Multi-modal data fusion AI cloud computing analysis system

Abstract

The invention relates to the technical field of cloud computing analysis, in particular to a multi-modal data fusion AI cloud computing analysis system. Comprising an ethical conflict resolution module, an antagonistic modal purification module, a phase change critical energy consumption optimization module, a multi-modal time sequence calibration module, a federal modal distillation module, a metadata differential algebraic management module, a quantum-classical hybrid fusion module and a dynamic unloading strategy execution module. And the ethical conflict resolution module is used for ethical analysis and dynamic liability chain tracing. Through cross-modal deep interaction, dynamic ethical conflict resolution, antagonistic noise suppression, energy consumption-precision optimization and other technologies, the utilization rate, the system robustness and the calculation efficiency of multi-modal data are remarkably improved, the ethical and safety problems are solved, a comprehensive solution is provided for multi-modal AI application, and the multi-modal AI algorithm has good application prospects. The method can be flexibly applied to the fields of automatic driving, e-commerce customer service systems, medical image diagnosis, industrial Internet of Things, smart agriculture and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud computing analysis, and particularly to a multi-modal data fusion AI cloud computing analysis system. Background Art

[0002] Multi-modal data refers to a collection of data from different sources with different forms or representations. Common modalities include: text, image, audio, video, sensor data, and biological signals. The characteristics of multi-modal data lie in its heterogeneity and complementarity. For example, in autonomous driving, lidar provides precise distance information, while cameras provide rich visual semantic information.

[0003] Generally, most traditional AI cloud computing analysis methods are single-modal analysis, which only models and analyzes a single modality. Classical machine learning or deep learning models are used to process data, and multi-modal data is simply combined through methods such as feature concatenation or weighted averaging. For example, the text feature vector and the image feature vector are concatenated and then input into a classifier. All data is uploaded to the cloud for processing, relying on powerful computing resources and using a distributed computing framework for large-scale data processing. This single-modal analysis method of computational analysis cannot utilize the complementarity of multi-modal data, resulting in low information utilization rate. Feature concatenation or weighted averaging cannot capture the complex interaction relationships between modalities, resulting in poor fusion effects. Centralized cloud computing requires a large amount of data transmission, resulting in high latency and high energy consumption. It lacks the ability to model cross-cultural ethical conflicts, is prone to generating biased or discriminatory decisions, and adversarial attacks can easily affect the global system through a single modality. Moreover, the metadata formats of different modalities vary greatly, making it difficult to manage and analyze them uniformly.

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

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

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

[0007] The present invention proposes a multi-modal data fusion AI cloud computing analysis system, including an ethical conflict resolution module, an adversarial modality purification module, a phase transition critical energy consumption optimization module, a multi-modal time series calibration module, a federated modality distillation module, a metadata differential algebra governance module, a quantum-classical hybrid fusion module, and a dynamic offloading strategy execution module;

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

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

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

[0011] The multi-modal temporal calibration module is used for ultra-chaotic timestamp synchronization and causal convolution resampling;

[0012] The federated modality distillation module is used for zero-shot modality 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 modality encoding and classical attention decoupling;

[0015] The dynamic offloading strategy execution module is used for reinforcement learning offloading decision-making 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 ethical analysis unit performs hypergraph modeling on multi-modal ethical attributes through a quantum annealing algorithm, which is used to encode gestures and speech modalities under different cultural backgrounds into qubit states, and finds the path of minimizing ethical conflicts through the quantum tunneling effect;

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

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

[0020] The modality adversarial immune network uses the inverse mapping mechanism of a generative adversarial network, measures the feature offset of the infrared image's attacked modality through the Wasserstein distance, and is used to generate adversarial samples of adversarial noise for training a robust fusion model;

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

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

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

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

[0025] Preferably, the multi-modal time series calibration module further includes a hyperchaotic timestamp synchronization unit and a causal convolution resampling unit;

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

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

[0028] Preferably, the federated modality distillation module further includes a zero-shot modality distiller and a gradient confusion detection unit;

[0029] The zero-shot modality 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 uses the persistent homology method in topological data analysis to detect abnormal Betti number distribution patterns in federated learning gradients.

[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 uses a differential algebraic equation solver to map the heterogeneous metadata of FLIR thermal imagers and Hikvision cameras to a unified Lie group space;

[0033] The dynamic environment coupling unit models the impact 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 modality encoder and a classical attention decoupling unit;

[0035] The quantum modality encoder uses a quantum variational encoder to map electroencephalogram signals and questionnaire texts to an entangled state in a quantum Hilbert space;

[0036] The classical attention decoupling unit is dynamically gated by a spiking neural network and is used to simulate the biological integration mechanism of the prefrontal cortex of the brain for 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 decision maker is based on the multi-agent deep deterministic policy gradient algorithm and is used to optimize the visual / speech modality processing location in real time in the cloud-edge-end architecture;

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

[0040] The present invention also proposes an analysis method for a multi-modal data fusion AI cloud computing analysis system, including the following steps:

[0041] S1. Model the multi-modal ethical attributes and quantify the modal contribution weights through the quantum annealing algorithm and the causal forest algorithm to trace the root cause of ethical deviations;

[0042] S2. Use the generative adversarial network and contrast learning technology to generate adversarial samples to enhance robustness and isolate the semantic noise of the text and visual modalities;

[0043] S3. Dynamically optimize the energy consumption-accuracy phase transition threshold and compress the multi-modal interaction matrix based on the thermodynamic model and tensor network decomposition;

[0044] S4. Use the chaotic oscillator and the deep causal convolutional network to align asynchronous data streams and perform super-resolution resampling on low-frame-rate modalities;

[0045] S5. Realize cross-client modal migration 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 environmental interference deviations;

[0047] S7. Realize quantum-classical hybrid feature fusion and decouple attention interference through the quantum variational encoder and the spiking neural network;

[0048] S8. Optimize the cloud-edge-end offloading strategy and search for the optimal fusion architecture based on multi-agent reinforcement learning and the Pareto evolutionary algorithm.

[0049] Compared with the prior art, the beneficial effects of the present invention are:

[0050] The present invention captures the complex interaction relationships between modalities through technologies such as quantum-classical hybrid fusion and tensor network decomposition, improves the information utilization rate, uses the quantum annealing algorithm and the causal forest algorithm to model cross-cultural ethical attributes, traces and eliminates ethical biases, blocks adversarial attacks and asymmetric noise propagation through generative adversarial networks and contrastive learning technologies, dynamically optimizes the cloud-edge-end computing load and reduces energy consumption based on the thermodynamic model and the Pareto evolutionary algorithm, aligns multi-source asynchronous data streams using chaotic oscillators and deep causal convolutional networks to ensure temporal consistency, and realizes cross-client modality migration while protecting data privacy, improving the model generalization ability. Through differential algebraic equations and stochastic differential equations, it unifies heterogeneous metadata mapping and eliminates parameter system differences. Using quantum variational encoders and spiking neural networks, it solves the representational conflict between discrete and continuous modalities. In summary, the present invention significantly improves the utilization rate of multi-modal data, system robustness and computing efficiency through technologies such as cross-modal deep interaction, dynamic ethical conflict resolution, adversarial noise suppression, and energy consumption-accuracy optimization, while solving ethical and security problems, providing a comprehensive solution for multi-modal AI applications, and can be flexibly applied to fields such as autonomous driving, e-commerce customer service systems, medical image diagnosis, industrial Internet of Things, and smart agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 Shows the topology diagram of the multi-modal data fusion AI cloud computing analysis system of the present invention;

[0052] Figure 2 Shows the flowchart of the multi-modal data fusion AI cloud computing analysis method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0054] Embodiment 1, please refer to Figure 1 , the present invention proposes a multi-modal data fusion AI cloud computing analysis system, which includes an ethical conflict resolution module, an adversarial modality purification module, a phase transition critical energy consumption optimization module, a multi-modal temporal calibration module, a federated modality distillation module, a metadata differential algebraic governance module, a quantum-classical hybrid fusion module, and a dynamic offloading strategy execution module;

[0055] The ethical conflict resolution module of this system is used for ethical analysis and dynamic responsibility chain tracing. The adversarial modality purification module of this system is used for the inverse mapping mechanism of the generative adversarial network (GAN) and semantic field isolation. The phase transition critical energy consumption optimization module of this system is used for thermodynamic phase transition control and. The multi-modal time series calibration module of this system is used for ultra-chaotic timestamp synchronization and causal convolution resampling. The federated modality distillation module of this system is used for zero-shot modality 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 modality encoding and classical attention decoupling. 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 further includes an ethical analysis unit and a dynamic responsibility chain tracing unit;

[0057] Among them, furthermore, the ethical analysis unit performs hypergraph modeling on multi-modal ethical attributes through the quantum annealing algorithm, which is used to encode gestures and speech modalities under different cultural backgrounds into qubit states, and find the path of minimizing ethical conflicts through the quantum tunneling effect;

[0058] Among them, furthermore, the dynamic responsibility chain tracing unit constructs a modality-decision causal graph using the causal forest algorithm, and uses the Shapley value to decompose the contribution degree of each modality to the final decision.

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

[0060] Among them, furthermore, the modality adversarial immune network uses the inverse mapping mechanism of the generative adversarial network, measures the feature offset of the infrared image under the attacked modality through the Wasserstein distance, and is used to generate adversarial samples of adversarial noise for training the robustness fusion model;

[0061] Among them, furthermore, the semantic field isolation unit is based on the manifold cutting technology of contrastive learning, which is used to forcibly separate the ironic semantics of the text modality from the visual modality features to an orthogonal subspace in the latent space.

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

[0063] Among them, furthermore, the thermodynamic phase transition controller dynamically calculates the energy consumption-accuracy phase transition critical point for visual modality processing and speech modality processing through the Gibbs free energy formula of the non-equilibrium thermodynamics model;

[0064] Among them, further, the energy manifold compression unit uses the tensor network decomposition algorithm to compress the multi-modal interaction matrix into a low-rank MP structure, reducing FLOPs while maintaining cross-modal correlation.

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

[0066] Among them, further, the hyperchaotic timestamp synchronization unit generates non-linear timestamps based on the Lorenz system chaotic oscillator, and uses the Lyapunov exponent to align the asynchronous data streams of the lidar and the camera;

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

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

[0069] Among them, further, the zero-shot modality 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;

[0070] Among them, further, the gradient confusion detection unit uses the persistent homology method in topological data analysis to detect abnormal Betti number distribution patterns in the federated learning gradient.

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

[0072] Among them, further, the manifold metadata parser uses a differential algebraic equation solver to map the heterogeneous metadata of the FLIR thermal imager and the Hikvision camera to a unified Lie group space;

[0073] Among them, further, the dynamic environment coupling unit models the impact of atmospheric turbulence on satellite images based on stochastic differential equations and is used to generate a metadata compensation coefficient matrix.

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

[0075] Among them, further, the quantum modality encoder uses a quantum variational encoder to map electroencephalogram signals and questionnaire texts to an entangled state in the quantum Hilbert space;

[0076] Among them, further, the classical attention decoupling unit is dynamically gated by a spiking neural network and is used to simulate the biological integration mechanism of the prefrontal cortex of the brain for multimodal information.

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

[0078] Among them, further, the reinforcement learning offloading decision maker is based on the multi-agent deep deterministic policy gradient algorithm and is used to optimize the visual / speech modality processing location in real time in the cloud-edge-end architecture;

[0079] Among them, further, the energy-aware neural architecture search unit simultaneously optimizes the three-objective function of model FLOPs, memory occupancy, and multimodal interaction efficiency through the Pareto evolutionary algorithm.

[0080] Embodiment 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. Specifically, it includes the following steps:

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

[0082] S1.1. Multimodal ethical attribute encoding, representing gesture and speech modality data under different cultural backgrounds as a hypergraph G=(V, E), where the nodes V represent modality features and the hyperedges E represent ethical attribute associations;

[0083] Using the quantum annealing algorithm to map the hypergraph nodes to qubit states:

[0084]

[0085] where α i is the complex amplitude and |i> is the ground state;

[0086] Objective function:

[0087]

[0088] where J ij represents the ethical conflict intensity between modalities, is the PauliZ operator. Minimize H through the quantum tunneling effect;

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

[0090]

[0091] S1.3. Construction of Modal Decision Causal Graph: Based on the causal forest algorithm, construct a multi-modal decision causal graph C = (M, D), where M is the modal node and D is the decision node, and calculate the Shapley value:

[0092]

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

[0094] S1.4. Tracing of Ethical Deviation:

[0095] Sort according to the Shapley value and identify the modal node with the highest contribution Trace the root cause of ethical deviation;

[0096] S2. Adversarial Modal Noise Suppression and Feature Isolation:

[0097] S2.1. Generation of Adversarial Samples: Use the inverse mapping mechanism of the generative adversarial network (GAN) to calculate the Wasserstein distance between the attacked modality x and the normal sample x′:

[0098]

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

[0100] Generate adversarial samples:

[0101]

[0102] where ∈ is the perturbation intensity and J is the loss function;

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

[0104]

[0105] where λ is the adversarial loss weight;

[0106] S2.3. Semantic Field Manifold Cutting: Use contrastive learning technology to construct the latent space representations z T , z V ;

[0107] Optimization objective:

[0108]

[0109] where τ is the temperature parameter and z k is the negative sample;

[0110] S2.4. Orthogonal Subspace Projection: Project z T and z V onto the orthogonal subspace to block the propagation of semantic noise;

[0111] S3. Dynamic Optimization of the Phase Change Critical Point of Energy Consumption Accuracy:

[0112] S3.1. Thermodynamic Phase Change Calculation: Based on the Gibbs free energy formula:

[0113] G = H - TS;

[0114] Calculate the phase change critical point T for visual modality (edge side) and speech modality (cloud side) processing c , where H is enthalpy (energy consumption), S is entropy (accuracy), and T is temperature (computing load);

[0115] S3.2. Data Offloading Strategy Decision: If T > T c , offload the visual modality processing to the cloud; otherwise, keep it at the edge side;

[0116] S3.3. Compression of the Multimodal Interaction Matrix: 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: Reduce FLOPs through low-rank approximation while preserving cross-modal correlations;

[0120] S4. Spatiotemporal Calibration of Multi-source Asynchronous Data Streams:

[0121] S4.1. Generation of Chaotic Timestamps: Generate non-linear timestamps based on the Lorenz system:

[0122]

[0123] where σ, ρ, β are parameters;

[0124] S4.2. Alignment of Asynchronous Data Streams: Align the asynchronous data streams of lidar and camera through the Lyapunov exponent:

[0125]

[0126] ;

[0127] S4.3. Resampling of Low Frame Rate Modalities: Use a deep causal convolutional network to perform super-resolution resampling on the low frame rate modality x L :

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

[0129] where W is the convolutional 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 is the encoder;

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

[0135]

[0136] where f T , f S are the teacher and student models, respectively;

[0137] S5.3. Gradient Topology Analysis: Calculate the Betti number β of the gradient distribution k = rank(H k ), where H k is the k-th homology group;

[0138] S5.4. Malicious Gradient Filtering: If β k is abnormal, it is determined as a malicious gradient and filtered;

[0139] S6. Unified Manifold Mapping of Heterogeneous Metadata:

[0140] S6.1. Metadata Differential Algebra Solving: Use differential algebraic equations:

[0141]

[0142] Map heterogeneous metadata to the Lie group space;

[0143] S6.2. Environmental Interference Modeling: Based on stochastic differential equations:

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

[0145] Generate the metadata compensation coefficient matrix;

[0146] S7. Feature Fusion in the Quantum-Classical Hybrid Space:

[0147] S7.1. Quantum Modal Encoding: Mapping the electroencephalogram signal x to the quantum state |ψ> = U(θ)|0> using a quantum variational encoder, where U(θ) is a variational quantum circuit;

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

[0149] S8. Dynamic Offloading and Architecture Search at the Cloud-Edge-Terminal:

[0150] S8.1. Reinforcement Learning Offloading Decision: Optimizing the offloading strategy 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 the FLOPs, f2 is the memory occupancy, and f3 is the modal interaction efficiency.

[0156] Through the above steps, the present invention captures the complex interaction relationships between modalities, improves the information utilization rate, models cross-cultural ethical attributes, traces and eliminates ethical biases, blocks adversarial attacks and asymmetric noise propagation, dynamically optimizes the cloud-edge-terminal computing load, reduces energy consumption, aligns multi-source asynchronous data streams, ensures temporal consistency, realizes cross-client modal migration, improves the model generalization ability, unifies heterogeneous metadata mapping, eliminates parameter system differences, and solves the representation theory conflict between discrete and continuous modalities by using techniques such as quantum-classical hybrid fusion and tensor network decomposition.

[0157] In summary, the present invention significantly improves the utilization rate of multi-modal data, system robustness, and computational efficiency through techniques such as cross-modal deep interaction, dynamic ethical conflict resolution, adversarial noise suppression, and energy consumption-accuracy optimization. At the same time, it solves ethical and security problems, provides a comprehensive solution for multi-modal AI applications, and can be flexibly applied to fields such as autonomous driving, e-commerce customer service systems, medical image diagnosis, industrial Internet of Things, and smart agriculture.

[0158] In the description of this specification, the descriptions referring to the terms "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0159] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and utilize the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A multi-modal data fusion AI cloud computing analysis system, characterized in that, It includes an ethical conflict resolution module, an adversarial modality purification module, a phase transition critical energy consumption optimization module, a multi-modal temporal calibration module, a federated modality distillation module, a metadata differential algebra governance module, a quantum-classical hybrid fusion module, and a dynamic offloading strategy execution module; The ethical conflict resolution module is used for ethical analysis and dynamic responsibility chain tracing; The adversarial modality purification module is used for the inverse mapping mechanism of the generative adversarial network and semantic field isolation; The phase transition critical energy consumption optimization module is used for thermodynamic phase transition control and; The multi-modal temporal calibration module is used for hyperchaotic timestamp synchronization and causal convolution resampling; The federated modality distillation module is used for zero-shot modality 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 modality encoding and classical attention decoupling; The dynamic offloading strategy execution module is used for reinforcement learning offloading decision-making and energy-aware neural architecture search.

2. The multimodal data fusion AI cloud computing analysis system according to claim 1, wherein The ethical conflict resolution module further includes an ethical analysis unit and a dynamic responsibility chain tracing unit; The ethical analysis unit performs hypergraph modeling on multi-modal ethical attributes through the quantum annealing algorithm, which is used to encode gestures and speech modalities in different cultural backgrounds into qubit states, and finds the path of minimizing ethical conflicts through the quantum tunneling effect; The dynamic responsibility chain tracing unit constructs a modality-decision causal graph using the causal forest algorithm, and uses the Shapley value to decompose the contribution of each modality to the final decision.

3. The multimodal data fusion AI cloud computing analysis system according to claim 2, wherein The adversarial modality purification module further includes a modality adversarial immune network and a semantic field isolation unit; The modality adversarial immune network uses the inverse mapping mechanism of the generative adversarial network, measures the feature offset of the infrared image's attacked modality through the Wasserstein distance, and is used to generate adversarial samples of adversarial noise for training a robust fusion model; The semantic field isolation unit is based on the manifold cutting technology of contrastive learning, and is used to forcibly separate the ironic semantics of the text modality from the visual modality features to an orthogonal subspace in the latent space.

4. The multimodal data fusion AI cloud computing analysis system according to claim 3, wherein The phase transition critical energy consumption optimization module further includes a thermodynamic phase transition controller and an energy manifold compression unit; The thermodynamic phase transition controller dynamically calculates the energy consumption-accuracy phase transition critical point for visual modality processing and speech modality processing through the Gibbs free energy formula of the non-equilibrium thermodynamics model; The energy manifold compression unit uses the tensor network decomposition algorithm to compress the multi-modal interaction matrix into a low-rank MP structure, reducing FLOPs while maintaining cross-modal correlation.

5. The multimodal data fusion AI cloud computing analysis system according to claim 4, wherein, The multi-modal temporal calibration module further includes a hyperchaotic timestamp synchronization unit and a causal convolution resampling unit; The hyperchaotic timestamp synchronization unit generates a non-linear timestamp based on the Lorenz system chaotic oscillator, and uses the Lyapunov exponent to align the asynchronous data streams of lidar and camera; The causal convolution resampling unit performs super-resolution resampling on the low-frame-rate modality through a deep causal convolution network, maintaining causal consistency with the high-frame-rate modality.

6. The multimodal data fusion AI cloud computing analysis system according to claim 5, wherein, The federated modality distillation module further includes a zero-shot modality distiller and a gradient confusion detection unit; The zero-shot modality 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 uses the persistent homology method in topological data analysis to detect abnormal Betti number distribution patterns in federated learning gradients.

7. The multimodal data fusion AI cloud computing analysis system according to claim 6, wherein The metadata differential algebra governance module further includes a manifold metadata parser and a dynamic environment coupling unit. The manifold metadata parser uses a differential algebraic equation solver to map heterogeneous metadata of a FLIR thermal imager and a Hikvision camera to a unified Lie group space. The dynamic environment coupling unit models the impact of atmospheric turbulence on satellite images based on stochastic differential equations and is used to generate a metadata compensation coefficient matrix.

8. The multimodal data fusion AI cloud computing analysis system according to claim 7, wherein, The quantum-classical hybrid fusion module further includes a quantum modality encoder and a classical attention decoupling unit. The quantum modality encoder uses a quantum variational encoder to map electroencephalogram signals and questionnaire texts to an entangled state in a quantum Hilbert space. The classical attention decoupling unit is dynamically gated by a spiking neural network and is used to simulate the biological integration mechanism of the prefrontal cortex of the brain for multimodal information.

9. The multimodal data fusion AI cloud computing analysis system according to claim 8, characterized in that, The dynamic offloading strategy execution module further includes a reinforcement learning offloading decision maker and an energy-aware neural architecture search unit. The reinforcement learning offloading decision maker is based on the 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-end architecture. The energy-aware neural architecture search unit simultaneously optimizes a three-objective function of model FLOPs, memory occupancy, and multimodal interaction efficiency through the Pareto evolutionary algorithm.

10. The analysis method of the multimodal data fusion AI cloud computing analysis system according to any one of claims 1-9, characterized in that, It includes the following steps: S1. Model multimodal ethical attributes and quantify modal contribution weights through a quantum annealing algorithm and a causal forest algorithm to trace the root causes of ethical biases. S2. Use a generative adversarial network and contrastive learning techniques to generate adversarial samples to enhance robustness and isolate semantic noise in text and visual modalities. S3. Dynamically optimize the energy consumption-accuracy phase transition threshold and compress the multimodal interaction matrix based on a thermodynamic model and tensor network decomposition. S4. Use a chaotic oscillator and a deep causal convolutional network to align asynchronous data streams and perform super-resolution resampling on low-frame-rate modalities. S5. Achieve 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 environmental interference biases. S7. Achieve quantum-classical hybrid feature fusion and decouple attention interference through a quantum variational encoder and a spiking neural network. S8. Optimize the cloud-edge-end offloading strategy and search for the optimal fusion architecture based on multi-agent reinforcement learning and the Pareto evolutionary algorithm.

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