A method and system for reading biometric data of ID card based on encryption machine

Abstract

The present invention relates to the field of data processing technology and is a method and system for reading biometric data from an ID card based on an encryption machine. The specific method includes: classifying feature points into three categories: core anchor points, variable feature points, and environmentally sensitive points, to obtain a feature point topology map; calculating the encryption granularity of data of different feature categories to obtain an encryption policy table; constructing a dynamic obfuscation function to encrypt the data; selecting a data reading strategy based on environmental credibility assessment results, obtaining historical decryption logs and a feature point drift matrix, identifying abnormal drift areas, dynamically tuning the weights of the encryption granularity decision function based on the drift situation, resampling the drift area features, and outputting an updated encryption policy parameter set. The present invention solves the problem in the prior art that hardware encryption and software encryption have different protection granularities for biometric data, and that encryption granularity mismatches are prone to occur due to the lack of a fine-grained access interface during dynamic collaborative encryption.

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 an ID card based on an encryption machine. Background Art

[0002] In the field of biometric recognition and encryption, existing technologies generally face the following challenges: Traditional encryption granularity inherently conflicts with biometric data structures. Hardware encryption modules (such as secure elements, or SEs) typically employ fixed-block encryption mechanisms, while software-level feature comparison requires the extraction of fine-grained biometric features (such as fingerprint minutiae and iris texture). This forces the system to decrypt the entire data set, exposing sensitive information to memory for extended periods and forcing software to compromise security to meet functional requirements. Furthermore, existing encryption strategies are insufficiently adaptable to dynamic environmental changes. In multimodal biometric recognition scenarios, fluctuations in environmental parameters such as temperature and humidity, and sensor accuracy drift, can significantly impact the stability of feature point coordinates. Static encryption models lack the ability to dynamically adjust granularity based on environmental risk levels, leading to security policy overload or insufficient protection in high-interference environments. Furthermore, there is a lack of effective compensation mechanisms for the gradual drift of biometric features caused by long-term use (such as fingerprint surface wear and iris texture degradation). Existing systems often use fixed thresholds to determine feature validity, which can lead to increased false positives and encryption policy failure over time. Although existing biometric protection schemes attempt to improve through environmental perception or dynamic block encryption, they still have problems such as the fragmentation of time and space dimensions and delayed parameter updates, making them difficult to defend against 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 biometric data provided by hardware encryption and software encryption is different, and the lack of fine-grained access interface during dynamic collaborative encryption easily leads to encryption granularity mismatch. A method and system for reading biometric data of ID cards based on an encryption machine are proposed.

[0004] In order to achieve the above-mentioned object, the technical solution of a method for reading biometric data of an ID card based on an encryption machine of the present invention comprises the following steps:

[0005] Collect multimodal biometric raw data, classify feature points into three categories: core anchor points, variable feature points, and environmentally sensitive points, and obtain a feature point topology map;

[0006] Real-time perception of the data reading environment is performed, and the feature point topology map is imported into the category differentiation strategy to calculate the encryption granularity of data of different feature categories and obtain the encryption strategy table;

[0007] The environmental entropy source is generated by using environmental parameters such as Shannon entropy, and the original biometric data is combined with the encryption strategy table to construct a dynamic obfuscation function to encrypt the data;

[0008] Evaluate the environmental credibility in real time and select the data reading strategy based on the environmental credibility evaluation results;

[0009] Obtain historical decryption logs and feature point drift matrices, identify abnormal drift areas, dynamically tune the weights of the encryption granularity decision function based on the drift situation, resample the drift area features, and output the updated encryption policy parameter set.

[0010] Collect multimodal biometric raw data, classify feature points into three categories: core anchor points, variable feature points, and environmentally sensitive points, and obtain a feature point topology map, including:

[0011] S11: Collecting original biometric coordinate data and temperature, humidity, and pressure parameters monitored in real time by sensors, and correcting the original coordinates using an environmental noise compensation formula;

[0012] At the same time, based on the environment-related sliding mean and variance standardization reference coordinate system, the dynamically calibrated standardized feature coordinates are output;

[0013] S12: Determine the adaptive time window based on 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 correlation quantification strategy, thereby generating a spatiotemporal correlation matrix that integrates the environmental weights;

[0014] 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;

[0015] Based on the diagonal elements of the spatiotemporal correlation matrix, the environmental offset, and the difference between the current environment and the nominal environment, the stability score of the environmental adaptation of each feature point is obtained;

[0016] S14: Based on the stability score, a stability gradient field is constructed to monitor the spatial trend of the stability of feature points. The feature points are divided into three categories: core anchor points, variable feature points, and environmentally sensitive points. The topological map of feature points with spatial topological relationships is output.

[0017] Real-time perception of the data reading environment is performed, and the feature point topology map is imported into the category differentiation strategy to calculate the encryption granularity of data of different feature categories, and obtain the encryption strategy table, including:

[0018] S21: Collect data to read the original values ​​of environmental parameters, as well as the nominal mean and standard deviation corresponding to each environmental parameter, and normalize the collected data to synthesize the environmental safety factor;

[0019] S22: Collect normalized environmental parameters, as well as the mean value of characteristic stability, the maximum data dimension, and the environmental safety factor. Design different forms of membership functions for device safety level, network delay, and sensor accuracy to obtain their nonlinear relationship with weights. Then associate the membership function values ​​with the mean value of characteristic stability, the inverse of the environmental safety factor, and the maximum data dimension, and dynamically adjust the weight parameters through normalized calculations. , generating weight parameters associated with the environment state ;

[0020] S23: Obtain characteristic data dimensions, stability scores, stability gradients, environmental safety factors, and environmental entropy, and construct dimension-safety coupling items ;

[0021] Constructing stability constraint function ,Combining the stability score and the degree of environmental deviation to constrain the granularity;

[0022] Finally, based on the ratio of the two and combined with the adjustment of the environmental entropy value, the encryption granularity level is calculated ;

[0023] S24: extracting the classification results of feature points, the encryption granularity level, the minimum stability score, and the environmental sensitivity in the feature point topology map, and generating an encrypted block mapping table integrating the spatial topology structure and the security level;

[0024] S3 includes:

[0025] S31: Collect the equipment running time, environmental parameters and their gradients, and equipment aging factors to generate a composite entropy value. Multiply the composite entropy value by Then round down to get an integer, and take the modulus of the above integer , map the result to 128-bit binary space, and use quantum key distribution to distribute the initial seed Perform XOR operation with the result of modular operation to generate the final 128-bit quantum noise sequence .

[0026] S32: Extract the stability score, stability gradient, and quantum noise sequence of the feature points. Design the noise intensity function based on the stability score and gradient. Inject modulated quantum noise into the AES-encrypted feature data. After layered obfuscation operation, output the core anchor point ciphertext.

[0027] S33: Generate a hash value containing time and space information through SHA3-512, use cyclic shift and XOR to build a quantum-resistant hash chain, and output a quantum-safe hash chain with time and space correlation;

[0028] S34: Dynamically adjust block encryption strength based on granularity level, using XMSS hashing to construct a Merkle tree with block entropy;

[0029] Construct an extended Merkle signature tree with a block entropy binding mechanism;

[0030] S35: Encrypt sensitive data using AES and then perform cyclic shift, combine EdDSA and NTRU to generate a dual-algorithm composite signature, and output a quantum-resistant signature;

[0031] S4 includes: S41: real-time environmental parameters, nominal environmental parameters, characteristic average stability, environmental parameter nominal standard deviation, dynamic weight of each environmental parameter is calculated by exponential function, and normalized environmental comprehensive deviation is obtained according to the dynamic weight ;

[0032] S42: extracting the device operating time, initial trust threshold, aging factor, environmental parameter entropy, and minimum feature stability score, setting a basic threshold, and modifying the basic threshold according to the environmental parameter entropy and the minimum feature stability score to obtain an adaptive trust threshold;

[0033] S43: Correct the loss function gradient by feature stability gradient amplification;

[0034] 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;

[0035] S44: Generates a memory self-destruct trigger signal and imports feature stability, environmental deviation, decryption priority, and maximum survival time into the risk perception strategy to quantify the survival time of the plaintext memory;

[0036] S5 includes: S51: extracting the average change rate of historical environmental parameters in the historical decryption log , feature point coordinates, feature region center coordinates, feature point stability score and initial time scale ;

[0037] Dynamically adjust the time scale according to the environmental change rate and simultaneously calculate the spatial correlation weights of feature points and feature area centers ;

[0038] Combined time scale and spatial correlation weights to generate spatiotemporally coupled dynamic convolution kernels ;

[0039] S52: Calculate the drift energy accumulation through spatiotemporal coupled dynamic convolution kernel, based on the median of historical drift energy and absolute median difference Generate a dynamic threshold and 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. Filter out the set of abnormal feature points with excessive drift energy.

[0040] S53: Quantify the abnormality degree of feature points 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 priority of feature matching and environmental compensation;

[0041] S54: Construct a drift compensation model to generate a compensation factor for the stability of the point. Combine the historical stability and the compensation factor, assign weights according to the drift energy ranking, and generate a new stability score.

[0042] In addition, the present invention provides a system for reading biometric data of an ID card based on an encryption machine, which includes the following modules:

[0043] Topology map output module, encryption strategy acquisition module, encryption module, data reading module and encryption parameter update module;

[0044] The topology map output module is used to collect multimodal biometric raw data, classify feature points into three categories: core anchor points, variable feature points, and environmental sensitive points, and obtain a feature point topology map;

[0045] The encryption strategy acquisition module is used to perceive the data reading environment in real time, import the feature point topology map into the category differentiation strategy to calculate the encryption granularity of different feature category data, and obtain the encryption strategy table;

[0046] The encryption module generates an environmental entropy source using environmental parameters such as Shannon entropy, combines the original biometric data with the encryption strategy table, and constructs a dynamic obfuscation function to encrypt the data;

[0047] The data reading module is used to evaluate the environmental credibility in real time and select a data reading strategy based on the environmental credibility evaluation result;

[0048] The encryption parameter update module is used to obtain historical decryption logs and feature point drift matrices, identify abnormal drift areas, dynamically tune the weights of the encryption granularity decision function according to the drift situation, resample the drift area features, and output an updated encryption policy parameter set.

[0049] Compared with the prior art, the technical effects of the present invention are as follows:

[0050] The present invention achieves a breakthrough in the biometric data security protection system by integrating the dynamic attributes of biometrics with the environmental parameter perception mechanism. In response to the core problems of existing biometric encryption technology, such as rigid encryption granularity, poor environmental adaptability and weak resistance to quantum attacks, this solution achieves significant technical effects through a multi-dimensional collaborative mechanism: First, based on the spatiotemporal 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 blocking mode, so that the memory exposure surface of the core anchor point is reduced compared with the traditional solution. At the same time, the classification boundary is dynamically adjusted through the stability gradient field, and the feature recognition accuracy is improved in the scenario of environmental mutation; secondly, the environment-feature coupled encryption granularity decision model (S2) of the present invention dynamically adjusts the weight parameters through the fuzzy membership function, so that the encryption granularity can adapt to the device security level and network in real time. Delay and sensor accuracy. Tests have shown that the fixed granularity strategy can greatly reduce the time consumption of the data reading process in complex environments on mobile devices. In addition, the quantum noise mapping mechanism (S3) binds the environmental entropy with the device aging factor to generate a 128-bit quantum noise-resistant sequence. Combined with the layered confusion function, the quantum attack resistance of the AES256 ciphertext is increased to 128 bits, thereby increasing the security margin. The dynamic decryption engine (S4) in the present invention realizes real-time optimization of the decryption priority through the environmental offset and dual-threshold verification mechanism, and at the same time improves the feature matching efficiency by correcting the loss function through the stability gradient. At the same time, the present invention constructs a full life cycle maintenance system for biometrics through the spatiotemporal convolution drift detection algorithm, which maintains a high drift compensation accuracy for fingerprint data used for 12 months, and reduces the system error rate by combining the parameter dynamic tuning mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0052] Figure 1 A schematic flow chart of a method for reading biometric data from an ID card based on an encryption machine according to the present invention;

[0053] Figure 2 The diagram is a structural diagram of a system for reading biometric data of an ID card based on an encryption machine according to the present invention. DETAILED DESCRIPTION

[0054] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0055] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0056] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0057] Example 1:

[0058] like Figure 1 As shown, a method for reading biometric data of a certificate card based on an encryption machine according to an embodiment of the present invention is as follows: Figure 1 As shown, the specific steps are as follows:

[0059] S1: Collect multimodal biometric raw data, classify feature points into three categories: core anchor points, variable feature points, and environmentally sensitive points, and obtain a feature point topology map;

[0060] S11: Collecting original biometric coordinate data and temperature, humidity, and pressure parameters monitored in real time by sensors, and correcting the original coordinates using an environmental noise compensation formula;

[0061] For example, in this embodiment, a specific implementation method of correcting the original coordinates using an environmental noise compensation formula is provided, specifically:

[0062] ;

[0063] in, are the original coordinates (physical quantities such as position, distance, etc., whose dimensions depend on the sensor type);

[0064] is the coordinate after compensation;

[0065] is the device calibration factor, used to calibrate differences across sensors;

[0066] is the change in environmental parameters (temperature / humidity / pressure);

[0067] is the sensitivity of the biosignature to environmental parameters;

[0068] 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.

[0069] At the same time, based on the environment-related sliding mean and variance standardization reference coordinate system, the dynamically calibrated standardized feature coordinates are output;

[0070] 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 degree of association of different feature points in the time series is quantified to generate a spatiotemporal association matrix that integrates the environmental weights.

[0071] For example, in this embodiment, a strategy for determining an adaptive time window is provided, specifically:

[0072] ;

[0073] Where T is the time window length; is the environmental disturbance threshold; is the environmental gradient;

[0074] 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.

[0075] It should also be noted that through Adjust the window's sensitivity to disturbances to avoid extreme values ​​that cause the window to be too long or too short;

[0076] For example, in this embodiment, the feature association quantification strategy is specifically as follows:

[0077] ;

[0078] 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 t-th sampling;

[0079] It should be noted that generating a spatiotemporal correlation matrix that integrates environmental weights can intuitively reflect the characteristics of the dependency between features being modulated by the environment.

[0080] 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;

[0081] For example, in this embodiment, an output formula of an environmental sensitivity model is provided, specifically:

[0082] ;

[0083] in, is the environmental sensitivity of feature i;

[0084] Environmental parameters The standard deviation of the measurement;

[0085] It should be noted that the environmental sensitivity model can intuitively quantify the range of feature point coordinate fluctuations caused by environmental measurement errors.

[0086] Based on the diagonal elements of the spatiotemporal correlation matrix, the environmental offset, and the difference between the current environment and the nominal environment, the stability score of the environmental adaptation of each feature point is obtained;

[0087] For example, in this embodiment, the stability score of each feature point environment adaptation is specifically:

[0088] ;

[0089] in, Score the stability of the i-th feature point; is the characteristic autocorrelation degree, which is used to reflect the stability of the time series; E is the current environmental parameter, is the nominal environmental condition;

[0090] It should be noted that When the term causes the environment to deviate from the nominal conditions, the denominator increases, Reduce and enhance the strictness of stability judgment;

[0091] It should be noted that the molecule Used to characterize the temporal stability of the feature itself;

[0092] Denominator Characterizes environmental sensitivity, and the ratio of the two balances intrinsic stability and environmental impact;

[0093] It should be noted that the environmental adaptive stability score of each feature point is used to quantify the stability of each feature point in different environments.

[0094] S14: Based on the stability score, a stability gradient field is constructed to monitor the spatial trend of the stability of feature points. The feature points are divided into three categories: core anchor points, variable feature points, and environmentally sensitive points. The topological map of feature points with spatial topological relationships is output.

[0095] Exemplarily, in this embodiment, a method for constructing a stable gradient field based on Kriging interpolation is provided, specifically:

[0096] ;

[0097] in, is the stability gradient; x, y are spatial coordinates;

[0098] For example, in this embodiment, a specific implementation method for classifying feature points into three categories: core anchor points, variable feature points, and environmentally sensitive points is provided, specifically including:

[0099] The dynamic classification boundary is calculated by the statistics of the stability score. The feature points are divided into three categories: core anchor points, variable feature points and environmental sensitive points, combining the stability score, gradient size and coordinate variance of the feature points.

[0100] The dynamic classification boundaries specifically include:

[0101] ;

[0102] ;

[0103] in, are the classification thresholds respectively; is the information entropy of the gradient field, which is used to reflect the degree of disorder of the spatial stability distribution; is the skewness of the stability score, which is used to reflect the symmetry of the score distribution;

[0104] It should be noted that It is negatively correlated with the gradient entropy. That is, when the stability space is chaotic (entropy is large), the threshold of the core anchor point is lowered to avoid excessive strictness; conversely, the threshold is increased to shrink the stable area.

[0105] It should also be noted that Positively correlated with skewness. If the score is right-skewed (most points are stable), increase the threshold of the low-sensitivity zone and expand the transition zone; otherwise, adjust the range of the sensitive zone.

[0106] Exemplarily, in this embodiment, the division of feature points includes:

[0107] when and When , the feature points are divided into core anchor points;

[0108] when and When , the feature points are divided into variable feature points;

[0109] when and When , the feature points are classified as environmental sensitive points.

[0110] S2: Real-time perception of the data reading environment is performed, and the feature point topology map is imported into the category differentiation strategy to calculate the encryption granularity of data of different feature categories and obtain the encryption strategy table;

[0111] S21: Collect data to read the original values ​​of environmental parameters, as well as the nominal mean and standard deviation corresponding to each environmental parameter, and normalize the collected data to synthesize the environmental safety factor;

[0112] For example, in this embodiment, a specific implementation method for synthesizing environmental safety factors is provided, including: adaptively allocating weights according to the absolute value of a normalized parameter, and multiplying the weights by the value of the normalized parameter after being transformed by a hyperbolic tangent function, and accumulating the multiplications;

[0113] The calculation strategy of the adaptive weight is as follows:

[0114] ; 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, a larger value of the dynamic environmental security factor indicates a more secure environment, and the dynamic environmental security factor provides a quantitative basis for environmental security for subsequent encryption strategies;

[0115] S22: Collect normalized environmental parameters, as well as the mean value of characteristic stability, the maximum data dimension, and the environmental safety factor. Design different forms of membership functions for device safety level, network delay, and sensor accuracy to obtain their nonlinear relationship with weights. Then associate the membership function values ​​with the mean value of characteristic stability, the inverse of the environmental safety factor, and the maximum data dimension, and dynamically adjust the weight parameters through normalized calculations. , generating weight parameters associated with the environment state ;

[0116] For example, in this embodiment, a device security level membership function is provided:

[0117] ;

[0118] in, Indicates the equipment safety level, h1 is the curvature constant. It should be noted that this function makes the higher the equipment safety level, the lower the membership degree. The closer it is to 1;

[0119] Exemplarily, in this embodiment, a network delay membership function is provided:

[0120] ;

[0121] in, is the network delay value, is the delay threshold. The higher the network delay, the higher the membership The closer it is to 0;

[0122] Exemplarily, in this embodiment, a sensor accuracy membership function is provided:

[0123] ;

[0124] in, is the sensor accuracy, are the lower and upper limits of the precision interval, and , this function realizes the sensor accuracy in the interval Linear mapping within .

[0125] For example, in this embodiment, a weight parameter associated with the environmental state is provided. The generation implementation method is as follows:

[0126] ;

[0127] in, They are fuzzy weight parameters, used to adjust the priority of encryption, transmission, and storage; is the characteristic stability mean; is the total membership degree;

[0128] is the environmental safety factor; is the maximum data dimension;

[0129] S23: Obtain characteristic data dimensions, stability scores, stability gradients, environmental safety factors, and environmental entropy, and construct dimension-safety coupling items ;

[0130] For example, in this embodiment, a dimension-security coupling item is provided. The construction strategy is as follows:

[0131] ;

[0132] in, is the feature data dimension; is the characteristic stability gradient;

[0133] Constructing stability constraint function ,Combining the stability score and the degree of environmental deviation to constrain the granularity;

[0134] For example, in this embodiment, a stability constraint function is provided: The construction strategy is as follows:

[0135] ;

[0136] in, Read the nominal environmental parameters under ideal environment for data;

[0137] Finally, based on the ratio of the two and combined with the adjustment of the environmental entropy value, the encryption granularity level is calculated ;

[0138] For example, in this embodiment, an encryption granularity level is provided. The acquisition strategy is as follows:

[0139] ;

[0140] It should be noted that It is a positive integer. The larger the value, the finer the encryption granularity. is the environmental entropy value;

[0141] S24: extracting the classification results of feature points, the encryption granularity level, the minimum stability score, and the environmental sensitivity in the feature point topology map, and generating an encrypted block mapping table integrating the spatial topology structure and the security level;

[0142] Exemplarily, in this embodiment, a specific mapping strategy for an encrypted block mapping table is provided, including: atomically encrypting the core anchor point and injecting quantum noise generated by the stability score and time information;

[0143] Specifically, it is encrypted using the encryption function Enc, and an XOR operation is performed with the quantum noise, and all the processed results are combined to obtain the encrypted set of core anchor points;

[0144] It should be noted that quantum noise is achieved by performing an XOR operation on the stability score of the feature point and the timestamp, and then taking the hash value of the XOR result. Modulo obtained;

[0145] According to the encryption granularity level, the block size of variable feature points is dynamically adjusted through the sine function, and spatial grid division is performed based on environmental sensitivity;

[0146] Specifically, the block size is calculated based on the encryption granularity level and the maximum encryption granularity level. Using the sine function, the ratio of the encryption granularity level to the maximum encryption granularity level is mapped to a certain range, and the integer block size is obtained through the rounding function;

[0147] For environmentally sensitive points, full-segment encryption is used, and the number of cyclic shift bits is determined based on the minimum stability score for obfuscation.

[0148] Specifically: For sensitive segment data, first use the Advanced Encryption Standard AES to encrypt it, and then calculate the minimum stability score of the feature points in the sensitive segment. Determine the number of cyclic shift bits, and perform a cyclic left shift ROL on the encrypted result. The number of cyclic shift bits is , and get the obfuscated result.

[0149] S3: Generate an environmental entropy source using environmental parameters such as Shannon entropy, combine the original biometric data with the encryption strategy table, and build a dynamic obfuscation function to encrypt the data;

[0150] S31: Collect the equipment running time, environmental parameters and their gradients, and equipment aging factors to generate a composite entropy value. Multiply the composite entropy value by Then round down to get an integer, and take the modulus of the above integer , map the result to 128-bit binary space, and use quantum key distribution to distribute the initial seed Perform XOR operation with the result of modular operation to generate the final 128-bit quantum noise sequence .

[0151] For example, in this embodiment, a composite entropy value is provided. The calculation strategy is as follows:

[0152] ;

[0153] is the probability distribution of the environmental parameter k;

[0154] is the rate of change of environmental parameters, indicating the real-time fluctuation range of environmental parameters;

[0155] Exemplarily, in this embodiment, a strategy for generating a quantum noise sequence is provided, specifically:

[0156] ;

[0157] The initial seed for quantum key distribution is an unpredictable seed generated through a quantum channel (such as the original key generated by the BB84 protocol).

[0158] S32: Extract the stability score, stability gradient, and quantum noise sequence of the feature points. Design the noise intensity function based on the stability score and gradient. Inject modulated quantum noise into the AES-encrypted feature data. After layered obfuscation operation, output the core anchor point ciphertext.

[0159] It should be noted that the core anchor ciphertext is a ciphertext block with stability-aware noise;

[0160] For example, in this embodiment, the noise intensity control function is specifically:

[0161] ;

[0162] is the noise intensity factor, which controls the size of the aliasing noise. It is a non-negative integer. The larger its value, the stronger the noise.

[0163] is the stability score of feature point i. The lower the stability, the higher the noise intensity. is the stability gradient, which indicates the severity of the stability change in the neighborhood of the feature point, such as the mean of the spatial derivative of the fingerprint pattern clarity.

[0164] For example, in this embodiment, a specific implementation of a layered obfuscation operation is provided: ;

[0165] is the obfuscated ciphertext block, feature data The encrypted and noise-modulated result;

[0166] S33: Generate a hash value containing time and space information through SHA3-512, use cyclic shift and XOR to build a quantum-resistant hash chain, and output a quantum-safe hash chain with time and space correlation;

[0167] For example, in this embodiment, the homomorphic hash value containing spatiotemporal information Specifically: ;

[0168] For example, in this embodiment, the steps for constructing a hash chain of core anchor points are as follows:

[0169] Initialize the previous hash value. For example, in this embodiment, the initial hash value is a fixed initial value or the hash value of the previous chain node;

[0170] Perform a circular left shift (ROL) on the previous hash value, multiplying 10 by the stability score and rounding down the result as the number of shift bits.

[0171] XOR the shifted result with the current hash value to get the hash value of the current chain node;

[0172] Repeat the above steps so that each hash value depends on the stability score of both the previous chain node and the current feature, forming a chain association;

[0173] S34: Dynamically adjust block encryption strength based on granularity level, using XMSS hashing to construct a Merkle tree with block entropy;

[0174] For example, in this embodiment, the block encryption strength is dynamically adjusted according to the granularity level, specifically:

[0175] ; For the jth data block, including The reorganization result of characteristic ciphertext blocks; is the maximum encryption granularity;

[0176] For example, in this embodiment, XMSS hash is used to construct a Merkle tree containing block entropy, specifically:

[0177] ;

[0178] All are Merkle tree nodes;

[0179] is the block entropy value, which is the uncertainty measure of the data within the block;

[0180] Construct an extended Merkle signature tree with a block entropy binding mechanism;

[0181] S35: Encrypt sensitive data using AES and then perform cyclic shift, combine EdDSA and NTRU to generate a dual-algorithm composite signature, and output a quantum-resistant signature;

[0182] In this embodiment, specifically: first, the sensitive data is encrypted using the AES256 algorithm;

[0183] Calculate the number of bits N of the cyclic left shift by multiplying 103 by (1 minus the minimum stability score of the feature points in the sensitive segment) and rounding the result down.

[0184] Perform an N-bit circular left shift (ROL) operation on the encrypted result to obtain the final encrypted data;

[0185] Connect the encrypted sensitive segment data, environmental entropy value, and timestamp in sequence;

[0186] The concatenated data is signed using the EdDSA algorithm, and the stability gradient ∇S is signed using the NTRU algorithm.

[0187] Multiply the signature results of the above two algorithms to obtain the final composite digital signature.

[0188] S4: Evaluate the environmental credibility in real time and select a data reading strategy based on the environmental credibility evaluation results;

[0189] S41: Real-time environmental parameters, nominal environmental parameters, characteristic average stability, nominal standard deviation of environmental parameters, dynamic weights of each environmental parameter are calculated through exponential function, and normalized environmental comprehensive deviation is obtained according to the dynamic weights ;

[0190] It should be noted that the greater the parameter change rate and the more stable the feature, the lower the weight, which reflects the low sensitivity of the stable feature to environmental changes.

[0191] It should be noted that the larger the value of the normalized environmental comprehensive deviation is, the more serious the deviation of the environment from the nominal state is and the lower the credibility is;

[0192] For example, in this embodiment, an implementation method for calculating the dynamic weight of each environmental parameter by using an exponential function is provided, specifically:

[0193] For the kth environmental parameter (such as temperature, humidity, air pressure), calculate the absolute difference between the real-time value and the nominal value of the kth environmental parameter , reflecting the instantaneous fluctuation amplitude of the parameter;

[0194] Characteristic Average Stability (i.e., the average of the stability scores of all feature points) regulates the effect of the rate of change;

[0195] The rate of change is converted into the dynamic weight of each environmental parameter through the exponential function, specifically:

[0196] ;

[0197] in, is the dynamic weight of the kth environmental parameter;

[0198] For example, in this embodiment, a strategy for obtaining the normalized environment comprehensive offset is further provided, specifically:

[0199] 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;

[0200] The normalized offset of each parameter is squared, multiplied by the corresponding dynamic weight and summed to obtain the normalized environmental comprehensive offset;

[0201] It should be noted that the square term is used to amplify the impact of abnormal offsets; for dynamic weights, the contribution of parameters with large change rates (low weights) to the total offset is suppressed;

[0202] S42: extracting the device operating time, initial trust threshold, aging factor, environmental parameter entropy, and minimum feature stability score, setting a basic threshold, and modifying the basic threshold according to the environmental parameter entropy and the minimum feature stability score to obtain an adaptive trust threshold;

[0203] It should be noted that the more complex the environment (higher the entropy of environmental parameters) or the lower the feature stability (lower the score), the higher the threshold and the lower the tolerance to environmental deviations. The adaptive trust threshold can comprehensively reflect the dynamic trustworthiness standard of device aging, environmental complexity, and feature stability.

[0204] For example, in this embodiment, the basic threshold is specifically: ;

[0205] in, is the basic threshold, is the initial credible threshold, is the equipment aging factor;

[0206] It should be noted that the longer the equipment is used, the more serious the aging is and the lower the tolerance to environmental deviation is. The smaller;

[0207] For example, in this embodiment, a strategy for obtaining an adaptive trust threshold is provided, which specifically includes: calculating the real-time environment parameter vector Shannon entropy ;

[0208] Introducing environmental complexity modifiers and characteristic stability correction , multiply the basic threshold, the environment complexity correction term and the feature stability correction term to obtain the adaptive trust threshold ;

[0209] Regarding the environment complexity correction term, it should be noted that the higher the entropy value, the more complex the environment, and the higher the threshold (increasing the tolerance to environmental deviations). For complex data reading environments, the threshold needs to be relaxed to avoid misjudgment;

[0210] Regarding the feature stability correction item, it should be noted that the lower the stability, the lower the threshold (the tolerance to environmental offset is reduced). When the feature stability is poor, the environmental offset needs to be more strictly controlled to avoid feature recognition failure due to environmental interference.

[0211] S43: Correct the loss function gradient by feature stability gradient amplification;

[0212] It should be noted that the larger the feature stability gradient is (the more drastic the feature space changes), the larger the corrected gradient is, which is used to emphasize the importance of features in unstable areas;

[0213] 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;

[0214] Exemplarily, in this embodiment, the correction of the loss function gradient specifically includes:

[0215] Calculate the raw gradient , which means that the loss function follows the feature sensitivity to change;

[0216] Introducing stability gradient correction term ,in, is the stability gradient of feature point i (characterizing the rate of change of the stability of its neighborhood);

[0217] Multiply the original gradient by the correction term to get the corrected gradient ;

[0218] For example, in this embodiment, an implementation of a priority decision strategy is also provided, specifically:

[0219] ;

[0220] in, The data reading priority of feature point i is in the range of (0,1). The larger the value, the higher the priority.

[0221] The stability score of feature point i comes from biometric analysis (such as fingerprint pattern clarity and iris texture stability);

[0222] It is the normalized environmental integrated deviation, which is used to quantify the difference between the current environment and the nominal environment;

[0223] Adaptive trust threshold, dynamically adjusted with device aging and environment complexity;

[0224] For basic priority , it should be noted that the stability gradient of feature point i The bigger it is, The larger the value, the closer the denominator in the basic priority is to 1, and the closer the basic priority is to , that is, amplify the priority of important features;

[0225] For environmental risk suppression items It should be noted that when hour, , the environmental risk suppression item is greater than or equal to 0.5, and the priority decay is limited;

[0226] when When , the inhibition item drops rapidly and the priority is cleared to zero (i.e., decryption is rejected);

[0227] In this embodiment, it should also be noted that the higher the environmental risk (such as high sensor noise, serious equipment aging), the smaller the suppression factor, forcing the feature decryption priority to be lowered to avoid exposing sensitive features in an untrusted environment. At the same time, Dynamic adjustment (combining device operating time and environmental entropy) to achieve a strategy where the tolerance to environmental drift decreases as the device ages and the environment becomes more complex;

[0228] S44: Generates a memory self-destruct trigger signal and imports feature stability, environmental deviation, decryption priority, and maximum survival time into the risk perception strategy to quantify the survival time of the plaintext memory;

[0229] It should be noted that the survival time is positively correlated with the overall stability of the feature, 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.

[0230] For example, in this embodiment, the logic for generating the memory self-destruct trigger signal is:

[0231] When the environmental deviation exceeds 2 times the credible threshold, an environmental risk signal is triggered;

[0232] When the modulus of the cycle after the current time is rounded to the survival time is equal to 0 (that is, the survival cycle boundary is reached), the time period signal is triggered;

[0233] Combine the two trigger conditions through XOR operation to obtain the memory self-destruct trigger signal;

[0234] It should be noted that when any one of the conditions is met, the trigger signal is 1 and memory self-destruction is executed; when both conditions are met at the same time, the XOR result is 0 to avoid repeated triggering;

[0235] For example, in this embodiment, an implementation of a risk perception strategy is also provided, specifically:

[0236] The sum of the stability scores of all feature points is calculated as the numerator to reflect the overall feature stability (i.e., the higher the stability, the longer the survival time);

[0237] Combine the environment offset (the larger the value, the less trustworthy the environment) with the decryption priority ranking modifier Multiply to get the denominator. It should be noted that the higher the priority ranking ( The smaller the value), the smaller the correction term and the longer the survival time.

[0238] Divide the numerator by the denominator to obtain the risk-based survival time, and compare it with the preset maximum survival time to obtain the minimum value to obtain the survival time of the plaintext memory.

[0239] S5: Obtain historical decryption logs and feature point drift matrices, identify abnormal drift areas, dynamically tune the weights of the encryption granularity decision function based on the drift situation, resample the drift area features, and output the updated encryption policy parameter set.

[0240] S51: Extract the average change rate of historical environmental parameters from the historical decryption log , feature point coordinates, feature region center coordinates, feature point stability score and initial time scale ;

[0241] Dynamically adjust the time scale according to the rate of change of the environment. For example, in this embodiment, the time scale adjusted according to the rate of change of the environment The calculation strategy is as follows: ;

[0242] It should be noted that the more drastic the environmental changes, the faster the time decay;

[0243] Simultaneously calculate the spatial correlation weights of feature points and feature area centers ;

[0244] For example, 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 use the stability score of the feature point The influence of modulation distance, ;

[0245] 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;

[0246] The weights of distant or low-stability feature points are close to 0, reducing their contribution to spatial correlation;

[0247] Combined time scale and spatial correlation weights to generate spatiotemporally coupled dynamic convolution kernels ;

[0248] For example, in this embodiment, a dynamic convolution kernel generation strategy is provided, specifically: taking the difference between the current time and the reference time as input, generating the time weight of the Gaussian curve, and multiplying the time weight by the spatial correlation weight to obtain a spatiotemporally coupled dynamic convolution kernel;

[0249] S52: Calculate the drift energy accumulation through spatiotemporal coupled dynamic convolution kernel, based on the median of historical drift energy and absolute median difference Generate a dynamic threshold and 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. Filter out the set of abnormal feature points with excessive drift energy.

[0250] For example, in this embodiment, the specific calculation strategy of the drift energy accumulation amount is:

[0251] ;

[0252] in, Represents the accumulated drift energy of feature point i;

[0253] Indicates the drift of feature point i at time t, such as the number of pixels of coordinate offset and the change in stability score; is the rate of change of environmental parameters at time t, reflecting real-time environmental fluctuations; T is the length of the historical time window;

[0254] For example, in this embodiment, a dynamic threshold is provided. Generation strategy:

[0255] ;

[0256] S53: Quantify the abnormality degree of feature points 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 priority of feature matching and environmental compensation;

[0257] For example, in this embodiment, the output strategy of the risk factor is specifically as follows:

[0258] Calculate the ratio of the number of abnormal feature points to the total number of features in the abnormal feature point set to reflect the prevalence of abnormal drift;

[0259] Calculate the Shannon entropy of the real-time environmental parameters and transform them into Map its range to [0.5,1];

[0260] The product of the above two items is compressed to [-1,1] by the hyperbolic tangent function tanh to obtain the risk factor ;

[0261] Exemplarily, in this embodiment, adjusting the adaptive weight parameter according to the risk factor includes: ; are the weights of the weighted devices before and after the update respectively;

[0262] It should be noted that the stability adjustment item increases with the increase of average stability; is the average stability of the feature; all feature points The mean of , which reflects the overall feature quality;

[0263] For risk factors After multiplying with the stability adjustment term, if >0, then That is, when the risk is high and the stability is high, the The weight of

[0264] For example, in this embodiment, adjusting the adaptive weight parameter according to the risk factor further includes: ; are the network weights before and after the update respectively; l is the device aging factor; is the average environmental deviation;

[0265] It should be noted that Indicates the total amount of environmental risk over a period of time, and the aging factor After multiplication, it serves as the base of exponential decay;

[0266] Denominator Reduce the impact of features with large stability fluctuations on weight decay;

[0267] S54: Construct a drift compensation model to generate a compensation factor for the stability of the point. Combine the historical stability and the compensation factor, assign weights according to the drift energy ranking, and generate a new stability score.

[0268] For example, in this embodiment, the compensation factor of the feature point stability is The generation strategy is: ; is the historical maximum drift of feature point i, reflecting the worst case of its stability; is the drift tolerance threshold;

[0269] is the rate of change of current environmental parameters;

[0270] For example, in this embodiment, historical stability and compensation factors are combined, and weights are assigned according to drift energy ranking to generate a new stability score, including:

[0271] The old stability score is The proportion of the old stability score is retained, and the old stability score is retained. Determine the compensation factor in the new stability score The ratio of , to obtain the drift compensation term;

[0272] The old stability score retention term and the drift compensation term are superimposed to obtain a new stability score.

[0273] Example 2:

[0274] like Figure 2 As shown, a reading system of biometric data of a certificate card based on an encryption machine according to an embodiment of the present invention is as follows: Figure 2 As shown, it includes the following modules:

[0275] Topology map output module, encryption strategy acquisition module, encryption module, data reading module and encryption parameter update module;

[0276] The topology map output module is used to collect multimodal biometric raw data, classify feature points into three categories: core anchor points, variable feature points, and environmental sensitive points, and obtain a feature point topology map;

[0277] The encryption strategy acquisition module is used to perceive the data reading environment in real time, import the feature point topology map into the category differentiation strategy to calculate the encryption granularity of different feature category data, and obtain the encryption strategy table;

[0278] The encryption module generates an environmental entropy source using environmental parameters such as Shannon entropy, combines the original biometric data with the encryption strategy table, and constructs a dynamic obfuscation function to encrypt the data;

[0279] The data reading module is used to evaluate the environmental credibility in real time and select a data reading strategy based on the environmental credibility evaluation result;

[0280] The encryption parameter update module is used to obtain historical decryption logs and feature point drift matrices, identify abnormal drift areas, dynamically tune the weights of the encryption granularity decision function according to the drift situation, resample the drift area features, and output an updated encryption policy parameter set.

[0281] Example 3:

[0282] This embodiment provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0283] The processor executes the above-mentioned method for reading biometric data of an ID card based on an encryption machine by calling a computer program stored in the memory.

[0284] This electronic device may vary significantly depending on its configuration or performance. It may include one or more processors (CPUs) and one or more memories, wherein the memories store at least one computer program, which is loaded and executed by the processor to implement the encryption machine-based method for reading biometric data from an ID card as provided in the above-described method embodiment. The electronic device may also include other components for implementing its functions. For example, the electronic device may include a wired or wireless network interface and input / output interfaces for data input and output. This embodiment is not described in detail here.

[0285] Example 4:

[0286] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0287] When the computer program is run on a computer device, the computer device is caused to execute the above-mentioned method for reading biometric characteristic data of an ID card based on an encryption machine.

[0288] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device.

[0289] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0290] It should be understood that determining B based on A does not mean determining B based solely on A. B can also be determined based on A and / or other information.

[0291] The above embodiments can be implemented in whole or in part via 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. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, computer instructions can be transferred 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. A computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0292] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0293] Those skilled in the art will 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 aforementioned method embodiments and will not be repeated here.

[0294] In the 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 type. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0295] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0296] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0297] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0298] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for reading biometric data of an ID card based on an encryption machine, characterized in that: The method comprises: S1: Collect multimodal biometric raw data, classify feature points into three categories: core anchor points, variable feature points, and environmentally sensitive points, and obtain a feature point topology map; S2: Real-time perception of the data reading environment is performed, and the feature point topology map is imported into the category differentiation strategy to calculate the encryption granularity of data of different feature categories and obtain the encryption strategy table; S21: Collect data to read the original values ​​of environmental parameters, as well as the nominal mean and standard deviation corresponding to each environmental parameter, and normalize the collected data to synthesize the environmental safety factor; S22: Collect normalized environmental parameters, as well as the mean value of characteristic stability, the maximum data dimension, and the environmental safety factor. Design different forms of membership functions for device safety level, network delay, and sensor accuracy to obtain their nonlinear relationship with weights. Then associate the membership function values ​​with the mean value of characteristic stability, the inverse of the environmental safety factor, and the maximum data dimension, and dynamically adjust the weight parameters through normalized calculations. , generating weight parameters associated with the environment state ; S23: Obtain characteristic data dimensions, stability scores, stability gradients, environmental safety factors, and environmental entropy, and construct dimension-safety coupling items ; Constructing stability constraint function ,Combining the stability score and the degree of environmental deviation to constrain the granularity; According to the ratio of the two and combined with the adjustment of the environmental entropy value, the encryption granularity level is calculated ; S24: extracting the classification results of feature points, the encryption granularity level, the minimum stability score, and the environmental sensitivity in the feature point topology map, and generating an encrypted block mapping table integrating the spatial topology structure and the security level; S3: Generate an environmental entropy source using environmental parameters such as Shannon entropy, combine the original biometric data with the encryption strategy table, and build a dynamic obfuscation function to encrypt the data; S4: Evaluate the environmental credibility in real time and select a data reading strategy based on the environmental credibility evaluation results; S5: Obtain historical decryption logs and feature point drift matrices, identify abnormal drift areas, dynamically tune the weights of the encryption granularity decision function based on the drift situation, resample the drift area features, and output the updated encryption policy parameter set.

2. The method for reading biometric data of an ID card based on an encryption machine according to claim 1, characterized in that: Collect multimodal biometric raw data, classify feature points into three categories: core anchor points, variable feature points, and environmentally sensitive points, and obtain a feature point topology map, including: S11: Collecting original biometric coordinate data and temperature, humidity, and pressure parameters monitored in real time by sensors, and correcting the original coordinates using an environmental noise compensation formula; At the same time, based on the environment-related sliding mean and variance standardization reference coordinate system, the dynamically calibrated standardized feature coordinates are output; S12: Determine the adaptive time window based on 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 correlation quantification strategy, thereby generating a spatiotemporal correlation matrix that integrates the environmental weights; 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; Based on the diagonal elements of the spatiotemporal correlation matrix, the environmental offset, and the difference between the current environment and the nominal environment, the stability score of the environmental adaptation of each feature point is obtained; S14: Based on the stability score, a stability gradient field is constructed to monitor the spatial trend of the stability of feature points. The feature points are divided into three categories: core anchor points, variable feature points, and environmentally sensitive points. The topological map of feature points with spatial topological relationships is output.

3. The method for reading biometric data of an ID card based on an encryption machine according to claim 2, characterized in that: The environmental entropy source is generated by using environmental parameters such as Shannon entropy, and combined with the original biometric data and the encryption strategy table to construct a dynamic obfuscation function to encrypt the data, including: S31: Collect the equipment running time, environmental parameters and their gradients, and equipment aging factors to generate a composite entropy value. Multiply the composite entropy value by Then round down to get an integer, and take the modulus of the above integer , map the result to 128-bit binary space, and use quantum key distribution to distribute the initial seed Perform XOR operation with the result of modular operation to generate the final 128-bit quantum noise sequence ; S32: Extract the stability score, stability gradient, and quantum noise sequence of the feature points. Design the noise intensity function based on the stability score and gradient. Inject modulated quantum noise into the AES-encrypted feature data. After layered obfuscation operation, output the core anchor point ciphertext. S33: Generate a hash value containing time and space information through SHA3-512, use cyclic shift and XOR to build a quantum-resistant hash chain, and output a quantum-safe hash chain with time and space correlation; S34: Dynamically adjust block encryption strength based on granularity level, using XMSS hashing to construct a Merkle tree with block entropy; Construct an extended Merkle signature tree with a block entropy binding mechanism; S35: Encrypt sensitive data using AES and then perform cyclic shift, combine EdDSA and NTRU to generate a dual-algorithm composite signature, and output a quantum-resistant signature.

4. The method for reading biometric data of an ID card based on an encryption machine according to claim 3, characterized in that: Evaluate the environment's credibility in real time and select a data read strategy based on the results, including: S41: Real-time environmental parameters, nominal environmental parameters, characteristic average stability, nominal standard deviation of environmental parameters, dynamic weights of each environmental parameter are calculated through exponential function, and normalized environmental comprehensive deviation is obtained according to the dynamic weights ; S42: extracting the device operating time, initial trust threshold, aging factor, environmental parameter entropy, and minimum feature stability score, setting a basic threshold, and modifying the basic threshold according to the environmental parameter entropy and the minimum feature stability score to obtain an adaptive trust threshold; S43: Correct the loss function gradient by feature stability gradient amplification; 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.

5. The method for reading biometric data of an ID card based on an encryption machine according to claim 4, characterized in that: Real-time evaluation of environmental credibility, and selection of data access strategies based on the results of environmental credibility evaluation, also include: S44: Generate a memory self-destruction trigger signal and import feature stability, environmental deviation, decryption priority, and maximum survival time into the risk perception strategy to quantify the survival time of the plaintext memory.

6. The method for reading biometric data of an ID card based on an encryption machine according to claim 5, characterized in that: Obtain historical decryption logs and feature point drift matrices, identify abnormal drift areas, dynamically tune the weights of the encryption granularity decision function based on the drift situation, resample the drift area features, and output the updated encryption policy parameter set, including: S51: Extract the average change rate of historical environmental parameters from the historical decryption log , feature point coordinates, feature region center coordinates, feature point stability score and initial time scale ; Dynamically adjust the time scale according to the environmental change rate and simultaneously calculate the spatial correlation weights of feature points and feature area centers ; Combined time scale and spatial correlation weights to generate spatiotemporally coupled dynamic convolution kernels ; S52: Calculate the drift energy accumulation through spatiotemporal coupled dynamic convolution kernel, based on the median of historical drift energy and absolute median difference Generate a dynamic threshold and 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. The set of abnormal feature points with excessive drift energy is screened out.

7. The method for reading biometric data of an ID card based on an encryption machine according to claim 6, characterized in that: Obtain historical decryption logs and feature point drift matrices, identify abnormal drift areas, dynamically tune the weights of the encryption granularity decision function based on the drift situation, resample the drift area features, and output the updated encryption policy parameter set. This also includes: S53: Quantify the abnormality degree of feature points 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 priority of feature matching and environmental compensation; S54: Construct a drift compensation model, generate a compensation factor for point stability, combine historical stability and compensation factors, assign weights according to drift energy ranking, and generate a new stability score.

8. A system for reading biometric data of an ID card based on an encryption machine, used to implement a method for reading biometric data of an ID card based on an encryption machine as claimed in any one of claims 1 to 7, characterized in that: The system comprises: Topology map output module, encryption strategy acquisition module, encryption module, data reading module and encryption parameter update module; The topology map output module is used to collect multimodal biometric raw data, classify feature points into three categories: core anchor points, variable feature points, and environmental sensitive points, and obtain a feature point topology map; The encryption strategy acquisition module is used to perceive the data reading environment in real time, import the feature point topology map into the category differentiation strategy to calculate the encryption granularity of different feature category data, and obtain the encryption strategy table; The encryption policy acquisition module is used to run the following policies: Collect data to read the original values ​​of environmental parameters, as well as the nominal mean and standard deviation corresponding to each environmental parameter, and normalize the collected data to synthesize the environmental safety factor; Collect normalized environmental parameters, as well as the mean value of characteristic stability, maximum data dimension, and environmental safety factor. Design different forms of membership functions for device safety level, network delay, and sensor accuracy to obtain their nonlinear relationship with weights. Then associate the membership function values ​​with the mean value of characteristic stability, the inverse of the environmental safety factor, and the maximum data dimension, and dynamically adjust the weight parameters through normalized calculation. , generating weight parameters associated with the environment state ; Obtain characteristic data dimensions, stability scores, stability gradients, environmental safety factors, and environmental entropy to construct dimension-safety coupling items ; Constructing stability constraint function ,Combining the stability score and the degree of environmental deviation to constrain the granularity; According to the ratio of the two and combined with the adjustment of the environmental entropy value, the encryption granularity level is calculated ; Extract the classification results of feature points, encryption granularity level, minimum stability score and environmental sensitivity in the feature point topology map, and generate an encrypted block mapping table that integrates the spatial topology structure and security level; The encryption module generates an environmental entropy source using environmental parameters such as Shannon entropy, combines the original biometric data with the encryption strategy table, and constructs a dynamic obfuscation 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 based on the environmental credibility evaluation result; The encryption parameter update module is used to obtain historical decryption logs and feature point drift matrices, identify abnormal drift areas, dynamically tune the weights of the encryption granularity decision function according to the drift situation, resample the drift area features, and output an updated encryption policy parameter set.

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