Intelligent user data encryption protection method and system

Abstract

The present invention discloses an intelligent user data encryption protection method and system, which includes presetting, obfuscation processing, hybrid mapping, encryption processing, and encryption optimization processing. The present invention belongs to the field of data protection technology, and specifically refers to an intelligent user data encryption protection method and system. This solution enhances the unpredictability of data protection through multi-level encryption and obfuscation processing, and finds a good balance between efficiency and security through XOR diffusion and hybrid mapping; increases the diversity and complexity of the encryption process by measuring local security and global security at different levels; improves the overall encryption strength through fitness value-based selection and non-fixed multiple pairing mechanism; introduces an aging correction mechanism to enhance the diversity of encrypted sequences by dynamically generating a common allele set; uses an archive set to store all potential optimal solutions, improving the security and anti-attack capability of the ciphertext; and ultimately improves the data encryption protection effect.

Description

Technical Field

[0001] The present invention relates to the field of data protection, and in particular to an intelligent user data encryption protection method and system. Background Art

[0002] User data encryption protection methods refer to a series of technologies and algorithms designed to encrypt sensitive user data to ensure that it cannot be accessed, modified, or stolen by unauthorized third parties during storage, transmission, or use. However, typical user data encryption protection methods suffer from excessive computational complexity, poor ability to process large amounts of data, and susceptibility to pattern analysis attacks, which in turn leads to low encryption security. They also employ fixed encryption methods, lack real-time adaptation to ciphertext changes, and are susceptible to differential attacks, resulting in poor data encryption protection effectiveness. Summary of the Invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides an intelligent user data encryption protection method. In view of the problems that general user data encryption protection methods have excessive computational complexity, poor ability to process large-scale data, and susceptibility to pattern analysis attacks, which in turn lead to low encryption protection security, this solution enhances the complexity and unpredictability of data protection through multi-level encryption and obfuscation processing. During the encryption process, the data is gradually disturbed and diffused through XOR diffusion and hybrid mapping steps, which significantly improves the ability to resist statistical analysis attacks and pattern analysis attacks; the elliptic curve encryption algorithm is used to reduce the computational and storage overhead, and a good balance is found between efficiency and security; the general user data encryption protection method has the problem of using a fixed encryption method. The scheme lacks real-time adaptation to ciphertext changes and is vulnerable to differential attacks, which leads to poor data encryption protection. This scheme increases the diversity and complexity of the encryption process by measuring local security and global security at different levels; continuously reduces encryption weaknesses and improves the overall encryption strength through fitness value-based selection and non-fixed multiple pairing mechanism; introduces an aging correction mechanism to maintain continuous improvement of encryption effect in long-term iterations; enhances the diversity of encrypted sequences by dynamically generating public allele sets; uses archive sets to store all potential optimal solutions, ensuring that the global optimal ciphertext arrangement can be continuously updated in multiple iteration cycles, improving the security and anti-attack ability of the ciphertext; and ultimately improves the data encryption protection effect.

[0004] The technical solution adopted by the present invention is as follows: The intelligent user data encryption protection method provided by the present invention comprises the following steps:

[0005] Step S1: pre-setting;

[0006] Step S2: obfuscation processing;

[0007] Step S3: hybrid mapping;

[0008] Step S4: encryption processing;

[0009] Step S5: Encryption optimization processing.

[0010] Furthermore, in step S1, the preset is to obtain the user data to be encrypted. Assuming that the user data to be encrypted is a binary stream, it is expressed as: ; Where D is the original data sequence to be encrypted by the user; L is the length; and are the 1st and Lth original data respectively.

[0011] Furthermore, in step S2, the obfuscation process is to use numerical integration to solve the Lorenz system for L consecutive iterations to obtain data at L time points, and use the Rossler attractor to calculate a set of three-dimensional vectors at each time point. ; expressed as: ;in, 、 and are the time derivatives of the variables X, Y, and Z in the Lorenz system, respectively; A, B, and C are behavioral parameters; normalized and mapped to the index [1, L], expressed as: ; Among them, max(X) and min(X) are all The maximum and minimum values of ; It is the index after mapping, indicating the order of the positions after the Lorenz system is confused, and is ultimately used for byte scrambling; the scrambled byte sequence is expressed as: ;in, It is an obfuscated byte sequence; It is the original data of the corresponding index; according to the Lorenz confusion and the mapped index, the original data is scrambled to generate the obfuscated data sequence .

[0012] Furthermore, in step S3, the hybrid mapping is to generate two sequences by iteratively combining Logistic mapping and PWLCM. , and introduce a dynamic adjustment mechanism; expressed as: ; ; ; ;in, and are the Logistic mapping values of the n+1th and nth iterations respectively; is the mapping parameter; and are the PWLCM mapping values of the n+1th and nth iterations, respectively; P1 is the mapping threshold; is to introduce random noise; and It is a dynamic regulatory factor; and is the maximum adjustment factor; and is the minimum adjustment factor; is the entropy value; mixed normalization is expressed as: ;in, is the normalized sequence after mixing; XOR diffusion is expressed as: ;in, It is the byte after XOR diffusion; is the normalized value of the i-th mixture.

[0013] Furthermore, in step S4, the encryption process specifically includes the following steps:

[0014] Step S41: Select an elliptic curve equation, expressed as: ; ; Where a and b are the parameters of the elliptic curve; p is the prime modulus; x and y are the coordinates on the elliptic curve; E is an ellipse;

[0015] Step S42: Generate private key and public key, expressed as: ; ; Where K is the private key; P is the public key; G is the base point of the elliptic curve;

[0016] Step S43: Byte curve point mapping; defining mapping function , ; recorded as ;in, is the mapped byte , is mapped to a point on the elliptic curve; It is defined in The set of elliptic curve points on ; O is the point at infinity of the curve;

[0017] Step S44: For each Random Numbers : ; ; ; ;in, is a randomly selected number ; is based on The calculated elliptic curve points participate in the encryption process; is the product of the public keys; It is the encrypted ciphertext part; is the ciphertext pair obtained after encryption; the ciphertext sequence obtained after ECC encryption It is regarded as a gene string of length L and is encrypted and optimized.

[0018] Furthermore, in step S5, the encryption optimization process specifically includes the following steps:

[0019] Step S51: Each individual u is a possible ciphertext permutation scheme; perform performance evaluation on the individual; calculate the adjacent correlation coefficient sequence , taking the average ; Design three-level metrics; individual total fitness value Expressed as: : ; Where u is the individual index; k is the metric adjustment weight; It is the underlying local metric that measures the correlation between adjacent ciphertexts; It is a mid-level global metric that measures NPCR and UACI; It is a top-level global metric that measures entropy; is the Pareto front set; is the distance between the individual and the optimal value; the definition principle of the middle-level global metric and the top-level global metric is the same as the bottom-level local metric; It is the maximum value of the distance between all individuals and the optimal value;

[0020] Step S52: Establish an aging correction mechanism; set the population size , each iteration updates Individual, average lifespan , the starting point of aging , lifespan limit ; Final individual fitness value after aging correction Expressed as:

[0021] ; Where t is the current surviving generation of the individual; is the starting generation of aging;

[0022] Step S53: Non-fixed multiple pairings; calculate the probability of selected pairing , expressed as: ; Where M is the current population size; v is the individual index of the population; each iteration randomly generates Pairs, duplication is allowed; each pair produces only one offspring; for each position, the offspring gene has a 50% probability of inheriting the father's gene and a 50% probability of inheriting the mother's gene;

[0023] Step S54: Dynamic public allele set; for each gene locus, the top T most frequent values of all individuals are taken as the public set; let the overall probability of mutation be , the probability of falling into the public set is , the size of the public set is T, and the size of the external set is ; Then the probability of each value mutation in the set is ; The mutation probability of each value outside the set is ;

[0024] Step S55: File set maintenance; calculation of elimination index , expressed as: ;in, The corresponding index of the individual in the previous generation of files Archived fitness values; each generation is Eliminate from small to large Individuals; individuals that are eliminated but belong to the Pareto frontier of the current population are added to the archive; the Pareto status of individuals in the archive is re-evaluated in each generation, and those that no longer meet the requirements are removed; at the end, the archive is the global optimal Pareto ciphertext permutation set;

[0025] Step S56: After iterating G generations, output a set of ciphertext arrangements with the best performance in the archive set as the final enhanced ciphertext, thereby achieving data encryption protection.

[0026] The intelligent user data encryption protection system provided by the present invention includes a presetting module, an obfuscation processing module, a hybrid mapping module, an encryption processing module and an encryption optimization processing module;

[0027] The preset module represents the user data to be encrypted as a binary stream sequence;

[0028] The obfuscation processing module uses the Lorenz system to generate an obfuscation sequence through numerical integration, mapping the original data to a new index position;

[0029] The hybrid mapping module generates a hybrid sequence through Logistic mapping and PWLCM iteration, and performs XOR diffusion;

[0030] The encryption processing module uses the elliptic curve encryption algorithm to generate a public key and a private key, and maps the encrypted data points to the elliptic curve through mapping to complete the encryption process;

[0031] The encryption optimization processing module optimizes the encryption arrangement and outputs the optimal encryption arrangement solution.

[0032] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0033] (1) In view of the problems that general user data encryption protection methods have excessive computational complexity, poor ability to process large-scale data, and susceptibility to pattern analysis attacks, which in turn lead to low encryption protection security, this scheme enhances the complexity and unpredictability of data protection through multi-level encryption and obfuscation processing. During the encryption process, the data is gradually disturbed and diffused through XOR diffusion and hybrid mapping steps, significantly improving the ability to resist statistical analysis attacks and pattern analysis attacks; the elliptic curve encryption algorithm is used to reduce computing and storage overhead, finding a good balance between efficiency and security.

[0034] (2) In view of the problem that general user data encryption protection methods use fixed encryption methods, lack real-time adaptation to ciphertext changes, and are vulnerable to differential attacks, which leads to poor data encryption protection effects, this scheme increases the diversity and complexity of the encryption process by measuring local security and global security at different levels; continuously reduces encryption weaknesses and improves the overall encryption strength through fitness value-based selection and non-fixed multiple pairing mechanisms; introduces an aging correction mechanism to maintain continuous improvement of encryption effects in long-term iterations; enhances the diversity of encryption sequences by dynamically generating public allele sets; uses archive sets to store all potential optimal solutions, ensuring that the global optimal ciphertext arrangement can be continuously updated in multiple iteration cycles, improving the security and anti-attack ability of the ciphertext; and ultimately improves the data encryption protection effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A flow chart of the intelligent user data encryption protection method provided by the present invention;

[0036] Figure 2 Flowchart of the intelligent user data encryption protection system.

[0037] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0039] In the description of the present invention, it should be understood that terms such as "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, they should not be understood as limiting the present invention.

[0040] Example 1, see Figure 1 The present invention provides an intelligent user data encryption protection method, which includes the following steps:

[0041] Step S1: Presetting; representing the user data to be encrypted as a binary stream sequence;

[0042] Step S2: Obfuscation processing: using the Lorenz system to generate an obfuscated sequence through numerical integration, mapping the original data to a new index position;

[0043] Step S3: Mixed mapping: generate mixed sequences through Logistic mapping and PWLCM iteration, and perform XOR diffusion;

[0044] Step S4: encryption processing; using the elliptic curve encryption algorithm to generate a public key and a private key, and mapping the encrypted data point to the elliptic curve through mapping to complete the encryption process;

[0045] Step S5: Encryption optimization processing; by optimizing the encryption arrangement, the optimal encryption arrangement scheme is output.

[0046] Example 2, see Figure 1 This embodiment is based on the above embodiment. In step S1, the preset setting is to obtain the user data to be encrypted. Assume that the user data to be encrypted is a binary stream, which is expressed as: ; Where D is the original data sequence to be encrypted by the user; L is the length; and are the 1st and Lth original data respectively.

[0047] Example 3, see Figure 1 This embodiment is based on the above embodiment. In step S2, the obfuscation process is to use numerical integration to solve the Lorenz system for L consecutive iterations to obtain data at L time points, and use the Rossler attractor to increase the oscillation frequency and chaotic characteristics. Each time point has a set of three-dimensional vectors ; expressed as: ;in, 、 and are the time derivatives of the variables X, Y, and Z in the Lorenz system, respectively; A, B, and C are behavioral parameters; normalized and mapped to the index [1, L], expressed as: ; Among them, max(X) and min(X) are all The maximum and minimum values of ; It is the index after mapping, indicating the order of the positions after the Lorenz system is confused, and is ultimately used for byte scrambling; the scrambled byte sequence is expressed as: ;in, It is an obfuscated byte sequence; It is the original data of the corresponding index; according to the Lorenz confusion and the mapped index, the original data is scrambled to generate the obfuscated data sequence .

[0048] Example 4, see Figure 1 This embodiment is based on the above embodiment. In step S3, the hybrid mapping is to generate two sequences by iteratively combining Logistic mapping and PWLCM. , and introduces a dynamic adjustment mechanism to automatically adjust according to the complexity and changes of encrypted data to improve encryption adaptability; expressed as: ; ; ; ;in, and are the Logistic mapping values of the n+1th and nth iterations respectively; is the mapping parameter; and are the PWLCM mapping values of the n+1th and nth iterations, respectively; P1 is the mapping threshold; is to introduce random noise; and It is a dynamic regulatory factor; and is the maximum adjustment factor; and is the minimum adjustment factor; is the entropy value; mixed normalization is expressed as: ;in, is the normalized sequence after mixing; XOR diffusion is expressed as: ;in, It is the byte after XOR diffusion; is the normalized value of the i-th mixture.

[0049] Example 5, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. In step S4, the encryption process specifically includes the following steps:

[0050] Step S41: Select an elliptic curve equation, expressed as: ; ; Where a and b are the parameters of the elliptic curve; p is the prime modulus; x and y are the coordinates on the elliptic curve; E is an ellipse;

[0051] Step S42: Generate private key and public key, expressed as: ; ; Where K is the private key; P is the public key; G is the base point of the elliptic curve;

[0052] Step S43: Byte curve point mapping; defining mapping function , ; recorded as ;in, is the mapped byte , is mapped to a point on the elliptic curve; It is defined in The set of elliptic curve points on ; O is the point at infinity of the curve;

[0053] Step S44: For each Random Numbers : ; ; ; ;in, is a randomly selected number ; is based on The calculated elliptic curve points participate in the encryption process; is the product of the public keys; It is the encrypted ciphertext part; is the ciphertext pair obtained after encryption; the ciphertext sequence obtained after ECC encryption It is regarded as a gene string of length L and is encrypted and optimized.

[0054] By performing the above operations, we can address the problems of excessive computational complexity, poor ability to process large-scale data, and susceptibility to pattern analysis attacks in general user data encryption protection methods, which in turn lead to low encryption protection security. This solution enhances the complexity and unpredictability of data protection through multi-level encryption and obfuscation processing. During the encryption process, the data is gradually disturbed and diffused through XOR diffusion and hybrid mapping steps, significantly improving the ability to resist statistical analysis attacks and pattern analysis attacks; the elliptic curve encryption algorithm is used to reduce computing and storage overhead, finding a good balance between efficiency and security.

[0055] Example 6, see Figure 1This embodiment is based on the above embodiment. In step S5, the encryption optimization process specifically includes the following steps:

[0056] Step S51: Each individual u is a possible ciphertext permutation scheme; perform performance evaluation on the individual; calculate the adjacent correlation coefficient sequence , taking the average In order to balance the local security of low correlation coefficient of adjacent ciphertexts and the global security of high overall entropy, a three-layer metric is designed; the total fitness value of an individual Expressed as: : ; Where u is the individual index; k is the metric adjustment weight; It is the underlying local metric that measures the correlation between adjacent ciphertexts; It is a mid-level global metric that measures NPCR and UACI; It is a top-level global metric that measures entropy; is the Pareto front set; is the distance between the individual and the optimal value; the definition principle of the middle-level global metric and the top-level global metric is the same as the bottom-level local metric; It is the maximum value of the distance between all individuals and the optimal value;

[0057] Step S52: Establish an aging correction mechanism; set the population size , each iteration updates Individual, average lifespan , the starting point of aging , lifespan limit ; Final individual fitness value after aging correction Expressed as:

[0058] ; Where t is the current surviving generation of the individual; is the starting generation of aging;

[0059] Step S53: Non-fixed multiple pairings; calculate the probability of selected pairing , expressed as: ; Where M is the current population size; v is the individual index of the population; each iteration randomly generates Pairs, duplication is allowed; each pair produces only one offspring; for each position, the offspring gene has a 50% probability of inheriting the father's gene and a 50% probability of inheriting the mother's gene;

[0060] Step S54: Dynamic public allele set; for each gene locus, the top T most frequent values of all individuals are taken as the public set; let the overall probability of mutation be , the probability of falling into the public set is , the size of the public set is T, and the size of the external set is ; Then the probability of each value mutation in the set is ; The mutation probability of each value outside the set is ;

[0061] Step S55: File set maintenance; calculation of elimination index , expressed as: ;in, The corresponding index of the individual in the previous generation of files Archived fitness values; each generation is based on Eliminate from small to large Individuals; individuals that are eliminated but belong to the Pareto frontier of the current population are added to the archive; the Pareto status of individuals in the archive is re-evaluated in each generation, and those that no longer meet the requirements are removed; at the end, the archive is the global optimal Pareto ciphertext permutation set;

[0062] Step S56: After G generations of iteration, a set of ciphertext permutations with the minimum correlation coefficient, maximum entropy, and optimal NPCR / UACI in the archive set is output as the final enhanced ciphertext, thereby achieving data encryption protection.

[0063] By performing the above operations, it is found that the general user data encryption protection method has the problem of using a fixed encryption method, lacking real-time adaptation to ciphertext changes, and being vulnerable to differential attacks, which leads to poor data encryption protection. This scheme increases the diversity and complexity of the encryption process by measuring local security and global security at different levels; continuously reduces encryption weaknesses and improves the overall encryption strength through fitness value-based selection and non-fixed multiple pairing mechanism; introduces an aging correction mechanism to maintain continuous improvement of encryption effect in long-term iterations; enhances the diversity of encryption sequences by dynamically generating public allele sets; uses archive sets to store all potential optimal solutions, ensuring that the global optimal ciphertext arrangement can be continuously updated in multiple iteration cycles, improving the security and anti-attack ability of the ciphertext; and ultimately improves the data encryption protection effect.

[0064] Example 7, see Figure 2 This embodiment is based on the above embodiment. The intelligent user data encryption protection system provided by the present invention includes a presetting module, an obfuscation processing module, a hybrid mapping module, an encryption processing module and an encryption optimization processing module;

[0065] The preset module represents the user data to be encrypted as a binary stream sequence;

[0066] The obfuscation processing module uses the Lorenz system to generate an obfuscation sequence through numerical integration, mapping the original data to a new index position;

[0067] The hybrid mapping module generates a hybrid sequence through Logistic mapping and PWLCM iteration, and performs XOR diffusion;

[0068] The encryption processing module uses the elliptic curve encryption algorithm to generate a public key and a private key, and maps the encrypted data points to the elliptic curve through mapping to complete the encryption process;

[0069] The encryption optimization processing module optimizes the encryption arrangement and outputs the optimal encryption arrangement solution.

[0070] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0071] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and alterations can be made to the embodiments without departing from the principles and spirit of the invention.

[0072] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. An intelligent user data encryption protection method, characterized by: The method comprises the following steps: Step S1: Presetting; representing the user data to be encrypted as a binary stream sequence; Step S2: Obfuscation processing: using the Lorenz system to generate an obfuscated sequence through numerical integration, mapping the original data to a new index position; Step S3: Mixed mapping: generate mixed sequences through Logistic mapping and PWLCM iteration, and perform XOR diffusion; Step S4: encryption processing; using the elliptic curve encryption algorithm to generate a public key and a private key, and mapping the encrypted data point to the elliptic curve through mapping to complete the encryption process; Step S5: Encryption optimization processing; by optimizing the encryption arrangement, outputting the optimal encryption arrangement scheme; In step S5, the encryption optimization process specifically includes the following steps: Step S51: Each individual u is a possible ciphertext permutation scheme; perform performance evaluation on the individual; calculate the adjacent correlation coefficient sequence , taking the average ; Design three-level metrics; individual total fitness value Expressed as: : ; Where u is the individual index; k is the metric adjustment weight; It is the underlying local metric that measures the correlation between adjacent ciphertexts; It is a mid-level global metric that measures NPCR and UACI; It is a top-level global metric that measures entropy; is the Pareto front set; is the distance between the individual and the optimal value; the definition principle of the middle-level global metric and the top-level global metric is the same as the bottom-level local metric; It is the maximum value of the distance between all individuals and the optimal value; Step S52: Establish an aging correction mechanism; set the population size , each iteration updates Individual, average lifespan , the starting point of aging , lifespan limit ; Final individual fitness value after aging correction Expressed as: ; Where t is the current surviving generation of the individual; is the starting generation of aging; Step S53: Non-fixed multiple pairings; calculate the probability of selected pairing , expressed as: ; Where M is the current population size; v is the individual index of the population; each iteration randomly generates Pairs, duplication is allowed; each pair produces only one offspring; for each position, the offspring gene has a 50% probability of inheriting the father's gene and a 50% probability of inheriting the mother's gene; Step S54: dynamic public allele set; Step S55: File set maintenance; calculation of elimination index , expressed as: ;in, The corresponding index of the individual in the previous generation of files Archived fitness values; each generation is Eliminate from small to large Individuals; individuals that are eliminated but belong to the Pareto frontier of the current population are added to the archive; the Pareto status of individuals in the archive is re-evaluated in each generation, and those that no longer meet the requirements are removed; at the end, the archive is the global optimal Pareto ciphertext permutation set; Step S56: After iterating G generations, output a set of ciphertext arrangements with the best performance in the archive set as the final enhanced ciphertext, thereby achieving data encryption protection.

2. The intelligent user data encryption protection method according to claim 1, characterized in that: In step S2, the obfuscation process is to use numerical integration to solve the Lorenz system for L consecutive iterations to obtain data at L time points, and use the Rossler attractor to have a set of three-dimensional vectors at each time point. ; expressed as: ;in, 、 and are the time derivatives of the variables X, Y, and Z in the Lorenz system, respectively; A, B, and C are behavioral parameters; normalized and mapped to the index [1, L], expressed as: ; Among them, max(X) and min(X) are all The maximum and minimum values of ; It is the index after mapping, indicating the order of the positions after the Lorenz system is confused, and is ultimately used for byte scrambling; the scrambled byte sequence is expressed as: ;in, It is an obfuscated byte sequence; It is the original data of the corresponding index; according to the Lorenz confusion and the mapped index, the original data is scrambled to generate the obfuscated data sequence .

3. The intelligent user data encryption protection method according to claim 2, characterized in that: In step S3, the hybrid mapping is to generate two sequences by iteratively combining Logistic mapping and PWLCM. , and introduce a dynamic adjustment mechanism; expressed as: ; ; ; ;in, and are the Logistic mapping values of the n+1th and nth iterations respectively; is the mapping parameter; and are the PWLCM mapping values of the n+1th and nth iterations, respectively; P1 is the mapping threshold; is to introduce random noise; and It is a dynamic regulatory factor; and is the maximum adjustment factor; and is the minimum adjustment factor; is the entropy value; mixed normalization is expressed as: ;in, is the normalized sequence after mixing; XOR diffusion is expressed as: ;in, It is the byte after XOR diffusion; is the normalized value of the i-th mixture.

4. The intelligent user data encryption protection method according to claim 3, characterized in that: In step S4, the encryption process specifically includes the following steps: Step S41: Select an elliptic curve equation, expressed as: ; ; Where a and b are the parameters of the elliptic curve; p is the prime modulus; x and y are the coordinates on the elliptic curve; E is an ellipse; Step S42: Generate private key and public key, expressed as: ; ; Where K is the private key; P is the public key; G is the base point of the elliptic curve; Step S43: Byte curve point mapping; defining mapping function , ; recorded as ;in, is the mapped byte , is mapped to a point on the elliptic curve; It is defined in The set of elliptic curve points on ; O is the point at infinity of the curve; Step S44: For each Random Numbers : ; ; ; ;in, is a randomly selected number ; is based on The calculated elliptic curve points participate in the encryption process; is the product of the public keys; It is the encrypted ciphertext part; is the ciphertext pair obtained after encryption; the ciphertext sequence obtained after ECC encryption It is regarded as a gene string of length L and is encrypted and optimized.

5. The intelligent user data encryption protection method according to claim 4, characterized in that: In step S5, the step S54: dynamic public allele set; for each gene locus, the top T values that appear most frequently in all individuals are taken as the public set; let the overall probability of mutation be , the probability of falling into the public set is , the size of the public set is T, and the size of the external set is ; Then the probability of each value mutation in the set is ; The mutation probability of each value outside the set is .

6. The intelligent user data encryption protection method according to claim 5, characterized in that: In step S1, the preset is to obtain the user data to be encrypted. Assume that the user data to be encrypted is a binary stream, which is expressed as: ; Where D is the original data sequence to be encrypted by the user; L is the length; and are the 1st and Lth original data respectively.

7. An intelligent user data encryption protection system, configured to implement the intelligent user data encryption protection method according to any one of claims 1 to 6, characterized in that: It includes a pre-setting module, an obfuscation processing module, a hybrid mapping module, an encryption processing module and an encryption optimization processing module; The preset module represents the user data to be encrypted as a binary stream sequence; The obfuscation processing module uses the Lorenz system to generate an obfuscation sequence through numerical integration, mapping the original data to a new index position; The hybrid mapping module generates a hybrid sequence through Logistic mapping and PWLCM iteration, and performs XOR diffusion; The encryption processing module uses the elliptic curve encryption algorithm to generate a public key and a private key, and maps the encrypted data points to the elliptic curve through mapping to complete the encryption process; The encryption optimization processing module optimizes the encryption arrangement and outputs the optimal encryption arrangement solution.

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