Method and system for generating high-security key based on hardware features

Abstract

The invention discloses a method and a system for generating a high-security key based on hardware features. The method comprises the steps of extracting terminal hardware features, analyzing feature levels and performing feature fusion. And reinforcing the fused features by using a noise processing algorithm based on a chaos theory. And generating a high-security key based on the enhanced fusion feature. According to the method and the system for generating the high-security key based on the hardware features, the hardware features are effectively utilized, and high-quality basic data are provided for subsequent key generation. The potential of hardware features is fully mined, and the randomness and stability of feature data are improved. And deep optimization and randomness enhancement of feature data are realized for feature enhancement, and the security of the key is effectively improved. Finally, the pseudo-random number sequence is converted into a high-security key through a secure hash algorithm, and it is ensured that the key is difficult to crack by an attacker. The generation of a high-quality key is realized, and the security of data transmission and storage is ensured.

Description

Technical Field

[0001] The present invention relates to the field of information security technology, and in particular to a method and system for generating a high-security key based on hardware features. Background Art

[0002] With the rapid development of information technology, data security and privacy protection have become increasingly important. In the process of information transmission and storage, the security of keys is directly related to the confidentiality and integrity of data. Traditional key generation methods mainly rely on software algorithms. Although these algorithms have been developed and optimized for many years, they still face certain inherent security issues, such as insufficient randomness of key generation and vulnerability to attacks. In addition, with the continuous advancement of quantum computing technology, traditional encryption methods based on mathematical problems may face severe challenges in the future.

[0003] At present, hardware security has become a new research direction, especially the method of generating keys using hardware features has received widespread attention. Hardware features such as CPU temperature fluctuations and clock jitter have natural randomness and difficult-to-copy characteristics, which can effectively improve the randomness and security of keys. However, the existing key generation methods based on hardware features have certain limitations, such as the difficulty in fully utilizing the randomness and stability of different hardware features, and the lack of complexity in feature fusion and processing. These problems lead to certain security risks in the generated keys in practical applications. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that when the prior art uses hardware features to generate keys, it often fails to fully tap the potential of the features, resulting in insufficient randomness and stability of the keys, making them vulnerable to attacks.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for generating a high-security key based on hardware features, comprising: extracting terminal hardware features, analyzing feature levels, and performing feature fusion.

[0007] A noise processing algorithm based on chaos theory is used to enhance the fused features.

[0008] Generate a highly secure key based on the enhanced fusion features.

[0009] As a preferred solution of the method for generating high-security keys based on hardware features described in the present invention, the terminal hardware features include CPU temperature fluctuations, clock jitter, memory electrical characteristics, hard disk read and write time characteristics, CPU voltage fluctuations, network delays, electromagnetic intensity fluctuations and power supply noise.

[0010] As a preferred solution of the method for generating a high-security key based on hardware features of the present invention, the feature level analysis includes first dividing the feature level:

[0011]

[0012] Among them, L i represents the i-th feature level value, i represents the feature level. T represents time, F i represents the hardware characteristics of the i-th terminal, σ(F i (t)) represents the feature F i The standard deviation at time t.

[0013] A total of three feature levels are obtained, including primary features, secondary features and tertiary features.

[0014] The first-level features are highly stable and random, including CPU temperature fluctuations and clock jitter.

[0015] Secondary features are features with medium stability and moderate randomness, including memory electrical characteristics, hard disk read and write time characteristics, and CPU voltage fluctuations.

[0016] Level 3 features are less stable and less random, including network delays, electromagnetic intensity fluctuations, and power supply noise.

[0017] The standard deviation and mean are calculated for the terminal hardware features. The standard deviation indicates the dispersion of the feature values, and the mean indicates the average level of the feature. The feature hierarchy is calculated using correlation analysis to identify the interactions and dependencies between features, expressed as:

[0018]

[0019] Among them, C ij Representation feature F i and feature F j The correlation coefficient, μ i Feature F i The mean value, μ j Representation feature F j The mean value, F i (k) represents the i-th eigenvalue at the k-th time point, F j (k) represents the jth eigenvalue at the kth time point, and N represents the total number of eigenvalue data points.

[0020] As a preferred solution of the method for generating a high-security key based on hardware features of the present invention, the feature fusion includes performing same-level fusion according to the divided feature levels, which is expressed as:

[0021]

[0022] Among them, S L represents the feature set belonging to the Lth level, F fusion (L) represents the features after L-th level fusion, Representation feature F i The rate of change over time, Representation feature F i The integral of the rate of change of a feature measures the magnitude of its change over time.

[0023] As a preferred solution of the method for generating a high-security key based on hardware features described in the present invention, the noise processing algorithm based on chaos theory is used to strengthen the fused features, including first processing the primary features and the secondary features, normalizing the fused primary features and the secondary features, and inputting them into the Logistic mapping formula to generate an initial chaotic sequence, which is expressed as:

[0024] x n+1 =rx n (1-x n )

[0025] Among them, x n represents the nth value of the chaotic sequence, r represents the chaotic mapping parameter, which is 3.57~4.

[0026] The initial chaotic sequence is transformed to generate an enhanced random number sequence R n :

[0027]

[0028] Among them, S (12) n represents the intermediate random number sequence between the primary feature and the secondary feature, λ represents the attenuation coefficient, R (12) n It represents the final enhanced random number sequence of the primary and secondary features, and T represents the integration time.

[0029] The three-level features are processed, the fused three-level features are normalized, and input into the Logistic mapping formula to generate the initial chaotic sequence. Due to the low stability and poor randomness of the three-level features, on the basis of the first and second level feature processing, the particularity of the three-level features is considered, and nonlinear enhancement transformation terms and high-level score correction terms are added, which are expressed as:

[0030]

[0031] Among them, S n (3)represents the intermediate random number sequence of the third-level features, E represents the nonlinear enhancement transformation, x n i represents the i-th power of the n-th chaotic sequence value. m represents a constant, and t represents a time variable.

[0032]

[0033] Among them, R n (3) represents the final enhanced random number sequence of the three-level features. M represents the integral correction term, S k Represents the kth intermediate random value. represents a nonlinear term.

[0034] As a preferred solution of the method for generating a high-security key based on hardware features described in the present invention, the method for generating a high-security key based on the enhanced fusion features includes firstly generating a pseudo-random number using a PRNG, and generating a pseudo-random number sequence using a recursive formula of a linear congruential generator:

[0035] S n+1 =(a·S n +c)modm

[0036] Among them, Sn represents the nth pseudo-random number, a represents the PRNG parameter, c represents the PRNG parameter, mod represents the modulus operation, and m represents the PRNG parameter.

[0037] The generated sequence S n Normalized to the range of 0 to 1, a pseudo-random number sequence is obtained:

[0038]

[0039] The normalized sequence is collected to obtain the pseudo-random number sequence P.

[0040] The enhanced first-level features, second-level features, and third-level features are merged into a sequence:

[0041]

[0042] in, Represents the exclusive-or operation.

[0043] In order to increase the complexity and unpredictability of the sequence, the cross-influence of adjacent sequences is introduced so that each value in the sequence depends not only on the current value, but also on the previous and next values:

[0044]

[0045] in, represents the n-1th value in the fused sequence, Indicates the n+1th value in the fused sequence.

[0046] As a preferred solution of the method for generating a high-security key based on hardware features of the present invention, wherein: the generation of a high-security key based on the enhanced fusion feature also includes combining the fusion sequence T of the primary, secondary and tertiary features n Multiple superpositions with sequence P are performed, expressed as:

[0047]

[0048] Among them, P n-1 Indicates the pseudo-random number sequence value generated by the PRNG at the previous point in time.

[0049] Finally, multiple hash operations are performed to mix and generate a highly secure key sequence:

[0050]

[0051] Among them, K n Represents the nth value in the generated key sequence, SHA-512() represents a cryptographic hash function, and MD5() represents a cryptographic hash function.

[0052] A system for generating a high-security key based on hardware features, characterized in that:

[0053] The feature hierarchical fusion module extracts terminal hardware features, analyzes feature levels, and performs feature fusion.

[0054] The feature enhancement module uses a noise processing algorithm based on chaos theory to enhance the fused features.

[0055] The key generation module generates a highly secure key based on the enhanced fusion features.

[0056] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0057] A computer-readable storage medium stores a computer program, which implements the steps of the method described above when executed by a processor.

[0058] The beneficial effects of the present invention are as follows: the effective utilization of hardware features is achieved, and high-quality basic data is provided for subsequent key generation. The potential of hardware features is fully tapped to improve the randomness and stability of feature data. Feature enhancement achieves deep optimization and randomness enhancement of feature data, effectively improving the security of keys. Finally, the pseudo-random number sequence is converted into a high-security key through a secure hash algorithm, ensuring that the key is difficult to be cracked by attackers. The generation of high-quality keys is achieved, ensuring the security of data transmission and storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] 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. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0060] Figure 1 An overall flow chart of a method for generating a high-security key based on hardware features provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0062] Example 1

[0063] Reference Figure 1 , as an embodiment of the present invention, provides a method for generating a high-security key based on hardware features, comprising:

[0064] S1: Extract terminal hardware features, analyze feature levels, and perform feature fusion.

[0065] Terminal hardware characteristics include CPU temperature fluctuations, clock jitter, memory electrical characteristics, hard disk read and write time characteristics, CPU voltage fluctuations, network delays, electromagnetic intensity fluctuations, and power supply noise.

[0066] It should be noted that a low-intrusive sampling strategy is used when collecting features to ensure that the impact on device performance is minimized during the collection process. The low-intrusive sampling strategy is designed mainly to reduce interference with the normal operation of the device, and through intermittent sampling, ensure that the device is not affected by the sampling process when it is under high load.

[0067] Analyzing the feature hierarchy includes first dividing the feature hierarchy:

[0068]

[0069] Among them, L i represents the i-th feature level value, i represents the feature level. T represents time, F i represents the hardware characteristics of the i-th terminal, σ(F i (t)) represents the feature F i The standard deviation at time t.

[0070] It should be noted that the change rate integral measures the magnitude and frequency of a feature's change over time. By integrating the feature's change rate, the dynamics and stability of the feature can be quantified. The determination of the feature level L is based on the feature's change rate integral.

[0071] A total of three feature levels are obtained, including primary features, secondary features and tertiary features.

[0072] The first-level features are highly stable and random, including CPU temperature fluctuations and clock jitter.

[0073] Secondary features are features with medium stability and moderate randomness, including memory electrical characteristics, hard disk read and write time characteristics, and CPU voltage fluctuations.

[0074] Level 3 features are less stable and less random, including network delays, electromagnetic intensity fluctuations, and power supply noise.

[0075] Calculate the standard deviation and mean of the terminal hardware features. The standard deviation indicates the dispersion of the feature values, and the mean indicates the average level of the feature. Use correlation analysis to calculate the feature hierarchy and identify the interactions and dependencies between features, expressed as:

[0076]

[0077] Among them, C ij Representation feature F i and feature F j The correlation coefficient, μ i Feature F i The mean value, μ j Representation feature F j The mean value, F i (k) represents the i-th eigenvalue at the k-th time point, F j (k) represents the jth eigenvalue at the kth time point, and N represents the total number of eigenvalue data points.

[0078] It should be noted that correlation analysis can identify the interactions and dependencies between features and help optimize the feature fusion process.

[0079] Feature fusion includes the same-level fusion according to the divided feature levels, which can be expressed as:

[0080]

[0081] Among them, S L represents the feature set belonging to the Lth level, F fusion (L) represents the features after L-th level fusion, Representation feature F i The rate of change over time, Representation feature F i The integral of the rate of change of a feature measures the magnitude of its change over time.

[0082] It should be noted that the feature same-level fusion formula takes into account the change rate integral and correlation of the features by combining the various features. It is used to measure the dynamics of the features, and the correlation matrix C ij It is used to adjust the interaction between features.

[0083] Furthermore, this design ensures that the fused features can not only retain the uniqueness of each feature, but also enhance the diversity and complexity of the features by considering the correlation between features, thereby improving the security of the key.

[0084] S2: Use a noise processing algorithm based on chaos theory to enhance the fused features.

[0085] It should be noted that since the first-level and second-level features have higher randomness and stability, and the third-level features have poor randomness, it is necessary to design an additional processing algorithm for the third-level features separately to give full play to the advantages of each level of features and improve overall security.

[0086] First, the primary and secondary features are processed, the fused primary and secondary features are normalized, and input into the Logistic mapping formula to generate the initial chaotic sequence, which is expressed as:

[0087] x n+1 =rx n (1-x n )

[0088] Among them, x n represents the nth value of the chaotic sequence, r represents the chaotic mapping parameter, which is 3.57~4.

[0089] The initial chaotic sequence is transformed to generate an enhanced random number sequence R n :

[0090]

[0091] Among them, S (12) n represents the intermediate random number sequence between the primary feature and the secondary feature, λ represents the attenuation coefficient, R (12) n It represents the final enhanced random number sequence of the primary and secondary features, and T represents the integration time.

[0092] It should be noted that the randomness of the third-level features is poor. On the basis of the first- and second-level feature processing, more complex transformation items are added to ensure that the output random numbers have higher randomness and complexity.

[0093] The three-level features are processed, the fused three-level features are normalized, and input into the Logistic mapping formula to generate the initial chaotic sequence. Due to the low stability and poor randomness of the three-level features, on the basis of the first and second level feature processing, the particularity of the three-level features is considered, and nonlinear enhancement transformation terms and high-level score correction terms are added, which are expressed as:

[0094]

[0095] Among them, S n (3) represents the intermediate random number sequence of the third-level features, E represents the nonlinear enhancement transformation, x n i represents the i-th power of the n-th chaotic sequence value. m represents a constant, and t represents a time variable.

[0096]

[0097] Among them, R n (3) represents the final enhanced random number sequence of the three-level features. M represents the integral correction term, S k Represents the kth intermediate random value. represents a nonlinear term.

[0098] It should be noted that by designing different processing algorithms for the primary, secondary and tertiary features respectively, and utilizing the high sensitivity and complex dynamic characteristics of chaos theory, the randomness and unpredictability of the fused features are enhanced, providing a high-quality random number foundation for subsequent key generation.

[0099] S3: Generate a highly secure key based on the enhanced fusion features.

[0100] First, use PRNG to generate pseudo-random numbers, and use the recursive formula of the linear congruential generator to generate a pseudo-random number sequence:

[0101] S n+1 =(a·Sn +c)modm

[0102] Among them, Sn represents the nth pseudo-random number, a represents the PRNG parameter, c represents the PRNG parameter, mod represents the modulus operation, and m represents the PRNG parameter.

[0103] It should be noted that a, c, and m are all large prime numbers.

[0104] The generated sequence S n Normalized to the range of 0 to 1, a pseudo-random number sequence is obtained:

[0105]

[0106] The normalized sequence is collected to obtain the pseudo-random number sequence P.

[0107] The enhanced first-level features, second-level features, and third-level features are merged into a sequence:

[0108]

[0109] in, Represents the exclusive-or operation.

[0110] In order to increase the complexity and unpredictability of the sequence, the cross-influence of adjacent sequences is introduced so that each value in the sequence depends not only on the current value, but also on the previous and next values:

[0111]

[0112] in, represents the n-1th value in the fused sequence, Indicates the n+1th value in the fused sequence.

[0113] Fusion sequence T combining primary, secondary and tertiary features n Multiple superpositions with sequence P are performed, expressed as:

[0114]

[0115] Among them, P n-1 Indicates the pseudo-random number sequence value generated by the PRNG at the previous point in time.

[0116] Finally, multiple hash operations are performed to mix and generate a highly secure key sequence:

[0117]

[0118] Among them, K n Represents the nth value in the generated key sequence, SHA-512() represents a cryptographic hash function, and MD5() represents a cryptographic hash function.

[0119] The above embodiments also include a system for generating a high-security key based on hardware features, specifically:

[0120] The feature hierarchical fusion module extracts terminal hardware features, analyzes feature levels, and performs feature fusion.

[0121] The feature enhancement module uses a noise processing algorithm based on chaos theory to enhance the fused features.

[0122] The key generation module generates a highly secure key based on the enhanced fusion features.

[0123] The computer device may be a server. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data cluster data of a power monitoring system. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method is implemented.

[0124] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0125] Example 2

[0126] An embodiment of the present invention provides a method and system for generating a high-security key based on hardware features. In order to verify the beneficial effects of the present invention, a scientific demonstration is carried out through simulation experiments.

[0127] This example selects 20 terminal devices with different hardware configurations, including desktops, laptops, and servers. The hardware characteristics of these terminal devices include CPU temperature fluctuations, clock jitter, memory electrical characteristics, hard disk read and write time characteristics, CPU voltage fluctuations, network delays, electromagnetic intensity fluctuations, and power supply noise.

[0128] The general process of the prior art:

[0129] Single hardware feature collection: only collect one or several hardware features, such as CPU temperature fluctuations.

[0130] Directly use PRNG to generate keys: Use a simple pseudo-random number generator such as a linear congruential generator (LCG) to generate keys without complex fusion or enhancement processing.

[0131] Limited key length and strength: Due to the lack of multi-level processing and fusion of hardware characteristics, the length and strength of the generated keys are often limited, usually 128 bits or less.

[0132] The process of the method of the present invention is as follows: First, the data of these hardware features are collected. A specific software tool is installed on each terminal device to monitor and record the above hardware features in real time. The data collection period is set to 24 hours, and the collection frequency is once per second. The collected data includes the original value of each hardware feature and its change trend.

[0133] Next, the collected hardware feature data is analyzed and processed. According to the stability and randomness of the features, they are divided into three levels: primary features (CPU temperature fluctuations, clock jitter), secondary features (memory electrical characteristics, hard disk read and write time characteristics, and CPU voltage fluctuations), and tertiary features (network delay, electromagnetic intensity fluctuations, and power supply noise). The standard deviation and mean of each feature are calculated to indicate its dispersion and average level. Correlation analysis is used to identify the interactions and dependencies between features and generate feature hierarchy values.

[0134] In the feature fusion stage, the features of the same level are fused at the same level. For the first and second level features, they are normalized and input into the Logistic mapping formula to generate the initial chaotic sequence, and then transformed to generate the enhanced random number sequence. The third level features are normalized and transformed with nonlinear enhancement to generate the final enhanced features.

[0135] Generate a pseudo-random number sequence and normalize it to obtain a pseudo-random number sequence P.

[0136] The enhanced sequence after the fusion of the primary, secondary and tertiary features is combined with the pseudo-random number sequence P, and a highly secure key is generated through complex nonlinear transformation and multi-level superposition. The cross-influence of adjacent sequences is introduced so that each value depends not only on the current value, but also on the previous and next values. Finally, multiple hash operations are performed to mix and generate the final highly secure key sequence. The experimental results are shown in Table 1.

[0137] Table 1 Experimental results

[0138]

[0139]

[0140] Through the above experimental data, we can clearly see the hardware characteristic values ​​of each terminal device and its impact on the key generation. The hardware characteristic values ​​of each measuring point, such as CPU temperature fluctuation, clock jitter, memory electrical characteristics, etc., are slightly different, but the key strength generated by feature fusion, enhancement and PRNG combination reaches 256 bits. This shows that the method of the present invention has high consistency and stability under different hardware configurations.

[0141] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for generating a high-security key based on hardware features, characterized in that: include: Extract terminal hardware features, analyze feature levels, and perform feature fusion; Use the noise processing algorithm based on chaos theory to enhance the fused features; Generate a highly secure key based on the enhanced fusion features.

2. The method for generating a high-security key based on hardware features as claimed in claim 1, characterized in that: The terminal hardware characteristics include CPU temperature fluctuations, clock jitter, memory electrical characteristics, hard disk read and write time characteristics, CPU voltage fluctuations, network delays, electromagnetic intensity fluctuations and power supply noise.

3. The method for generating a high-security key based on hardware features as claimed in claim 2, characterized in that: The analysis feature hierarchy includes first dividing the feature hierarchy: Among them, L i represents the i-th feature level value, i represents the feature level; T represents time, F i represents the hardware characteristics of the i-th terminal, σ(F i (t)) represents the feature F i The standard deviation at time t; A total of three levels of features are obtained, including primary features, secondary features, and tertiary features; Level 1 features are features with high stability and strong randomness, including CPU temperature fluctuations and clock jitter; Secondary features are features with medium stability and moderate randomness, including memory electrical characteristics, hard disk read and write time characteristics, and CPU voltage fluctuations; Level 3 features are less stable and less random, including network delays, electromagnetic intensity fluctuations, and power supply noise; The standard deviation and mean are calculated for the terminal hardware features; the standard deviation represents the dispersion of the feature values, and the mean represents the average level of the feature; the feature hierarchy is calculated using correlation analysis to identify the interactions and dependencies between the features, expressed as: Among them, C ij Representation feature F i and feature F j The correlation coefficient, μ i Feature F i The mean value, μ j Representation feature F j The mean value, F i (k) represents the i-th eigenvalue at the k-th time point, F j (k) represents the jth eigenvalue at the kth time point, and N represents the total number of eigenvalue data points.

4. The method for generating a high-security key based on hardware features as claimed in claim 3, characterized in that: The feature fusion includes performing same-level fusion according to the divided feature levels, which is expressed as: Among them, S L represents the feature set belonging to the Lth level, F fusion (L) represents the features after L-th level fusion, Representation feature F i The rate of change over time, Representation feature F i The integral of the rate of change of a feature measures the magnitude of its change over time.

5. The method for generating a high-security key based on hardware features as claimed in claim 4, characterized in that: The noise processing algorithm based on chaos theory is used to strengthen the fused features, including first processing the primary features and the secondary features, normalizing the fused primary features and the secondary features, and inputting them into the Logistic mapping formula to generate an initial chaotic sequence, which is expressed as: x n+1 =rx n (1-x n ) Among them, x n represents the nth value of the chaotic sequence, r represents the chaotic mapping parameter, which is 3.57 to 4; The initial chaotic sequence is transformed to generate an enhanced random number sequence R n : Among them, S (12) n represents the intermediate random number sequence between the primary feature and the secondary feature, λ represents the attenuation coefficient, R (12) n represents the final enhanced random number sequence of the primary and secondary features, and T represents the integration time; The three-level features are processed, the fused three-level features are normalized, and input into the Logistic mapping formula to generate the initial chaotic sequence. Due to the low stability and poor randomness of the three-level features, on the basis of the first and second level feature processing, the particularity of the three-level features is considered, and nonlinear enhancement transformation terms and high-level score correction terms are added, which are expressed as: Among them, S n (3) represents the intermediate random number sequence of the third-level features, E represents the nonlinear enhancement transformation, x n i represents the i-th power of the n-th chaotic sequence value; m represents a constant, and t represents a time variable; Among them, R n (3) represents the final enhanced random number sequence of the three-level features; M represents the integral correction term, S k represents the kth intermediate random value; represents a nonlinear term.

6. The method for generating a high-security key based on hardware features as claimed in claim 5, characterized in that: The method of generating a highly secure key based on the enhanced fusion feature includes firstly generating a pseudo-random number using a PRNG, and then generating a pseudo-random number sequence using a recursive formula of a linear congruential generator: S n+1 =(a·S n +c)modm Wherein, Sn represents the nth pseudo-random number, a represents the PRNG parameter, c represents the PRNG parameter, mod represents the modulus operation, and m represents the PRNG parameter; The generated sequence S n Normalized to the range of 0 to 1, a pseudo-random number sequence is obtained: The normalized sequence is collected to obtain a pseudo-random number sequence P; The enhanced first-level features, second-level features, and third-level features are merged into a sequence: Among them, ⊕ represents the exclusive OR operation; In order to increase the complexity and unpredictability of the sequence, the cross-influence of adjacent sequences is introduced so that each value in the sequence depends not only on the current value, but also on the previous and next values: in, represents the n-1th value in the fused sequence, Indicates the n+1th value in the fused sequence.

7. The method for generating a high-security key based on hardware features as claimed in claim 6, characterized in that: The generation of a highly secure key based on the enhanced fusion feature also includes combining the fusion sequence T of the primary, secondary and tertiary features. n Multiple superpositions with sequence P are performed, expressed as: Among them, P n-1 Represents the pseudo-random number sequence value generated by the PRNG at the previous time point; Finally, multiple hash operations are performed to mix and generate a highly secure key sequence: Among them, K n Represents the nth value in the generated key sequence, SHA-512() represents a cryptographic hash function, and MD5() represents a cryptographic hash function.

8. A system for generating a high-security key based on hardware features using the method according to any one of claims 1 to 7, characterized in that: Feature hierarchical fusion module, extracts terminal hardware features, analyzes feature levels, and performs feature fusion; The feature enhancement module uses a noise processing algorithm based on chaos theory to enhance the fused features; The key generation module generates a highly secure key based on the enhanced fusion features.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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