## 3 Method Implementation  
When a fault occurs in a consumer electronic device, users can upload diagnostic information, including fault images, descriptions (either as voice or text), and odor data from sensors. If only a single type of information is provided, such as an image, the model helps users add more information, for example, by providing a text input box or a voice module. This ensures comprehensive data collection for the model. The collected information is then analyzed using graph neural networks and a multimodal knowledge graph to identify the root cause of the fault. Finally, the results are transformed into a user-friendly report using a text generation model, making the diagnosis easy to understand. Following this approach, after collecting user-provided fault information, the model extracts features from various modalities of fault data. For images, the model first pre-processes the fault image by resizing and normalizing it. The image is then input into a ResNet network, where it passes through convolutional and pooling layers to obtain an initial feature map. This feature map is further refined through residual blocks to extract deeper features. Finally, after global pooling and a fully connected layer, the image feature vector is obtained. For text data, the model preprocesses the fault description using the BertTokenizer to tokenize the text, adding special markers and positional encodings to create an input vector. This input vector is then fed into BERT's encoding layer, where self-attention mechanisms generate context-aware feature vectors. Feature pooling is applied to produce the text feature vector. For audio data, the model preprocesses the fault audio by adjusting the sampling rate and segmenting the audio into frames. These frames are then input into a Long Short-Term Memory (LSTM) model, which processes each frame sequentially to generate a probability distribution over text. This distribution is passed through a decoder to produce output text, which is then input into the BERT model to generate the audio feature vector. For other modalities, data dimensionality expansion is applied before extracting feature vectors. For example, for odor data collected by sensors, the model maps the one-dimensional odor data to a higher-dimensional feature space, capturing the characteristics of the olfactory data more effectively and resulting in an olfactory feature vector. After extracting feature vectors from image, text, audio, and olfactory modalities, the model uses a modality fusion module to integrate these feature vectors. The fusion module employs a contrastive learning strategy that maximizes the similarity between related modality features while minimizing the similarity between unrelated ones, producing a fused multimodal feature vector. This process ensures the seamless integration of information from different modalities, resulting in a comprehensive input for subsequent fault detection and diagnosis. The multimodal feature vector is then fed into GraphSAGE for inference. At this stage, the model maps the multimodal feature vector as nodes within a graph and establishes adjacency relationships to construct the graph. GraphSAGE is then used to sample neighboring nodes and aggregate their features. Through multi-level feature aggregation, each layer of GraphSAGE updates the node feature vectors, allowing each node representation to include both its own features and those of its neighbors. This process enables the node features to capture information from nodes at greater distances. By sampling and aggregating with GraphSAGE, the model utilizes the multimodal feature vectors to infer the fault diagnosis results. The fault results obtained from GraphSAGE are input into a pre-constructed multimodal knowledge graph, where reasoning is used to identify fault points and corresponding diagnostic solutions for consumer electronics.