A data index security access method and related equipment

Abstract

The present application discloses a data index security access method and related equipment, which relates to the field of data processing technology. The method includes: in response to a data access instruction, based on the dynamic behavior fingerprint of the requesting terminal, generating a dynamic index topology structure that matches the current network delay jitter value; based on the spatiotemporal distribution characteristics of the dynamic behavior fingerprint, performing real-time reconstruction on the dynamic index topology structure to generate an anti-collision verification path; injecting quantum noise signals into the anti-collision verification path to form a cross-layer verification channel; performing asymmetric hash chain verification on the target data index through the cross-layer verification channel to generate a verification result containing a fuzzy topology label; dynamically remapping the original index node according to the fuzzy topology label to output a secure data index; and accessing data based on the secure data index. The present application realizes the dynamicity, unpredictability and anti-parsing capability of the index path, which can comprehensively improve the security and anti-attack capability in the data access process.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and more specifically, to a data index secure access method and related equipment. Background Art

[0002] With the rapid development of technologies such as big data, cloud computing, and the Internet of Things, cross-network transmission and distributed access of data are becoming increasingly frequent. How to improve the security of the data indexing process while ensuring access efficiency has become an important research topic in the field of information security. Especially in scenarios with high data protection requirements such as finance, telecommunications, government affairs, and military industry, the predictability of index paths and the resolvability of data identifiers can easily become breakthrough points for attackers. Therefore, building a dynamic, tamper-proof, and difficult-to-reproduce data index security access mechanism is of great significance to ensuring the overall security of the system.

[0003] In existing technologies, data index structures generally adopt static design patterns, with fixed index paths and a single verification process. Attackers can predict, tamper with, or steal data indexes through methods such as path collisions, data replay, and behavioral simulation. Traditional methods are particularly inadequate in protecting against threats such as counterfeit terminals and advanced persistent attacks. Furthermore, current data verification mechanisms generally lack multi-layered dynamic adaptability, relying mostly on static rules or symmetric verification algorithms, making them vulnerable to security risks such as man-in-the-middle attacks, side-channel eavesdropping, and path parsing in complex environments. Consequently, these technologies are plagued by technical issues such as easy data leakage and ineffective access control. Summary of the Invention

[0004] The Summary of the Invention section of this application introduces a series of simplified concepts that will be further described in detail in the Detailed Description of the Invention section. The Summary of the Invention section of this application is not intended to limit the key features and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0005] The data index security access method and related equipment provided in this application can achieve the dynamic, unpredictable and anti-analysis capabilities of the index path by integrating multiple mechanisms such as dynamic behavioral fingerprints, network characteristics, quantum noise, cross-layer verification and fuzzy remapping, and can comprehensively improve the security and anti-attack capabilities during the data access process.

[0006] In the first aspect, the present application provides a method for secure access to data indexes, comprising: in response to a data access instruction, generating a dynamic index topology structure that matches the current network delay jitter value based on the dynamic behavior fingerprint of the requesting terminal; performing real-time reconstruction on the dynamic index topology structure based on the spatiotemporal distribution characteristics of the dynamic behavior fingerprint to generate a collision-resistant verification path; injecting quantum noise signals into the collision-resistant verification path to form a cross-layer verification channel; performing asymmetric hash chain verification on the target data index through the cross-layer verification channel to generate a verification result containing a fuzzy topology label; dynamically remapping the original index node according to the fuzzy topology label to output a parsing-resistant secure data index; and accessing data based on the secure data index.

[0007] In some embodiments, the dynamic index topology structure that matches the current network delay jitter value is generated based on the dynamic behavior fingerprint of the requesting terminal, including: obtaining the touch trajectory characteristics and network fluctuation characteristics of the requesting terminal within a preset time window; performing nonlinear fusion of the touch trajectory characteristics and network fluctuation characteristics through a convolution chaos algorithm to generate a topology generation factor; and constructing the dynamic index topology structure in a complex domain space according to the topology generation factor, wherein the dimensional parameters of the dynamic index topology structure are dynamically adjusted by the current network delay jitter value.

[0008] In some embodiments, the dynamic index topology structure is reconstructed in real time based on the spatiotemporal distribution characteristics of the dynamic behavior fingerprint to generate an anti-collision verification path, including: decomposing the spatiotemporal distribution characteristics into sequence fluctuation characteristics in the time dimension and trajectory distribution characteristics in the space dimension; calculating the dynamic correlation matrix based on the entropy value of the sequence fluctuation characteristics and the variance of the trajectory distribution characteristics; adjusting the connection weights of the dynamic index topology structure in real time based on the dynamic correlation matrix to generate a reconstructed topology network with a random perturbation factor; screening the node paths of the reconstructed topology network through a preset anti-collision algorithm, and retaining the paths that meet the preset anti-collision conditions as the anti-collision verification paths, wherein the anti-collision conditions include that the path conflict probability is less than a first preset threshold and the number of path jumps is greater than a second preset threshold.

[0009] In some embodiments, injecting a quantum noise signal into the anti-collision verification path to form a cross-layer verification channel includes: obtaining path parameters of the anti-collision verification path, wherein the path parameters include path length, node distribution density, and hopping frequency; generating a quantum noise signal matching the path parameters based on a quantum random number generator, wherein the spectrum width of the quantum noise signal is positively correlated with the hopping frequency; convolving the quantum noise signal with the topological parameters of the anti-collision verification path through a chaotic modulation algorithm to generate a phase perturbation factor; injecting the phase perturbation factor into the physical layer and logical layer boundary of the anti-collision verification path to form a cross-layer verification channel, wherein the verification data packet of the cross-layer verification channel carries a noise fingerprint at the physical layer and embeds a dynamic hash tag at the logical layer.

[0010] In some embodiments, the asymmetric hash chain verification is performed on the target data index through the cross-layer verification channel to generate a verification result including a fuzzy topology tag, including: constructing an asymmetric hash chain structure based on the dynamic hash tag and the phase perturbation factor, wherein the public key of the asymmetric hash chain structure is generated by the noise fingerprint of the physical layer of the cross-layer verification channel, and the private key of the asymmetric hash chain structure is generated by the expiration timestamp of the dynamic hash tag of the logical layer; cross-verifying the node attributes of the target data index to generate an initial verification result; using the asymmetric hash chain structure to perform iterative signing on the initial verification result to obtain a multi-level verification signature; comparing the multi-level verification signature with a preset anti-interference threshold, and if the threshold condition is met, extracting the topological distribution characteristics of the multi-level verification signature; performing fuzzy encoding on the topological distribution characteristics based on the random masking factor in the quantum noise signal to generate the fuzzy topology tag, wherein the generation process of the fuzzy topology tag is dynamically associated with the dimensional parameters of the dynamic index topology structure.

[0011] In some embodiments, the dynamic remapping of the original index node according to the fuzzy topological label and the output of the anti-parsing security data index include: parsing the quantum noise spectrum characteristics and topological distribution parameters in the fuzzy topological label to generate an anti-parsing mapping rule; dynamically offsetting the storage location and access permission label of the original index node based on the anti-parsing mapping rule to generate an implicit mapping relationship; performing nonlinear obfuscation on the implicit mapping relationship according to the dimension parameters of the dynamic index topological structure and the current system timestamp to generate a randomized index mapping table; injecting a dynamic obfuscation factor into the randomized index mapping table to generate the anti-parsing security data index, wherein the generation frequency of the dynamic obfuscation factor is synchronously updated with the hopping frequency of the quantum noise signal.

[0012] In some embodiments, the data index security access method also includes: when abnormal parsing behavior for the security data index is detected, generating a virtual bait index set based on the distribution characteristics of the fuzzy topological marker, and covering the virtual bait index set to the original index node; dynamically adjusting the topological parameters of the virtual bait index set based on the hopping frequency of the quantum noise signal to generate an obfuscated index network with attack tracing function; when the virtual node in the obfuscated index network is activated for access, extracting the attacker's behavioral fingerprint characteristics and triggering the data self-destruction instruction of the distributed storage node.

[0013] In the second aspect, the present application also provides a data index security access device, including: a topology structure generation unit, which is used to generate a dynamic index topology structure that matches the current network delay jitter value based on the dynamic behavior fingerprint of the requesting terminal in response to a data access instruction; a verification path generation unit, which is used to perform real-time reconstruction of the dynamic index topology structure based on the spatiotemporal distribution characteristics of the dynamic behavior fingerprint, and generate a collision-resistant verification path; a verification channel generation unit, which is used to inject quantum noise signals into the collision-resistant verification path to form a cross-layer verification channel; a topology label generation unit, which is used to perform asymmetric hash chain verification on the target data index through the cross-layer verification channel to generate a verification result containing a fuzzy topology label; a data index generation unit, which is used to dynamically remap the original index node according to the fuzzy topology label, and output a security data index that is anti-parsing; and a data access unit, which is used to access data based on the security data index.

[0014] In a third aspect, the present application further provides an electronic device comprising: a memory and a processor, wherein the processor is configured to implement the steps of the data index secure access method described in the first aspect when executing a computer program stored in the memory.

[0015] In a fourth aspect, the present application further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the data index secure access method described in the first aspect.

[0016] In a fifth aspect, the present application also provides a computer program product, including a computer program or computer executable instructions. When the computer program or computer executable instructions are executed by a processor, the data index security access method provided in the embodiment of the present application is implemented.

[0017] In summary, this application utilizes the dynamic behavioral fingerprint and network delay characteristics of the requesting terminal to construct a dynamic index structure, which can dynamically respond to different user environments, making it difficult for illegal terminals to reproduce the access mode of legitimate terminals, and effectively avoiding the problem that static structures are easily predicted or copied; by analyzing the spatiotemporal distribution characteristics of dynamic behavioral fingerprints, the index structure is adjusted in real time, which can prevent different requests from falling into the same index path, prevent the data index path from being collided or enumerated, and effectively defend against brute force cracking and path speculation attacks; using quantum noise signals to disrupt the signal characteristics of the verification path and create uncertainty, which can make traditional electronic monitoring and side channel analysis methods ineffective and improve the stealth and randomness of path verification; constructing a verification mechanism through channels across network layers, making it impossible for attackers to crack the system at a single layer, enhancing the hierarchical security of the system, using asymmetric hash chains to achieve data tamper-proofing and request traceability, and improving the consistency and credibility of access behavior and index content; using fuzzy processing to cover up the real index structure can prevent attackers from inferring the original topology structure by analyzing the verification results, and then further strengthen the protection mechanism through dynamic remapping to ensure that the index cannot be reversely reconstructed even if it is intercepted. In summary, the data index security access method provided in this application realizes the dynamic, unpredictable and anti-analysis capabilities of the index path by integrating multiple mechanisms such as dynamic behavioral fingerprints, network characteristics, quantum noise, cross-layer verification and fuzzy remapping, which can comprehensively improve the security and anti-attack capabilities in the data access process. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present description. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0019] Figure 1 A flowchart of a method for securely accessing data indexes provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of the structure of a data index security access device provided in an embodiment of the present application;

[0021] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] In the specification, claims, and drawings of this application, terms such as "first," "second," "third," "fourth," and the like (if any) are used to distinguish similar objects, rather than to describe a particular order or precedence. Therefore, it is understood that these terms can be used interchangeably where appropriate, so that the described embodiments can be implemented in a different order, unless otherwise specified in the drawings or descriptions. In addition, the terms "is" and "has" and any variations thereof in this application are intended to cover all possible constituent elements on a non-exclusive basis. For example, a process, method, system, product, or apparatus that includes several steps or units is not necessarily limited to the steps or units explicitly listed, but may also include other steps or units that are not explicitly listed, or steps or units that are inherent to the process, method, product, or apparatus.

[0023] In this application, a "module" or "unit" refers to a computer program or portion of a computer program that has a specific function and works in conjunction with other related components to achieve a predetermined goal. These modules or units can be implemented using software, hardware (e.g., processing circuitry or memory), or a combination of both. One or more processors or memories can implement one or more modules or units. Furthermore, each module or unit can be part of a larger module or unit.

[0024] The technical solutions in this application will be described in detail below in conjunction with the accompanying drawings in the embodiments. It should be noted that the embodiments described are only part of this application, not all embodiments. In the following description, the "some embodiments" mentioned are only a subset of all possible embodiments, which may be the same or different subsets, and different embodiments can be combined with each other without conflict.

[0025] Figure 1 This is a flow chart of a data index security access method provided by an embodiment of the present application. For example, see Figure 1 The data index secure access method provided in the embodiment of the present application may include the following steps 101 to 106:

[0026] Step 101 , in response to a data access instruction, generating a dynamic index topology structure that matches a current network delay jitter value based on a dynamic behavior fingerprint of a requesting terminal;

[0027] In some examples, data read (query) or write (upload) requests initiated by users or applications can be triggered by system calls, API requests, or front-end operation commands, such as a user clicking "View File." The requesting terminal refers to the device initiating the data access instruction, which could be a mobile phone, computer, or IoT terminal. It can be identified by its unique identifier, such as device ID, MAC address, or IP address. A dynamic behavioral fingerprint is a combination of fine-grained behavioral features exhibited by the requesting terminal within a specific time window. It can be used to identify and dynamically model user behavior. Dynamic behavioral fingerprints can include touch trajectory, sliding speed, press force, click interval, and number of retries. These fingerprints can be collected through a front-end SDK or behavior perception module. The current network delay jitter value represents the fluctuation in data transmission delay over a specific time period. It is an important indicator of network stability and can be calculated in real time by sending ICMP ping packets, measuring HTTP request RTT, or using the RTTStats interface in WebRTC. For example, the delay is measured every 100ms over 1 second, and the difference between the maximum and minimum recorded delays is the jitter value, for example, 30ms. The dynamic index topology is a dynamically generated data index graph structure. The construction rules of its nodes and edges are driven by user behavior fingerprints and network status. It is real-time and unpredictable and can be automatically constructed by combining the aforementioned fingerprints and network parameters through algorithm engines (such as chaos algorithms and graph structure generators).

[0028] For example, when a user initiates a request to view a ciphertext file in the cloud (i.e., a data access instruction), the terminal is identified as an Android device, and the touch trajectory is recorded as "L-shaped quick sliding, lightly tapping 3 times" within a 3-second time window; combined with the current network jitter value of 15ms, the behavior is fused with the network fluctuation characteristics through a convolutional chaos algorithm to generate a dynamic index topology structure.

[0029] By implementing step 101, an index structure is dynamically generated by combining the behavioral characteristics of the user terminal and the real-time network status, so that the index has personalization and timeliness, and is difficult to be counterfeited or reproduced by attackers. This effectively avoids the problem that traditional static index structures are easily predictable and reused, and enhances the system's anti-intrusion capabilities from the source.

[0030] Step 102: Based on the spatiotemporal distribution characteristics of the dynamic behavior fingerprint, the dynamic index topology structure is reconstructed in real time to generate an anti-collision verification path;

[0031] In some examples, spatiotemporal distribution characteristics refer to the dynamic distribution characteristics of dynamic behavioral fingerprints in both time and space. The temporal dimension reflects the temporal regularity and rhythmic intensity of behavioral events (e.g., click frequency and sliding rhythm), while the spatial dimension reflects changes in operation location, trajectory range, and path direction. Based on the existing dynamic index topology, its node connections, path priorities, or structural form can be dynamically updated based on the current spatiotemporal behavioral characteristics to form a new index topology. Anti-collision verification paths are selected from the reconstructed index topology to identify paths with low path overlap probability, high node hop diversity, and strong randomness. These paths are used for subsequent security verification operations. Pre-set screening criteria such as "path conflict probability less than a certain threshold and path hop count greater than another threshold" can be used to perform a path optimization algorithm based on the node access history and the perturbation factor of the reconstructed graph. For example, in the reconstructed index topology, there are paths A and B. Path A has been requested three times in the past second and has a hop count of one. Path B has no access records and has a hop count of three. Therefore, path B is preferred as the anti-collision verification path.

[0032] Exemplarily, based on the initial dynamic index graph generated in step 101, the rhythm of the user's behavior on the time axis (such as operations concentrated in the first 1 second) and the trajectory range in the spatial dimension (such as concentrated in the upper right area of ​​the screen) are further collected, and these spatiotemporal features are decomposed into time entropy and spatial variance, and a dynamic correlation matrix is ​​constructed; then, the matrix drives the local connections of the index graph structure to recombine to form a perturbation topology.

[0033] By implementing step 102, the temporal and spatial distribution characteristics of terminal behavior are dynamically analyzed, the index path structure is adjusted in real time, and a high degree of variability and non-repeatability are introduced, which can effectively prevent multiple access requests from falling into the same path, reduce the risk of path collision and enumeration attacks, and improve path security and access isolation.

[0034] Step 103: injecting quantum noise signals into the anti-collision verification path to form a cross-layer verification channel;

[0035] In some examples, quantum noise signals are non-deterministic, high-entropy random signals generated based on quantum uncertainty principles (such as the uncertainty principle). They are unpredictable, non-replicable, and irreversible. The hardware layer can generate true quantum random sequences through quantum random number generators, such as those based on photon scattering, electron tunneling, and quantum decoherence. If the software layer lacks quantum hardware support, it can use high-entropy pseudo-random sources to simulate quantum noise characteristics, such as dynamic changes in the noise spectrum and non-periodic perturbations. A cross-layer verification channel refers to a multi-layer collaborative verification channel that runs through the communication physical layer (such as link characteristics) and the logical layer (such as protocol tags and data structures). It is a cross-layer verification channel formed by embedding quantum noise signals in the collision-resistant verification path, which can achieve joint verification of the authenticity and consistency of the data path.

[0036] By implementing step 103, a quantum-level random noise signal is introduced, so that the path verification process has a dual perturbation mechanism at the physical layer and the logical layer, which greatly increases the difficulty for attackers to crack the path through side-channel analysis or protocol monitoring, and realizes the concealment, randomness and anti-detection properties of the verification path.

[0037] Step 104: Perform asymmetric hash chain verification on the target data index through a cross-layer verification channel to generate a verification result including a fuzzy topology tag;

[0038] In some examples, the target data index is the index information pointing to the specific data item in the data access instruction. It serves as the entry point for data retrieval and retrieval, and is a unique, structured location identifier, such as a hash address, B+ tree path, hash table key, or encrypted file handle. Symmetric hash chain verification is a verification method that combines asymmetric encryption mechanisms (such as RSA / ECC) with hash chain structures (such as Merkle chains or hash ladders). It ensures data path integrity and identity unforgeability through the sequential consistency of chained hashes and the uniqueness of asymmetric signatures. Fuzzy topology labels are structural labels generated by obfuscating the actual path information in the index topology structure through perturbation, encryption, and shifting. These labels do not directly expose the actual path, but can be used to restore the verification results in the verification channel. The verification results are the final composite verification output, indicating that the index path is valid after multiple layers of verification, and include hash chain signatures, path consistency proofs, and fuzzy label matching.

[0039] For example, after the user requests the target data index of "contract.pdf" through the cross-layer verification channel, its current path node sequence is first constructed as a hash chain and signed by the client's private key; the server verifies the hash chain order and signature validity through the corresponding public key; at the same time, the collision-resistant verification path is perturbed, and its fuzzy mark is matched and compared with the path structure in the verification channel, and finally a structured verification result is generated that includes path consistency proof and fuzzy mark recognition.

[0040] Through the implementation of step 104, combined with the asymmetric hash mechanism and fuzzy topology marking, the non-forgeability and non-tamperability of data index verification can be ensured, while protecting the topology structure from being inferred or restored, enhancing the reliability and anti-inference ability of index verification, and achieving dual protection of content authenticity and path privacy.

[0041] Step 105: Dynamically remap the original index nodes according to the fuzzy topological labels and output a parsing-resistant secure data index;

[0042] In some examples, the original index node refers to the location node that directly maps the target data before topological perturbation and fuzzification processing are performed, representing a real, resolvable data location reference. It can be generated by the index construction engine during the initial index topology generation (such as step 101) and can be the primary key ID of a record in the database, the inode in the file system, the shard location identifier in the distributed storage, etc. For example, the original index node corresponding to the file "contract.pdf" is IDnode_42, and the path is [root→doc→legal→node_42]. Dynamic remapping refers to remapping the original index node to a new node ID or path address at the logical or physical layer based on the fuzzified topological label or behavioral perturbation results to prevent static path exposure or reuse. The perturbation factor in the fuzzified topological label (such as the perturbation hash value or the path offset matrix) can be used as a mapping seed to apply a dynamic path transformation algorithm, such as perturbation graph matching or random jump mapping table. The anti-parsing secure data index is the final index result generated after dynamic remapping. It is anti-parsing, which means that external observers cannot easily restore the original data location it points to, ensuring the confidentiality and non-determinism of the access path and data location.

[0043] Exemplarily, after the verification is completed in step 104, the mask and jump sequence used to perturb the path are extracted from the fuzzy topology mark, and combined with the user terminal behavior perturbation factor, such as the trajectory change hash or touch rate, a dynamic perturbation matrix is ​​calculated. The matrix is ​​applied to the original index node mapping table, dynamically offset to the new index address, and encrypted to form an "anti-parsing security data index". Finally, the index is submitted to the data scheduling module as a new access identifier to realize real data retrieval.

[0044] Through the implementation of step 105, the original node positions and access paths are re-encoded and obfuscated, making the index structure dynamic and hidden. Even if it is intercepted, it is difficult to restore the real index path, effectively resisting attacks such as structural analysis and reverse tracing, and ensuring the long-term security of the index.

[0045] Step 106, accessing data based on the security data index;

[0046] In some examples, the anti-parsing security data index generated in the previous stage is used to locate and complete the read (query, download) or write (upload, modify) operations of the target data. This does not directly rely on the real path or node ID, but instead uses encrypted security indexes and decoding mechanisms for data interaction, thereby ensuring the confidentiality of data location and index structure during transmission and retrieval.

[0047] Through the implementation of step 106, data access is performed based on a security index that has undergone multi-layer verification and dynamic protection, ensuring that the data access process is both safe and reliable, and has traceability, tamper-proofness, and environmental adaptability, effectively achieving end-to-end data protection and access control.

[0048] In summary, the embodiments of the present application utilize the dynamic behavior fingerprint and network delay characteristics of the requesting terminal to construct a dynamic index structure, which can dynamically respond to different user environments, making it difficult for illegal terminals to reproduce the access mode of legitimate terminals, and effectively avoiding the problem that static structures are easily predicted or copied; by analyzing the spatiotemporal distribution characteristics of dynamic behavior fingerprints and adjusting the index structure in real time, it is possible to prevent different requests from falling into the same index path, prevent the data index path from being collided or enumerated, and effectively defend against brute force cracking and path speculation attacks; using quantum noise signals to disrupt the signal characteristics of the verification path and create uncertainty, it is possible to render traditional electronic monitoring and side channel analysis methods ineffective, and improve the stealth and randomness of path verification; by constructing a verification mechanism through channels across network layers, it is impossible for attackers to crack the system at a single layer, thereby enhancing the hierarchical security of the system; using asymmetric hash chains to achieve data tamper-proofing and request traceability, and improving the consistency and credibility of access behavior and index content; using fuzzy processing to conceal the true index structure can prevent attackers from inferring the original topology structure by analyzing the verification results, and then further strengthen the protection mechanism through dynamic remapping to ensure that the index cannot be reversely reconstructed even if it is intercepted. In summary, the data index security access method provided in the embodiment of the present application realizes the dynamic, unpredictable and anti-analysis capabilities of the index path by integrating multiple mechanisms such as dynamic behavioral fingerprints, network characteristics, quantum noise, cross-layer verification and fuzzy remapping, which can comprehensively improve the security and anti-attack capabilities in the data access process.

[0049] In some embodiments, the aforementioned generation of a dynamic index topology structure that matches the current network delay jitter value based on the dynamic behavior fingerprint of the requesting terminal may include: obtaining the touch trajectory characteristics and network fluctuation characteristics of the requesting terminal within a preset time window; performing nonlinear fusion of the touch trajectory characteristics and network fluctuation characteristics through a convolution chaos algorithm to generate a topology generation factor; and constructing a dynamic index topology structure in a complex domain space according to the topology generation factor, wherein the dimensional parameters of the dynamic index topology structure are dynamically adjusted by the current network delay jitter value.

[0050] In some examples, the preset time window is the time range for sampling and counting terminal behavior data within a fixed time period (such as 1 second, 3 seconds, etc.), which can be set by the client behavior perception module. For example, a timer is started through the JavaScript front-end or mobile SDK to collect touch coordinates every 100ms. Touch trajectory features refer to the characteristic information formed by the movement path of the user's finger on the screen, including direction, speed, degree of curvature, click point sequence, etc. The touchmove, touchstart, and touchend event data can be collected through the front-end to generate a trajectory point set, and then Bezier curve fitting, angle change detection and other methods are used to extract features. Network fluctuation characteristics refer to the delay change, jitter value, packet loss rate and other parameters of the network per unit time that reflect the stability and dynamics of the network. The convolutional chaos algorithm uses nonlinear mixing of input signals (behavior trajectories and network fluctuations) to produce a highly complex output. It combines convolution operations with chaotic maps (such as logistic, tent, and henon maps) to increase the uncertainty and sensitivity of the output. For example, the touch trajectory features [0.3, 0.5, 0.9] and network fluctuations [15, 22, 10] are convolved and used as initial states for the logistic chaotic map, outputting a chaotic sequence [0.47, 0.81, 0.13]. The topology generation factor is a set of high-entropy parameters or vectors output by the convolutional chaos algorithm. It serves as a seed for constructing the index graph structure (node ​​positions, edge weights, hierarchical relationships, etc.). It can be a complex array or a chaotic perturbation matrix. Topology generation factors can be used to generate dynamic index topology structures in complex space (i.e., a mathematical space containing real and imaginary parts). The complex domain can introduce structural changes such as phase and rotation to increase the perturbation dimension of the graph. Graph construction engines (such as graph neural networks and graph chaos modeling systems) can be used to arrange nodes in complex coordinate space and set edge connection relationships based on complex weights to obtain dynamic index topology structures. The complexity of the index structure, such as the number of node layers and the number of path branches, can be dynamically adjusted according to the current network jitter level (i.e., the delay fluctuation range). The greater the jitter, the more complex the structure. For example, if the network jitter is < 0ms, the topology dimension maintains the basic value, such as a 3rd-order tree; if the jitter is > 50ms, it is increased to a high-order multi-branch graph structure.

[0051] For example, in a user data access request, the terminal performs a series of operations such as swiping and clicking within a 3-second time window. At the same time, the network fluctuation detection module records that the current dynamic index topology structure is 32ms. The trajectory features [0.3, 0.6, 0.2] and fluctuation features [15, 20, 18] are extracted and input into the convolutional chaos algorithm to obtain the topology generation factor such as [0.78+0.1i, 0.43-0.2i]. Subsequently, nodes are arranged in the complex domain graph space to construct a dynamic index topology graph, and the topology is adjusted to a 4-layer bifurcation structure according to the jitter value. This graph becomes the basis for access index generation, and subsequent processes continue to reconstruct, verify and scramble based on this structure.

[0052] Through the implementation of the above embodiments, terminal characteristics such as touch trajectories and network fluctuations are introduced, and the chaotic algorithm and complex domain are used to construct the index topology, so that the generated index structure is highly individualized and dynamic, which can significantly reduce the risk of index paths being predicted, copied or reproduced, and eliminate the security risks brought by static structures from the source.

[0053] In some embodiments, the aforementioned step 102 may include: decomposing the aforementioned spatiotemporal distribution characteristics into sequence fluctuation characteristics in the time dimension and trajectory distribution characteristics in the spatial dimension; calculating the dynamic correlation matrix based on the entropy value of the sequence fluctuation characteristics and the variance of the trajectory distribution characteristics; adjusting the connection weights of the dynamic index topology structure in real time based on the dynamic correlation matrix to generate a reconstructed topology network with a random perturbation factor; screening the node paths of the reconstructed topology network through a preset anti-collision algorithm, and retaining the paths that meet the preset anti-collision conditions as anti-collision verification paths, wherein the anti-collision conditions may include the path conflict probability being less than a first preset threshold and the number of path jumps being greater than a second preset threshold.

[0054] In some examples, sequence fluctuation features describe the distribution and rhythmic change characteristics of user behavior events (such as clicks and slides) on the timeline, reflecting the temporal regularity of behavior. Behavioral events (such as touch timestamps) can be converted into time series (such as click intervals and slide rates), and their entropy, variance, or FFT spectrum can be calculated to obtain sequence fluctuation features. Trajectory distribution features describe the spatial distribution pattern of user operation trajectories (such as finger slides), including distribution range, path density, trajectory angle distribution, etc. The position distribution can be extracted from the touch point coordinate set and quantified using statistical indicators (such as standard deviation, variance, and center of gravity offset) to obtain trajectory distribution features. The dynamic correlation matrix is ​​a matrix structure used to describe the similarity or correlation strength between spatiotemporal behavior features. The sequence entropy value and trajectory variance can be input into a correlation calculation model (such as cosine similarity, inverse Euclidean distance function, mutual information, etc.) to generate a dynamic correlation matrix. The pre-set anti-collision algorithm is used to identify and select paths with low path overlap probability and high hop characteristics, ensuring path diversity and security. This algorithm can be implemented as a path scoring algorithm, using a collision probability function and a hop statistics mechanism, combined with historical access frequency and graph structure perturbation factors. The pre-set anti-collision conditions serve as criteria for path selection, ensuring that selected paths exhibit low collision rates and high variability within the current structure. These conditions can be set based on empirical data or adversarial simulation training results, such as collision probability <20% and hop count >3. The first pre-set threshold sets the maximum path collision probability (e.g., the probability of a path being shared by multiple users). A lower threshold generally ensures security and can be set based on access logs or simulated attack analysis. For example, a value of 0.05 specifies that the probability of a path being simultaneously requested must be less than 5%. The second pre-set threshold sets the minimum path hop count to ensure path variability and unpredictability. This threshold can be set based on the average hop count of the topology graph.

[0055] For example, in a data access, the user's click and slide operations within a 3-second time window are recorded, and the behavioral entropy of the time dimension is extracted as 0.86, and the spatial trajectory distribution variance is 0.72. The calculated dynamic correlation matrix shows that user behavior has a medium correlation level in space and time, driving the connection probability between the middle-level nodes in the index graph to be perturbation-weighted; then, conflict analysis and jump statistics are performed on all paths in the reconstructed topology, and paths that meet the "conflict probability <0.05 and number of jumps ≥4" are screened out as anti-collision verification paths, which are finally used in the index verification stage.

[0056] Through the implementation of the above embodiments, the dynamic behavior characteristics are refined into two dimensions of time and space, and mathematical models such as entropy and variance are introduced for path reconstruction, which can effectively improve the randomness and anti-collision ability of the path, avoid multiple requests falling into the same path, significantly reduce the probability of path conflicts and data collisions, and improve index stability and attack defense capabilities.

[0057] In some embodiments, the aforementioned step 103 may include: obtaining path parameters of the anti-collision verification path, where the path parameters may include path length, node distribution density and hopping frequency; generating a quantum noise signal that matches the path parameters based on a quantum random number generator, wherein the spectrum width of the quantum noise signal is positively correlated with the hopping frequency; convolving the quantum noise signal with the topological parameters of the anti-collision verification path through a chaotic modulation algorithm to generate a phase perturbation factor; injecting the phase perturbation factor into the physical layer and logical layer boundary of the anti-collision verification path to form a cross-layer verification channel, wherein the verification data packet of the cross-layer verification channel carries a noise fingerprint at the physical layer and embeds a dynamic hash tag at the logical layer.

[0058] In some examples, path parameters are quantitative indicators of the physical and logical characteristics of the collision-resistant verification path. In addition to path length (such as the total number of hop nodes), node density (the number of nodes per path segment), and hop frequency (the number of path structure changes per unit time), they can also include derived parameters such as path entropy and node similarity. The spectral width of the quantum noise signal must be dynamically adapted to the hop frequency to achieve signal-path coupling. For example, when the hop frequency is 2 Hz, a quantum random number generator (such as a quantum entropy source based on an avalanche diode) generates a broadband noise signal with a spectral width of (hopping frequency × 1 kHz), achieving dynamic coverage in the time-frequency domain. The chaotic modulation algorithm can employ a logistic-tent composite mapping model to nonlinearly convolve the quantum noise signal with path topology parameters (such as the node connection matrix and edge weight distribution) to generate a perturbation factor with phase rotation properties (such as a phase shift angle θ = 0.78π±Δ). This factor can dynamically change the carrier phase of signal transmission and the basis for generating logical-layer hash tags. The verification data packets of the cross-layer verification channel are superimposed with quantum noise fingerprints through orthogonal frequency division multiplexing subcarriers at the physical layer, which appears as random dispersion of the signal constellation diagram; at the logical layer, the SHA3-512 hash iteration is driven by the perturbation factor to generate a dynamic tag carrying a timestamp and behavioral entropy, achieving physical-logical dual anonymity.

[0059] For example, assuming a collision-resistant verification path with a length of 5 hops, a node density of 3 nodes / hop, and a hopping frequency of 3 Hz, a quantum random number generator generates a quantum noise signal with a spectral width of 3 kHz (frequency band 2.5-5.5 kHz). The noise signal is modulated with the path connection matrix using a logistic chaotic map (μ = 3.99, x0 = 0.42), generating a phase perturbation factor θ = [0.21π, 0.67π, 1.32π]. At the physical layer, θ is injected into the phase compensation module of the orthogonal frequency division multiplexing pilot subcarrier, causing the channel estimate to deviate by ±θ from the true value. At the logical layer, θ is used as a control parameter for hash iterations to generate the dynamic signature "9f86d08...3b9d5." If an attacker intercepts a data packet, the quantum noise at the physical layer distorts the channel characteristics, and the dynamic hash at the logical layer cannot match the historical pattern, ultimately triggering verification failure.

[0060] Through the implementation of the above embodiments, the quantum noise signal is dynamically matched with the path parameters, and is injected into the physical and logical layers through phase perturbations to build a "cross-layer" verification mechanism. This can effectively defend against side-channel attacks, traffic analysis, and single-layer cracking methods, making the verification process highly concealed and unpredictable, and enhancing the depth of the system's multi-layer security protection.

[0061] In some embodiments, the aforementioned step 104 may include: constructing an asymmetric hash chain structure based on a dynamic hash tag and a phase perturbation factor, wherein the public key of the asymmetric hash chain structure is generated by the noise fingerprint of the physical layer of the cross-layer verification channel, and the private key of the asymmetric hash chain structure is generated by the expiration timestamp of the dynamic hash tag of the logical layer; cross-verifying the node attributes of the target data index to generate an initial verification result; using the asymmetric hash chain structure to perform iterative signing on the initial verification result to obtain a multi-level verification signature; comparing the multi-level verification signature with a preset anti-interference threshold, and if the threshold condition is met, extracting the topological distribution characteristics of the multi-level verification signature; performing fuzzy encoding on the topological distribution characteristics based on the random mask factor in the quantum noise signal to generate a fuzzy topological tag, wherein the generation process of the fuzzy topological tag is dynamically associated with the dimensional parameters of the dynamic index topological structure.

[0062] In some examples, a dynamic hash tag is a time-varying identifier generated by the logical layer verification packet. It can include a hash value composed of a behavioral entropy hash (e.g., SHA3-512 (user click rhythm + sliding trajectory)) and a timestamp salt (e.g., UTC millisecond time truncated to hexadecimal). Its expiration timestamp is dynamically set using a preset time-to-live (TTL) policy; for example, it is valid for 10 seconds after the logical tag is generated. A phase perturbation factor, acting as a chaotic modulation parameter, participates in the generation of the initial vector for hash iterations during the asymmetric hash chain construction process. For example, the value θ is used to adjust the hash round offset. Cross-validation involves a multi-dimensional comparison of node attributes of the target data index, such as the node ID hash, storage location metadata, and access permission labels. This comparison includes logical consistency (e.g., matching the index path with user permissions) and physical consistency (e.g., whether the actual response latency of the storage node is consistent with the topology prediction). If the logical layer hash chain signature matches the public key decryption result of the physical layer noise fingerprint, the initial verification result is "trusted." Multi-level verification signatures are generated by nesting chained hashes and asymmetric signatures. For example, the client's private key signs the initial verification result at the first level, and the server's private key re-signs the first-level signature result at the second level, forming a hierarchical verification chain. The preset anti-interference threshold is modeled based on historical attack data and is set as the lower limit of the confidence score for the multi-level signature, such as a first-level signature similarity of >95% and a secondary signature latency fluctuation of <5ms. The threshold conditions can be adjusted through the dynamic policy engine. The random masking factor is a random number sequence extracted from the quantum noise signal, such as a 32-bit fragment intercepted from a quantum random source. It is used to perform XOR masking and shift obfuscation on topological distribution features such as node connectivity and path weight distribution. The masking strength is associated with the dimensionality parameters of the dynamic index topology (such as the number of node layers N). For example, when N=4, the mask is cyclically left-shifted by (4 mod 8) = 4 bits.

[0063] Through the implementation of the above embodiments, the public and private keys of the asymmetric hash chain are generated respectively by the physical layer and the logical layer, a strong verification chain is constructed through dynamic hashing and multi-level signatures, and fuzzy coding is achieved by combining quantum noise, so that data index verification has the triple protection of anti-tampering, traceability and anti-analysis, ensuring index consistency, verification authenticity and structural privacy.

[0064] In some embodiments, the aforementioned step 105 may include: parsing the quantum noise spectrum characteristics and topological distribution parameters in the fuzzy topological label to generate anti-parsing mapping rules; dynamically offsetting the storage location and access permission label of the original index node based on the anti-parsing mapping rules to generate an implicit mapping relationship; performing nonlinear obfuscation on the implicit mapping relationship according to the dimension parameters of the dynamic index topological structure and the current system timestamp to generate a randomized index mapping table; injecting a dynamic obfuscation factor into the randomized index mapping table to generate an anti-parsing security data index, wherein the generation frequency of the dynamic obfuscation factor is synchronized with the jump frequency of the quantum noise signal.

[0065] In some examples, anti-parseability mapping rules refer to a set of rules that map the real addresses or identifiers of original index nodes to non-directly resolvable virtual nodes after perturbation. Quantum noise spectral characteristics (e.g., bandwidth, modulation pattern) and topological distribution parameters (e.g., node offset matrix, connection disorder index) contained in fuzzy topological labels can be parsed and used as input parameters of the perturbation function to derive the anti-parseability mapping rules. An implicit mapping relationship refers to a non-explicit, non-reversible logical correspondence between the original index node and its perturbed node. Based on the anti-parseability mapping rules, the physical address, path label, access rights, and other attributes of the original node can be perturbed and offset without preserving reversible paths. A randomized index mapping table is a table structure that remaps the original index node to a set of time-varying, unpredictable index addresses after multi-dimensional perturbation. This is a transitional latent data structure that uses topological dimension parameters (e.g., number of layers, node fan-out rate) and system timestamps (e.g., UNIX time, UTC nanoseconds) as seeds for the obfuscation function to generate table entries that scramble node IDs or path logic. The dynamic obfuscation factor is a set of time-sensitive perturbation parameters injected into the index mapping table to enhance the unpredictability and anti-reconstruction capability of the index path. The update frequency of the dynamic obfuscation factor is synchronized with the jump frequency of the quantum noise signal. It can be generated in real time based on a quantum entropy source, or simulated by a pseudo-quantum random number generator to generate high-frequency perturbations.

[0066] For example, in the process of generating a secure data index, the disturbance spectrum [8Hz, 12Hz] and the offset parameter [Δx=3,θ=π / 6] are first parsed from the fuzzy topology mark to generate an anti-parsing mapping rule; then, the real position and access label of the node IDnode_42 are perturbed and offset according to the rule to generate an implicit mapping {IDnode_42→IDδ_Y}; a nonlinear confusion path is constructed based on the current network jitter dimension parameter of 4 layers and the system timestamp 1725817223 to form a randomized index mapping table; in this structure, the confusion factor σ(t) dynamically generated by the quantum noise spectrum is injected every 100ms, and finally the secure data index S_index(IDδ_Y) with anti-parsing capability is output.

[0067] Through the implementation of the above embodiments, fuzzy tag parsing is used to generate anti-parsing mapping rules, and a dynamic obfuscation mechanism is introduced to disrupt the direct relationship between the original index and the access rights, thereby constructing an irreversible and difficult-to-restore implicit index path, which can significantly improve the reverse reasoning difficulty and overall anti-parsing capability of the index layer.

[0068] In some embodiments, the aforementioned data index security access method may also include: when abnormal parsing behavior for the security data index is detected, generating a virtual bait index set based on the distribution characteristics of the fuzzy topological marker, and covering the virtual bait index set to the original index node; dynamically adjusting the topological parameters of the virtual bait index set based on the hopping frequency of the quantum noise signal to generate an obfuscated index network with attack tracing function; when the virtual node in the obfuscated index network is activated for access, extracting the attacker's behavioral fingerprint characteristics and triggering the data self-destruction instruction of the distributed storage node.

[0069] In some examples, abnormal parsing behavior refers to attempts to access, reconstruct, scan, or reverse engineer secure data index structures in an unauthorized or non-standard manner. This behavior is often accompanied by abnormal parsing frequencies, failed path reconstructions, and inconsistent hopping patterns. The behavior monitoring module can analyze index access logs to detect abnormal behavior characteristics (such as repeated parsing of the same node, abnormal path hopping sequences, and access rates exceeding normal thresholds). A virtual decoy index set is a constructed set of pseudo-index nodes that exist logically but are not physically bound to real data. These nodes are used to mislead attackers' parsing logic. Virtual nodes are generated based on the distribution characteristics of fuzzy topological markers (such as densely populated areas and frequently accessed paths), overwriting the original nodes or embedding them into the topological structure. An obfuscated index network is a dynamic virtual topology network consisting of decoy nodes, deformed paths, and perturbed connection relationships. It is used to mislead attackers, trap parsing behavior, and trace it back to its source. An attacker's behavioral fingerprint refers to the set of behavioral traces revealed when accessing a decoy index, including request patterns, path selection strategies, timing, and terminal identification. System behavior recorders (such as front-end JS SDKs, border gateway logs, and distributed link tracing) can be used to collect behavioral traces and extract feature vectors. For example, if the access path preference is breadth-first, the jump rate is less than 0.2, and the UA string is used for automated scripting tools, the behavioral fingerprint can be marked as a potential bot-type attack fingerprint. Data self-destruction commands are instructions sent to distributed data nodes for forced destruction, delinking, and encryption overwriting. They are used to proactively destroy data to prevent leakage when a threat is encountered. When the obfuscated index node is activated (i.e., the decoy is accessed) and the attack fingerprint is confirmed to be valid, the central control node broadcasts a signed self-destruction command to the data nodes.

[0070] For example, during an index access process, it was detected that the IP address 203.0.113.78 quickly and recursively accessed multiple index paths in a short period of time. The access behavior showed path scanning characteristics and disguised UA information. After being identified as abnormal parsing behavior, a virtual bait index set containing 32 nodes was immediately generated based on the fuzzy topological characteristics of the hotspot node, and some of the node structures were overlaid on the target real node path to form a confused index network; the bait network dynamically generates an irregular multi-layer path structure through the current quantum noise signal hopping frequency of 13Hz. When the attacker accesses node_fakeA, behavioral fingerprint extraction is triggered, and its hopping pattern, trigger timestamp and UA characteristics are recorded to identify it as a medium- to high-risk attack behavior; then a signed self-destruct instruction is issued to the real data node involved, and data overwriting and off-chain operations are performed to ensure that sensitive data cannot be recovered in the attacker's access path.

[0071] Through the implementation of the above embodiments, a bait index network and attack tracing mechanism are introduced. When malicious behavior is detected, the virtual node and data self-destruction process is triggered. It has automatic countermeasures, source tracing and dynamic response capabilities, and can enhance the system's perception and defense capabilities against active attacks.

[0072] In some embodiments, the aforementioned preset anti-collision algorithm may include: traversing all candidate paths of the reconstructed topological network, calculating the conflict probability and number of jumps of each path; if the conflict probability of the candidate path is less than a first preset threshold and the number of jumps is greater than a second preset threshold, marking it as a valid anti-collision path; prioritizing the valid anti-collision path according to its topological distance and network load parameters, and generating a final set of anti-collision verification paths.

[0073] In some examples, the collision probability is calculated by counting the request frequency of each path based on historical access logs. For example, if path A was accessed 100 times in the past hour, and combining it with path structural similarity, such as node sequence overlap rate and hop direction consistency, the collision probability P_collision = P(path reused | structural similarity > 0.7) is calculated using a Bayesian conditional probability model. The hop count is calculated as follows: when traversing the path node sequence, if the difference in the hash values ​​of adjacent node IDs exceeds a preset threshold (for example, the Hamming distance between SHA256(node_i) and SHA256(node_j) > 128 bits), it is counted as a valid hop. The priority sorting algorithm uses a multi-objective optimization model. It normalizes topological distance (such as the number of hops) into a latency cost factor (α = number of hops × single-hop baseline latency (e.g., 5ms / hop). The network load parameter is converted into a bandwidth occupancy weight (β = 1 / (current link utilization + ε). The final priority score (Score) = (1 / α) × β × log(number of hops) is used, prioritizing paths with high scores. The first preset threshold can be set to a collision probability (P_collision) < 0.1, meaning the path reuse probability is less than 10%. The second preset threshold is set to a number of hops ≥ 5.

[0074] Through the implementation of the above embodiments, through the quantified conflict probability and dynamic load-aware priority decision-making mechanism, it is possible to optimize path selection efficiency while ensuring low collision risk, thereby achieving a balance between security and performance; multi-dimensional threshold control makes path screening environmentally adaptive, making it difficult for attackers to break through the verification logic through fixed patterns, and significantly improving the anti-enumeration and anti-replay capabilities of the index path.

[0075] In some embodiments, the aforementioned method for generating the random perturbation factor may include: generating a chaotic perturbation sequence based on the singular value decomposition result of the current system timestamp and the dynamic correlation matrix; and convolving the chaotic perturbation sequence with a preset white noise signal to generate a random perturbation factor.

[0076] In some examples, the chaotic perturbation sequence is generated by converting the current system timestamp, such as the UNIX timestamp 1620000000.123456, into a floating-point seed value, such as taking the decimal part 0.123456 as the initial value x0, performing singular value decomposition (SVD) on the dynamic correlation matrix to obtain a singular value vector [σ1, σ2, σ3], which is normalized and used as the chaotic mapping parameter; for example, using the improved Logistic chaotic system: x n+1 = μ·σ k ·x n (1-x n ), where μ=3.999, σ k The cyclically selected singular value components are generated by a hardware true random number generator. A preset white noise signal is convolved with the chaotic sequence in the time-frequency domain: the two signals are Fourier transformed in the frequency domain, point-multiplied, and then inversely transformed back to the time domain to generate the mixed perturbation factor. The resulting random perturbation factor must have a Lyapunov exponent greater than 0.5 (strong chaotic characteristics) and an autocorrelation coefficient less than 0.1 (low periodicity).

[0077] For example, let the dynamic correlation matrix SVD decomposition get σ = [0.92, 0.35, 0.18], the system timestamp is 1620000000.123456, the initial x0 = 0.123456; the first round of iteration takes σ k =0.92, calculate x1=3.999×0.92×0.123456×(1-0.123456)=0.415; after 100 iterations, the chaotic sequence [0.415,0.732,0.218,...] is generated. The white noise signal segment is [0.312,-0.087,0.654,...], and after convolution, the random perturbation factors [0.123×0.312, 0.732×(-0.087), 0.218×0.654,...]=[0.038, -0.064, 0.142,...] are generated. If an attacker attempts to reverse the deduction, the original parameters cannot be restored due to the sensitivity of the chaotic initial value (δx0=1e-6, resulting in an error of >90% after 100 steps).

[0078] Through the implementation of the above embodiments, the system time entropy source and the mathematical perturbation of matrix decomposition are combined to generate a chaotic sequence with irreversible characteristics, and then the randomness is enhanced by physical-level white noise, so that the disturbance factor has both algorithmic complexity and physical non-cloning properties, effectively resisting reverse attacks based on model inference.

[0079] In some embodiments, the aforementioned chaotic modulation algorithm may include: inputting quantum noise signals and topological parameters into a preset chaotic system to generate a chaotic modulation sequence; performing segmented weighting on the chaotic modulation sequence according to the node distribution density of the anti-collision verification path to generate a phase perturbation factor, wherein the initial parameters of the chaotic system are dynamically set by the spatiotemporal distribution characteristics of the dynamic behavior fingerprint.

[0080] In some examples, the preset chaotic system adopts a three-dimensional Lorenz system, and its differential equation parameters (σ, ρ, β) are dynamically set by the spatiotemporal distribution characteristics of the dynamic behavior fingerprint: σ = 10 × (spatial trajectory variance) + 5, ρ = 28 × (time series entropy), β = 8 / 3 × (spatial distribution kurtosis). The quantum noise signal is converted into a digital sequence Q = [q1, q2, ..., q n ], with topological parameters including the node connection matrix C and the edge weight vector W. The chaotic modulation process is as follows: Q and C are input into the Lorenz system, three-dimensional chaotic trajectory points (x, y, z) are iteratively generated, and the z component is used to form the chaotic modulation sequence. The piecewise weighting strategy is set according to the node distribution density d (number of nodes / unit path length): when d > 3, Hanning window weighting is applied to the chaotic sequence; when d ≤ 3, exponential decay weighting is used, ultimately generating a phase perturbation factor θ = arctan(Σ(weighted chaotic sequence)) with path adaptation characteristics.

[0081] For example, a collision-resistant verification path has a node density of d = 4, a dynamic behavior fingerprint with a spatiotemporal variance of 0.7, temporal entropy of 0.9, and spatial kurtosis of 2.1. The Lorenz parameters σ = 10 × 0.7 + 5 = 12, ρ = 28 × 0.9 = 25.2, and β = 8 / 3 × 2.1 = 5.6 are calculated. The quantum noise signal Q = [0.12, -0.33, 0.78, ...] and the connection matrix C are input into the Lorenz system, and the z sequence [0.45, -0.12, 0.67, ...] is iteratively generated. Because d = 4 > 3, a Hanning window is used to weight the sequence [0.45 × 0.08, -0.12 × 0.25, 0.67 × 0.45, ...]. The phase perturbation θ is calculated as arctan(0.036 - 0.03 + 0.302) = 0.98π. After this factor is injected into the physical layer pilot phase, the constellation diagram presents an asymmetric vortex shape; the logical layer hash mark is generated "c4ca42...b67" due to the θ offset, which attackers cannot crack using a fixed phase template.

[0082] Through the implementation of the above embodiments, the dynamic configuration of chaotic parameters driven by behavioral fingerprints is used to achieve deep binding between the modulation process and the terminal characteristics. The segmented weighted mechanism enables the phase perturbation factor to have path topology adaptability, creates channel feature confusion at the physical layer, and strengthens hash unpredictability at the logical layer, forming a cross-layer collaborative defense system.

[0083] In some embodiments, the verification data packet construction method of the aforementioned cross-layer verification channel may include: adding a phase offset based on a quantum noise signal to the original data packet at the physical layer to generate a noise fingerprint data frame; asymmetrically encrypting the noise fingerprint data frame at the logical layer and embedding a dynamic hash tag based on the current timestamp; wherein the generation key of the dynamic hash tag is synchronously updated with the expiration time of the encryption index key.

[0084] In some examples, the physical layer phase offset is added by using a quantum random number generator (QRNG) to generate a uniformly distributed phase noise sequence θ∈[0,2π). Orthogonal frequency division multiplexing (OFDM) is used to inject θ into the phase compensation module of the pilot subcarriers. Specifically, N_pilot pilot subcarriers are inserted into each OFDM symbol, and each pilot point is subjected to a phase perturbation of θ_i=QRNG() mod 2π to generate a noise fingerprint data frame. Logical layer asymmetric encryption uses elliptic curve cryptography (ECC). The public key Pub_Key is preset by the system center, and the private key Pri_Key=KDF(expiration timestamp||quantum noise entropy), where KDF is a SHA-3-based key derivation function. The dynamic hash marker is generated using the formula Hash_Marker=SHA3-512(UTC timestamp||θ_i sequence||packet payload). The marker's validity period is strictly synchronized with the Pri_Key's TTL (time to live). For example, the key pair is updated every 30 seconds, and expired markers automatically expire.

[0085] For example, when a user requests to access an encrypted file, the physical layer QRNG generates θ=[0.5π, 1.2π, 2.7π] and applies corresponding phase offsets to the pilot positions 1, 5, and 9 of the OFDM symbol. After the data frame is transmitted through the channel, the receiving end detects that the pilot point phase is distorted to [0.52π, 1.18π, 2.65π]. The difference from the original θ is used for noise fingerprint verification. The logical layer uses the expiration timestamp 2023-09-20T14:30:00Z to generate Pri_Key=KDF(1621596600||QRNG_entropy), and performs a hash operation on Hash_Marker=SHA3-512("1621596600_0.5π_1.2π_2.7π_ <payload>") for ECC signing. If the attacker attempts to reuse the old key at 14:30:15, decryption will be rejected due to TTL expiration.

[0086] Through the implementation of the above embodiments, the deep coupling of physical-layer quantum noise injection and logical-layer time-sensitive keys enables verification packets to simultaneously conceal channel characteristics and maintain key dynamics. This prevents attackers from recovering phase perturbation patterns through physical-layer signal analysis, nor can they circumvent logical-layer verification by replaying old hash tags, achieving cross-layer collaborative protection.

[0087] In some embodiments, the aforementioned method for generating the random mask factor may include: extracting energy distribution parameters of the quantum noise signal in a preset frequency band; generating a chaotic mask sequence based on the energy distribution parameters and the connection weights of the dynamic index topology structure; and normalizing the chaotic mask sequence to obtain the random mask factor.

[0088] In some examples, the energy distribution parameters are extracted by performing a short-time Fourier transform (STFT) on the quantum noise signal, selecting a preset frequency band (e.g., 3kHz-5kHz) to calculate the energy integral E=Σ|X(f)|²Δf, and normalizing it to the energy proportion P=E / Σ full-band energy. The generation of the chaotic mask sequence uses an improved Henon map: x n+1 =1-α·P·x n ²+y n ,y n+1 =β·C_ij·x n , where C_ij is the connection weight from node i to node j in the dynamic index topology, α=1.4, β=0.3 are the chaos parameters. The normalization process linearly transforms the iteratively generated x sequence to the integer range [0,255], and generates the mask factor Mask=floor(255*(x n -x_min) / (x_max-x_min)).

[0089] Through the implementation of the above embodiments, the quantum noise spectrum characteristics are dynamically bound to the topological connection relationship, and a high-entropy mask sequence is generated through the chaotic system; the energy-aware chaotic parameter adjustment mechanism makes the mask factor environmentally adaptive, and the normalization processing ensures compatibility with the data format, effectively resisting mask cracking attacks based on statistical analysis.

[0090] In some embodiments, the aforementioned cross-validation may include: traversing the nodes of the target data index, calculating the mapping relationship between the attribute hash value of each node and the dynamic index topology structure; if the deviation between the node attribute hash value and the mapping relationship exceeds a third preset threshold, marking it as an abnormal node and triggering a path backtracking mechanism; after removing the abnormal node, re-executing the asymmetric hash chain verification.

[0091] In some examples, the cross-validated node attribute hash value calculation method is: perform SHA-256 hashing on the node's storage path, access permission label, and physical location metadata to generate a 64-byte hash value H_node. The mapping relationship of the dynamic index topology is established through the chaotic correlation matrix C, and the node hash deviation calculation formula is The third preset threshold is set to 0.1 (i.e., a hash deviation exceeding 10% is considered abnormal). After the path backtracking mechanism is triggered, the system traverses the last five nodes along the verification path in reverse, recalculates their hash chain signatures, and updates the topology structure.

[0092] For example, the hash value of a node , after calculation of the chaos matrix C, we get , deviation degree , marked as an abnormal node. The system traced back to the previous node ID_25, regenerated the hash chain, and built a new topology. After removing the ID_42 node, the verification pass rate increased to 99.7%.

[0093] Through the implementation of the above embodiments, through quantitative hash deviation detection and dynamic path backtracking, real-time credibility verification of index nodes is achieved, which effectively prevents node tampering and path hijacking attacks and ensures the integrity and consistency of the verification chain.

[0094] In some embodiments, the method for generating the aforementioned anti-analytic mapping rules may include: extracting the energy peak distribution of quantum noise spectrum characteristics in a preset frequency band; constructing a complex domain anti-analytic weight matrix based on the energy peak distribution; inputting the topological distribution parameters into a preset anti-analytic function to generate an initial mapping rule; and performing weighted correction on the initial mapping rule through the anti-analytic weight matrix to generate a final anti-analytic mapping rule.

[0095] In some examples, the energy peak distribution is extracted by performing power spectral density (PSD) analysis on the quantum noise signal and detecting the first three main peak frequencies f1, f2, and f3 and their amplitudes A1, A2, and A3 within a preset frequency band (e.g., 3kHz-5kHz). The complex-domain anti-analytic weight matrix W is constructed using the formula W_ij = A_k·e^(j2πf_kτ_ij), where τ_ij is the topological transition delay from node i to j. The anti-analytic function uses the Fourier perturbation model F(x)=FFT(x)·W. The initial mapping rule R_init is generated from the node connectivity and weights, and the final rule R_final = Normalize(R_init ⊙ F(R_init)), where ⊙ is the Hadamard product.

[0096] For example, the main quantum noise peaks detected are f1 = 3.5 kHz (A1 = 0.8), f2 = 4.1 kHz (A2 = 0.6), and f3 = 4.7 kHz (A3 = 0.4), with a node transition delay of τ_12 = 5 ms. We construct W_12 = 0.8e^(j2π×3500×0.005) = 0.8e^(j35π) = 0.8(-1+0j) = -0.8. The initial rule R_init = [0.3, 0.7, 0.5] is transformed by F(x) to obtain R_final = [0.3×(-0.8), 0.7×0.6, 0.5×0.4] = [-0.24, 0.42, 0.2], which is normalized to [0, 0.66, 0.34]. An attacker cannot infer the original topology parameters by observing the final rule.

[0097] Through the implementation of the above embodiments, the quantum noise spectrum characteristics are encoded into a complex weight matrix, and the nonlinearity and irreversibility of the mapping rule are enhanced through frequency domain transformation, so that the anti-analysis rule has both physical noise binding and mathematical chaos characteristics, which significantly improves the difficulty of reverse engineering the index path.

[0098] In some embodiments, the aforementioned method for generating the dynamic confusion factor may include: calculating the confusion factor update period based on the hopping frequency of the quantum noise signal; generating a chaotic sequence according to the hash value of the current index mapping table and the system timestamp in each update period; and normalizing and truncating the chaotic sequence to obtain the dynamic confusion factor.

[0099] In some examples, the confusion factor update period T = 1 / (2×hopping frequency), such as hopping frequency f = 10 Hz, then T = 50 ms. The chaotic sequence is generated using the Logistic-Tent hybrid model: n+1 = (μ×x n (1-x n ) + λ×(1-2|x n -0.5|)) mod 1, where μ=3.99, λ=0.7, and the initial value x0=SHA3(current mapping table hash||timestamp) mod 1. The normalized truncation will be x n Mapping to [0,255] integers: Mask = floor(255×x n ).

[0100] For example, with a hopping frequency of 15 Hz, T ≈ 33 ms, the current hash = 0x5a3d, and the timestamp 1620000000, we calculate x0 = SHA3("5a3d1620000000"), the last 8 bytes of which = 0.723. Iterating the Logistic-Tent algorithm yields x1 = 0.632, x2 = 0.417, and so on. Taking x5 = 0.891, we obtain Mask = floor(255 × 0.891) = 227 (0xE3). Data byte 0x6A is obfuscated to 0x6A ⊕ 0xE3 = 0x89, making it impossible for an attacker to infer the original value from 0x89.

[0101] Through the implementation of the above embodiment, the obfuscation rhythm is dynamically adjusted based on the noise hopping frequency, and the strong randomness of the cryptographic hash and chaos model is combined to ensure that the obfuscation factor is time-varying and unpredictable, effectively resisting replay attacks and pattern analysis.

[0102] In some embodiments, the execution method of the aforementioned data self-destruction instruction may include: locating the associated distributed storage node based on the attacker's behavioral fingerprint characteristics; sending an encryption instruction carrying a self-destruction key to the storage node, where the self-destruction key is generated by the dimensional parameters of the dynamic index topology structure; after the storage node verifies the validity of the self-destruction key, it performs multiple overwrite erases on the target data block and destroys the corresponding index mapping record.

[0103] In some examples, the self-destruct key is generated using the formula K_selfdestruct = SHA3(dimension parameter d || time window ID), where d is the number of topological layers and the time window ID = floor(current time / self-destruct period). Encryption instructions use AES-GCM mode, with an additional authentication tag TAG = HMAC(K_selfdestruct, node ID). Data overwrites are performed using the DoD 5220.22-M standard, performing three random pattern overwrites (0x00 → 0xFF → random code).

[0104] For example, with dimension parameter d = 4 and time window ID = 1620000000 / 300 = 5400000, K_selfdestruct = SHA3("45400000") = 0x8d3a...c9f is generated. Node ID = 192.168.1.42 has a tag = HMAC(0x8d3a, "192.168.1.42") = 0x7e5f...a9. After the node verifies the tag is valid, it overwrites the data block: writing all 0s in the first round, all 1s in the second round, padding with random numbers in the third round, and finally deleting the index record.

[0105] Through the implementation of the above embodiments, the self-destruct keys bound to topology parameters and military-grade erasure standards ensure that sensitive data is quickly and irreversibly destroyed in the event of an intrusion, while preventing key reuse attacks and achieving immediate and thorough attack response.

[0106] In some embodiments, the aforementioned step 106 may include: parsing the randomized index mapping table in the aforementioned security data index, extracting the dynamic obfuscation factor and the implicit mapping relationship; generating a dynamic decryption key based on the implicit mapping relationship, wherein the dynamic decryption key is jointly generated by the dynamic obfuscation factor and the hash value of the current system timestamp; locating the target data block according to the node distribution characteristics of the randomized index mapping table, and performing hierarchical decryption on the target data block using the dynamic decryption key to obtain initial plaintext data; performing secondary integrity verification on the initial plaintext data, and the secondary integrity verification may include verifying the consistency of the Fourier phase perturbation characteristics of the target data block and the spectral characteristics of the quantum noise signal; if the verification passes, deobfuscating the decrypted data to generate the final plaintext data and return it to the requesting terminal, and at the same time updating the dimension parameters of the dynamic index topology structure to trigger the index reconstruction of the next cycle.

[0107] In some examples, the dynamic decryption key is generated by concatenating the dynamic obfuscation factor σ (e.g., 0xE3) with the current system timestamp t (e.g., UNIX time 1620000000), then inputting the result into the SHA3-512 hash function to generate a 256-bit key K_decrypt = SHA3-512(σ||t)[0:31]. The first 32 bytes are then truncated as the AES-256 key. Layered decryption employs a cascaded decryption scheme: the first layer uses K_decrypt to decrypt the outer ciphertext of the data block, obtaining the intermediate ciphertext C_mid. The second layer decrypts C_mid using the phase key K_phase = FFT(Q_noise)[3kHz-5kHz frequency band amplitude sequence] generated from the quantum noise spectrum. During secondary integrity verification, the Fourier transform phase spectrum Φ_data of the decrypted data is calculated and compared with the phase perturbation baseline Φ_baseline acquired in real time by a quantum noise source for cosine similarity. If the similarity exceeds 0.9, the two are considered identical. The deobfuscation process uses the XOR operation, using the lower 8 bits of σ to restore the data byte by byte.

[0108] For example, a user requests the encrypted file "financial report.docx." The system parses the security index S_index, extracting σ = 0xE3 and the implicit mapping {ID δ_Y → physical address 192.168.5.42:7049}. Based on the current timestamp 1620000000, K_decrypt is generated, which is SHA3-512("E31620000000"), with the first 32 bytes being 0x5a3d...c7f2. After locating the target data block, the first-level AES decryption yields C_mid, which is then decrypted using phase rotation using K_phase = [0.78π, 1.23π, 0.56π]. The calculated similarity between the decrypted data phase spectrum Φ_data and the real-time quantum noise Φ_baseline is 0.93. After verification, the data is XOR-ed and deobfuscated using σ = 0xE3, and the plaintext is returned to the user terminal. Simultaneously, the topology dimension parameter is increased from 4 to 5, triggering reconstruction.

[0109] Through the implementation of the above embodiments, the dynamic decryption key is deeply bound to the timestamp and obfuscation factor, so that the key for each access is unique and timely, effectively defending against key reuse attacks; the layered decryption mechanism is combined with physical layer quantum feature verification to ensure data integrity and source authenticity; deobfuscation processing and dynamic update of topology parameters form a closed-loop protection, making it impossible for attackers to obtain persistent access capabilities through a single cracking, thereby realizing full life cycle security protection of data access.

[0110] Furthermore, as an implementation of the aforementioned method embodiment, the present application also provides a data index security access device for implementing the aforementioned method embodiment. This device embodiment corresponds to the aforementioned method embodiment. For ease of reading, this data index security access device embodiment will no longer describe the details of the aforementioned method embodiment one by one, but it should be clear that the device in the embodiment of the present application can implement all the contents of the aforementioned method embodiment. Figure 2 As shown, the data index security access device 20 includes: a topology structure generation unit 201, a verification path generation unit 202, a verification channel generation unit 203, a topology label generation unit 204, a data index generation unit 205 and a data access unit 206, wherein the topology structure generation unit 201 is used to generate a dynamic index topology structure that matches the current network delay jitter value based on the dynamic behavior fingerprint of the requesting terminal in response to the data access instruction; the verification path generation unit 202 is used to perform real-time reconstruction of the dynamic index topology structure based on the spatiotemporal distribution characteristics of the dynamic behavior fingerprint to generate a collision-resistant verification path; the verification channel generation unit 203 is used to inject quantum noise signals into the collision-resistant verification path to form a cross-layer verification channel; the topology label generation unit 204 is used to perform asymmetric hash chain verification on the target data index through the cross-layer verification channel to generate a verification result including a fuzzy topology label; the data index generation unit 205 is used to dynamically remap the original index node according to the fuzzy topology label and output a security data index that is anti-parsing; the data access unit 206 is used to access data based on the security data index.

[0111] In some embodiments, the topology structure generation unit 201 is further used to obtain the touch trajectory characteristics and network fluctuation characteristics of the requesting terminal within a preset time window; perform nonlinear fusion of the touch trajectory characteristics and network fluctuation characteristics through a convolution chaos algorithm to generate a topology generation factor; and construct a dynamic index topology structure in a complex domain space according to the topology generation factor, wherein the dimension parameters of the dynamic index topology structure are dynamically adjusted by the current network delay jitter value.

[0112] In some embodiments, the verification path generation unit 202 is also used to decompose the spatiotemporal distribution characteristics into sequence fluctuation characteristics in the time dimension and trajectory distribution characteristics in the space dimension; calculate the dynamic correlation matrix based on the entropy value of the sequence fluctuation characteristics and the variance of the trajectory distribution characteristics; adjust the connection weights of the dynamic index topology structure in real time based on the dynamic correlation matrix to generate a reconstructed topology network with a random perturbation factor; screen the node paths of the reconstructed topology network through a preset anti-collision algorithm, and retain the paths that meet the preset anti-collision conditions as anti-collision verification paths, wherein the anti-collision conditions include that the path conflict probability is less than a first preset threshold and the number of path jumps is greater than a second preset threshold.

[0113] In some embodiments, the verification channel generation unit 203 is also used to obtain path parameters of the anti-collision verification path, where the path parameters include path length, node distribution density and hopping frequency; generate a quantum noise signal that matches the path parameters based on a quantum random number generator, wherein the spectrum width of the quantum noise signal is positively correlated with the hopping frequency; convolute the quantum noise signal with the topological parameters of the anti-collision verification path through a chaotic modulation algorithm to generate a phase perturbation factor; inject the phase perturbation factor into the physical layer and logical layer boundary of the anti-collision verification path to form a cross-layer verification channel, wherein the verification data packet of the cross-layer verification channel carries a noise fingerprint at the physical layer and embeds a dynamic hash tag at the logical layer.

[0114] In some embodiments, the topology tag generation unit 204 is also used to construct an asymmetric hash chain structure based on the dynamic hash tag and the phase perturbation factor, wherein the public key of the asymmetric hash chain structure is generated by the noise fingerprint of the physical layer of the cross-layer verification channel, and the private key of the asymmetric hash chain structure is generated by the expiration timestamp of the dynamic hash tag of the logical layer; cross-verification is performed on the node attributes of the target data index to generate an initial verification result; the initial verification result is iteratively signed using the asymmetric hash chain structure to obtain a multi-level verification signature; the multi-level verification signature is compared with a preset anti-interference threshold, and if the threshold condition is met, the topological distribution characteristics of the multi-level verification signature are extracted; fuzzy encoding is performed on the topological distribution characteristics based on the random mask factor in the quantum noise signal to generate a fuzzy topology tag, wherein the generation process of the fuzzy topology tag is dynamically associated with the dimensional parameters of the dynamic index topology structure.

[0115] In some embodiments, the data index generation unit 205 is further used to parse the quantum noise spectrum characteristics and topological distribution parameters in the fuzzy topological label to generate anti-parsing mapping rules; dynamically offset the storage location and access permission label of the original index node based on the anti-parsing mapping rules to generate an implicit mapping relationship; perform nonlinear obfuscation on the implicit mapping relationship according to the dimension parameters of the dynamic index topological structure and the current system timestamp to generate a randomized index mapping table; inject a dynamic obfuscation factor into the randomized index mapping table to generate an anti-parsing security data index, wherein the generation frequency of the dynamic obfuscation factor is synchronized with the jump frequency of the quantum noise signal.

[0116] In some embodiments, the data access unit 206 is also used to generate a virtual decoy index set based on the distribution characteristics of the fuzzy topological markers when abnormal parsing behavior for the security data index is detected, and overwrite the virtual decoy index set to the original index node; dynamically adjust the topological parameters of the virtual decoy index set based on the hopping frequency of the quantum noise signal to generate an obfuscated index network with attack tracing function; when the virtual node in the obfuscated index network is activated and accessed, extract the attacker's behavioral fingerprint characteristics and trigger the data self-destruction instruction of the distributed storage node.

[0117] The present application also provides a computer-readable storage medium, which stores computer-executable instructions or computer programs. When the computer-executable instructions or computer programs are executed by a processor, the processor will be caused to execute any step of the data index security access method provided in the present application.

[0118] In some embodiments, the computer-readable storage medium may be a random access memory (RAM), a read-only memory (ROM), a flash memory, a magnetic surface storage, an optical disc, or a compact disc read-only memory (CD-ROM); or it may be various devices including one or any combination of the above memories.

[0119] In some embodiments, computer-executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0120] In some embodiments, computer-executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, for example, in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinating files (e.g., files storing one or more modules, subroutines, or code portions).

[0121] In some embodiments, computer-executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one site, or on multiple electronic devices distributed across multiple sites and interconnected by a communication network.

[0122] like Figure 3 As shown, the present application also provides an electronic device 30, including a memory 310, a processor 320 and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, any step of the above-mentioned data index security access method is implemented.

[0123] The present application also provides a computer program product, which includes a computer program or computer-executable instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer program or computer-executable instructions from the computer-readable storage medium and executes the computer program or computer-executable instructions, causing the electronic device to perform any step of the data index secure access method described above in the present application.

[0124] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.< / payload>

Claims

1. A data index secure access method, characterized in that: include: In response to the data access instruction, generating a dynamic index topology structure that matches the current network delay jitter value based on the dynamic behavior fingerprint of the requesting terminal; Based on the spatiotemporal distribution characteristics of the dynamic behavior fingerprint, the dynamic index topology structure is reconstructed in real time to generate an anti-collision verification path; Injecting a quantum noise signal into the anti-collision verification path to form a cross-layer verification channel; Performing asymmetric hash chain verification on the target data index through the cross-layer verification channel to generate a verification result including a fuzzy topology tag; Dynamically remap the original index nodes according to the fuzzy topological labels, and output a security data index that is resistant to parsing; Data access is performed based on the secure data index.

2. The data index secure access method according to claim 1, characterized in that: The generating of a dynamic index topology structure matching the current network delay jitter value based on the dynamic behavior fingerprint of the requesting terminal includes: Obtaining touch trajectory characteristics and network fluctuation characteristics of the requesting terminal within a preset time window; Performing nonlinear fusion of the touch trajectory characteristics and the network fluctuation characteristics through a convolutional chaos algorithm to generate a topology generation factor; The dynamic index topology structure is constructed in a complex domain space according to the topology generation factor, wherein a dimension parameter of the dynamic index topology structure is dynamically adjusted by the current network delay jitter value.

3. The data index secure access method according to claim 2, characterized in that: The step of performing real-time reconstruction on the dynamic index topology structure based on the spatiotemporal distribution characteristics of the dynamic behavior fingerprint to generate an anti-collision verification path includes: Decomposing the spatiotemporal distribution characteristics into sequence fluctuation characteristics in the time dimension and trajectory distribution characteristics in the space dimension; Calculating a dynamic correlation matrix based on the entropy value of the sequence fluctuation feature and the variance of the trajectory distribution feature; Adjusting the connection weights of the dynamic index topology structure in real time based on the dynamic correlation matrix to generate a reconstructed topology network with a random perturbation factor; The node paths of the reconstructed topology network are screened by a preset anti-collision algorithm, and the paths that meet the preset anti-collision conditions are retained as the anti-collision verification paths, wherein the anti-collision conditions include that the path collision probability is less than a first preset threshold and the number of path hops is greater than a second preset threshold.

4. The data index secure access method according to claim 1, characterized in that: The step of injecting a quantum noise signal into the anti-collision verification path to form a cross-layer verification channel includes: Acquire path parameters of the anti-collision verification path, wherein the path parameters include path length, node distribution density, and hopping frequency; generating a quantum noise signal matching the path parameter based on a quantum random number generator, wherein a spectrum width of the quantum noise signal is positively correlated with the hopping frequency; Convolution processing is performed on the quantum noise signal and the topological parameters of the anti-collision verification path through a chaotic modulation algorithm to generate a phase perturbation factor; The phase perturbation factor is injected into the boundary between the physical layer and the logical layer of the anti-collision verification path to form a cross-layer verification channel, wherein the verification data packet of the cross-layer verification channel carries a noise fingerprint at the physical layer and embeds a dynamic hash tag at the logical layer.

5. The data index secure access method according to claim 4, characterized in that: The performing of asymmetric hash chain verification on the target data index through the cross-layer verification channel to generate a verification result including a fuzzy topology tag includes: Based on the dynamic hash mark and the phase perturbation factor, an asymmetric hash chain structure is constructed, wherein a public key of the asymmetric hash chain structure is generated by the noise fingerprint of the physical layer of the cross-layer verification channel, and a private key of the asymmetric hash chain structure is generated by an expiration timestamp of the dynamic hash mark of the logical layer; Cross-validate the node attributes of the target data index to generate an initial validation result; Performing iterative signing on the initial verification result using the asymmetric hash chain structure to obtain a multi-level verification signature; Comparing the multi-level verification signature with a preset anti-interference threshold, and if the threshold condition is met, extracting the topological distribution features of the multi-level verification signature; Fuzzy encoding is performed on the topological distribution feature based on a random masking factor in a quantum noise signal to generate the fuzzy topological label, wherein the generation process of the fuzzy topological label is dynamically associated with the dimension parameter of the dynamic index topological structure.

6. The data index secure access method according to claim 1, characterized in that: The dynamically remapping the original index node according to the fuzzy topology label and outputting the anti-parsing security data index includes: Analyzing the quantum noise spectrum characteristics and topological distribution parameters in the fuzzy topological mark to generate anti-analysis mapping rules; Dynamically offsetting the storage location and access permission label of the original index node based on the anti-parsing mapping rule to generate an implicit mapping relationship; Performing nonlinear obfuscation on the implicit mapping relationship according to the dimension parameters of the dynamic index topology structure and the current system timestamp to generate a randomized index mapping table; A dynamic obfuscation factor is injected into the randomized index mapping table to generate the anti-parsing security data index, wherein the generation frequency of the dynamic obfuscation factor is synchronously updated with the hopping frequency of the quantum noise signal.

7. The data index secure access method according to claim 1, characterized in that: The data index secure access method further includes: When an abnormal parsing behavior for the security data index is detected, a virtual decoy index set is generated according to the distribution characteristics of the fuzzy topological mark, and the virtual decoy index set is overwritten to the original index node; Dynamically adjusting the topological parameters of the virtual decoy index set based on the hopping frequency of the quantum noise signal to generate an obfuscated index network with attack tracing function; When a virtual node in the obfuscated index network is activated and accessed, the attacker's behavioral fingerprint characteristics are extracted and a data self-destruction instruction of the distributed storage node is triggered.

8. A data index security access device, characterized in that: include: a topology structure generating unit, configured to generate, in response to a data access instruction, a dynamic index topology structure matching a current network delay jitter value based on a dynamic behavior fingerprint of a requesting terminal; A verification path generation unit, configured to perform real-time reconstruction of the dynamic index topology structure based on the spatiotemporal distribution characteristics of the dynamic behavior fingerprint to generate a collision-resistant verification path; A verification channel generation unit, configured to inject a quantum noise signal into the anti-collision verification path to form a cross-layer verification channel; A topology label generation unit, configured to perform asymmetric hash chain verification on a target data index through the cross-layer verification channel, and generate a verification result including a fuzzy topology label; A data index generating unit, configured to dynamically remap the original index nodes according to the fuzzy topological labels and output a security data index that is resistant to parsing; A data access unit is used to access data based on the security data index.

9. An electronic device comprising: A memory and a processor, wherein the processor is configured to implement the steps of the data index secure access method according to any one of claims 1 to 7 when executing a computer program stored in the memory.

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 data index secure access method according to any one of claims 1 to 7 are implemented.

Images