Multimodal data fusion AI cloud computing analysis system

Abstract

The present invention relates to the field of cloud computing analysis technology, specifically a multimodal data fusion AI cloud computing analysis system, comprising an ethical conflict resolution module, an adversarial modal purification module, a phase change critical energy consumption optimization module, a multimodal timing calibration module, a federated modal distillation module, a metadata differential-algebraic governance module, a quantum-classical hybrid fusion module, and a dynamic unloading policy execution module. The ethical conflict resolution module is used for ethical analysis and dynamic chain of responsibility tracing. Through technologies such as cross-modal deep interaction, dynamic ethical conflict resolution, adversarial noise suppression, and energy-precision optimization, the present invention significantly improves the utilization rate, system robustness, and computational efficiency of multimodal data, while simultaneously addressing ethical and security issues. This system provides a comprehensive solution for multimodal AI applications and can be flexibly applied to fields such as autonomous driving, e-commerce customer service systems, medical imaging diagnosis, the Industrial Internet of Things, and smart agriculture.

Description

Technical Field

[0001] The present invention relates to the field of cloud computing analysis technology, and specifically to a multimodal data fusion AI cloud computing analysis system. Background Art

[0002] Multimodal data refers to a collection of data from different sources in different forms or representations. Common modalities include text, images, audio, video, sensor data, and biosignals. Multimodal data is characterized by its heterogeneity and complementarity. For example, in autonomous driving, lidar provides precise distance information, while cameras provide rich visual semantic information.

[0003] Traditional AI cloud computing analysis methods are generally single-modal analysis, modeling and analyzing only a single modality. They use classic machine learning or deep learning models to process data and simply combine multimodal data through methods like feature concatenation or weighted averaging. For example, text feature vectors and image feature vectors are concatenated and fed into a classifier. All data is then uploaded to the cloud for processing, relying on powerful computing resources and using distributed computing frameworks for large-scale data processing. This computational analysis method, single-modal analysis, fails to leverage the complementarity of multimodal data, resulting in low information utilization. Feature concatenation or weighted averaging cannot capture the complex interactions between modalities, leading to poor fusion effects. Centralized cloud computing requires large amounts of data transmission, resulting in high latency and energy consumption. It lacks the ability to model cross-cultural ethical conflicts, making it prone to biased or discriminatory decision-making. Adversarial attacks can easily affect the global system through a single modality. Metadata formats vary significantly across modalities, making unified management and analysis difficult.

[0004] Based on this, the present invention provides a multimodal data fusion AI cloud computing analysis system to solve the technical problems raised above. Summary of the Invention

[0005] The purpose of the present invention is to provide a multimodal data fusion AI cloud computing analysis system to solve the problems mentioned in the background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The present invention proposes a multimodal data fusion AI cloud computing analysis system, which includes an ethical conflict resolution module, an adversarial modal purification module, a phase change critical energy consumption optimization module, a multimodal timing calibration module, a federated modal distillation module, a metadata differential algebraic governance module, a quantum-classical hybrid fusion module, and a dynamic unloading strategy execution module.

[0008] The ethical conflict resolution module is used for ethical analysis and dynamic responsibility chain tracing;

[0009] The adversarial modal purification module is used for the inverse mapping mechanism and semantic field isolation of the generative adversarial network (GAN);

[0010] The phase change critical energy consumption optimization module is used for thermodynamic phase change control and;

[0011] The multimodal timing calibration module is used for hyperchaotic timestamp synchronization and causal convolution resampling;

[0012] The federated modal distillation module is used for zero-shot modal distillation and gradient confusion detection;

[0013] The metadata differential algebra governance module is used for manifold metadata parsing and dynamic environment coupling;

[0014] The quantum-classical hybrid fusion module is used for quantum modal encoding and classical attention decoupling;

[0015] The dynamic offloading strategy execution module is used to reinforce learning offloading decisions and energy-aware neural architecture search.

[0016] Preferably, the ethical conflict resolution module further includes an ethical analysis unit and a dynamic responsibility chain tracing unit;

[0017] The ethics analysis unit uses a quantum annealing algorithm to perform hypergraph modeling on multimodal ethical attributes, encoding gestures and voice modalities in different cultural backgrounds into quantum bit states, and finding a path to minimize ethical conflicts through the quantum tunneling effect;

[0018] The dynamic responsibility chain traceability unit adopts the causal forest algorithm to construct a modality-decision causal graph, and uses the Shapley value to decompose the contribution of each modality to the final decision.

[0019] Preferably, the adversarial modal purification module further includes a modal adversarial immune network and a semantic field isolation unit;

[0020] The modal adversarial immune network uses the inverse mapping mechanism of the generative adversarial network and the Wasserstein distance to measure the feature offset of the attacked modality of the infrared image to generate adversarial samples against noise for training the robust fusion model;

[0021] The semantic field isolation unit is based on the manifold cutting technology of contrastive learning, which is used to forcibly separate the sarcastic semantics of the text modality and the visual modality features into orthogonal subspaces in the latent space.

[0022] Preferably, the phase change critical energy consumption optimization module further comprises a thermodynamic phase change controller and an energy manifold compression unit;

[0023] The thermodynamic phase change controller dynamically calculates the energy consumption-accuracy phase change critical point of the visual mode for processing and the speech mode for processing through the Gibbs free energy formula of the non-equilibrium thermodynamic model;

[0024] The energy manifold compression unit is used to compress the multimodal interaction matrix into a low-rank MP structure through a tensor network decomposition algorithm, reducing FLOPs while maintaining cross-modal correlation.

[0025] Preferably, the multimodal timing calibration module further includes a hyperchaotic timestamp synchronization unit and a causal convolution resampling unit;

[0026] The hyperchaotic timestamp synchronization unit generates a nonlinear timestamp based on a Lorenz system chaotic oscillator, and uses the Lyapunov exponent to align the asynchronous data streams of the lidar and the camera;

[0027] The causal convolution resampling unit performs super-resolution resampling on the low frame rate modality through a deep causal convolutional network to maintain causal consistency with the high frame rate modality.

[0028] Preferably, the federated modal distillation module further comprises a zero-sample modal distiller and a gradient confusion detection unit;

[0029] The zero-shot modal distiller is based on a cross-modal knowledge distillation framework and is used to construct a shared latent space projection between client A with only text data and client B with only image data.

[0030] The gradient confusion detection unit is used to detect abnormal Betti number distribution patterns in federated learning gradients through a persistent homology method in topological data analysis.

[0031] Preferably, the metadata differential algebra governance module further includes a manifold metadata parser and a dynamic environment coupling unit;

[0032] The manifold metadata parser is used to map the heterogeneous metadata of FLIR thermal imagers and Hikvision cameras into a unified Lie group space through a differential algebraic equation solver;

[0033] The dynamic environment coupling unit models the influence of atmospheric turbulence on satellite images based on stochastic differential equations, and is used to generate a metadata compensation coefficient matrix.

[0034] Preferably, the quantum-classical hybrid fusion module further includes a quantum modal encoder and a classical attention decoupling unit;

[0035] The quantum modal encoder is used to map the brain wave signal and the questionnaire text into an entangled state in the quantum Hilbert space through a quantum variational encoder;

[0036] The classical attention decoupling unit is dynamically gated by a pulse neural network to simulate the biological integration mechanism of the brain's prefrontal cortex on multimodal information.

[0037] Preferably, the dynamic offloading strategy execution module further includes a reinforcement learning offloading decision maker and an energy-aware neural architecture search unit;

[0038] The reinforcement learning offloading decider is based on a multi-agent deep deterministic policy gradient algorithm and is used to optimize the visual / speech modality processing location in real time in a cloud-edge architecture.

[0039] The energy-aware neural architecture search unit simultaneously optimizes the three objective functions of model FLOPs, memory usage and multimodal interaction efficiency through the Pareto evolution algorithm.

[0040] The present invention also proposes an analysis method based on a multimodal data fusion AI cloud computing analysis system, comprising the following steps:

[0041] S1. Using the quantum annealing algorithm and causal forest algorithm, we model multimodal ethical attributes and quantify modal contribution weights to trace the root causes of ethical deviations.

[0042] S2. Generate adversarial examples using generative adversarial networks and contrastive learning techniques to enhance robustness and isolate semantic noise from text and visual modalities.

[0043] S3. Based on thermodynamic models and tensor network decomposition, dynamically optimize the energy-accuracy phase transition threshold and compress the multimodal interaction matrix;

[0044] S4. Aligning asynchronous data streams and super-resolution resampling of low-frame-rate modalities using chaotic oscillators and deep causal convolutional networks.

[0045] S5. Enable cross-client modality transfer and defend against malicious gradient attacks through knowledge distillation and topological data analysis.

[0046] S6. Use differential algebraic equations and stochastic differential equations to unify heterogeneous metadata mapping and correct for environmental interference bias;

[0047] S7. Quantum-classical hybrid feature fusion and decoupling of attention interference through quantum variational encoders and spiking neural networks.

[0048] S8. Based on multi-agent reinforcement learning and Pareto evolution algorithm, optimize the cloud-edge offloading strategy and search for the optimal fusion architecture.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] This invention uses quantum-classical hybrid fusion, tensor network decomposition and other technologies to capture the complex interactive relationships between modalities, improve information utilization, use quantum annealing algorithms and causal forest algorithms to model cross-cultural ethical attributes, trace and eliminate ethical deviations, and use generative adversarial networks and contrastive learning techniques to block adversarial attacks and asymmetric noise propagation. Based on thermodynamic models and Pareto evolution algorithms, it dynamically optimizes cloud-edge computing loads and reduces energy consumption. It uses chaotic oscillators and deep causal convolutional networks to align multi-source asynchronous data streams to ensure timing consistency. While protecting data privacy, it achieves cross-client modal migration and improves model generalization. Ability, through differential algebraic equations and stochastic differential equations, unified heterogeneous metadata mapping, eliminate parameter system differences, and use quantum variational encoders and pulse neural networks to resolve the representation theory conflicts between discrete and continuous modes. In summary, the present invention significantly improves the utilization rate, system robustness and computing efficiency of multimodal data through cross-modal deep interaction, dynamic ethical conflict resolution, adversarial noise suppression, energy consumption-precision optimization and other technologies, while solving ethical and security issues, providing a comprehensive solution for multimodal AI applications, which can be flexibly applied to autonomous driving, e-commerce customer service systems, medical imaging diagnosis, industrial Internet of Things and smart agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The topology diagram of the multimodal data fusion AI cloud computing analysis system of the present invention is shown;

[0052] Figure 2 The flowchart of the multimodal data fusion AI cloud computing analysis method of the present invention is shown. DETAILED DESCRIPTION

[0053] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0054] Example 1, please refer to Figure 1 The present invention proposes a multimodal data fusion AI cloud computing analysis system, which includes an ethical conflict resolution module, an adversarial modal purification module, a phase change critical energy consumption optimization module, a multimodal timing calibration module, a federated modal distillation module, a metadata differential algebraic governance module, a quantum-classical hybrid fusion module, and a dynamic unloading strategy execution module.

[0055] The ethical conflict resolution module of this system is used for ethical analysis and dynamic responsibility chain tracing, the adversarial modal purification module of this system is used for the inverse mapping mechanism and semantic field isolation of the generative adversarial network (GAN), the phase change critical energy consumption optimization module of this system is used for thermodynamic phase change control and, the multimodal timing calibration module of this system is used for hyperchaotic timestamp synchronization and causal convolution resampling, the federated modal distillation module of this system is used for zero-sample modal distillation and gradient confusion detection, the metadata differential algebra governance module of this system is used for manifold metadata parsing and dynamic environment coupling, the quantum-classical hybrid fusion module of this system is used for quantum modal encoding and classical attention decoupling, and the dynamic offloading strategy execution module of this system is used for reinforcement learning offloading decision-making and energy-aware neural architecture search.

[0056] In this embodiment, it should also be noted that the ethical conflict resolution module also includes an ethical analysis unit and a dynamic responsibility chain tracing unit;

[0057] Furthermore, the ethics analysis unit uses a quantum annealing algorithm to perform hypergraph modeling of multimodal ethical attributes, encoding gestures and speech modalities in different cultural backgrounds into quantum bit states, and using the quantum tunneling effect to find a path to minimize ethical conflicts.

[0058] Furthermore, the dynamic responsibility chain traceability unit adopts the causal forest algorithm to construct a modal-decision causal graph, and uses the Shapley value to decompose the contribution of each modal to the final decision.

[0059] In this embodiment, it should also be noted that the adversarial modal purification module also includes a modal adversarial immune network and a semantic field isolation unit;

[0060] Furthermore, the modal adversarial immune network uses the inverse mapping mechanism of the generative adversarial network to measure the feature offset of the attacked modality of the infrared image using the Wasserstein distance, and is used to generate adversarial samples against noise for training the robust fusion model.

[0061] Furthermore, the semantic field isolation unit is based on the manifold cutting technology of contrastive learning, which is used to forcibly separate the sarcastic semantics of the text modality and the visual modality features into orthogonal subspaces in the latent space.

[0062] In this embodiment, it should also be noted that the phase change critical energy consumption optimization module also includes a thermodynamic phase change controller and an energy manifold compression unit;

[0063] Furthermore, the thermodynamic phase change controller dynamically calculates the energy consumption-accuracy phase change critical point between the visual mode processing and the speech mode processing through the Gibbs free energy formula of the non-equilibrium thermodynamic model;

[0064] Furthermore, the energy manifold compression unit is used to compress the multimodal interaction matrix into a low-rank MP structure through a tensor network decomposition algorithm, reducing FLOPs while maintaining cross-modal correlation.

[0065] In this embodiment, it should also be noted that the multimodal timing calibration module further includes a hyperchaotic timestamp synchronization unit and a causal convolution resampling unit;

[0066] Furthermore, the hyperchaotic timestamp synchronization unit generates nonlinear timestamps based on the Lorenz system chaotic oscillator, which is used to align the asynchronous data streams of the lidar and camera through the Lyapunov exponent;

[0067] Furthermore, the causal convolution resampling unit performs super-resolution resampling on the low frame rate modality through a deep causal convolutional network to maintain causal consistency with the high frame rate modality.

[0068] In this embodiment, it should also be noted that the federated modal distillation module also includes a zero-sample modal distiller and a gradient confusion detection unit;

[0069] Furthermore, the zero-shot modal distiller is based on a cross-modal knowledge distillation framework to construct a shared latent space projection between client A with only text data and client B with only image data.

[0070] Furthermore, the gradient confusion detection unit is used to detect abnormal Betti number distribution patterns in federated learning gradients through the persistent homology method in topological data analysis.

[0071] In this embodiment, it should also be noted that the metadata differential algebra governance module also includes a manifold metadata parser and a dynamic environment coupling unit;

[0072] Furthermore, the manifold metadata parser is used to map the heterogeneous metadata of FLIR thermal imagers and Hikvision cameras into a unified Lie group space through a differential algebraic equation solver.

[0073] Furthermore, the dynamic environment coupling unit models the impact of atmospheric turbulence on satellite images based on stochastic differential equations to generate metadata compensation coefficient matrices.

[0074] In this embodiment, it should also be noted that the quantum-classical hybrid fusion module also includes a quantum modal encoder and a classical attention decoupling unit;

[0075] Furthermore, the quantum modal encoder is used to map the brainwave signal and the questionnaire text into the entangled state of the quantum Hilbert space through the quantum variational encoder;

[0076] Furthermore, the classic attention decoupling unit is dynamically gated through a pulse neural network to simulate the biological integration mechanism of the brain's prefrontal cortex for multimodal information.

[0077] In this embodiment, it should also be noted that the dynamic offloading strategy execution module also includes a reinforcement learning offloading decision maker and an energy-aware neural architecture search unit;

[0078] Furthermore, the reinforcement learning offloading decision maker is based on a multi-agent deep deterministic policy gradient algorithm, which is used to optimize the location of visual / speech modality processing in real time in the cloud-edge architecture.

[0079] Furthermore, the energy-aware neural architecture search unit uses the Pareto evolution algorithm to simultaneously optimize the three objective functions of model FLOPs, memory usage, and multimodal interaction efficiency.

[0080] Example 2, please refer to Figure 2 In practical applications, based on the above system, the present invention also proposes an analysis method based on a multimodal data fusion AI cloud computing analysis system, which specifically includes the following steps:

[0081] S1. Cross-cultural ethical attribute modeling and conflict resolution:

[0082] S1.1. Multimodal ethical attribute encoding: Gesture and speech modal data from different cultural backgrounds are represented as a hypergraph G = (V, E), where nodes V represent modal features and hyperedges E represent ethical attribute associations.

[0083] Use the quantum annealing algorithm to map hypergraph nodes into quantum bit states:

[0084]

[0085] Where, α i is the complex amplitude, |i> is the ground state;

[0086] Objective function:

[0087]

[0088] Where, J ij Indicates the intensity of ethical conflict between modalities, is the PauliZ operator. H is minimized through quantum tunneling effect;

[0089] S1.2. Ethical conflict path search: Use a quantum annealer (such as DWave) to solve the minimum cut problem of the hypergraph, find the fusion path with the least ethical conflict, and output the optimal path:

[0090]

[0091] S1.3. Modal decision causal graph construction: Based on the causal forest algorithm, a multimodal decision causal graph C = (M, D) is constructed, where M is the modal node and D is the decision node. The Shapley value is calculated as:

[0092]

[0093] Where v(S) is the decision contribution of subset S;

[0094] S1.4. Sources of ethical deviations:

[0095] Sort by Shapley value and identify the modal node with the highest contribution Tracing the roots of ethical deviations;

[0096] S2. Adversarial modal noise suppression and feature isolation:

[0097] S2.1. Adversarial sample generation: Using the inverse mapping mechanism of the Generative Adversarial Network (GAN), the Wasserstein distance between the attacked modality x and the normal sample x′ is calculated:

[0098]

[0099] Where Γ(x,x′) is the joint distribution;

[0100] Generate adversarial examples:

[0101]

[0102] Where, ∈ is the perturbation intensity, J is the loss function;

[0103] S2.2. Robust model training: Add adversarial examples to the training set and optimize the fusion model loss function:

[0104]

[0105] Where λ is the adversarial loss weight;

[0106] S2.3. Semantic Field Manifold Cutting: Using contrastive learning techniques, construct the latent space representation z of the text modality T and the visual modality V T ,z V ;

[0107] Optimization goal:

[0108]

[0109] Where τ is the temperature parameter, z k is a negative sample;

[0110] S2.4. Orthogonal subspace projection: Orthogonalize z by GramSchmidt T With z V Projecting to orthogonal subspace to block the propagation of semantic noise;

[0111] S3. Dynamic optimization of critical point of phase change of energy consumption and accuracy:

[0112] S3.1. Thermodynamic phase transition calculation: Based on the Gibbs free energy formula:

[0113] G = H-TS;

[0114] Compute the critical point T of the phase transition between visual modality (edge) and speech modality (cloud) processing c , where H is enthalpy (energy consumption), S is entropy (accuracy), and T is temperature (computational load);

[0115] S3.2. Data offloading strategy determination: If T>T c , offload visual modality processing to the cloud; otherwise, keep it at the edge;

[0116] S3.3. Multimodal interaction matrix compression: Use the tensor network decomposition algorithm to decompose the interaction matrix A into a low-rank MPS structure:

[0117]

[0118] Where, is the local tensor;

[0119] S3.4. Computational complexity optimization: reducing FLOPs through low-rank approximation while preserving cross-modal correlation;

[0120] S4. Spatiotemporal alignment of multi-source asynchronous data streams:

[0121] S4.1. Chaotic timestamp generation: nonlinear timestamp generation based on Lorenz system:

[0122]

[0123] Where σ, ρ, β are parameters;

[0124] S4.2. Alignment of asynchronous data streams: via Lyapunov exponents:

[0125]

[0126] Align the asynchronous data streams of the lidar and camera;

[0127] S4.3. Low frame rate modality resampling: Using deep causal convolutional networks to resample low frame rate modalities x L Perform super-resolution resampling:

[0128] x H =Conv1D(x L ,W)

[0129] Where W is the convolution kernel weight;

[0130] S5. Federated Modal Knowledge Distillation and Attack Defense:

[0131] S5.1. Shared Latent Space Projection: Construct a shared latent space between client A (text) and client B (image):

[0132] Z=f θ (x)

[0133] Where, f θ For the encoder;

[0134] S5.2. Knowledge Distillation Loss Optimization: Optimize distillation loss:

[0135]

[0136] Where, f T ,f S They are teacher and student models respectively;

[0137] S5.3. Gradient Topology Analysis: Calculating the Betti Number β of the Gradient Distribution k =rank(H k ), where H k is the kth order homology group;

[0138] S5.4. Malicious gradient filtering: If β k Abnormal,determined as malicious gradient and filtered;

[0139] S6. Unified manifold mapping of heterogeneous metadata:

[0140] S6.1. Metadata Differential Algebraic Solving: Using Differential Algebraic Equations:

[0141]

[0142] Mapping heterogeneous metadata to Lie group space;

[0143] S6.2. Environmental disturbance modeling: based on stochastic differential equations:

[0144] dX t =μ(X t ,t)dt+σ(X t ,t)dW t

[0145] Generate metadata compensation coefficient matrix;

[0146] S7. Feature Fusion of Quantum-Classical Hybrid Spaces:

[0147] S7.1. Quantum modal encoding: Use a quantum variational encoder to map the EEG signal x to the quantum state |ψ〉=U(θ)|0〉, where U(θ) is the variational quantum circuit.

[0148] S7.2. Classical Attention Decoupling: Decoupling the attention weights of discrete symbols and continuous signals through a dynamic gating mechanism in a spiking neural network.

[0149] S8. Cloud-edge dynamic offloading and architecture search:

[0150] S8.1. Reinforcement Learning for Offloading Decisions: Optimizing Offloading Strategies Based on the MADDPG Algorithm:

[0151]

[0152] Where R is the reward function;

[0153] S8.2. Pareto architecture search, using the Pareto evolutionary algorithm to optimize the objective function:

[0154] F=(f1,f2,f3)

[0155] Where f1 is FLOPs, f2 is memory usage, and f3 is modal interaction efficiency.

[0156] Through the above steps, the present invention uses quantum-classical hybrid fusion, tensor network decomposition and other technologies to capture the complex interactive relationship between modalities, improve information utilization, use quantum annealing algorithm and causal forest algorithm to model cross-cultural ethical attributes, trace and eliminate ethical deviations, and use generative adversarial networks and contrastive learning technology to block adversarial attacks and asymmetric noise propagation. Based on thermodynamic models and Pareto evolution algorithms, it dynamically optimizes cloud-edge computing loads and reduces energy consumption. It uses chaotic oscillators and deep causal convolutional networks to align multi-source asynchronous data streams to ensure timing consistency. While protecting data privacy, it realizes cross-client modal migration and improves model generalization capabilities. It uses differential algebraic equations and stochastic differential equations to unify heterogeneous metadata mapping and eliminate parameter system differences. It uses quantum variational encoders and pulse neural networks to resolve representation conflicts between discrete and continuous modes.

[0157] In summary, the present invention significantly improves the utilization rate of multimodal data, system robustness and computing efficiency through technologies such as cross-modal deep interaction, dynamic ethical conflict resolution, adversarial noise suppression, and energy consumption-precision optimization, while solving ethical and security issues. It provides a comprehensive solution for multimodal AI applications and can be flexibly applied to fields such as autonomous driving, e-commerce customer service systems, medical imaging diagnosis, industrial Internet of Things, and smart agriculture.

[0158] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0159] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. Multimodal data fusion AI cloud computing analysis system, characterized by: It includes an ethical conflict resolution module, an adversarial modal purification module, a phase change critical energy consumption optimization module, a multimodal timing calibration module, a federated modal distillation module, a metadata differential algebraic governance module, a quantum-classical hybrid fusion module, and a dynamic unloading strategy execution module. The ethical conflict resolution module is used for ethical analysis and dynamic responsibility chain tracing; The adversarial modal purification module is used for the inverse mapping mechanism and semantic field isolation of the generative adversarial network; The phase change critical energy consumption optimization module is used for thermodynamic phase change control and energy manifold compression; The multimodal timing calibration module is used for hyperchaotic timestamp synchronization and causal convolution resampling; The federated modal distillation module is used for zero-shot modal distillation and gradient confusion detection; The metadata differential algebra governance module is used for manifold metadata parsing and dynamic environment coupling; The quantum-classical hybrid fusion module is used for quantum modal encoding and classical attention decoupling; The dynamic offloading strategy execution module is used to reinforce learning offloading decisions and energy-aware neural architecture search; The ethical conflict resolution module also includes an ethical analysis unit and a dynamic responsibility chain tracing unit; The ethics analysis unit uses a quantum annealing algorithm to perform hypergraph modeling on multimodal ethical attributes, encoding gestures and voice modalities in different cultural backgrounds into quantum bit states, and finding a path to minimize ethical conflicts through the quantum tunneling effect; The dynamic responsibility chain traceability unit uses the causal forest algorithm to construct a modality-decision causal graph, and uses the Shapley value to decompose the contribution of each modality to the final decision; The adversarial modal purification module further includes a modal adversarial immune network and a semantic field isolation unit; The modal adversarial immune network uses the inverse mapping mechanism of the generative adversarial network and the Wasserstein distance to measure the feature offset of the attacked modality of the infrared image to generate adversarial samples against noise for training the robust fusion model; The semantic field isolation unit is based on the manifold cutting technology of contrastive learning, which is used to forcibly separate the sarcastic semantics of the text modality and the visual modality features into orthogonal subspaces in the latent space; The phase change critical energy consumption optimization module also includes a thermodynamic phase change controller and an energy manifold compression unit; The thermodynamic phase change controller dynamically calculates the energy consumption-accuracy phase change critical point of the visual mode for processing and the speech mode for processing through the Gibbs free energy formula of the non-equilibrium thermodynamic model; The energy manifold compression unit is used to compress the multimodal interaction matrix into a low-rank MP structure through a tensor network decomposition algorithm, reducing FLOPs while maintaining cross-modal correlation; The multimodal timing calibration module further includes a hyperchaotic timestamp synchronization unit and a causal convolution resampling unit; The hyperchaotic timestamp synchronization unit generates a nonlinear timestamp based on a Lorenz system chaotic oscillator, and uses the Lyapunov exponent to align the asynchronous data streams of the lidar and the camera; The causal convolution resampling unit performs super-resolution resampling on the low frame rate modality through a deep causal convolutional network to maintain causal consistency with the high frame rate modality; The federated modal distillation module also includes a zero-shot modal distiller and a gradient confusion detection unit; The zero-shot modal distiller is based on a cross-modal knowledge distillation framework and is used to construct a shared latent space projection between client A with only text data and client B with only image data. The gradient confusion detection unit is used to detect abnormal Betti number distribution patterns in federated learning gradients through a persistent homology method in topological data analysis.

2. The multimodal data fusion AI cloud computing analysis system according to claim 1 is characterized in that: The metadata differential algebra governance module also includes a manifold metadata parser and a dynamic environment coupling unit; The manifold metadata parser is used to map the heterogeneous metadata of FLIR thermal imagers and Hikvision cameras into a unified Lie group space through a differential algebraic equation solver; The dynamic environment coupling unit models the influence of atmospheric turbulence on satellite images based on stochastic differential equations, and is used to generate a metadata compensation coefficient matrix.

3. The multimodal data fusion AI cloud computing analysis system according to claim 2 is characterized in that: The quantum-classical hybrid fusion module also includes a quantum modal encoder and a classical attention decoupling unit; The quantum modal encoder is used to map the brain wave signal and the questionnaire text into an entangled state in the quantum Hilbert space through a quantum variational encoder; The classical attention decoupling unit is dynamically gated by a pulse neural network to simulate the biological integration mechanism of the brain's prefrontal cortex on multimodal information.

4. The multimodal data fusion AI cloud computing analysis system according to claim 3 is characterized in that: The dynamic offloading strategy execution module also includes a reinforcement learning offloading decision maker and an energy-aware neural architecture search unit; The reinforcement learning offloading decider is based on a multi-agent deep deterministic policy gradient algorithm and is used to optimize the visual / speech modality processing location in real time in a cloud-edge architecture. The energy-aware neural architecture search unit simultaneously optimizes the three objective functions of model FLOPs, memory usage and multimodal interaction efficiency through the Pareto evolution algorithm.

5. The analysis method of the multimodal data fusion AI cloud computing analysis system according to claim 4 is characterized in that: The following steps are involved: S1. Using the quantum annealing algorithm and causal forest algorithm, we model multimodal ethical attributes and quantify modal contribution weights to trace the root causes of ethical deviations. S2. Generate adversarial examples using generative adversarial networks and contrastive learning techniques to enhance robustness and isolate semantic noise from text and visual modalities. S3. Based on thermodynamic models and tensor network decomposition, dynamically optimize the energy-accuracy phase transition threshold and compress the multimodal interaction matrix; S4. Aligning asynchronous data streams and super-resolution resampling of low-frame-rate modalities using chaotic oscillators and deep causal convolutional networks. S5. Enable cross-client modality transfer and defend against malicious gradient attacks through knowledge distillation and topological data analysis. S6. Use differential algebraic equations and stochastic differential equations to unify heterogeneous metadata mapping and correct for environmental interference bias; S7. Quantum-classical hybrid feature fusion and decoupling of attention interference through quantum variational encoders and spiking neural networks. S8. Based on multi-agent reinforcement learning and Pareto evolution algorithm, optimize the cloud-edge offloading strategy and search for the optimal fusion architecture.

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