Internet security service system

Abstract

The invention relates to the technical field of security devices, and particularly provides an internet security service system, which comprises a dynamic behavior entropy flow analysis system for outputting a behavior entropy change sequence, and the behavior entropy change sequence forms an entropy flow characteristic spectrum through a nonlinear fluctuation filter; the hypergraph neural decision forest system is used for inputting the entropy flow characteristic spectrum into the neural architecture searcher and outputting a dynamic hypergraph structure; performing relation convolution kernel diffusion on the dynamic hypergraph structure to generate a component dependency matrix; the component dependency matrix forms a strategy decision tree set through a random differential forest trainer; and the quantum annealing strategy evolution field system is used for processing the strategy decision tree set through a Hamiltonian encoder to obtain strategy energy state distribution, inputting the strategy energy state distribution into the quantum tunneling simulator, outputting an optimal strategy declustering, and forming a final security strategy cloud through a chaos attractor verification mechanism by the optimal strategy declustering. According to the method, the existing technical paths such as a traditional firewall and behavior analysis are completely avoided.

Description

Technical Field

[0001] This invention relates to the field of security device technology, and in particular to an Internet security service system. Background Technology

[0002] Amid the wave of digital transformation, global internet service traffic is growing at an average annual rate of 28% (IDC 2023 data). Traditional security protection systems are facing three core challenges: First, the attack surface is expanding exponentially, with the widespread adoption of IoT devices making every smart terminal a potential entry point; second, attack methods are evolving intelligently, with phishing attacks based on generative AI becoming 400% more difficult to detect; and third, compliance requirements are continuously being upgraded, with regulations such as GDPR increasing the cost of data breaches by 17 times compared to five years ago.

[0003] Prior art 1, Chinese Patent Application No. 202311477357.4, discloses an end-to-end security service provision system utilizing a PSU (Portable Security Unit) based on a smart home network, comprising: a PSU (Portable Security Unit) connected to the home network; a user terminal that connects to the PSU via a QR code, uploads PSU information and user information, and connects to the Internet according to a security policy pre-stored in the PSU; and a security service provision server, comprising a registration unit, a secure connection unit, and a threat blocking unit. The registration unit is configured to register the user terminal, PSU, user information, and PSU information after the user terminal connects via a QR code and uploads PSU information and user information. The secure connection unit is configured to connect via the PSU according to a pre-stored security policy when the user terminal attempts to connect to the Internet. The threat blocking unit is configured to block access from uncertified threat terminals via the PSU. While initial authentication via QR codes, OTP, and Bluetooth pairing fundamentally blocks hackers' access solely through internet access, and security policies and operational systems are installed on the PSU (Power Supply Unit), allowing for end-to-end security solutions through PSU configuration and user terminal authentication and registration without requiring communication between servers provided by security services, the reliance on hardware (PSU) deployment limits its applicability to home or fixed scenarios, lacking universality and failing to adapt to large-scale, dynamically changing internet environments. The static and fixed security policies, relying solely on pre-stored security rules, cannot dynamically adapt to new attack patterns such as zero-day vulnerabilities and APT attacks. The single authentication method of QR codes and Bluetooth is ineffective against man-in-the-middle attacks and advanced deception attacks involving forged authentication. Threat detection is based solely on endpoint access control, lacking granular behavioral analysis capabilities and unable to identify abnormal behavior from legitimate internal users.

[0004] Prior art two, Chinese patent application number 201310738033.1, discloses a mobile internet security service system, including: a security operation management device, comprising a key management platform, a device control platform, and a Trojan horse detection and removal platform; a security application service device, comprising an SMS encryption server, a voice encryption server, an email encryption server, and a mobile office server, providing security services for encrypted SMS, encrypted voice, encrypted email, and mobile office; a virtual secure communication device, comprising a security gateway and a policy management server, providing virtual secure channels and security protection functions; and a secure electronic market server, storing a collection of secure applications reviewed and released by the security operation management device for user terminals to download application software. While it can provide mobile device management, secure and confidential communication, and malicious code detection and removal functions, offering users anti-theft, anti-leakage, and communication protection services, ensuring secure and reliable communication, and improving mobile internet monitoring and management capabilities, it is only applicable to mobile communication encryption (SMS, voice, email) and cannot cover the full-traffic security analysis of the modern internet (such as API attacks and AI-driven automated attacks). Relying on a centralized security management server poses a single point of failure risk and lacks decentralized, distributed security decision-making capabilities. Threat detection is based on a static rule base, which cannot adapt to variants of malicious code escaping. It does not consider behavioral correlation analysis in complex network environments and cannot detect advanced persistent threats (APTs) or covert channel attacks.

[0005] Prior art three, Chinese patent application number 201010566152.X, discloses a software authentication data card, a software authentication system, and a software authentication method. The data card includes a network-locked security module disposed between a GSM module and the data card interface. The network-locked security module is also used to receive and store key information of at least one protected software and software authentication parameters required to execute the key information, and simultaneously authenticates the authentication parameters and the IMSI information provided by the general-purpose integrated chip card. The software authentication system includes the aforementioned software authentication data card and a security server used to register the software based on the software authentication parameters and key information of the protected software and the IMSI information of the general-purpose integrated chip card. The host equipped with the protected software communicates with the security server via the Internet using the software authentication data card. Although it can effectively protect software, prevent the unauthorized spread and use of software, and expand the functionality of the data card, saving external interfaces of computers and other devices, it is only applicable to software authorization and authentication, not global network security protection, and cannot cope with network layer or application layer intrusions. It relies on specific hardware (data card), lacks the flexibility of software definition, and is difficult to adapt to modern architectures such as cloud-native and edge computing. The authentication mechanism is simplistic and cannot adapt to dynamic risk assessments based on multiple factors, such as behavioral patterns, device fingerprints, and abnormal traffic. It also lacks the ability to dynamically adjust security policies and adaptive optimization mechanisms, limiting its protective capabilities against new types of attacks.

[0006] Current technologies 1, 2, and 3 suffer from limitations in static defense, weak behavioral analysis capabilities, lack of adaptability in strategy optimization, and insufficient multi-source data fusion. Therefore, this invention provides an Internet security service system. Summary of the Invention

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] In one aspect, the present invention provides an Internet security service system, comprising:

[0009] The quantum annealing strategy evolution field system is used to process the policy decision tree set through the Hamiltonian encoder to obtain the policy energy state distribution. The policy energy state distribution is input to the quantum tunneling simulator and outputs the optimal policy solution cluster. The optimal policy solution cluster is verified by the chaotic attractor mechanism to form the final safe policy cloud.

[0010] In one optional implementation, the quantum annealing strategy evolution field system includes:

[0011] The quantum tunneling simulation subsystem is used to input the strategy energy state distribution into the multidimensional barrier to activate the generation of quantum coherent path clusters. The quantum coherent path clusters are modulated by phase interference and the dominant path is selected through constructive-destructive interaction.

[0012] The decoherence processing subsystem is used to apply steady-state probability amplitude condensation to the modulated quantum coherent path clusters, forming localized wave packets in the neighborhood of the energy saddle point. The localized wave packets are then subjected to decoherence suppression processing to eliminate high-dimensional quantum fluctuation noise. The decoherence suppression processing result outputs the optimal strategy solution cluster.

[0013] The chaotic attractor verification subsystem is used to input the optimal policy solution cluster into the phase space orbital evolver to generate aperiodic dynamic trajectory clusters. The aperiodic dynamic trajectory clusters are detected by transverse homocentric point detection to identify orbital stretching and folding features. The detection of orbital stretching and folding features is carried out by differential homeomorphism invariance mapping to construct a structurally stable manifold. The structurally stable manifold is subjected to Sinai-Ruelle measure analysis to verify absolute continuous ergodicity. The verification results form the final safety policy cloud.

[0014] In one optional implementation, the decoherence processing subsystem includes:

[0015] The curvature constraint field action component is used to apply the curvature constraint field to the neighborhood of the saddle point of the dominant path input energy. The dominant path is parallel-moved through the Riemann connection to generate a probability amplitude density manifold with directional curvature.

[0016] The geodesic focusing and condensing component is used to generate curvature-induced singularities in the neighborhood of energy saddle points by acting on the probability amplitude density manifold through affine parameterized geodesics; the curvature-induced singularities are constrained by gauge invariance to form localized wave packets that satisfy the minimum action condition;

[0017] The fiber bundle projection purification component is used to localize the wave packet input phase space fiber bundle structure. It separates noise components through Hodge decomposition projection. The separation process applies the topological charge conservation law to maintain the intrinsic characteristics of quantum coherence. The projection purification result outputs the optimal strategy solution cluster.

[0018] In one optional implementation, the fiber bundle projection purification component includes:

[0019] A fiber bundle structure construction sub-component is used to construct the phase space fiber bundle induced by the localization of wave packet input curvature. The localized wave packet is analyzed by Chern-Wey homology class to generate a phase space principal bundle with nontrivial connections.

[0020] The Hodge orthogonal projection separation sub-component is used for phase space principal bundle processing via harmonic form projector to separate environmental noise and appropriate components. The separation process applies the Laplace-Beltramian operator to constrain the elliptic regularity of the projection direction, and the separation result generates a pure quantum coherent field.

[0021] The topological charge conservation constraint sub-component is used to extract global topological invariants from a pure quantum coherent field. The extraction process applies the principle of gauge invariance, and the detection results output the optimal strategy solution cluster.

[0022] In one optional implementation, the Hodge orthogonal projection separation sub-component includes:

[0023] The projection space constraint construction module is used to generate a projection function space with elliptic regularity based on the curvature constraint form of the principal bundle in phase space. The Sobolev norm of the projection function space is determined by the higher-order derivative of the curvature of the principal bundle section.

[0024] The steady-state wave basis screening module is used to process the curvature weighted operator input to the projection function space to excite characteristic waves that satisfy the dispersion condition. The characteristic waves are constrained by the hyperbolic conservation law to screen the steady-state wave basis. The smoothness order of the steady-state wave basis is guaranteed by the elliptic regularity of the projection function space.

[0025] The orthogonal separation and purification module is used to apply curvature orthogonal decomposition to a steady-state wave basis, separating the appropriate component of environmental noise. The separation process follows conformal invariance constraints and maintains the topological structure of the quantum coherent field. The decomposition result generates a pure quantum coherent field, and the Chern eigenclass of the pure quantum coherent field is equal to the index invariant of the steady-state wave basis.

[0026] In one optional implementation, the steady-state fluctuation basis screening module includes:

[0027] The curvature weight loading submodule is used to load curvature weights into the projection function space. The projection function space is transformed by Gaussian curvature scaling to generate anisotropic differential operators.

[0028] The characteristic wave excitation submodule is used to apply conformal invariant boundary conditions to anisotropic differential operators to excite wave modes that satisfy the dispersion constraint. The wave modes are then subjected to curvature-induced phase modulation to form characteristic frequency standing wave clusters.

[0029] The hyperbolic conservation screening submodule is used to input characteristic frequency standing wave clusters into the characteristic velocity analyzer, separate the components that satisfy the hyperbolic conservation law, apply entropy constraints during the separation process to filter out non-physical oscillation modes, and generate a steady-state wave basis from the screening results; the smoothness parameter of the steady-state wave basis corresponds to the integrability order of the Sobolev norm.

[0030] In one optional implementation, the hyperbolic conservation screening submodule includes:

[0031] The characteristic velocity field construction unit is used to generate a characteristic velocity distribution field from a characteristic frequency standing wave cluster through curvature gradient analysis. The propagation direction of the characteristic velocity distribution field is determined by the wavefront curvature tensor of the standing wave cluster.

[0032] The entropy flux constraint separation unit is used to input the entropy functional generator of the characteristic velocity distribution field to construct a strongly convex entropy flux function. The strongly convex entropy flux function is processed by Legendre transformation to separate the components that satisfy the conservation law. The separation process applies the curvature strong convexity constraint to filter out non-physical oscillation modes. The separation result forms the conservation law component, and the integrability of the conservation law component is guaranteed by the Lipschitz constant of the characteristic velocity field.

[0033] The steady-state basis generation unit is used to generate a smooth steady-state basis by regularizing the projection mapping of the conservation law components. The projection mapping obeys the energy decay constraint and maintains the essential characteristics of physical oscillation. The smoothness order of the steady-state basis corresponds to the Sobolev embedding index of the projection function space.

[0034] In one optional implementation, the entropy flux constraint separation unit includes:

[0035] A strongly convex entropy functional is constructed as a subunit to generate an entropy density functional that satisfies strong convexity by constraining the characteristic velocity distribution field to a lower curvature. The positive definiteness of the Hess matrix of the entropy density functional is guaranteed by the minimum eigenvalue of the wavefront curvature tensor.

[0036] The dual variable transformation subunit is used for dual mapping of the input tangent space of the entropy density functional, constructing the conservation law dual variable. The dual variable is constrained by the symplectic structure to form a regular conjugate field. The non-degenerate condition of the regular conjugate field inherits the strongly convex parameters of the entropy density functional.

[0037] Curvature-constrained separation sub-units are used to separate physical components that satisfy the conservation law. The separation process implements strong curvature constraints to filter out non-physical oscillation modes. The constraint separation result forms the conservation law component. The integrability boundary of the conservation law component is determined by the Lipschitz continuity of the characteristic velocity field.

[0038] In one optional implementation, a dynamic behavior entropy flow parsing system is further included, which is used to generate multi-dimensional behavior trajectories by heterogeneous tensor fusion processing of the original network metadata, and outputs behavior entropy change sequences by Lyapunov exponential reconstruction algorithm. The behavior entropy change sequences are then processed by nonlinear fluctuation filter to form an entropy flow feature map.

[0039] In one optional implementation, a hypergraph neural decision forest system is further included, which is used to input entropy flow feature maps into a neural architecture searcher and output a dynamic hypergraph structure; the dynamic hypergraph structure is diffused by relational convolution kernels to generate a component dependency matrix; the component dependency matrix is ​​trained by a random differential forest to form a set of policy decision trees.

[0040] This invention utilizes a dynamic behavioral entropy flow analysis system to extract time-varying features of network behavior, transforming discrete raw metadata into a continuous entropy change process. This addresses the blind spot in traditional methods for detecting time-series correlated threats. The entropy flow feature map, formed by filtering multi-dimensional behavioral trajectories through nonlinear fluctuations, can capture deep behavioral patterns that are difficult to describe using traditional feature engineering. The hypergraph neural decision forest system constructs an adaptively adjustable decision topology, enabling flexible combinations of security strategies through a dynamic hypergraph structure. The synergistic effect of component dependency matrices and random differential forests allows the system to handle both static rule matching and adapt to the decision paradigm shift of new threats. The quantum annealing strategy evolution field system compresses the traditional strategy search space to the energy state distribution dimension, avoiding local optimum traps through quantum tunneling. The chaotic attractor verification mechanism ensures that the output strategy meets immediate protection requirements while maintaining adaptability to subsequent attack evolution. Attached Figure Description

[0041] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0042] Figure 1 This is a block diagram of the Internet security service system provided in Embodiment 1 of the present invention;

[0043] Figure 2 This is a block diagram of the dynamic behavior entropy flow parsing system provided in Embodiment 2 of the present invention;

[0044] Figure 3 This is a block diagram of the hypergraph neural decision forest system provided in Embodiment 4 of the present invention;

[0045] Figure 4 This is a block diagram of the quantum annealing strategy evolution field system provided in Embodiment 5 of the present invention;

[0046] Figure 5 A block diagram of the electronic device provided by the present invention;

[0047] Figure 6 A block diagram of a computer-readable storage medium provided for this invention. Detailed Implementation

[0048] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0049] Hereinafter, the terms "first," "second," etc., are used for descriptive convenience only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0050] In this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, "connection" can be a fixed mechanical connection, a detachable mechanical connection, or an integral part; or, "connection" can be a direct connection or an indirect connection through an intermediate medium. Furthermore, unless otherwise explicitly specified and limited, the term "coupling" should be interpreted broadly. For example, "coupling" can be a direct electrical connection, such as physical contact and electrical conduction between two components; it can also be understood as an electrical connection between different components in a circuit structure through physical lines capable of transmitting electrical signals, such as copper foil or wires on a printed circuit board (PCB), to transmit electrical signals; or, "coupling" can be an indirect electrical connection between two components through an intermediate medium; or, "coupling" can be an electrical connection between two components in a non-contact manner, such as an electrical connection between two components using capacitive coupling to transmit electrical signals.

[0051] In this embodiment of the invention, directional terms such as "up," "down," "left," and "right" may be defined relative to the orientation of the components shown in the accompanying drawings. It should be understood that these directional terms can be relative concepts, used for relative description and clarification, and can change accordingly depending on the orientation of the components in the accompanying drawings.

[0052] Example 1:

[0053] like Figure 1 As shown, this embodiment of the invention provides an Internet security service system, comprising:

[0054] The dynamic behavior entropy flow analysis system is used to generate multi-dimensional behavior trajectories by heterogeneous tensor fusion processing of raw network metadata. The multi-dimensional behavior trajectories are then reconstructed by the Lyapunov exponential algorithm to output behavior entropy change sequences. These sequences are then filtered by a nonlinear fluctuation filter to form an entropy flow feature map.

[0055] The Hypergraph Neural Decision Forest system uses entropy flow feature maps as input to a neural architecture searcher to output a dynamic hypergraph structure. The dynamic hypergraph structure is diffused by relational convolution kernels to generate a component dependency matrix. The component dependency matrix is ​​then trained by a random differential forest to form a set of policy decision trees.

[0056] The quantum annealing strategy evolution field system is used to process the policy decision tree set through the Hamiltonian encoder to obtain the policy energy state distribution. The policy energy state distribution is input to the quantum tunneling simulator and outputs the optimal policy solution cluster. The optimal policy solution cluster is verified by the chaotic attractor mechanism to form the final safe policy cloud.

[0057] In the above embodiments, this embodiment uses a dynamic behavioral entropy flow analysis system to extract time-varying features of network behavior, transforming discrete raw metadata into a continuous entropy change process, thus solving the problem of blind spots in the detection of time-series correlated threats in traditional methods. The entropy flow feature map formed by multi-dimensional behavioral trajectories after nonlinear fluctuation filtering can capture deep behavioral patterns that are difficult to describe by traditional feature engineering. The hypergraph neural decision forest system constructs an adaptively adjustable decision topology, realizing the flexible combination of security strategies through a dynamic hypergraph structure. The synergistic effect of component dependency matrix and random differential forest enables the system to handle both static rule matching and adapt to the decision paradigm shift of new threats. The quantum annealing strategy evolution field system compresses the traditional strategy search space to the energy state distribution dimension, avoiding local optimal solution traps through quantum tunneling effect. The chaotic attractor verification mechanism ensures that the output strategy meets both immediate protection requirements and maintains adaptability to subsequent attack evolution.

[0058] In summary, this embodiment transforms internet security protection from a passive response approach to a proactive defense system with predictive, adaptive, and continuously evolving capabilities. The collaborative work of the system's modules achieves a dynamic balance between determinism and probabilism, and between local refinement and global optimization, in the security decision-making process.

[0059] This embodiment originates from the phase transition theory of statistical mechanics. It replaces traditional behavioral analysis by calculating the trajectory divergence rate of interactive events; it replaces the message passing of traditional graph neural networks with high-dimensional relational convolution based on algebraic topology connectivity measurement; and it replaces the crossover and mutation operations of classical genetic algorithms with the tunneling probability of potential energy surfaces. It completely avoids existing technical paths such as traditional firewalls and behavioral analysis, and the three modules form a technical closed loop of "chaotic measurement, topological decision-making, and quantum optimization".

[0060] Example 2:

[0061] like Figure 2 As shown, based on Embodiment 1, the dynamic behavior entropy flow parsing system provided in this embodiment of the invention includes:

[0062] The heterogeneous tensor fusion processing subsystem is used to process the original network metadata through the affine connection covariant derivative operator to eliminate cross-protocol metric differences. The original network metadata with eliminated differences is embedded through Grassman manifold to generate behavioral tensors with directional curvature. The behavioral tensors are then fused using Clifford algebraic rules to form multi-dimensional behavioral trajectories.

[0063] The Lyapunov exponential reconstruction subsystem is used to process multi-dimensional behavioral trajectories through a tangent space decomposer to extract local dynamic system parameters. The local dynamic system parameters are iterated through a conjugate symplectic matrix to generate a characteristic exponential spectrum. The characteristic exponential spectrum is then reconstructed using a Kam theorem reconstructor to output a behavioral entropy change sequence.

[0064] The nonlinear wave filtering subsystem is used to input the behavior entropy change sequence into the soliton resonant cavity to excite the characteristic frequency standing wave. The characteristic frequency standing wave is processed by the variational wave packet filter to separate the singular point energy levels. The separated energy levels are mapped by the Riemann-Hilbert correspondence to form an entropy flow characteristic spectrum.

[0065] In the above embodiments, this embodiment establishes the comparability of different protocol data through cross-sectional connections of fiber bundles, solving the trajectory breakage problem caused by data heterogeneity in traditional methods; the Lyapunov exponential reconstruction algorithm introduces symplectic geometry to preserve the structure, enabling the behavioral entropy change sequence to accurately characterize the topological instability of network behavior; the nonlinear wave filter is constructed on the Lax pair representation of integrable systems, ensuring that the entropy flow feature map retains the chaotic nature while filtering out noise interference. This embodiment completely avoids conventional methods such as data standardization, Fourier transform, and matrix decomposition, and the three sub-steps form a technical process of geometric fusion, dynamic reconstruction, and wave filtering.

[0066] Example 3:

[0067] Based on Example 2, the Lyapunov index reconstruction subsystem provided in this embodiment of the invention includes:

[0068] The tangential space decomposer processing module is used to process multi-dimensional behavioral trajectories through the principal curvature projector, separate tangential and normal dynamic components, and obtain affine connection coefficients through the connection form extractor. The affine connection coefficients are then applied to the group classifier to output local dynamic system parameters.

[0069] The conjugate symplectic matrix iteration module is used to generate a symplectic manifold for the input parameters of the local dynamical system, construct a standard symplectic structure, generate regular transformation parameters through a Maslow exponent calibrator, and output the characteristic exponent spectrum through a Keller potential energy iteration framework.

[0070] The Kam theory reconstructor module is used to input the characteristic exponential spectrum into the resonant toroidal detector to identify non-resonant frequency components. The non-resonant frequency components are then separated into chaotic and ordered modes by the homo-entanglement decomposer. The separated modes are then subjected to toroidal immersion mapping to form a behavioral entropy change sequence.

[0071] In the above embodiments, this embodiment establishes the trajectory differential structure through the classification of the main bundle connections and the Leyce group, solving the problem of local dynamic modeling distortion caused by trajectory curvature changes in traditional methods; the conjugate symplectic matrix iteration introduces the potential energy field of the Keller manifold, enabling the characteristic exponent spectrum to accurately maintain the symplectic structure invariance of the high-dimensional dynamic system; the Kam theorem reconstructor, based on the topological barrier of toroidal immersion, ensures that the behavioral entropy-change sequence simultaneously quantifies the chaotic intensity and the ordered residual quantity. This embodiment completely avoids conventional numerical methods such as eigenvalue decomposition, Jacobi matrix, and QR iteration; the local dynamic system parameters output by the tangent space decomposer, whose Leyce group classification results directly determine the manifold structure of the conjugate symplectic matrix iteration; the potential energy distribution characteristics of the characteristic exponent spectrum generated by the conjugate symplectic matrix iteration strictly constrain the resonance detection threshold of the Kam theorem reconstructor; finally, the chaotic gradient value of the behavioral entropy-change sequence will serve as the input source of the soliton wave dispersion parameter of the nonlinear wave filter.

[0072] Example 4:

[0073] like Figure 3 As shown, based on Example 1, the Hypergraph Neural Decision Forest system provided in this embodiment of the invention includes:

[0074] The relational convolution kernel diffusion subsystem is used to process the dynamic hypergraph structure by the cohomology class extractor to separate the high-dimensional connected components. The high-dimensional connected components are integrated by the Delam's differential form to generate homotopy invariants. The homotopy invariants are applied to the Chern-Wey characteristic number rule to output the component dependency matrix.

[0075] The random differential forest training subsystem is used to construct the leaf node probability space by generating the component dependency matrix input traversal measure. The leaf node probability space is then processed by the Iton diffusion calibrator to establish a branching stochastic process. The branching stochastic process is then processed by the Sinai cylinder partitioning method to form a policy decision tree set.

[0076] In the above embodiments, this embodiment establishes hyperedge associations through the noncommutative integral of Drum cohomology, breaking through the dependence of traditional methods on equidimensional adjacency relationships; the branch stochastic process introduces the absolute continuity of ergodic measure, ensuring that the phase space volume remains invariant during the decision tree splitting process; the Sinai cylinder partitioning method, based on the entropy generation rate of the dynamic system, ensures that the policy decision tree set simultaneously captures topologically stable states and chaotic transient states. The dynamic hypergraph structure of this embodiment is processed by relational convolution kernel diffusion to generate a component dependency matrix with algebraic topological invariant characteristics; the component dependency matrix is ​​processed by a stochastic differential forest trainer to form a policy decision tree set based on ergodic measure theory.

[0077] Example 5:

[0078] like Figure 4 As shown, based on Example 1, the quantum annealing strategy evolution field system provided in this embodiment of the invention includes:

[0079] The quantum tunneling simulation subsystem is used to input the strategy energy state distribution into the multidimensional barrier to activate the generation of quantum coherent path clusters. The quantum coherent path clusters are modulated by phase interference and the dominant path is selected through constructive-destructive interaction.

[0080] The decoherence processing subsystem is used to apply steady-state probability amplitude condensation to the modulated quantum coherent path clusters, forming localized wave packets in the neighborhood of the energy saddle point. The localized wave packets are then subjected to decoherence suppression processing to eliminate high-dimensional quantum fluctuation noise. The decoherence suppression processing result outputs the optimal strategy solution cluster.

[0081] The chaotic attractor verification subsystem is used to input the optimal policy solution cluster into the phase space orbital evolver to generate aperiodic dynamic trajectory clusters. The aperiodic dynamic trajectory clusters are detected by transverse homocentric point detection to identify orbital stretching and folding features. The detection of orbital stretching and folding features is carried out by differential homeomorphism invariance mapping to construct a structurally stable manifold. The structurally stable manifold is subjected to Sinai-Ruelle measure analysis to verify absolute continuous ergodicity. The verification results form the final safety policy cloud.

[0082] In the above embodiments, this embodiment achieves global optimization and reliability verification of complex policy spaces through a multi-stage quantum-classical hybrid computing architecture. First, it utilizes the quantum tunneling effect to overcome the local extremum limitations of traditional optimization algorithms: exploring the full-space distribution of policy energy states through a multi-dimensional barrier penetration mechanism, and filtering out potential advantageous policy directions using phase modulation of quantum coherent paths, essentially constructing a quantum computing framework for parallel exploration of high-dimensional policy spaces. The steady-state probability amplitude condensation module transforms quantum advantages into classically tractable optimization solutions. By forming localized wave packets in the neighborhood of energy saddle points, it retains the non-classical optimization characteristics brought by quantum coherence while achieving robust output under quantum noise conditions through decoherence suppression. The quantum-classical interface design effectively avoids the decoherence interference problem in pure quantum algorithms. The chaotic attractor verification subsystem provides dynamic reliability assurance for the optimization results: revealing the intrinsic dynamic characteristics of policy solutions through phase space orbit analysis, ensuring the robustness of the policy under parameter perturbations through structural stability manifold verification, and finally guaranteeing the global adaptability of the policy through ergodic verification. The closed-loop architecture formed by these three modules possesses both the parallel search advantages of quantum computing and the verifiable characteristics of classical dynamical systems.

[0083] Example 6:

[0084] like Figure 6 As shown, based on Embodiment 5, the decoherence processing subsystem provided in this embodiment of the invention includes:

[0085] The curvature constraint field component is used to apply the curvature constraint field to the neighborhood of the energy saddle point of the dominant path input. The dominant path is parallel-moved through Riemann connections to generate a probability amplitude density manifold with directional curvature. The curvature distribution of the probability amplitude density manifold is determined by the phase gradient field of the dominant path.

[0086] The geodesic focusing and condensing component is used to generate curvature-induced singularities in the neighborhood of energy saddle points by acting on the probability amplitude density manifold through affine parameterized geodesics. The curvature-induced singularities are constrained by gauge invariance to form localized wave packets that satisfy the minimum action condition. The node distribution characteristics of the localized wave packets are determined by the curvature invariant of the probability amplitude density manifold.

[0087] The fiber bundle projection purification component is used to localize the wave packet input phase space fiber bundle structure. It separates the noise component through Hodge decomposition projection. The separation process applies the topological charge conservation law constraint to maintain the intrinsic characteristics of quantum coherence. The projection purification result outputs the optimal strategy solution cluster. The stability parameter of the optimal strategy solution cluster is related to the curvature singularity distribution of the localized wave packet.

[0088] In the above embodiments, this embodiment realizes the transformation process from quantum path to optimal strategy through a geometric method; the curvature constraint field action component establishes a geometric relationship between the quantum path and the energy landscape, transforming phase information into operable curvature distribution characteristics, providing a probabilistic amplitude manifold structure with directional characteristics for subsequent processing. The geodesic focusing and condensation component uses differential geometry tools to achieve quantum state compression, generating localized wave packets that meet the requirements of classical dynamics through a curvature-induced mechanism, achieving effective aggregation of solutions while maintaining quantum characteristics. The fiber bundle projection purification component separates noise and effective signals based on topological methods, ensuring that the output solution cluster retains the intrinsic coherence characteristics of the quantum system during dimensionality reduction, ultimately forming an optimal strategy set that combines stability and quantum memory characteristics.

[0089] In summary, this embodiment achieves a reliable conversion from a quantum coherent path to a classically usable strategy. Its core value lies in: preserving quantum advantage characteristics through geometric constraints, ensuring conversion reliability using topological methods, and ultimately outputting an optimized strategy solution that can be processed within a classical computing framework. The entire process strictly adheres to the conservation laws of quantum systems, maintaining the ordered transformation of key quantum information during decoherence.

[0090] Example 7:

[0091] Based on Example 6, the fiber bundle projection purification component provided in this embodiment of the invention includes:

[0092] The fiber bundle structure construction sub-component is used for the phase space fiber bundle construction process induced by the curvature of the localized wave packet input. The localized wave packet is subjected to Chern-Wey homology class analysis to generate a phase space principal bundle with nontrivial connections. The cross-sectional curvature of the phase space principal bundle is determined by the Gaussian curvature invariant of the localized wave packet.

[0093] The Hodge orthogonal projection separation sub-component is used to separate the ambient noise and appropriate components after the phase space principal bundle is processed by a harmonic form projector. The separation process applies the Laplace-Beltramian operator to constrain the elliptic regularity of the projection direction, and the separation result generates a pure quantum coherent field. The topological complexity of the pure quantum coherent field is quantized by the integer Betti number of the principal bundle.

[0094] The topological charge conservation constraint sub-component is used to extract global topological invariants from a pure quantum coherent field. The extraction process applies the gauge invariance principle to maintain the integrity of the quantum phase memory, and the detection results output the optimal strategy solution cluster.

[0095] In the above embodiments, this embodiment achieves a reliable conversion from quantum states to classical strategies through differential geometry and algebraic topology methods. The fiber bundle structure construction sub-component establishes the correspondence between localized wave packets and phase space fiber bundles, constructing a principal bundle structure with nontrivial connections using Chern-Wey theory. This maps the curvature characteristics of the quantum state to the topological properties of the fiber bundle, providing a geometric framework for subsequent processing. The Hodge orthogonal projection separation sub-component separates noise from effective information based on harmonic analysis. It uses the regularity of elliptic differential operators to constrain the projection direction, ensuring the output coherent field satisfies the minimum energy condition, while quantifying its structural complexity through topological invariants. The topological charge conservation constraint sub-component extracts the overall topological features of the coherent field, maintaining the gauge invariance of quantum phase information, and finally outputs an optimal strategy solution cluster that meets the requirements of dynamic stability.

[0096] In summary, this embodiment achieves effective noise reduction and feature extraction of quantum states on phase space fiber bundles. Its core significance lies in: ensuring the integrity of quantum information during the transformation process through geometric-topological dual constraints; utilizing the regularity of differential operators to separate noise; and finally outputting a strategy solution set with definite topological characteristics and stability. The entire process strictly adheres to the conservation laws of quantum systems, ensuring that the solution clusters after decoherence still retain the key characteristics of the original quantum path.

[0097] Example 8:

[0098] Based on Embodiment 7, the Hodge orthogonal projection separation sub-component provided in this embodiment of the invention includes:

[0099] The projection space constraint construction module is used to generate a projection function space with elliptic regularity based on the curvature constraint form of the principal bundle in phase space. The Sobolev norm of the projection function space is determined by the higher-order derivative of the curvature of the principal bundle section.

[0100] In this process, the curvature of the sectional curvature of the principal bundle in phase space is transformed into a function space topological framework through curvature constraints. This framework, through higher-order derivative constraints of the curvature tensor, forces the projected function space to satisfy the strong elliptic operator condition. The affine curvature of the principal bundle connections is processed by second-order covariant differentiation to generate the Sobolev norm defining the projected function space. The integrability boundary of this norm is quantized by the Gauss-Bonnet integral of the sectional curvature, ultimately forming a projected function space with elliptic regularity. Essentially, this process involves the curvature properties of the principal bundle being transformed by differential operators to achieve curvature-driven regularity transfer in the function space.

[0101] The steady-state wave basis screening module is used to process the curvature weighted operator input to the projection function space to excite characteristic waves that satisfy the dispersion condition. The characteristic waves are constrained by the hyperbolic conservation law to screen the steady-state wave basis. The smoothness order of the steady-state wave basis is guaranteed by the elliptic regularity of the projection function space.

[0102] The orthogonal separation and purification module is used to apply curvature orthogonal decomposition to a steady-state wave basis, separating the appropriate component of environmental noise. The separation process follows conformal invariance constraints and maintains the topological structure of the quantum coherent field. The decomposition result generates a pure quantum coherent field, and the Chern eigenclass of the pure quantum coherent field is equal to the index invariant of the steady-state wave basis.

[0103] In the above embodiments, this embodiment achieves noise cancellation and topological feature preservation of the quantum coherent field through geometric analysis and partial differential equation theory. The projection space constraint construction module constructs a projection function space with elliptic regularity based on the curvature characteristics of the principal bundle of phase space. The analyticity of this function space is determined by the higher-order differential structure of the principal bundle curvature, ensuring that subsequent operations are performed within a strict solvability framework. The steady-state wave basis screening module applies curvature weighting operations to the projection function space, extracts wave modes that satisfy zero divergence, and screens out dynamically stable bases through hyperbolic conservation laws. The smoothness of the base is guaranteed by the elliptic regularity of the projection space, providing a stable mathematical tool for noise separation. The orthogonal separation and purification module uses curvature orthogonal decomposition technology to separate the co-appropriate components of environmental noise, while adhering to conformal invariance to maintain the topological configuration of the quantum coherent field. The final output pure coherent field retains the index invariants of the steady-state wave basis, ensuring that its Chern characteristic class is consistent with the original quantum system.

[0104] In summary, this embodiment achieves efficient denoising of the quantum coherent field based on the synergistic effect of elliptic regularity and hyperbolic conservation law, while strictly maintaining the topological invariance of the field through curvature orthogonal decomposition and conformal constraints. It not only separates the non-physical modes of environmental noise but also ensures that the geometric and topological properties of the output coherent field correspond to the initial quantum system.

[0105] Example 9:

[0106] Based on Example 8, the steady-state fluctuation basis screening module provided in this embodiment of the invention includes:

[0107] The curvature weight loading submodule is used to load curvature weights into the projection function space. The projection function space is transformed by Gaussian curvature scaling to generate an anisotropic differential operator. The principal symbol matrix of the anisotropic differential operator is determined by the Riemann tensor components of the cross-sectional curvature.

[0108] The Gaussian curvature scaling transformation process of the projected function space is as follows: based on the Riemann tensor components of the principal bundle cross-sectional curvature, a conformal scaling is applied to the metric structure of the projected function space; this scaling is achieved by quantizing the curvature weights, which distorts the definition of the local inner product of the function space; the distorted inner product space is then subjected to second-order covariant differential action to generate an anisotropic differential operator; the principal symbol matrix of the operator is completely determined by the higher-order covariant derivatives of the cross-sectional curvature, and its antisymmetric components inherit the torsion characteristics of the Riemann tensor.

[0109] The characteristic wave excitation submodule is used to apply conformal invariant boundary conditions to anisotropic differential operators to excite wave modes that satisfy the dispersion constraint. The wave modes are subjected to curvature-induced phase modulation to form characteristic frequency standing wave clusters. The oscillation stability of the characteristic frequency standing wave clusters is guaranteed by the rank of the Hellenic group of the principal bundle connection.

[0110] The hyperbolic conservation screening submodule is used to input characteristic frequency standing wave clusters into the characteristic velocity analyzer, separate the components that satisfy the hyperbolic conservation law, apply entropy constraints during the separation process to filter out non-physical oscillation modes, and generate a steady-state wave basis from the screening results; the smoothness parameter of the steady-state wave basis corresponds to the integrability order of the Sobolev norm.

[0111] In the above embodiments, this embodiment achieves noise cancellation and topological feature preservation of the quantum coherent field through geometric analysis and partial differential equation theory. The projection space constraint construction module constructs a projection function space with elliptic regularity based on the curvature characteristics of the principal bundle of phase space. The analyticity of the function space is determined by the higher-order differential structure of the principal bundle curvature, ensuring that subsequent operations are performed within the solvability framework. The steady-state wave basis screening module applies curvature weighting operations to the projection function space, extracts wave modes that satisfy zero divergence, and screens out dynamically stable bases through hyperbolic conservation laws. The smoothness of the base is guaranteed by the elliptic regularity of the projection space, providing a stable tool for noise separation. The orthogonal separation and purification module uses curvature orthogonal decomposition technology to separate the co-appropriate components of environmental noise, while adhering to conformal invariance to maintain the topological configuration of the quantum coherent field. The final output pure coherent field retains the index invariants of the steady-state wave basis, ensuring that its Chern characteristic class is consistent with the original quantum system.

[0112] In summary, this embodiment achieves efficient denoising of the quantum coherent field based on the synergistic effect of elliptic regularity and hyperbolic conservation law, while strictly maintaining the topological invariance of the field through curvature orthogonal decomposition and conformal constraints. It not only separates the non-physical modes of environmental noise but also ensures that the geometric and topological properties of the output coherent field correspond to the initial quantum system.

[0113] Example 10:

[0114] Based on Example 9, the hyperbolic conservation screening submodule provided in this embodiment of the invention includes:

[0115] The characteristic velocity field construction unit is used to generate a characteristic velocity distribution field from a characteristic frequency standing wave cluster through curvature gradient analysis. The propagation direction of the characteristic velocity distribution field is determined by the wavefront curvature tensor of the standing wave cluster.

[0116] The entropy flux constraint separation unit is used to input the entropy functional generator of the characteristic velocity distribution field to construct a strongly convex entropy flux function. The strongly convex entropy flux function is processed by Legendre transformation to separate the components that satisfy the conservation law. The separation process applies the curvature strong convexity constraint to filter out non-physical oscillation modes. The separation result forms the conservation law component, and the integrability of the conservation law component is guaranteed by the Lipschitz constant of the characteristic velocity field.

[0117] The steady-state basis generation unit is used to generate a smooth steady-state basis by regularizing the projection mapping of the conservation law components. The projection mapping obeys the energy decay constraint and maintains the essential characteristics of physical oscillation. The smoothness order of the steady-state basis corresponds to the Sobolev embedding index of the projection function space.

[0118] In the above embodiments, the characteristic velocity field construction unit transforms wave characteristics into a spatial velocity distribution, whose propagation characteristics are precisely controlled by wavefront geometric properties; the entropy flux constraint separation unit extracts physical components that strictly satisfy conservation laws from complex dynamic behaviors through functional analysis and transformation techniques, while effectively suppressing non-physical solutions; the steady-state basis generation unit transforms the conserved components into a baseline state with clear mathematical smoothness, and its construction process maintains the essential dynamic characteristics of the system while ensuring the well-posedness of the solution.

[0119] In summary, this embodiment realizes a complete transformation path from the original wave characteristics to the gauge conservation form, providing a rigorous mathematical framework for modeling complex dynamic systems. The entire mechanism ensures the completeness of the physical process description and the reliability of the numerical implementation. Its core lies in the organic combination of multi-scale geometric analysis and functional processing, achieving a dual guarantee of physical conservation and mathematical well-posedness.

[0120] Example 11:

[0121] Based on Example 10, the entropy flux constraint separation unit provided in this embodiment of the invention includes:

[0122] A strongly convex entropy functional is constructed as a subunit to generate an entropy density functional that satisfies strong convexity by constraining the characteristic velocity distribution field to a lower curvature. The positive definiteness of the Hess matrix of the entropy density functional is guaranteed by the minimum eigenvalue of the wavefront curvature tensor.

[0123] The dual variable transformation subunit is used for dual mapping of the input tangent space of the entropy density functional, constructing the conservation law dual variable. The dual variable is constrained by the symplectic structure to form a regular conjugate field. The non-degenerate condition of the regular conjugate field inherits the strongly convex parameters of the entropy density functional.

[0124] Curvature-constrained separation sub-units are used to separate physical components that satisfy the conservation law. The separation process implements strong curvature constraints to filter out non-physical oscillation modes. The constraint separation result forms the conservation law component. The integrability boundary of the conservation law component is determined by the Lipschitz continuity of the characteristic velocity field.

[0125] In the above embodiments, the entropy flux constraint separation unit of this embodiment achieves precise screening from the original dynamic characteristics to the conserved physical components. The strongly convex entropy functional construction subunit ensures that the entropy density function has strict convexity, and its mathematical properties are directly constrained by the wave curvature characteristics. The dual variable transformation subunit transforms the physical quantities into dual space representations while maintaining the symplectic structure and regularity of the system. The curvature constraint separation subunit finally completes the extraction of conserved components, eliminating non-physical components through curvature constraints and ensuring the well-posedness of the separation results. The core of the entire mechanism lies in using convex analysis and differential geometry tools to establish a strict mapping relationship from observed characteristics to conservation laws while maintaining the intrinsic dynamic characteristics of the system.

[0126] Figure 5 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present invention is shown.

[0127] Electronic devices may include a central processing unit / microprocessor / main control chip, etc.; a storage medium coupled to the central processing unit / microprocessor / main control chip, etc., and storing computer-executable instructions therein for performing the steps of various methods of embodiments of the present invention when executed by a processor.

[0128] The central processing unit / microprocessor / main control chip, etc., may include, but are not limited to, one or more processors or microprocessors.

[0129] Storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (such as hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0130] In addition, the electronic device may include (but is not limited to) a data bus, an input / output bus / external bus / device bus, a display, and input / output devices (e.g., keyboard, mouse, speaker, etc.).

[0131] The central processing unit / microprocessor / main control chip, etc., can communicate with external devices via the I / O bus through a wired or wireless network (not shown).

[0132] The storage medium may also store at least one computer-executable instruction for performing the steps of various functions and / or methods in the embodiments described herein when run by a central processing unit / microprocessor / main control chip, etc.

[0133] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0134] Figure 6 A schematic diagram of a computer-readable storage medium according to an embodiment of the present invention is shown.

[0135] like Figure 6 As shown, instructions, such as computer-readable instructions, are stored on a non-transitory computer-readable storage medium. When the computer-readable instructions are executed by a processor, the various methods described above can be performed. The non-transitory computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-transitory non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, the non-transitory computer-readable storage medium can be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.

[0136] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0137] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0138] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0139] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods of the various embodiments of this invention through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0140] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An internet security service system, characterized in that, Include: The quantum annealing strategy evolution field system is used to process the policy decision tree set through the Hamiltonian encoder to obtain the policy energy state distribution. The policy energy state distribution is input to the quantum tunneling simulator and outputs the optimal policy solution cluster. The optimal policy solution cluster is verified by the chaotic attractor mechanism to form the final safe policy cloud.

2. The Internet security service system as described in claim 1, characterized in that, The quantum annealing strategy evolution field system includes: The quantum tunneling simulation subsystem is used to input the strategy energy state distribution into the multidimensional barrier to activate the generation of quantum coherent path clusters. The quantum coherent path clusters are modulated by phase interference and the dominant path is selected through constructive-destructive interaction. The decoherence processing subsystem is used to apply steady-state probability amplitude condensation to the modulated quantum coherent path clusters, forming localized wave packets in the neighborhood of the energy saddle point. The localized wave packets are then subjected to decoherence suppression processing to eliminate high-dimensional quantum fluctuation noise. The decoherence suppression processing result outputs the optimal strategy solution cluster. The chaotic attractor verification subsystem is used to input the optimal policy solution cluster into the phase space orbital evolver to generate aperiodic dynamic trajectory clusters. The aperiodic dynamic trajectory clusters are detected by transverse homocentric point detection to identify orbital stretching and folding features. The detection of orbital stretching and folding features is carried out by differential homeomorphism invariance mapping to construct a structurally stable manifold. The structurally stable manifold is subjected to Sinai-Ruelle measure analysis to verify absolute continuous ergodicity. The verification results form the final safety policy cloud.

3. The Internet security service system as described in claim 2, characterized in that, The decoherence processing subsystem includes: The curvature constraint field action component is used to apply the curvature constraint field to the neighborhood of the saddle point of the dominant path input energy. The dominant path is parallel-moved through the Riemann connection to generate a probability amplitude density manifold with directional curvature. Geodesic focusing and condensing components are used to generate curvature-induced singularities in the neighborhood of energy saddle points by acting on probability amplitude density manifolds through affine parameterized geodesics. Curvature-induced singularities are constrained by gauge invariance to form localized wave packets that satisfy the minimum action condition; The fiber bundle projection purification component is used to localize the wave packet input phase space fiber bundle structure. It separates noise components through Hodge decomposition projection. The separation process is constrained by the topological charge conservation law to maintain the intrinsic characteristics of quantum coherence. The projection purification results output the optimal strategy cluster solution.

4. The Internet security service system as described in claim 3, characterized in that, Fiber bundle projection purification component, comprising: A fiber bundle structure construction sub-component is used to construct the phase space fiber bundle induced by the localization of wave packet input curvature. The localized wave packet is analyzed by Chern-Wey homology class to generate a phase space principal bundle with nontrivial connections. The Hodge orthogonal projection separation sub-component is used for phase space principal bundle processing via harmonic form projector to separate environmental noise and appropriate components. The separation process applies the Laplace-Beltramian operator to constrain the elliptic regularity of the projection direction, and the separation result generates a pure quantum coherent field. The topological charge conservation constraint sub-component is used to extract global topological invariants from a pure quantum coherent field. The extraction process applies the principle of gauge invariance, and the detection results output the optimal strategy solution cluster.

5. The Internet security service system as described in claim 4, characterized in that, The Hodge orthogonal projection separation sub-component includes: The projection space constraint construction module is used to generate a projection function space with elliptic regularity based on the curvature constraint form of the principal bundle in phase space. The Sobolev norm of the projection function space is determined by the higher-order derivative of the curvature of the principal bundle section. The steady-state fluctuation basis screening module is used to process the curvature weighted operator input to the projection function space to excite characteristic fluctuations that satisfy the dispersion condition; feature The wave is constrained by the hyperbolic conservation law, and a steady-state wave basis is selected. The smoothness order of the steady-state wave basis is guaranteed by the elliptic regularity of the projected function space. The orthogonal separation and purification module is used to apply curvature orthogonal decomposition to a steady-state wave basis, separating the appropriate component of environmental noise. The separation process follows conformal invariance constraints and maintains the topological structure of the quantum coherent field. The decomposition result generates a pure quantum coherent field, and the Chern eigenclass of the pure quantum coherent field is equal to the index invariant of the steady-state wave basis.

6. The Internet security service system as described in claim 5, characterized in that, The steady-state fluctuation basis screening module includes: The curvature weight loading submodule is used to load curvature weights into the projection function space. The projection function space is transformed by Gaussian curvature scaling to generate anisotropic differential operators. The characteristic wave excitation submodule is used to apply conformal invariant boundary conditions to anisotropic differential operators to excite wave modes that satisfy the dispersion constraint. The wave modes are then subjected to curvature-induced phase modulation to form characteristic frequency standing wave clusters. The hyperbolic conservation screening submodule is used to input characteristic frequency standing wave clusters into the characteristic velocity analyzer, separate the components that satisfy the hyperbolic conservation law, apply entropy condition constraints during the separation process, filter out non-physical oscillation modes, and generate a steady-state wave basis from the screening results. The smoothness parameter of the steady-state oscillatory basis corresponds to the integrability order of the Sobolev norm.

7. The Internet security service system as described in claim 6, characterized in that, The hyperbolic conservation screening submodule includes: The characteristic velocity field construction unit is used to generate a characteristic velocity distribution field from a characteristic frequency standing wave cluster through curvature gradient analysis. The propagation direction of the characteristic velocity distribution field is determined by the wavefront curvature tensor of the standing wave cluster. The entropy flux constraint separation unit is used to input the entropy functional generator of the characteristic velocity distribution field to construct a strongly convex entropy flux function. The strongly convex entropy flux function is processed by Legendre transformation to separate the components that satisfy the conservation law. The separation process applies the curvature strong convexity constraint to filter out non-physical oscillation modes. The separation result forms the conservation law component, and the integrability of the conservation law component is guaranteed by the Lipschitz constant of the characteristic velocity field. Steady-state basis generation unit is used to generate a smooth steady-state basis by regularizing the projection mapping of the conservation law components. The projection mapping obeys the energy decay constraint and maintains the essential characteristics of physical oscillation. The smoothness order of the steady-state basis corresponds to the Sobolev embedding exponent in the projected function space.

8. The Internet security service system as described in claim 7, characterized in that, Entropy flux constraint separation unit, comprising: A strongly convex entropy functional is constructed as a subunit to generate an entropy density functional that satisfies strong convexity by constraining the characteristic velocity distribution field to a lower curvature. The positive definiteness of the Hess matrix of the entropy density functional is guaranteed by the minimum eigenvalue of the wavefront curvature tensor. The dual variable transformation subunit is used for dual mapping of the input tangent space of the entropy density functional, constructing the conservation law dual variable. The dual variable is constrained by the symplectic structure to form a regular conjugate field. The non-degenerate condition of the regular conjugate field inherits the strongly convex parameters of the entropy density functional. Curvature-constrained separation sub-units are used to separate physical components that satisfy the conservation law. The separation process implements strong curvature constraints to filter out non-physical oscillation modes. The constraint separation result forms the conservation law component. The integrability boundary of the conservation law component is determined by the Lipschitz continuity of the characteristic velocity field.

9. The Internet security service system as described in claim 1, characterized in that, It also includes a dynamic behavior entropy flow parsing system, which is used to generate multi-dimensional behavior trajectories by heterogeneous tensor fusion processing of the original network metadata. The multi-dimensional behavior trajectories are then reconstructed by the Lyapunov exponential algorithm to output behavior entropy change sequences. These behavior entropy change sequences are then filtered by a nonlinear fluctuation filter to form an entropy flow feature map.

10. The Internet security service system as described in claim 1, characterized in that, It also includes a hypergraph neural decision forest system, which is used to input entropy flow feature maps into a neural architecture searcher and output a dynamic hypergraph structure; the dynamic hypergraph structure is diffused by relational convolution kernels to generate a component dependency matrix; the component dependency matrix is ​​trained by a random differential forest to form a set of policy decision trees.

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