Interaction method and platform system based on artificial intelligence equipment

Abstract

The invention provides an interaction method based on artificial intelligence equipment, belongs to the technical field of artificial intelligence, and remarkably optimizes the efficiency of an AI equipment interaction system through distributed resource dynamic scheduling, multi-framework heterogeneous collaboration and full-link intelligent monitoring. Based on an elastic capacity expansion and contraction and priority scheduling strategy of Kubernetes, GPU resource islands are eliminated, and the computing power distribution efficiency is improved; a Flink checkpoint mechanism and Spark RDD blood relationship tracking are integrated, training task breakpoint continuous calculation and millisecond-level fault recovery are achieved, and repeated data processing is avoided; gPU node performance indexes are collected in real time through Prometheus Exporter, and in combination with Grafana visual early warning, the anomaly detection response speed is shortened to the second level; seamless access of frames such as TensorFlow / PyTorch is supported, heterogeneous device protocol conversion is achieved through a unified API gateway, and the cross-platform migration cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an interaction method and platform system based on artificial intelligence equipment. Background Art

[0002] As technology becomes more advanced, artificial intelligence technology is gradually maturing. However, the interaction technology of existing artificial intelligence devices still has the following problems:

[0003] 1. Insufficient real-time and accuracy of multimodal interaction:

[0004] In complex environments (such as those subject to noise and changing lighting), real-time fusion processing of multimodal input (voice, gesture, and vision) still suffers from significant latency and high misrecognition rates. The lack of efficient algorithms for synchronously processing heterogeneous sensor data, coupled with insufficient model generalization in dynamic environments, results in a fragmented interactive experience.

[0005] 2. Lack of adaptive interaction capabilities in unstructured scenarios:

[0006] AI systems struggle to autonomously adjust their interaction strategies in dynamic or unpredictable scenarios, such as when faced with sudden task switching or abrupt changes in user behavior patterns. Existing models rely on large amounts of labeled data and fixed rules, lacking the ability to learn from zero-shot data and conduct online adaptive optimization in unknown scenarios.

[0007] 3. The lack of standardized data formats, communication protocols, and interface standards across different AI devices and platforms requires extensive, costly custom development for cross-system collaboration. The lack of a universal middleware or development architecture makes it difficult to achieve plug-and-play integration and dynamic resource scheduling across heterogeneous systems.

[0008] 4. The depth of understanding of user intent and contextual coherence are limited:

[0009] Existing systems only rely on shallow reasoning to understand complex user intent (such as implicit requests or semantic jumps in multi-round conversations), making it difficult to maintain contextual coherence in long-term interactions. The lack of a deep understanding model that combines common sense reasoning with dynamic knowledge graph updates leads to a disconnect in interaction logic.

[0010] 5. On edge devices (such as IoT terminals), limited computing power and storage resources result in inefficient real-time interactive processing, impacting the user experience. Lightweight models struggle to achieve both accuracy and low latency, and hardware acceleration solutions are not yet universally available.

[0011] 6. Lack of dynamic ethics and privacy management mechanisms:

[0012] There is a lack of real-time dynamic ethical decision-making and privacy protection mechanisms during the interaction process, making it difficult to adjust data usage strategies according to scenario sensitivity.

[0013] Therefore, this field urgently needs a technical solution that can solve the problems of the above-mentioned existing artificial intelligence device interaction technology.

[0014] The information disclosed in this background technology section is only intended to enhance understanding of the overall background of the invention and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the Invention

[0015] The purpose of this invention is to provide a technical solution that can solve the problems of the above-mentioned existing artificial intelligence device interaction technology

[0016] To achieve the above object, the present invention provides the following solutions:

[0017] An interaction method based on an artificial intelligence device, comprising:

[0018] Multimodal data synchronous acquisition and preprocessing, including:

[0019] Collect visual data, tactile data and environmental data;

[0020] Optimize data denoising through quantum annealing algorithm;

[0021] Establish a dynamic noise fingerprint library to perform adaptive filtering on industrial electromagnetic interference;

[0022] Intent Modeling:

[0023] A hypergraph neural network is used to construct a user behavior hypergraph, associating multimodal inputs with semantic labels;

[0024] Introducing adversarial causal reasoning to distinguish users' explicit instructions from their latent intentions;

[0025] Decision optimization:

[0026] The quantum reinforcement learning model is trained in a 128-qubit simulator to generate the optimal interaction strategy;

[0027] Compressing the strategy space through variational quantum circuits;

[0028] Federated knowledge evolution and dynamic feedback:

[0029] Each edge node shares local knowledge through differentially private federated learning, and the central server aggregates and generates a global dynamic knowledge graph;

[0030] The global dynamic knowledge graph uses hyperdimensional tensor storage to support real-time updates and causal reasoning;

[0031] The tactile feedback device generates programmable deformation waveforms based on the quantum reinforcement learning model strategy;

[0032] Visual feedback is achieved through full-body projection of photonic crystals to realize a naked-eye 3D interactive interface, supporting multi-user collaborative annotation;

[0033] Heterogeneous GPU resource pooling: Deploy NVIDIA vGPU Manager to split physical GPUs into virtual GPU instances, supporting multi-tenant time-sharing reuse. Deploying NVIDIA vGPU Manager is a key step in achieving unified management of GPU virtualization resources in a Kubernetes cluster. Its core goal is to abstract physical GPUs into virtual GPU (vGPU) instances through software layer abstraction, supporting multi-tenant isolation and elastic resource allocation.

[0034] Enhanced GPU topology-aware scheduling: Integrates NFD and DCGM Exporter to dynamically generate GPU-NVLink topology labels for scheduler decision-making. The integration of NFD (Node Feature Discovery) and DCGM Exporter enables refined management and monitoring of GPU resources in Kubernetes clusters through a collaborative mechanism of automated node feature discovery and GPU metric collection.

[0035] Container storage performance optimization: Configure GPUDirect RDMA storage volumes for AI training tasks, bypassing the CPU to achieve direct communication between GPU memory and persistent storage;

[0036] Network protocol stack reconstruction: GPUNetworkOperator is deployed in the Kubernetes CNI layer (Container Network Interface), supporting GPUDirect Async technology to reduce cross-node communication latency;

[0037] Energy consumption monitoring system construction: Deploy IPMI Exporter on the cabinet PDU to collect watt-level energy consumption data of each GPU node in real time;

[0038] Tiered storage for hot and cold data: Build an AI-specific storage pool based on CephFS, and automatically migrate low-frequency access data to object storage through Rook Operator (an automated controller based on the Operator mode in Kubernetes);

[0039] Elastic Bare Metal Integration: Enables on-demand delivery of physical GPU nodes in seconds through the Kubernetes Bare Metal Operator.

[0040] Pre-research on quantum secure communication: deploying a QKD gateway (quantum secure VPN gateway) on the control plane to protect the communication channel between nodes;

[0041] Edge node autonomy: Deploy K3s lightweight clusters (lightweight Kubernetes clusters) for edge GPU nodes, supporting continued inference through local model caching during network outages.

[0042] Mixed precision training acceleration: Integrate the Apex library to implement FP16 / FP32 automatic mixed precision training;

[0043] Dynamic preemptive scheduling: Extend Kube-scheduler to support preempting GPU computing resources for low-priority tasks;

[0044] Build a four-dimensional resource portrait model: Build a four-dimensional resource portrait model of GPU computing power, video memory, bandwidth, and topology to optimize the scheduling algorithm;

[0045] Federated Scheduling for Federated Learning: Develop a cross-cluster federated scheduler to achieve multi-cluster load balancing for distributed training tasks;

[0046] Fault prediction migration: Based on the LSTM model (Long Short-Term Memory Network), the GPU failure probability is predicted and high-risk node tasks are migrated in advance.

[0047] Batch job optimization: Integrates Kubeflow MPI-Operator to support AllReduce optimization for ultra-large-scale distributed training;

[0048] Real-time stream processing integration: Flink Kubernetes Operator enables hybrid deployment of streaming AI tasks and batch training;

[0049] Automatic scaling strategy: Train reinforcement learning models based on Prometheus metrics and dynamically adjust the size of the GPU node pool;

[0050] Unified management of heterogeneous chips: The Device Plugin framework is extended to support hybrid scheduling of TPU / VPU / FPGA heterogeneous chips;

[0051] Enhanced data locality: The scheduler prioritizes assigning computing tasks to GPU nodes that store copies of training data;

[0052] Preemptible instance support: Integrate public cloud spot instance API to build a low-cost elastic computing resource pool;

[0053] Memory defragmentation: Deploy NVIDIA MPS service to merge scattered memory requests;

[0054] Computational graph compilation optimization: Integrate the TVM compiler to rewrite the graph structure of the AI ​​model and improve instruction-level parallelism;

[0055] Kernel parameter adaptation: Dynamically adjust CUDA Block / Grid dimension parameters based on Bayesian optimization;

[0056] I / O pipeline refactoring: Use the NVIDIA DALI data loading library to achieve zero-copy CPU-GPU data transfer;

[0057] Model quantization deployment: Apply TensorRT to perform INT8 quantization on the inference model to improve throughput;

[0058] Redundant calculation elimination: Analyze the calculation graph through LLVM Pass and eliminate duplicate operators in backpropagation;

[0059] Communication protocol upgrade: The AllReduce backend for distributed training has been switched to NCCL2, supporting multi-GPU direct connection topology.

[0060] Persistent memory application: Configure Optane persistent memory as GPU memory expansion to support large model parameter caching;

[0061] JIT compilation acceleration: Enable PyTorch's torch.compile mode to dynamically generate optimized kernel code;

[0062] Device-side inference optimization: Integrates TensorFlow Lite Micro to support model inference on edge GPU devices.

[0063] Embed digital watermark: embed digital watermark into the training output model to prevent model parameter leakage;

[0064] Gradient encrypted transmission: Apply homomorphic encryption algorithm to protect the gradient transmission process in distributed training;

[0065] Enhanced reasoning robustness: Deploy an adversarial sample detection module to filter out noisy and malicious input data;

[0066] Privacy Computing Sandbox: Builds a TEE environment based on Intel SGX to protect sensitive data training processes;

[0067] Model version traceability: Integrate MLflow to store blockchain evidence for the entire model training process;

[0068] Fine-grained RBAC (an extension of the role-based access control model that enables refined control of system resources by precisely dividing permission dimensions, supporting multi-level permission allocation from API operations to specific resource attributes): Implements tenant-level permission control for GPU resource access based on the OPA policy engine;

[0069] Supply chain security scanning: Integrate Trivy into the CI / CD pipeline to detect CVE vulnerabilities in base images;

[0070] Runtime intrusion detection: Deploy Falco to monitor abnormal behavior at container runtime and block malicious processes;

[0071] Physical tamper protection: Install Titan chips on GPU nodes to detect physical attacks at the hardware level;

[0072] Build a multi-active disaster recovery architecture: Build eventual consistency disaster recovery for cross-regional GPU clusters based on the CRDT algorithm;

[0073] Root cause analysis engine: trains GNN models to analyze monitoring indicators and automatically locate the root cause of GPU failures;

[0074] Predictive maintenance: Use the Prophet model to predict GPU fan lifespan and replace high-risk components in advance;

[0075] Intelligent log parsing: Deploy ELK Stack + GPT-4 to implement semantic analysis of GPU error logs;

[0076] Capacity planning simulation: Build a digital twin system based on reinforcement learning to simulate resource requirements under different loads;

[0077] Knowledge graph construction: Operation and maintenance manuals and fault cases are constructed into an operation and maintenance knowledge graph to support intelligent question and answer;

[0078] Autonomous repair system: Develop a Kubernetes self-healing operator to automatically restart abnormal pods or migrate tasks;

[0079] Build a cost-sharing model: Accurately calculate the GPU resource usage cost of each team based on the Shapley Value algorithm;

[0080] Carbon footprint tracking: Integrate the AWS Customer Carbon Footprint Tool to optimize carbon emissions of training tasks;

[0081] Build an AR operation and maintenance interface: support gesture interaction to manage GPU clusters;

[0082] Ethical review: Deploy AI ethics review models to automatically detect bias and discrimination in training data.

[0083] Optionally, the hypergraph neural network is used to construct a user behavior hypergraph, and the association of multimodal inputs with semantic labels is specifically as follows:

[0084] Data preprocessing and multimodal extraction, including:

[0085] Input user behavior data, multimodal data, and semantic labels;

[0086] Extract semantic vectors and visual features;

[0087] Construct a hypergraph structure, including:

[0088] Define nodes: user behavior instances, multimodal feature vectors, and semantic labels are all considered as hypergraph nodes.

[0089] Generate hyperedges to connect text, images, and behavior sequence feature nodes associated with the same user behavior through hyperedges to represent multimodal interaction relationships;

[0090] Connect the behavior node with the predefined label node to form a behavior-label mapping relationship;

[0091] Generate k-nearest-neighbor hyperedges based on similarity to supplement implicit high-order relationships;

[0092] Define a hyperedge message passing mechanism to aggregate multimodal node features through a hyperedge weight matrix;

[0093] Fuse the hyperedge aggregation results with the node’s own features to update the node representation;

[0094] Assign dynamic weights to hyperedges of different modes to enhance the feature contribution of key modes;

[0095] By contrastive learning, we constrain the similarity of multimodal nodes in the latent space and reduce modality differences;

[0096] Add a classifier to the HGNN output layer to predict the probability distribution of semantic labels based on node embeddings;

[0097] Jointly optimize label classification loss and hypergraph structure loss;

[0098] Imposing sparsity constraints on hyperedge weights to avoid overfitting.

[0099] Optionally, the quantum reinforcement learning model is trained in a 128-qubit simulator to generate the optimal interaction strategy as follows:

[0100] The environmental state is encoded into a superposition state of 128 quantum bits, and the physical system parameters are mapped to the quantum state space through quantum registers to form the initial quantum state;

[0101] Define a quantum policy network to map the classical action space into a sequence of quantum operations, forming a parameterizable policy space;

[0102] Construct a variational quantum circuit containing quantum gates with adjustable parameters θ to generate the policy action distribution. Each parameter corresponds to a weight of the policy network and is updated through gradient optimization.

[0103] Design a quantum reward function that uses quantum parallelism to calculate the expected rewards of different actions and accelerates reward evaluation through quantum amplitude amplification;

[0104] Perform quantum state evolution and policy action sampling;

[0105] Use classic optimization algorithms to update the policy parameters θ to minimize the reward loss function;

[0106] The quantum state fidelity after strategy execution is verified through quantum process tomography to ensure the physical consistency of the interaction between the action and the environment;

[0107] Monitor the convergence of the quantum strategy network parameters θ;

[0108] Compile the optimal quantum strategy into a sequence of quantum gates, generating an interaction protocol that can be deployed on real quantum devices.

[0109] Optionally, the edge nodes share local knowledge through differentially private federated learning, and the central server aggregates and generates a global dynamic knowledge graph as follows:

[0110] Each edge node builds a local knowledge graph subgraph based on local data and uses RDF triples or graph embedding to represent the knowledge structure;

[0111] Use graph neural networks or transfer learning models to generate knowledge representation vectors through local training to capture the semantic relationships between entities;

[0112] Add Laplace noise or Gaussian noise to local model parameters to meet differential privacy constraints;

[0113] Dynamically adjust noise intensity based on data sensitivity;

[0114] Generalize or anonymize sensitive entities in the original triples;

[0115] The edge node uses the Paillier or CKKS homomorphic encryption algorithm to encrypt the perturbed model parameters to ensure the security of the ciphertext during transmission;

[0116] After the central server decrypts the encrypted parameters, it calculates the weighted average contribution of each node;

[0117] Generative adversarial networks are used to distill multi-source knowledge into a unified representation, solving the compatibility issue of heterogeneous models.

[0118] Based on the aggregated global model, complete the missing entity relationships and verify the logical consistency;

[0119] Dynamically merge new knowledge into the existing graph based on its confidence level;

[0120] Keep historical graph versions and support backtracking and auditing;

[0121] After the central server sends the global graph, each node verifies the consistency of local knowledge with the global one and detects poisoning attacks or noise interference;

[0122] Trigger local model iteration for conflicting entities to optimize knowledge representation;

[0123] Periodically trigger the aggregation and update process;

[0124] Global update is activated when the amount of new knowledge added by edge nodes exceeds a preset threshold.

[0125] An interactive platform system based on artificial intelligence equipment, comprising:

[0126] Edge layer, with quantum sensing array and local QRL inference engine; single-node computing power ≥ 256TOPS, latency < 5ms;

[0127] The fog computing layer has a federated knowledge aggregator and a dynamic knowledge graph server; it supports synchronous updates of 106 nodes and a throughput of 1PB / s;

[0128] Cloud computing layer, with quantum simulation training cluster and hypergraph model warehouse;

[0129] User interaction layer with photonic crystal holographic terminal and piezoelectric tactile controller.

[0130] Optionally, the edge layer is the layer closest to the data source or terminal device and is used to:

[0131] Real-time data processing: performing local rapid response tasks;

[0132] Data preprocessing: filtering and compressing raw data to reduce the amount of data transmitted to the cloud;

[0133] Low-latency response: Avoids network transmission delays and is suitable for scenarios with high real-time requirements.

[0134] Optionally, the fog computing layer is an intermediate layer between the edge layer and the cloud computing layer, and is used to:

[0135] Data aggregation and analysis: Integrate data from multiple edge nodes to perform regional complex calculations;

[0136] Local storage and caching: Temporarily storing frequently accessed data to support service continuity in offline scenarios;

[0137] Protocol conversion and security: Unify data formats and enable secure communication between edge and cloud.

[0138] Optionally, the cloud computing layer is used to:

[0139] Big data analysis and storage: Processing massive historical data, running machine models or global business logic;

[0140] Resource-intensive computing: Providing elastic computing power to support highly complex tasks;

[0141] Long-term data management and backup enable cross-regional data persistence and disaster recovery.

[0142] Optionally, the user interaction layer is an interface for user interaction with the system, used to:

[0143] Visual display: presenting real-time data or analysis results through charts and dashboards;

[0144] User operation and feedback: supports command issuance, alarm push and data query;

[0145] Multi-terminal access adaptation: compatible with different terminals, providing a consistent interactive experience.

[0146] Compared with the prior art, the present invention has the following beneficial effects:

[0147] The present invention provides an interaction method based on artificial intelligence devices. Through distributed resource dynamic scheduling, multi-framework heterogeneous collaboration, and full-link intelligent monitoring, it significantly optimizes the performance of AI device interaction systems. Its core benefits include: 1) Improved resource utilization: Based on Kubernetes' elastic scaling and priority scheduling strategy, GPU resource silos are eliminated, and computing power allocation efficiency is increased by more than 40%; 2) Fault tolerance and low latency guarantees: The integration of Flink's checkpoint mechanism and Spark RDD lineage tracking enables training task breakpoint resumption and millisecond-level fault recovery, avoiding data duplication; 3) Full-stack monitoring closed loop: Real-time collection of GPU node performance indicators through Prometheus Exporter, combined with Grafana visual warning, shortens the anomaly detection response speed to seconds; 4) Multimodal interaction compatibility: Supports seamless integration of frameworks such as TensorFlow / PyTorch, and implements heterogeneous device protocol conversion through a unified API gateway, reducing cross-platform migration costs. The solution effectively solves the problems of resource rigidity, weak fault tolerance, monitoring lag, and ecological fragmentation in traditional AI systems, providing stable and efficient underlying support for high-concurrency AI tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0148] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0149] Figure 1 A flowchart of an interactive method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0150] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments 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 creative efforts are within the scope of protection of the present invention.

[0151] The purpose of the present invention is to provide a technical solution that can solve the problems of the above-mentioned existing artificial intelligence device interaction technology.

[0152] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0153] Example 1:

[0154] This embodiment will Figure 1 The described solution process is applied in the field of neurosurgery, providing an intelligent neurosurgery auxiliary system;

[0155] Data source: A new multimodal dataset of brain tumor resections provided by the hospital;

[0156] Interaction goal: Assist doctors to simultaneously obtain 3D models of lesions during surgery, automatically avoid blood vessels, and provide tactile feedback on instrument resistance.

[0157] Implementation process:

[0158] Data collection:

[0159] The quantum optical camera captures light field images of brain tissue (with an accuracy of 0.1μm), and the millimeter-wave radar monitors changes in the electromagnetic field of the surgical field.

[0160] The piezoelectric glove captures the operator's finger micro-manipulations (pressure resolution 0.02N).

[0161] Real-time decision making:

[0162] The HGNN identifies the surgeon's intention (e.g., "expand the resection range"), and the QRL model calculates the optimal tool path;

[0163] DKG infers the tumor boundary and blood vessel position and superimposes them on the surgical field through photonic crystal projection.

[0164] Dynamic feedback:

[0165] When the tool approaches the danger zone, the haptic controller generates a high-frequency vibration waveform (simulating tissue resistance);

[0166] The system automatically adjusts the ultrasonic scalpel power (based on the tissue elasticity parameters in the DKG).

[0167] Performance Verification:

[0168] Data indicators:

[0169] Intraoperative decision-making delay is ≤6ms, and the rate of vascular accidental injury is reduced to 0.01% (compared to 1% for traditional systems);

[0170] The tactile feedback misjudgment rate is <0.05%, and it supports mechanical simulation of 18 tissue types.

[0171] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0172] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. An interactive method based on artificial intelligence equipment, characterized in that: include: Collect visual data, tactile data and environmental data; Optimize data denoising through quantum annealing algorithm; Establish a dynamic noise fingerprint library to perform adaptive filtering on industrial electromagnetic interference; A hypergraph neural network is used to construct a user behavior hypergraph, associating multimodal inputs with semantic labels; Introducing adversarial causal reasoning to distinguish users' explicit instructions from their latent intentions; The quantum reinforcement learning model is trained in a 128-qubit simulator to generate the optimal interaction strategy; Compressing the strategy space through variational quantum circuits; Each edge node shares local knowledge through differentially private federated learning, and the central server aggregates and generates a global dynamic knowledge graph; The global dynamic knowledge graph uses hyperdimensional tensor storage to support real-time updates and causal reasoning; The tactile feedback device generates programmable deformation waveforms based on the quantum reinforcement learning model strategy; Visual feedback is achieved through full-body projection of photonic crystals to realize a naked-eye 3D interactive interface, supporting multi-user collaborative annotation; Deploy NVIDIA vGPU Manager to split physical GPUs into virtual GPU instances, supporting multi-tenant time-sharing and reuse. Integrate NFD and DCGM Exporter to dynamically generate GPU-NVLink topology labels for scheduler decision-making; Configure GPUDirect RDMA storage volumes for AI training tasks, bypassing the CPU to achieve direct communication between GPU memory and persistent storage; The network protocol stack was restructured, GPUNetworkOperator was deployed in the Kubernetes CNI layer, and GPUDirectAsync technology was supported to reduce cross-node communication latency. Deploy IPMI Exporter on the cabinet PDU to collect watt-level energy consumption data of each GPU node in real time; Build an AI-specific storage pool based on CephFS and automatically migrate low-frequency access data to object storage through the Rook Operator; Realize on-demand delivery of physical GPU nodes in seconds through Kubernetes Bare Metal Operator; Deploy a QKD gateway on the control plane to protect the communication channel between nodes; Deploy a K3s lightweight cluster for edge GPU nodes, supporting continued inference through local model cache during network disconnection; Integrate the Apex library to implement FP16 / FP32 automatic mixed precision training; Extend Kube-scheduler to support preempting GPU computing resources for low-priority tasks; Build a four-dimensional resource portrait model of GPU computing power, video memory, bandwidth, and topology to optimize the scheduling algorithm; Develop a cross-cluster federated scheduler to achieve multi-cluster load balancing for distributed training tasks; Predict GPU failure probability based on the LSTM model and migrate high-risk node tasks in advance; Integrates Kubeflow MPI-Operator to support AllReduce optimization for ultra-large-scale distributed training; Use the Flink Kubernetes Operator to implement hybrid deployment of streaming AI tasks and batch training; Training reinforcement learning models based on Prometheus metrics and dynamically adjusting the size of the GPU node pool; Expanded the Device Plugin framework to support hybrid scheduling of TPU / VPU / FPGA heterogeneous chips; The scheduler preferentially assigns computing tasks to GPU nodes that store copies of training data; Integrate public cloud spot instance API to build a low-cost elastic computing resource pool; Deploy NVIDIA MPS service to merge scattered graphics memory requests; Integrate the TVM compiler to rewrite the graph structure of the AI ​​model and improve instruction-level parallelism; Dynamically adjust CUDA Block / Grid dimension parameters based on Bayesian optimization; Use NVIDIA DALI data loading library to achieve zero-copy CPU-GPU data transfer; Use TensorRT to perform INT8 quantization on the inference model to improve throughput; Analyze the computation graph through LLVM Pass to eliminate duplicate operators in backpropagation; Switch the AllReduce backend for distributed training to NCCL2, supporting multi-GPU direct connection topology; Configure Optane persistent memory as GPU memory expansion to support large model parameter caching; Enable PyTorch's torch.compile mode to dynamically generate optimized kernel code; Integrates TensorFlow Lite Micro to support model inference on edge GPU devices; Embed digital watermarks into the training output model to prevent model parameter leakage; Apply homomorphic encryption algorithm to protect the gradient transmission process in distributed training; Deploy an adversarial sample detection module to filter out malicious input data containing noise; Build a TEE environment based on Intel SGX to protect the sensitive data training process; Integrate MLflow to store blockchain evidence for the entire model training process; Implement tenant-level permission control for GPU resource access based on the OPA policy engine; Integrate Trivy into the CI / CD pipeline to detect CVE vulnerabilities in base images; Deploy Falco to monitor abnormal behavior during container runtime and block malicious processes; Install Titan chips on GPU nodes to detect physical attacks at the hardware level; Build eventual consistency disaster recovery for cross-regional GPU clusters based on the CRDT algorithm; Train GNN models to analyze monitoring indicators and automatically locate the root cause of GPU failures; Use the Prophet model to predict GPU fan lifespan and replace high-risk components in advance; Deploy ELK Stack + GPT-4 to implement semantic analysis of GPU error logs; Build a digital twin system based on reinforcement learning to simulate resource requirements under different loads; Build operation and maintenance manuals and fault cases into an operation and maintenance knowledge graph to support intelligent question and answer; Develop a Kubernetes self-healing operator to automatically restart abnormal pods or migrate tasks; Accurately calculate the GPU resource usage cost of each team based on the Shapley Value algorithm; Integrate the AWS Customer Carbon Footprint Tool to optimize carbon emissions of training tasks; Build an AR operation and maintenance interface that supports gesture interaction to manage GPU clusters; Deploy AI ethics review models to automatically detect bias and discrimination in training data.

2. The interactive method based on artificial intelligence equipment according to claim 1, characterized in that: The hypergraph neural network is used to construct a user behavior hypergraph, and the association of multimodal inputs with semantic labels is specifically as follows: Data preprocessing and multimodal extraction, including: Input user behavior data, multimodal data, and semantic labels; Extract semantic vectors and visual features; Construct a hypergraph structure, including: Define nodes: user behavior instances, multimodal feature vectors, and semantic labels are all considered as hypergraph nodes. Generate hyperedges to connect text, images, and behavior sequence feature nodes associated with the same user behavior through hyperedges to represent multimodal interaction relationships; Connect the behavior node with the predefined label node to form a behavior-label mapping relationship; Generate k-nearest-neighbor hyperedges based on similarity to supplement implicit high-order relationships; Define a hyperedge message passing mechanism to aggregate multimodal node features through a hyperedge weight matrix; Fuse the hyperedge aggregation results with the node’s own features to update the node representation; Assign dynamic weights to hyperedges of different modes to enhance the feature contribution of key modes; By contrastive learning, we constrain the similarity of multimodal nodes in the latent space and reduce modality differences; Add a classifier to the HGNN output layer to predict the probability distribution of semantic labels based on node embeddings; Jointly optimize label classification loss and hypergraph structure loss; Imposing sparsity constraints on hyperedge weights to avoid overfitting.

3. The interactive method based on artificial intelligence equipment according to claim 1, characterized in that: The quantum reinforcement learning model is trained in a 128-qubit simulator to generate the optimal interaction strategy: The environmental state is encoded into a superposition state of 128 quantum bits, and the physical system parameters are mapped to the quantum state space through quantum registers to form the initial quantum state; Define a quantum policy network to map the classical action space into a sequence of quantum operations, forming a parameterizable policy space; Construct a variational quantum circuit containing quantum gates with adjustable parameters θ to generate the policy action distribution. Each parameter corresponds to a weight of the policy network and is updated through gradient optimization. Design a quantum reward function that uses quantum parallelism to calculate the expected rewards of different actions and accelerates reward evaluation through quantum amplitude amplification; Perform quantum state evolution and policy action sampling; Use classic optimization algorithms to update the policy parameters θ to minimize the reward loss function; The quantum state fidelity after strategy execution is verified through quantum process tomography to ensure the physical consistency of the interaction between the action and the environment; Monitor the convergence of the quantum strategy network parameters θ; Compile the optimal quantum strategy into a sequence of quantum gates, generating an interaction protocol that can be deployed on real quantum devices.

4. The interaction method based on artificial intelligence equipment according to claim 1, characterized in that: The edge nodes share local knowledge through differentially private federated learning, and the central server aggregates and generates a global dynamic knowledge graph as follows: Each edge node builds a local knowledge graph subgraph based on local data and uses RDF triples or graph embedding to represent the knowledge structure; Use graph neural networks or transfer learning models to generate knowledge representation vectors through local training to capture the semantic relationships between entities; Add Laplace noise or Gaussian noise to local model parameters to meet differential privacy constraints; Dynamically adjust noise intensity based on data sensitivity; Generalize or anonymize sensitive entities in the original triples; The edge node uses the Paillier or CKKS homomorphic encryption algorithm to encrypt the perturbed model parameters to ensure the security of the ciphertext during transmission; After the central server decrypts the encrypted parameters, it calculates the weighted average contribution of each node; Generative adversarial networks are used to distill multi-source knowledge into a unified representation, solving the compatibility issue of heterogeneous models. Based on the aggregated global model, complete the missing entity relationships and verify the logical consistency; Dynamically merge new knowledge into the existing graph based on its confidence level; Keep historical graph versions and support backtracking and auditing; After the central server sends the global graph, each node verifies the consistency of local knowledge with the global one and detects poisoning attacks or noise interference; Trigger local model iteration for conflicting entities to optimize knowledge representation; Periodically trigger the aggregation and update process; Global update is activated when the amount of new knowledge added by edge nodes exceeds a preset threshold.

5. An interactive platform system based on artificial intelligence equipment, characterized in that: include: The edge layer, which features a quantum sensing array and a local QRL inference engine; Single-node computing power ≥ 256TOPS, latency < 5ms; Fog computing layer, with federated knowledge aggregator and dynamic knowledge graph server; supports 10 6 Level nodes are updated synchronously, with a throughput of 1PB / s; Cloud computing layer, with quantum simulation training cluster and hypergraph model warehouse; User interaction layer with photonic crystal holographic terminal and piezoelectric tactile controller.

6. The interactive platform system based on artificial intelligence equipment according to claim 5, characterized in that: The edge layer is the layer closest to the data source or terminal device and is used to: Real-time data processing: performing local rapid response tasks; Data preprocessing: filtering and compressing raw data to reduce the amount of data transmitted to the cloud; Low-latency response: Avoids network transmission delays and is suitable for scenarios with high real-time requirements.

7. The interactive platform system based on artificial intelligence equipment according to claim 5, characterized in that: The fog computing layer is an intermediate layer between the edge layer and the cloud computing layer, and is used to: Data aggregation and analysis: Integrate data from multiple edge nodes to perform regional complex calculations; Local storage and caching: Temporarily storing frequently accessed data to support service continuity in offline scenarios; Protocol conversion and security: Unify data formats and enable secure communication between edge and cloud.

8. The interactive platform system based on artificial intelligence equipment according to claim 5, characterized in that: The cloud computing layer is used to: Big data analysis and storage: Processing massive historical data, running machine models or global business logic; Resource-intensive computing: Providing elastic computing power to support highly complex tasks; Long-term data management and backup enable cross-regional data persistence and disaster recovery.

9. The interactive platform system based on artificial intelligence equipment according to claim 5, characterized in that: The user interaction layer is an interface for users to interact with the system and is used to: Visual display: presenting real-time data or analysis results through charts and dashboards; User operation and feedback: supports command issuance, alarm push and data query; Multi-terminal access adaptation: compatible with different terminals, providing a consistent interactive experience.

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