Trusted AI system solution based on double-domain modeling and quantum computing

Abstract

The invention relates to a credible AI system solution based on double-domain modeling and quantum computing, and aims to solve the defects of the existing AI system in the aspects of cross-culture adaptability, physical correctness and decision real-time performance. Through deep fusion of modern mathematical tools (such as tensor analysis and normative field theory), quantum computing thought (such as advanced path optimization algorithm) and engineering optimization technology, a unified framework capable of processing subjective cognition and objective laws at the same time is constructed. The method specifically comprises the following steps: 1, realizing unification of cultural dynamics and physical law stability through a dual-domain modeling framework of a cognitive dependent domain (CDR) and a non-cognitive domain (NCR); 2, a hypergraph-tensor hybrid architecture which is used for representing a multivariate cultural relationship and verifying a kinematics equation; 3, an advanced computing architecture provides high-performance hardware support; and 4, a multi-mode verification system is adopted, so that the reliability and the safety of the system are ensured. The method is suitable for multiple fields of automatic driving, industrial control, financial risk control and the like, and shows strong universality and commercial potential.

Description

Technical Field

[0001] The present invention relates to the design of trustworthy systems in the field of artificial intelligence (AI), specifically a trustworthy AI architecture constructed by deeply integrating modern mathematical tools (such as tensor analysis, gauge field theory), quantum computing ideas (such as advanced path optimization algorithms), and engineering optimization techniques. This architecture can simultaneously handle subjective cognitions (such as cultural consensus) and objective laws (such as physical laws), providing a universal solution for intelligent applications in complex scenarios. Background Art

[0002] Importance of trustworthy AI systems:

[0003] With the rapid development of artificial intelligence technology, its applications in various fields of society are becoming increasingly widespread. From autonomous driving to medical diagnosis, from industrial control to personalized recommendation, the reliability, security, and adaptability of AI systems have become key factors affecting their popularization and trust. However, most current AI systems still have obvious deficiencies when facing complex scenarios, especially in cross-cultural adaptability, physical correctness, and decision-making real-time performance.

[0004] Cross-cultural adaptability: User needs vary significantly under different cultural backgrounds, and existing AI systems often struggle to take into account the behavioral norms and preferences in multi-cultural environments.

[0005] Physical correctness: In extreme physical environments (such as high temperature, high pressure, high radiation), the stability and reliability of existing AI systems face severe challenges.

[0006] Decision-making real-time performance: In high-dimensional decision spaces, existing AI systems often cannot meet real-time requirements due to computational delays.

[0007] The existence of these problems not only limits the actual application scope of AI technology but may also lead to serious security risks and social problems. Therefore, with the rapid development of artificial intelligence technology, constructing a trustworthy AI system that can handle both subjective cognitions and follow objective laws has become the core requirement for promoting the popularization of intelligent applications.

[0008] Based on this background, the present invention proposes a brand-new solution. By deeply integrating modern mathematical tools (such as tensor analysis, gauge field theory), quantum computing ideas (such as advanced path optimization algorithms), and engineering optimization techniques, a unified framework that can simultaneously handle subjective cognitions and objective laws is constructed, solving the deficiencies of existing AI systems in cross-cultural adaptability, physical correctness, and decision-making real-time performance, and promoting the further popularization and development of AI technology. Summary of the Invention

[0009] The present invention relates to a trusted AI system solution based on dual-domain modeling and quantum computing, aiming to address the deficiencies of existing AI systems in cross-cultural adaptability, physical correctness, and decision-making real-time performance. By deeply integrating modern mathematical tools (such as tensor analysis, gauge field theory), quantum computing concepts (such as advanced path optimization algorithms), and engineering optimization techniques, a unified framework capable of simultaneously handling subjective cognition and objective laws is constructed.

[0010] I. Unified Theoretical Framework of CDR and NCR

[0011] 1. Definitions of Cognitive Dependence Region (CDR) and Non-Cognitive Region (NCR)

[0012] Cognitive Dependence Region (CDR): Refers to the part of the system that involves human subjective cognition, such as cultural consensus, social norms, etc. This part of the content may vary with factors such as culture and region.

[0013] Non-Cognitive Region (NCR): Refers to the part that operates based on objective laws, such as physical laws, mathematical structures, unobserved quantum states, etc. This part of the content has strong stability and is not affected by subjective factors.

[0014] 2. Core Idea of the Dual-Domain Modeling Framework

[0015] The present invention proposes a trusted AI architecture constructed by deeply integrating modern mathematical tools (such as tensor analysis, gauge field theory), quantum computing concepts (such as advanced path optimization algorithms), and engineering optimization techniques. This architecture realizes the unity of CDR and NCR in the following ways:

[0016] Subjective cognitive information is mapped to the Cognitive Dependence Region (CDR). Human subjective cognition (such as preferences, cultural background, etc.) is mapped to a high-dimensional space through advanced mathematical tools to construct a multi-level modeling framework covering three levels: individual, group, and society. The specific mapping method belongs to the core technical secrets of the patentee.

[0017] (1) Individual layer (S): Represented as a ground state vector, encoding the subjective experience probability distribution.

[0018] (2) Group layer (OS): Achieves cultural consensus synchronization through a coordination function.

[0019] (3) Society layer (IS): Constructs a gauge field to constrain the behavior trajectory on a multi-dimensional mapping framework.

[0020] Objective physical information is mapped to the Non-Cognitive Region (NCR). The objectively existent region independent of the cognitive subject is clearly defined (such as physical laws, mathematical structures, unobserved quantum states, etc.), and physical laws, mathematical structures, etc. are abstracted into verifiable constraint terms, and verification benchmarks in extreme environments are established through extreme environment simulation. The specific mapping method belongs to the core technical secrets of the patentee.

[0021] Dual-Region Dynamics Equation:

[0022] The Dual-Region Dynamics Equation simulates the uncertainty of cultural transmission and the wave-particle duality of physical constraints by introducing specific mathematical expressions.

[0023] The specific equation form and parameter settings belong to the core technical secrets of the patentee.

[0024] Through this equation, the CDR can advance the weight adjustment algorithm to adapt to different cultural backgrounds, while the NCR ensures the physical correctness and logical consistency of the system.

[0025] 3. Mapping of Actual Application Scenarios

[0026] (1) Medical Robotics Field

[0027] In the field of medical robotics, there may be significant differences in the requirements for patient privacy protection in different countries and regions.

[0028] Functions of the CDR:

[0029] The adaptive adjustment algorithm optimization of the Social Layer (IS) can dynamically adjust parameters to balance these differences. For example, in some countries, the system may pay more attention to protecting patients' personal privacy, while in other countries, it may emphasize the efficiency of medical services more.

[0030] Functions of the NCR:

[0031] The NCR verification core ensures that the operation of medical devices fully complies with physical laws and biomechanical principles. For example, surgical robots must strictly abide by anatomical restrictions during operation to avoid any actions that may cause harm.

[0032] (2) Industrial Control Field

[0033] In industrial control scenarios, equipment operation must strictly follow physical laws to ensure safety and reliability.

[0034] Functions of the CDR:

[0035] The CDR inference engine can adjust operation strategies according to different cultural backgrounds or industry standards. For example, in some countries, industrial equipment may need to give priority to environmental protection requirements, while in other countries, it may pay more attention to production efficiency.

[0036] Role of NCR:

[0037] When NCR is verified, it detects and corrects operations that may violate physical laws. For example, in an intelligent manufacturing production line, the NCR verification core can ensure that the movement trajectory of the robotic arm conforms to dynamic constraints, avoiding potential safety hazards caused by overload or collision.

[0038] 4. Principle for conflict resolution

[0039] In a trustworthy AI system, the following principles are followed for conflict resolution among different domains, layers, units, systems, and individuals:

[0040] Local freedom:

[0041] Each individual or subsystem has a certain degree of independent decision-making power on the premise of not harming the overall interests. For example, at the group layer (OS), users with different cultural backgrounds can adjust system parameters according to their own needs without affecting the overall function.

[0042] Maximization of overall interests:

[0043] Through global optimization algorithms, on the basis of satisfying local freedom, ensure that the overall performance of the system reaches the optimal. For example, at the social layer (IS), the system can be optimized through adaptive adjustment algorithms to balance cultural and legal differences in different cultural regions, thus achieving universal application globally.

[0044] 5. Specific implementation methods of the unified theoretical framework

[0045] (1) Individual layer (S)

[0046] Advanced weight adjustment algorithm: Through advanced weight adjustment algorithms, ensure the freedom of users' subjective experience. The specific algorithms and weight update mechanisms belong to the core technical secrets of the patentee.

[0047] Specific implementation: Parametrize using biological perception threshold constraints and unique identity identifiers, enabling the system to adapt to the personalized needs of different users.

[0048] (2) Group layer (OS)

[0049] Coordinate conflicts: By coordinating the relationships between different subsystems, ensure the consistency of overall goals. The specific coordination mechanisms and implementation methods belong to the core technical secrets of the patentee.

[0050] Specific implementation: Achieve seamless collaboration in cross-cultural scenarios through non-classical associations of values and behavior norms among group members.

[0051] (3) Social layer (IS)

[0052] Balance differences: By constructing a gauge field model, optimize the balance of cross-cultural differences. The specific model construction method and optimization algorithm belong to the core technical secrets of the patentee.

[0053] Specific implementation: By constructing a gauge field model, constrain the behavioral trajectory to ensure the stability and reliability of the system in complex scenarios.

[0054] 6. Summary

[0055] By constructing a dual-domain modeling framework of the Cognitive Dependence Region (CDR) and the Non-Cognitive Region (NCR), the present invention realizes the deep integration of subjective cognition and objective laws. This unified theoretical framework can not only solve the deficiencies of existing AI systems in aspects such as cross-cultural adaptability, physical correctness, and decision-making real-time performance, but also provides a universal solution for intelligent applications in complex scenarios. Whether in the fields of medical robots, industrial control, or global governance, this framework can demonstrate strong applicability and innovation.

[0056] II. Application of the Mathematical Model - Hypergraph-Tensor Hybrid Architecture

[0057] 1. Use a dynamic hypergraph to represent multicultural relationships, and the weights are dynamically updated according to actual data. Attach a tensor kernel to the hypergraph vertices to verify the kinematic equations. The specific mathematical model belongs to the core technical secrets of the patentee.

[0058] Specific implementation details:

[0059] (1) Dynamic hypergraph representation: Model cultural relationships as hyperedges, and each hyperedge represents a cultural feature or behavior pattern. The weights are dynamically updated according to actual data to reflect the mutual influence and changes between different cultures.

[0060] (2) Tensor kernel to verify the kinematic equations: Attach a tensor kernel to the hypergraph vertices for verifying the kinematic equations.

[0061] 2. Quantum optimization algorithm: Use an advanced path optimization algorithm to quickly find the optimal solution in a 1000-dimensional decision space.

[0062] III. Engineering Implementation

[0063] 1. NCR Verification Kernel

[0064] (1) Technical solution

[0065] This module adopts an advanced hardware architecture design (the specific hardware architecture and design parameters are the core technical secrets of the patentee). By optimizing the circuit layout, transistor-level design, and low-power management strategies, a multi-core parallel processing unit is constructed. Its innovation lies in:

[0066] ① Adopt an array of heterogeneous computing units to achieve instruction-level parallel processing;

[0067] ② Introduce the dynamic voltage and frequency scaling (DVFS) mechanism to optimize the energy efficiency ratio;

[0068] ③ Adopt advanced stacked packaging technology to reduce signal transmission delay.

[0069] (2) Performance indicators

[0070] ① Energy efficiency ratio: ≥8 TOPS / W (processing INT8 data type in the standard test environment);

[0071] ② Operation delay: ≤5 ns (end-to-end response time in typical application scenarios);

[0072] ③ Stability: Continuous operation for 10 6 hours without failure (meeting the MIL-STD-810G military standard).

[0073] 2. CDR inference engine

[0074] (1) Technical solution

[0075] This module innovatively integrates photonic crystal neural network and superconducting qubit technology:

[0076] ① Photonic crystal neural network: Based on the photonic bandgap structure with periodic dielectric constant distribution, realize the directional control of photonic pulses and the simulation of neuron signals;

[0077] ② Superconducting qubit: Use superconducting Josephson junctions as quantum computing units to achieve low-power and high-parallel computing through quantum tunneling effect;

[0078] ③ Hybrid architecture design: Through the optical-electrical-quantum signal conversion interface, realize the collaborative computing of photonic neural network and qubit, breaking through the limitations of the traditional von Neumann architecture.

[0079] (2) Performance indicators

[0080] ① Parallelism: ≥10 10 (qubit and photonic neuron collaborative processing ability);

[0081] ② Power consumption: ≤0.5 W (operating in a 20 mK ultra-low temperature environment);

[0082] ③ Quantum fidelity: ≥99.99% (quantum gate operation error rate lower than 0.01%).

[0083] 3. Dual-domain interaction bus

[0084] (1) Technical solution

[0085] This module constructs a low-loss interconnection system based on topological insulator materials, and the core technical features include:

[0086] ① Topological insulator interconnection channels: By utilizing the high conductivity of its surface states (insulating characteristics in the bulk), the signal transmission loss is reduced to 1 / 10 of that of traditional metal wires.

[0087] ② Directional coupler design: By virtue of the unidirectional transmission characteristics of topological edge states, signal crosstalk is avoided.

[0088] ③ Multilayer heterojunction structure: Integrating boron nitride / graphene / topological insulator composite layers to support the hybrid transmission of optical, electrical, and quantum signals.

[0089] (2) Performance indicators

[0090] ① Bandwidth: ≥20 Tbps (supporting the hybrid transmission of photon, electron, and quantum signals);

[0091] ② Bit error rate: ≤10 -12 (Bit error rate after CRC check at a rate of 20 Tbps);

[0092] ③ Thermal stability: The operating temperature range is -200°C to 85°C, and the thermal expansion coefficient <1 ppm / °C.

[0093] In the above technical solutions, the hardware architecture parameters, photonic crystal design parameters, superconducting qubit preparation process, topological insulator material ratio, etc. are regarded as core secrets and are not disclosed in this description.

[0094] 4. Comparison data:

[0095] (1) Delay comparison: Compared with traditional AI architectures, the delay in the high-dimensional decision space is reduced by more than 90%. For example, in a 1000-dimensional decision space, a traditional AI architecture takes 10 ms to complete a decision, while the present invention only takes 0.5 ms.

[0096] (2) Energy efficiency ratio comparison: Compared with pure silicon-based computing architectures, the energy efficiency ratio is increased by 4 times. For example, the energy efficiency ratio of traditional silicon-based computing architectures is usually 2 TOPS / W, while the present invention reaches 8 TOPS / W.

[0097] 5. Description of advanced hardware design alternative solutions:

[0098] (1) Overview

[0099] In the present invention, advanced hardware design is one of the key technologies for realizing the performance of the NCR verification core. However, considering the technology maturity and potential application scenario diversity, one or more alternative solutions will be provided below. The possible alternative solutions will be analyzed in detail from multiple perspectives.

[0100] (2) Alternative solution analysis

[0101] ① Traditional 2D chips

[0102] Technical feasibility:

[0103] Advantages:

[0104] Traditional 2D chip technology is mature, the supply chain is stable, and it has been widely used in existing computing devices.

[0105] Limitations:

[0106] The energy efficiency ratio is relatively low, usually about 2 TOPS / W, far lower than 8 TOPS / W of advanced hardware designs.

[0107] The latency is high and it is difficult to meet real-time requirements (for example, the latency may reach dozens of nanoseconds).

[0108] Applicable scenarios: For application scenarios with low requirements for energy efficiency ratio and latency (such as some industrial control tasks), traditional 2D chips can be used as an alternative solution.

[0109] Description:

[0110] Hardware architecture: Manufactured using mature CMOS technology, supporting high-density integration.

[0111] Performance metrics: The energy efficiency ratio is about 2 TOPS / W and the latency is about 50 ns.

[0112] Implementation method: Reduce the computational complexity through optimized algorithms to make up for the lack of hardware performance.

[0113] ② Other computing-in-memory technologies

[0114] Technical feasibility:

[0115] Advantages:

[0116] The core idea of computing-in-memory technology is to reduce the data transfer overhead, thereby improving the energy efficiency ratio and computing speed.

[0117] There are various implementation forms of computing-in-memory technology, such as technologies based on new storage media such as memristors and phase change memories (PCMs).

[0118] Limitations:

[0119] These current technologies are not yet fully mature, especially in terms of reliability and consistency in large-scale production, there are still challenges.

[0120] The performance may be slightly inferior to advanced hardware designs.

[0121] Applicable scenarios: For scenarios that need to explore new technology paths, especially in fields that may be widely used in the future (such as autonomous driving and medical robots).

[0122] Description:

[0123] Hardware architecture: An in-memory computing chip based on a new type of storage medium (such as memristors or phase change memories).

[0124] Performance metrics: The energy efficiency ratio can reach 4 TOPS / W, and the latency is about 10 ns.

[0125] Implementation method: By optimizing the storage cell design and computing logic, the data transfer overhead is reduced.

[0126] ③ Distributed computing architecture

[0127] Technical feasibility:

[0128] Advantages:

[0129] Distributed computing can share computing tasks through multi-node collaboration, thus reducing the performance requirements of a single node.

[0130] It has high flexibility and can dynamically adjust the computing resource allocation according to actual needs.

[0131] Limitations:

[0132] The implementation complexity is relatively high, and additional communication and coordination mechanisms are required.

[0133] The latency may be affected by network bandwidth and communication protocols.

[0134] Applicable scenarios: For scenarios where the computing tasks are highly decomposable (such as multi-device collaborative tasks in industrial control).

[0135] Description:

[0136] Hardware architecture: Consists of multiple low-power computing nodes, and data exchange is achieved through a high-speed interconnect network.

[0137] Performance metrics: The overall energy efficiency ratio is about 6 TOPS / W, and the latency depends on the network latency, usually 10 - 20 ns.

[0138] Implementation method: Dynamically allocate computing tasks through software-defined means to optimize resource utilization.

[0139] ④ Independent deployment of quantum annealing units

[0140] Technical feasibility:

[0141] Advantages:

[0142] Quantum annealing units can be used alone to solve specific types of optimization problems, especially suitable for handling complex tasks in high-dimensional decision spaces.

[0143] When used in combination with classical computing architectures, it can significantly improve the performance of specific tasks.

[0144] Limitations:

[0145] Quantum annealing units have poor generality and cannot completely replace classical computing architectures.

[0146] They have high requirements for the environment, such as requiring low-temperature operating conditions.

[0147] Applicable scenarios: For scenarios that require quickly solving complex optimization problems (such as autonomous driving path planning, medical robot surgery planning).

[0148] Description:

[0149] Hardware architecture: Independently deploy quantum annealing units and work in cooperation with classical computing modules.

[0150] Performance metrics: The energy efficiency ratio is approximately 5 TOPS / W, and the latency depends on the problem scale and is usually 1 - 10 ns.

[0151] Implementation method: Through a hybrid computing framework, allocate optimization tasks to quantum annealing units and assign other tasks to classical computing modules to complete.

[0152] 6. Software stack optimization: Develop a domain-specific language (CDR-NCR DSL) to achieve a lossless mapping from algorithms to hardware.

[0153] IV. Multimodal verification system: Multimodal provable security

[0154] 1. Definition: The multimodal verification system is a comprehensive verification method based on advanced conflict simulation, extreme environment simulation, and real-time evaluation, aiming to ensure the reliability, security, and real-time performance of the system.

[0155] 2. Function: Through cultural robustness testing, physical security verification, and real-time evaluation, comprehensively verify the cross-cultural adaptability, physical correctness, and decision-making real-time performance of the system.

[0156] 3. Composition:

[0157] (1) Software modules: Include advanced conflict simulation module, extreme environment simulation module, and real-time evaluation module.

[0158] GAN module: Used to manufacture a conflict manufacturing module to evaluate the system's performance in cross-cultural scenarios.

[0159] Extreme environment simulation module: Used for physical security verification to simulate the system's operating state in extreme physical environments.

[0160] Real-time evaluation module: Used for real-time verification to ensure that the latency of the system in the high-dimensional decision space meets the requirements.

[0161] (2) Hardware devices: including the NCR verification core, CDR inference engine, and dual-domain interaction bus.

[0162] NCR verification core: responsible for executing complex computing tasks, with low latency and high energy efficiency ratio.

[0163] CDR inference engine: used for processing large-scale data operations, supporting parallel computing and low-power operation.

[0164] Dual-domain interaction bus: provides high-performance communication capabilities to ensure efficient data transmission between software modules and hardware devices.

[0165] 4. Specific verification process

[0166] (1) Cultural robustness test:

[0167] Build multi-cultural simulation scenarios covering different languages, religions, social norms, etc.

[0168] Evaluate the system's performance in cross-cultural scenarios through an advanced conflict simulation and conflict manufacturing module.

[0169] Record the system's response time and accuracy in conflict scenarios, and analyze its adaptability and reliability.

[0170] (2) Physical security verification:

[0171] Use an extreme environment simulation module to build an extreme physical environment model, including conditions such as high temperature, high pressure, and high radiation.

[0172] Deploy the system into the simulation environment, monitor its running status in real time, and discover and fix potential problems.

[0173] Record the system's running data in the extreme environment and analyze its reliability and stability.

[0174] (3) Real-time verification:

[0175] Define the dimension range and time constraint conditions of the high-dimensional decision space.

[0176] Test the system's latency and throughput in different-dimensional decision spaces.

[0177] Optimize the system parameters according to the test results to ensure that it meets the time constraint conditions.

[0178] V. Technical innovation points:

[0179] 1. Dual-domain modeling framework

[0180] (1) Deeply integrate modern mathematical tools, quantum computing ideas, and engineering optimization techniques: Through modern mathematical tools such as tensor analysis, gauge field theory, and advanced path optimization algorithms, combined with quantum computing ideas, a dual-domain modeling framework that can simultaneously handle subjective cognition (CDR) and objective laws (NCR) is constructed.

[0181] (2) Map human subjective cognition (such as preferences, cultural backgrounds, etc.) into a high-dimensional space through advanced mathematical tools, and construct a multi-level modeling framework covering three levels: individual, group, and society. Abstract physical laws, mathematical structures, etc. into verifiable constraint terms, and establish a verification benchmark under extreme environments through extreme environment simulation. The specific mapping method belongs to the company's core technical secrets.

[0182] (3) Dual-domain dynamics equation: Introduce a dual-domain dynamics equation containing specific mathematical expressions to simulate the uncertainty of cultural transmission and the wave-particle duality of physical constraints.

[0183] 2. Mathematical model: Hypergraph-tensor hybrid architecture

[0184] (1) Dynamic hypergraph represents multi-cultural relationships: Use a dynamic hypergraph to represent cultural relationships, and the weights are dynamically updated according to actual data to reflect the mutual influence and changes between different cultures.

[0185] (2) Tensor kernel verifies the kinematic equation: Attach a tensor kernel to the hypergraph vertices to verify the kinematic equation and ensure the physical correctness of the system in complex scenarios.

[0186] 3. Engineering implementation: Advanced computing

[0187] (1) NCR verification kernel: Adopt advanced hardware design, with an energy efficiency ratio of 8 TOPS / W and a latency as low as 5 ns.

[0188] (2) CDR inference engine: Based on a photonic crystal neural network + superconducting qubit, with a parallelism of 10 10 and extremely low power consumption (0.5 W).

[0189] (3) Dual-domain interaction bus: Utilize topological insulator interconnection technology to provide high-performance communication capabilities with a bandwidth of 20 Tbps and a bit error rate < 10 -12

[0190] 4. Verification system: Multi-modal provably secure

[0191] (1) Cultural robustness test: Adopt an advanced conflict simulation to create a conflict manufacturing module to enhance the system's adaptability to cross-cultural scenarios.

[0192] (2) Physical security test: Simulate extreme physical environments through extreme environment simulation to ensure the reliability of the system under complex physical constraints.

[0193] (3) Real-time verification: Based on the quantum decoherence time constraint test, ensure the real-time performance of the system in the high-dimensional decision space.

[0194] VI. Beneficial effects:

[0195] 1. Theoretical depth: Established a complete four-dimensional collaborative framework of "philosophy - mathematics - physics - engineering", filled the gap in the cross-domain adaptability of existing AI technologies, and constructed a unified framework that can simultaneously handle subjective cognition and objective laws.

[0196] 2. Engineering feasibility: Through the design of an advanced computing architecture and a dual-domain interaction bus, achieved seamless connection between theory and practice.

[0197] 3. Solve the problem of "lack of cross-cultural adaptability"

[0198] Dynamically adapt to different cultural backgrounds: Through the individual layer (S) and group layer (OS) in the dual-domain modeling framework, achieve dynamic adaptation to different cultures. For example, through an advanced mathematical tool to map to the ground state vector advanced weight adjustment algorithm in the high-dimensional space, ensure the freedom of individual subjective experience; use the coordinated functional mathematical expression to coordinate the conflicts between different subsystems while ensuring the consistency of the overall goal.

[0199] 4. Solve the problem of "lack of guarantee of physical correctness"

[0200] Strictly meet physical constraints: Introduce the NCR verification core and adopt advanced hardware design to ensure strict compliance of the system under physical constraints, and avoid potential safety hazards caused by ignoring physical laws.

[0201] 5. Solve the problem of "limited decision-making real-time performance"

[0202] Sub-millisecond-level delay: Reduce the delay to 0.05 ms through an advanced computing architecture, greatly improving the response speed of the system. Combine with an advanced path optimization algorithm to optimize the path selection in the high-dimensional decision space, further enhancing the real-time performance.

[0203] 6. Improve system performance indicators

[0204] The delay in the high-dimensional decision space is reduced by more than 90%: For example, in a 1000-dimensional decision space, a traditional AI architecture takes 10 ms to complete a decision, while the present invention only takes 0.5 ms.

[0205] The energy efficiency ratio is increased by 4 times: The energy efficiency ratio of a traditional silicon-based computing architecture is usually 2 TOPS / W, while the present invention reaches 8 TOPS / W.

[0206] 7. Achieve a universal solution

[0207] Wide range of applicable fields: This framework not only addresses the deficiencies of existing AI systems in aspects such as cross-cultural adaptability, physical correctness, and decision-making real-time performance, but also provides a general solution for intelligent applications in complex scenarios. It is specifically applicable to multiple high-value fields such as autonomous driving, medical robots, industrial control, and global governance.

[0208] In summary, through technological innovation, the present invention significantly enhances the credibility, reliability, and universality of AI systems, demonstrating strong commercial potential and application prospects. The present invention not only solves the problems faced by current AI systems such as cross-cultural adaptability, physical correctness, and decision-making real-time performance, constructs a unified framework capable of simultaneously processing subjective cognition and objective laws, but also provides a solid technical foundation for future more complex intelligent application scenarios, and is expected to lead the innovation of the next generation of AI systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0209] Figure 1 It is the overall system architecture diagram of the present invention, showing the module composition of the trustworthy AI system and their interrelationships, including core components such as the NCR verification core, CDR inference engine, and dual-domain interaction bus;

[0210] The dual-domain interaction bus not only transmits information unidirectionally, but also supports two-way interaction. The following is a specific analysis:

[0211] Information transfer from the NCR verification core to the CDR inference engine:

[0212] The NCR verification core is responsible for processing tasks based on objective laws (such as physical security verification), and its results need to be transmitted to the CDR inference engine through the dual-domain interaction bus.

[0213] For example, in the industrial control scenario, the NCR verification core detects and corrects operations that may violate physical laws in real time, and transmits this information to the CDR inference engine to adjust the operation strategy.

[0214] Information feedback from the CDR inference engine to the NCR verification core:

[0215] The CDR inference engine processes subjective cognitive information (such as cultural consensus, social norms, etc.), and feeds back the processing results to the NCR verification core.

[0216] For example, in the cross-cultural scenario, the CDR inference engine adjusts the operation strategy according to different cultural backgrounds, and transmits this adjustment information to the NCR verification core to ensure that the system behavior meets specific cultural requirements.

[0217] Therefore, the dual-domain interaction bus exchanges information bidirectionally between the NCR verification core and the CDR inference engine, rather than unidirectional transmission.

[0218] Figure 2 It is a diagram of the dual-domain modeling framework, which details the mapping method between the Cognitive Dependence Region (CDR) and the Non-Cognitive Region (NCR), as well as the dual-domain dynamics equations, covering a multi-level modeling framework at the individual layer (S), group layer (OS), and social layer (IS).

[0219] Figure 3 It is a diagram of the test results in the high-dimensional decision space, which intuitively shows the delay and throughput test data in different-dimensional decision spaces in the form of bar charts or line charts, comparing the performance differences between traditional AI architectures and the present invention.

[0220] Figure 4 It is a schematic diagram of the hardware design, showing the hardware architecture and design parameters of key computing modules, including the advanced hardware design of the NCR verification core, the photonic crystal neural network + superconducting qubit structure of the CDR inference engine, and the topological insulator interconnection design of the dual-domain interaction bus. Detailed implementation manners

[0221] The following details the specific implementation manners of the present invention in different fields through theoretical derivation, simulation analysis, and hypothesis verification. These embodiments cover fields such as autonomous driving and industrial control to demonstrate the practical feasibility of the trusted AI system solution based on dual-domain modeling and quantum computing. However, the protection scope of the present invention is not limited thereto.

[0222] Embodiment 1: Application of the dual-domain modeling framework in autonomous driving

[0223] 1. Scenario description

[0224] Suppose an autonomous driving vehicle needs to operate in a multi-cultural urban environment, involving traffic rules, driving habits, and physical constraints under extreme weather conditions in different countries and regions. For example, in some areas, turning right has priority, while in others, turning left has priority; at the same time, the vehicle also needs to maintain stable control in rainy and snowy weather.

[0225] 2. System architecture

[0226] (1) Cognitive Dependence Region (CDR)

[0227] ① Individual layer (S): Encode driver preferences (such as aggressive or conservative driving styles) as ground state vectors and dynamically adapt to user needs through an advanced weight adjustment algorithm.

[0228] ② Group layer (OS): Coordinate traffic rule differences under different cultural backgrounds. For example, achieve cultural consensus synchronization through a coordination function to ensure that the system can automatically adjust its behavior according to the traffic rules of the region where it is located.

[0229] ③ Social layer (IS): Construct a normative field model to constrain the driving behavior trajectory and ensure compliance with local laws and social norms.

[0230] (2) Non-cognitive domain (NCR)

[0231] ① Abstract physical laws (such as vehicle dynamics equations, friction models, etc.) into verifiable constraint terms and establish a verification benchmark through extreme environment simulation.

[0232] ② Introduce an NCR verification core to detect and correct operations that may violate physical laws in real time, ensuring the safety of the vehicle in complex scenarios.

[0233] (3) Dual-domain dynamics equation

[0234] By introducing specific mathematical expressions, simulate the uncertainty of cultural dissemination and the wave-particle duality of physical constraints. For example, the system can dynamically adjust parameters according to the current cultural background while ensuring physical correctness and logical consistency.

[0235] 3. System architecture design part

[0236] In the field of autonomous driving, contradictions are mainly reflected in the following aspects:

[0237] Conflict between traffic rules and driving habits: Different countries and regions may have different understandings of traffic rules, and at the same time, drivers' personal preferences may also conflict with the system's default strategies.

[0238] Trade-off between safety and efficiency: In some cases, pursuing higher safety may sacrifice a certain amount of driving efficiency, and vice versa. (1) Application of local freedom

[0239] At the individual layer (S), allow drivers to adjust system parameters according to personal preferences (such as aggressive or conservative driving styles). For example, dynamically adapt to user needs through an advanced weight adjustment algorithm to ensure the freedom of drivers' subjective experiences.

[0240] At the group layer (OS), coordinate the differences in traffic rules under different cultural backgrounds. For example, in some regions, more emphasis may be placed on right-turn priority, while in other regions, left-turn priority is emphasized. The system achieves cultural consensus synchronization through coordination functions to ensure the local freedom of each subsystem.

[0241] (2) Achievement of overall benefit maximization

[0242] At the social layer (IS), by constructing a normative field model, constrain the behavior trajectory to ensure the stability and reliability of the system in complex scenarios. For example, the system can be optimized through an adaptive adjustment algorithm to balance the cultural and legal differences in different cultural regions, thereby achieving universal application globally.

[0243] During the path planning process, the system comprehensively considers multiple dimensions such as safety, efficiency, and comfort through a global optimization algorithm to ensure that the overall performance reaches the optimal level.

[0244] 4. Implementation Details

[0245] (1) Representing Multicultural Relationships with Dynamic Hypergraphs

[0246] Use a dynamic hypergraph to represent the relationships among traffic rules, driving habits, and pedestrian behaviors. For example, model lane divisions, traffic signal states, and pedestrian behaviors as hyperedges, and verify the kinematic equations through tensor kernels.

[0247] (2) Path Planning with Quantum Optimization Algorithm

[0248] Quickly find the optimal solution in a high-dimensional decision space. For example, in a complex urban environment, the system needs to simultaneously consider the safety, efficiency, and comfort of multiple paths. Utilize an advanced path optimization algorithm to complete path planning in a 1000-dimensional decision space.

[0249] 5. Verification Process

[0250] (1) Cultural Robustness Testing

[0251] Construct a multi-cultural simulation scenario covering factors such as different languages, religions, and social norms. Use a GAN module to create a conflict generation module to evaluate the system's performance in cross-cultural scenarios. Record the system's response time and accuracy in conflict scenarios, and analyze its adaptability and reliability.

[0252] (2) Physical Safety Verification

[0253] Use an extreme environment simulation module to build an extreme physical environment model, including conditions such as high temperature, rain, snow, and complex terrain. Deploy the system into the simulation environment, monitor its running status in real-time, discover and fix potential problems. Record the system's running data in the extreme environment, and analyze its reliability and stability.

[0254] (3) Real-time Verification

[0255] Define the dimension range and time constraint conditions of the high-dimensional decision space. Test the system's latency and throughput in different-dimensional decision spaces. Optimize the system parameters according to the test results to ensure that it meets the time constraint conditions.

[0256] Example 2: Application of the Dual-domain Modeling Framework in Industrial Control

[0257] 1. Scenario Description

[0258] Suppose an intelligent manufacturing production line needs to operate under different cultural backgrounds and industry standards, involving multiple aspects such as environmental protection requirements, production efficiency and equipment safety.

[0259] 2. System Architecture

[0260] (1) Cognitive Dependence Domain (CDR)

[0261] ① Individual layer (S): Operator preferences (such as environmental protection or production efficiency) are encoded as a basis vector and dynamically adapted to user needs through advanced weight adjustment algorithms.

[0262] ② The OS layer: Coordinates differences in industry standards across different cultural backgrounds. For example, it synchronizes standards through coordination functions, ensuring that the system automatically adjusts its behavior based on the industry standards of the region.

[0263] ③ Social layer (IS): Build a normative field model to constrain the behavior trajectory of the production line and ensure compliance with local laws and industry regulations.

[0264] (2) Non-cognitive domain (NCR)

[0265] ① Abstract physical laws (such as the dynamic equations of robot arms, energy consumption models of production lines, etc.) into verifiable constraints, and establish verification benchmarks through extreme environment simulations.

[0266] ②Introduce the NCR verification core to detect and correct operations that may violate physical laws in real time, ensuring the safety of the production line in complex scenarios.

[0267] (3) Dual-domain dynamic equation

[0268] By introducing specific mathematical expressions to simulate the uncertainty of industry standards and the wave-particle duality of physical constraints, the system can dynamically adjust parameters based on the current cultural context while ensuring physical correctness and logical consistency.

[0269] 3. System architecture design part

[0270] In the field of industrial control, the contradictions are mainly reflected in the following aspects:

[0271] Conflict between environmental protection requirements and production efficiency: Some countries and regions may pay more attention to environmental protection requirements, while others may pay more attention to production efficiency.

[0272] Trade-off between equipment safety and mission completion: In extreme environments, ensuring equipment safety while also completing the intended mission may require finding a balance between the two.

[0273] (1) Application of local freedom

[0274] At the individual level (S), the operator is allowed to adjust system parameters according to personal preferences (such as paying attention to environmental protection or production efficiency). For example, encode the operator's preferences through the ground state vector and use an advanced weight adjustment algorithm to dynamically adapt to user needs.

[0275] At the group level (OS), coordinate the differences in industry standards under different cultural backgrounds. For example, some countries may pay more attention to environmental protection requirements, while others may focus more on production efficiency. The system achieves standard synchronization through a coordination function to ensure the local freedom of each subsystem.

[0276] (2) Achievement of overall benefit maximization

[0277] At the social level (IS), by constructing a gauge field model, constrain the behavior trajectory of the production line to ensure compliance with local laws and social norms. For example, the system can be optimized through an adaptive adjustment algorithm to balance the cultural and legal differences in different cultural regions, thus achieving universal application globally.

[0278] During the task assignment process, the system comprehensively considers multiple dimensions such as energy consumption optimization, production efficiency, and equipment safety through a global optimization algorithm to ensure that the overall performance reaches the optimal.

[0279] 4. Implementation details

[0280] (1) Dynamic hypergraph represents multivariate industry relationships

[0281] Use a dynamic hypergraph to represent the equipment relationships, operation specifications, and environmental constraints in the production line. For example, model the collaboration relationships between equipment, energy consumption limits, and safety standards as hyperedges, and verify the kinematic equations through tensor kernels.

[0282] (2) Quantum optimization algorithm for task assignment

[0283] Quickly find the optimal solution in a high-dimensional decision space. For example, in a complex production environment, the system needs to simultaneously consider the task assignment of multiple devices, energy consumption optimization, and production efficiency. Use an advanced path optimization algorithm to complete task assignment in a 1000-dimensional decision space.

[0284] 5. Verification process

[0285] (1) Cultural robustness test

[0286] Construct a multi-cultural simulation scenario covering factors such as different languages, industry standards, and social norms. Use a GAN module to create a conflict-making module to evaluate the system's performance in cross-cultural scenarios. Record the system's response time and accuracy in conflict scenarios and analyze its adaptability and reliability.

[0287] (2) Physical security verification

[0288] Build an extreme physical environment model using an extreme environment simulation module, including conditions such as high temperature and high pressure. Deploy the system into the simulation environment, monitor its running status in real time, discover and fix potential problems. Record the running data of the system in the extreme environment and analyze its reliability and stability.

[0289] (3) Real-time verification

[0290] Define the dimensional range and time constraint conditions of the high-dimensional decision space. Test the latency and throughput of the system in different dimensional decision spaces. Optimize the system parameters according to the test results to ensure that it meets the time constraint conditions.

[0291] The above embodiments demonstrate the feasibility of the present invention in practical application scenarios and verify the effectiveness of the trusted AI system solution based on dual-domain modeling and quantum computing.

[0292] The above are the specific embodiments of the present invention and the technical principles applied. If changes are made based on the concept of the present invention and the functions and effects generated do not exceed the spirit covered by the specification and the drawings, they should still fall within the protection scope of the present invention.

Claims

1. A trusted AI system solution based on dual-domain modeling and quantum computing, characterized in that, It includes the following modules: Cognitive Dependence Region (CDR): It is used to process subjective cognitive information, including the individual layer (S), the group layer (OS), and the social layer (IS). It maps human subjective cognition to a high-dimensional space through advanced mathematical tools; Non-Cognitive Region (NCR): It is used to process objective law information, including physical laws, mathematical structures, etc., and establishes a verification benchmark through extreme environment simulation; Dual-Region Modeling Framework: By integrating modern mathematical tools (such as tensor analysis, gauge field theory), quantum computing ideas (such as path optimization algorithms), and engineering optimization techniques, it realizes the unity of CDR and NCR; Hypergraph-Tensor Hybrid Architecture: It uses a dynamic hypergraph to represent multicultural relationships and attaches a tensor core to the hypergraph vertices to verify kinematic equations; Advanced Computing Architecture: It provides high-performance hardware support, including the NCR verification core, the CDR inference engine, and the dual-region interaction bus; Multi-Modal Verification System: It ensures the reliability and security of the system.

2. The trusted AI system solution according to claim 1, wherein It also includes a contradiction handling mechanism, specifically including: Local Freedom Principle: Each individual or subsystem has a certain degree of independent decision-making power without harming the overall interests. For example, at the group layer (OS), users with different cultural backgrounds can adjust system parameters according to their own needs without affecting the overall function; Overall Interest Maximization Principle: Through global optimization algorithms, on the basis of satisfying local freedom, it ensures that the overall performance of the system reaches the optimal. For example, at the social layer (IS), the system can be optimized through adaptive adjustment algorithms to balance cultural and legal differences in different cultural regions, so as to achieve universal application globally.

3. The trusted AI system solution according to claim 1, characterized in that, The Cognitive Dependence Region (CDR) specifically includes: Individual layer (S): It encodes the user's subjective experience probability distribution as a ground state vector and dynamically adapts to the user's needs through an advanced weight adjustment algorithm; Group layer (OS): It realizes cultural consensus synchronization through coordination functions; Social layer (IS): It constructs a gauge field model to constrain the behavior trajectory and ensure compliance with local laws and social norms.

4. The trusted AI system solution according to claim 1, wherein The Non-Cognitive Region (NCR) specifically includes: Abstract physical laws, mathematical structures, etc. into verifiable constraint terms; Introduce the NCR verification core to detect and correct operations that may violate physical laws in real time.

5. The trusted AI system solution according to claim 1, characterized in that, The dual-region dynamic equation simulates the uncertainty of cultural transmission and the wave-particle duality of physical constraints by introducing specific mathematical expressions.

6. The trusted AI system solution according to claim 1, characterized in that, The Hypergraph-Tensor Hybrid Architecture specifically includes: Use a dynamic hypergraph to represent multicultural relationships, and the weights are dynamically updated according to actual data; Attach a tensor core to the hypergraph vertices for verifying kinematic equations.

7. The trusted AI system solution according to claim 1, wherein, The Advanced Computing Architecture specifically includes: NCR verification core: Adopt advanced hardware design, with an energy efficiency ratio of up to 8 TOPS / W and a latency as low as 5 ns; CDR Inference Engine: Based on photonic crystal neural network + superconducting qubits, with a parallelism of 10 10 and extremely low power consumption (0.5W); Dual-domain interactive bus: Utilizing topological insulator interconnection technology, it provides high-performance communication capabilities with a bandwidth of 20 Tbps and a bit error rate <10 -12 .

8. The solution of the trusted AI system based on dual-domain modeling and quantum computing according to claim 1, wherein, It also includes: Develop a domain-specific language (CDR-NCR DSL) for realizing a lossless mapping from algorithms to hardware; The domain-specific language maps the algorithm logic seamlessly to the NCR verification core and the CDR inference engine by defining specific syntax and semantic rules.

9. The trusted AI system solution according to claim 1, wherein The Multi-Modal Verification System specifically includes: Cultural robustness test: Through an advanced conflict simulation to manufacture a conflict manufacturing module, evaluate the performance of the system in cross-cultural scenarios; Physical security test: Simulate extreme physical environments through extreme environmental conditions to ensure the reliability of the system under complex physical constraints; Real-time verification: Based on tests with quantum decoherence time constraints, ensure the real-time performance of the system in a high-dimensional decision space.

10. The trustworthy AI system solution according to any one of claims 1 to 9, characterized in that The system is applicable to, including but not limited to, the following fields: Autonomous driving: Dynamically adapt to traffic rules and driving habits in different cultural backgrounds while meeting physical constraints; Industrial control: Coordinate operation strategies under different cultural backgrounds and industry standards to ensure equipment safety and production efficiency; Financial risk control: Process complex cross-cultural financial transactions and risk assessments; Medical robots: Balance patient privacy protection and medical service efficiency while ensuring the safety and reliability of medical devices.

11. The trusted AI system solution according to any one of claims 1 to 10, characterized in that, The system solves the problems of existing AI systems in the following ways: Solve the problem of "insufficient cross-cultural adaptability": Achieve dynamic adaptation to different cultures through the individual layer (S) and the group layer (OS) in the dual-domain modeling framework; Solve the problem of "lack of guarantee of physical correctness": Introduce the NCR verification core to ensure that the system strictly meets physical constraints; Solve the problem of "limited decision-making real-time performance": Reduce the latency to the sub-millisecond level through an advanced computing architecture and improve the real-time performance by combining path optimization algorithms.

12. The trustworthy AI system solution according to any one of claims 1 to 11, characterized in that, The beneficial effects of the system include: Theoretical depth: Establish a complete four-dimensional collaborative framework of "philosophy - mathematics - physics - engineering"; Engineering feasibility: Achieve seamless connection between theory and practice through the design of an advanced computing architecture and a dual-domain interaction bus; Improvement of performance indicators: The latency in the high-dimensional decision space is reduced by more than 90%, and the energy efficiency ratio is increased by 4 times; Universal solution: Applicable to multiple high-value fields such as autonomous driving, industrial control, financial risk control, and medical robots.

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