Disordered factor self-organizing bidirectional calculation data security processing method and system

Abstract

The invention relates to the technical field of information security and data encryption, and discloses a disordered factor self-organizing bidirectional calculation data security processing method and system, and the method sequentially comprises the steps: data vectorization, double-layer Levy segmentation, hyperchaotic transformation, non-bijection decomposition and distributed storage. The method comprises the following steps of: converting original data into a disordered factor set by constructing an irreversible disturbance model, distributing the disordered factor set to a plurality of geographically distributed edge nodes, and finishing approximate data recovery through an optimization recombination mechanism with a gradient regular term when a legal recovery voucher exists. By constructing a perturbation irreversible factorization mechanism and combining distributed mapping storage of geographical distribution and a limited recovery algorithm with regular modulation, physical isolation, recoverability after perturbation and long-term confidentiality of data are guaranteed on the premise of not depending on a secret key; the overall security and recovery robustness of the system in the aspects of node faults, data leakage, quantum cracking risks and the like in an edge computing scene are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of information security and data encryption, and in particular to a method and system for securely processing disordered factor self-organizing bidirectional computing data. Background Art

[0002] As network computing systems continue to migrate to the edge, data generation, processing, and application are increasingly moving away from central servers, becoming highly distributed. In scenarios such as smart cities, telemedicine, and the Industrial Internet of Things, large amounts of sensitive information are generated and temporarily stored directly on edge nodes, placing higher demands on data privacy and availability. In this context, implementing secure, efficient, and controllable data persistence and recovery mechanisms has become a pressing technical challenge in edge computing applications.

[0003] Existing technologies generally rely on traditional encryption algorithms to protect edge data. Their core focus is controlling data access and decryption through key mechanisms. However, this approach carries an inherent single point of risk: if the key is stolen or mismanaged, the overall security of the system will be compromised. Furthermore, the central scheduling architecture lacks resilience in the event of node failure or attack, making data unrecoverable. Furthermore, recovery mechanisms for disturbed or incomplete data still rely on precise reconstruction, lacking a flexible solution to maintain data availability under complex conditions, limiting recovery capabilities.

[0004] In the face of emerging attack methods such as quantum computing, the vulnerability of traditional security models is becoming increasingly apparent, and existing encryption systems based on mathematical complexity are gradually facing the risk of being breached. Therefore, building innovative mechanisms that do not rely on keys, support irreversible perturbations, and achieve controlled recovery under limited conditions has become a key direction for improving the security and reliability of edge data. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a method and system for securely processing bidirectional computing data with disordered factors, which solves the problems of weak data storage security, strong key dependence, and uncontrolled recovery of disturbed data in edge computing environments.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for securely processing disordered factor self-organized bidirectional computing data, comprising the following steps:

[0007] S1. Data vectorization: convert the original data into a decimal integer vector;

[0008] S2, primary Levy segmentation: randomly segmenting the decimal vector based on Levy flight distribution to generate a first-level sub-vector set;

[0009] S3, hyperchaotic transformation: performing nonlinear mapping on the first-level sub-vector set through a three-dimensional tensor coupled chaotic system, and outputting a chaotic mapping vector set;

[0010] S4, secondary Levy segmentation: performing secondary Levy distribution segmentation on the chaotic map vector set to generate a second-level sub-vector set;

[0011] S5, non-bijective decomposition: applying an irreversible function transformation to the second-level sub-vector set to generate an unordered factor set that meets the entropy threshold;

[0012] S6. Distributed storage: distributing the set of disordered factors to geographically distributed edge nodes;

[0013] S7. Limited recovery: When valid recovery credentials are verified, optimized reorganization is performed based on the set of disorder factors.

[0014] Preferably, the Levy flight distribution in step S2 adopts a stable distribution with parameter α=1.5, and its step length generation formula is:

[0015]

[0016] Where μ and v are independent and identically distributed standard normal random variables, α is the shape parameter of the Levy distribution, and s is the Levy flight step length;

[0017] The random segmentation points in the S2 step are obtained by modular operation. Dynamically determined, where s k is the k-th generated Levy flight step, m is the dimension of the original decimal vector, p i is the i-th dynamic segmentation point.

[0018] Preferably, the dynamic equation of the three-dimensional tensor coupled chaotic system in step S3 is:

[0019]

[0020] Where γ>3.7, W is the dynamic weight matrix, the chaotic sequence is generated by the fourth-order Runge-Kutta method, x, y, z are state variables, γ is the control parameter, is the dissipation coefficient, 28 is the critical parameter, and 10 is the damping coefficient.

[0021] Preferably, the irreversible function transformation in step S5 includes:

[0022] Perform a circular left shift x<<<s on each subvector i , displacement s i Taken from Chaos Sequence;

[0023] Apply a random mask matrix M iPerform bitwise XOR ⊙M i ;

[0024] The combined operation is

[0025] Preferably, the step S5 further includes entropy feedback control:

[0026] Calculate the disorder factor information entropy, the formula is:

[0027]

[0028] Where H is the entropy value, v is the data value, p(v) is the probability distribution, and log2 is the logarithmic base;

[0029] When H>0.01 bit, return to S4 for re-segmentation.

[0030] Preferably, the optimized reorganization in step S7 is achieved by solving the following formula:

[0031]

[0032] in, is the optimization variable, f is a non-bijective function, F i is a known parameter, N is a scalar parameter, is the gradient operator, is the L2 norm, ||·||1 is the L1 norm, and 0.1 is the regularization coefficient;

[0033] The calculation is terminated when the recovery error rate ERR ≥ 37%, where:

[0034]

[0035] Where m is a scalar parameter, To recover data, x j is the original data.

[0036] A second aspect of the present invention provides a system for securely processing disordered factor self-organizing bidirectional computing data, which is used to execute the above-mentioned method for securely processing disordered factor self-organizing bidirectional computing data, comprising:

[0037] Data preprocessing module, used to convert raw data into standard digital vector format;

[0038] Chaos calculation module, used to perform Levy flight distribution segmentation and hyperchaotic transformation processing;

[0039] Non-bijective decomposition module, used to perform irreversible function transformation on data fragments;

[0040] Edge storage network to manage distributed storage and location obfuscation of data fragments;

[0041] Data recovery engine for performing limited optimized reorganization based on storage fragmentation

[0042] Preferably, the chaos computing module includes:

[0043] The Levy parameter configuration unit is used to dynamically configure the α=1.5 parameter of the Levy flight distribution to control the randomness of the data segmentation;

[0044] Tensor coupling operation unit, used for real-time calculation of tensor coupling terms in three-dimensional chaotic systems;

[0045] Chaos iteration accelerator, used for hardware acceleration of the fourth-order Runge-Kutta method for solving chaotic differential equations.

[0046] Preferably, the edge storage network includes:

[0047] Fragment tag encryption unit, used to generate unpredictable UUID tags based on quantum random numbers;

[0048] Correlation analyzer, used to detect and block the storage of logical correlation factors on the same physical node;

[0049] Dynamic migration engine, used to periodically trigger random migration of fragment physical locations.

[0050] Preferably, the data recovery engine includes:

[0051] Gradient calculation unit, used for hardware-accelerated gradient norm calculation to achieve fast evaluation of regularization terms;

[0052] Error monitor, used to monitor the recovery error rate in real time and force the calculation to terminate when ERR ≥ 37%;

[0053] A projection optimizer is used to perform steepest descent iterations in a constrained solution space to find a local optimal solution.

[0054] The present invention provides a method and system for securely processing disordered factor self-organized bidirectional computing data. It has the following beneficial effects:

[0055] 1. By adopting a geographically distributed mapping storage technology solution with a post-disturbance disordered factor set, the present invention achieves physical decoupling and logical isolation of data across multiple edge nodes, achieving the technical effect of redundancy and resilience in different network topologies. Compared with the existing centralized architecture that is unable to cope with local node failures, this solution effectively avoids the risk of single point failures and improves data availability in regional disaster scenarios.

[0056] 2. The present invention adopts a variational optimization model with L1 sparse regularization and L2 error constraints to achieve limited precision reconstruction of disturbed data, achieving the technical effect of performing data recovery in a high-interference environment without relying on the original plaintext or key. Compared with the existing recovery mechanism based on precise key restoration, the present invention solves the problems of complex key management and insufficient uniqueness of the recovery path, and enhances the system's data recovery resilience under uncontrollable conditions.

[0057] 3. The present invention constructs an irreversible non-bijective perturbation function system and combines it with a mapping index mechanism to ensure that data fragments cannot be reversed to their original form at the structural layer, achieving the technical effect that even if an attacker obtains all edge node data, the valid content cannot be restored. Compared with the existing method that relies on the strength of the encryption algorithm, the present invention solves the problem that traditional public key systems are easily cracked under the quantum computing model, and significantly improves long-term data confidentiality and resistance to algorithm leakage. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 is a flow chart of the method of the present invention;

[0059] Figure 2 Flowchart of the system framework of the present invention;

[0060] Figure 3 This is a framework diagram of the chaos computing module of the present invention;

[0061] Figure 4 A framework diagram of the edge storage network of the present invention;

[0062] Figure 5 This is a framework diagram of the data recovery engine of the present invention. DETAILED DESCRIPTION

[0063] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0064] Please see the attached Figure 1 The embodiment of the present invention provides a method for securely processing disordered factor self-organized bidirectional computing data, comprising the following steps:

[0065] S1. Data vectorization: convert the original data into a decimal integer vector;

[0066] This step involves converting the raw data into a decimal integer vector for subsequent encryption processing. This process involves extracting the original information from the data and converting it into a standard format that is easy to calculate and perform subsequent encryption algorithms.

[0067] First, the original data X={x1,x2,...,x m} need to be digitized, and each data item x i Convert to a decimal integer. This process is preferably based on specific encoding rules (such as ASCII code or other encoding formats) to ensure that the converted data can be used as input for subsequent encryption operations.

[0068] The formula derivation process is as follows:

[0069] Original data vectorization: Let original data X={x1,x2,...,x m}, where x i is the i-th element of the original data. i Convert to decimal integer using standard encoding to get vector X int ={x′1,x′2,...,x′ m}, where each x′ i is x i The converted decimal value.

[0070] Numericalization steps: For example, for character data, if ASCII encoding is used, each character x i The conversion relationship is: x′ i =ASCII(x i ) For binary data, a direct conversion rule is used to ensure that each data point x i are mapped to the corresponding decimal integers.

[0071] Through this vectorization process, each item of data is converted into digital form, ensuring that subsequent chaotic encryption operations can be performed correctly.

[0072] Implementation process description:

[0073] During the vectorization process, the original data is converted into integer values one by one using pre-defined encoding rules (such as ASCII, UTF-8, etc.). These integer values will serve as input for subsequent algorithms, ensuring that the data can be encrypted smoothly through the mathematical model and algorithm process.

[0074] In physical implementation, data is converted into integer vectors through hardware or software encoding and decoding modules to complete the data preprocessing process. After the data conversion, the encryption step size of the data can be generated through a random number generator or chaotic sequence to achieve random data segmentation and encryption.

[0075] This implementation effectively converts raw data into a standardized digital format, laying the foundation for subsequent encryption processing. This vectorization process ensures that subsequent mathematical models and encryption algorithms can adapt to a variety of input data types, improving the applicability and scalability of the encryption system.

[0076] S2, primary Levy segmentation: randomly segment the decimal vector based on the Levy flight distribution to generate the first-level sub-vector set;

[0077] In one embodiment of the present invention, the decimal integer vector X=x1, x2, ..., x obtained in step S1 is m}, the primary sub-vector segmentation is achieved through random step generation and dynamic index calculation based on Levy flight distribution.

[0078] First, a stable Levy distribution with parameter α = 1.5 is used to generate a random step length sequence. The step length generation formula is as follows:

[0079]

[0080] in, are independent and identically distributed standard normal random variables; α = 1.5 represents the shape parameter of the Levy distribution; s is the Levy flight step length.

[0081] Then, the step sequence is accumulated and the split point position is determined by modular operation. The split point calculation formula is as follows:

[0082]

[0083] Among them, p i Indicates the index position of the i-th segmentation point, s k is the k-th generated Levy flight step, and m is the dimension of the original decimal vector.

[0084] To ensure the validity and uniqueness of the segmentation points, the calculated segmentation points {p1, p2, ...} are sorted and duplicated to obtain the final segmentation point sequence P = {p1, p2, ..., p r},satisfy:

[0085] 0 <p1<p2<…<p r <m;

[0086] According to the segmentation point sequence p, the input vector X is divided into r+1 sub-vectors:

[0087]

[0088] Among them, p0=-1 represents the virtual starting point; p r+1 =m-1 represents the virtual end point; each subvector X (i)The data segments of X are complete and do not overlap; (i) is the i-th sub-vector segment; It means combining all sub-vector segments to form the complete original vector X, satisfying no overlap and no omission.

[0089] The above-mentioned primary Levy segmentation process is performed by the chaos computing module in the system. This module consists of the following three physical units:

[0090] Levy parameter configuration unit: used to set and lock the shape parameter α = 1.5, control the long tail characteristics of the step size distribution, and determine the sparsity of the jump distribution;

[0091] Tensor coupling operation unit: cooperates with the distribution generation logic to perform tensor-level modeling of the interaction between step sequences and time series, and provides a mapping interface for the accumulation and propagation of step sequences;

[0092] Chaos Iteration Accelerator: Accelerates the solution of the coupling process between chaotic state variables and distribution changes through the fourth-order Runge-Kutta method, providing high-concurrency generation support.

[0093] The Levy parameter configuration unit provides a system-level control interface for coordinating with the master control scheduling module to set distribution parameters. The tensor coupling operation unit is nested within the multi-channel chaotic tensor intermediate processing layer and synchronously constructs the index state in conjunction with the real-time input step stream.

[0094] The chaotic iterative accelerator integrates a hardware floating-point operation unit to perform real-time differential advancement of the index update path under the influence of the step size, providing periodic drive for step size accumulation and split point calculation.

[0095] The entire chaos computing module is directly coupled with the data preprocessing module of the previous stage of the system through a logical connection interface, receives the vector X as the input data stream, and outputs the segmented sub-vector set to the subsequent non-bijective decomposition module.

[0096] Through this implementation structure, the dynamic presentation of Levy distribution characteristics during the segmentation process can be ensured, the unpredictability of sub-vectors in dimension, position and structure can be improved, and the system's resistance to statistical recombination attacks can be enhanced.

[0097] S3, hyperchaotic transformation: nonlinearly map the first-level sub-vector set through a three-dimensional tensor coupled chaotic system and output a chaotic map vector set;

[0098] In an embodiment of the present invention, after completing the primary Levy segmentation in step S2, the obtained sub-vector set {X(i)}i=0r{X(i)}i=0r is introduced into the three-dimensional tensor coupled chaotic system, and a nonlinear mapping operation is performed to generate a chaotic mapping vector set.

[0099] First, the continuous-time dynamic model of the system is established, which is described as follows:

[0100]

[0101] Where x, y, and z are state variables; γ>3.7 is a control parameter; W is a dynamic weight matrix constructed from the input subvectors; Represents a tensor multiplication operation, which is used to enhance the coupling between system variables; coefficients 10, 28 and are the damping factor, critical control parameter and dissipation coefficient respectively.

[0102] The construction process of the dynamic weight tensor W is as follows:

[0103] W (i) =reshape(X (i) ,d1×d2×d3);

[0104] Among them, X (i) is the i-th input subvector; d1, d2, d3 are tensor dimension parameters, satisfying d1·d2·d3=|X (i) |, ensure the integrity of tensor reconstruction; W (i) The dynamic tensor used by the current iteration.

[0105] The above differential equations are numerically integrated using the fourth-order Runge-Kutta method. The specific discrete iterative form is:

[0106]

[0107] Among them, x n 、y n and z n is the state variable value at the nth iteration, x n+1 、y n+1 and z n+1 is the updated value of the state variable for the n+1th iteration, k1, l2, and m1 represent the calculated values of the Runge-Kutta intermediate slopes of the differential equations for x, y, and z, respectively;

[0108] The intermediate variables are calculated as follows:

[0109]

[0110] Among them, f x ,f y ,f z They represent the applied function terms of x, y, and z in the system equations, h is the time step, l1, l2, l3, and l4 are The four estimated slopes, m1, m2, m3 and m4, are for The four estimated slopes of k1, k2, k3 and k4 are Four estimated slopes for .

[0111] After each round of iteration, the system obtains the state variable sequence It is combined into a three-channel chaotic mapping result, which is constructed as follows:

[0112]

[0113] Among them, Y (i) represents the chaotic output after the i-th sub-vector mapping, which serves as the input of the subsequent non-bijective decomposition step. and is the state variable sequence of the i-th sub-vector at the end of the iteration, and concat(·) is the concatenation operation.

[0114] The above transformation process is completed by the chaos computing module at the system level, and its module structure is as follows:

[0115] Levy parameter configuration unit: provides the setting of α=1.5 to support the front-end random segmentation;

[0116] Tensor coupling operation unit: constructs the weight tensor W according to the input sub-vector and performs Operation;

[0117] Chaos Iteration Accelerator: A hardware-based implementation of the fourth-order Runge-Kutta algorithm for real-time solution of continuous-time dynamics model equations.

[0118] The modules are connected in parallel via a data bus to ensure the transmission efficiency between input vector switching and tensor generation, and to support the continuous evolution of the chaotic system state variables in high-dimensional space.

[0119] Through the tensor-coupled chaotic mapping mechanism in this embodiment, high-dimensional nonlinear mapping of the primary segmentation vector is achieved, which effectively improves the irreversibility and complexity of the data in the transformation space and enhances the defense capability against statistical reconstruction attacks.

[0120] S4, secondary Levy segmentation: perform secondary Levy distribution segmentation on the chaotic map vector set to generate the second-level sub-vector set;

[0121] In the embodiment of the present invention, after completing the chaotic mapping in step S3, the system obtains a set of chaotic mapping vectors The vector set has a complex structure, enhanced dimensionality, and has high nonlinearity and high entropy characteristics.

[0122] In order to further improve the irreversibility and randomness of the data, before entering the non-bijective decomposition, it is necessary to perform secondary Levy distribution partitioning on the vector set to form a second-level sub-vector set as the basic input for data scrambling in the next stage.

[0123] First, we establish the Levy flight distribution model and define the probability control mechanism of secondary segmentation. The Levy distribution is a heavy-tailed distribution, and its probability density function is defined as follows:

[0124]

[0125] Among them, β is the offset parameter, which is set to zero to indicate a symmetric distribution; μ is the offset starting point, which is generally set to the minimum segmentable position of the vector; c is the scale parameter, which affects the distribution width and can be combined with Y (i) Length adaptive setting

[0126] The Levy distribution controller is set in the system, and for each Y (i) The random sliding window selection operation based on this distribution is performed as follows:

[0127] Initialize variable index j = 0 and set vector length to N = |Y (i) |.

[0128] Generate random numbers of length l from the Levy distribution j ~L(s;α,β), perform rounding operation

[0129] Extract sub-segments Generate new subvectors

[0130] Update index j = j + l j , repeat steps S2 to S4 until j≥N.

[0131] The above operation is expressed as a piecewise function:

[0132] wherel j ~L(s;α=1.5);

[0133] Among them, Y (i) is the i-th chaotic map vector output in the previous stage, is the jth sub-sub-vector generated in this stage, segment(·) is a piecewise function, which is a sub-vector of length l from the source vector. j Extract the fragment.

[0134] To prevent sub-segment lengths from being too small, which can lead to information fragmentation, or being too large, which can affect diversity, the system introduces threshold constraints:

[0135]

[0136] Among them, l min With l max It is a system configurable parameter, preferably automatically adjusted by the data preprocessing module according to the original data dimension.

[0137] The physical implementation of this process is completed by the interface components between the chaos calculation module and the non-bijective decomposition module, including:

[0138] The distribution generation unit is connected to the Levy parameter configuration unit and the tensor output port, and is used to output a random length sequence that meets the parameter settings;

[0139] A segmentation controller, the controller is connected to the mapping result cache unit and is used for dynamically extracting sub-vectors according to length and writing them into an intermediate storage array;

[0140] Output reconstruction unit, used to uniformly structure the extraction results and generate secondary sub-vector sets

[0141] Through the implementation of this step, the system can further perform high-dimensional perturbations based on the enhanced chaotic structure, construct a multi-scale sub-vector set with heavy-tailed distribution characteristics, provide highly diverse data inputs for the next non-bijective transformation, and effectively improve the irreversibility and encryption strength of the overall system.

[0142] S5, non-bijective decomposition: apply irreversible function transformation to the second-level sub-vector set to generate a set of disordered factors that meet the entropy threshold;

[0143] In this embodiment, the secondary sub-vector set obtained in step S4 is completed The non-bijective transformation logic is input in sequence, multiple irreversible perturbation operations are performed, and finally the disordered factor set is output. As the core input of the encryption system.

[0144] First, for each input subvector Distributed Chaos Control Sequence K represents the number of perturbation iterations, and its value depends on the system configuration and is generally not less than 3.

[0145] The chaos control sequence comes from the three-channel chaotic state vector Y output in step S3 (i) , obtained by subsampling function Submap transformation:

[0146]

[0147] in, is the kth item of the i-th chaotic sequence, is the cyclic shift of the i,j subvector in the kth round of operation, with a value range of [0,7]. mod(·,8) is the modulo function to ensure that the shift does not exceed the single-byte limit.

[0148] Then, the system generates a random mask matrix for each round of operation Its generation is determined by a pseudo-random generator (PRG) and a perturbation key seed to ensure that each execution is unique.

[0149] The combined non-bijective perturbation function is defined as follows:

[0150]

[0151] in, To represent the subvector, each byte is shifted left bit by bit. Bit, ⊙ represents bitwise AND operation or bitwise mask, represents the bitwise XOR operation, which is used to fuse the results of multiple rounds of perturbations. f(·) is the final non-bijective perturbation function

[0152] To ensure the irreversibility of the output results, all Both have The same dimensions are generated independently in each round and cannot be reused.

[0153] After the perturbation is completed, the system enters the entropy feedback module and calculates each disorder factor vector Shannon entropy of:

[0154]

[0155] in, is the Shannon entropy value of the disordered factor vector, v is the byte value, the value range is [0,255], and p(v) is the probability of occurrence of the disordered factor with the median value v.

[0156] The entropy value is set by the system threshold H min Make a judgment. If it satisfies:

[0157]

[0158] Among them, H min The minimum entropy threshold set for the system is preferably set to 0.01 bit.

[0159] The disturbance is considered valid and the output as a legal disorder factor; if not satisfied, return to step S4 and re-execute the secondary Levy segmentation.

[0160] This process is completed by the non-bijective decomposition module. Through the implementation of this step, the source data sub-vector can be strongly nonlinearly perturbed and the structure disrupted to ensure that the generated result does not have a reversible mapping relationship. At the same time, the entropy feedback mechanism is used to achieve dynamic control of the output quality, providing a high-intensity and high-confusion input foundation for subsequent embedded coding or channel mapping.

[0161] S6, distributed storage: distribute the unordered factor set to geographically distributed edge nodes;

[0162] After completing step S5, the system obtains the disordered factor set This collection is highly obfuscated and irreversible, making it suitable for disaster-tolerant distributed storage in edge environments.

[0163] First, establish the geographic node topology model G = (V, E), where:

[0164]

[0165] Among them, V is the set of all edge storage nodes, E is the set of reachable communication links between nodes, and M is the total number of edge nodes.

[0166] Secondly, through the node allocation function Map the disorder factor to a specific edge node. This function relies on the hash index and node status feedback to make decisions and is defined as follows:

[0167]

[0168] Among them, h(·) is a hash function that inputs data of arbitrary length and outputs a fixed bit length index, v * The edge node of the selected storage target.

[0169] The system further Compile segment index labels Contains the original sub-vector number, perturbation round and mapping node position for subsequent search and recovery. The label structure is as follows:

[0170]

[0171] Among them, ID i,j is the atomic vector sequence number, K is the perturbation round, which is consistent with the perturbation number in step S5, v * The edge node identifier mapped by the current sub-vector.

[0172] Then, through the encrypted communication channel After being distributed to the corresponding edge nodes, the system uses end-to-end security protocols (such as DTLS or TLS) to protect the channel to ensure that the data is not tampered with or monitored during transmission.

[0173] Through the above process, the system can achieve distributed split storage of highly obfuscated data without relying on a central control node, effectively improving the system robustness against deletion attacks, traffic monitoring, and node failures.

[0174] In actual deployment, the system can further combine the node load factor λ(v m ) to dynamically remap to achieve load balancing control. This factor is defined as:

[0175]

[0176] The system is based on λ(v m ) Determine whether to perform node replacement or multi-copy redistribution to achieve dynamic avoidance of high-risk nodes and replica disaster recovery mechanism.

[0177] By implementing this step, the disordered data fragments after irreversible disturbance can be mapped to the distributed edge storage cluster in a structured manner, providing a storage mechanism with encryption, discreteness and traceability, and meeting the comprehensive needs of edge data tamper prevention and disaster recovery in high-security scenarios.

[0178] S7. Restricted recovery: When validating the recovery credentials, perform an optimized reorganization based on the set of disordered factors.

[0179] After completing S6, the system stores the scrambled set of unordered factors and their index tags through edge nodes. To restore the original data, the system first verifies the recovery credentials, including access authorization, data hash verification, and node consistency verification, to ensure that the requester has legitimate authority.

[0180] After the certificate is verified, the system enters the optimization and reorganization process, the goal is to Reconstruct the approximate original data vector This is achieved by solving the following objective function:

[0181]

[0182] in, is the optimization variable, F i is a known parameter, known and stored in the edge node, N is a scalar parameter, is the gradient operator, which means The first-order gradient of is used to constrain the reconstruction smoothness, is the L2 norm, ||·||1 is the L1 norm, 0.1 is the regularization coefficient, and f is a non-bijective function.

[0183] The above optimization problem adopts a variational solution strategy, performs iterative approximation through the alternating minimization method, and utilizes the computing resources of edge nodes in parallel to accelerate the optimization process.

[0184] The system evaluates the current recovery data after each iteration The recovery error rate ERR between the original data is defined as follows:

[0185]

[0186] Where m is a scalar parameter, To recover data, x jis the original data.

[0187] If ERR ≥ 37%, the system determines that the current data cannot be effectively recovered, terminates the optimization process and feeds back a recovery failure signal.

[0188] Through this implementation, the system can reconstruct the original input data within a limited error range, effectively realizing the "conditionally reversible" recovery function for high-security scenarios. At the same time, combined with the irreversibility of the perturbation function, it ensures that unauthorized recovery cannot obtain usable information.

[0189] In specific deployments, it can be further expanded to support multi-round and multi-replica joint reconstruction strategies, enhance fault tolerance in the case of partial node loss, or introduce reconstruction of trusted blockchain record paths to improve operation traceability.

[0190] The disordered factor self-organizing bidirectional computing data security processing system described below and the disordered factor self-organizing bidirectional computing data security processing method described above can be referenced to each other.

[0191] Please see the attached Figure 2 The disordered factor self-organizing bidirectional computing data security processing system is used to execute the above-mentioned disordered factor self-organizing bidirectional computing data security processing method, including:

[0192] Data preprocessing module, used to convert raw data into standard digital vector format;

[0193] Chaos calculation module, used to perform Levy flight distribution segmentation and hyperchaotic transformation processing;

[0194] Non-bijective decomposition module, used to perform irreversible function transformation on data fragments;

[0195] Edge storage network to manage distributed storage and location obfuscation of data fragments;

[0196] A data recovery engine that performs limited optimized reorganization based on storage fragmentation.

[0197] Please see the attached Figure 3 , the chaos computing module includes:

[0198] The Levy parameter configuration unit is used to dynamically configure the α=1.5 parameter of the Levy flight distribution to control the randomness of the data segmentation;

[0199] Tensor coupling operation unit, used for real-time calculation of tensor coupling terms in three-dimensional chaotic systems;

[0200] Chaos iteration accelerator, used for hardware acceleration of the fourth-order Runge-Kutta method for solving chaotic differential equations.

[0201] Please see the attached Figure 4, the edge storage network includes:

[0202] Fragment tag encryption unit, used to generate unpredictable UUID tags based on quantum random numbers;

[0203] Correlation analyzer, used to detect and block the storage of logical correlation factors on the same physical node;

[0204] Dynamic migration engine, used to periodically trigger random migration of fragment physical locations.

[0205] Please see the attached Figure 5 , the data recovery engine includes:

[0206] Gradient calculation unit, used for hardware-accelerated gradient norm calculation to achieve fast evaluation of regularization terms;

[0207] Error monitor, used to monitor the recovery error rate in real time and force the calculation to terminate when ERR ≥ 37%;

[0208] A projection optimizer is used to perform steepest descent iterations in a constrained solution space to find a local optimal solution.

[0209] The system of this embodiment can be used to execute the above method embodiments, and its principles and technical effects are similar, so they will not be repeated here.

[0210] Test Example: Comparative Experiment between the Data Perturbation Storage and Restricted Recovery Method of the Present Invention and AES-256 Encryption Technology

[0211] Experimental objectives:

[0212] The data perturbation storage and restricted recovery method proposed in this invention is compared with the traditional AES-256 encryption algorithm in terms of anti-attack capability, encryption speed, and performance under quantum computing attacks.

[0213] Experimental environment and equipment:

[0214] Hardware environment:

[0215] Processor: Intel i9 10-core 3.6GHz;

[0216] Memory: 64GB DDR4;

[0217] Storage: 1TB SSD;

[0218] Operating system: Linux Ubuntu 20.04;

[0219] Software environment:

[0220] Implementation of the present invention: Using Python to implement perturbation storage, regularized optimization recovery algorithm and quantum attack simulation;

[0221] AES-256 encryption algorithm: Use the OpenSSL library for encryption and decryption operations.

[0222] Experimental process:

[0223] Experimental data preparation:

[0224] Dataset: Use a 1GB text file containing randomly generated character data.

[0225] Experimental plan:

[0226] Two processes are performed on the 1GB data:

[0227] Solution 1: Use the perturbation storage and restricted recovery method of the present invention to perform data storage and recovery.

[0228] Solution 2: Use the AES-256 encryption algorithm to encrypt and store data.

[0229] Step 1: Data encryption and storage

[0230] Scheme 1 (method of the present invention):

[0231] Convert the original data to a vector of decimal integers.

[0232] Randomly split the data based on the Levy distribution.

[0233] Nonlinear mapping of subvector sets using a three-dimensional tensor-coupled chaotic system.

[0234] Perform quadratic Levy distribution partitioning on the chaotic map vector set.

[0235] Apply an irreversible function to the second-level subvector set to generate an unordered factor set.

[0236] The generated unordered factor set is distributed to multiple geographically distributed edge nodes (simulating a distributed storage environment).

[0237] Option 2 (AES-256 encryption):

[0238] Use the AES-256 encryption algorithm to encrypt a 1GB file and store the encrypted data.

[0239] Step 2: Attack Simulation

[0240] Anti-brute force cracking ability:

[0241] For solution 1, if you try to brute force the perturbed data, you will not be able to recover the original data anyway.

[0242] For solution 2, brute force attacks are simulated to try to decrypt the ciphertext data. Theoretically, 2^256 attempts are required.

[0243] Anti-AI pattern recognition attacks:

[0244] Solution 1: Use a machine learning model (such as a deep neural network) to try to identify and predict patterns in the perturbed data. However, due to the use of an irregular Levy flight distribution, the model cannot identify any patterns.

[0245] Solution 2: Use machine learning models for pattern recognition. AES encrypted data can be cracked through pattern recognition due to its statistical properties.

[0246] Resistance to quantum computing attacks:

[0247] Solution 1: Simulate quantum computing attacks to test the ability of quantum computers to crack mathematical irreversibility (non-bijective perturbation function systems). According to current research on quantum computing attacks, attacks relying on quantum computing cannot crack the perturbation model of this invention.

[0248] Solution 2: Use Shor's algorithm to simulate quantum computing attacks, and AES-256 encryption will be cracked.

[0249] Step 3: Recovery and Decryption

[0250] Scheme 1 (method of the present invention):

[0251] Using legal recovery credentials, perform an optimized reorganization based on the unordered factor set.

[0252] The perturbed data is reconstructed with limited precision through an optimization model with a gradient regularization term to restore results close to the original data.

[0253] Option 2 (AES-256 encryption):

[0254] Decrypt the encrypted data using the AES-256 decryption key.

[0255] Experimental data and result analysis:

[0256]

[0257] Experimental conclusion:

[0258] Brute force resistance: The proposed method has an ∞ brute force resistance rating because the perturbed data is irreversible and the attacker cannot recover the original data. AES-256, on the other hand, requires 2^256 attempts, which is extremely time-consuming and relies on key management.

[0259] Anti-AI pattern recognition attack: The method of the present invention uses irregular Levy flight distribution, which is not easily recognized by machine learning models, avoiding the problem of statistical cracking; however, AES-256 may be cracked when attackers use AI for pattern recognition due to its statistical characteristics.

[0260] Anti-quantum computing attack: The method of the present invention is based on irreversible perturbation and quantum computing resistance design and cannot be cracked by quantum computers; while AES-256 is vulnerable to Shor's algorithm in a quantum computing environment.

[0261] Encryption / Recovery Speed: The encryption and recovery speed of the proposed method is superior to AES-256. Encrypting a 1GB file takes only 0.5 seconds, while AES-256 takes 2 seconds, a 4x improvement.

[0262] The experiment shows that the perturbation storage and restricted recovery method proposed in the present invention has significant advantages in terms of anti-attack capability, encryption speed and resistance in future quantum computing environments.

[0263] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for securely processing disordered factor self-organized bidirectional computing data, characterized in that: The following steps are involved: S1. Data vectorization: convert the original data into a decimal integer vector; S2, primary Levy segmentation: randomly segmenting the decimal vector based on Levy flight distribution to generate a first-level sub-vector set; S3, hyperchaotic transformation: performing nonlinear mapping on the first-level sub-vector set through a three-dimensional tensor coupled chaotic system, and outputting a chaotic mapping vector set; S4, secondary Levy segmentation: performing secondary Levy distribution segmentation on the chaotic map vector set to generate a second-level sub-vector set; S5, non-bijective decomposition: applying an irreversible function transformation to the second-level sub-vector set to generate an unordered factor set that meets the entropy threshold; S6. Distributed storage: distributing the set of disordered factors to geographically distributed edge nodes; S7. Limited recovery: When valid recovery credentials are verified, optimized reorganization is performed based on the set of disorder factors.

2. The method for securely processing disordered factor self-organizing bidirectional computing data according to claim 1, characterized in that: The Levy flight distribution in step S2 adopts a stable distribution with parameter α=1.5, and its step length generation formula is: Where μ and v are independent and identically distributed standard normal random variables, α is the shape parameter of the Levy distribution, and s is the Levy flight step length; The random segmentation points in the S2 step are obtained by modular operation. Dynamically determined, where s k is the k-th generated Levy flight step, m is the dimension of the original decimal vector, p i is the i-th dynamic segmentation point.

3. The method for securely processing disordered factor self-organizing bidirectional computing data according to claim 1, characterized in that: The dynamic equation of the three-dimensional tensor coupled chaotic system in step S3 is: Where γ>3.7, W is the dynamic weight matrix, the chaotic sequence is generated by the fourth-order Runge-Kutta method, x, y, z are state variables, γ is the control parameter, is the dissipation coefficient, 28 is the critical parameter, and 10 is the damping coefficient.

4. The method for securely processing disordered factor self-organizing bidirectional computing data according to claim 1, characterized in that: The irreversible function transformation in step S5 includes: Perform a circular left shift x<<<s on each subvector i , displacement s i Taken from Chaos Sequence; Apply a random mask matrix M i Perform bitwise XOR ⊙M i ; The combined operation is 5. The method for securely processing disordered factor self-organizing bidirectional computing data according to claim 1, characterized in that: The S5 step also includes entropy feedback control: Calculate the disorder factor information entropy, the formula is: Where H is the entropy value, v is the data value, p(v) is the probability distribution, and log2 is the logarithmic base; When H>0.01 bit, return to S4 for re-segmentation.

6. The method for securely processing disordered factor self-organizing bidirectional computing data according to claim 1, characterized in that: The optimized reorganization in step S7 is achieved by solving the following equation: in, is the optimization variable, f is a non-bijective function, F i is a known parameter, N is a scalar parameter, is the gradient operator, is the L2 norm, ||·||1 is the L1 norm, and 0.1 is the regularization coefficient; The calculation is terminated when the recovery error rate ERR ≥ 37%, where: Where m is a scalar parameter, To recover data, x j is the original data.

7. The disorder factor self-organizing bidirectional computing data security processing system is characterized by: The method for securely processing disordered factor self-organizing bidirectional computing data according to any one of claims 1 to 6 comprises: Data preprocessing module, used to convert raw data into standard digital vector format; Chaos calculation module, used to perform Levy flight distribution segmentation and hyperchaotic transformation processing; Non-bijective decomposition module, used to perform irreversible function transformation on data fragments; Edge storage network to manage distributed storage and location obfuscation of data fragments; A data recovery engine that performs limited optimized reorganization based on storage fragmentation.

8. The disordered factor self-organizing bidirectional computing data security processing system according to claim 7 is characterized in that: The chaos computing module includes: The Levy parameter configuration unit is used to dynamically configure the α=1.5 parameter of the Levy flight distribution to control the randomness of the data segmentation; Tensor coupling operation unit, used for real-time calculation of tensor coupling terms in three-dimensional chaotic systems; Chaos iteration accelerator, used for hardware acceleration of the fourth-order Runge-Kutta method for solving chaotic differential equations.

9. The disordered factor self-organizing bidirectional computing data security processing system according to claim 7 is characterized in that: The edge storage network includes: Fragment tag encryption unit, used to generate unpredictable UUID tags based on quantum random numbers; Correlation analyzer, used to detect and block the storage of logical correlation factors on the same physical node; Dynamic migration engine, used to periodically trigger random migration of fragment physical locations.

10. The disordered factor self-organizing bidirectional computing data security processing system according to claim 7 is characterized in that: The data recovery engine includes: Gradient calculation unit, used for hardware-accelerated gradient norm calculation to achieve fast evaluation of regularization terms; Error monitor, used to monitor the recovery error rate in real time and force the calculation to terminate when ERR ≥ 37%; A projection optimizer is used to perform steepest descent iterations in a constrained solution space to find a local optimal solution.

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