Certificate card biological characteristic data reading method and system based on encryption machine

Abstract

The invention relates to the technical field of data processing, in particular to a method and system for reading biological characteristic data of a certificate card based on an encryption machine, and the method specifically comprises the steps: dividing characteristic points into three types of core anchor points, variable characteristic points and environment sensitive points, and obtaining a characteristic point topological graph; calculating encryption granularity of data of different feature categories to obtain an encryption strategy table; constructing a dynamic obfuscation function to encrypt the data; and according to an environment credibility evaluation result, selecting a data reading strategy, obtaining a historical decryption log and a feature point drift matrix, identifying an abnormal drift region, dynamically tuning the weight of an encryption granularity decision function according to the drift condition, resampling the drift region features, and outputting an updated encryption strategy parameter set. According to the method and the device, the problem that in the prior art, hardware encryption and software encryption have different protection granularities on biological characteristic data, and encryption granularity mismatching easily occurs due to lack of a fine-grained access interface during dynamic collaborative encryption is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and is a method and system for reading biometric data of a credential card based on an encryption machine. Background Art

[0002] In the fields of biometric recognition and encryption, the existing technologies generally face the following challenges: there is an inherent contradiction between the traditional encryption granularity and the biometric data structure. Hardware encryption modules (such as Secure Element, SE) usually adopt a fixed data block encryption mechanism, while when performing feature comparison at the software layer, fine-grained biometric features (such as fingerprint minutiae, iris texture) need to be extracted, resulting in the system having to decrypt all data, causing sensitive information to be exposed in memory for a long time, and forcing the software to sacrifice security to meet functional requirements when processing. At the same time, the existing encryption strategies are insufficient in adapting to dynamic changes in the environment. Especially in multi-modal biometric scenarios, fluctuations in environmental parameters such as temperature and humidity changes, sensor accuracy drift, etc. will significantly affect the stability of feature point coordinates, and static encryption models cannot achieve dynamic adjustment of granularity based on environmental risk levels, resulting in security policy overload or insufficient protection in high-interference environments. In addition, there is a lack of an effective compensation mechanism for the progressive drift of biometric features caused by long-term use (such as fingerprint epidermis wear, iris texture degradation). Existing systems mostly use fixed thresholds to determine the validity of features, which is likely to cause an increase in the false recognition rate and the failure of encryption strategies over time. Although existing biometric protection schemes attempt to improve through environmental perception or dynamic block encryption, there are still problems such as the disconnection in the time and space dimensions and the lag in parameter updates, making it difficult to resist side-channel attacks. Summary of the Invention

[0003] The technical problem to be solved by the present invention is that, in the existing technology, the protection granularity of hardware encryption and software encryption for biometric data is different, and it is easy to have an encryption granularity mismatch due to the lack of a fine-grained access interface during dynamic collaborative encryption. A method and system for reading biometric data of a credential card based on an encryption machine are proposed.

[0004] In order to achieve the above object, the technical solution of a method for reading biometric data of a credential card based on an encryption machine according to the present invention includes the following steps: Collect multi-modal biometric raw data, classify feature points into three categories: core anchor points, variable feature points, and environment-sensitive points, and obtain a feature point topology map; Perform real-time perception of the data reading environment, import the feature point topology map into a category discrimination strategy to calculate the encryption granularity of different feature category data, and obtain an encryption policy table; Generate an environmental entropy source with environmental parameters such as Shannon entropy, combine the original biometric data with the encryption policy table, and construct a dynamic confusion function to encrypt the data; Evaluate the credibility of the environment in real time, and select a data reading strategy according to the evaluation result of the environment credibility; Obtain historical decryption logs and the feature point drift matrix, identify abnormal drift regions, dynamically tune the weights of the encryption granularity decision function according to the drift situation, and resample the feature of the drift region to output an updated set of encryption policy parameters.

[0005] Collect multi-modal biometric raw data, classify feature points into three categories: core anchor points, variable feature points, and environment-sensitive points, and obtain a feature point topology map, including: S11: Collect the raw biometric coordinate data and the temperature, humidity, and pressure parameters monitored by the sensor in real time, and correct the raw coordinates through the environmental noise compensation formula; At the same time, based on the sliding mean and variance standardized reference coordinate system related to the environment, output the dynamically calibrated standardized feature coordinates; S12: Determine an adaptive time window according to the environmental perturbation threshold and the environmental change gradient, and quantify the correlation degree of different feature points in the time series according to the feature correlation quantization strategy to generate a spatio-temporal correlation matrix integrating environmental weights; S13: Collect the measurement standard deviation of environmental parameters, calculate the offset generated by each feature point affected by the environment, and construct an environmental sensitivity model; Based on the diagonal elements of the spatio-temporal correlation matrix, the environmental offset, and the difference between the current environment and the nominal environment, obtain the stability score of each feature point adapted to the environment; S14: Based on the stability score, construct a stability gradient field, monitor the spatial change trend of the feature point stability, classify the feature points into three categories: core anchor points, variable feature points, and environment-sensitive points, and output a feature point topology map with spatial topological relationships.

[0006] Perceive the data reading environment in real time, import the feature point topology map into the category discrimination strategy to calculate the encryption granularity of different feature category data, and obtain an encryption policy table, including: S21: Collect the original values of the environmental parameters of the data reading environment, as well as the nominal mean and standard deviation corresponding to each environmental parameter, and synthesize an environmental security factor after normalizing the collected data; S22: Collect the normalized environmental parameters, as well as the feature stability mean, the maximum data dimension, and the environmental security factor, design different forms of membership functions for the device security level, network latency, and sensor accuracy respectively, and obtain their non-linear relationships with the weights; then associate the membership function values with the feature stability mean, the reciprocal of the environmental security factor, and the maximum data dimension respectively, and dynamically adjust the weight parameters through normalization calculation to generate weight parameters associated with the environmental state ; S23: Obtain the feature data dimension, stability score, stability gradient, environmental security factor, and environmental entropy, and construct a dimension-security coupling term ; Construct a stability constraint function , and combine the stability score and the environmental deviation degree to constrain the granularity; Finally, calculate the encryption granularity level according to the ratio of the two and in combination with the adjustment of the environmental entropy value ; S24: Extract the feature point classification result, encryption granularity level, minimum stability score, and environmental sensitivity in the feature point topology map, and generate an encryption block mapping table that combines the spatial topology structure and the security level; S3 includes: S31: Collect the device operation duration, environmental parameters and their gradients, and device aging factor, generate a composite entropy value, multiply the composite entropy value by and then take the floor to get an integer, take the modulus of the above integer , map the result to a 128-bit binary space, and use the initial seed of quantum key distribution to perform an exclusive OR operation with the result of the modulo operation to generate a final 128-bit quantum noise sequence .

[0007] S32: Extract the feature point stability score, stability gradient, and quantum noise sequence, design a noise intensity function according to the stability score and gradient, inject the modulated quantum noise into the AES-encrypted feature data, and output the core anchor point ciphertext after hierarchical confusion operation; S33: Generate a hash value containing space-time information through SHA3-512, construct a quantum-resistant hash chain using cyclic shift and exclusive OR, and output a quantum-secure hash chain with space-time correlation; S34: Dynamically adjust the block encryption intensity according to the granularity level, and use XMSS hash to construct a Merkle tree containing block entropy; Construct an extended Merkle signature tree with a block entropy binding mechanism; S35: Cyclically shift the sensitive data after AES encryption, and combine EdDSA and NTRU to generate a dual-algorithm composite signature, and output a quantum-resistant signature; S4 includes: S41: Calculate the dynamic weights of each environmental parameter through an exponential function based on real-time environmental parameters, nominal environmental parameters, average feature stability, and nominal standard deviation of environmental parameters, and obtain the normalized environmental comprehensive deviation degree according to the dynamic weights ; S42: Extract the device running time, initial trust threshold, aging factor, environmental parameter entropy, and minimum feature stability score, set the base threshold, and correct the base threshold according to the environmental parameter entropy and minimum feature stability score to obtain the adaptive trust threshold; S43: Modify the loss function gradient through feature stability gradient amplification; Import the feature stability, modified gradient, and environmental risk factor into the priority decision strategy to obtain the decryption priority of feature points with environmental risk suppression; S44: Generate a memory self-destruction trigger signal, and import the feature stability, environmental deviation degree, decryption priority, and maximum survival time into the risk perception strategy to quantify the survival time of the plaintext memory; S5 includes: S51: Extract the average change rate of historical environmental parameters , feature point coordinates, central coordinates of the feature region, feature point stability score, and initial time scale ; Dynamically adjust the time scale according to the environmental change rate, and synchronously calculate the spatial correlation weight between the feature point and the center of the feature region ; Combine the time scale and the spatial correlation weight to generate a spatio-temporal coupled dynamic convolution kernel ; S52: Calculate the drift energy accumulation through the spatio-temporal coupled dynamic convolution kernel, and generate a dynamic threshold based on the median of the historical drift energy and the absolute median difference. Compare the drift energy accumulation with the dynamic threshold. When the drift energy accumulation is greater than the dynamic threshold, it indicates that the feature point is abnormal, and filter out the set of abnormal feature points with excessive drift energy; S53: Quantify the feature point abnormality degree and environmental complexity through the hyperbolic tangent function, output the risk factor, adjust the adaptive weight parameter according to the risk factor, and dynamically adjust the priorities of feature matching and environmental compensation; S54: Construct a drift compensation model, generate a compensation factor for feature point stability, combine the historical stability and the compensation factor, allocate weights according to the drift energy ranking, and generate a new stability score; In addition, a reading system for biometric data of a credential card based on an encryption machine according to the present invention includes the following modules: A topology map output module, an encryption policy acquisition module, an encryption module, a data reading module, and an encryption parameter update module; The topology map output module is used to collect multi-modal biometric raw data, classify the feature points into three categories: core anchor points, variable feature points, and environment-sensitive points, and obtain a feature point topology map; The encryption policy acquisition module is used to perceive the data reading environment in real time, import the feature point topology map into the category discrimination policy to calculate the encryption granularity of different feature category data, and obtain an encryption policy table; The encryption module generates an environmental entropy source with environmental parameters such as Shannon entropy, combines the original biometric data with the encryption policy table, and constructs a dynamic confusion function to encrypt the data; The data reading module is used to evaluate the environmental credibility in real time, and select a data reading strategy according to the evaluation result of the environmental credibility; The encryption parameter update module is used to obtain historical decryption logs and the feature point drift matrix, identify abnormal drift regions, dynamically tune the weights of the encryption granularity decision function according to the drift situation, and resample the features in the drift regions, and output the updated encryption policy parameter set.

[0008] Compared with the prior art, the technical effects of the present invention are as follows: The present invention realizes a breakthrough in the biometric data security protection system by integrating the dynamic attributes of biometrics and the environmental parameter perception mechanism. Aiming at the core problems existing in the existing biometric encryption technologies, such as rigid encryption granularity, poor environmental adaptability, and weak resistance to quantum attacks, this solution achieves significant technical effects through a multi-dimensional cooperation mechanism: First, based on the spatio-temporal covariance matrix and the dynamic stability quantization model (S1), a three-layer classification system of biometric feature points is constructed, breaking through the traditional static block mode, reducing the memory exposure surface of the core anchor points compared with the traditional solution, and at the same time dynamically adjusting the classification boundary through the stability gradient field, improving the feature recognition accuracy in the case of environmental mutations; Second, the environment-feature coupling encryption granularity decision model (S2) in the present invention dynamically adjusts the weight parameters through the fuzzy membership function, making the encryption granularity adapt to the device security level, network latency, and sensor accuracy in real time. After testing, it greatly reduces the time consumption during the data reading process compared with the fixed granularity strategy in the complex mobile environment; In addition, the quantum noise mapping mechanism (S3) binds the environmental entropy to the device aging factor, generates a 128-bit anti-quantum noise sequence, and combines the hierarchical confusion function to increase the anti-quantum attack strength of the AES256 ciphertext to 128 bits, improving the security margin; The dynamic decryption engine (S4) in the present invention realizes the real-time optimization of the decryption priority through the environmental offset degree and the double-threshold verification mechanism, and at the same time corrects the loss function through the stability gradient, improving the feature comparison efficiency; At the same time, in the present invention, a full-life cycle maintenance system of biometrics is constructed through the spatio-temporal convolution drift detection algorithm, maintaining a high drift compensation accuracy for fingerprint data used continuously for 12 months, and combining the parameter dynamic tuning mechanism to reduce the system false recognition rate. Description of the Drawings

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. Among them: Figure 1 It is a schematic flowchart of a method for reading biometric data of a certificate card based on an encryption machine according to the present invention; Figure 2 It is a schematic structural diagram of a system for reading biometric data of a certificate card based on an encryption machine according to the present invention. Detailed implementation manners

[0010] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific implementation manners of the present invention in conjunction with the drawings of the specification.

[0011] In the following description, many specific details are set forth to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0012] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or alternative embodiment that is mutually exclusive with other embodiments.

[0013] Embodiment 1: As Figure 1 shown, a method for reading biometric data of a certificate card based on an encryption machine according to an embodiment of the present invention, as Figure 1 shown, includes the following specific steps: S1: Collect multi-modal biometric raw data, classify the feature points into three categories: core anchor points, variable feature points, and environment-sensitive points, and obtain a feature point topology map; S11: Collect the original biometric coordinate data and the temperature, humidity, and pressure parameters monitored by the sensor in real time, and correct the original coordinates through the environmental noise compensation formula; Exemplarily, in this embodiment, a specific implementation manner for correcting the original coordinates through the environmental noise compensation formula is provided, specifically as: ; Among them, is the original coordinate (a physical quantity, such as position, distance, etc., whose dimension depends on the type of sensor); is the coordinate after compensation; is the device calibration factor, used to calibrate differences across sensors; is the change in environmental parameters (temperature / humidity / pressure); is the sensitivity of biological characteristics to environmental parameters; It should be noted that the impact of environmental disturbances on the original data is quantified and offset by multiplying the environmental sensitivity by the change amount.

[0014] At the same time, the reference coordinate system is standardized based on the sliding mean and variance related to the environment, and the standardized feature coordinates after dynamic calibration are output; S12: Based on the standardized feature coordinates, the adaptive time window is determined according to the environmental disturbance threshold and the environmental change gradient. According to the feature association quantification strategy, the association degree of different feature points in the time series is quantified to generate a spatiotemporal association matrix that integrates the environmental weights. Exemplarily, in this embodiment, a strategy for determining an adaptive time window is provided, specifically: ; Where T is the time window length; is the environmental disturbance threshold; for environmental gradients; It should be noted that T is inversely proportional to the square of the environmental gradient, and drastic environmental changes ( When the environment is stable, the window is extended to accumulate more data. It should also be noted that through Adjust the sensitivity of the window to disturbances to avoid extreme values ​​that cause the window to be too long or too short; Exemplarily, in this embodiment, the feature association quantification strategy is specifically: ; in, is the correlation between features i and j; is the standardized feature coordinate; is the mean of feature i; is the environmental attenuation factor; is the environmental offset of the tth sampling; It should be noted that the generation of a spatiotemporal correlation matrix that integrates environmental weights can intuitively reflect the characteristics of the dependency between features being modulated by the environment.

[0015] S13: Collect the standard deviation of environmental parameter measurements, calculate the offset of each feature point affected by the environment, and build an environmental sensitivity model; Exemplarily, in this embodiment, an output formula of an environmental sensitivity model is provided, specifically: ; Wherein, is the environmental sensitivity of feature i; is the environmental parameter of the measurement standard deviation; It should be noted that the environmental sensitivity model can intuitively quantify the range of fluctuations in the coordinates of feature points caused by environmental measurement errors.

[0016] Based on the diagonal elements of the spatio-temporal correlation matrix, the environmental offset, and the difference between the current environment and the nominal environment, obtain the stability score of environmental adaptation for each feature point; Exemplarily, in this embodiment, the stability score of environmental adaptation for each feature point is specifically: ; Wherein, is the stability score of the i-th feature point; is the feature self-correlation degree, which is used to reflect the time series stability; E is the current environmental parameter, is the nominal environmental condition; It should be noted that When the item makes the environment deviate from the nominal condition, the denominator increases, decreases, enhancing the strictness of stability judgment; It should be noted that the numerator is used to characterize the time stability of the feature itself; The denominator characterizes environmental sensitivity, and the ratio of the two balances the intrinsic stability and environmental impact; It should be noted that the environmental adaptation stability score of each feature point is used to quantify the stability degree of each feature point in different environments.

[0017] S14: Based on the stability score, construct a stability gradient field, monitor the spatial change trend of the feature point stability, divide the feature points into three categories: core anchor points, variable feature points, and environmentally sensitive points, and output a feature point topology map with spatial topological relationships.

[0018] Exemplarily, in this embodiment, a construction implementation manner of a stability gradient field based on Kriging interpolation is provided, specifically: ; Wherein, is the stability gradient; x, y are spatial coordinates; Exemplarily, in this embodiment, a specific implementation manner of dividing feature points into three categories, namely core anchor points, variable feature points, and environment-sensitive points, is further provided, which specifically includes: Calculate the dynamic classification boundary through the statistic of the stability score, and combine the stability score, gradient magnitude, and coordinate variance of the feature points to divide the feature points into three categories: core anchor points, variable feature points, and environment-sensitive points; The specific dynamic classification boundary includes: ; ; wherein, are the classification thresholds respectively; is the information entropy of the gradient field, which is used to reflect the degree of chaos of the spatial stability distribution; is the skewness of the stability score, which is used to reflect the symmetry of the score distribution; It should be noted that, is negatively correlated with the gradient entropy, that is, when the spatial stability distribution is chaotic (large entropy), the threshold of the core anchor point is reduced to avoid being overly strict; conversely, the threshold is increased to narrow the stable area.

[0019] It should also be noted that, is positively correlated with the skewness. If the score is right-skewed (most points are stable), the threshold of the low-sensitivity area is increased to expand the transition area; conversely, the range of the sensitive area is adjusted.

[0020] Exemplarily, in this embodiment, the division of feature points includes: When and at the same time, the feature point is divided into a core anchor point; When and at the same time, the feature point is divided into a variable feature point; When and at the same time, the feature point is divided into an environment-sensitive point.

[0021] S2: Perceive the data reading environment in real time, import the feature point topology map into the category discrimination strategy to calculate the encryption granularity of different feature category data, and obtain the encryption strategy table; S21: Collect the original values of the environmental parameters of the data reading environment, as well as the nominal mean and standard deviation corresponding to each environmental parameter, and synthesize the environmental security factor after normalizing the collected data; Exemplarily, in this embodiment, a specific implementation manner of synthesizing the environmental security factor is provided, including: adaptively allocating weights according to the absolute value of the normalized parameters, and multiplying and accumulating the weights with the values obtained by transforming the normalized parameters through the hyperbolic tangent function; Among them, the calculation strategy of the adaptive weight is specifically as follows: ; is the dynamic weight of the environmental parameter k; is the real-time value of the environmental parameter; it should be noted that in this embodiment, the larger the value of the dynamic environmental safety factor, the safer the environment, and the dynamic environmental safety factor provides a quantitative basis for environmental safety for subsequent encryption strategies; S22: Collect the normalized environmental parameters, the mean value of feature stability, the maximum data dimension, and the environmental safety factor, design different forms of membership functions for the device security level, network latency, and sensor accuracy respectively, and obtain their non-linear relationship with the weight; then associate the membership function values with the mean value of feature stability, the reciprocal of the environmental safety factor, and the maximum data dimension respectively, and adjust the weight parameters dynamically through normalization calculation to generate the weight parameters associated with the environmental state ; Exemplarily, in this embodiment, a membership function for the device security level is provided: ; Among them, represents the device security level, h1 is the curvature constant, and it should be noted that this function makes the higher the device security level, the membership degree tends to 1 more; Exemplarily, in this embodiment, a membership function for network latency is provided: ; Among them, is the network latency value, is the latency threshold, the higher the network latency, the membership degree tends to 0 more; Exemplarily, in this embodiment, a membership function for sensor accuracy is provided: ; Among them, is the sensor accuracy, are the lower and upper limits of the accuracy interval, and , this function realizes the linear mapping of the sensor accuracy within the interval ;

[0022] Exemplarily, in this embodiment, a generation implementation method of the weight parameter associated with the environmental state is provided, specifically as follows: ; Among them, are the fuzzy weight parameters respectively, used to adjust the priorities of encryption, transmission, and storage; is the mean of feature stability; is the sum of membership degrees; is the environmental safety factor; is the maximum data dimension; S23: Obtain the feature data dimension, stability score, stability gradient, environmental safety factor, and environmental entropy, and construct the dimension-safety coupling term ; Exemplarily, in this embodiment, a construction strategy for the dimension-safety coupling term is provided, specifically: ; where is the feature data dimension; is the feature stability gradient; Construct the stability constraint function , and combine the stability score and the environmental deviation degree to constrain the granularity; Exemplarily, in this embodiment, a construction strategy for the stability constraint function is provided, specifically: ; where is the nominal environmental parameter under the ideal data reading environment; Finally, according to the ratio of the two and combined with the adjustment of the environmental entropy value, the encryption granularity level is calculated ; Exemplarily, in this embodiment, a strategy for obtaining the encryption granularity level is provided, specifically: ; It should be noted that is a positive integer, and the larger the value, the finer the encryption granularity; is the environmental entropy value; S24: Extract the feature point classification result, encryption granularity level, minimum stability score, and environmental sensitivity in the feature point topology map, and generate an encrypted block mapping table that combines the spatial topology and the security level; Exemplarily, in this embodiment, a specific mapping strategy for the encrypted block mapping table is provided, including: atomizing and encrypting the core anchor points, and injecting quantum noise generated by the stability score and time information; Specifically: Encrypt it using the encryption function Enc, perform an exclusive OR operation with the quantum noise, and perform a union operation on all the processed results to obtain the set of encrypted core anchor points; It should be noted that the quantum noise is obtained by performing an exclusive OR operation on the stability score and timestamp of the feature point, then taking the hash value of the exclusive OR result, and finally Obtained by modulo operation; According to the encryption granularity level, dynamically adjust the block size of variable feature points through the sine function, and perform spatial grid division based on environmental sensitivity; Specifically: Calculate the block size according to the encryption granularity level and the maximum encryption granularity level. Use the sine function to map the ratio of the encryption granularity level to the maximum encryption granularity level to a certain range, and obtain the integer block size through the rounding function; Perform whole-segment encryption on environmentally sensitive points, and determine the number of cyclic shift bits in combination with the lowest stability score for confusion processing; Specifically: For the data in the sensitive section, first encrypt it using the Advanced Encryption Standard (AES), and then determine the number of cyclic shift bits according to the lowest stability score of the feature points in the sensitive section Perform a cyclic left shift operation ROL on the encrypted result, and the number of cyclic shift bits is to obtain the confused result.

[0023] S3: Generate an environmental entropy source with environmental parameters such as Shannon entropy, and combine the original biometric data with the encryption policy table to construct a dynamic confusion function to encrypt the data; S31: Collect the device running duration, environmental parameters and their gradients, and device aging factors to generate a composite entropy value. Multiply the composite entropy value by and then round down to obtain an integer. Take the modulo of the above integer to map the result to a 128-bit binary space, and use the initial seed of quantum key distribution to perform an exclusive OR operation with the modulo operation result to generate a final 128-bit quantum noise sequence .

[0024] Exemplarily, in this embodiment, a calculation strategy for the composite entropy value is provided, specifically: ; is the probability distribution of the environmental parameter k; is the environmental parameter change rate, indicating the real-time fluctuation amplitude of the environmental parameter; Exemplarily, in this embodiment, a generation strategy for the quantum noise sequence is provided, specifically: ; is the initial seed of quantum key distribution, an unpredictable seed generated through a quantum channel (such as the raw key generated by the BB84 protocol).

[0025] S32: Extract the stability score of feature points, the stability gradient, and the quantum noise sequence. Design a noise intensity function based on the stability score and gradient, inject the modulated quantum noise into the feature data after AES encryption, and output the core anchor point ciphertext after hierarchical confusion operations; It should be noted that the core anchor point ciphertext is a ciphertext block with stability-aware noise; Exemplarily, in this embodiment, the noise intensity control function is specifically: ; is the noise intensity factor, which controls the magnitude of the confusion noise. It is a non-negative integer, and the larger its value, the stronger the noise; is the stability score of feature point i. The lower the stability, the higher the noise intensity; is the stability gradient, which represents the severity of the stability change within the neighborhood of the feature point, such as the mean value of the spatial derivative of the fingerprint pattern clarity.

[0026] Exemplarily, in this embodiment, a specific implementation manner of the hierarchical confusion operation is provided: ; is the confused ciphertext block, and the feature data is the result of encryption and noise modulation; S33: Generate a hash value containing spatio-temporal information through SHA3-512, construct a quantum-resistant hash chain using cyclic shift and exclusive OR, and output a quantum-secure hash chain with spatio-temporal correlation; Exemplarily, in this embodiment, the homomorphic hash value containing spatio-temporal information is specifically: ; Exemplarily, in this embodiment, the steps to construct the hash chain of the core anchor point are as follows: Initialize the previous hash value. Exemplarily, in this embodiment, the initial hash value is a fixed initial value or the hash value of the previous chain node; Perform a cyclic left shift (ROL) operation on the previous hash value, and use the result of rounding down the product of 10 and the stability score as the shift number of bits; Perform an exclusive OR operation on the shifted result and the current hash value to obtain the hash value of the current chain node; Repeat the above steps to make each hash value depend on both the previous chain node and the stability score of the current feature simultaneously, forming a chain-like association; S34: Dynamically adjust the block encryption strength according to the granularity level, and use the XMSS hash to construct a Merkle tree containing block entropy; Exemplarily, in this embodiment, dynamically adjusting the block encryption strength according to the granularity level is specifically: ; is the j-th data chunk, containing the recombination result of feature ciphertext blocks; is the maximum encryption granularity; Exemplarily, in this embodiment, an XMSS hash is used to construct a Merkle tree with block entropy, specifically: ; are all Merkle tree nodes; is the block entropy value, a measure of the uncertainty of the data within the block; Construct an extended Merkle signature tree with a block entropy binding mechanism; S35: After encrypting the sensitive data through AES, perform circular shift, combine EdDSA and NTRU to generate a dual-algorithm composite signature, and output a quantum-resistant signature; In this embodiment, specifically: First, use the AES256 algorithm to encrypt the sensitive data; Calculate the number of bits N for circular left shift, specifically: Multiply 103 by (1 minus the lowest stability score of the feature points within the sensitive section), and take the floor of the result; Perform an N-bit circular left shift operation (ROL) on the encrypted result to obtain the final encrypted data; Connect the encrypted sensitive section data, environmental entropy value, and timestamp in sequence; Use the EdDSA algorithm to sign the concatenated data, and at the same time use the NTRU algorithm to sign the stability gradient ∇S; Multiply the signature results of the above two algorithms to obtain the final composite digital signature.

[0027] S4: Evaluate the environmental credibility in real time, and select a data reading strategy according to the evaluation result of the environmental credibility; S41: For real-time environmental parameters, nominal environmental parameters, average feature stability, and nominal standard deviation of environmental parameters, calculate the dynamic weights of each environmental parameter through an exponential function, and obtain the normalized environmental comprehensive deviation degree according to the dynamic weights ; It should be noted that the greater the parameter change rate and the more stable the feature, the lower the weight, reflecting the low sensitivity of stable features to environmental changes.

[0028] It should be noted that the larger the value of the normalized environmental comprehensive deviation degree, the more serious the deviation of the environment from the nominal state and the lower the credibility; Exemplarily, in this embodiment, an implementation method for calculating the dynamic weights of each environmental parameter through an exponential function is provided, specifically: For the k-th environmental parameter (such as temperature, humidity, air pressure), calculate the absolute difference between the real-time value and the nominal value of the k-th environmental parameter , reflecting the instantaneous fluctuation amplitude of the parameter; At the same time, use the characteristic average stability (i.e., the average of the stability scores of all characteristic points) to adjust the influence of the change rate; Convert the change rate into the dynamic weight of each environmental parameter through an exponential function, specifically: ; Wherein, is the dynamic weight of the k-th environmental parameter; Exemplarily, in this embodiment, a strategy for obtaining the normalized environmental comprehensive offset degree is also provided, specifically: For each environmental parameter, calculate the deviation between its actual value and the nominal value, and divide it by the nominal standard deviation (the nominal standard deviation can reflect the normal fluctuation range of the parameter) to obtain the normalized offset; After squaring the normalized offsets of each parameter, multiply them by the corresponding dynamic weights and sum them up to obtain the normalized environmental comprehensive offset degree; It should be noted that the square term is used to amplify the influence of abnormal offsets; for the dynamic weights, the contribution of parameters with a large change rate (low weights) to the total offset is suppressed; S42: Extract the device running time, initial trust threshold, aging factor, environmental parameter entropy, and the lowest characteristic stability score, set the base threshold, and correct the base threshold according to the environmental parameter entropy and the lowest characteristic stability score to obtain the adaptive trust threshold; It should be noted that the more complex the environment (the higher the environmental parameter entropy) or the lower the characteristic stability (the smaller the score), the higher the threshold, and the tolerance for environmental offsets is reduced. The adaptive trust threshold can comprehensively reflect the dynamic trust standard of device aging, environmental complexity, and characteristic stability; Exemplarily, in this embodiment, the base threshold is specifically: ; Wherein, is the base threshold, is the initial trust threshold, is the device aging factor; It should be noted that the longer the device is used, the more serious the aging, and the lower the tolerance for environmental offsets, that is is smaller; Exemplarily, in this embodiment, a strategy for obtaining the adaptive trust threshold is provided, which specifically includes: calculating the Shannon entropy of the real-time environmental parameter vector ; Introduce the environmental complexity correction term and characteristic stability modifier , multiply the basic threshold, the environmental complexity correction term and the feature stability correction term to obtain the adaptive credible threshold ; Regarding the environmental complexity correction item, it should be noted that the higher the entropy value, the more complex the environment, and the higher the threshold (the tolerance to environmental deviation is improved). For complex data reading environments, the threshold needs to be relaxed to avoid misjudgment; Regarding the feature stability correction item, it should be noted that the lower the stability, the lower the threshold (the tolerance to environmental deviation is reduced). When the feature stability is poor, the environmental deviation needs to be more strictly controlled to avoid feature recognition failure due to environmental interference.

[0029] S43: Correct the loss function gradient by feature stability gradient amplification; It should be noted that the larger the feature stability gradient (the more drastic the feature space changes), the larger the corrected gradient is, which is used to emphasize the feature importance of the unstable area; The feature stability, correction gradient and environmental risk factors are introduced into the priority decision strategy to obtain the feature point decryption priority with environmental risk suppression; Exemplarily, in this embodiment, the correction of the loss function gradient specifically includes: Compute the original gradient , indicating that the loss function varies with the feature sensitivity to change; Introducing the stability gradient correction term ,in, is the stability gradient of feature point i (characterizing the rate of change of the stability of its neighborhood); Multiply the original gradient by the correction term to get the corrected gradient ; Exemplarily, in this embodiment, an implementation method of a priority decision strategy is also provided, specifically: ; in, The data reading priority of feature point i is in the range of (0,1). The larger the value, the higher the priority. The stability score of feature point i comes from biometric analysis (such as fingerprint pattern clarity and iris texture stability); It is the normalized environmental comprehensive deviation, which is used to quantify the difference between the current environment and the nominal environment; The adaptive trust threshold is dynamically adjusted with device aging and environmental complexity; For basic priority , It should be noted that the stability gradient of feature point i The larger it is, the larger it is, the closer the denominator in the basic priority is to 1, and the closer the basic priority is to , that is, the priority of important features is amplified; For the environmental risk suppression term , it should be noted that when , , the environmental risk suppression term is greater than or equal to 0.5, and the priority attenuation is limited; When , the suppression term drops rapidly and the priority is cleared (i.e., decryption is rejected); In this embodiment, it should also be noted that the higher the environmental risk (such as large sensor noise and serious equipment aging), the smaller the suppression factor, which forces the decryption priority of features to be reduced, avoiding the exposure of sensitive features in an untrusted environment. At the same time, through dynamic adjustment (combining device running time and environmental entropy), a strategy is implemented where the older the device and the more complex the environment, the lower the tolerance for environmental deviation; S44: Generate a memory self-destruction trigger signal, and import the feature stability, environmental deviation degree, decryption priority, and maximum survival time into the risk perception strategy to quantify the survival time of the plaintext memory; It should be noted that the survival time is positively correlated with the total feature stability and negatively correlated with the environmental risk and decryption priority ranking. Taking the minimum value of the calculated value and the maximum survival time can avoid the risk of excessive survival.

[0030] Exemplarily, in this embodiment, the generation logic of the memory self-destruction trigger signal is as follows: When the environmental deviation degree exceeds 2 times the trusted threshold, trigger an environmental risk signal; When the remainder of the current time modulo the period obtained by rounding the survival time is equal to 0 (i.e., reaching the survival cycle boundary), trigger a time period signal; Combine the two trigger conditions through exclusive OR operation to obtain the memory self-destruction trigger signal; It should be noted that when any one of the conditions is satisfied, the trigger signal is 1 and memory self-destruction is executed; when both conditions are satisfied, the exclusive OR result is 0 to avoid repeated triggering; Exemplarily, in this embodiment, an implementation method of the risk perception strategy is also provided, specifically: Calculate the sum of the stability scores of all feature points as the numerator term, which is used to reflect the overall feature stability (i.e., the higher the stability, the longer the survival time); Multiply the environmental deviation degree (the larger its value, the more untrusted the environment) by the decryption priority ranking correction term to obtain the denominator term. It should be noted that the higher the priority ranking ( The smaller the value is, the smaller the correction term is and the longer the survival time is.

[0031] Divide the numerator by the denominator to obtain the risk-based survival time, and compare it with the preset maximum survival time to take the minimum value to obtain the survival time of the plaintext memory.

[0032] S5: Obtain historical decryption logs and the feature point drift matrix, identify abnormal drift regions, dynamically tune the weights of the encryption granularity decision function according to the drift situation, and resample the drift region features, and output the updated encryption policy parameter set.

[0033] S51: Extract the average change rate of historical environment parameters, feature point coordinates, center coordinates of the feature region, feature point stability score, and initial time scale ; Dynamically adjust the time scale according to the environmental change rate. Exemplarily, in this embodiment, the time scale adjusted with the environmental change rate The calculation strategy is specifically as follows ; It should be noted that the more drastic the environmental change is, the faster the time decay is; Synchronously calculate the spatial correlation weight between the feature point and the center of the feature region ; Exemplarily, in this embodiment, the spatial correlation weight The calculation strategy is: calculate the Euclidean distance between the feature point and the center of the region, and modulate the influence of the distance with the feature point stability score ; ; It should be noted that the closer the feature point is to the center of the region or the higher the stability is, the greater the weight is, and the maximum value is 1; For feature points with long distances or low stability, the weight approaches 0, reducing their contribution to spatial correlation; Combine the time scale and the spatial correlation weight to generate a spatio-temporal coupled dynamic convolution kernel ; Exemplarily, in this embodiment, a generation strategy for the dynamic convolution kernel is provided, specifically: using the difference between the current time and the reference time as the input, generating the time weight of the Gaussian curve, and multiplying the time weight by the spatial correlation weight to obtain the spatio-temporal coupled dynamic convolution kernel; S52: Calculate the drift energy accumulation amount through the spatio-temporal coupled dynamic convolution kernel, based on the median of the historical drift energy Generate a dynamic threshold, compare the drift energy accumulation with the dynamic threshold. When the drift energy accumulation is greater than the dynamic threshold, it indicates that the feature point is abnormal, and filter out the set of abnormal feature points with excessive drift energy; Exemplarily, in this embodiment, the specific calculation strategy for the drift energy accumulation is as follows: ; Where, represents the drift energy accumulation of feature point i; represents the drift amount of feature point i at time t, such as the number of pixel offsets in coordinates and the change value of the stability score; is the change rate of the environmental parameter at time t, reflecting the real-time environmental fluctuation; T is the length of the historical time window; Exemplarily, in this embodiment, a generation strategy for a dynamic threshold is provided: ; S53: Quantify the abnormality degree of the feature point and the environmental complexity through the hyperbolic tangent function, output the risk factor, and adjust the adaptive weight parameter according to the risk factor to dynamically adjust the priorities of feature matching and environmental compensation; Exemplarily, in this embodiment, the output strategy of the risk factor is specifically as follows: Calculate the ratio of the number of abnormal feature points in the set of abnormal feature points to the total number of feature points, which is used to reflect the universality of abnormal drift; Calculate the Shannon entropy of the real-time environmental parameters, and map its range to [0.5, 1] through a linear transformation ; Compress the product of the above two items to [-1, 1] through the hyperbolic tangent function tanh to obtain the risk factor ; Exemplarily, in this embodiment, adjusting the adaptive weight parameter according to the risk factor includes: ; are the weights of the weight device before and after update respectively; It should be noted that the stability adjustment term increases as the average stability increases; is the feature average stability; the mean value of all feature points reflects the overall feature quality; For the risk factor after multiplying with the stability adjustment term, if > 0, then that is, when it is high risk and high stability, significantly increase the weight; Exemplarily, in this embodiment, adjusting the adaptive weight parameter according to the risk factor further includes: ; are the network weights before and after update respectively; l is the device aging factor; is the average environmental offset; It should be noted that represents the total environmental risk within a period of time, which is used as the base of exponential decay after being multiplied by the aging factor ; denominator reduces the influence of the characteristic of large stability fluctuation on weight decay; S54: Construct a drift compensation model to generate a compensation factor for the stability of feature points. Combine the historical stability and the compensation factor, and allocate weights according to the drift energy ranking to generate a new stability score; Exemplarily, in this embodiment, the generation strategy of the compensation factor for the stability of feature points is as follows: ; is the historical maximum drift amount of feature point i, reflecting the worst case of its stability; is the drift tolerance threshold; is the current environmental parameter change rate; Exemplarily, in this embodiment, combining the historical stability and the compensation factor, and allocating weights according to the drift energy ranking to generate a new stability score includes: Retain the old stability score at a ratio of to obtain the retained item of the old stability score, and determine the ratio of the compensation factor in the new stability score through to obtain the drift compensation item; Superimpose the retained item of the old stability score and the drift compensation item to obtain the new stability score.

[0034] Embodiment 2: As Figure 2 shown, a reading system for biometric data of a certificate card based on an encryption machine according to an embodiment of the present invention, as Figure 2 shown, includes the following modules: a topology map output module, an encryption policy acquisition module, an encryption module, a data reading module, and an encryption parameter update module; The topology map output module is used to collect multi-modal biometric raw data, classify feature points into three categories: core anchor points, variable feature points, and environment-sensitive points, and obtain a feature point topology map; The encryption policy acquisition module is used to perceive the data reading environment in real time, import the feature point topology map into a category discrimination strategy to calculate the encryption granularity of different feature category data, and obtain an encryption policy table; The encryption module generates an environmental entropy source based on the Shannon entropy of environmental parameters, combines the original biometric data with the encryption policy table, and constructs a dynamic confusion function to encrypt the data; The data reading module is used to evaluate the environmental credibility in real time and select a data reading strategy according to the evaluation result of the environmental credibility; The encryption parameter update module is used to obtain the historical decryption log and the feature point drift matrix, identify the abnormal drift area, dynamically tune the weight of the encryption granularity decision function according to the drift situation, and resample the features in the drift area, and output the updated encryption policy parameter set.

[0035] Embodiment Three: This embodiment provides an electronic device, including: a processor and a memory, wherein, a computer program that can be called by the processor is stored in the memory; The processor executes the above-mentioned method for reading biometric data of a credential card based on an encryption machine by calling the computer program stored in the memory.

[0036] This electronic device may have relatively large differences due to different configurations or performances, and can include one or more processors (Central Processing Units, CPU) and one or more memories. Among them, at least one computer program is stored in the memory, and this computer program is loaded and executed by the processor to implement the method for reading biometric data of a credential card based on an encryption machine provided by the above method embodiment. This electronic device can also include other components for implementing the functions of the device. For example, this electronic device can also have components such as wired or wireless network interfaces and input / output interfaces for data input and output. This embodiment will not be elaborated here.

[0037] Embodiment Four: This embodiment proposes a computer-readable storage medium, on which a rewritable computer program is stored; When the computer program runs on a computer device, it enables the computer device to execute the above-mentioned method for reading biometric data of a credential card based on an encryption machine.

[0038] For example, the computer-readable storage medium can be a read-only memory (Read-Only Memory, abbreviated as: ROM), a random access memory (Random Access Memory, abbreviated as: RAM), a compact disc read-only memory (Compact Disc Read-Only Memory, abbreviated as: CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0039] It should be understood that in various embodiments of the present application, the sequence numbers of the above processes do not imply the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0040] It should be understood that determining B according to A does not mean determining B only according to A, but also B can be determined according to A and / or other information.

[0041] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains a collection of one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0042] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present invention can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0043] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0044] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only one way, and in actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0045] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0046] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0047] In the description of this specification, the description with reference to terms such as "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0048] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification only illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for reading biometric data of a document card based on an encryption machine, characterized in that, The method includes: S1: Collect multi-modal biometric raw data, classify feature points into three categories: core anchor points, variable feature points, and environment-sensitive points, and obtain a feature point topology map; S2: Sense the data reading environment in real time, import the feature point topology map into a category discrimination strategy to calculate the encryption granularity of different feature category data, and obtain an encryption policy table; S3: Generate an environmental entropy source with environmental parameters such as Shannon entropy, combine the original biometric data with the encryption policy table, and construct a dynamic confusion function to encrypt the data; S4: Evaluate the environmental credibility in real time, and select a data reading strategy according to the evaluation result of the environmental credibility; S5: Obtain the historical decryption log and the feature point drift matrix, identify the abnormal drift area, dynamically tune the weight of the encryption granularity decision function according to the drift situation, and resample the features in the drift area, and output the updated encryption policy parameter set.

2. The method for reading biometric data of a document card based on an encryption machine according to claim 1, wherein Collect multi-modal biometric raw data, classify feature points into three categories: core anchor points, variable feature points, and environment-sensitive points, and obtain a feature point topology map, including: S11: Collect the original biometric coordinate data and the temperature, humidity, and pressure parameters monitored by the sensor in real time, and correct the original coordinates through the environmental noise compensation formula; At the same time, based on the sliding mean and variance related to the environment to standardize the reference coordinate system, output the dynamically calibrated standardized feature coordinates; S12: Determine an adaptive time window according to the environmental disturbance threshold and the environmental change gradient, and quantify the correlation degree of different feature points in the time series according to the feature association quantization strategy, and generate a spatio-temporal correlation matrix integrating the environmental weight; S13: Collect the measurement standard deviation of environmental parameters, calculate the offset generated by each feature point affected by the environment, and construct an environmental sensitivity model; Based on the diagonal elements of the spatio-temporal correlation matrix, the environmental offset, and the difference between the current environment and the nominal environment, obtain the stability score of each feature point adapted to the environment; S14: Based on the stability score, construct a stability gradient field, monitor the spatial change trend of the feature point stability, classify the feature points into three categories: core anchor points, variable feature points, and environment-sensitive points, and output a feature point topology map with spatial topological relationships.

3. The method for reading biometric data of a document card based on an encryption machine according to claim 2, characterized in that, Sense the data reading environment in real time, import the feature point topology map into a category discrimination strategy to calculate the encryption granularity of different feature category data, and obtain an encryption policy table, including: S21: Collect the original values of environmental parameters in the data reading environment, as well as the nominal mean and standard deviation corresponding to each environmental parameter, and synthesize an environmental safety factor after normalizing the collected data; S22: Collect the normalized environmental parameters, the mean of feature stability, the maximum data dimension, and the environmental safety factor. Design different forms of membership functions for the device security level, network latency, and sensor accuracy respectively to obtain their non-linear relationships with the weights. Then, associate the membership function values with the mean of feature stability, the reciprocal of the environmental safety factor, and the maximum data dimension respectively, and dynamically adjust the weight parameters through normalization calculation , and generate weight parameters associated with the environmental state ; S23: Obtain the dimension of feature data, stability score, stability gradient, environmental safety factor, and environmental entropy, and construct a dimension-safety coupling term ; Construct a stability constraint function , and combine the stability score with the degree of environmental deviation to constrain the granularity; Calculate the encryption granularity level based on the ratio of the two and in combination with the adjustment of the environmental entropy value ; S24: Extract the feature point classification result, encryption granularity level, minimum stability score, and environmental sensitivity in the feature point topology map, and generate an encryption block mapping table integrating the spatial topological structure and the security level.

4. The method for reading biometric data of a credential card based on an encryption machine according to claim 3, wherein Generate an environmental entropy source with environmental parameters such as Shannon entropy, combine the original biometric data with the encryption policy table, and construct a dynamic confusion function to encrypt the data, including: S31: Collect the running duration of the acquisition device, environmental parameters and their gradients, and the device aging factor, generate a composite entropy value, multiply the composite entropy value by and then round down to get an integer, take the modulus of the above integer , map the result to a 128-bit binary space, and use the initial seed of quantum key distribution to perform an exclusive OR operation with the result of the modulo operation to generate a final 128-bit quantum noise sequence ; S32: Extract the stability score of feature points, the stability gradient, and the quantum noise sequence. Design a noise intensity function based on the stability score and gradient, inject the modulated quantum noise into the feature data after AES encryption, and output the core anchor point ciphertext after hierarchical confusion operations; S33: Generate a hash value containing spatio-temporal information through SHA3-512, construct a quantum-resistant hash chain using circular shift and exclusive OR, and output a quantum-secure hash chain with spatio-temporal correlation; S34: Dynamically adjust the block encryption strength according to the granularity level, and use the XMSS hash to construct a Merkle tree containing block entropy; Construct an extended Merkle signature tree with a block entropy binding mechanism; S35: After encrypting the sensitive data through AES, perform circular shift, combine EdDSA and NTRU to generate a dual-algorithm composite signature, and output a quantum-resistant signature.

5. A method for reading biometric data of a credential card based on an encryption machine according to claim 4, wherein, Evaluate the environmental credibility in real time. According to the evaluation result of the environmental credibility, select a data reading strategy, including: S41: Calculate the dynamic weights of each environmental parameter through an exponential function based on the real-time environmental parameters, nominal environmental parameters, characteristic average stability, and nominal standard deviation of environmental parameters, and obtain the normalized environmental comprehensive deviation degree according to the dynamic weights. ; S42: Extract the device running time, the initial trust threshold, the aging factor, the environmental parameter entropy, and the lowest feature stability score. Set a basic threshold, and correct the basic threshold according to the environmental parameter entropy and the lowest feature stability score to obtain an adaptive trust threshold; S43: Amplify and correct the gradient of the loss function through the feature stability gradient; Import the feature stability, the corrected gradient, and the environmental risk factor into the priority decision strategy to obtain the decryption priority of feature points with environmental risk suppression.

6. The method for reading biometric data of a credential card based on an encryption machine according to claim 5, wherein Evaluate the environmental credibility in real time. According to the evaluation result of the environmental credibility, select a data reading strategy, which also includes: S44: Generate a memory self-destruction trigger signal, and import the feature stability, the environmental deviation degree, the decryption priority, and the maximum survival time into the risk perception strategy to quantify the survival time of the plaintext memory.

7. A method for reading biometric data of a credential card based on an encryption machine according to claim 6, characterized in that, Obtain the historical decryption log and the feature point drift matrix, identify the abnormal drift area, dynamically tune the weight of the encryption granularity decision function according to the drift situation, and resample the features in the drift area, and output the updated encryption policy parameter set, including: S51: Extract the average change rate of historical environment parameters, feature point coordinates, center coordinates of feature regions, feature point stability scores, and initial time scales from historical decryption logs , feature point coordinates, center coordinates of feature regions, feature point stability scores, and initial time scales ; Dynamically adjust the time scale according to the environmental change rate, and synchronously calculate the spatial correlation weight between the feature points and the center of the feature region ; Combined with the time scale and the spatial correlation weight, a spatio-temporal coupled dynamic convolution kernel is generated ; S52: Calculate the drift energy accumulation amount through a spatio-temporal coupled dynamic convolution kernel, and generate a dynamic threshold based on the median of historical drift energy and the absolute median difference. Compare the drift energy accumulation amount with the dynamic threshold. When the drift energy accumulation amount is greater than the dynamic threshold, it indicates that the feature point is abnormal, and an abnormal feature point set with excessive drift energy is screened out. and the absolute median difference Generate a dynamic threshold, compare the drift energy accumulation amount with the dynamic threshold. When the drift energy accumulation amount is greater than the dynamic threshold, it indicates that the feature point is abnormal, and an abnormal feature point set with excessive drift energy is screened out.

8. A method for reading biometric data of a document card based on an encryption machine according to claim 7, characterized in that Obtain the historical decryption log and the feature point drift matrix, identify the abnormal drift area, dynamically tune the weight of the encryption granularity decision function according to the drift situation, and resample the features in the drift area, and the updated encryption policy parameter set also includes: S53: Quantify the abnormality degree of feature points and the environmental complexity through the hyperbolic tangent function, output the risk factor, adjust the adaptive weight parameter according to the risk factor, and dynamically adjust the priority of feature matching and environmental compensation; S54: Construct a drift compensation model, generate a compensation factor for the stability of feature points, combine the historical stability and the compensation factor, allocate weights according to the drift energy ranking, and generate a new stability score.

9. A reading system for biometric data of a document card based on an encryption machine, which is used to implement a reading method for biometric data of a document card based on an encryption machine as described in any one of claims 1-8, characterized in that, The system includes: A topology map output module, an encryption policy acquisition module, an encryption module, a data reading module, and an encryption parameter update module; The topology map output module is used to collect the original multi-modal biometric data, classify the feature points into three categories: core anchor points, variable feature points, and environment-sensitive points, and obtain the feature point topology map; The encryption policy acquisition module is used to perceive the data reading environment in real time, import the feature point topology map into the category discrimination policy to calculate the encryption granularity of different feature category data, and obtain an encryption policy table; The encryption module generates an environmental entropy source with environmental parameters such as Shannon entropy, combines the original biometric data with the encryption policy table, and constructs a dynamic confusion function to encrypt the data; The data reading module is used to evaluate the environmental credibility in real time and select a data reading strategy according to the evaluation result of the environmental credibility; The encryption parameter update module is used to obtain historical decryption logs and the feature point drift matrix, identify abnormal drift regions, dynamically tune the weights of the encryption granularity decision function according to the drift situation, and resample the features of the drift regions, and output the updated encryption policy parameter set.

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