interference. SNR estimation can be achieved through various techniques, such as noise estimation based on voice activity detection (VAD), minimum statistics, and spectral subtraction. In the weighted average calculation, signal frames with higher SNR estimates have a larger weight, meaning that high-quality signal frames contribute more to the final acoustic entropy feature value, while the contribution of signal frames with an SNR lower than a preset SNR threshold (even if they pass the continuity check, they may still have some noise) is correspondingly reduced. This weighting strategy effectively suppresses interference from signals with an SNR lower than the preset SNR threshold, ensuring the robustness and accuracy of the final acoustic entropy feature value.

[0047] In this embodiment, a time continuity verification mechanism is used to effectively identify, eliminate, or weaken the impact of sudden noise on frame-level entropy calculation, avoiding contamination of the overall acoustic entropy feature value by abnormal data points. Secondly, by introducing a weighted average calculation based on the signal-to-noise ratio estimate, high-quality signal frames dominate the synthesis process, further enhancing the acoustic entropy feature value's resistance to environmental noise and its stability. This integrated method, combining noise detection and quality weighting, ensures that the extracted acoustic entropy feature value can more accurately characterize the instantaneous randomness of the environmental sound wave signal, thus providing a more reliable random source for subsequent one-time dynamic session key generation, significantly enhancing the security and reliability of the electronic information dynamic authentication method based on mobile terminal payment.

[0048] In one feasible implementation, the step of generating a one-time dynamic session key based on the first intermediate hash value using a preset key generation algorithm includes: mapping the first intermediate hash value to a scalar value under an elliptic curve cryptosystem as a private key seed; performing a scalar multiplication operation between the private key seed and a preset base point of the elliptic curve cryptosystem to generate an elliptic curve point; and extracting data of a preset length from the coordinate data of the elliptic curve point as the one-time dynamic session key.

[0049] In this embodiment, firstly, the first intermediate hash value is mapped to a scalar value under elliptic curve cryptography to serve as the private key seed. Specifically, elliptic curve cryptography (ECC) is a public-key encryption method based on elliptic curve mathematics, whose security relies on the difficulty of solving the elliptic curve discrete logarithm problem. The private key seed is the basis for generating the elliptic curve private key and is usually a large integer. Mapping the hash value to the private key seed aims to leverage the randomness and irreversibility of the hash value to provide a high-entropy, unpredictable starting point for subsequent key generation. This mapping can be implemented in various ways, for example, by directly interpreting the hash value as an integer within a specific range, or by converting it into a private key seed that meets specific length and range requirements through a key derivation function (KDF).

[0050] Secondly, the private key seed is multiplied by a preset base point of the elliptic curve cryptography system to generate an elliptic curve point. In elliptic curve cryptography, scalar multiplication is a core operation; that is, the private key (a scalar integer) is added multiple times to the preset elliptic curve base point (a point on an elliptic curve) to obtain a new elliptic curve point. This new elliptic curve point is usually regarded as the public key. Through this operation, the randomness of the private key is securely extended to the public key, and it is computationally infeasible to deduce the private key from the public key, thus ensuring the security of key generation.

[0051] Finally, a preset length of data is extracted from the coordinate data of the elliptic curve points to serve as the one-time dynamic session key. Elliptic curve points are typically represented by their x and y coordinates in a Cartesian coordinate system. This coordinate data itself is highly random and unpredictable because it is derived from complex mathematical operations based on the private key and base points. By truncating or combining bit sequences of a specific length from this coordinate data, a one-time dynamic session key for symmetric encryption can be securely derived. The extracted length is preset to ensure that the generated session key meets the key length requirements of the target symmetric encryption algorithm (such as AES). For example, a portion of the x or y coordinate can be truncated, or both can be combined, to increase the entropy and complexity of the key.

[0052] In this embodiment, leveraging the mathematical properties of elliptic curve cryptography, a high-entropy first intermediate hash value generated based on ambient sound wave signals and user identification codes is securely converted into a one-time dynamic session key. This key generation mechanism based on hash values ​​and ECC ensures that a completely new and unpredictable session key is generated for each payment request, effectively avoiding the risks of key reuse and replay attacks. Since the private key seed originates from dynamically changing sound entropy characteristics and user identification codes, and undergoes hashing, the generated session key possesses high randomness and uniqueness. Simultaneously, the security of elliptic curve cryptography guarantees that even if an attacker intercepts encrypted transaction data and public key information, it is difficult to reverse-engineer the private key or session key, thus greatly enhancing the security of payment transactions. This dynamic, high-entropy key generation method provides robust security for mobile terminal payments, effectively addressing the security vulnerabilities of traditional static or predictable keys in payment scenarios.

[0053] In one feasible implementation, the step of mapping the first intermediate hash value to a scalar value under elliptic curve cryptography as a private key seed includes: taking the first intermediate hash value as an initial input and performing a bit-by-bit cyclic XOR operation with the perturbation vector derived from the acoustic entropy feature value to obtain a preliminary seed; inputting the preliminary seed into an expansion function based on a nonlinear feedback shift register to generate a length-expanded pseudo-random sequence; truncating the first N bits of the pseudo-random sequence and interpreting it as an integer value represented by the first N bits that falls within the range of private key values ​​required by the elliptic curve cryptography, as the final private key seed; wherein N is greater than the length of the preliminary seed so that the entropy of the private key seed meets the entropy security requirements of the elliptic curve cryptography for the private key.

[0054] In this embodiment, the first intermediate hash value is the result of deterministically concatenating the acoustic entropy feature value extracted from the real-time acquired ambient sound wave signal with the user identification code pre-stored locally on the mobile terminal, followed by encrypted hashing. It possesses cryptographic properties such as one-wayness and collision resistance, but its randomness primarily originates from the combination of the acoustic entropy feature value and the user identification code. The perturbation vector derived from the acoustic entropy feature value refers to generating a bit sequence that matches the length of the first intermediate hash value or can be cyclically XORed by further processing the real-time extracted acoustic entropy feature value (e.g., converting it into a bit sequence of a specific length using a secure hash function or pseudo-random function). The core of this perturbation vector lies in its origin from the instantaneous randomness of the real-time ambient sound wave signal, which introduces highly dynamic and unpredictable external random factors into the generation of the private key seed. Bitwise cyclic XOR is a common method for performing bitwise operations on two bit sequences. When the lengths of the two input sequences are inconsistent, the shorter sequence is cyclically repeated to match the length of the longer sequence. The XOR operation is characterized by the fact that when one input is random, the output will also be random even if the other input is fixed. By XORing a relatively stable first intermediate hash value with a highly dynamic perturbation vector, the randomness of both can be effectively mixed, thereby generating a more random and unpredictable initial seed.

[0055] In this embodiment, the initial seed is obtained through an XOR operation, which enhances its randomness. However, to meet the requirements of elliptic curve cryptography regarding private key length and entropy, it is usually necessary to extend its length. An extension function based on a nonlinear feedback shift register (NLFSR) is a cryptographic primitive that uses a combination of nonlinear logic gates (such as AND, OR, NOT, etc.) and shift registers to generate pseudo-random sequences. Compared to a linear feedback shift register (LFSR), NLFSR has higher linear complexity and can generate pseudo-random sequences with longer periods and better statistical properties, thus effectively resisting linear attacks. This extension function uses the initial seed as the initial state or part of the input and generates a pseudo-random sequence much longer than the initial seed through iterative computation.

[0056] In this embodiment, the private key of the Elliptic Curve Cryptography (ECC) system is an integer within a specific range. Therefore, truncating the first N bits from the generated pseudo-random sequence is to obtain a binary sequence that meets the ECC private key length requirements. Interpreting the truncated first N bits as an integer value and ensuring that it is within the range of private key values ​​required by the Elliptic Curve Cryptography system is a necessary step in generating a valid ECC private key. If the truncated value is outside the range, modulo operations or other processing are required to bring it into the valid range. N being greater than the length of the initial seed is crucial. It means that the NLFSR expansion function not only increases the length of the sequence, but more importantly, it "amplifies" the entropy of the initial seed to a certain extent. Even if the initial seed may have some statistical bias or insufficient entropy, after sufficient nonlinear expansion and appropriate truncation, the final private key seed can obtain higher effective entropy, thereby significantly enhancing its randomness and resistance to guessing, meeting the cryptographic requirements of a high security level.

[0057] In this embodiment, through the above technical solution, this application effectively solves the problems of insufficient randomness, low entropy, and incomplete compliance with the elliptic curve cryptography (ECC) private key value range requirements when generating a one-time dynamic session key. Specifically, by performing a bit-by-bit XOR operation between the first intermediate hash value and the perturbation vector derived from the real-time acoustic entropy feature value, this application cleverly injects instantaneous randomness from the environment into the private key seed generation process, making the initial seed more dynamic and unpredictable, greatly increasing the difficulty for attackers to predict or reconstruct the private key seed. Furthermore, by using an extension function based on a nonlinear feedback shift register to extend the length of the initial seed, and ensuring that the final truncated private key seed has an N-bit length greater than the initial seed length, this not only guarantees that the private key seed meets the length requirements of the ECC, but more importantly, the nonlinear extension process effectively improves the entropy and statistical randomness of the private key seed, giving it stronger cryptographic security. The final generated private key seed is not only highly random and unpredictable, but also strictly conforms to the private key value range of the elliptic curve cryptosystem, thereby ensuring that the subsequently generated one-time dynamic session key has extremely high security strength, effectively resisting various key guessing and replay attacks, and significantly improving the overall security and reliability of the electronic information dynamic authentication method based on mobile terminal payment.

[0058] In one feasible implementation, the step of extracting data of a preset length from the coordinate data of the elliptic curve points to serve as the one-time dynamic session key includes: obtaining the binary representations of the abscissa and ordinate of the elliptic curve points; truncating the lower M bits of the abscissa and the higher M bits of the ordinate; where M is a preset value; cross-mixing the two truncated bit sequences; converting the mixed bit sequence into a hexadecimal string, and taking its first L characters as the one-time dynamic session key; where L is determined according to the key length required by the target encryption algorithm.

[0059] In this embodiment, the binary representations of the x and y coordinates of the elliptic curve points are obtained. Elliptic curve points are typically represented by a pair of large integer coordinates (x, y). Converting these integer coordinates to their binary form facilitates subsequent bit-level operations and precise data extraction. For example, this can be achieved using standard integer-to-binary string conversion functions, ensuring that the length of the binary representation is sufficient to contain all the information from the original coordinates.

[0060] In this embodiment, the lower M bits of the horizontal coordinate and the upper M bits of the vertical coordinate are extracted, where M is a preset value. This step aims to extract specific portions of bit data from the two coordinates of an elliptic curve point. Selecting the lower M bits of the horizontal coordinate and the upper M bits of the vertical coordinate (or other combinations depending on specific security requirements) effectively utilizes the entropy from two independent coordinates, increasing the source of key randomness. The value of M can be adjusted according to the required key strength and the parameters of the elliptic curve to ensure that the number of extracted bits provides sufficient entropy without introducing excessive redundancy. For example, the required bit segment can be precisely extracted using bitmasking and bit shifting operations.

[0061] In this embodiment, the two truncated bit sequences are cross-mixed. This mixing operation, by alternately inserting bits from the horizontal and vertical axes into the new sequence, further enhances the diffusion and confusion of the key. For example, if the two truncated bit sequences are A and B, the mixed arrangement can form a pattern of A1B1A2B2... This cross-mixing ensures that any small change in the original coordinates can have a wide impact on the final key, thereby improving the key's resistance to differential analysis and making the individual bits of the key more correlated, making it difficult to infer the whole from partial information.

[0062] In this embodiment, the final step is to convert the mixed bit sequence into a hexadecimal string, and take its first L characters as the one-time dynamic session key, where L is determined according to the key length required by the target encryption algorithm. Converting a binary sequence into a hexadecimal string is a common representation method, facilitating storage, transmission, and use in various encryption libraries. Crucially, by truncating the first L characters, it ensures that the generated one-time dynamic session key has a precise length matching the target encryption algorithm (e.g., AES-128, AES-256, etc.). This not only guarantees the usability of the key but also avoids security vulnerabilities or performance issues that may result from key length mismatches.

[0063] In this embodiment, obtaining the binary representations of the horizontal and vertical coordinates provides a foundation for subsequent refined operations. Selectively truncating the lower M bits of the horizontal coordinate and the upper M bits of the vertical coordinate ensures the diversity of the key's entropy sources, avoiding the bias that may exist with a single coordinate or continuous bit segment. Furthermore, the two truncated bit sequences are cross-mixed and arranged, greatly enhancing the key's diffusion and obfuscation, fully utilizing its randomness, and improving its resistance to attacks. Finally, the mixed bit sequence is converted into a hexadecimal string and the first L characters are truncated, ensuring that the generated one-time dynamic session key not only has sufficient randomness and security, but also that its length L precisely matches the requirements of subsequent encryption algorithms, thereby guaranteeing the compatibility and efficiency of the encrypted transaction data process. This refined key extraction mechanism significantly improves the overall security and robustness of the electronic information dynamic authentication method based on mobile terminal payments.

[0064] In one feasible implementation, the payment server synchronously reconstructs the acoustic entropy feature value based on the payment request initiation time information, and generates a corresponding verification key accordingly to complete the decryption and authentication of the encrypted transaction data. This includes: the payment server, based on the received payment request initiation time information, queries a reference acoustic entropy feature matching the time window of the received time from a pre-established acoustic feature benchmark library associated with the mobile terminal; determines the reconstructed acoustic entropy feature value based on the queried reference acoustic entropy feature; concatenates the reconstructed acoustic entropy feature value with a user identity code stored on the payment server corresponding to the mobile terminal to generate second concatenated data; performs an encrypted hash operation on the second concatenated data to generate a second intermediate hash value; inputs the second intermediate hash value into the same key generation algorithm as the mobile terminal for calculation to generate the verification key, and uses the verification key to decrypt and authenticate the encrypted transaction data.

[0065] In this embodiment, the payment server, based on the received payment request initiation time information, queries a pre-established acoustic feature benchmark library associated with the mobile terminal for reference acoustic entropy features that match the time window of that time. After receiving the payment request initiation time information from the mobile terminal, the payment server uses this time information as the basis for the query. The payment server pre-maintains an acoustic feature benchmark library associated with each mobile terminal, which stores historical acoustic entropy feature values ​​or their statistical information collected and uploaded by the mobile terminal at different time points. The payment server searches the benchmark library for records that match the received payment request initiation time information in terms of time. The matching process can employ various strategies; for example, it can simply search for the few records closest to that time, or define a time window and search for all records falling within that time window. This step aims to provide the server with a reference value that is highly correlated with the real-time acoustic entropy feature value of the mobile terminal to cope with dynamic changes in ambient sound waves and transmission delays.

[0066] Based on this, the reconstructed acoustic entropy feature value is determined using the retrieved reference acoustic entropy features. After retrieving reference acoustic entropy features that match the information at the time the payment request was initiated, the payment server needs to determine a reconstructed acoustic entropy feature value for subsequent key generation based on these reference features. This reconstructed acoustic entropy feature value aims to simulate or represent, as closely as possible, the acoustic entropy feature value extracted by the mobile terminal when the payment request was initiated. For example, if multiple reference acoustic entropy features are retrieved, they can be averaged, calculated using the median, or other statistical methods can be used to obtain a representative value. This step is crucial for ensuring the consistency of the acoustic entropy feature values ​​between the server and the mobile terminal.

[0067] In this embodiment, the reconstructed acoustic entropy feature value is then concatenated with the user identification code corresponding to the mobile terminal, stored on the payment server, to generate the second concatenated data. To maintain consistency with the key generation process of the mobile terminal, the payment server deterministically concatenates the determined reconstructed acoustic entropy feature value with the user identification code corresponding to the mobile terminal initiating the payment request, stored locally. The user identification code is a unique sequence of numbers or characters that identifies the user, ensuring the personalization and security of key generation. The concatenation method can be a simple string or byte sequence concatenation, but it must be exactly the same as the way the mobile terminal generated the first concatenated data to ensure input consistency for subsequent hash operations.

[0068] Furthermore, the second concatenated data undergoes a cryptographic hash operation to generate a second intermediate hash value. After generating the second concatenated data, the payment server uses the same cryptographic hash algorithm as the mobile terminal (such as SHA-256, SHA-3, etc.) to perform a hash operation on the second concatenated data, thereby generating a fixed-length second intermediate hash value. This hash operation is unidirectional, irreversible, and extremely sensitive to even minor changes in the input data, ensuring data integrity and the security of subsequent key generation.

[0069] In this embodiment, the second intermediate hash value is finally input into the same key generation algorithm as the mobile terminal to generate a verification key, which is then used to decrypt and authenticate the encrypted transaction data. The payment server takes the generated second intermediate hash value as input and feeds it into a preset key generation algorithm identical to that of the mobile terminal. This key generation algorithm (e.g., an algorithm based on elliptic curve cryptography) converts the second intermediate hash value into a one-time dynamic key, i.e., the verification key. Once the verification key is generated, the payment server uses it to decrypt the encrypted transaction data sent by the mobile terminal. If decryption is successful and the transaction data content matches expectations, it indicates that the verification key is consistent with the one-time dynamic session key generated by the mobile terminal, thus completing the authentication of the transaction data and confirming the legality of the transaction and the user's identity.

[0070] In this embodiment, through the above technical solution, the payment server no longer needs to acquire ambient sound wave signals in real time. Instead, it uses a pre-established acoustic feature benchmark library and payment request initiation time information to intelligently query and determine a reference acoustic entropy feature that matches the acoustic entropy feature value generated by the mobile terminal. This reconstruction method based on historical data and time window matching effectively overcomes the challenges posed by the instantaneous randomness of ambient sound waves and tolerates clock deviations and network latency between the mobile terminal and the server, significantly improving the accuracy and stability of the server-side reconstructed acoustic entropy feature value. By ensuring strict consistency between the server and the mobile terminal in acoustic entropy feature values, user identification codes, hash operations, and key generation algorithms, the payment server can reliably generate a verification key that is exactly the same as that of the mobile terminal, thereby successfully decrypting encrypted transaction data and completing authentication. This not only ensures the security of the payment process and prevents man-in-the-middle attacks and replay attacks, but also improves the smoothness and success rate of the user's payment experience, avoiding transaction failures caused by mismatched acoustic entropy feature values.

[0071] In one feasible implementation, the step of the payment server querying a reference acoustic entropy feature matching the time window of the received payment request initiation time information from a pre-established acoustic feature benchmark library associated with the mobile terminal includes: determining a query time window with a time length greater than the real-time acquisition time window based on the payment request initiation time information and a preset sliding window strategy, to accommodate clock deviations and network transmission delays between the mobile terminal and the server; extracting all historical acoustic entropy feature records within the query time window from the acoustic feature benchmark library and calculating their statistical distribution characteristics; using a pre-trained anomaly detection model to filter all extracted historical records and remove abnormal records that significantly deviate from the statistical distribution characteristics; and taking the median or time-decrease-based weighted average of the remaining records as the final reference acoustic entropy feature to enhance the stability and anti-interference of the matching.

[0072] In this embodiment, firstly, based on the payment request initiation time information and a preset sliding window strategy, a query time window with a duration longer than the real-time acquisition time window is determined to accommodate clock deviations and network transmission delays between the mobile terminal and the server. This strategy aims to solve the time synchronization problem between the mobile terminal and the server, because network transmission delays and device clock drift may cause a deviation between the payment request initiation time information received by the server and the actual time when the mobile terminal acquires the acoustic entropy feature value. By setting a query time window centered on the payment request initiation time information and extending a certain duration before and after it, for example, if the time window for the mobile terminal to acquire the acoustic entropy feature value is 5 seconds, the server can set a query time window of 10 seconds or 15 seconds, covering a few seconds before and after the payment request initiation time information, ensuring that the query time window is wide enough to accommodate time offsets caused by clock deviations and network delays.

[0073] In this embodiment, next, all historical acoustic entropy feature records within the query time window are extracted from the acoustic feature benchmark library, and their statistical distribution characteristics are calculated. The acoustic feature benchmark library is a database storing acoustic entropy feature values ​​collected by a specific mobile terminal at different time points. Based on the query time window determined in the previous step, the server retrieves all acoustic entropy feature values ​​falling within that time window and their corresponding timestamps from the benchmark library, and performs statistical analysis on these extracted acoustic entropy feature value sets, such as calculating the mean, variance, median, mode, quartiles, or probability density function, to describe the central tendency and dispersion of the acoustic entropy feature values ​​within the time window, providing a data foundation for subsequent anomaly detection and reference feature determination.

[0074] In this embodiment, a pre-trained anomaly detection model is used to filter all extracted historical records, removing abnormal records that significantly deviate from the statistical distribution characteristics. This anomaly detection model can be a pre-trained model based on statistical methods (such as Z-score, IQR), machine learning methods (such as Isolation Forest, One-Class SVM), or deep learning methods (such as Autoencoder), which learns patterns in normal acoustic entropy features to identify outliers. The historical acoustic entropy feature records with extracted and calculated statistical distribution characteristics are input into the model. Based on its learned rules, the model marks and removes records that significantly deviate from the normal pattern, thereby ensuring the purity and reliability of the data used for subsequent reference feature determination.

[0075] In this embodiment, the median or time-decay-based weighted average of the remaining records after filtering is taken as the final reference acoustic entropy feature to enhance the stability and anti-interference capability of the matching. If the median is used, the filtered acoustic entropy feature values ​​are sorted, and the value at the middle position is taken. The median is insensitive to outliers and provides a robust measure of central tendency. If a time-decay-based weighted average is used, the acoustic entropy feature record closer to the time of payment request initiation is considered to have greater reference value. Therefore, these records are assigned higher weights; for example, the weights can be designed to be proportional to the inverse of the time difference or to use an exponential decay function. By using the median or weighted average, the instantaneous fluctuations of ambient sound waves can be smoothed out, reducing the impact of noise at a single moment on the reference feature, making the acoustic entropy feature value reconstructed by the server closer to the actual acoustic entropy feature value generated by the mobile terminal.

[0076] In this embodiment, the above technical solution effectively solves the problem of inaccurate matching of acoustic entropy feature values ​​between the mobile terminal and the payment server due to clock deviation, network latency, and the instantaneous randomness of ambient sound waves. By introducing a sliding window strategy, the server can search for historical acoustic entropy features over a wider time range, thereby compensating for uncertainties in time synchronization. Furthermore, statistical analysis of historical records and the use of anomaly detection models to remove abnormal data ensure the purity and reliability of the acoustic entropy feature data used for reference. Finally, by calculating the median or a time-decay-based weighted average, a more representative and stable reference acoustic entropy feature can be extracted from multiple reliable historical records, effectively smoothing out instantaneous fluctuations caused by environmental noise, significantly enhancing the accuracy and anti-interference capability of the server-side reconstructed acoustic entropy feature values, and thus improving the stability and security of the entire dynamic authentication process.

[0077] In the embodiments of this application, the electronic information dynamic authentication method based on mobile terminal payment includes responding to a payment request initiated by the mobile terminal by activating the microphone of the mobile terminal, acquiring real-time collected ambient sound wave signals, and processing the ambient sound wave signals to extract sound entropy feature values ​​characterizing the instantaneous randomness of the information; performing deterministic data concatenation of the sound entropy feature values ​​with the user identity code pre-stored locally on the mobile terminal to generate first concatenated data; performing encrypted hash operation on the first concatenated data to generate a first intermediate hash value; generating a one-time dynamic session key based on the first intermediate hash value using a preset key generation algorithm; encrypting transaction information containing payment elements using the one-time dynamic session key to generate encrypted transaction data; and sending the encrypted transaction data and payment request initiation time information together to the payment server, so that the payment server can synchronously reconstruct the sound entropy feature values ​​based on the payment request initiation time information and generate a corresponding verification key to complete the decryption and authentication of the encrypted transaction data. By capturing ambient sound wave signals in real time to extract sound entropy feature values ​​and dynamically generating one-time session keys, the uniqueness and unpredictability of the transaction source are ensured, thereby improving the security of the transaction.

[0078] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the electronic information dynamic authentication method based on mobile terminal payment in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0079] This application also provides an electronic information dynamic authentication system based on mobile terminal payment, see reference. Figure 2 The electronic information dynamic authentication system based on mobile terminal payment includes: a memory 10, a processor 20, and an electronic information dynamic authentication program based on mobile terminal payment stored on the memory 10 and executable on the processor 20. The electronic information dynamic authentication program based on mobile terminal payment is configured to implement the steps of the electronic information dynamic authentication method based on mobile terminal payment.

[0080] The electronic information dynamic authentication system based on mobile terminal payment provided in this application, employing the electronic information dynamic authentication method based on mobile terminal payment in the above embodiments, can improve transaction security. Compared with the prior art, the beneficial effects of the electronic information dynamic authentication system based on mobile terminal payment provided in this application are the same as those of the electronic information dynamic authentication method based on mobile terminal payment provided in the above embodiments, and other technical features of the electronic information dynamic authentication system based on mobile terminal payment are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0081] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0082] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. All equivalent structural transformations made under the technical concept of this application using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of this application.

Claims

1. An electronic information dynamic authentication method based on mobile terminal payment, characterized in that, The method includes: In response to a payment request initiated by a mobile terminal, the microphone of the mobile terminal is activated to acquire real-time ambient sound wave signals, and the ambient sound wave signals are processed to extract sound entropy feature values ​​that characterize the randomness of their instantaneous information. The acoustic entropy feature value is deterministically concatenated with the user identification code pre-stored locally on the mobile terminal to generate the first concatenated data; Perform a cryptographic hash operation on the first concatenated data to generate a first intermediate hash value; Based on the first intermediate hash value, a one-time dynamic session key is generated by performing calculations using a preset key generation algorithm. The transaction information containing payment elements is encrypted using the one-time dynamic session key to generate encrypted transaction data; The encrypted transaction data and the payment request initiation time information are sent to the payment server together, so that the payment server can synchronously reconstruct the acoustic entropy feature value based on the payment request initiation time information, and generate a corresponding verification key to complete the decryption and authentication of the encrypted transaction data.

2. The electronic information dynamic authentication method based on mobile terminal payment according to claim 1, characterized in that, The steps of processing the ambient sound wave signal to extract acoustic entropy feature values ​​that characterize the instantaneous randomness of its information include: The ambient sound wave signal is converted into a digital signal sequence, and the digital signal sequence is divided into multiple consecutive signal frames; For at least one of the signal frames, it is converted from the time domain to the frequency domain to obtain the spectral data of the signal frame; Calculate the frame-level entropy value of the signal frame based on the spectrum data; The acoustic entropy feature value is obtained by combining the frame-level entropy values ​​of multiple signal frames.

3. The method for dynamic electronic information authentication based on mobile terminal payment as described in claim 2, characterized in that, The step of converting at least one of the signal frames from the time domain to the frequency domain to obtain the spectral data of the signal frame includes: A preset window function is added to the signal frame to reduce spectral leakage; Perform a Fast Fourier Transform or an improved Discrete Cosine Transform on the windowed signal frame to obtain a preliminary spectrum; The preliminary spectrum is logarithmically compressed to simulate the characteristics of human hearing, thereby obtaining the spectrum data.

4. The method for dynamic electronic information authentication based on mobile terminal payment as described in claim 2, characterized in that, The step of combining the frame-level entropy values ​​of multiple signal frames to obtain the acoustic entropy feature value includes: The frame-level entropy value of each signal frame is checked for temporal continuity. If the difference between the frame-level entropy values ​​of adjacent frames exceeds a preset threshold, it is determined that the current signal frame is affected by sudden noise interference. For the frame-level entropy values ​​that pass the continuity check, a weighted average is calculated based on the signal-to-noise ratio (SNR) estimates of each signal frame. The higher the SNR estimate, the greater the weight of the signal frame, in order to suppress interference from signals with an SNR lower than a preset SNR threshold, and thus obtain the final acoustic entropy feature value.

5. The method for dynamic electronic information authentication based on mobile terminal payment as described in claim 1, characterized in that, The steps for generating a one-time dynamic session key based on the first intermediate hash value using a preset key generation algorithm include: The first intermediate hash value is mapped to a scalar value under the elliptic curve cryptography system to serve as a private key seed; The private key seed is multiplied by a preset base point of the elliptic curve cryptosystem to generate elliptic curve points. Data of a preset length is extracted from the coordinate data of the elliptic curve points to serve as the one-time dynamic session key.

6. The method for dynamic electronic information authentication based on mobile terminal payment as described in claim 5, characterized in that, The step of mapping the first intermediate hash value to a scalar value under elliptic curve cryptography to serve as a private key seed includes: The first intermediate hash value is used as the initial input, and a bit-by-bit cyclic XOR operation is performed with the perturbation vector derived from the acoustic entropy feature value to obtain the initial seed. The initial seed is input into an extension function based on a nonlinear feedback shift register to generate a pseudo-random sequence with extended length. The first N bits of the pseudo-random sequence are extracted and interpreted as an integer value represented by the first N bits that falls within the range of private key values ​​required by the elliptic curve cryptosystem, and used as the final private key seed; wherein N is greater than the length of the initial seed so that the entropy of the private key seed meets the entropy security requirements of the elliptic curve cryptosystem for the private key.

7. The method for dynamic electronic information authentication based on mobile terminal payment as described in claim 5, characterized in that, The step of extracting data of a preset length from the coordinate data of the elliptic curve points to serve as the one-time dynamic session key includes: Obtain the binary representation of the x-coordinate and y-coordinate of the points on the elliptic curve; Extract the lower M bits of the horizontal axis and the higher M bits of the vertical axis; where M is a preset value; The two extracted bit sequences are cross-mixed and arranged. The mixed bit sequence is converted into a hexadecimal string, and the first L characters are taken as the one-time dynamic session key; where L is determined according to the key length required by the target encryption algorithm.

8. The method for dynamic electronic information authentication based on mobile terminal payment as described in claim 1, characterized in that, The payment server synchronously reconstructs the acoustic entropy feature value based on the payment request initiation time information, and generates a corresponding verification key accordingly to complete the decryption and authentication of the encrypted transaction data, including: The payment server, based on the received payment request initiation time information, queries a reference acoustic entropy feature that matches the time window of the time from a pre-established acoustic feature benchmark library associated with the mobile terminal. Based on the retrieved reference acoustic entropy features, the reconstructed acoustic entropy feature values ​​are determined; The reconstructed acoustic entropy feature value is concatenated with the user identification code corresponding to the mobile terminal stored on the payment server to generate the second concatenated data; Perform a cryptographic hash operation on the second concatenated data to generate a second intermediate hash value; The second intermediate hash value is input into the same key generation algorithm as the mobile terminal for calculation to generate the verification key, and the verification key is used to decrypt and authenticate the encrypted transaction data.

9. The method for dynamic electronic information authentication based on mobile terminal payment as described in claim 1, characterized in that, The step of the payment server querying a reference acoustic entropy feature that matches the time window of the received payment request from a pre-established acoustic feature reference library associated with the mobile terminal, based on the received payment request initiation time information, includes: Based on the payment request initiation time information and combined with the preset sliding window strategy, a query time window with a time length greater than the real-time acquisition time window is determined to adapt to the clock deviation and network transmission delay between the mobile terminal and the server. Extract all historical acoustic entropy feature records within the query time window from the acoustic feature benchmark library, and calculate their statistical distribution characteristics; Using a pre-trained anomaly detection model, all extracted historical records are filtered to remove abnormal records that significantly deviate from the statistical distribution characteristics. For the remaining records after filtering, the median of their entropy values ​​or the weighted average based on time decay is taken as the final reference acoustic entropy feature to enhance the stability and anti-interference of the matching.

10. A dynamic electronic information authentication system based on mobile terminal payment, characterized in that, The mobile terminal payment-based electronic information dynamic authentication system includes: a memory, a processor, and a mobile terminal payment-based electronic information dynamic authentication program stored in the memory and executable on the processor. The mobile terminal payment-based electronic information dynamic authentication program is configured to implement the steps of the mobile terminal payment-based electronic information dynamic authentication method as described in any one of claims 1 to 9.

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