Adaptive distributed compression system based on multimodal understanding

Abstract

To provide a data compression system that combines large-scale language models (LLMs) with distributed computing to solve the problems that conventional compression techniques only consider statistical redundancy and are unable to exploit semantic structure or contextual information, and furthermore, large-scale model-based compression requires high resources and fails to fully exploit the interrelationships in multimodal data.SOLUTION: The present invention achieves highly efficient compression even in resource-constrained environments, by utilizing the interrelationships between different data formats using a multimodal understanding module, selecting an optimal model according to the data characteristics and communication environment using an adaptive model selection module, utilizing shared knowledge between terminals via a distributed collaborative compression module, transferring the compression capabilities of large-scale models using a knowledge distillation module to lightweight models, and integrating efficient coding using an adaptive arithmetic coding module.SELECTED DRAWING: None

Description

Technical Field

[0001] The present invention relates to data compression technology, particularly an innovative data compression system that combines large language models (LLMs) and distributed computing. Specifically, it relates to a system that achieves ultra-high efficiency reversible compression for various data formats such as text, images, audio, and video through collaborative understanding between terminals and adaptive model selection. Further, the present invention is related to technologies aimed at a dramatic improvement in data transfer efficiency in next-generation communication technologies (such as 6G), edge computing, mobile devices, IoT systems, etc. Additionally, it relates to the application of advanced artificial intelligence technologies in resource-constrained environments, particularly a new paradigm of data compression architecture based on semantic understanding and distributed processing of multimodal data.

Background Art

[0002] Data compression technology has been researched and developed for many years as the foundation of digital communication. Conventional compression technologies are mainly based on the information-theoretic framework established by Claude Shannon, and standard compression methods such as ZIP, FLAC, PNG, H.264 / H.265, etc. are widely popular. These methods achieve compression by detecting statistical redundancies in the data and converting them into more efficient representations. For example, entropy coding techniques such as Huffman coding and run-length coding, predictive coding, and transform coding (DCT, wavelet transform, etc.) are widely used.

[0003] These traditional compression technologies are restricted by the theoretical limits (Shannon entropy) based on Shannon's information theory and only consider the statistical properties of the data. Therefore, there is an inherent limitation that the semantic structure and context information of the data cannot be fully utilized. For example, for the sentence "New York is a city in the United States", without understanding the semantic relationship between "New York" and "the United States", only compression based on the statistical properties of the string can be performed.

[0004] In recent years, according to the research by Li et al. (arXiv:2407.07723v3), "LMCompress" leveraging large language models (LLMs) has been proposed. Based on the insight that "understanding is compression", this technology realizes a compression rate far exceeding conventional compression technologies by utilizing the understanding ability of large models. LMCompress presents a new paradigm that realizes compression based on in-depth understanding of data by approximating the computationally infeasible concept of Solomonoff induction with large models.

[0005] Specifically, LMCompress achieves a compression rate approximately 4 times that of bz2 for text data, approximately 2 times that of JPEG-XL for image data, approximately 2 times that of FLAC for audio data, and approximately 2 times that of H.264 for video data. This epoch-making achievement indicates that large models can understand and utilize not only the statistical characteristics of data but also semantic structures and context information.

[0006] However, the following issues remain in LMCompress. 1. The computational resource requirements of large models are high, making it difficult to implement in resource-constrained environments such as mobile terminals. For example, large models such as iGPT (image-GPT) and LLaMA3 have tens of billions to hundreds of billions of parameters and require hundreds of GB of memory and high-performance GPUs. These resource requirements are difficult to meet in battery-powered mobile devices and IoT devices. 2. Due to compression by a single model, the interrelationships of multimodal data cannot be fully utilized. For example, there is a strong correlation between slide images and narration audio in a presentation file, but in LMCompress, since these are treated as independent data, compression that utilizes complementary information cannot be achieved. 3. There is a lack of an adaptive compression strategy according to the communication environment and data characteristics. Since LMCompress uses a fixed model, it cannot provide optimal compression for environments where communication conditions such as bandwidth and latency change, or for situations with different data complexities. For example, in a low-bandwidth environment, the processing speed may need to be prioritized over the compression ratio, but such an adaptive switch cannot be made. 4. The framework for cooperative compression in a distributed environment has not been established. There is a possibility of further improving communication efficiency by having the sender and receiver utilize shared knowledge to perform compression and decompression cooperatively, but such a cooperative approach is not adopted in LMCompress. 5. A method for efficiently transferring the knowledge of large models to lightweight models has not been established. To achieve high-efficiency compression even in resource-constrained environments, a technology for distilling the compression ability of large models into lightweight models is required, but this aspect is not considered in LMCompress.

[0007] These issues have become important barriers in the practical application of LMCompress. Especially when considering applications in resource-constrained environments such as mobile devices and IoT devices, it is essential to solve these problems. Also, in next-generation communication technologies (such as 6G), a compression system that can adapt to various communication environments and operate cooperatively between terminals is required.

Prior Art Documents

Non-Patent Documents

[0008]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0009] The problems to be solved by the present invention are as follows. 1. To provide a lightweight and high-performance compression system that can operate efficiently even in resource-constrained environments (such as mobile terminals). In resource-constrained environments such as mobile devices, IoT devices, and wearable devices, it is a challenge to achieve compression performance comparable to that of large models. Specifically, there is a need for a lightweight compression system that can suppress the memory usage to several GB or less, efficiently utilize the processing capabilities of CPUs / GPUs, and minimize battery consumption. For example, a smartphone needs to meet the constraint conditions of a memory usage of 2 GB or less, a compression processing speed of 10 MB or more per second, and a battery consumption of 5% or less per hour. 2. To establish a more advanced compression method that utilizes the interrelationships between multimodal data. It is an issue to develop a method for understanding the interrelationships between different modalities such as text, images, audio, and video, and improving the compression rate based on that understanding. For example, compression that utilizes the semantic correspondence between slide images and narration audio in a presentation file, the complementary relationship between text and images on a web page, the synchronization relationship between video and audio tracks in a video, etc. is required. By doing so, it aims to improve the compression rate by 15 - 30 percent or more compared to the case of compressing each modality independently. 3. Construction of an adaptive compression framework that dynamically selects the optimal compression strategy according to the communication environment and data characteristics It is an issue to develop a framework that dynamically selects the optimal compression model and compression parameters according to communication parameters such as bandwidth, delay, packet loss rate, and data characteristics such as data complexity, structural features, and predictability. For example, in a high-bandwidth environment, a model that maximizes the compression rate is selected, and in a low-bandwidth environment, a model that prioritizes processing speed is selected, and an adaptive switch according to the situation is required. Also, optimization considering terminal states such as battery level and CPU / GPU load is necessary. 4. Maximization of communication efficiency through cooperative compression between multiple terminals It is an issue to develop a method for maximizing communication efficiency by having the sender and receiver cooperate in compression and decompression using shared knowledge. Specifically, when both terminals have common domain knowledge, a technology that minimizes communication overhead by omitting information that can be inferred from that knowledge and transmitting only the differential information is required. Also, in a continuous communication session, it is also an issue to construct a shared context based on past communication content and realize compression that utilizes that context. 5. Establishment of knowledge distillation technology for efficiently transferring the compression ability of large-scale models to lightweight models The challenge is to develop a technology for efficiently distilling the compression capabilities of large-scale models (such as iGPT and LLaMA3) into lightweight models with 1 / 10 to 1 / 100 the number of parameters. Specifically, techniques are required for training lightweight models using the prediction distribution of large-scale models as teacher signals and maximizing the approximation accuracy of the prediction distribution, as well as for constructing domain-specific lightweight models through knowledge distillation specialized for specific domains. 6. Realization of a Secure Compression System Considering Privacy and Security In the compression of data containing personal information and confidential information, the development of technologies to ensure privacy and security is an issue. Specifically, privacy protection through in-terminal processing, secure knowledge sharing based on differential privacy, and efficient integration of encryption and compression are required. In particular, when handling highly confidential information such as medical data and financial data, it is important to achieve highly efficient compression while maintaining the confidentiality of the data. By comprehensively solving these issues, we aim to establish the basic technology for an ultra-high-efficiency data transfer system that can support next-generation communication technologies (such as 6G).

Means for Solving the Problems

[0010] The present invention proposes an "adaptive distributed compression system based on multimodal understanding". This system extends the "compression based on understanding", which is the basic concept of LMCompress, and realizes more efficient and flexible data compression by combining cooperative understanding and adaptive model selection in a distributed environment. This system is composed of the following main components. 1. Multimodal Understanding Module: Understand and utilize the interrelationships between multiple data formats The multimodal understanding module analyzes the interrelationships between different data formats (such as text, images, audio, videos, etc.) and extracts complementary information. Specifically, it uses a cross-modal attention mechanism to model the semantic correspondence between different modalities and constructs a shared semantic representation. For example, in the case of a presentation file, it analyzes the semantic correspondence between slide images and narration audio, and identifies slide content inferable from the narration audio and narration content inferable from the slide images. This can efficiently reduce redundant information compared to compressing each modality independently. In addition, the multimodal understanding module constructs a cross-modal prediction model between different modalities. This model reduces the amount of predictable information by predicting information of one modality from information of another modality. For example, it becomes possible to predict a part of a slide image from audio content or predict audio features from video frames. 2. Adaptive Model Selection Module: Selects the optimal model according to data characteristics and communication environment The adaptive model selection module dynamically selects the optimal model from multiple compression models based on data characteristics (such as complexity, structural features, predictability, etc.) and communication environment (such as bandwidth, latency, packet loss rate, etc.). Specifically, it provides the following functions. a) Data Complexity Evaluation Function: Quantitatively evaluates the complexity of data using techniques such as entropy estimation, structural feature extraction, and predictability evaluation. For example, in the case of image data, features such as edge density and texture complexity are extracted to calculate a complexity score. b) Communication environment monitoring function: Continuously monitor the current communication environment using technologies such as bandwidth measurement, delay estimation, and packet loss detection. This enables dynamic adjustment of the compression strategy according to changes in communication conditions. c) Terminal status monitoring function: Monitor terminal status such as CPU / GPU usage rate, memory usage, and battery remaining level, and perform optimal model selection according to resource constraints. d) Model performance prediction function: Predict performance indicators such as the compression rate, computational cost, and memory usage of each model, and select the optimal model for the current situation. This prediction is performed by a machine learning model based on past execution data. e) Optimal model selection algorithm: Use a multi-objective optimization algorithm to perform optimal model selection considering multiple goals such as compression rate, processing speed, and resource usage. For example, emphasize the compression rate in an environment with limited bandwidth, and emphasize processing efficiency when the battery remaining level is low, etc., and perform weighting according to the situation. 3. Distributed cooperative compression module: Perform compression processing cooperatively among multiple terminals The distributed cooperative compression module realizes cooperative compression and decompression by leveraging the shared knowledge between the sender and the receiver. Specifically, it provides the following functions. a) Shared knowledge management function: Manage the knowledge base (domain knowledge, past communication content, general world knowledge, etc.) shared between the sender and the receiver. This knowledge base is implemented as a distributed key-value store, enabling efficient access and update. b) Differential information extraction function: Separate information inferable from the shared knowledge and differential information. Specifically, predict the transmission data based on the shared knowledge and extract only the prediction error (differential information). This differential information is much smaller in size than the original data. c) Cooperative encoding function: Efficiently encode the differential information. Use techniques such as arithmetic coding and range coding optimized for the statistical characteristics of the prediction error to compress the differential information with high efficiency. d) Knowledge synchronization function: Regularly synchronize the knowledge bases between terminals. Use techniques such as differential updates and incremental learning to maintain the consistency of the knowledge base with minimal communication overhead. e) Context construction function: In a continuous communication session, construct a shared context based on past communication content. This context has a hierarchical structure of short-term memory (recent communication content) and long-term memory (frequently occurring patterns), serving as the basis for efficient compression. 4. Knowledge distillation module: Distill the knowledge of large models into lightweight models The knowledge distillation module efficiently transfers the compression ability of large models (such as iGPT, LLaMA3, etc.) to lightweight models. Specifically, it provides the following functions. a) Teacher model selection function: Select the large model with the highest performance as the teacher model. For example, select iGPT for image compression and LLaMA3 for text compression, etc. b) Student model design function: Design a lightweight student model according to resource constraints. For example, significantly reduce the model size by reducing the number of layers and the dimension of hidden layers in the Transformer architecture, or applying quantization and pruning. c) Distillation learning function: Train the student model to mimic the prediction distribution of the teacher model. Specifically, use the next token prediction probability distribution output by the teacher model as the teacher signal to optimize the parameters of the student model. At this time, use an objective function that combines the knowledge distillation loss (such as KL divergence) and the direct prediction loss (such as cross-entropy). d) Domain adaptation function: Perform knowledge distillation specialized for data in a specific domain. For example, by constructing a lightweight model specialized for text data in the medical domain or specific types of image data, achieve higher compression performance than general-purpose models. e) Quantization and pruning function: Further reduce the size of the student model after distillation. For example, by applying weight quantization (such as converting from 32-bit floating-point to 8-bit integer) and model pruning (removing less important parameters), reduce the model size and computational cost. 5. Adaptive arithmetic coding module: Efficiently encode data based on the predicted distribution The adaptive arithmetic coding module efficiently encodes data based on the predicted distribution output by the generative model. Specifically, it provides the following functions. a) Probability distribution conversion function: Convert the predicted distribution output by the model into a form suitable for arithmetic coding. For example, discretize a continuous probability distribution or perform special processing to efficiently handle low-probability events. b) Adaptive coding function: Dynamically adjust the coding parameters according to the characteristics of the data. For example, perform adaptive processing such as increasing accuracy in high-entropy regions and prioritizing computational efficiency in low-entropy regions. c) Parallel processing function: To achieve high-speed processing of large-scale data, divide the data into multiple chunks and encode them in parallel. At this time, use an efficient parallelization algorithm that takes into account the dependency relationships between chunks. d) Error tolerance function: Realize coding with tolerance to communication errors. For example, ensure robustness against packet loss and corruption by adding redundancy to important data parts or incorporating error detection and correction codes. By integrating these main components, the present invention realizes the following operation process. 1. Data input phase: Input the data to be compressed and perform preprocessing (such as noise removal, normalization, etc.) if necessary. 2. Multimodal Understanding Phase: Analyze the interrelationships between different modalities and extract complementary information. For example, in the case of a presentation file, analyze the semantic correspondence between the slide images and the narration audio. 3. Model Selection Phase: Select the optimal compression model based on data characteristics, communication environment, and terminal status. For example, select a high-performance model for complex image data and a lightweight model for simple text data. 4. Differential Information Extraction Phase: Extract differential information based on the shared knowledge between the sender and the receiver. For example, if both terminals share specific domain knowledge, omit the information inferable from that knowledge and extract only the differential information. 5. Encoding Phase: Compress the differential information using the selected model and adaptive arithmetic coding. At this time, perform efficient coding based on the predicted distribution output by the model. 6. Transmission Phase: Transmit the compressed data from the sender to the receiver. Apply error detection and correction codes and encryption as necessary. 7. Decoding Phase: Decode the compressed data at the receiver and reconstruct the original data in combination with the shared knowledge. 8. Knowledge Update Phase: Update the shared knowledge based on the communication content to improve the compression efficiency for the next time. These phases are dynamically adjusted according to the characteristics of the data and the communication environment. For example, when real-time performance is required, prioritize the processing speed, and when batch processing is involved, maximize the compression ratio, etc., and optimizations according to the situation are carried out.

Advantages of the Invention

[0011] According to the present invention, the following effects can be obtained. 1. Achieve both a significant reduction in resource usage and high-efficiency compression With the knowledge distillation module of the present invention, it is possible to achieve a compression rate equal to or higher than that of LMCompress while reducing the resource usage by up to 90 percent. Specifically, the following effects can be obtained. - Memory usage: Reduced from about 10 - 20 GB of LMCompress to about 1 - 2 GB in the present invention (a reduction of about 90 percent) - Computational cost: Reduced from several tens of TFLOPS (tera floating - point operations per second) of LMCompress to about several TFLOPS in the present invention (a reduction of about 80 - 90 percent) - Battery consumption: Reduced by about 85 percent in mobile devices compared to conventional large - scale model - based compression As a result, high - efficiency compression becomes possible even in resource - constrained environments such as smartphones, tablets, and IoT devices. For example, even in a smartphone with 8 GB of RAM, a compression performance of over 95 percent of a large - scale model can be achieved with a memory usage of about several hundred MB. 2. Improvement in compression rate by leveraging the inter - relationship of multimodal data With the multimodal understanding module of the present invention, by leveraging the inter - relationship between different data formats, an improvement in compression rate of 15 - 30 percent can be achieved compared to single - modality compression. Specifically, the following effects can be obtained. - Presentation file (slide image + narration audio): An improvement in compression rate of about 25 percent compared to individual compression - Multimedia web page (text + image): An improvement in compression rate of about 20 percent compared to individual compression - Video content (video + audio): An improvement in compression rate of about 15 - 30 percent compared to individual compression - Medical data (medical image + diagnostic report): An improvement in compression rate of about 30 - 40 percent compared to individual compression In particular, in the case of multimodal data with a strong correlation relationship (e.g., medical images and diagnostic reports), by effectively utilizing complementary information, a significant improvement in the compression ratio can be achieved. 3. Optimization by Adaptive Model Selection According to the Communication Environment The adaptive model selection module of the present invention can exhibit optimal compression performance under various network conditions. Specifically, the following effects can be obtained. - High-bandwidth environment (5G / Wi-Fi, etc.): Select a high-performance model that maximizes the compression ratio to achieve a compression ratio equal to or higher than that of LMCompress. - Medium-bandwidth environment (4G / LTE, etc.): Select an intermediate model that balances the compression ratio and processing speed to improve the processing speed by 2 - 3 times. - Low-bandwidth environment (3G / satellite communication, etc.): Select a lightweight model that prioritizes processing speed to improve the processing speed by 5 - 10 times. - Variable-bandwidth environment: Dynamically switch the model according to the change in bandwidth to always maintain optimal compression performance. In addition, it is also possible to select a model according to the remaining battery level and CPU / GPU load of the terminal, and optimization considering the user experience can be realized, such as switching to a power-saving mode when the remaining battery level is low. 4. Dramatic Improvement in Communication Efficiency by Distributed Cooperative Compression The distributed cooperative compression module of the present invention can improve the communication efficiency between multiple terminals by up to 40 percent. Specifically, the following effects can be obtained. - Domain-specific communication (medical, legal, financial, etc.): By utilizing shared domain knowledge, the communication volume can be reduced by 30 - 40 percent. - Continuous communication session: By utilizing the shared context based on past communication content, the communication volume can be reduced by 20 - 30 percent. - Multi-device environment: By sharing knowledge between multiple devices, the overall communication efficiency can be improved by 15 - 25 percent. - Edge-cloud collaborative environment: Optimize traffic and processing load through efficient role sharing between edge devices and cloud servers In particular, in the communication in specialized fields (e.g., patient data sharing between medical institutions), by effectively utilizing the shared specialized knowledge, a significant improvement in communication efficiency can be achieved. 5. Performance improvement of lightweight models through knowledge distillation With the knowledge distillation module of the present invention, high-efficiency compression can be achieved even in resource-constrained environments such as mobile terminals. Specifically, the following effects can be obtained. - Number of parameters: Maintain 90 - 95 percent of the compression performance while reducing the number of parameters of the large-scale model to 1 / 10 to 1 / 100 - Inference speed: Achieve a 5 - 20 times speedup compared to the large-scale model - Domain-specific model: Improve the compression rate by 10 - 20 percent compared to the general lightweight model with a lightweight model specialized for a specific domain - Quantization and pruning: Further reduce the model size by 50 - 75 percent while suppressing the performance degradation within 5 percent For example, a lightweight model specialized for medical image compression has 1 / 50 of the number of parameters of the large-scale model, but can exhibit a compression performance of more than 95 percent of the large-scale model for medical images. 6. Ensuring privacy and security With the privacy protection function of the present invention, safe compression of data including personal information and confidential information becomes possible. Specifically, the following effects can be obtained. - In-terminal processing: Minimize the risk of data leakage by completing the processing of confidential data within the terminal - Differential privacy: Apply privacy protection technology during knowledge sharing to prevent the leakage of personal information - Integration with encryption: Efficiently combine compression and encryption to achieve both security and efficiency - Access control: Appropriately control access to shared knowledge and prevent unauthorized terminals from using the knowledge Of particular note, while inheriting the basic concept of "understanding-based compression" of LMCompress, the present invention introduces new technical elements such as multimodal understanding, adaptive model selection, distributed collaborative compression, and knowledge distillation to realize a more practical and efficient compression system. This significantly improves not only the theoretical compression performance but also the usability and adaptability in real environments.

Embodiments for Carrying Out the Invention

[0012] Regarding the embodiments of the present invention's "adaptive distributed compression system based on multimodal understanding", the detailed configuration, operating principle, implementation method, and specific usage examples of each module will be described. The present invention provides a new paradigm in data compression technology and realizes performance beyond the limits of conventional compression technologies by combining the understanding ability of large language models (LLMs) and the flexibility of distributed computing. ## 1. Overall System Architecture As shown in FIG. 1, the overall system of the present invention is composed of the following five main modules. 1. Multimodal Understanding Module 2. Adaptive Model Selection Module 3. Distributed Collaborative Compression Module 4. Knowledge Distillation Module 5. Adaptive Arithmetic Coding Module These modules form a series of processing flows from data input to compressed data output and operate in cooperation with each other. Each module is implemented as an independent software component and is designed to enable parallel processing and distributed processing as needed. The overall processing flow of the system is as follows. 1. Input data (text, image, audio, video, or a combination thereof) is input into the multimodal understanding module 2. The multimodal understanding module analyzes the interrelationships between different modalities and constructs a shared semantic representation 3. The adaptive model selection module selects the optimal compression model based on data characteristics, communication environment, and terminal state 4. The distributed collaborative compression module extracts differential information by leveraging the shared knowledge between the sender and the receiver 5. The knowledge distillation module transfers the compression ability of the selected large-scale model to a lightweight model (if necessary) 6. The adaptive arithmetic coding module efficiently encodes differential information based on the predicted distribution 7. The compressed data is output and transmitted through the communication channel These processes are dynamically adjusted according to the type of data and the communication environment. For example, in the case of single-modal data, only some functions of the multimodal understanding module are used, and in a high-performance environment, the knowledge distillation module is bypassed, and an optimal processing path according to the situation is selected The following describes the detailed embodiments of each module ## 2. Embodiments of the Multimodal Understanding Module The multimodal understanding module is a component that understands and utilizes the interrelationships between different data formats (such as text, image, audio, video, etc.). The detailed implementation method and operating principle of this module will be described 2.1 Module Composition The multimodal understanding module is composed of the following sub-modules 1. Modality-specific feature extractor 2. Cross-modal correlation analyzer 3. Shared semantic representation generator 4. Inter-modal predictor 5. Redundant Information Identifier These sub-modules operate in cooperation as a pipeline process to extract correlation information and redundant information from the input data. 2.2 Implementation of Feature Extractors for Different Modalities The feature extractor for each modality extracts semantic features from the input data using a feature extraction model specialized for each data format. The specific implementation is as follows. # 2.2.1 Text Feature Extractor The text feature extractor extracts contextual representations from text data using a pre-trained language model (such as BERT, RoBERTa, LLaMA, etc.). The implementation includes the following processing steps. 1. Text preprocessing: Tokenize the input text and convert it into the input format of the model 2. Encoding: Use a pre-trained language model to convert the token sequence into a contextual representation vector 3. Feature extraction: Extract semantically important features (such as keywords, topics, sentiment, etc.) from the contextual representation vector 4. Hierarchical representation construction: Construct hierarchical feature representations at the character, word, phrase, sentence, and paragraph levels The output of the text feature extractor is a set of feature vectors representing the semantic structure of the text. This feature vector is used as the input to the subsequent cross-modal correlation analyzer. # 2.2.2 Image Feature Extractor The image feature extractor extracts visual features from image data using a pre-trained vision model (such as ResNet, ViT, CLIP, etc.). The implementation includes the following processing steps. 1. Image preprocessing: Resize, normalize the input image, and convert it into the input format of the model 2. Feature Extraction: Use a pre-trained vision model to extract visual feature vectors from an image. 3. Object Detection: Detect important elements such as objects, text, and faces in the image, and extract their location information and features. 4. Hierarchical Representation Construction: Construct hierarchical feature representations at the pixel, patch, object, and scene levels. The output of the image feature extractor is a set of feature vectors that represent the visual structure and content of the image. # 2.2.3 Audio Feature Extractor The audio feature extractor uses a pre-trained audio model (such as Wav2Vec2, HuBERT, etc.) to extract acoustic features from audio data. The implementation includes the following processing steps. 1. Audio Preprocessing: Convert the input audio to an appropriate sampling rate and normalize it. 2. Feature Extraction: Use techniques such as short-time Fourier transform (STFT) and Mel-frequency cepstral coefficients (MFCC) to extract basic acoustic features. 3. Deep Feature Extraction: Use a pre-trained audio model to extract deep feature vectors from the audio. 4. Audio Event Detection: Detect important events (such as speech, music, environmental sounds, etc.) in the audio, and extract their time information and features. 5. Hierarchical Representation Construction: Construct hierarchical feature representations at the frame, segment, utterance, and conversation levels. The output of the audio feature extractor is a set of feature vectors that represent the temporal structure and content of the audio. # 2.2.4 Video Feature Extractor The video feature extractor uses a pre-trained video model (such as I3D, SlowFast, VideoSwin, etc.) to extract spatio-temporal features from video data. The implementation includes the following processing steps. 1. Video Preprocessing: Split the input video into frames and convert it to an appropriate resolution and frame rate. 2. Spatial feature extraction: Extract spatial features from each frame using an image feature extractor 3. Temporal feature extraction: Extract features that capture the temporal relationship between frames 4. Spatiotemporal feature integration: Integrate spatial and temporal features to construct the spatiotemporal representation of the video 5. Action and event detection: Detect actions and events in the video and extract their temporal information and features 6. Hierarchical representation construction: Construct hierarchical feature representations at the frame, clip, scene, and story levels The output of the video feature extractor is a set of feature vectors that represent the spatiotemporal structure and content of the video 2.3 Implementation of the Cross-modal Correlation Analyzer The cross-modal correlation analyzer analyzes the correlation relationship between feature vectors of different modalities and models the semantic correspondence relationship between modalities. The implementation includes the following processing steps 1. Feature space alignment: Convert feature vectors of different modalities into a common embedding space 2. Correlation matrix calculation: Calculate the correlation coefficients between elements of each modality in the common embedding space and construct a correlation matrix 3. Correlation clustering: Cluster groups of elements with strong correlations based on the correlation matrix 4. Correlation graph construction: Construct a correlation graph that represents the correlation relationship between modalities The following techniques are used in the implementation of the cross-modal correlation analyzer # 2.3.1 Joint Embedding Learning To map feature vectors of different modalities into a common embedding space, joint embedding learning is implemented. Specifically, the following methods are combined and used 1. Contrastive learning: Optimize the distance in the common embedding space using corresponding modality pairs (e.g., image and its caption) as positive examples and random pairs as negative examples. 2. Triplet loss: For the triplets of anchor, positive example, and negative example, minimize the distance between the anchor and the positive example and maximize the distance between the anchor and the negative example. 3. Cross-modal attention mechanism: Use a Transformer-based attention mechanism to model the interaction between different modalities. As the model architecture of joint embedding learning, adopt the following neural network structures. ``` class JointEmbeddingModel(nn.Module): def __init__(self, modality_encoders, embedding_dim=512): super(JointEmbeddingModel, self).__init__() self.modality_encoders = nn.ModuleDict(modality_encoders) self.projection_heads = nn.ModuleDict({ modality: nn.Linear(encoder.output_dim, embedding_dim) for modality, encoder in modality_encoders.items() }) self.cross_attention = CrossModalAttention(embedding_dim) def forward(self, inputs): # Extract features of each modality features = { modality: encoder(inputs[modality]) for modality, encoder in self.modality_encoders.items() } # Projection to the common embedding space embeddings = { modality: self.projection_heads[modality](features[modality]) for modality in features } # Interaction by cross-modal attention mechanism aligned_embeddings = self.cross_attention(embeddings) return aligned_embeddings, features ```

[0013] # 2.3.2 Correlation matrix calculation Calculate the correlation coefficients between each modality element in the common embedding space and construct a correlation matrix. Indicators such as cosine similarity and normalized mutual information are used for the calculation of correlation coefficients. ``` def compute_correlation_matrix(embeddings_a, embeddings_b): # Calculation of the correlation matrix based on cosine similarity embeddings_a_norm = F.normalize(embeddings_a, p=2, dim=1) embeddings_b_norm = F.normalize(embeddings_b, p=2, dim=1) correlation_matrix = torch.mm(embeddings_a_norm, embeddings_b_norm.transpose(0, 1)) return correlation_matrix ``` # 2.3.3 Correlation Clustering and Correlation Graph Construction Based on the correlation matrix, cluster groups of elements with strong correlations and construct a correlation graph that represents the correlation relationships between modalities. ``` def build_correlation_graph(correlation_matrix, threshold = 0.7): # Extract element pairs with correlations above the threshold correlations = correlation_matrix.clone() correlations[correlations < threshold]= 0 # Construction of the correlation graph graph = nx.Graph() rows, cols = torch.nonzero(correlations, as_tuple = True) for i, j in zip(rows.tolist(), cols.tolist()): graph.add_edge(('modality_a', i), ('modality_b', j), weight = correlations[i, j].item()) # Clustering by community detection communities = nx.community.greedy_modularity_communities(graph, weight='weight') return graph, communities ``` 2.4 Implementation of the Common Semantic Expression Generator Based on the output of the cross-modal correlation analyzer, the common semantic expression generator extracts semantic expressions common to different modalities and constructs a unified expression. The implementation includes the following processing steps. 1. Application of the multimodal transformer: Using the feature vectors of different modalities as input, perform deep processing using the cross-modal attention mechanism 2. Semantic clustering: Cluster the common semantic expressions for elements with similar meanings 3. Hierarchical expression construction: Construct a hierarchical structure that gradually refines the expression from abstract concepts to specific details # 2.4.1 Implementation of the Multimodal Transformer The multimodal transformer takes the feature vectors of different modalities as input and performs deep processing using the cross-modal attention mechanism. The following architecture is adopted for the implementation. ``` class MultimodalTransformer(nn.Module): def __init__(self, modality_dims, hidden_dim=768, num_heads=12, num_layers=6): super(MultimodalTransformer, self).__init__() # Input projection layers for each modality self.input_projections = nn.ModuleDict({ modality: nn.Linear(dim, hidden_dim) for modality, dim in modality_dims.items() }) # Modality identification embedding self.modality_embeddings = nn.ParameterDict({ modality: nn.Parameter(torch.randn(1, 1, hidden_dim)) for modality in modality_dims }) # Transformer layer encoder_layer = nn.TransformerEncoderLayer( d_model=hidden_dim, nhead=num_heads, dim_feedforward=hidden_dim * 4, batch_first=True ) self.transformer_encoder = nn.TransformerEncoder( encoder_layer, num_layers=num_layers ) # Output projection layer self.output_projection = nn.Linear(hidden_dim, hidden_dim) def forward(self, inputs): # Project the features of each modality to a common dimension projected_inputs = { modality: self.input_projections[modality](inputs[modality]) for modality in inputs } # Add modality identification embeddings embedded_inputs = { modality: projected + self.modality_embeddings[modality].expand(projected.size(0), projected.size(1), -1) for modality, projected in projected_inputs.items() } # Concatenate features of all modalities concatenated_inputs = torch.cat([embedded_inputs[modality] for modality in sorted(embedded_inputs.keys())], dim=1) # Apply the Transformer encoder transformer_output = self.transformer_encoder(concatenated_inputs) # Output projection shared_representation = self.output_projection(transformer_output) return shared_representation ``` # 2.4.2 Semantic Clustering and Hierarchical Representation Construction Cluster the shared semantic representation into semantically similar elements and construct a hierarchical representation. The following approach is adopted for the implementation. ``` def semantic_clustering(shared_representation, num_clusters=10): # Apply K-means clustering kmeans = KMeans(n_clusters=num_clusters) cluster_assignments = kmeans.fit_predict(shared_representation.detach().cpu().numpy()) cluster_centers = kmeans.cluster_centers_ # Apply hierarchical clustering hierarchical_clustering = AgglomerativeClustering( n_clusters=None, distance_threshold=0.5, linkage='ward' ) hierarchical_assignments = hierarchical_clustering.fit_predict(cluster_centers) # Construct hierarchical representation hierarchical_representation = { 'cluster_assignments': cluster_assignments, 'cluster_centers': cluster_centers, 'hierarchical_structure': hierarchical_assignments } return hierarchical_representation ```

[0014] 2.5 Implementation of Inter-Modality Predictor The inter-modality predictor constructs a model that predicts information of one modality from information of another modality. The implementation includes the following processing steps. 1. Construction of Cross-modal Generation Model: Construct a model that generates from one modality to another 2. Construction of Prediction Probability Distribution Model: Construct a model that represents the uncertainty of the generation result as a probability distribution 3. Prediction Quality Evaluation: Evaluate the quality of the prediction results and distinguish between parts with high and low prediction accuracy # 2.5.1 Implementation of Cross-modal Generation Model The cross-modal generation model is implemented based on architectures such as conditional generative adversarial network (cGAN) and variational autoencoder (VAE). The following shows an implementation example of a VAE-based cross-modal generation model. ``` class CrossModalVAE(nn.Module): def __init__(self, source_dim, target_dim, latent_dim=256): super(CrossModalVAE, self).__init__() # Encoder network self.encoder = nn.Sequential( nn.Linear(source_dim, 512), nn.ReLU(), nn.Linear(512, 256), nn.ReLU() ) # Latent variable parameters self.fc_mu = nn.Linear(256, latent_dim) self.fc_logvar = nn.Linear(256, latent_dim) # Decoder network self.decoder = nn.Sequential( nn.Linear(latent_dim, 256), nn.ReLU(), nn.Linear(256, 512), nn.ReLU(), nn.Linear(512, target_dim) ) def encode(self, x): h = self.encoder(x) mu = self.fc_mu(h) logvar = self.fc_logvar(h) return mu, logvar def reparameterize(self, mu, logvar): std = torch.exp(0.5 * logvar) eps = torch.randn_like(std) z = mu + eps * std return z def decode(self, z): return self.decoder(z) def forward(self, x): mu, logvar = self.encode(x) z = self.reparameterize(mu, logvar) x_recon = self.decode(z) return x_recon, mu, logvar ``` # 2.5.2 Implementation of the Prediction Probability Distribution Model The prediction probability distribution model represents the output of the generative model as a probability distribution. For example, the Gaussian mixture model (GMM) can be used to model the uncertainty of predictions. ``` class PredictionDistributionModel(nn.Module): def __init__(self, input_dim, output_dim, num_components=5): super(PredictionDistributionModel, self).__init__() self.num_components = num_components # Common feature extractor self.feature_extractor = nn.Sequential( nn.Linear(input_dim, 512), nn.ReLU(), nn.Linear(512, 256), nn.ReLU() ) # Mixture weight network self.mixture_weights = nn.Sequential( nn.Linear(256, num_components), nn.Softmax(dim=1) ) # Mean network self.means = nn.Linear(256, num_components * output_dim) # Variance network self.logvars = nn.Linear(256, num_components * output_dim) def forward(self, x): # Feature extraction features = self.feature_extractor(x) # Mixing weights weights = self.mixture_weights(features) # Means and variances means = self.means(features).view(-1, self.num_components, output_dim) logvars = self.logvars(features).view(-1, self.num_components, output_dim) return weights, means, logvars def sample(self, x, num_samples=1): weights, means, logvars = self.forward(x) # Component selection component_indices = torch.multinomial(weights, num_samples, replacement=True) # Get the means and variances of the selected components batch_indices = torch.arange(x.size(0)).unsqueeze(1).expand(-1, num_samples) selected_means = means[batch_indices, component_indices] selected_logvars = logvars[batch_indices, component_indices] # Sampling std = torch.exp(0.5 * selected_logvars) eps = torch.randn_like(std) samples = selected_means + eps * std return samples ```

[0015] # 2.5.3 Implementation of Prediction Quality Evaluation Prediction quality evaluation assesses the quality of the prediction results and distinguishes between parts with high and low prediction accuracy. The following methods are adopted for implementation. ``` def evaluate_prediction_quality(true_values, predicted_distribution): weights, means, logvars = predicted_distribution # Calculate the log-likelihood of each component log_likelihoods = [] for i in range(means.size(1)): mean = means[:, i, :] logvar = logvars[:, i, :] weight = weights[:, i].unsqueeze(1) # Log-likelihood of Gaussian distribution log_likelihood = -0.5 * (logvar + ((true_values - mean) ** 2) / torch.exp(logvar)) log_likelihood = log_likelihood.sum(dim=1, keepdim=True) log_likelihood = log_likelihood + torch.log(weight) log_likelihoods.append(log_likelihood) # Calculate the log-likelihood of the mixture model using the log-sum-exp log_likelihoods = torch.cat(log_likelihoods, dim=1) log_likelihood = torch.logsumexp(log_likelihoods, dim=1) # Calculate the prediction quality score (higher is better prediction) prediction_quality = log_likelihood return prediction_quality ``` 2.6 Implementation of Redundant Information Identifier The redundant information identifier identifies redundant information between different modalities based on the outputs of the cross-modal correlation analyzer and the inter-modal predictor. The implementation includes the following processing steps. 1. Redundant information identification based on correlation: Based on the correlation matrix, identify element pairs with correlation coefficients exceeding the threshold as redundant information 2. Redundant information identification based on prediction: Identify parts with high prediction accuracy from one modality to another as redundant information 3. Construct a redundant information map: Construct a map representing the identified redundant information ``` def identify_redundant_information(correlation_matrix, prediction_quality, correlation_threshold = 0.8, prediction_threshold = 0.9): # Redundant information identification based on correlation correlation_redundancy = correlation_matrix.clone() correlation_redundancy[correlation_redundancy < correlation_threshold] = 0 correlation_redundancy[correlation_redundancy >= correlation_threshold] = 1 # Prediction-based redundant information identification prediction_redundancy = torch.zeros_like(prediction_quality) prediction_redundancy[prediction_quality >= prediction_threshold] = 1 # Construction of redundant information map redundancy_map = { 'correlation_based': correlation_redundancy, 'prediction_based': prediction_redundancy } return redundancy_map ``` 2.7 Integration of Multimodal Understanding Module Integrate the above sub-modules to construct a multimodal understanding module. The integrated architecture is as follows. ``` class MultimodalUnderstandingModule: def __init__(self, config): # Initialization of modality-specific feature extractors self.feature_extractors = { 'text': TextFeatureExtractor(config['text_model']), 'image': ImageFeatureExtractor(config['image_model']), 'audio': AudioFeatureExtractor(config['audio_model']), 'video': VideoFeatureExtractor(config['video_model']) } # Initialization of Cross-modal Correlation Analyzer self.correlation_analyzer = CrossModalCorrelationAnalyzer( modality_dims={ modality: extractor.output_dim for modality, extractor in self.feature_extractors.items() }, embedding_dim=config['embedding_dim'] ) # Initialization of Shared Semantic Representation Generator self.shared_representation_generator = MultimodalTransformer( modality_dims={ modality: extractor.output_dim for modality, extractor in self.feature_extractors.items() }, hidden_dim=config['transformer_dim'], num_heads=config['num_heads'], num_layers=config['num_layers'] ) # Initialization of Cross-modal Predictor self.cross_modal_predictors = {} for source_modality in self.feature_extractors: for target_modality in self.feature_extractors: if source_modality != target_modality: key = f"{source_modality}_to_{target_modality}" self.cross_modal_predictors[key] = CrossModalVAE( source_dim=self.feature_extractors[source_modality].output_dim, target_dim=self.feature_extractors[target_modality].output_dim, latent_dim=config['latent_dim'] ) # Initialization of Redundancy Identifier self.redundancy_identifier = RedundancyIdentifier( correlation_threshold=config['correlation_threshold'], prediction_threshold=config['prediction_threshold'] ) def process(self, inputs): # Identify available modalities available_modalities = { ​ if data is not None and modality in self.feature_extractors } if len(available_modalities) < 2: # Simplified processing for single modality return self._process_single_modality(available_modalities) # Feature extraction features = { modality: self.feature_extractors[modality](data) for modality, data in available_modalities.items() } # Cross-modal correlation analysis correlation_matrix, correlation_graph = self.correlation_analyzer(features) # Shared semantic representation generation shared_representation = self.shared_representation_generator(features)

[0016] # Inter-modal prediction predictions = {} prediction_qualities = {} for source_modality in available_modalities: for target_modality in available_modalities: if source_modality != target_modality: key = f"{source_modality}_to_{target_modality}" predictor = self.cross_modal_predictors[key] # Generation of predicted distribution predicted_distribution = predictor(features[source_modality]) # Evaluation of prediction quality quality = evaluate_prediction_quality( features[target_modality], predicted_distribution ) predictions[key] = predicted_distribution prediction_qualities[key] = quality # Identification of redundant information redundancy_maps = {} for source_modality in available_modalities: for target_modality in available_modalities: if source_modality != target_modality: key = f"{source_modality}_to_{target_modality}" redundancy_maps[key] = self.redundancy_identifier( correlation_matrix[source_modality][target_modality], prediction_qualities[key] ) # Integration of Results result = { 'features': features, 'correlation_matrix': correlation_matrix, 'correlation_graph': correlation_graph, 'shared_representation': shared_representation, 'predictions': predictions, 'prediction_qualities': prediction_qualities, 'redundancy_maps': redundancy_maps } return result def _process_single_modality(self, available_modalities): # Implementation of single modality processing modality = list(available_modalities.keys())[0] data = available_modalities[modality] # Feature extraction features = { modality: self.feature_extractors[modality](data) } # Integration of results result = { 'features': features, 'correlation_matrix': None, 'correlation_graph': None, 'shared_representation': features[modality], 'predictions': {}, 'prediction_qualities': {}, 'redundancy_maps': {} } return result ``` ## 3. Embodiments of the Adaptive Model Selection Module The adaptive model selection module is a component that dynamically selects the optimal compression model based on data characteristics, communication environment, and terminal state. The detailed implementation method and operating principle of this module will be described. 3.1 Module Composition The adaptive model selection module is composed of the following sub-modules. 1. Data Complexity Evaluator 2. Communication Environment Monitor 3. Terminal State Monitor 4. Model Performance Predictor 5. Optimal Model Selector These sub-modules select the optimal compression model based on the input data and environmental information. 3.2 Implementation of the Data Complexity Evaluator The data complexity evaluator evaluates the complexity, structural features, and predictability of the input data. The implementation includes the following processing steps. 1. Entropy Estimation: Estimate the entropy of the data and evaluate the density of the information volume 2. Structural Feature Extraction: Extract the structural features of the data and evaluate the regularity and repeating patterns 3. Predictability Evaluation: Evaluate the predictability of the data using a small-scale prediction model 4. Complexity Score Calculation: Integrate the above evaluation results to calculate the complexity score of the data # 3.2.1 Implementation of Entropy Estimation To estimate the entropy of the data, implement a method according to the data format. ``` def estimate_entropy(data, data_type): if data_type == 'text': return estimate_text_entropy(data) elif data_type == 'image': return estimate_image_entropy(data) elif data_type == 'audio': return estimate_audio_entropy(data) elif data_type == 'video': return estimate_video_entropy(data) else: raise ValueError(f"Unsupported data type: {data_type}") def estimate_text_entropy(text_data): # Calculation of n-gram entropy n_gram_entropies = {} for n in range(1, 6): # Calculate from 1-gram to 5-gram n_grams = extract_n_grams(text_data, n) n_gram_counts = Counter(n_grams) total_n_grams = sum(n_gram_counts.values()) # Calculate the probability distribution of n-grams n_gram_probs = {n_gram: count / total_n_grams for n_gram, count in n_gram_counts.items()} # Calculate entropy entropy = -sum(prob * math.log2(prob) for prob in n_gram_probs.values()) n_gram_entropies[n] = entropy # Calculate conditional entropy conditional_entropies = {} for n in range(2, 6): conditional_entropies[n] = n_gram_entropies[n] - n_gram_entropies[n-1] # Integrate the results result = { 'n_gram_entropies': n_gram_entropies, 'conditional_entropies': conditional_entropies, 'average_entropy': sum(n_gram_entropies.values()) / len(n_gram_entropies) } return result def estimate_image_entropy(image_data): # Calculate the entropy of the image # Convert to grayscale if len(image_data.shape) == 3: gray_image = cv2.cvtColor(image_data, cv2.COLOR_RGB2GRAY) else: gray_image = image_data # Calculation of histogram hist = cv2.calcHist([gray_image], [0], None,

[0256] , [0, 256]) hist = hist / hist.sum()

[0017] # Calculation of entropy entropy = -np.sum(hist * np.log2(hist + 1e-10)) # Calculation of local entropy local_entropy = cv2.filter2D(gray_image, -1, np.ones((7, 7)) / 49) local_entropy = -np.sum(local_entropy * np.log2(local_entropy + 1e-10)) / (gray_image.shape[0] * gray_image.shape[1]) # Integration of results result = { 'global_entropy': entropy, 'local_entropy': local_entropy, 'average_entropy': (entropy + local_entropy) / 2 } return result def estimate_audio_entropy(audio_data): # Calculation of audio entropy # Calculation of spectrogram f, t, Sxx = signal.spectrogram(audio_data, fs=16000) # Normalization of spectrogram Sxx_norm = Sxx / np.sum(Sxx) # Calculation of entropy entropy = -np.sum(Sxx_norm * np.log2(Sxx_norm + 1e-10)) # Calculation of temporal entropy temporal_entropy = -np.sum(np.mean(Sxx_norm, axis=0) * np.log2(np.mean(Sxx_norm, axis=0) + 1e-10)) # Calculation of spectral entropy spectral_entropy = -np.sum(np.mean(Sxx_norm, axis=1) * np.log2(np.mean(Sxx_norm, axis=1) + 1e-10)) # Integration of results result = { 'total_entropy': entropy, 'temporal_entropy': temporal_entropy, 'spectral_entropy': spectral_entropy, 'average_entropy': (entropy + temporal_entropy + spectral_entropy) / 3 } return result def estimate_video_entropy(video_data): # Entropy calculation of video frame_entropies = [] # Calculate the entropy for each frame for frame in video_data: frame_entropy = estimate_image_entropy(frame) frame_entropies.append(frame_entropy['average_entropy']) # Calculate the temporal entropy frame_diffs = np.diff(video_data, axis=0) temporal_entropies = [] for frame_diff in frame_diffs: gray_diff = cv2.cvtColor(frame_diff, cv2.COLOR_RGB2GRAY) hist = cv2.calcHist([gray_diff], [0], None,

[0256] , [0, 256]) hist = hist / (hist.sum() + 1e-10) temporal_entropy = -np.sum(hist * np.log2(hist + 1e-10)) temporal_entropies.append(temporal_entropy) # Integrate the results result = { 'frame_entropies': frame_entropies, 'average_frame_entropy': np.mean(frame_entropies), 'temporal_entropies': temporal_entropies, 'average_temporal_entropy': np.mean(temporal_entropies), 'total_entropy': np.mean(frame_entropies) + np.mean(temporal_entropies) } return result ``` # 3.2.2 Implementation of Structural Feature Extraction An implementation example for extracting the structural features of data and evaluating regularity and repetition patterns is shown. ``` def extract_structural_features(data, data_type): if data_type == 'text': return extract_text_structural_features(data) elif data_type == 'image': return extract_image_structural_features(data) elif data_type == 'audio': return extract_audio_structural_features(data) elif data_type == 'video': return extract_video_structural_features(data) else: raise ValueError(f"Unsupported data type: {data_type}") def extract_text_structural_features(text_data): # Extract structural features of text # Analyze grammatical structure doc = nlp(text_data) # Part-of-Speech Distribution pos_counts = Counter([token.pos_ for token in doc]) pos_distribution = {pos: count / len(doc) for pos, count in pos_counts.items()} # Complexity of Dependency Relationships dependency_depths = [] for sent in doc.sents: depths = {} for token in sent: depth = 0 current = token while current.head != current: depth += 1 current = current.head depths[token] = depth dependency_depths.append(max(depths.values()) if depths else 0) avg_dependency_depth = sum(dependency_depths) / len(dependency_depths) if dependency_depths else 0 # Complexity of Parse Trees parse_tree_depths = [] for sent in doc.sents: parse_tree = nltk.Tree.fromstring(sent._.parse_string) parse_tree_depths.append(parse_tree.height()) avg_parse_tree_depth = sum(parse_tree_depths) / len(parse_tree_depths) if parse_tree_depths else 0 # Integration of results result = { 'pos_distribution': pos_distribution, 'avg_dependency_depth': avg_dependency_depth, 'avg_parse_tree_depth': avg_parse_tree_depth, 'structural_complexity_score': (avg_dependency_depth + avg_parse_tree_depth) / 2 } return result def extract_image_structural_features(image_data): # Extract structural features of the image # Convert to grayscale if len(image_data.shape) == 3: gray_image = cv2.cvtColor(image_data, cv2.COLOR_RGB2GRAY) else: gray_image = image_data # Edge detection edges = cv2.Canny(gray_image, 100, 200) edge_density = np.sum(edges > 0) / (edges.shape[0] * edges.shape[1]) # Texture analysis glcm = greycomatrix(gray_image, [1], [0, np.pi / 4, np.pi / 2, 3*np.pi / 4], levels=256, symmetric=True, normed=True) contrast = greycoprops(glcm, 'contrast').mean() homogeneity = greycoprops(glcm, 'homogeneity').mean() energy = greycoprops(glcm, 'energy').mean() correlation = greycoprops(glcm, 'correlation').mean()

[0018] # Frequency analysis f_transform = np.fft.fft2(gray_image) f_transform_shifted = np.fft.fftshift(f_transform) magnitude_spectrum = 20 * np.log(np.abs(f_transform_shifted) + 1) # Ratio of high-frequency components high_freq_ratio = np.sum(magnitude_spectrum > np.mean(magnitude_spectrum)) / magnitude_spectrum.size # Integration of results result = { 'edge_density': edge_density, 'texture_features': { 'contrast': contrast, 'homogeneity': homogeneity, 'energy': energy, 'correlation': correlation }, 'high_freq_ratio': high_freq_ratio, 'structural_complexity_score': (edge_density + (1 - homogeneity) + high_freq_ratio) / 3 } return result def extract_audio_structural_features(audio_data): # Structural feature extraction of speech # Calculate the spectrogram f, t, Sxx = signal.spectrogram(audio_data, fs=16000) # Formant structure analysis formants = librosa.feature.spectral_centroid(y=audio_data, sr=16000)[0] formant_variability = np.std(formants) / np.mean(formants) # Analysis of rhythmic structure onset_env = librosa.onset.onset_strength(y=audio_data, sr=16000) tempo, beats = librosa.beat.beat_track(onset_envelope=onset_env, sr=16000) beat_regularity = np.std(np.diff(beats)) / np.mean(np.diff(beats)) if len(beats) > 1 else 0 # Harmonic structure analysis harmonic, percussive = librosa.effects.hpss(audio_data) harmonic_ratio = np.sum(harmonic**2) / (np.sum(harmonic**2) + np.sum(percussive**2)) # Result integration result = { 'formant_variability': formant_variability, 'beat_regularity': beat_regularity, 'harmonic_ratio': harmonic_ratio, 'structural_complexity_score': (formant_variability + beat_regularity + (1 - harmonic_ratio)) / 3 } return result def extract_video_structural_features(video_data): # Video structural feature extraction frame_features = [] # Extract structural features for each frame for frame in video_data: frame_feature = extract_image_structural_features(frame) frame_features.append(frame_feature['structural_complexity_score']) # Temporal structure analysis frame_diffs = np.diff(video_data, axis=0) motion_magnitudes = np.sum(np.abs(frame_diffs), axis=(1, 2, 3)) motion_variability = np.std(motion_magnitudes) / np.mean(motion_magnitudes) if np.mean(motion_magnitudes) > 0 else 0 # Detection of scene changes scene_changes = [] for i in range(1, len(motion_magnitudes)): if motion_magnitudes[i] > 3 * np.mean(motion_magnitudes): scene_changes.append(i) scene_change_frequency = len(scene_changes) / len(video_data) # Integration of results result = { 'average_frame_complexity': np.mean(frame_features), 'motion_variability': motion_variability, 'scene_change_frequency': scene_change_frequency, 'structural_complexity_score': (np.mean(frame_features) + motion_variability + scene_change_frequency) / 3 } return result ``` # 3.2.3 Implementation of Predictability Evaluation An implementation example of evaluating the predictability of data using a small-scale prediction model is shown. ``` def evaluate_predictability(data, data_type): if data_type == 'text': return evaluate_text_predictability(data) elif data_type == 'image': return evaluate_image_predictability(data) elif data_type == 'audio': return evaluate_audio_predictability(data) elif data_type == 'video': return evaluate_video_predictability(data) else: raise ValueError(f"Unsupported data type: {data_type}") def evaluate_text_predictability(text_data): # Evaluate the predictability of text # Tokenization tokens = nltk.word_tokenize(text_data) # Split into training data and test data train_size = int(len(tokens) * 0.8) train_tokens = tokens[:train_size] test_tokens = tokens[train_size:] # Build an n-gram model n_gram_models = {} n_gram_perplexities = {} for n in range(1, 6): n_gram_model = nltk.lm.MLE(n) n_gram_model.fit(nltk.util.ngrams(train_tokens, n), train_tokens) # Calculation of perplexity perplexity = 0 for i in range(n, len(test_tokens)): context = tuple(test_tokens[i - n:i]) token = test_tokens[i] prob = n_gram_model.score(token, context) perplexity -= math.log2(prob if prob > 0 else 1e - 10) perplexity = 2 ** (perplexity / (len(test_tokens) - n)) n_gram_perplexities[n] = perplexity n_gram_models[n] = n_gram_model # Calculation of predictability score (reciprocal of perplexity, the higher the more predictable) predictability_score = 1 / np.mean(list(n_gram_perplexities.values())) # Integration of results result = { 'n_gram_perplexities': n_gram_perplexities, 'average_perplexity': np.mean(list(n_gram_perplexities.values())), 'predictability_score': predictability_score } return result

[0019] def evaluate_image_predictability(image_data): # Evaluation of image predictability # Convert to grayscale if len(image_data.shape) == 3: gray_image = cv2.cvtColor(image_data, cv2.COLOR_RGB2GRAY) else: gray_image = image_data # Split the image into patches patches = extract_patches_2d(gray_image, (8, 8)) # Split into training and test data train_size = int(len(patches) * 0.8) train_patches = patches[:train_size] test_patches = patches[train_size:] # Dimensionality reduction by principal component analysis pca = PCA(n_components=20) train_features = pca.fit_transform(train_patches.reshape(train_patches.shape[0], -1)) test_features = pca.transform(test_patches.reshape(test_patches.shape[0], -1)) # Construction of the linear regression model model = LinearRegression() # Predict the central pixel of the patch X_train = train_features y_train = train_patches[:, 4, 4] model.fit(X_train, y_train) # Evaluate the prediction accuracy X_test = test_features y_test = test_patches[:, 4, 4] y_pred = model.predict(X_test) mse = mean_squared_error(y_test, y_pred) r2 = r2_score(y_test, y_pred) # Calculate the predictability score (R2 score, the higher the more predictable) predictability_score = max(0, r2) # Integrate the results result = { 'mse': mse, 'r2': r2, 'predictability_score': predictability_score } return result def evaluate_audio_predictability(audio_data): # Prediction Possibility Evaluation of Voice # Split into Frames frame_length = 512 hop_length = 256 frames = librosa.util.frame(audio_data, frame_length=frame_length, hop_length=hop_length).T # Split into Training Data and Test Data train_size = int(len(frames) * 0.8) train_frames = frames[:train_size] test_frames = frames[train_size:] # Feature Extraction train_features = np.array([librosa.feature.mfcc(y=frame, sr=16000, n_mfcc=13).flatten() for frame in train_frames]) test_features = np.array([librosa.feature.mfcc(y=frame, sr=16000, n_mfcc=13).flatten() for frame in test_frames]) # Construction of Autoregressive Model model = AR(train_features) model_fit = model.fit() # Evaluation of Prediction Accuracy predictions = model_fit.predict(start=len(train_features), end=len(train_features) + len(test_features) - 1) mse = mean_squared_error(test_features, predictions) # Calculation of predictability score (reciprocal of MSE, the higher the more predictable) predictability_score = 1 / (1 + mse) # Integration of results result = { 'mse': mse, 'predictability_score': predictability_score } return result def evaluate_video_predictability(video_data): # Evaluation of video predictability frame_predictabilities = [] # Evaluate predictability for each frame for i in range(1, len(video_data)): prev_frame = video_data[i-1] curr_frame = video_data[i] # Calculation of optical flow prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_RGB2GRAY) curr_gray = cv2.cvtColor(curr_frame, cv2.COLOR_RGB2GRAY) flow = cv2.calcOpticalFlowFarneback(prev_gray, curr_gray, None, 0.5, 3, 15, 3, 5, 1.2, 0) # Prediction of the next frame based on the flow h, w = prev_gray.shape flow_map = np.zeros((h, w, 2), np.float32) flow_map[:,:,0] = flow[:,:,0] flow_map[:,:,1] = flow[:,:,1] predicted_frame = cv2.remap(prev_frame, flow_map, None, cv2.INTER_LINEAR) # Evaluation of prediction accuracy mse = np.mean((predicted_frame - curr_frame) ** 2) psnr = 10 * np.log10((255 ** 2) / mse) if mse > 0 else 100 # Frame predictability score (PSNR, the higher the more predictable) frame_predictability = min(1.0, psnr / 50) frame_predictabilities.append(frame_predictability) # Overall predictability score predictability_score = np.mean(frame_predictabilities) # Integration of results result = { 'frame_predictabilities': frame_predictabilities, 'predictability_score': predictability_score } return result ``` # 3.2.4 Implementation of Complexity Score Calculation An implementation example is shown for integrating the above evaluation results and calculating the complexity score of the data. ``` def calculate_complexity_score(data, data_type): # Entropy estimation entropy_result = estimate_entropy(data, data_type) entropy_score = entropy_result['average_entropy'] / 8.0 # Normalize by the maximum entropy of 8-bit data # Structural feature extraction structural_result = extract_structural_features(data, data_type) structural_score = structural_result['structural_complexity_score'] # Predictability evaluation predictability_result = evaluate_predictability(data, data_type) predictability_score = predictability_result['predictability_score'] # Calculation of complexity score (considering entropy and structural complexity, adjusted by predictability) complexity_score = (0.4 * entropy_score + 0.4 * structural_score) * (1.0 - 0.2 * predictability_score) # Normalize the score to the range of 0-1 complexity_score = min(1.0, max(0.0, complexity_score)) # Integration of results result = { 'entropy_result': entropy_result, 'entropy_score': entropy_score, 'structural_result': structural_result, 'structural_score': structural_score, 'predictability_result': predictability_result, 'predictability_score': predictability_score, 'complexity_score': complexity_score } return result ```

[0020] 3.3 Implementation of Communication Environment Monitor The communication environment monitor monitors communication parameters such as bandwidth, latency, and packet loss rate. The implementation includes the following processing steps. 1. Bandwidth measurement: Periodically send small-scale probe packets and measure the throughput. 2. Latency estimation: Measure the round-trip time (RTT) and estimate the network latency. 3. Packet loss detection: Compare the number of transmitted and received packets and calculate the packet loss rate. 4. Communication quality score calculation: Integrate the above measurement results and calculate the communication quality score. ``` class NetworkMonitor: def __init__(self, config): self.config = config self.history_length = config.get('history_length', 10) self.bandwidth_history = [] self.latency_history = [] self.packet_loss_history = [] self.last_measurement_time = 0 self.measurement_interval = config.get('measurement_interval', 30) # seconds def measure_bandwidth(self): # Implementation of bandwidth measurement # Send small probe packets and measure throughput try: start_time = time.time() # Probe size (bytes) probe_size = self.config.get('probe_size', 100000) # Generate probe data probe_data = b'0' * probe_size # Probe server URL probe_url = self.config.get('probe_url', 'http: / / speedtest.example.com / probe') # Send the probe response = requests.post(probe_url, data=probe_data, timeout=10) end_time = time.time() # Time taken for transmission (seconds) elapsed_time = end_time - start_time # Bandwidth calculation (bits / second) bandwidth = (probe_size * 8) / elapsed_time # Update history self.bandwidth_history.append(bandwidth) if len(self.bandwidth_history) > self.history_length: self.bandwidth_history.pop(0) return bandwidth except Exception as e: logger.error(f"Bandwidth measurement failed: {e}") return None def measure_latency(self): # Implementation of latency measurement # Measure RTT (Round-Trip Time) try: # Hostname of the ping target ping_target = self.config.get('ping_target', 'example.com') # Execute the ping command ping_count = self.config.get('ping_count', 5) ping_result = subprocess.run(['ping', '-c', str(ping_count), ping_target], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, timeout=10) # Result analysis output = ping_result.stdout # RTT extraction rtt_pattern = r'min / avg / max / mdev = ([\d.]+) / ([\d.]+) / ([\d.]+) / ([\d.]+)' match = re.search(rtt_pattern, output) if match: min_rtt, avg_rtt, max_rtt, mdev_rtt = map(float, match.groups()) # History update self.latency_history.append(avg_rtt) if len(self.latency_history) > self.history_length: self.latency_history.pop(0) return { 'min': min_rtt, 'avg': avg_rtt, 'max': max_rtt, 'mdev': mdev_rtt } else: return None except Exception as e: logger.error(f"Latency measurement failed: {e}") return None def measure_packet_loss(self): # Implementation of packet loss measurement try: # Hostname of the ping target ping_target = self.config.get('ping_target', 'example.com') # Execution of the ping command ping_count = self.config.get('ping_count', 20) ping_result = subprocess.run(['ping', '-c', str(ping_count), ping_target], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, timeout=30) # Analysis of the results output = ping_result.stdout # Extraction of the number of transmitted and received packets packets_pattern = r'(\d+) packets transmitted, (\d+) received' match = re.search(packets_pattern, output) if match: transmitted, received = map(int, match.groups()) # Calculation of the packet loss rate packet_loss = (transmitted - received) / transmitted if transmitted > 0 else 0 # Update of the history self.packet_loss_history.append(packet_loss) if len(self.packet_loss_history) > self.history_length: self.packet_loss_history.pop(0) return packet_loss else: return None except Exception as e: logger.error(f"Packet loss measurement failed: {e}") return None def calculate_network_quality_score(self):

[0021] # Calculation of communication quality score current_time = time.time() # Check measurement interval if current_time - self.last_measurement_time >= self.measurement_interval: # Perform a new measurement bandwidth = self.measure_bandwidth() latency = self.measure_latency() packet_loss = self.measure_packet_loss() self.last_measurement_time = current_time # Calculation of bandwidth score (the higher the better) if self.bandwidth_history: avg_bandwidth = sum(self.bandwidth_history) / len(self.bandwidth_history) max_expected_bandwidth = self.config.get('max_expected_bandwidth', 100000000) # 100 Mbps bandwidth_score = min(1.0, avg_bandwidth / max_expected_bandwidth) else: bandwidth_score = 0.5 # Default value # Calculation of latency score (the lower, the better) if self.latency_history: avg_latency = sum(self.latency_history) / len(self.latency_history) max_acceptable_latency = self.config.get('max_acceptable_latency', 500) # 500 ms latency_score = 1.0 - min(1.0, avg_latency / max_acceptable_latency) else: latency_score = 0.5 # Default value # Calculation of packet loss score (the lower, the better) if self.packet_loss_history: avg_packet_loss = sum(self.packet_loss_history) / len(self.packet_loss_history) max_acceptable_packet_loss = self.config.get('max_acceptable_packet_loss', 0.1) # 10% packet_loss_score = 1.0 - min(1.0, avg_packet_loss / max_acceptable_packet_loss) else: packet_loss_score = 0.5 # Default value # Calculation of communication quality score (weighted average) bandwidth_weight = self.config.get('bandwidth_weight', 0.4) latency_weight = self.config.get('latency_weight', 0.4) packet_loss_weight = self.config.get('packet_loss_weight', 0.2) network_quality_score = ( bandwidth_weight * bandwidth_score + latency_weight * latency_score + packet_loss_weight * packet_loss_score ) # Integration of results result = { 'bandwidth': { 'current': self.bandwidth_history[-1] if self.bandwidth_history else None, 'average': sum(self.bandwidth_history) / len(self.bandwidth_history) if self.bandwidth_history else None, 'score': bandwidth_score }, 'latency': { 'current': self.latency_history[-1] if self.latency_history else None, 'average': sum(self.latency_history) / len(self.latency_history) if self.latency_history else None, 'score': latency_score }, 'packet_loss': { 'current': self.packet_loss_history[-1] if self.packet_loss_history else None, 'average': sum(self.packet_loss_history) / len(self.packet_loss_history) if self.packet_loss_history else None, 'score': packet_loss_score }, 'network_quality_score': network_quality_score } return result ``` 3.4 Implementation of the Terminal State Monitor The terminal state monitor monitors the terminal state such as CPU / GPU usage, memory usage, battery level, etc. The implementation includes the following processing steps. 1. CPU / GPU Monitoring: Monitor the current CPU / GPU usage and available computing resources 2. Memory Monitoring: Monitor the current memory usage and available memory capacity 3. Battery Monitoring: Monitor the current battery level and consumption rate 4. Resource Constraint Score Calculation: Integrate the above monitoring results to calculate the resource constraint score ``` class DeviceMonitor: def __init__(self, config): self.config = config self.history_length = config.get('history_length', 10) self.cpu_usage_history = [] self.memory_usage_history = [] self.battery_level_history = [] self.gpu_usage_history = [] self.thermal_state_history = [] self.last_measurement_time = 0 self.measurement_interval = config.get('measurement_interval', 5) # seconds def measure_cpu_usage(self): # Measure CPU usage try: cpu_percent = psutil.cpu_percent(interval=1) # Update history self.cpu_usage_history.append(cpu_percent) if len(self.cpu_usage_history) > self.history_length: self.cpu_usage_history.pop(0) return cpu_percent except Exception as e: logger.error(f"CPU usage measurement failed: {e}") return None def measure_memory_usage(self): # Measurement of memory usage try: memory = psutil.virtual_memory()

[0022] # Update of history if avg_cpu_temp: self.thermal_state_history.append(avg_cpu_temp) if len(self.thermal_state_history) > self.history_length: self.thermal_state_history.pop(0) return { 'temperatures': temperatures, 'avg_cpu_temp': avg_cpu_temp } else: return None except Exception as e: logger.error(f"Thermal state measurement failed: {e}") return None def calculate_resource_constraint_score(self): # Calculation of resource constraint score current_time = time.time() # Check measurement interval if current_time - self.last_measurement_time >= self.measurement_interval: # Execute a new measurement cpu_usage = self.measure_cpu_usage() memory_usage = self.measure_memory_usage() battery_level = self.measure_battery_level() gpu_usage = self.measure_gpu_usage() thermal_state = self.measure_thermal_state() self.last_measurement_time = current_time # Calculate the CPU usage score (the higher, the stricter the constraint) if self.cpu_usage_history: avg_cpu_usage = sum(self.cpu_usage_history) / len(self.cpu_usage_history) cpu_threshold = self.config.get('cpu_threshold', 80) # 80% cpu_score = min(1.0, avg_cpu_usage / cpu_threshold) else: cpu_score = 0.5 # Default value # Calculate the memory usage score (the higher, the stricter the constraint) if self.memory_usage_history: avg_memory_usage = sum(self.memory_usage_history) / len(self.memory_usage_history) memory_threshold = self.config.get('memory_threshold', 90) # 90% memory_score = min(1.0, avg_memory_usage / memory_threshold) else: memory_score = 0.5 # Default value # Calculate battery remaining score (lower is more restrictive) if self.battery_level_history: avg_battery_level = sum(self.battery_level_history) / len(self.battery_level_history) battery_threshold = self.config.get('battery_threshold', 20) # 20% battery_score = 1.0 - min(1.0, avg_battery_level / 100) # Relax constraints if battery is charging battery_info = self.measure_battery_level() if battery_info and battery_info['plugged']: battery_score *= 0.5 else: battery_score = 0.5 # Default value # Calculating GPU utilization score (higher is more constrained) if self.gpu_usage_history: avg_gpu_usage = sum(self.gpu_usage_history) / len(self.gpu_usage_history) gpu_threshold = self.config.get('gpu_threshold', 80) # 80% gpu_score = min(1.0, avg_gpu_usage / gpu_threshold) else: gpu_score = 0.5 # default value # Calculating the thermal condition score (higher is more restrictive) if self.thermal_state_history: avg_temp = sum(self.thermal_state_history) / len(self.thermal_state_history) temp_threshold = self.config.get('temp_threshold', 80) # 80℃ thermal_score = min(1.0, avg_temp / temp_threshold) else: thermal_score = 0.5 # default value # Calculate resource constraint score (weighted average) cpu_weight = self.config.get('cpu_weight', 0.2) memory_weight = self.config.get('memory_weight', 0.2) battery_weight = self.config.get('battery_weight', 0.3) gpu_weight = self.config.get('gpu_weight', 0.2) thermal_weight = self.config.get('thermal_weight', 0.1) resource_constraint_score = ( cpu_weight * cpu_score + memory_weight * memory_score + battery_weight * battery_score + gpu_weight * gpu_score + thermal_weight * thermal_score )

[0023] # Integration of Results result = { 'cpu': { 'current': self.cpu_usage_history[-1] if self.cpu_usage_history else None, 'average': sum(self.cpu_usage_history) / len(self.cpu_usage_history) if self.cpu_usage_history else None, 'score': cpu_score }, 'memory': { 'current': self.memory_usage_history[-1] if self.memory_usage_history else None, 'average': sum(self.memory_usage_history) / len(self.memory_usage_history) if self.memory_usage_history else None, 'score': memory_score }, 'battery': { 'current': self.battery_level_history[-1] if self.battery_level_history else None, 'average': sum(self.battery_level_history) / len(self.battery_level_history) if self.battery_level_history else None, 'score': battery_score }, 'gpu': { 'current': self.gpu_usage_history[-1] if self.gpu_usage_history else None, 'average': sum(self.gpu_usage_history) / len(self.gpu_usage_history) if self.gpu_usage_history else None, 'score': gpu_score }, 'thermal': { 'current': self.thermal_state_history[-1] if self.thermal_state_history else None, 'average': sum(self.thermal_state_history) / len(self.thermal_state_history) if self.thermal_state_history else None, 'score': thermal_score }, 'resource_constraint_score': resource_constraint_score } return result ``` 3.5 Implementation of the Model Performance Predictor The model performance predictor predicts performance metrics such as the compression ratio, computational cost, and memory usage of each compression model. The implementation includes the following processing steps. 1. Model profiling: Build the performance profile of each compression model 2. Regression model construction: Based on past execution data, construct a regression model that models the relationship between data complexity and each performance metric 3. Performance prediction: Predict the performance metrics of each model based on the current data complexity 4. Confidence interval calculation: Calculate the confidence interval for each prediction value to represent the uncertainty of the prediction value ``` class ModelPerformancePredictor: def __init__(self, config): self.config = config self.model_profiles = config.get('model_profiles', {}) self.regression_models = {} self.training_data = { 'complexity_scores': [], 'compression_ratios': {}, 'processing_times': {}, 'memory_usages': {}, 'energy_consumptions': {} } # Initialize the regression model for the performance metrics of each model for model_name in self.model_profiles: self.regression_models[model_name] = { 'compression_ratio': None, 'processing_time': None, 'memory_usage': None, 'energy_consumption': None } self.training_data['compression_ratios'][model_name] = [] self.training_data['processing_times'][model_name] = [] self.training_data['memory_usages'][model_name] = [] self.training_data['energy_consumptions'][model_name] = [] def update_training_data(self, complexity_score, model_name, performance_metrics): # Update training data self.training_data['complexity_scores'].append(complexity_score) if 'compression_ratio' in performance_metrics: self.training_data['compression_ratios'][model_name].append(performance_metrics['compression_ratio']) if 'processing_time' in performance_metrics: self.training_data['processing_times'][model_name].append(performance_metrics['processing_time']) if 'memory_usage' in performance_metrics: self.training_data['memory_usages'][model_name].append(performance_metrics['memory_usage']) if 'energy_consumption' in performance_metrics: self.training_data['energy_consumptions'][model_name].append(performance_metrics['energy_consumption']) # Update the regression model if enough training data has been accumulated min_samples = self.config.get('min_samples_for_regression', 10) if len(self.training_data['complexity_scores']) >= min_samples: self._update_regression_models(model_name) def _update_regression_models(self, model_name): # Update the regression model X = np.array(self.training_data['complexity_scores']).reshape(-1, 1) # Update the compression ratio model if len(self.training_data['compression_ratios'][model_name]) >= len(X): y = np.array(self.training_data['compression_ratios'][model_name]) self.regression_models[model_name]['compression_ratio'] = RandomForestRegressor().fit(X, y) # Update the processing time model if len(self.training_data['processing_times'][model_name]) >= len(X): y = np.array(self.training_data['processing_times'][model_name]) self.regression_models[model_name]['processing_time'] = RandomForestRegressor().fit(X, y)

[0024] # Predict the memory usage if self.regression_models[model_name]['memory_usage'] is not None: X = np.array([complexity_score]).reshape(-1, 1) memory_usage_model = self.regression_models[model_name]['memory_usage'] memory_usage_pred = memory_usage_model.predict(X)[0] memory_usage_std = np.std([tree.predict(X)[0] for tree in memory_usage_model.estimators_]) memory_usage_ci = ( max(0, memory_usage_pred - 1.96 * memory_usage_std), memory_usage_pred + 1.96 * memory_usage_std ) model_predictions['memory_usage'] = { 'value': memory_usage_pred, 'confidence_interval': memory_usage_ci } else: default_profile = self.model_profiles[model_name] model_predictions['memory_usage'] = { 'value': default_profile.get('memory_usage', 1.0), 'confidence_interval': (0.8, 1.2) } # Prediction of energy consumption if self.regression_models[model_name]['energy_consumption'] is not None: X = np.array([complexity_score]).reshape(-1, 1) energy_consumption_model = self.regression_models[model_name]['energy_consumption'] energy_consumption_pred = energy_consumption_model.predict(X)[0] energy_consumption_std = np.std([tree.predict(X)[0] for tree in energy_consumption_model.estimators_]) energy_consumption_ci = ( max(0, energy_consumption_pred - 1.96 * energy_consumption_std), energy_consumption_pred + 1.96 * energy_consumption_std ) model_predictions['energy_consumption'] = { 'value': energy_consumption_pred, 'confidence_interval': energy_consumption_ci } else: default_profile = self.model_profiles[model_name] model_predictions['energy_consumption'] = { 'value': default_profile.get('energy_consumption', 1.0), 'confidence_interval': (0.8, 1.2) } predictions[model_name] = model_predictions return predictions ``` 3.6 Implementation of the Optimal Model Selector The optimal model selector uses a multi-objective optimization algorithm to perform optimal model selection considering multiple objectives such as compression rate, processing speed, and resource usage. The implementation includes the following processing steps. 1. Define the objective function: Define an objective function that integrates multiple objectives 2. Adaptive weighting: Dynamically adjust the weight of each goal based on data complexity, communication quality, and resource constraints. 3. Model selection: Apply the predictive performance of each model to the objective function and select the model with the highest score. 4. Feedback learning: Update the prediction model and weighting parameters based on actual execution results. ``` class OptimalModelSelector: def __init__(self, config): self.config = config self.available_models = config.get('available_models', []) self.objective_weights = config.get('objective_weights', { 'compression_ratio': 0.4, 'processing_time': 0.3, 'memory_usage': 0.2, 'energy_consumption': 0.1 }) self.adaptation_factors = config.get('adaptation_factors', { 'complexity_score': { 'compression_ratio': 0.5, 'processing_time': -0.3, 'memory_usage': -0.1, 'energy_consumption': -0.1 }, 'network_quality_score': { 'compression_ratio': -0.5, 'processing_time': 0.3, 'memory_usage': 0.1, 'energy_consumption': 0.1 }, 'resource_constraint_score': { 'compression_ratio': -0.2, 'processing_time': -0.3, 'memory_usage': -0.3, 'energy_consumption': -0.2 } }) self.learning_rate = config.get('learning_rate', 0.01) def calculate_adaptive_weights(self, complexity_score, network_quality_score, resource_constraint_score): # Calculation of weights according to the situation adaptive_weights = self.objective_weights.copy() # Adjustment based on complexity score for objective, factor in self.adaptation_factors['complexity_score'].items(): adaptive_weights[objective] += factor * complexity_score # Adjustment based on network quality score for objective, factor in self.adaptation_factors['network_quality_score'].items(): adaptive_weights[objective] += factor * (1.0 - network_quality_score) # Adjustment based on resource constraint score for objective, factor in self.adaptation_factors['resource_constraint_score'].items(): adaptive_weights[objective] += factor * resource_constraint_score

[0025] # Normalization of weights total_weight = sum(self.objective_weights.values()) if total_weight > 0: self.objective_weights = {obj: weight / total_weight for obj, weight in self.objective_weights.items()} return { 'prediction_error': prediction_error, 'updated_weights': self.objective_weights } ``` 3.7 Integration of Adaptive Model Selection Module Integrate the above sub - modules to construct an adaptive model selection module. The integrated architecture is as follows. ``` class AdaptiveModelSelectionModule: def __init__(self, config): self.config = config # Initialization of Data Complexity Evaluator self.complexity_evaluator = DataComplexityEvaluator(config.get('complexity_evaluator_config', {})) # Initialization of Communication Environment Monitor self.network_monitor = NetworkMonitor(config.get('network_monitor_config', {})) # Initialization of Terminal State Monitor self.device_monitor = DeviceMonitor(config.get('device_monitor_config', {})) # Initialization of Model Performance Predictor self.performance_predictor = ModelPerformancePredictor(config.get('performance_predictor_config', {})) # Initialization of Optimal Model Selector self.model_selector = OptimalModelSelector(config.get('model_selector_config', {})) # Retention of Selection History self.selection_history = [] self.max_history_length = config.get('max_history_length', 100) def select_model(self, data, data_type): # Evaluation of Data Complexity complexity_result = self.complexity_evaluator.calculate_complexity_score(data, data_type) complexity_score = complexity_result['complexity_score'] # Monitoring of communication environment network_result = self.network_monitor.calculate_network_quality_score() network_quality_score = network_result['network_quality_score'] # Monitoring of terminal status device_result = self.device_monitor.calculate_resource_constraint_score() resource_constraint_score = device_result['resource_constraint_score'] # Prediction of model performance performance_predictions = self.performance_predictor.predict_performance(complexity_score) # Selection of optimal model selection_result = self.model_selector.select_optimal_model( complexity_score, network_quality_score, resource_constraint_score, performance_predictions ) optimal_model = selection_result['optimal_model'] # Saving the selection result selection_record = { 'timestamp': time.time(), 'data_type': data_type, 'complexity_score': complexity_score, 'network_quality_score': network_quality_score, 'resource_constraint_score': resource_constraint_score, 'selected_model': optimal_model, 'adaptive_weights': selection_result['adaptive_weights'], 'objective_scores': selection_result['objective_scores'] } self.selection_history.append(selection_record) if len(self.selection_history) > self.max_history_length: self.selection_history.pop(0) # Integrating the results result = { 'complexity_result': complexity_result, 'network_result': network_result, 'device_result': device_result, 'performance_predictions': performance_predictions, 'selection_result': selection_result, 'selected_model': optimal_model } return result def update_with_feedback(self, selected_model, actual_performance, predicted_performance): # Feedback learning update_result = self.model_selector.update_weights( actual_performance, predicted_performance, selected_model ) # Update of performance predictor complexity_score = self.selection_history[-1]['complexity_score'] self.performance_predictor.update_training_data( complexity_score, selected_model, actual_performance ) return update_result ``` ## 4. Embodiments of the distributed collaborative compression module The distributed collaborative compression module is a component that realizes collaborative compression and decompression by leveraging the shared knowledge between the sender and the receiver. The detailed implementation method and operating principle of this module will be described. 4.1 Module Composition The distributed collaborative compression module consists of the following sub-modules. 1. Shared Knowledge Manager 2. Differential Information Extractor 3. Collaborative Encoder 4. Knowledge Synchronizer 5. Context Builder These sub-modules are symmetrically implemented on the sender and receiver sides to achieve collaborative processing between the two terminals. 4.2 Implementation of Shared Knowledge Manager The Shared Knowledge Manager manages the knowledge base shared between the sender and the receiver. The implementation includes the following processing steps. 1. Knowledge Representation: Design a data structure to efficiently represent shared knowledge 2. Initial Knowledge Construction: Construct the initial knowledge shared by both terminals before the start of communication 3. Knowledge Update Mechanism: Implement a mechanism to update the shared knowledge based on the communication content 4. Knowledge Consistency Assurance: Implement a mechanism to ensure the consistency of the knowledge base between the two terminals ``` class SharedKnowledgeManager: def __init__(self, config): self.config = config self.knowledge_base = {} self.knowledge_version = 0 self.knowledge_hash = None self.domain_specific_knowledge = {} self.general_world_knowledge = {} self.communication_history = [] self.max_history_length = config.get('max_history_length', 1000)

[0026] # Loading initial knowledge self._load_initial_knowledge() def _load_initial_knowledge(self): # Loading initial knowledge try: # Loading domain-specific knowledge domain_knowledge_path = self.config.get('domain_knowledge_path') if domain_knowledge_path and os.path.exists(domain_knowledge_path): with open(domain_knowledge_path, 'rb') as f: self.domain_specific_knowledge = pickle.load(f) # Loading general world knowledge world_knowledge_path = self.config.get('world_knowledge_path') if world_knowledge_path and os.path.exists(world_knowledge_path): with open(world_knowledge_path, 'rb') as f: self.general_world_knowledge = pickle.load(f) # Integration of knowledge bases self.knowledge_base = { 'domain_specific': self.domain_specific_knowledge, 'general_world': self.general_world_knowledge, 'communication_history': self.communication_history } # Calculation of knowledge hash self.knowledge_hash = self._calculate_knowledge_hash() logger.info(f"Initial knowledge loaded. Hash: {self.knowledge_hash}") except Exception as e: logger.error(f"Failed to load initial knowledge: {e}") # Use the default empty knowledge base self.knowledge_base = { 'domain_specific': {}, 'general_world': {}, 'communication_history': [] } self.knowledge_hash = self._calculate_knowledge_hash() def _calculate_knowledge_hash(self): # Calculate the hash value of the knowledge base knowledge_str = json.dumps(self.knowledge_base, sort_keys=True) return hashlib.sha256(knowledge_str.encode()).hexdigest() def get_knowledge(self, key=None, domain=None): # Retrieval of knowledge if key is None: # Return the entire knowledge base return self.knowledge_base if domain is None: # Search from all domains for d in ['domain_specific', 'general_world']: if key in self.knowledge_base[d]: return self.knowledge_base[d][key] # Search from communication history for entry in reversed(self.communication_history): if key in entry: return entry[key] return None else: # Search from the specified domain if domain in self.knowledge_base and key in self.knowledge_base[domain]: return self.knowledge_base[domain][key] return None def update_knowledge(self, key, value, domain='domain_specific'): # Update knowledge if domain not in self.knowledge_base: self.knowledge_base[domain] = {} self.knowledge_base[domain][key] = value self.knowledge_version += 1 self.knowledge_hash = self._calculate_knowledge_hash() return { 'key': key, 'domain': domain, 'version': self.knowledge_version, 'hash': self.knowledge_hash } def add_to_communication_history(self, entry): # Add to communication history self.communication_history.append(entry) if len(self.communication_history) > self.max_history_length: self.communication_history.pop(0) self.knowledge_base['communication_history'] = self.communication_history self.knowledge_version += 1 self.knowledge_hash = self._calculate_knowledge_hash() return { 'history_length': len(self.communication_history), 'version': self.knowledge_version, 'hash': self.knowledge_hash } def get_knowledge_metadata(self): # Get the metadata of the knowledge base return { 'version': self.knowledge_version, 'hash': self.knowledge_hash, 'domain_specific_size': len(self.domain_specific_knowledge), 'general_world_size': len(self.general_world_knowledge), 'communication_history_length': len(self.communication_history) } def export_knowledge(self, path=None): # Export the knowledge base if path is None: path = self.config.get('knowledge_export_path', 'knowledge_base.pkl') try: with open(path, 'wb') as f: pickle.dump(self.knowledge_base, f) logger.info(f"Knowledge base exported to {path}") return True except Exception as e: logger.error(f"Failed to export knowledge base: {e}") return False def import_knowledge(self, path): # Import of knowledge base try: with open(path, 'rb') as f: imported_knowledge = pickle.load(f) # Update of knowledge base self.knowledge_base.update(imported_knowledge)

[0027] # Update of each component if 'domain_specific' in imported_knowledge: self.domain_specific_knowledge.update(imported_knowledge['domain_specific']) if 'general_world' in imported_knowledge: self.general_world_knowledge.update(imported_knowledge['general_world']) if 'communication_history' in imported_knowledge: self.communication_history.extend(imported_knowledge['communication_history']) if len(self.communication_history) > self.max_history_length: self.communication_history = self.communication_history[-self.max_history_length:] self.knowledge_version += 1 self.knowledge_hash = self._calculate_knowledge_hash() logger.info(f"Knowledge base imported from {path}. New hash: {self.knowledge_hash}") return True except Exception as e: logger.error(f"Failed to import knowledge base: {e}") return False def verify_knowledge_consistency(self, remote_metadata): # Verification of knowledge base consistency if remote_metadata['hash'] == self.knowledge_hash: return { 'consistent': True, 'local_version': self.knowledge_version, 'remote_version': remote_metadata['version'] } else: return { 'consistent': False, 'local_version': self.knowledge_version, 'remote_version': remote_metadata['version'], 'local_hash': self.knowledge_hash, 'remote_hash': remote_metadata['hash'] } ``` 4.3 Implementation of Differential Information Extractor The differential information extractor separates information inferable from shared knowledge and differential information. The implementation includes the following processing steps. 1. Prediction model construction: Construct a model that predicts the transmitted data based on shared knowledge 2. Prediction execution: Use the constructed model to predict the transmitted data 3. Difference calculation: Compare the prediction results with the actual data and calculate the differential information 4. Importance evaluation: Evaluate the importance of each part of the differential information and prioritize it ``` class DifferentialInformationExtractor: def __init__(self, config, shared_knowledge_manager): self.config = config self.shared_knowledge_manager = shared_knowledge_manager self.prediction_models = {} # Initialize prediction models for each data type for data_type in ['text', 'image', 'audio', 'video']: self._initialize_prediction_model(data_type) def _initialize_prediction_model(self, data_type): # Initialization of the prediction model according to the data type if data_type == 'text': self.prediction_models[data_type] = self._initialize_text_prediction_model() elif data_type == 'image': self.prediction_models[data_type] = self._initialize_image_prediction_model() elif data_type == 'audio': self.prediction_models[data_type] = self._initialize_audio_prediction_model() elif data_type == 'video': self.prediction_models[data_type] = self._initialize_video_prediction_model() def _initialize_text_prediction_model(self): # Initialization of the text prediction model model_config = self.config.get('text_prediction_model', {}) model_type = model_config.get('type', 'n-gram') if model_type == 'n-gram': return NgramPredictionModel(model_config) elif model_type == 'neural': return NeuralTextPredictionModel(model_config) else: raise ValueError(f"Unsupported text prediction model type: {model_type}") def _initialize_image_prediction_model(self): # Initialization of the image prediction model model_config = self.config.get('image_prediction_model', {}) model_type = model_config.get('type', 'cnn') if model_type == 'cnn': return CNNImagePredictionModel(model_config) elif model_type == 'transformer': return TransformerImagePredictionModel(model_config) else: raise ValueError(f"Unsupported image prediction model type: {model_type}") def _initialize_audio_prediction_model(self): # Initialization of the audio prediction model model_config = self.config.get('audio_prediction_model', {}) model_type = model_config.get('type', 'wavenet') if model_type == 'wavenet': return WaveNetPredictionModel(model_config) elif model_type == 'transformer': return TransformerAudioPredictionModel(model_config) else: raise ValueError(f"Unsupported audio prediction model type: {model_type}") def _initialize_video_prediction_model(self):

[0028] # Initialization of the video prediction model model_config = self.config.get('video_prediction_model', {}) model_type = model_config.get('type', 'conv3d') if model_type == 'conv3d': return Conv3DVideoPredictionModel(model_config) elif model_type == 'transformer': return TransformerVideoPredictionModel(model_config) else: raise ValueError(f"Unsupported video prediction model type: {model_type}") def extract_differential_information(self, data, data_type, context=None): # Extraction of differential information # Acquisition of prediction model if data_type not in self.prediction_models: raise ValueError(f"Unsupported data type: {data_type}") prediction_model = self.prediction_models[data_type] # Preparation of context information if context is None: context = {} # Acquisition of shared knowledge knowledge = self.shared_knowledge_manager.get_knowledge() # Execution of prediction prediction_result = prediction_model.predict(data, knowledge, context) predicted_data = prediction_result['prediction'] prediction_confidence = prediction_result['confidence'] # Differential calculation differential_info = self._calculate_differential(data, predicted_data, data_type) # Importance Evaluation importance_scores = self._evaluate_importance(differential_info, prediction_confidence, data_type) # Integration of Results result = { 'original_data': data, 'predicted_data': predicted_data, 'prediction_confidence': prediction_confidence, 'differential_info': differential_info, 'importance_scores': importance_scores } return result def _calculate_differential(self, original_data, predicted_data, data_type): # Differential calculation according to data type if data_type == 'text': return self._calculate_text_differential(original_data, predicted_data) elif data_type == 'image': return self._calculate_image_differential(original_data, predicted_data) elif data_type == 'audio': return self._calculate_audio_differential(original_data, predicted_data) elif data_type == 'video': return self._calculate_video_differential(original_data, predicted_data) else: raise ValueError(f"Unsupported data type: {data_type}") def _calculate_text_differential(self, original_text, predicted_text): # Calculation of text difference # Tokenization original_tokens = nltk.word_tokenize(original_text) predicted_tokens = nltk.word_tokenize(predicted_text) # Calculation of difference diff = [] matcher = difflib.SequenceMatcher(None, predicted_tokens, original_tokens) for tag, i1, i2, j1, j2 in matcher.get_opcodes(): if tag =='replace': diff.append({ 'type':'replace', 'predicted': predicted_tokens[i1:i2], 'original': original_tokens[j1:j2] }) elif tag == 'delete': diff.append({ 'type': 'delete', 'predicted': predicted_tokens[i1:i2], 'original': [] [[ID=4}} elif tag == 'insert': diff.append({ 'type': 'insert', 'predicted': [], 'original': original_tokens[j1:j2] [[ID=16}} elif tag == 'equal': diff.append({ 'type': 'equal', 'predicted': predicted_tokens[i1:i2], 'original': original_tokens[j1:j2] [[ID=28}} return diff def _calculate_image_differential(self, original_image, predicted_image): # Calculation of image difference # Conversion to NumPy array original_array = np.array(original_image) predicted_array = np.array(predicted_image) # Calculation of difference diff_array = original_array - predicted_array # Compression of difference (sparse representation) diff_sparse = {} non_zero_indices = np.where(np.abs(diff_array) > 5) # Keep only differences above the threshold for i in range(len(non_zero_indices[0])): y, x, c = non_zero_indices[0][i], non_zero_indices[1][i], non_zero_indices[2][i] if y not in diff_sparse: diff_sparse[y] = {} if x not in diff_sparse[y]: diff_sparse[y][x] = {} diff_sparse[y][x][c] = int(diff_array[y, x, c]) return { 'diff_sparse': diff_sparse, 'non_zero_count': len(non_zero_indices[0]), 'total_pixels': original_array.size / / 3 } def _calculate_audio_differential(self, original_audio, predicted_audio): # Differential calculation of audio # Convert to NumPy array original_array = np.array(original_audio) predicted_array = np.array(predicted_audio)

[0029] # Calculate the difference diff_array = original_array - predicted_array # Difference in the frequency domain original_fft = np.fft.rfft(original_array) predicted_fft = np.fft.rfft(predicted_array) diff_fft = original_fft - predicted_fft # Extraction of important frequency components magnitude = np.abs(diff_fft) threshold = np.mean(magnitude) + 2 * np.std(magnitude) # Consider components above mean + 2 standard deviations as important important_indices = np.where(magnitude > threshold)[0] important_components = { int(idx): (float(diff_fft[idx].real), float(diff_fft[idx].imag)) for idx in important_indices } return { 'time_domain_diff': diff_array.tolist(), 'freq_domain_diff': important_components, 'important_component_count': len(important_components), 'total_components': len(diff_fft) } def _calculate_video_differential(self, original_video, predicted_video): # Calculation of video differential # Calculate the difference for each frame frame_diffs = [] for i in range(len(original_video)): original_frame = original_video[i] predicted_frame = predicted_video[i] if i < len(predicted_video) else np.zeros_like(original_frame) # Frame difference frame_diff = self._calculate_image_differential(original_frame, predicted_frame) frame_diffs.append(frame_diff) # Motion prediction difference motion_diff = {} if len(original_video) > 1: for i in range(1, len(original_video)): # Actual motion original_flow = cv2.calcOpticalFlowFarneback( cv2.cvtColor(original_video[i - 1], cv2.COLOR_RGB2GRAY), cv2.cvtColor(original_video[i], cv2.COLOR_RGB2GRAY), None, 0.5, 3, 15, 3, 5, 1.2, 0 ) # Predicted motion if i < len(predicted_video): predicted_flow = cv2.calcOpticalFlowFarneback( cv2.cvtColor(predicted_video[i - 1], cv2.COLOR_RGB2GRAY), cv2.cvtColor(predicted_video[i], cv2.COLOR_RGB2GRAY), None, 0.5, 3, 15, 3, 5, 1.2, 0 ) else: predicted_flow = np.zeros_like(original_flow) # Motion difference flow_diff = original_flow - predicted_flow # Keep only important motion differences magnitude = np.sqrt(flow_diff[:, :, 0] ** 2 + flow_diff[:, :, 1] ** 2) threshold = np.mean(magnitude) + 2 * np.std(magnitude) important_indices = np.where(magnitude > threshold) motion_diff[i] = { 'indices': [(int(y), int(x)) for y, x in zip(important_indices[0], important_indices[1])], 'values': [(float(flow_diff[y, x, 0]), float(flow_diff[y, x, 1])) for y, x in zip(important_indices[0], important_indices[1])] } return { 'frame_diffs': frame_diffs, 'motion_diff': motion_diff, 'total_frames': len(original_video), 'predicted_frames': len(predicted_video) } def _evaluate_importance(self, differential_info, prediction_confidence, data_type): # Importance evaluation of differential information if data_type == 'text': return self._evaluate_text_importance(differential_info, prediction_confidence) elif data_type == 'image': return self._evaluate_image_importance(differential_info, prediction_confidence) elif data_type == 'audio': return self._evaluate_audio_importance(differential_info, prediction_confidence) elif data_type == 'video': return self._evaluate_video_importance(differential_info, prediction_confidence) else: raise ValueError(f"Unsupported data type: {data_type}") def _evaluate_text_importance(self, text_diff, prediction_confidence): # Importance Evaluation of Text Differences importance_scores = [] for diff_item in text_diff: if diff_item['type'] == 'equal': # The importance of matching parts is low importance_scores.append(0.1) else: # Importance evaluation of non-matching parts original_tokens = diff_item['original'] # The importance of proper nouns, numerical values, technical terms, etc. is high token_importance = [] for token in original_tokens: importance = 0.5 # Default importance

[0030] # Importance adjustment based on part of speech pos_tag = nltk.pos_tag([token])[0][1] if pos_tag.startswith('NNP'): # Proper noun importance += 0.3 elif pos_tag == 'CD': # Numerical value importance += 0.3 elif pos_tag.startswith('JJ'): # Adjective importance += 0.1 elif pos_tag.startswith('RB'): # Adverb importance += 0.1 # Matching with the technical term dictionary domain_knowledge = self.shared_knowledge_manager.get_knowledge(domain='domain_specific') if token.lower() in domain_knowledge: importance += 0.2 # Adjustment based on prediction confidence if token in prediction_confidence: importance *= (1.0 - prediction_confidence[token]) token_importance.append(importance) # Importance of the entire difference (maximum value) if token_importance: importance_scores.append(max(token_importance)) else: importance_scores.append(0.5) return importance_scores def _evaluate_image_importance(self, image_diff, prediction_confidence): # Evaluate the importance of image differences diff_sparse = image_diff['diff_sparse'] non_zero_count = image_diff['non_zero_count'] total_pixels = image_diff['total_pixels'] # Importance map per pixel importance_map = np.zeros((max(diff_sparse.keys()) + 1 if diff_sparse else 1, max(diff_sparse[y].keys()) + 1 if diff_sparse and y in diff_sparse else 1, 3)) for y in diff_sparse: for x in diff_sparse[y]: for c in diff_sparse[y][x]: # Importance based on the magnitude of the difference diff_value = abs(diff_sparse[y][x][c]) importance = min(1.0, diff_value / 255.0) # Adjustment based on prediction confidence if (y, x) in prediction_confidence: importance *= (1.0 - prediction_confidence[(y, x)]) importance_map[y, x, c] = importance # Importance evaluation at the region level # Divide the image into blocks for evaluation block_size = 8 height, width = importance_map.shape[:2] blocks_h = height / / block_size + (1 if height % block_size > 0 else 0) blocks_w = width / / block_size + (1 if width % block_size > 0 else 0) block_importance = np.zeros((blocks_h, blocks_w)) for i in range(blocks_h): for j in range(blocks_w): y_start = i * block_size y_end = min((i + 1) * block_size, height) x_start = j * block_size x_end = min((j + 1) * block_size, width) block = importance_map[y_start:y_end, x_start:x_end, :] block_importance[i, j] = np.mean(block) # Overall importance metric overall_importance = non_zero_count / total_pixels if total_pixels > 0 else 0 return { 'pixel_importance': importance_map.tolist(), 'block_importance': block_importance.tolist(), 'overall_importance': float(overall_importance) } def _evaluate_audio_importance(self, audio_diff, prediction_confidence): # Evaluate the importance of audio difference time_domain_diff = np.array(audio_diff['time_domain_diff']) freq_domain_diff = audio_diff['freq_domain_diff'] # Importance evaluation in the time domain # Importance based on RMS energy window_size = 1024 num_windows = len(time_domain_diff) / / window_size + (1 if len(time_domain_diff) % window_size > 0 else 0) time_importance = np.zeros(num_windows) for i in range(num_windows): start = i * window_size end = min((i + 1) * window_size, len(time_domain_diff)) window = time_domain_diff[start:end] rms = np.sqrt(np.mean(window**2)) time_importance[i] = min(1.0, rms / 0.1) # 0.1 is the normalization coefficient # Importance evaluation in the frequency domain # Weighting considering human auditory characteristics freq_importance = {} for idx, (real, imag) in freq_domain_diff.items(): magnitude = np.sqrt(real**2 + imag**2) # Frequency-based weighting (emphasize mid-range frequencies) freq = int(idx) freq_weight = 1.0 if freq < 500: # Low frequency range freq_weight = 0.7 + 0.3 * (freq / 500) elif freq > 5000: # High frequency range freq_weight = 0.7 + 0.3 * (1.0 - min(1.0, (freq - 5000) / 15000))

[0031] # Adjustment based on prediction confidence confidence = prediction_confidence.get(idx, 0.5) importance = magnitude * freq_weight * (1.0 - confidence) freq_importance[idx] = float(importance) # Overall importance metric overall_importance = np.mean(time_importance) if len(time_importance) > 0 else 0 return { 'time_importance': time_importance.tolist(), 'freq_importance': freq_importance, 'overall_importance': float(overall_importance) } def _evaluate_video_importance(self, video_diff, prediction_confidence): # Importance evaluation of video differences frame_diffs = video_diff['frame_diffs'] motion_diff = video_diff['motion_diff'] # Importance evaluation for each frame frame_importance = [] for i, frame_diff in enumerate(frame_diffs): # Evaluate the importance of each frame as an image frame_imp = self._evaluate_image_importance(frame_diff, prediction_confidence.get(f'frame_{i}', {})) frame_importance.append(frame_imp) # Importance evaluation of motion motion_importance = {} for i, diff in motion_diff.items(): indices = diff['indices'] values = diff['values'] # Importance based on the magnitude of motion importance_values = [] for (y, x), (dx, dy) in zip(indices, values): magnitude = np.sqrt(dx**2 + dy**2) importance = min(1.0, magnitude / 10.0) # 10.0 is the normalization coefficient # Adjustment based on prediction confidence confidence = prediction_confidence.get(f'motion_{i}_{y}_{x}', 0.5) importance *= (1.0 - confidence) importance_values.append(importance) motion_importance[i] = { 'indices': indices, 'importance': importance_values } # Detection and importance evaluation of scene changes scene_changes = [] scene_importance = [] for i in range(1, len(frame_importance)): prev_imp = frame_importance[i-1]['overall_importance'] curr_imp = frame_importance[i]['overall_importance'] # A sharp change in importance suggests a scene change if curr_imp > prev_imp * 2 or curr_imp < prev_imp * 0.5: scene_changes.append(i) scene_importance.append(1.0) # Scene changes are always important else: scene_importance.append(0.2) # Normal frame transition # Overall importance metric overall_importance = np.mean([frame['overall_importance'] for frame in frame_importance]) if frame_importance else 0 return { 'frame_importance': frame_importance, 'motion_importance': motion_importance, 'scene_changes': scene_changes, 'scene_importance': scene_importance, 'overall_importance': float(overall_importance) } ``` 4.4 Implementation of the Collaborative Encoder The collaborative encoder efficiently encodes differential information. The implementation includes the following processing steps. 1. Encoding scheme selection: Select the optimal encoding scheme according to the characteristics of the differential information 2. Utilization of the prediction distribution: Utilize the probability distribution output by the prediction model for encoding 3. Hierarchical encoding: Hierarchically encode the differential information according to the importance 4. Error-tolerant encoding: Implement encoding with tolerance to communication errors ``` class CollaborativeEncoder: def __init__(self, config): self.config = config self.encoding_schemes = { 'text': self._initialize_text_encoding_scheme(), 'image': self._initialize_image_encoding_scheme(), 'audio': self._initialize_audio_encoding_scheme(), 'video': self._initialize_video_encoding_scheme() } def _initialize_text_encoding_scheme(self): # Initialization of text encoding scheme scheme_config = self.config.get('text_encoding_scheme', {}) scheme_type = scheme_config.get('type', 'arithmetic') if scheme_type == 'arithmetic': return ArithmeticTextEncoder(scheme_config) elif scheme_type == 'huffman': return HuffmanTextEncoder(scheme_config) else: raise ValueError(f"Unsupported text encoding scheme: {scheme_type}")

[0032] def _initialize_image_encoding_scheme(self): # Initialization of image encoding scheme scheme_config = self.config.get('image_encoding_scheme', {}) scheme_type = scheme_config.get('type','residual') if scheme_type =='residual': return ResidualImageEncoder(scheme_config) elif scheme_type == 'wavelet': return WaveletImageEncoder(scheme_config) else: raise ValueError(f"Unsupported image encoding scheme: {scheme_type}") def _initialize_audio_encoding_scheme(self): # Initialize the audio coding scheme scheme_config = self.config.get('audio_encoding_scheme', {}) scheme_type = scheme_config.get('type', 'spectral') if scheme_type == 'spectral': return SpectralAudioEncoder(scheme_config) elif scheme_type == 'wavelet': return WaveletAudioEncoder(scheme_config) else: raise ValueError(f"Unsupported audio encoding scheme: {scheme_type}") def _initialize_video_encoding_scheme(self): # Initialize the video encoding scheme scheme_config = self.config.get('video_encoding_scheme', {}) scheme_type = scheme_config.get('type', 'motion_residual') if scheme_type == 'motion_residual': return MotionResidualVideoEncoder(scheme_config) elif scheme_type == 'wavelet': return WaveletVideoEncoder(scheme_config) else: raise ValueError(f"Unsupported video encoding scheme: {scheme_type}") def encode_differential(self, differential_result, data_type): # Encoding of differential information if data_type not in self.encoding_schemes: raise ValueError(f"Unsupported data type: {data_type}") encoder = self.encoding_schemes[data_type] # Obtaining differential information and importance scores differential_info = differential_result['differential_info'] importance_scores = differential_result['importance_scores'] prediction_confidence = differential_result['prediction_confidence'] # Execution of encoding encoding_result = encoder.encode(differential_info, importance_scores, prediction_confidence) # Addition of metadata metadata = { 'data_type': data_type, 'encoding_scheme': encoder.get_scheme_info(), 'original_size': self._calculate_original_size(differential_result['original_data'], data_type), 'compressed_size': len(encoding_result['encoded_data']), 'compression_ratio': None, # To be calculated later 'importance_threshold': encoding_result.get('importance_threshold', None), 'error_protection_level': encoding_result.get('error_protection_level', None) } # Calculation of compression ratio if metadata['original_size'] > 0: metadata['compression_ratio'] = metadata['original_size'] / metadata['compressed_size'] # Integration of results result = { 'encoded_data': encoding_result['encoded_data'], 'encoding_parameters': encoding_result.get('encoding_parameters', {}), 'metadata': metadata } return result def _calculate_original_size(self, data, data_type): # Calculate the size of the original data if data_type == 'text': return len(data.encode('utf-8')) elif data_type == 'image': return data.size * data.mode.count elif data_type == 'audio': return len(data) * 2 # Assume 16-bit samples elif data_type == 'video': return sum(frame.size * frame.mode.count for frame in data) else: return 0 ```

[0033] 4.5 Implementation of the Knowledge Synchronizer The knowledge synchronizer periodically synchronizes the knowledge bases between terminals. The implementation includes the following processing steps. 1. Differential update: Implement a differential update mechanism that transmits only the changed parts of the knowledge base 2. Priority-based synchronization: Determine the synchronization priority based on the importance and usage frequency of knowledge items 3. Background synchronization: Execute knowledge synchronization in the background during time periods with sufficient communication bandwidth 4. Conflict resolution: Implement a mechanism to resolve conflicts when different updates are made simultaneously on both terminals ``` class KnowledgeSynchronizer: def __init__(self, config, shared_knowledge_manager): self.config = config self.shared_knowledge_manager = shared_knowledge_manager self.sync_interval = config.get('sync_interval', 3600) # Default 1 hour self.last_sync_time = 0 self.sync_in_progress = False self.background_sync_enabled = config.get('background_sync_enabled', True) self.priority_thresholds = config.get('priority_thresholds', { 'high': 0.8, 'medium': 0.5, 'low': 0.2 }) self.conflict_resolution_strategy = config.get('conflict_resolution_strategy', 'timestamp') self.knowledge_change_log = [] def check_sync_needed(self, remote_metadata): # Check if synchronization is needed current_time = time.time() time_since_last_sync = current_time - self.last_sync_time # Check if regular synchronization is needed regular_sync_needed = time_since_last_sync >= self.sync_interval # When there is a knowledge base inconsistency consistency_check = self.shared_knowledge_manager.verify_knowledge_consistency(remote_metadata) inconsistency_detected = not consistency_check['consistent'] # Need for synchronization based on the change log changes_since_last_sync = len([log for log in self.knowledge_change_log if log['timestamp'] > self.last_sync_time]) changes_sync_needed = changes_since_last_sync > 0 return { 'sync_needed': regular_sync_needed or inconsistency_detected or changes_sync_needed, 'reason': { 'regular_sync': regular_sync_needed, 'inconsistency': inconsistency_detected, 'changes': changes_sync_needed }, 'time_since_last_sync': time_since_last_sync, 'consistency_check': consistency_check } def prepare_sync_data(self, sync_type='full', priority_level='all'): # Preparation of Synchronous Data if sync_type == 'full': # In the case of full synchronization, send the entire knowledge base return self._prepare_full_sync_data() elif sync_type == 'differential': # In the case of differential synchronization, send only the changed parts return self._prepare_differential_sync_data(priority_level) else: raise ValueError(f"Unsupported sync type: {sync_type}") def _prepare_full_sync_data(self): # Preparation of full synchronous data knowledge_base = self.shared_knowledge_manager.get_knowledge() metadata = self.shared_knowledge_manager.get_knowledge_metadata() # Serialize serialized_data = pickle.dumps(knowledge_base) return { 'sync_type': 'full', 'metadata': metadata, 'data': serialized_data, 'size': len(serialized_data) } def _prepare_differential_sync_data(self, priority_level='all'): # Preparation of Differential Synchronization Data # Extract Target Changes from the Change Log if priority_level == 'all': target_changes = self.knowledge_change_log else: priority_threshold = self.priority_thresholds.get(priority_level, 0) target_changes = [log for log in self.knowledge_change_log if log['priority'] >= priority_threshold] # Extract Only Changes Since the Last Synchronization recent_changes = [log for log in target_changes if log['timestamp'] > self.last_sync_time] # Construction of Change Data differential_data = { 'changes': [], 'deletions': [] } for change_log in recent_changes: if change_log['operation'] == 'update' or change_log['operation'] == 'add': key = change_log['key'] domain = change_log['domain'] value = self.shared_knowledge_manager.get_knowledge(key, domain) differential_data['changes'].append({ 'key': key, 'domain': domain, 'value': value, 'timestamp': change_log['timestamp'], 'priority': change_log['priority'] }) elif change_log['operation'] == 'delete': differential_data['deletions'].append({ 'key': change_log['key'], 'domain': change_log['domain'], 'timestamp': change_log['timestamp'] }) # Addition of metadata metadata = self.shared_knowledge_manager.get_knowledge_metadata()

[0034] # Serialization serialized_data = pickle.dumps(differential_data) return { 'sync_type': 'differential', 'metadata': metadata, 'data': serialized_data, 'size': len(serialized_data), 'change_count': len(differential_data['changes']) + len(differential_data['deletions'])} def apply_sync_data(self, sync_data): # Application of synchronization data try: # Deserialization of data if sync_data['sync_type'] == 'full': # Application of full synchronization data knowledge_base = pickle.loads(sync_data['data']) # Merge with the current knowledge base self._merge_knowledge_bases(knowledge_base) result = { 'success': True, 'sync_type': 'full', 'applied_changes': len(knowledge_base), } elif sync_data['sync_type'] == 'differential': # Application of differential synchronization data differential_data = pickle.loads(sync_data['data']) # Application of changes applied_changes = 0 for change in differential_data['changes']: if self._should_apply_change(change): self.shared_knowledge_manager.update_knowledge( change['key'], change['value'], change['domain'] ) applied_changes += 1 # Application of deletion applied_deletions = 0 for deletion in differential_data['deletions']: if self._should_apply_deletion(deletion): # Delete an item from the knowledge base domain = deletion['domain'] key = deletion['key'] if domain in self.shared_knowledge_manager.knowledge_base and \ key in self.shared_knowledge_manager.knowledge_base[domain]: del self.shared_knowledge_manager.knowledge_base[domain][key] applied_deletions += 1 result = { 'success': True, 'sync_type': 'differential', 'applied_changes': applied_changes, 'applied_deletions': applied_deletions, 'total_changes': len(differential_data['changes']), 'total_deletions': len(differential_data['deletions']) } else: raise ValueError(f"Unsupported sync type: {sync_data['sync_type']}") # Update synchronization time self.last_sync_time = time.time() return result except Exception as e: logger.error(f"Failed to apply sync data: {e}") return { 'success': False, 'error': str(e) } def _merge_knowledge_bases(self, remote_knowledge_base): # Merge knowledge bases local_knowledge_base = self.shared_knowledge_manager.knowledge_base # Merge by domain for domain, domain_data in remote_knowledge_base.items(): if domain not in local_knowledge_base: local_knowledge_base[domain] = {} # Merge by item for key, value in domain_data.items(): # Conflict resolution if domain in local_knowledge_base and key in local_knowledge_base[domain]: # If it exists in both, follow the conflict resolution strategy local_value = local_knowledge_base[domain][key] if self._resolve_conflict(key, local_value, value, domain) =='remote': local_knowledge_base[domain][key] = value else: # If it doesn't exist locally, adopt the remote value local_knowledge_base[domain][key] = value # Update the metadata of the knowledge base self.shared_knowledge_manager.knowledge_version += 1 self.shared_knowledge_manager.knowledge_hash = self.shared_knowledge_manager._calculate_knowledge_hash() def _should_apply_change(self, change): # Determine whether to apply the change key = change['key'] domain = change['domain'] timestamp = change['timestamp'] # Check if it exists locally local_value = self.shared_knowledge_manager.get_knowledge(key, domain) if local_value is None: # If it doesn't exist locally, apply it return True # Get the last update time from the changelog local_logs = [log for log in self.knowledge_change_log if log['key'] == key and log['domain'] == domain] if not local_logs: # Apply local changelog if no return True local_timestamp = max(log['timestamp'] for log in local_logs)

[0035] # Decision based on timestamp if self.conflict_resolution_strategy == 'timestamp': return timestamp > local_timestamp # Always apply (overwrite) elif self.conflict_resolution_strategy == 'remote_wins': return True # Never apply (keep) elif self.conflict_resolution_strategy == 'local_wins': return False return False def _resolve_conflict(self, key, local_value, remote_value, domain): # Conflict resolution if self.conflict_resolution_strategy == 'timestamp': # Get the last update time from the change log local_logs = [log for log in self.knowledge_change_log if log['key'] == key and log['domain'] == domain] if not local_logs: return'remote' # If there is no local change log, adopt the remote value local_timestamp = max(log['timestamp'] for log in local_logs) # Get the remote timestamp (since there is no remote change log, use the current time) remote_timestamp = time.time() return'remote' if remote_timestamp > local_timestamp else 'local' elif self.conflict_resolution_strategy =='remote_wins': return'remote' elif self.conflict_resolution_strategy == 'local_wins': return 'local' elif self.conflict_resolution_strategy =='merge': # Merge if the data types are mergeable if isinstance(local_value, dict) and isinstance(remote_value, dict): merged_value = local_value.copy() merged_value.update(remote_value) # Merge result is applied locally self.shared_knowledge_manager.update_knowledge(key, merged_value, domain) return'merged' elif isinstance(local_value, list) and isinstance(remote_value, list): # In the case of a list, combine while removing duplicates merged_value = list(set(local_value + remote_value)) # Merge result is applied locally self.shared_knowledge_manager.update_knowledge(key, merged_value, domain) return'merged' else: # If it cannot be merged, adopt the remote value return'remote' return'remote' # By default, adopt the remote value def log_knowledge_change(self, operation, key, domain, priority=0.5): # Record the log of knowledge change log_entry = { 'operation': operation, # 'add', 'update', 'delete' 'key': key, 'domain': domain, 'timestamp': time.time(), 'priority': priority } self.knowledge_change_log.append(log_entry) # Log size limit max_log_size = self.config.get('max_change_log_size', 1000) if len(self.knowledge_change_log) > max_log_size: self.knowledge_change_log = self.knowledge_change_log[-max_log_size:] return log_entry def start_background_sync(self, remote_endpoint): # Start background synchronization if not self.background_sync_enabled: return { 'status': 'disabled', 'message': 'Background synchronization is disabled' } if self.sync_in_progress: return { 'status': 'in_progress', 'message': 'Synchronization is already in progress' } # Perform synchronization in a background thread self.sync_in_progress = True threading.Thread(target=self._background_sync_task, args=(remote_endpoint,)).start() return { 'status': 'started', 'message': 'Background synchronization started' } def _background_sync_task(self, remote_endpoint): # Background sync task try: # Get metadata from a remote endpoint remote_metadata = self._fetch_remote_metadata(remote_endpoint) # Check if sync is needed sync_check = self.check_sync_needed(remote_metadata) if sync_check['sync_needed']: # Preparing sync data if sync_check['reason']['inconsistency']: # Full sync if there is a mismatch sync_data = self.prepare_sync_data(sync_type='full') else: # Otherwise, sync the differences sync_data = self.prepare_sync_data(sync_type='differential') # Sending synchronous data sync_result = self._send_sync_data(remote_endpoint, sync_data)

[0036] # Retrieving sync data from a remote location # In actual implementation, HTTP requests etc. are used # Dummy implementation here return None ``` ### 4.6 Implementing a Context Constructor The context constructor constructs a shared context based on past communication content during an ongoing communication session. Implementation includes the following processing steps: 1. Short-term memory management: Retaining the most recent communication content as short-term memory 2. Long-term memory building: Building frequently occurring patterns and important information into long-term memory 3. Hierarchical context model: Construct a hierarchical context model that integrates short-term and long-term memory 4. Context-based prediction: Use the constructed context to predict the next communication content ``` class ContextBuilder: def __init__(self, config, shared_knowledge_manager): self.config = config self.shared_knowledge_manager = shared_knowledge_manager # Short-term memory settings self.short_term_memory_size = config.get('short_term_memory_size', 10) self.short_term_memory = [] # Settings for long-term memory self.long_term_memory = {} self.long_term_memory_max_items = config.get('long_term_memory_max_items', 1000) self.long_term_memory_threshold = config.get('long_term_memory_threshold', 3) # Settings for context model self.context_model = {} self.context_decay_factor = config.get('context_decay_factor', 0.95) # Settings for prediction model self.prediction_model = None self._initialize_prediction_model() def _initialize_prediction_model(self): # Initialization of prediction model model_config = self.config.get('prediction_model', {}) model_type = model_config.get('type', 'n-gram') if model_type == 'n-gram': self.prediction_model = NgramContextPredictor(model_config) elif model_type == 'neural': self.prediction_model = NeuralContextPredictor(model_config) else: raise ValueError(f"Unsupported prediction model type: {model_type}") def update_context(self, communication_data): # Update context based on communication data # Update short-term memory self._update_short_term_memory(communication_data) # Update long-term memory self._update_long_term_memory(communication_data) # Update context model self._update_context_model() # Update prediction model self._update_prediction_model() # Reflect to knowledge base self._reflect_to_knowledge_base() return { 'short_term_memory_size': len(self.short_term_memory), 'long_term_memory_size': len(self.long_term_memory), 'context_model_size': len(self.context_model) } def _update_short_term_memory(self, communication_data): # Update short-term memory # Data preprocessing processed_data = self._preprocess_communication_data(communication_data) # Add to short-term memory self.short_term_memory.append({ 'data': processed_data, 'timestamp': time.time(), 'type': communication_data.get('type', 'unknown') }) # Size limit if len(self.short_term_memory) > self.short_term_memory_size: self.short_term_memory.pop(0) def _preprocess_communication_data(self, communication_data): # Preprocess communication data data_type = communication_data.get('type', 'unknown') data = communication_data.get('data', {}) if data_type == 'text': # Preprocess text data return self._preprocess_text_data(data) elif data_type == 'image': # Preprocess image data return self._preprocess_image_data(data) elif data_type == 'audio': # Preprocessing of audio data return self._preprocess_audio_data(data) elif data_type == 'video': # Preprocessing of video data return self._preprocess_video_data(data) else: # Other data types return data def _preprocess_text_data(self, text_data): # Preprocessing of text data # Tokenization tokens = nltk.word_tokenize(text_data) # Stop word removal stop_words = set(nltk.corpus.stopwords.words('english')) filtered_tokens = [token for token in tokens if token.lower() not in stop_words] # Stemming stemmer = nltk.stem.PorterStemmer() stemmed_tokens = [stemmer.stem(token) for token in filtered_tokens] # Part-of-speech tagging tagged_tokens = nltk.pos_tag(stemmed_tokens) # Named entity extraction entities = nltk.chunk.ne_chunk(tagged_tokens) return { 'original': text_data, 'tokens': tokens, 'filtered_tokens': filtered_tokens, 'stemmed_tokens': stemmed_tokens, 'tagged_tokens': tagged_tokens, 'entities': str(entities) # Convert to string and save } def _preprocess_image_data(self, image_data): # Preprocess image data # Image size height, width = image_data.shape[:2] # Image features features = { 'size': (height, width), 'channels': image_data.shape[2] if len(image_data.shape) > 2 else 1, 'mean_color': np.mean(image_data, axis=(0, 1)).tolist(), 'std_color': np.std(image_data, axis=(0, 1)).tolist() }

[0037] # Image hash (for similar image search) img_hash = imagehash.phash(Image.fromarray(image_data)) return { 'features': features, 'hash': str(img_hash) } def _preprocess_audio_data(self, audio_data): # Preprocessing of audio data # Audio features features = { 'duration': len(audio_data) / 16000, # Assume sampling rate of 16kHz 'rms_energy': np.sqrt(np.mean(audio_data**2)), 'zero_crossing_rate': np.sum(np.abs(np.diff(np.signbit(audio_data)))) / len(audio_data) } # Spectrogram features f, t, Sxx = signal.spectrogram(audio_data, fs=16000) spectral_features = { 'spectral_centroid': np.mean(np.sum(f[:, np.newaxis] * Sxx, axis=0) / np.sum(Sxx, axis=0)), 'spectral_bandwidth': np.mean(np.sqrt(np.sum(((f[:, np.newaxis] - np.mean(f))**2) * Sxx, axis=0) / np.sum(Sxx, axis=0))) } return { 'features': features, 'spectral_features': spectral_features } def _preprocess_video_data(self, video_data): # Preprocessing of video data # Video features num_frames = len(video_data) frame_rate = 30 # Assumption features = { 'num_frames': num_frames, 'duration': num_frames / frame_rate, 'frame_rate': frame_rate, 'resolution': video_data[0].shape[:2] if num_frames > 0 else (0, 0) } # Features of the representative frame if num_frames > 0: middle_frame = video_data[num_frames / / 2] middle_frame_features = self._preprocess_image_data(middle_frame) features['middle_frame'] = middle_frame_features return { 'features': features } def _update_long_term_memory(self, communication_data): # Update of long-term memory data_type = communication_data.get('type', 'unknown') data = communication_data.get('data', {}) # Feature extraction according to data type if data_type == 'text': features = self._extract_text_features(data) elif data_type == 'image': features = self._extract_image_features(data) elif data_type == 'audio': features = self._extract_audio_features(data) elif data_type == 'video': features = self._extract_video_features(data) else: features = [] # Update the frequency of feature occurrences for feature in features: if feature in self.long_term_memory: self.long_term_memory[feature]['count'] += 1 self.long_term_memory[feature]['last_seen'] = time.time() else: self.long_term_memory[feature] = { 'count': 1, 'first_seen': time.time(), 'last_seen': time.time(), 'type': data_type } # Size limit of long-term memory if len(self.long_term_memory) > self.long_term_memory_max_items: # Delete items with low frequency items_to_keep = sorted( self.long_term_memory.items(), key=lambda x: (x[1]['count'], x[1]['last_seen']), reverse=True )[:self.long_term_memory_max_items] self.long_term_memory = {k: v for k, v in items_to_keep} def _extract_text_features(self, text_data): # Feature extraction from text if isinstance(text_data, str): # In the case of raw text data tokens = nltk.word_tokenize(text_data) elif isinstance(text_data, dict) and 'tokens' in text_data: # In the case of preprocessed text data tokens = text_data['tokens'] else: tokens = [] # Word n-gram features = [] for n in range(1, 4): # 1-gram, 2-gram, 3-gram ngrams = list(nltk.ngrams(tokens, n)) for gram in ngrams: feature = f"text_ngram_{n}_{' '.join(gram)}" features.append(feature) # Named entity if isinstance(text_data, dict) and 'entities' in text_data: entities_str = text_data['entities'] # Analysis of named entities (simple implementation) entity_matches = re.findall(r'\(NE\s+(\w+)\s+([^)]+)\)', entities_str) for entity_type, entity_text in entity_matches: feature = f"text_entity_{entity_type}_{entity_text}" features.append(feature) return features def _extract_image_features(self, image_data): # Feature extraction from images features = [] if isinstance(image_data, dict) and 'features' in image_data: # In the case of pre - processed image data img_features = image_data['features']

[0038] # Size features if'size' in img_features: height, width = img_features['size'] aspect_ratio = width / height if height > 0 else 0 size_category = 'small' if height * width < 100000 else ('medium' if height * width <1000000> else 'large') features.append(f"image_size_{size_category}") features.append(f"image_aspect_{aspect_ratio:.1f}") # 色特徴 if 'mean_color' in img_features: mean_color = img_features['mean_color'] brightness = sum(mean_color) / len(mean_color) brightness_category = 'dark' if brightness < 85 else ('medium' if brightness < 170 else 'bright') features.append(f"image_brightness_{brightness_category}") # 画像ハッシュ if isinstance(image_data, dict) and 'hash' in image_data: features.append(f"image_hash_{image_data['hash']}") return features def _extract_audio_features(self, audio_data): # 音声からの特徴抽出 features = [] if isinstance(audio_data, dict) and 'features' in audio_data: # In the case of pre - processed audio data audio_features = audio_data['features'] # Length feature if 'duration' in audio_features: duration = audio_features['duration'] duration_category ='short' if duration < 5 else ('medium' if duration < 30 else 'long') features.append(f"audio_duration_{duration_category}") # Energy feature if 'rms_energy' in audio_features: energy = audio_features['rms_energy'] energy_category = 'low' if energy < 0.1 else ('medium' if energy < 0.3 else 'high') features.append(f"audio_energy_{energy_category}") return features def _extract_video_features(self, video_data): # Feature extraction from video features = [] if isinstance(video_data, dict) and 'features' in video_data: # For preprocessed video data video_features = video_data['features'] # Length characteristics if 'duration' in video_features: duration = video_features['duration'] duration_category = 'short' if duration < 10 else ('medium' if duration < 60 else 'long') features.append(f"video_duration_{duration_category}") # Resolution characteristics if 'resolution' in video_features: height, width = video_features['resolution'] resolution_category = 'low' if height * width < 100000 else ('medium' if height * width <1000000> else 'high') features.append(f"video_resolution_{resolution_category}") return features def _update_context_model(self): # Updating the context model # Applying time decay for key in self.context_model: self.context_model[key] *= self.context_decay_factor # Update from short-term memory for memory_item in self.short_term_memory: data_type = memory_item['type'] processed_data = memory_item['data'] # Feature extraction according to data type if data_type == 'text': features = self._extract_text_features(processed_data) elif data_type == 'image': features = self._extract_image_features(processed_data) elif data_type == 'audio': features = self._extract_audio_features(processed_data) elif data_type == 'video': features = self._extract_video_features(processed_data) else: features = [] # Activation of features for feature in features: if feature in self.context_model: self.context_model[feature] += 1.0 else: self.context_model[feature] = 1.0 # Update from long-term memory for feature, info in self.long_term_memory.items(): if info['count'] >= self.long_term_memory_threshold: if feature in self.context_model: self.context_model[feature] += 0.5 * (info['count'] / self.long_term_memory_threshold) else: self.context_model[feature] = 0.5 * (info['count'] / self.long_term_memory_threshold)

[0039] # Delete items below the threshold threshold = 0.1 self.context_model = {k: v for k, v in self.context_model.items() if v >= threshold} def _update_prediction_model(self): # Update the prediction model # Creating learning data from short-term memory training_data = [] for i in range(1, len(self.short_term_memory)): prev_item = self.short_term_memory[i-1] curr_item = self.short_term_memory[i] training_data.append({ 'prev': prev_item, 'curr': curr_item ) # Update the prediction model if training_data: self.prediction_model.update(training_data, self.context_model) def _reflect_to_knowledge_base(self): # Reflect context information to the knowledge base # Extract important items from long-term memory important_items = { feature: info for feature, info in self.long_term_memory.items() if info['count'] >= self.long_term_memory_threshold * 2 } # Add to the knowledge base for feature, info in important_items.items(): self.shared_knowledge_manager.update_knowledge( key=feature, value={ 'count': info['count'], 'first_seen': info['first_seen'], 'last_seen': info['last_seen'], 'type': info['type'] }, domain='communication_patterns' ) def predict_next_content(self, current_data=None): # Predict the following communication content if current_data is None and self.short_term_memory: # If the current data is not specified, use the latest short-term memory current_data = self.short_term_memory[-1] if current_data is None: return None # Execute the prediction prediction = self.prediction_model.predict(current_data, self.context_model) return prediction def get_context_state(self): # Get the current context state # Active features (in descending order of activation value) active_features = sorted( self.context_model.items(), key=lambda x: x[1], reverse=True )[:20] # Top 20 # Recent communication content recent_communications = { 'type': item['type'], 'timestamp': item['timestamp'] } for item in self.short_term_memory # Long-term memory statistics ​long_term_stats = { 'total_items': len(self.long_term_memory), 'by_type': {} } for feature, info in self.long_term_memory.items(): data_type = info['type'] if data_type not in long_term_stats['by_type']: long_term_stats['by_type'][data_type] = 0 long_term_stats['by_type'][data_type] += 1 return { 'active_features': active_features, 'recent_communications': recent_communications, 'long_term_stats': long_term_stats, 'prediction_model_state': self.prediction_model.get_state() } ```

[0040] 4.7 Integration of the Distributed Collaborative Compression Module Integrate the above sub-modules to construct a distributed collaborative compression module. The integrated architecture is as follows. ``` class DistributedCollaborativeCompressionModule: def __init__(self, config): self.config = config # Initialization of the Shared Knowledge Manager self.shared_knowledge_manager = SharedKnowledgeManager( config.get('shared_knowledge_manager_config', {}) ) # Initialization of the Differential Information Extractor self.differential_extractor = DifferentialInformationExtractor( config.get('differential_extractor_config', {}), self.shared_knowledge_manager ) # Initialization of the Collaborative Encoder self.collaborative_encoder = CollaborativeEncoder( config.get('collaborative_encoder_config', {}) ) # Initialization of the Knowledge Synchronizer self.knowledge_synchronizer = KnowledgeSynchronizer( config.get('knowledge_synchronizer_config', {}), self.shared_knowledge_manager ) # Initialization of the Context Builder self.context_builder = ContextBuilder( config.get('context_builder_config', {}), self.shared_knowledge_manager ) # Communication session information self.session_id = str(uuid.uuid4()) self.session_start_time = time.time() self.compression_history = [] def compress(self, data, data_type, context=None): # Data compression start_time = time.time() # Update context information if context is None: context = {} context.update({ 'session_id': self.session_id, 'session_time': time.time() - self.session_start_time }) # Update context builder self.context_builder.update_context({ 'type': data_type, 'data': data }) # Extract differential information differential_result = self.differential_extractor.extract_differential_information( data, data_type, context ) # Encode differential information encoding_result = self.collaborative_encoder.encode_differential( differential_result, data_type ) # Update compression history compression_record = { 'timestamp': time.time(), 'data_type': data_type, 'original_size': encoding_result['metadata']['original_size'], 'compressed_size': encoding_result['metadata']['compressed_size'], 'compression_ratio': encoding_result['metadata']['compression_ratio'], 'processing_time': time.time() - start_time } self.compression_history.append(compression_record) # Update knowledge base self._update_knowledge_base(data, data_type, differential_result) # Integrate results result = { 'encoded_data': encoding_result['encoded_data'], 'metadata': encoding_result['metadata'], 'context_state': self.context_builder.get_context_state(), 'knowledge_metadata': self.shared_knowledge_manager.get_knowledge_metadata(), 'compression_record': compression_record } return result def decompress(self, compressed_data, metadata): # Decompression of compressed data start_time = time.time() # Obtaining the data type data_type = metadata['data_type'] # Obtaining the encoding scheme encoding_scheme = metadata['encoding_scheme'] # Selecting the appropriate decoder if data_type == 'text': decoder = self._get_text_decoder(encoding_scheme) elif data_type == 'image': decoder = self._get_image_decoder(encoding_scheme) elif data_type == 'audio': decoder = self._get_audio_decoder(encoding_scheme) elif data_type == 'video': decoder = self._get_video_decoder(encoding_scheme) else: raise ValueError(f"Unsupported data type: {data_type}") # Decoding of differential information differential_info = decoder.decode(compressed_data, metadata) # Reconstruct the original data from shared knowledge and differential information reconstructed_data = self._reconstruct_data(differential_info, data_type) # Update context information self.context_builder.update_context({ 'type': data_type, 'data': reconstructed_data }) # Update the knowledge base self._update_knowledge_base(reconstructed_data, data_type, None) # Integrate the results result = { 'reconstructed_data': reconstructed_data, 'processing_time': time.time() - start_time, 'context_state': self.context_builder.get_context_state(), 'knowledge_metadata': self.shared_knowledge_manager.get_knowledge_metadata()} return result

[0041] def _get_text_decoder(self, encoding_scheme): # Obtaining the text decoder scheme_type = encoding_scheme.get('type', 'arithmetic') if scheme_type == 'arithmetic': return ArithmeticTextDecoder(encoding_scheme) elif scheme_type == 'huffman': return HuffmanTextDecoder(encoding_scheme) else: raise ValueError(f"Unsupported text encoding scheme: {scheme_type}") def _get_image_decoder(self, encoding_scheme): # Obtaining the image decoder scheme_type = encoding_scheme.get('type','residual') if scheme_type =='residual': return ResidualImageDecoder(encoding_scheme) elif scheme_type == 'wavelet': return WaveletImageDecoder(encoding_scheme) else: raise ValueError(f"Unsupported image encoding scheme: {scheme_type}") def _get_audio_decoder(self, encoding_scheme): # Obtaining the audio decoder scheme_type = encoding_scheme.get('type','spectral') if scheme_type =='spectral': return SpectralAudioDecoder(encoding_scheme) elif scheme_type == 'wavelet': return WaveletAudioDecoder(encoding_scheme) else: raise ValueError(f"Unsupported audio encoding scheme: {scheme_type}") def _get_video_decoder(self, encoding_scheme): # Obtaining the video decoder scheme_type = encoding_scheme.get('type','motion_residual') if scheme_type =='motion_residual': return MotionResidualVideoDecoder(encoding_scheme) elif scheme_type == 'wavelet': return WaveletVideoDecoder(encoding_scheme) else: raise ValueError(f"Unsupported video encoding scheme: {scheme_type}") def _reconstruct_data(self, differential_info, data_type): # Reconstruct the original data from the differential information # Generation of predicted data knowledge = self.shared_knowledge_manager.get_knowledge() context_state = self.context_builder.get_context_state() if data_type == 'text': return self._reconstruct_text_data(differential_info, knowledge, context_state) elif data_type == 'image': return self._reconstruct_image_data(differential_info, knowledge, context_state) elif data_type == 'audio': return self._reconstruct_audio_data(differential_info, knowledge, context_state) elif data_type == 'video': return self._reconstruct_video_data(differential_info, knowledge, context_state) else: raise ValueError(f"Unsupported data type: {data_type}") def _reconstruct_text_data(self, differential_info, knowledge, context_state): # Reconstruction of text data # Implementation omitted return "Reconstructed text data" def _reconstruct_image_data(self, differential_info, knowledge, context_state): # Reconstruction of image data # Implementation omitted return np.zeros((100, 100, 3), dtype=np.uint8) # Dummy image def _reconstruct_audio_data(self, differential_info, knowledge, context_state): # Reconstruction of audio data # Implementation omitted return np.zeros(16000) # Dummy audio (1 second, 16 kHz) def _reconstruct_video_data(self, differential_info, knowledge, context_state): # Reconstruction of video data # Implementation omitted return [np.zeros((100, 100, 3), dtype=np.uint8) for _ in range(30)] # Dummy video (30 frames) def _update_knowledge_base(self, data, data_type, differential_result): # Update of the knowledge base # Feature extraction according to the data type if data_type == 'text': features = self._extract_text_knowledge(data) elif data_type == 'image': features = self._extract_image_knowledge(data) elif data_type == 'audio': features = self._extract_audio_knowledge(data) elif data_type == 'video': features = self._extract_video_knowledge(data) else: features = {} # Add to the knowledge base for key, value in features.items(): self.shared_knowledge_manager.update_knowledge( key=key, value=value, domain=f"{data_type}_features" ) # Log knowledge changes self.knowledge_synchronizer.log_knowledge_change( operation='update', key=key, domain=f"{data_type}_features", priority=0.5 # Default priority ) def _extract_text_knowledge(self, text_data): # Knowledge Extraction from Text # Implementation Omitted return {} def _extract_image_knowledge(self, image_data): # Knowledge Extraction from Image # Implementation Omitted return {} def _extract_audio_knowledge(self, audio_data): # Knowledge Extraction from Audio # Implementation Omitted return {} def _extract_video_knowledge(self, video_data): # Knowledge Extraction from Video # Implementation Omitted return {}

[0042] def synchronize_knowledge(self, remote_endpoint): # Synchronization of Knowledge Base return self.knowledge_synchronizer.start_background_sync(remote_endpoint) def get_compression_statistics(self): # Retrieval of Compression Statistics Information if not self.compression_history: return { 'total_compressions': 0, 'average_ratio': 0, 'average_processing_time': 0, 'total_original_size': 0, 'total_compressed_size': 0, 'by_data_type': {} } total_original_size = sum(record['original_size'] for record in self.compression_history) total_compressed_size = sum(record['compressed_size'] for record in self.compression_history) # Statistics by data type by_data_type = {} for record in self.compression_history: data_type = record['data_type'] if data_type not in by_data_type: by_data_type[data_type] = { 'count': 0, 'total_original_size': 0, 'total_compressed_size': 0, 'total_processing_time': 0 } by_data_type[data_type]['count'] += 1 by_data_type[data_type]['total_original_size'] += record['original_size'] by_data_type[data_type]['total_compressed_size'] += record['compressed_size'] by_data_type[data_type]['total_processing_time'] += record['processing_time'] # Calculation of average value for data_type, stats in by_data_type.items(): stats['average_ratio'] = stats['total_original_size'] / stats['total_compressed_size'] if stats['total_compressed_size'] > 0 else 0 if stats['total_compressed_size'] > 0 else 0 stats['average_processing_time'] = stats['total_processing_time'] / stats['count'] if stats['count'] > 0 else 0 if stats['count'] > 0 else 0 return { 'total_compressions': len(self.compression_history), 'average_ratio': total_original_size / total_compressed_size if total_compressed_size > 0 else 0, 'average_processing_time': sum(record['processing_time'] for record in self.compression_history) / len(self.compression_history), 'total_original_size': total_original_size, 'total_compressed_size': total_compressed_size, 'by_data_type': by_data_type } ``` ## 5. Embodiment of the Knowledge Distillation Module The knowledge distillation module is a component that efficiently transfers the compression ability of a large-scale model to a lightweight model. The detailed implementation method and operating principle of this module will be described. 5.1 Module Composition The knowledge distillation module consists of the following sub-modules. 1. Teacher Model Selector 2. Student Model Designer 3. Distillation Learner 4. Domain Adaptor 5. Quantization and Pruning Device These sub-modules realize the process of efficiently transferring the knowledge of a large-scale model to a lightweight model. 5.2 Implementation of the Teacher Model Selector The teacher model selector selects the optimal large-scale model for each data format as the teacher model. The implementation includes the following processing steps. 1. Model Evaluation: Evaluate the compression performance of multiple large-scale models for each data format 2. Optimal Model Selection: Based on the evaluation results, select the optimal teacher model for each data format 3. Ensemble Construction: Construct an ensemble model that integrates the predictions of multiple large-scale models 4. Teacher Model Adaptation: Adapt the selected teacher model to a specific domain or task ``` class TeacherModelSelector: def __init__(self, config): self.config = config self.available_models = config.get('available_models', {}) self.evaluation_metrics = config.get('evaluation_metrics', { 'compression_ratio': 0.6, 'processing_time': 0.2, 'prediction_accuracy': 0.2 }) self.evaluation_results = {} self.selected_models = {} self.ensemble_models = {} def evaluate_models(self, data_type, evaluation_dataset): # Evaluation of each model if data_type not in self.available_models: raise ValueError(f"No available models for data type: {data_type}") models = self.available_models[data_type] results = {} for model_name, model_info in models.items(): # Loading the model model = self._load_model(model_name, model_info) # Executing the evaluation eval_result = self._evaluate_model(model, data_type, evaluation_dataset) results[model_name] = eval_result # Saving the evaluation result self.evaluation_results[data_type] = results return results def _load_model(self, model_name, model_info): # Loading the model model_type = model_info.get('type', 'pytorch') model_path = model_info.get('path') if model_type == 'pytorch': return self._load_pytorch_model(model_name, model_path) elif model_type == 'tensorflow': return self._load_tensorflow_model(model_name, model_path) elif model_type == 'onnx': return self._load_onnx_model(model_name, model_path) else: raise ValueError(f"Unsupported model type: {model_type}")

[0043] def _load_pytorch_model(self, model_name, model_path): # Loading the PyTorch model try: if model_name == 'iGPT': model = IGPTModel.from_pretrained(model_path) elif model_name == 'LLaMA3': model = LLaMA3Model.from_pretrained(model_path) else: model = torch.load(model_path) return model except Exception as e: logger.error(f"Failed to load PyTorch model {model_name}: {e}") return None def _load_tensorflow_model(self, model_name, model_path): # Loading TensorFlow model try: model = tf.keras.models.load_model(model_path) return model except Exception as e: logger.error(f"Failed to load TensorFlow model {model_name}: {e}") return None def _load_onnx_model(self, model_name, model_path): # Loading ONNX model try: model = onnx.load(model_path) return model except Exception as e: logger.error(f"Failed to load ONNX model {model_name}: {e}") return None def _evaluate_model(self, model, data_type, evaluation_dataset): # Evaluation of the model if model is None: return { 'compression_ratio': 0, 'processing_time': float('inf'), 'prediction_accuracy': 0, 'overall_score': 0 } # Evaluation according to the data type if data_type == 'text': return self._evaluate_text_model(model, evaluation_dataset) elif data_type == 'image': return self._evaluate_image_model(model, evaluation_dataset) elif data_type == 'audio': return self._evaluate_audio_model(model, evaluation_dataset) elif data_type == 'video': return self._evaluate_video_model(model, evaluation_dataset) else: raise ValueError(f"Unsupported data type: {data_type}") def _evaluate_text_model(self, model, evaluation_dataset): # Evaluation of the text model # Evaluation of the compression ratio compression_ratios = [] processing_times = [] prediction_accuracies = [] for text_sample in evaluation_dataset: # Original size original_size = len(text_sample.encode('utf-8')) # Measurement of processing time start_time = time.time() # Prediction by the model predictions = self._get_text_model_predictions(model, text_sample) # Processing time processing_time = time.time() - start_time # Evaluation of prediction accuracy prediction_accuracy = self._evaluate_text_prediction_accuracy(predictions, text_sample) # Estimation of compressed size compressed_size = self._estimate_compressed_size(predictions) # Compression ratio compression_ratio = original_size / compressed_size if compressed_size > 0 else 0 compression_ratios.append(compression_ratio) processing_times.append(processing_time) prediction_accuracies.append(prediction_accuracy) # Calculate the average value avg_compression_ratio = sum(compression_ratios) / len(compression_ratios) if compression_ratios else 0 avg_processing_time = sum(processing_times) / len(processing_times) if processing_times else 0 avg_prediction_accuracy = sum(prediction_accuracies) / len(prediction_accuracies) if prediction_accuracies else 0 # Normalization (take the reciprocal of the processing time) normalized_processing_time = 1.0 / (avg_processing_time + 1e-10) # Calculating the overall score overall_score = ( self.evaluation_metrics['compression_ratio'] * avg_compression_ratio + self.evaluation_metrics['processing_time'] * normalized_processing_time + self.evaluation_metrics['prediction_accuracy'] * avg_prediction_accuracy ) return { 'compression_ratio': avg_compression_ratio, 'processing_time': avg_processing_time, 'prediction_accuracy': avg_prediction_accuracy, 'overall_score': overall_score } def _evaluate_image_model(self, model, evaluation_dataset): # Evaluation of the image model # Implementation omitted (same structure as the text model) return { 'compression_ratio': 5.0, # Dummy value 'processing_time': 0.5, # Dummy value 'prediction_accuracy': 0.9, # Dummy value 'overall_score': 3.0 # Dummy value } def _evaluate_audio_model(self, model, evaluation_dataset): # Evaluation of the audio model # Implementation omitted (same structure as the text model) return { 'compression_ratio': 4.0, # Dummy value 'processing_time': 0.8, # Dummy value 'prediction_accuracy': 0.85, # Dummy value 'overall_score': 2.5 # Dummy value }

[0044] def _evaluate_video_model(self, model, evaluation_dataset): # Evaluation of video model # Implementation omitted (same structure as text model) return { 'compression_ratio': 6.0, # Dummy value 'processing_time': 2.0, # Dummy value 'prediction_accuracy': 0.8, # Dummy value 'overall_score': 2.8 # Dummy value } def _get_text_model_predictions(self, model, text_sample): # Predictions by text model # Implementation omitted return { 'token_predictions': [], 'confidence_scores': [] } def _evaluate_text_prediction_accuracy(self, predictions, text_sample): # Evaluation of text prediction accuracy # Implementation omitted return 0.9 # Dummy value def _estimate_compressed_size(self, predictions): # Estimation of Compression Size Based on Prediction # Implementation Omitted return 100 # Dummy Value def select_optimal_models(self, data_type): # Selection of Optimal Model if data_type not in self.evaluation_results: raise ValueError(f"No evaluation results for data type: {data_type}") results = self.evaluation_results[data_type] # Select Optimal Model Based on Score optimal_model = max(results.items(), key=lambda x: x[1]['overall_score'])[0] # Save Selection Result self.selected_models[data_type] = optimal_model return optimal_model def build_ensemble(self, data_type, ensemble_config=None): # Construction of Ensemble Model if data_type not in self.evaluation_results: raise ValueError(f"No evaluation results for data type: {data_type}") if ensemble_config is None: ensemble_config = self.config.get('ensemble_config', {}) ensemble_type = ensemble_config.get('type', 'weighted_average') top_k = ensemble_config.get('top_k', 3) # Select top models based on evaluation results results = self.evaluation_results[data_type] top_models = sorted( results.items(), key=lambda x: x[1]['overall_score'], reverse=True )[:top_k] # Build the ensemble if ensemble_type == 'weighted_average': ensemble = self._build_weighted_average_ensemble(data_type, top_models) elif ensemble_type =='stacking': ensemble = self._build_stacking_ensemble(data_type, top_models) elif ensemble_type == 'boosting': ensemble = self._build_boosting_ensemble(data_type, top_models) else: raise ValueError(f"Unsupported ensemble type: {ensemble_type}") # Saving Ensemble Results self.ensemble_models[data_type] = ensemble return ensemble def _build_weighted_average_ensemble(self, data_type, top_models): # Building Weighted Average Ensemble models = [] weights = [] for model_name, result in top_models: model_info = self.available_models[data_type][model_name] model = self._load_model(model_name, model_info) if model is not None: models.append(model) weights.append(result['overall_score']) # Normalizing Weights total_weight = sum(weights) if total_weight > 0: weights = [w / total_weight for w in weights] return { 'type': 'weighted_average', 'models': models, 'weights': weights, 'model_names': [model_name for model_name, _ in top_models] } def _build_stacking_ensemble(self, data_type, top_models): # Construction of stacking ensemble # Implementation omitted return { 'type':'stacking', 'models': [], 'meta_model': None, 'model_names': [model_name for model_name, _ in top_models] } def _build_boosting_ensemble(self, data_type, top_models): # Construction of boosting ensemble # Implementation omitted return { 'type': 'boosting', 'models': [], 'weights': [], 'model_names': [model_name for model_name, _ in top_models] } def adapt_teacher_model(self, data_type, domain=None, adaptation_config=None): # Domain adaptation of teacher model if data_type not in self.selected_models and data_type not in self.ensemble_models: raise ValueError(f"No selected or ensemble model for data type: {data_type}") if adaptation_config is None: adaptation_config = self.config.get('adaptation_config', {}) adaptation_type = adaptation_config.get('type', 'fine_tuning') # Obtain the model to be adapted if data_type in self.ensemble_models: model = self.ensemble_models[data_type] is_ensemble = True else: model_name = self.selected_models[data_type] model_info = self.available_models[data_type][model_name] model = self._load_model(model_name, model_info) is_ensemble = False

[0045] # Execute domain adaptation if adaptation_type == 'fine_tuning': adapted_model = self._adapt_by_fine_tuning(model, data_type, domain, is_ensemble) elif adaptation_type == 'prompt_engineering': adapted_model = self._adapt_by_prompt_engineering(model, data_type, domain, is_ensemble) elif adaptation_type == 'parameter_efficient_learning': adapted_model = self._adapt_by_parameter_efficient_learning(model, data_type, domain, is_ensemble) else: raise ValueError(f"Unsupported adaptation type: {adaptation_type}") return adapted_model def _adapt_by_fine_tuning(self, model, data_type, domain, is_ensemble): # Domain adaptation by fine-tuning # Implementation omitted return model def _adapt_by_prompt_engineering(self, model, data_type, domain, is_ensemble): # Domain adaptation by prompt engineering # Implementation omitted return model def _adapt_by_parameter_efficient_learning(self, model, data_type, domain, is_ensemble): # Domain adaptation by parameter-efficient learning # Implementation omitted return model def get_teacher_model(self, data_type, use_ensemble=False): # Obtaining the teacher model if use_ensemble and data_type in self.ensemble_models: return self.ensemble_models[data_type] elif data_type in self.selected_models: model_name = self.selected_models[data_type] model_info = self.available_models[data_type][model_name] return self._load_model(model_name, model_info) else: raise ValueError(f"No teacher model available for data type: {data_type}") ``` 5.3 Implementation of Student Model Designer The student model designer designs a lightweight student model according to resource constraints. The implementation includes the following processing steps. 1. Architecture design: Design a model architecture suitable for resource constraints 2. Modular design: Adopt a design that divides the model into multiple specialized modules and activates only the necessary modules according to the task 3. Adoption of efficient architecture: Adopt efficient architectures such as MobileNet, EfficientNet, SqueezeNet, etc. 4. Hardware optimization: Design the model according to the characteristics of the target hardware ``` class StudentModelDesigner: def __init__(self, config): self.config = config self.resource_constraints = config.get('resource_constraints', {}) self.architecture_templates = config.get('architecture_templates', {}) self.hardware_targets = config.get('hardware_targets', {}) self.designed_models = {} def design_student_model(self, data_type, teacher_model, target_hardware=None): # Design of the student model if target_hardware is None: target_hardware = self.config.get('default_hardware_target','mobile_cpu') # Obtaining resource constraints constraints = self._get_resource_constraints(target_hardware) # Analysis of the teacher model teacher_model_info = self._analyze_teacher_model(teacher_model, data_type) # Selection of architecture architecture = self._select_architecture(data_type, teacher_model_info, constraints) # Modular design modular_design = self._design_modular_architecture(architecture, teacher_model_info, constraints) # Hardware optimization optimized_design = self._optimize_for_hardware(modular_design, target_hardware) # Construction of student model student_model = self._build_student_model(optimized_design, data_type) # Saving the design result model_id = str(uuid.uuid4()) self.designed_models[model_id] = { 'model': student_model, 'data_type': data_type, 'target_hardware': target_hardware, 'constraints': constraints, 'architecture': architecture, 'design': optimized_design } return { 'model_id': model_id, 'model': student_model, 'architecture': architecture, 'design': optimized_design } def _get_resource_constraints(self, target_hardware): # Obtaining Resource Constraints if target_hardware in self.resource_constraints: return self.resource_constraints[target_hardware] else: # Default Constraints return { 'max_parameters': 10000000, # 10 million parameters 'max_model_size': 50000000, # 50MB 'max_memory_usage': 500000000, # 500MB 'max_inference_time': 1.0, # 1 second 'max_power_consumption': 1.0 # 1W } def _analyze_teacher_model(self, teacher_model, data_type): # Analyzing the Teacher Model if isinstance(teacher_model, dict) and 'type' in teacher_model and teacher_model['type'] == 'ensemble': # In the case of an ensemble model return self._analyze_ensemble_teacher_model(teacher_model, data_type) else: # In the case of a single model return self._analyze_single_teacher_model(teacher_model, data_type)

[0046] def _analyze_ensemble_teacher_model(self, ensemble_model, data_type): # Analysis of the ensemble teacher model models = ensemble_model.get('models', []) model_names = ensemble_model.get('model_names', []) # Analysis of each model model_analyses = [] for i, model in enumerate(models): model_name = model_names[i] if i < len(model_names) else f"model_{i}" analysis = self._analyze_single_teacher_model(model, data_type) model_analyses.append(analysis) # Analysis of the entire ensemble total_parameters = sum(analysis['num_parameters'] for analysis in model_analyses) total_model_size = sum(analysis['model_size'] for analysis in model_analyses) return { 'type': 'ensemble', 'num_models': len(models), 'model_names': model_names, 'model_analyses': model_analyses, 'num_parameters': total_parameters, 'model_size': total_model_size, 'data_type': data_type } def _analyze_single_teacher_model(self, model, data_type): # Analysis of single teacher model model_type = self._detect_model_type(model) if model_type == 'pytorch': return self._analyze_pytorch_model(model, data_type) elif model_type == 'tensorflow': return self._analyze_tensorflow_model(model, data_type) elif model_type == 'onnx': return self._analyze_onnx_model(model, data_type) else: # Default values when model information cannot be obtained return { 'type': 'unknown', 'num_parameters': 1000000000, # 1 billion parameters 'model_size': 4000000000, # 4GB 'architecture': 'unknown', 'layer_structure': [], 'data_type': data_type } def _detect_model_type(self, model): # Detect the type of the model if isinstance(model, torch.nn.Module): return 'pytorch' elif 'keras' in str(type(model)): return 'tensorflow' elif hasattr(model, 'graph'): return 'onnx' else: return 'unknown' def _analyze_pytorch_model(self, model, data_type): # Analysis of PyTorch model # Calculation of the number of parameters num_parameters = sum(p.numel() for p in model.parameters()) # Estimation of model size (in bytes) model_size = num_parameters * 4 # Assume 32-bit floating point # Obtaining architecture information architecture = model.__class__.__name__ # Obtaining layer structure layer_structure = [] for name, module in model.named_modules(): if len(list(module.children())) == 0: # Only terminal modules layer_info = { 'name': name, 'type': module.__class__.__name__, 'parameters': sum(p.numel() for p in module.parameters()) } layer_structure.append(layer_info) return { 'type': 'pytorch', 'num_parameters': num_parameters, 'model_size': model_size, 'architecture': architecture, 'layer_structure': layer_structure, 'data_type': data_type } def _analyze_tensorflow_model(self, model, data_type): # Analysis of TensorFlow model # Calculation of the number of parameters num_parameters = model.count_params() # Estimation of model size (in bytes) model_size = num_parameters * 4 # Assume 32-bit floating point # Obtaining architecture information architecture = model.__class__.__name__ # Obtaining layer structure layer_structure = [] for layer in model.layers: layer_info = { 'name': layer.name, 'type': layer.__class__.__name__, 'parameters': layer.count_params() } layer_structure.append(layer_info) return { 'type': 'tensorflow', 'num_parameters': num_parameters, 'model_size': model_size, 'architecture': architecture, 'layer_structure': layer_structure, 'data_type': data_type } def _analyze_onnx_model(self, model, data_type): # Analysis of ONNX model # Implementation omitted return { 'type': 'onnx', 'num_parameters': 1000000, # Dummy value 'model_size': 4000000, # Dummy value 'architecture': 'onnx_model', 'layer_structure': [], 'data_type': data_type } def _select_architecture(self, data_type, teacher_model_info, constraints): # Selection of architecture if data_type not in self.architecture_templates: raise ValueError(f"No architecture templates for data type: {data_type}") templates = self.architecture_templates[data_type] # Number of parameters of the teacher model teacher_params = teacher_model_info['num_parameters']

[0047] # Calculation of the reduction ratio based on constraints reduction_ratio = min( constraints['max_parameters'] / teacher_params, 1.0 # At most, it does not exceed the original size ) # Select the architecture based on the reduction ratio if reduction_ratio <= 0.01: # Very small model (1 / 100 or less) architecture_type = 'ultra_light' elif reduction_ratio <= 0.1: # Small model (1 / 10 or less) architecture_type = 'light' elif reduction_ratio <= 0.3: # Medium-sized model (1 / 3 or less) architecture_type ='medium' else: # Relatively large model architecture_type = 'large' # Selected architecture template if architecture_type in templates: architecture = templates[architecture_type].copy() else: # Default template architecture = templates.get('default', {}).copy() # Add teacher model information architecture['teacher_model_info'] = teacher_model_info architecture['reduction_ratio'] = reduction_ratio architecture['data_type'] = data_type return architecture def _design_modular_architecture(self, architecture, teacher_model_info, constraints): # Design modular architecture modular_design = architecture.copy() # Module design based on architecture type if architecture.get('type') == 'transformer': modular_design['modules'] = self._design_transformer_modules(architecture, teacher_model_info, constraints) elif architecture.get('type') == 'cnn': modular_design['modules'] = self._design_cnn_modules(architecture, teacher_model_info, constraints) elif architecture.get('type') == 'rnn': modular_design['modules'] = self._design_rnn_modules(architecture, teacher_model_info, constraints) else: modular_design['modules'] = self._design_generic_modules(architecture, teacher_model_info, constraints) # Definition of the connection relationship between modules modular_design['connections'] = self._define_module_connections(modular_design['modules']) # Definition of conditional execution paths modular_design['execution_paths'] = self._define_execution_paths(modular_design['modules'], modular_design['connections']) return modular_design def _design_transformer_modules(self, architecture, teacher_model_info, constraints): # Module design for Transformer architecture # Implementation omitted return [] def _design_cnn_modules(self, architecture, teacher_model_info, constraints): # CNN architecture module design # Implementation omitted return [] def _design_rnn_modules(self, architecture, teacher_model_info, constraints): # RNN architecture module design # Implementation omitted return [] def _design_generic_modules(self, architecture, teacher_model_info, constraints): # Generic architecture module design # Implementation omitted return [] def _define_module_connections(self, modules): # Definition of connection relationships between modules # Implementation omitted return {} def _define_execution_paths(self, modules, connections): # Definition of conditional execution paths # Implementation omitted return [] def _optimize_for_hardware(self, design, target_hardware): # Hardware optimization optimized_design = design.copy() if target_hardware not in self.hardware_targets: # If there is no target hardware information, return as is return optimized_design hardware_info = self.hardware_targets[target_hardware] # Optimization based on hardware characteristics if 'instruction_set' in hardware_info: optimized_design['instruction_set'] = hardware_info['instruction_set'] if 'compute_units' in hardware_info: optimized_design['parallelism'] = self._optimize_parallelism(design, hardware_info['compute_units']) if'memory_hierarchy' in hardware_info: optimized_design['memory_layout'] = self._optimize_memory_layout(design, hardware_info['memory_hierarchy']) if 'accelerators' in hardware_info: optimized_design['accelerator_ops'] = self._optimize_for_accelerators(design, hardware_info['accelerators']) return optimized_design def _optimize_parallelism(self, design, compute_units): # Optimization of parallel processing # Implementation omitted return {} def _optimize_memory_layout(self, design, memory_hierarchy): # Optimization of memory layout # Implementation omitted return {} def _optimize_for_accelerators(self, design, accelerators): # Optimization for accelerators # Implementation omitted return {}

[0048] def _build_student_model(self, design, data_type): # Construction of student model if data_type == 'text': return self._build_text_student_model(design) elif data_type == 'image': return self._build_image_student_model(design) elif data_type == 'audio': return self._build_audio_student_model(design) elif data_type == 'video': return self._build_video_student_model(design) else: raise ValueError(f"Unsupported data type: {data_type}") def _build_text_student_model(self, design): # Construction of the student model for text # Implementation omitted return None def _build_image_student_model(self, design): # Construction of the student model for images # Implementation omitted return None def _build_audio_student_model(self, design): # Construction of the student model for audio # Implementation omitted return None def _build_video_student_model(self, design): # Construction of the student model for videos # Implementation omitted return None def get_student_model(self, model_id): # Retrieval of the student model if model_id in self.designed_models: return self.designed_models[model_id] else: raise ValueError(f"No student model with ID: {model_id}") ``` 5.4 Implementation of the distillation learner The distillation learning device trains the student model to mimic the prediction distribution of the teacher model. The implementation includes the following processing steps. 1. Construction of distillation dataset: Construct a distillation dataset consisting of diverse data samples 2. Generation of teacher model predictions: Use the teacher model to generate the prediction distribution for each sample in the distillation dataset 3. Definition of distillation loss: Define a distillation loss that measures the similarity between the prediction distribution of the teacher model and that of the student model 4. Multi-objective optimization: Perform multi-objective optimization by combining the distillation loss and the direct prediction loss 5. Curriculum learning: Adopt curriculum learning to gradually progress the learning from simple samples to complex samples ``` class DistillationTrainer: def __init__(self, config): self.config = config self.distillation_datasets = {} self.training_history = {} self.trained_models = {} def prepare_distillation_dataset(self, data_type, raw_dataset, teacher_model): # Preparation of distillation dataset dataset_id = str(uuid.uuid4()) # Preprocessing of the dataset processed_dataset = self._preprocess_dataset(raw_dataset, data_type) # Generation of teacher model predictions teacher_predictions = self._generate_teacher_predictions(processed_dataset, teacher_model, data_type) # Construction of the distilled dataset distillation_dataset = { 'data_type': data_type, 'processed_samples': processed_dataset, 'teacher_predictions': teacher_predictions, 'size': len(processed_dataset) } # Saving the dataset self.distillation_datasets[dataset_id] = distillation_dataset return { 'dataset_id': dataset_id, 'size': len(processed_dataset), 'data_type': data_type } def _preprocess_dataset(self, raw_dataset, data_type): # Preprocessing the dataset if data_type == 'text': return self._preprocess_text_dataset(raw_dataset) elif data_type == 'image': return self._preprocess_image_dataset(raw_dataset) elif data_type == 'audio': return self._preprocess_audio_dataset(raw_dataset) elif data_type == 'video': return self._preprocess_video_dataset(raw_dataset) else: raise ValueError(f"Unsupported data type: {data_type}") def _preprocess_text_dataset(self, raw_dataset): # Preprocessing of text dataset processed_samples = [] for sample in raw_dataset: # Normalization of text normalized_text = self._normalize_text(sample) # Tokenization tokens = self._tokenize_text(normalized_text) processed_samples.append({ 'original': sample, 'normalized': normalized_text, 'tokens': tokens }) return processed_samples def _normalize_text(self, text): # Normalization of text # Convert to lowercase text = text.lower() # Remove extra whitespace text = ' '.join(text.split()) return text def _tokenize_text(self, text): # Tokenizing text # Implementation omitted return text.split() def _preprocess_image_dataset(self, raw_dataset): # Preprocessing the image dataset # Implementation omitted return [] def _preprocess_audio_dataset(self, raw_dataset): # Preprocessing the audio dataset # Implementation omitted return [] def _preprocess_video_dataset(self, raw_dataset): # Preprocessing the video dataset # Implementation omitted return []

[0049] def _generate_teacher_predictions(self, processed_dataset, teacher_model, data_type): # Generating predictions from the teacher model if data_type == 'text': return self._generate_text_teacher_predictions(processed_dataset, teacher_model) elif data_type == 'image': return self._generate_image_teacher_predictions(processed_dataset, teacher_model) elif data_type == 'audio': return self._generate_audio_teacher_predictions(processed_dataset, teacher_model) elif data_type == 'video': return self._generate_video_teacher_predictions(processed_dataset, teacher_model) else: raise ValueError(f"Unsupported data type: {data_type}") def _generate_text_teacher_predictions(self, processed_dataset, teacher_model): # Prediction generation for text teacher model teacher_predictions = [] for sample in processed_dataset: # Prediction by teacher model predictions = self._predict_with_text_model(teacher_model, sample) teacher_predictions.append(predictions) return teacher_predictions def _predict_with_text_model(self, model, sample): # Prediction by text model # Implementation omitted return { 'token_predictions': [], 'confidence_scores': [] } def _generate_image_teacher_predictions(self, processed_dataset, teacher_model): # Generate predictions of the image teacher model # Implementation omitted return [] def _generate_audio_teacher_predictions(self, processed_dataset, teacher_model): # Generate predictions of the audio teacher model # Implementation omitted return [] def _generate_video_teacher_predictions(self, processed_dataset, teacher_model): # Generate predictions of the video teacher model # Implementation omitted return [] def train_student_model(self, student_model, dataset_id, training_config=None): # Train the student model if dataset_id not in self.distillation_datasets: raise ValueError(f"No distillation dataset with ID: {dataset_id}") if training_config is None: training_config = self.config.get('training_config', {}) # Obtaining the dataset dataset = self.distillation_datasets[dataset_id] data_type = dataset['data_type'] # Executing the training if data_type == 'text': training_result = self._train_text_student_model(student_model, dataset, training_config) elif data_type == 'image': training_result = self._train_image_student_model(student_model, dataset, training_config) elif data_type == 'audio': training_result = self._train_audio_student_model(student_model, dataset, training_config) elif data_type == 'video': training_result = self._train_video_student_model(student_model, dataset, training_config) else: raise ValueError(f"Unsupported data type: {data_type}") # Saving the training result training_id = str(uuid.uuid4()) self.training_history[training_id] = { 'dataset_id': dataset_id, 'data_type': data_type, 'config': training_config, 'metrics': training_result['metrics'], 'duration': training_result['duration'] } # Saving the trained model self.trained_models[training_id] = training_result['model'] return { 'training_id': training_id, 'metrics': training_result['metrics'], 'duration': training_result['duration'], 'model': training_result['model'] } def _train_text_student_model(self, student_model, dataset, training_config): # Training the text student model start_time = time.time() # Obtaining training parameters num_epochs = training_config.get('num_epochs', 10) batch_size = training_config.get('batch_size', 32) learning_rate = training_config.get('learning_rate', 0.001) temperature = training_config.get('temperature', 2.0) alpha = training_config.get('alpha', 0.5) # Weight of distillation loss # Dataset split train_samples, val_samples = self._split_dataset(dataset['processed_samples'], dataset['teacher_predictions']) # Optimizer setup optimizer = torch.optim.Adam(student_model.parameters(), lr=learning_rate) # Loss function setup distillation_criterion = nn.KLDivLoss(reduction='batchmean') direct_criterion = nn.CrossEntropyLoss() # Curriculum learning setup if training_config.get('use_curriculum', False): train_samples, val_samples = self._setup_curriculum(train_samples, val_samples) # Training loop metrics = { 'train_loss': [], 'val_loss': [], 'train_accuracy': [], 'val_accuracy': [] [[ID=2}} for epoch in range(num_epochs):

[0050] # Training phase student_model.train() train_loss = 0.0 train_correct = 0 train_total = 0 # Batch processing for i in range(0, len(train_samples), batch_size): batch_samples = train_samples[i:i+batch_size] batch_predictions = dataset['teacher_predictions'][i:i+batch_size] # Preparation of batch data inputs, targets = self._prepare_text_batch(batch_samples) # Predictions of the teacher model teacher_logits = self._get_teacher_logits(batch_predictions) # Predictions of the student model student_logits = student_model(inputs) # Calculation of the distillation loss soft_targets = F.softmax(teacher_logits / temperature, dim=1) log_probs = F.log_softmax(student_logits / temperature, dim=1) distillation_loss = distillation_criterion(log_probs, soft_targets) * (temperature ** 2) # Calculation of direct prediction loss direct_loss = direct_criterion(student_logits, targets) # Total loss loss = alpha * distillation_loss + (1 - alpha) * direct_loss # Gradient reset and backpropagation optimizer.zero_grad() loss.backward() optimizer.step() # Update of statistics train_loss += loss.item() _, predicted = torch.max(student_logits, 1) train_total += targets.size(0) train_correct += (predicted == targets).sum().item() # Validation phase student_model.eval() val_loss = 0.0 val_correct = 0 val_total = 0 with torch.no_grad(): for i in range(0, len(val_samples), batch_size): batch_samples = val_samples[i:i+batch_size] batch_predictions = dataset['teacher_predictions'][len(train_samples) + i:len(train_samples) + i+batch_size] # Preparation of batch data inputs, targets = self._prepare_text_batch(batch_samples) # Predictions of the teacher model teacher_logits = self._get_teacher_logits(batch_predictions) # Predictions of the student model student_logits = student_model(inputs) # Calculation of the distillation loss soft_targets = F.softmax(teacher_logits / temperature, dim=1) log_probs = F.log_softmax(student_logits / temperature, dim=1) distillation_loss = distillation_criterion(log_probs, soft_targets) * (temperature ** 2) # Calculation of the direct prediction loss direct_loss = direct_criterion(student_logits, targets) # Total loss loss = alpha * distillation_loss + (1 - alpha) * direct_loss # Update of statistics val_loss += loss.item() _, predicted = torch.max(student_logits, 1) val_total += targets.size(0) val_correct += (predicted == targets).sum().item() # Record epoch statistics train_loss / = len(train_samples) / batch_size train_accuracy = train_correct / train_total val_loss / = len(val_samples) / batch_size val_accuracy = val_correct / val_total metrics['train_loss'].append(train_loss) metrics['train_accuracy'].append(train_accuracy) metrics['val_loss'].append(val_loss) metrics['val_accuracy'].append(val_accuracy) # Update curriculum learning if training_config.get('use_curriculum', False): train_samples, val_samples = self._update_curriculum(train_samples, val_samples, epoch, num_epochs) # Calculate training time duration = time.time() - start_time return { 'model': student_model, 'metrics': metrics, 'duration': duration } def _split_dataset(self, samples, predictions, val_ratio=0.2): # Splitting the dataset num_samples = len(samples) num_val = int(num_samples * val_ratio) # Shuffling indices = list(range(num_samples)) random.shuffle(indices) train_indices = indices[num_val:] val_indices = indices[:num_val] train_samples = [samples[i] for i in train_indices] val_samples = [samples[i] for i in val_indices] return train_samples, val_samples

[0051] def _prepare_text_batch(self, batch_samples): # Preparing the text batch # Implementation omitted return torch.randn(len(batch_samples), 100), torch.randint(0, 10, (len(batch_samples),)) def _get_teacher_logits(self, batch_predictions): # Acquisition of teacher model logits # Implementation omitted return torch.randn(len(batch_predictions), 10) def _setup_curriculum(self, train_samples, val_samples): # Setup of curriculum learning # Implementation omitted return train_samples, val_samples def _update_curriculum(self, train_samples, val_samples, current_epoch, total_epochs): # Update of curriculum learning # Implementation omitted return train_samples, val_samples def _train_image_student_model(self, student_model, dataset, training_config): # Training of image student model # Implementation omitted return { 'model': student_model, 'metrics': {}, 'duration': 0 } def _train_audio_student_model(self, student_model, dataset, training_config): # Training of audio student model # Implementation omitted return { 'model': student_model, 'metrics': {}, 'duration': 0 } ```python def _train_video_student_model(self, student_model, dataset, training_config): # Training of the video student model # Implementation omitted return { 'model': student_model, 'metrics': {}, 'duration': 0 } def evaluate_student_model(self, model, dataset_id): # Evaluation of the student model if dataset_id not in self.distillation_datasets: raise ValueError(f"No distillation dataset with ID: {dataset_id}") # Retrieval of the dataset dataset = self.distillation_datasets[dataset_id] data_type = dataset['data_type'] # Execution of the evaluation if data_type == 'text': evaluation_result = self._evaluate_text_student_model(model, dataset) elif data_type == 'image': evaluation_result = self._evaluate_image_student_model(model, dataset) elif data_type == 'audio': evaluation_result = self._evaluate_audio_student_model(model, dataset) elif data_type == 'video': evaluation_result = self._evaluate_video_student_model(model, dataset) else: raise ValueError(f"Unsupported data type: {data_type}") return evaluation_result def _evaluate_text_student_model(self, model, dataset): # Evaluation of the text student model # Implementation omitted return { 'accuracy': 0.9, 'perplexity': 10.5, 'kl_divergence': 0.05 } def _evaluate_image_student_model(self, model, dataset): # Evaluation of the image student model # Implementation omitted return { 'accuracy': 0.85, 'mse': 0.02, 'kl_divergence': 0.08 } def _evaluate_audio_student_model(self, model, dataset): # Evaluation of the audio student model # Implementation omitted return { 'accuracy': 0.8, 'mse': 0.03, 'kl_divergence': 0.1 } def _evaluate_video_student_model(self, model, dataset): # Evaluation of the video student model # Implementation omitted return { 'accuracy': 0.75, 'mse': 0.04, 'kl_divergence': 0.12 } def get_trained_model(self, training_id): # Retrieval of the trained model if training_id in self.trained_models: return self.trained_models[training_id] else: raise ValueError(f"No trained model with ID: {training_id}") ``` 5.5 Implementation of the domain adapter The domain adapter performs knowledge distillation specialized for data in a specific domain. The implementation includes the following processing steps. 1. Domain data collection: Collect a specialized dataset for the target domain (such as medical, legal, financial, etc.) 2. Domain-Specific Teacher Model Construction: Fine-tune a general large-scale model with domain data to construct a domain-specific teacher model 3. Domain-Specific Distillation: Use the domain-specific teacher model to perform knowledge distillation on domain data 4. Domain Adaptation Evaluation: Evaluate the domain adaptability of the distilled student model ```python class DomainAdapter: def __init__(self, config): self.config = config self.domain_datasets = {} self.domain_teacher_models = {} self.domain_student_models = {} self.adaptation_history = {} def collect_domain_data(self, domain_name, data_sources, data_type): # Collection of domain data dataset_id = str(uuid.uuid4()) # Data collection from data sources collected_data = self._collect_from_sources(data_sources, data_type) # Data preprocessing processed_data = self._preprocess_domain_data(collected_data, data_type, domain_name) # Construction of domain dataset domain_dataset = { 'domain_name': domain_name, 'data_type': data_type, 'raw_data': collected_data, 'processed_data': processed_data, 'size': len(processed_data) } # Saving the dataset self.domain_datasets[dataset_id] = domain_dataset return { 'dataset_id': dataset_id, 'domain_name': domain_name, 'data_type': data_type, 'size': len(processed_data) }

[0052] def _collect_from_sources(self, data_sources, data_type): # Collecting data from data sources collected_data = [] for source in data_sources: source_type = source.get('type', 'file') if source_type == 'file': file_data = self._collect_from_file(source, data_type) collected_data.extend(file_data) elif source_type == 'database': db_data = self._collect_from_database(source, data_type) collected_data.extend(db_data) elif source_type == 'api': api_data = self._collect_from_api(source, data_type) collected_data.extend(api_data) else: logger.warning(f"Unsupported data source type: {source_type}") return collected_data def _collect_from_file(self, source, data_type): # Data collection from file file_path = source.get('path') if not file_path or not os.path.exists(file_path): logger.warning(f"File not found: {file_path}") return [] # Reading according to data type if data_type == 'text': return self._read_text_file(file_path) elif data_type == 'image': return self._read_image_file(file_path) elif data_type == 'audio': return self._read_audio_file(file_path) elif data_type == 'video': return self._read_video_file(file_path) else: logger.warning(f"Unsupported data type for file collection: {data_type}") return [] def _read_text_file(self, file_path): # Reading text file try: with open(file_path, 'r', encoding='utf-8') as f: content = f.read() # Splitting by line lines = content.split('\n') # Removing blank lines lines = [line for line in lines if line.strip()] return lines except Exception as e: logger.error(f"Error reading text file {file_path}: {e}") return [] def _read_image_file(self, file_path): # Reading image file # Implementation omitted return [] def _read_audio_file(self, file_path): # Reading audio file # Implementation omitted return [] def _read_video_file(self, file_path): # Loading video file # Implementation omitted return [] def _collect_from_database(self, source, data_type): # Data collection from database # Implementation omitted return [] def _collect_from_api(self, source, data_type): # Data collection from API # Implementation omitted return [] def _preprocess_domain_data(self, raw_data, data_type, domain_name): # Preprocessing domain data if data_type == 'text': return self._preprocess_domain_text(raw_data, domain_name) elif data_type == 'image': return self._preprocess_domain_image(raw_data, domain_name) elif data_type == 'audio': return self._preprocess_domain_audio(raw_data, domain_name) elif data_type == 'video': return self._preprocess_domain_video(raw_data, domain_name) else: logger.warning(f"Unsupported data type for domain preprocessing: {data_type}") return [] def _preprocess_domain_text(self, raw_data, domain_name): # Domain-specific text preprocessing processed_data = [] # Domain-specific preprocessing settings domain_config = self.config.get('domain_preprocessing', {}).get(domain_name, {}) for text in raw_data: # Basic text preprocessing processed_text = text.strip() # Domain-specific preprocessing if domain_name =='medical': processed_text = self._preprocess_medical_text(processed_text, domain_config) elif domain_name == 'legal': processed_text = self._preprocess_legal_text(processed_text, domain_config) elif domain_name == 'financial': processed_text = self._preprocess_financial_text(processed_text, domain_config) processed_data.append({ 'original': text, 'processed': processed_text }) return processed_data def _preprocess_medical_text(self, text, config): # Preprocessing of medical text # Implementation omitted return text def _preprocess_legal_text(self, text, config): # Preprocessing of legal text # Implementation omitted return text def _preprocess_financial_text(self, text, config): # Preprocessing of financial text # Implementation omitted return text def _preprocess_domain_image(self, raw_data, domain_name): # Preprocessing of domain-specific images # Implementation omitted return [] def _preprocess_domain_audio(self, raw_data, domain_name): # Preprocessing of domain-specific audio # Implementation omitted return []

[0053] def _preprocess_domain_video(self, raw_data, domain_name): # Domain-specific video preprocessing # Implementation omitted return [] def build_domain_teacher_model(self, generic_teacher_model, dataset_id, adaptation_config=None): # Construction of domain-specific teacher model if dataset_id not in self.domain_datasets: raise ValueError(f"No domain dataset with ID: {dataset_id}") if adaptation_config is None: adaptation_config = self.config.get('teacher_adaptation_config', {}) # Acquisition of dataset dataset = self.domain_datasets[dataset_id] domain_name = dataset['domain_name'] data_type = dataset['data_type'] # Acquisition of adaptation type adaptation_type = adaptation_config.get('type', 'fine_tuning') # Execution of domain adaptation if adaptation_type == 'fine_tuning': adapted_model = self._adapt_teacher_by_fine_tuning(generic_teacher_model, dataset, adaptation_config) elif adaptation_type == 'prompt_engineering': adapted_model = self._adapt_teacher_by_prompt_engineering(generic_teacher_model, dataset, adaptation_config) elif adaptation_type == 'parameter_efficient_learning': adapted_model = self._adapt_teacher_by_parameter_efficient_learning(generic_teacher_model, dataset, adaptation_config) else: raise ValueError(f"Unsupported adaptation type: {adaptation_type}") # Evaluation of adaptation result evaluation_result = self._evaluate_domain_adaptation(adapted_model, dataset, generic_teacher_model) # Saving the adapted model model_id = str(uuid.uuid4()) self.domain_teacher_models[model_id] = { 'model': adapted_model, 'domain_name': domain_name, 'data_type': data_type, 'dataset_id': dataset_id, 'adaptation_type': adaptation_type, 'evaluation': evaluation_result } return { 'model_id': model_id, 'domain_name': domain_name, 'data_type': data_type, 'adaptation_type': adaptation_type, 'evaluation': evaluation_result } def _adapt_teacher_by_fine_tuning(self, teacher_model, dataset, config): # Domain adaptation by fine-tuning # Implementation omitted return teacher_model def _adapt_teacher_by_prompt_engineering(self, teacher_model, dataset, config): # Domain adaptation by prompt engineering # Implementation omitted return teacher_model def _adapt_teacher_by_parameter_efficient_learning(self, teacher_model, dataset, config): # Domain adaptation by parameter-efficient learning # Implementation omitted return teacher_model def _evaluate_domain_adaptation(self, adapted_model, dataset, generic_model): # Evaluation of domain adaptation data_type = dataset['data_type'] if data_type == 'text': return self._evaluate_text_domain_adaptation(adapted_model, dataset, generic_model) elif data_type == 'image': return self._evaluate_image_domain_adaptation(adapted_model, dataset, generic_model) elif data_type == 'audio': return self._evaluate_audio_domain_adaptation(adapted_model, dataset, generic_model) elif data_type == 'video': return self._evaluate_video_domain_adaptation(adapted_model, dataset, generic_model) else: logger.warning(f"Unsupported data type for domain adaptation evaluation: {data_type}") return {} def _evaluate_text_domain_adaptation(self, adapted_model, dataset, generic_model): # Evaluation of text domain adaptation # Implementation omitted return { 'perplexity_improvement': 30, 'domain_accuracy': 0.85, 'generic_accuracy': 0.65 } def _evaluate_image_domain_adaptation(self, adapted_model, dataset, generic_model): # Evaluation of image domain adaptation # Implementation details are omitted return {} def _evaluate_audio_domain_adaptation(self, adapted_model, dataset, generic_model): # Evaluation of audio domain adaptation # Implementation details are omitted return {} def _evaluate_video_domain_adaptation(self, adapted_model, dataset, generic_model): # Evaluation of video domain adaptation # Implementation details are omitted return {} def perform_domain_distillation(self, domain_teacher_model_id, student_model, distillation_config=None): # Execution of domain-specific distillation if domain_teacher_model_id not in self.domain_teacher_models: raise ValueError(f"No domain teacher model with ID: {domain_teacher_model_id}") if distillation_config is None: distillation_config = self.config.get('domain_distillation_config', {}) # Acquisition of teacher model information teacher_info = self.domain_teacher_models[domain_teacher_model_id] domain_name = teacher_info['domain_name'] data_type = teacher_info['data_type'] dataset_id = teacher_info['dataset_id'] teacher_model = teacher_info['model']

[0054] # Acquisition of the dataset if dataset_id not in self.domain_datasets: raise ValueError(f"Dataset not found for domain teacher model: {dataset_id}") dataset = self.domain_datasets[dataset_id] # Initialization of the distillation trainer distillation_trainer = DistillationTrainer(distillation_config) # Preparation of the distillation dataset distillation_dataset_info = distillation_trainer.prepare_distillation_dataset( data_type, dataset['processed_data'], teacher_model ) # Training of the student model training_result = distillation_trainer.train_student_model( student_model, distillation_dataset_info['dataset_id'], distillation_config ) # Domain adaptation evaluation domain_evaluation = self._evaluate_domain_student_model( training_result['model'], dataset, teacher_model, domain_name, data_type ) # Saving the results adaptation_id = str(uuid.uuid4()) self.domain_student_models[adaptation_id] = { 'model': training_result['model'], 'domain_name': domain_name, 'data_type': data_type, 'teacher_model_id': domain_teacher_model_id, 'training_metrics': training_result['metrics'], 'domain_evaluation': domain_evaluation} self.adaptation_history[adaptation_id] = { 'domain_name': domain_name, 'data_type': data_type, 'teacher_model_id': domain_teacher_model_id, 'dataset_id': dataset_id, 'training_duration': training_result['duration'], 'training_metrics': training_result['metrics'], 'domain_evaluation': domain_evaluation } return { 'adaptation_id': adaptation_id, 'domain_name': domain_name, 'data_type': data_type, 'training_metrics': training_result['metrics'], 'domain_evaluation': domain_evaluation, 'model': training_result['model'] } def _evaluate_domain_student_model(self, student_model, dataset, teacher_model, domain_name, data_type): # Evaluation of the domain-specific student model if data_type == 'text': return self._evaluate_text_domain_student(student_model, dataset, teacher_model, domain_name) elif data_type == 'image': return self._evaluate_image_domain_student(student_model, dataset, teacher_model, domain_name) elif data_type == 'audio': return self._evaluate_audio_domain_student(student_model, dataset, teacher_model, domain_name) elif data_type == 'video': return self._evaluate_video_domain_student(student_model, dataset, teacher_model, domain_name) else: logger.warning(f"Unsupported data type for domain student evaluation: {data_type}") return {} def _evaluate_text_domain_student(self, student_model, dataset, teacher_model, domain_name): # Evaluation of the text domain student model # Implementation omitted return { 'teacher_similarity': 0.92, 'domain_accuracy': 0.82, 'compression_efficiency': 0.88 } def _evaluate_image_domain_student(self, student_model, dataset, teacher_model, domain_name): # Evaluation of the student model for the image domain # Implementation is omitted return {} def _evaluate_audio_domain_student(self, student_model, dataset, teacher_model, domain_name): # Evaluation of the student model for the audio domain # Implementation is omitted return {} def _evaluate_video_domain_student(self, student_model, dataset, teacher_model, domain_name): # Evaluation of the student model for the video domain # Implementation is omitted return {} def get_domain_student_model(self, adaptation_id): # Obtain the domain-specific student model if adaptation_id in self.domain_student_models: return self.domain_student_models[adaptation_id] else: raise ValueError(f"No domain student model with ID: {adaptation_id}") ``` 5.6 Implementation of the Quantization and Pruning Device The quantization pruning optimizer further reduces the size of the student model after distillation. The implementation includes the following processing steps. 1. Weight quantization: Convert the weight parameters of the model to a low-precision representation 2. Activation quantization: Convert the activation outputs of the model to a low-precision representation 3. Model pruning: Remove parameters with low importance 4. Structural pruning: Remove the structural units of the model 5. Integration with knowledge distillation: Perform optimization in combination with knowledge distillation to minimize the accuracy degradation caused by quantization pruning ```python class QuantizationPruningOptimizer: def __init__(self, config): self.config = config self.optimized_models = {} self.optimization_history = {} def optimize_model(self, model, model_type, optimization_config=None): # Model optimization if optimization_config is None: optimization_config = self.config.get('optimization_config', {}) # Model evaluation before optimization pre_optimization_metrics = self._evaluate_model(model, model_type) # Construction of the optimization pipeline pipeline = self._build_optimization_pipeline(optimization_config)

[0055] # Execution of optimization optimized_model, optimization_stats = self._run_optimization_pipeline(model, model_type, pipeline) # Evaluation of the model after optimization post_optimization_metrics = self._evaluate_model(optimized_model, model_type) # Saving the optimization results optimization_id = str(uuid.uuid4()) self.optimized_models[optimization_id] = { 'model': optimized_model, 'model_type': model_type, 'optimization_stats': optimization_stats, 'pre_optimization_metrics': pre_optimization_metrics, 'post_optimization_metrics': post_optimization_metrics } self.optimization_history[optimization_id] = { 'model_type': model_type, 'optimization_config': optimization_config, 'optimization_stats': optimization_stats, 'pre_optimization_metrics': pre_optimization_metrics, 'post_optimization_metrics': post_optimization_metrics, 'performance_change': self._calculate_performance_change( pre_optimization_metrics, post_optimization_metrics ) } return { 'optimization_id': optimization_id, 'optimized_model': optimized_model, 'optimization_stats': optimization_stats, 'pre_optimization_metrics': pre_optimization_metrics, 'post_optimization_metrics': post_optimization_metrics, 'performance_change': self._calculate_performance_change( pre_optimization_metrics, post_optimization_metrics ) } def _evaluate_model(self, model, model_type): # Evaluation of the model if model_type == 'pytorch': return self._evaluate_pytorch_model(model) elif model_type == 'tensorflow': return self._evaluate_tensorflow_model(model) elif model_type == 'onnx': return self._evaluate_onnx_model(model) else: raise ValueError(f"Unsupported model type: {model_type}") def _evaluate_pytorch_model(self, model): # Evaluation of PyTorch model # Number of parameters num_parameters = sum(p.numel() for p in model.parameters()) # Model size model_size = num_parameters * 4 # Assume 32-bit floating point # Estimation of memory usage memory_usage = model_size * 3 # Considering weights, gradients, and optimization state # Measurement of inference time (with dummy input) input_shape = self._get_model_input_shape(model) dummy_input = torch.randn(*input_shape) start_time = time.time() with torch.no_grad(): _ = model(dummy_input) inference_time = time.time() - start_time return { 'num_parameters': num_parameters, 'model_size': model_size, 'memory_usage': memory_usage, 'inference_time': inference_time } def _get_model_input_shape(self, model): # Get the input shape of the model # Implementation omitted return (1, 3, 224, 224) # Dummy shape def _evaluate_tensorflow_model(self, model): # Evaluate the TensorFlow model # Implementation omitted return {} def _evaluate_onnx_model(self, model): # Evaluate the ONNX model # Implementation omitted return {} def _build_optimization_pipeline(self, optimization_config): # Build the optimization pipeline pipeline = [] # Add the quantization step if optimization_config.get('quantization', {}).get('enabled', False): quantization_config = optimization_config.get('quantization', {}) pipeline.append({ 'type': 'quantization', 'config': quantization_config ) # Addition of pruning step if optimization_config.get('pruning', {}).get('enabled', False): pruning_config = optimization_config.get('pruning', {}) pipeline.append({ 'type': 'pruning', 'config': pruning_config ) # Addition of integration step with knowledge distillation if optimization_config.get('distillation_fine_tuning', {}).get('enabled', False): distillation_config = optimization_config.get('distillation_fine_tuning', {}) pipeline.append({ 'type': 'distillation_fine_tuning', 'config': distillation_config ) return pipeline

[0056] def _run_optimization_pipeline(self, model, model_type, pipeline): # Execution of the optimization pipeline current_model = model optimization_stats = {} for step in pipeline: step_type = step['type'] step_config = step['config'] if step_type == 'quantization': current_model, step_stats = self._apply_quantization(current_model, model_type, step_config) optimization_stats['quantization'] = step_stats elif step_type == 'pruning': current_model, step_stats = self._apply_pruning(current_model, model_type, step_config) optimization_stats['pruning'] = step_stats elif step_type == 'distillation_fine_tuning': current_model, step_stats = self._apply_distillation_fine_tuning(current_model, model_type, step_config) optimization_stats['distillation_fine_tuning'] = step_stats return current_model, optimization_stats def _apply_quantization(self, model, model_type, quantization_config): # 量子化の適用 if model_type == 'pytorch': return self._apply_pytorch_quantization(model, quantization_config) elif model_type == 'tensorflow': return self._apply_tensorflow_quantization(model, quantization_config) elif model_type == 'onnx': return self._apply_onnx_quantization(model, quantization_config) else: raise ValueError(f"Unsupported model type for quantization: {model_type}") def _apply_pytorch_quantization(self, model, quantization_config): # Application of PyTorch quantization quantization_type = quantization_config.get('type', 'post_training') bit_width = quantization_config.get('bit_width', 8) if quantization_type == 'post_training': # Post-training quantization return self._apply_pytorch_post_training_quantization(model, bit_width) elif quantization_type == 'quantization_aware_training': # Quantization-aware training return self._apply_pytorch_quantization_aware_training(model, bit_width) elif quantization_type == 'dynamic_quantization': # Dynamic quantization return self._apply_pytorch_dynamic_quantization(model, bit_width) else: raise ValueError(f"Unsupported PyTorch quantization type: {quantization_type}") def _apply_pytorch_post_training_quantization(self, model, bit_width): # Post-training quantization in PyTorch try: # Move the model to CPU model = model.cpu() # Quantization settings if bit_width == 8: # 8-bit quantization quantized_model = torch.quantization.quantize_dynamic( model, {torch.nn.Linear, torch.nn.Conv2d}, dtype=torch.qint8 ) elif bit_width == 16: # 16-bit quantization (half-precision) quantized_model = model.half() else: raise ValueError(f"Unsupported bit width for PyTorch quantization: {bit_width}") # Quantization statistics original_size = sum(p.numel() * 4 for p in model.parameters()) # 32-bit floating point if bit_width == 8: quantized_size = sum(p.numel() * (8 / / 8) for p in quantized_model.parameters()) elif bit_width == 16: quantized_size = sum(p.numel() * (16 / / 8) for p in quantized_model.parameters()) size_reduction = 1.0 - (quantized_size / original_size) return quantized_model, { 'method': 'post_training_quantization', 'bit_width': bit_width, 'original_size': original_size, 'quantized_size': quantized_size, 'size_reduction': size_reduction } except Exception as e: logger.error(f"PyTorch post-training quantization failed: {e}") return model, { 'method': 'post_training_quantization', 'bit_width': bit_width, 'error': str(e), 'status': 'failed' } def _apply_pytorch_quantization_aware_training(self, model, bit_width): # PyTorch quantization-aware training # Implementation omitted return model, {} def _apply_pytorch_dynamic_quantization(self, model, bit_width): # PyTorch dynamic quantization # Implementation omitted return model, {} def _apply_tensorflow_quantization(self, model, quantization_config): # Apply TensorFlow quantization # Implementation omitted return model, {} def _apply_onnx_quantization(self, model, quantization_config): # Apply ONNX quantization # Implementation omitted return model, {}

[0057] def _apply_pruning(self, model, model_type, pruning_config): # Apply pruning if model_type == 'pytorch': return self._apply_pytorch_pruning(model, pruning_config) elif model_type == 'tensorflow': return self._apply_tensorflow_pruning(model, pruning_config) elif model_type == 'onnx': return self._apply_onnx_pruning(model, pruning_config) else: raise ValueError(f"Unsupported model type for pruning: {model_type}") def _apply_pytorch_pruning(self, model, pruning_config): # Application of PyTorch pruning pruning_type = pruning_config.get('type', 'magnitude') pruning_ratio = pruning_config.get('ratio', 0.5) if pruning_type == 'magnitude': # Magnitude-based pruning return self._apply_pytorch_magnitude_pruning(model, pruning_ratio) elif pruning_type == 'structured': # Structured pruning return self._apply_pytorch_structured_pruning(model, pruning_config) else: raise ValueError(f"Unsupported PyTorch pruning type: {pruning_type}") def _apply_pytorch_magnitude_pruning(self, model, pruning_ratio): # PyTorch magnitude-based pruning try: # Parameter importance evaluation importance = {} for name, param in model.named_parameters(): if param.dim() > 1: # Only target weights (exclude biases) importance[name] = torch.abs(param).detach() # Number of parameters to be pruned total_params = sum(param.numel() for name, param in model.named_parameters() if name in importance) prune_params = int(total_params * pruning_ratio) # Identify parameters with low importance all_importances = torch.cat([imp.flatten() for imp in importance.values()]) threshold = torch.kthvalue(all_importances, prune_params).values.item() # Create and apply pruning mask pruned_model = copy.deepcopy(model) zero_params = 0 for name, param in pruned_model.named_parameters(): if name in importance: mask = (importance[name] > threshold).float() param.data.mul_(mask) zero_params += (mask == 0).sum().item() actual_ratio = zero_params / total_params return pruned_model, { 'method':'magnitude_pruning', 'target_ratio': pruning_ratio, 'actual_ratio': actual_ratio, 'total_params': total_params, 'pruned_params': zero_params } except Exception as e: logger.error(f"PyTorch magnitude pruning failed: {e}") return model, { 'method':'magnitude_pruning', 'target_ratio': pruning_ratio, 'error': str(e), 'status': 'failed' } def _apply_pytorch_structured_pruning(self, model, pruning_config): # PyTorch Structural Pruning # Implementation omitted return model, {} def _apply_tensorflow_pruning(self, model, pruning_config): # Apply TensorFlow pruning # Implementation omitted return model, {} def _apply_onnx_pruning(self, model, pruning_config): # Apply ONNX pruning # Implementation omitted return model, {} def _apply_distillation_fine_tuning(self, model, model_type, distillation_config): # Integration with knowledge distillation # Implementation omitted return model, {} def _calculate_performance_change(self, pre_metrics, post_metrics): # Calculate performance change performance_change = {} for metric in pre_metrics: if metric in post_metrics: if pre_metrics[metric] != 0: relative_change = (post_metrics[metric] - pre_metrics[metric]) / pre_metrics[metric] performance_change[f"{metric}_change"] = relative_change return performance_change def get_optimized_model(self, optimization_id): # Retrieval of the optimized model if optimization_id in self.optimized_models: return self.optimized_models[optimization_id] else: raise ValueError(f"No optimized model with ID: {optimization_id}") ```

[0058] 5.7 Integration of the Knowledge Distillation Module Integrate the above sub-modules to construct a knowledge distillation module. The integrated architecture is as follows. ```python class KnowledgeDistillationModule: def __init__(self, config): self.config = config # Initialization of the teacher model selector self.teacher_selector = TeacherModelSelector( config.get('teacher_selector_config', {}) ) # Initialization of the student model designer self.student_designer = StudentModelDesigner( config.get('student_designer_config', {}) ) # Initialization of the distillation learner self.distillation_trainer = DistillationTrainer( config.get('distillation_trainer_config', {}) ) # Initialization of the domain adapter self.domain_adapter = DomainAdapter( config.get('domain_adapter_config', {}) ) # Initialization of the quantization and pruning optimizer self.optimization_optimizer = QuantizationPruningOptimizer( config.get('optimization_optimizer_config', {}) ) # Distillation process information self.distillation_processes = {} def distill_model(self, data_type, target_hardware=None, domain=None, evaluation_dataset=None): # Execute model distillation distillation_id = str(uuid.uuid4()) start_time = time.time() # Step 1: Selection of the teacher model teacher_selection_result = self._select_teacher_model(data_type, evaluation_dataset) teacher_model = teacher_selection_result['model'] # Step 2: Design of the student model student_design_result = self._design_student_model(data_type, teacher_model, target_hardware) student_model = student_design_result['model'] # Step 3: Domain adaptation (if specified) if domain: domain_adaptation_result = self._adapt_to_domain(teacher_model, student_model, data_type, domain) teacher_model = domain_adaptation_result['teacher_model'] domain_dataset_id = domain_adaptation_result['dataset_id'] else: domain_adaptation_result = None domain_dataset_id = None # Step 4: Perform knowledge distillation distillation_result = self._perform_distillation(teacher_model, student_model, data_type, domain_dataset_id) distilled_model = distillation_result['model'] # Step 5: Application of Quantization and Pruning optimization_result = self._optimize_model(distilled_model, data_type) optimized_model = optimization_result['optimized_model'] # Saving Distillation Process Information duration = time.time() - start_time self.distillation_processes[distillation_id] = { 'data_type': data_type, 'target_hardware': target_hardware, 'domain': domain, 'teacher_selection': teacher_selection_result, 'student_design': student_design_result, 'domain_adaptation': domain_adaptation_result, 'distillation': distillation_result, 'optimization': optimization_result, 'duration': duration, 'final_model': optimized_model } # Integration of Results result = { 'distillation_id': distillation_id, 'data_type': data_type, 'target_hardware': target_hardware, 'domain': domain, 'duration': duration, 'model': optimized_model, 'compression_stats': self._calculate_compression_stats( teacher_model, optimized_model ) } return result

[0059] def _select_teacher_model(self, data_type, evaluation_dataset): # Selection of teacher model logger.info(f"Selecting teacher model for data type: {data_type}") # Evaluation of available models evaluation_results = self.teacher_selector.evaluate_models(data_type, evaluation_dataset) # Selection of optimal model optimal_model = self.teacher_selector.select_optimal_models(data_type) # Ensemble construction (if configured) if self.config.get('use_ensemble_teacher', False): teacher_model = self.teacher_selector.build_ensemble(data_type) is_ensemble = True else: teacher_model = self.teacher_selector.get_teacher_model(data_type) is_ensemble = False return { 'model': teacher_model, 'optimal_model': optimal_model, 'evaluation_results': evaluation_results, 'is_ensemble': is_ensemble } def _design_student_model(self, data_type, teacher_model, target_hardware): # Design of the student model logger.info(f"Designing student model for data type: {data_type} and hardware: {target_hardware}") design_result = self.student_designer.design_student_model( data_type, teacher_model, target_hardware ) return design_result def _adapt_to_domain(self, teacher_model, student_model, data_type, domain): # Domain adaptation logger.info(f"Adapting models to domain: {domain}") # Collection of domain data data_sources = self.config.get('domain_data_sources', {}).get(domain, []) dataset_info = self.domain_adapter.collect_domain_data(domain, data_sources, data_type) # Domain adaptation of the teacher model adapted_teacher_info = self.domain_adapter.build_domain_teacher_model( teacher_model, dataset_info['dataset_id'] ) adapted_teacher_model = self.domain_adapter.domain_teacher_models[adapted_teacher_info['model_id']]['model'] return { 'teacher_model': adapted_teacher_model, 'dataset_id': dataset_info['dataset_id'], 'teacher_adaptation': adapted_teacher_info } def _perform_distillation(self, teacher_model, student_model, data_type, domain_dataset_id=None): # Execution of knowledge distillation logger.info(f"Performing knowledge distillation for data type: {data_type}") # Preparation of distillation dataset if domain_dataset_id: # Domain-specific distillation domain_info = self.domain_adapter.domain_datasets[domain_dataset_id] distillation_result = self.domain_adapter.perform_domain_distillation( teacher_model, student_model ) else: # General distillation # Preparation of distillation dataset raw_dataset = self._get_distillation_dataset(data_type) dataset_info = self.distillation_trainer.prepare_distillation_dataset( data_type, raw_dataset, teacher_model ) # Training of student model distillation_result = self.distillation_trainer.train_student_model( student_model, dataset_info['dataset_id'] ) return distillation_result def _get_distillation_dataset(self, data_type): # Acquisition of the distillation dataset # Implementation is omitted return [] def _optimize_model(self, model, data_type): # Optimization of the model logger.info(f"Optimizing distilled model for data type: {data_type}") # Detection of the model type model_type = self._detect_model_type(model) # Execution of the optimization optimization_result = self.optimization_optimizer.optimize_model( model, model_type ) return optimization_result def _detect_model_type(self, model): # Detection of the model type if isinstance(model, torch.nn.Module): return 'pytorch' elif 'keras' in str(type(model)): return 'tensorflow' elif hasattr(model, 'graph'): return 'onnx' else: return 'unknown'

[0060] def _calculate_compression_stats(self, teacher_model, student_model): # Calculation of compression statistics teacher_params = self._count_parameters(teacher_model) student_params = self._count_parameters(student_model) param_reduction = 1.0 - (student_params / teacher_params) if teacher_params > 0 else 0 teacher_size = self._estimate_model_size(teacher_model) student_size = self._estimate_model_size(student_model) size_reduction = 1.0 - (student_size / teacher_size) if teacher_size > 0 else 0 return { 'teacher_parameters': teacher_params, 'student_parameters': student_params, 'parameter_reduction': param_reduction, 'teacher_size': teacher_size, 'student_size': student_size, 'size_reduction': size_reduction } def _count_parameters(self, model): # Count the number of parameters in the model if isinstance(model, torch.nn.Module): return sum(p.numel() for p in model.parameters()) elif 'keras' in str(type(model)): return model.count_params() else: return 0 def _estimate_model_size(self, model): # Estimate the model size params = self._count_parameters(model) # Estimate the number of bytes per parameter if isinstance(model, torch.nn.Module): # In the case of a PyTorch model bytes_per_param = 4 # Assume 32-bit floating point for param in model.parameters(): if param.dtype == torch.float16: bytes_per_param = 2 elif param.dtype == torch.int8 or param.dtype == torch.uint8: bytes_per_param = 1 break else: # Default bytes_per_param = 4 return params * bytes_per_param def get_distilled_model(self, distillation_id): # Acquisition of distilled model if distillation_id in self.distillation_processes: return self.distillation_processes[distillation_id]['final_model'] else: raise ValueError(f"No distillation process with ID: {distillation_id}") def get_distillation_info(self, distillation_id): # Acquisition of distillation process information if distillation_id in self.distillation_processes: return self.distillation_processes[distillation_id] else: raise ValueError(f"No distillation process with ID: {distillation_id}") ``` ## 6. Embodiments of the Adaptive Arithmetic Encoding Module The adaptive arithmetic encoding module is a component that efficiently encodes data based on the predicted distribution output by the generative model. The detailed implementation method and operating principle of this module will be described. 6.1 Module Composition The adaptive arithmetic encoding module is composed of the following sub-modules. 1. Probability Distribution Transformer 2. Adaptive Encoder 3. Parallel Processor 4. Error-Tolerant Encoder These sub-modules implement a process for efficiently encoding the probability distribution output by the prediction model. 6.2 Implementation of Probability Distribution Transformer The probability distribution transformer converts the prediction distribution output by the model into a form suitable for arithmetic coding. The implementation includes the following processing steps. 1. Probability Distribution Normalization: Normalize the raw probability values output by the model so that their sum is 1. 2. Cumulative Distribution Function (CDF) Transformation: Calculate the cumulative distribution function from the normalized probability distribution. 3. Probability Distribution Quantization: Quantize the floating-point cumulative distribution function into a fixed-point representation. 4. Low-Probability Event Handling: Implement special processing for extremely low-probability events. ```python class ProbabilityDistributionTransformer: def __init__(self, config): self.config = config self.precision = config.get('precision', 16) # Bit precision self.min_probability = config.get('min_probability', 1e-10) self.special_handling_threshold = config.get('special_handling_threshold', 1e-6) self.use_log_domain = config.get('use_log_domain', True) def transform(self, raw_distribution): # Conversion of Probability Distribution # Input Distribution Format Check if isinstance(raw_distribution, torch.Tensor): return self._transform_torch_distribution(raw_distribution) elif isinstance(raw_distribution, np.ndarray): return self._transform_numpy_distribution(raw_distribution) elif isinstance(raw_distribution, dict): return self._transform_dict_distribution(raw_distribution) elif isinstance(raw_distribution, list): return self._transform_list_distribution(raw_distribution) else: raise ValueError(f"Unsupported distribution type: {type(raw_distribution)}") def _transform_torch_distribution(self, distribution): # Conversion of PyTorch Distribution # Convert to NumPy array on CPU numpy_distribution = distribution.detach().cpu().numpy() return self._transform_numpy_distribution(numpy_distribution)

[0061] def _transform_numpy_distribution(self, distribution): # Conversion of NumPy distribution # Convert to 1D array if distribution.ndim > 1: flat_distribution = distribution.reshape(-1) else: flat_distribution = distribution # Normalization of probability distribution normalized_distribution = self._normalize_distribution(flat_distribution) # Calculation of cumulative distribution function cdf = self._compute_cdf(normalized_distribution) # Quantization to fixed-point representation quantized_cdf = self._quantize_cdf(cdf) # Handling of low-probability events processed_distribution, special_events = self._handle_low_probability_events(normalized_distribution) return { 'normalized_distribution': normalized_distribution, 'cdf': cdf, 'quantized_cdf': quantized_cdf, 'processed_distribution': processed_distribution, 'special_events': special_events, 'original_shape': distribution.shape } def _transform_dict_distribution(self, distribution): # Conversion of dictionary-form distribution # Extraction of keys and probability values keys = list(distribution.keys()) values = np.array([distribution[k] for k in keys]) # Process as a NumPy distribution result = self._transform_numpy_distribution(values) # Addition of key information result['keys'] = keys return result def _transform_list_distribution(self, distribution): # Conversion of list-form distribution # Convert to a NumPy array numpy_distribution = np.array(distribution) return self._transform_numpy_distribution(numpy_distribution) def _normalize_distribution(self, distribution): # Normalization of probability distribution # Replace negative values with 0 distribution = np.maximum(distribution, 0) # If the sum is 0, return a uniform distribution if np.sum(distribution) <= 0: return np.ones_like(distribution) / len(distribution) # Normalization normalized = distribution / np.sum(distribution) # Application of minimum probability value normalized = np.maximum(normalized, self.min_probability) # Re-normalization normalized = normalized / np.sum(normalized) return normalized def _compute_cdf(self, distribution): # Calculation of cumulative distribution function return np.cumsum(distribution) def _quantize_cdf(self, cdf): # Quantization of cumulative distribution function # Quantize to 2^precision - 1 scale scale = (1 << self.precision) - 1 quantized = np.round(cdf * scale).astype(np.int32) # Ensure monotonic increase for i in range(1, len(quantized)): if quantized[i] <= quantized[i-1]: quantized[i] = quantized[i-1] + 1 # Adjust the maximum value to fit the scale if quantized[-1] < scale: quantized[-1] = scale return quantized def _handle_low_probability_events(self, distribution): # Handling of low-probability events special_events = [] processed_distribution = distribution.copy() # Identify events with probabilities below the threshold for i, prob in enumerate(distribution): if prob < self.special_handling_threshold: special_events.append(i) processed_distribution[i] = self.special_handling_threshold # Renormalization if special_events: processed_distribution = processed_distribution / np.sum(processed_distribution) return processed_distribution, special_events def inverse_transform(self, encoded_data, transformed_distribution): # Inverse transformation (used during decoding) # Implementation omitted return None ``` 6.3 Implementation of Adaptive Encoder The adaptive encoder dynamically adjusts the encoding parameters according to the characteristics of the data. The implementation includes the following processing steps. 1. Entropy Evaluation: Evaluate the local entropy of the data and identify regions with high and low information density 2. Precision Adaptation: Apply high-precision arithmetic coding in high-entropy regions and low-precision arithmetic coding in low-entropy regions 3. Context Adaptation: Dynamically adjust the encoding parameters based on the previous data 4. Resource Adaptation: Adjust the encoding precision according to the available computing resources ```python class AdaptiveEncoder: def __init__(self, config): self.config = config self.precision = config.get('precision', 16) self.max_precision = config.get('max_precision', 24) self.min_precision = config.get('min_precision', 8) self.entropy_threshold_high = config.get('entropy_threshold_high', 0.8) self.entropy_threshold_low = config.get('entropy_threshold_low', 0.3) self.resource_monitor = ResourceMonitor(config.get('resource_monitor_config', {}))

[0062] def encode(self, data, transformed_distribution, context=None): # Adaptive encoding of data # Process according to data type if isinstance(data, str): return self._encode_text(data, transformed_distribution, context) elif isinstance(data, np.ndarray) or isinstance(data, torch.Tensor): if len(data.shape) == 2 or len(data.shape) == 3: return self._encode_image(data, transformed_distribution, context) elif len(data.shape) == 1: return self._encode_audio(data, transformed_distribution, context) else: raise ValueError(f"Unsupported data shape: {data.shape}") elif isinstance(data, list) and all(isinstance(x, np.ndarray) for x in data): return self._encode_video(data, transformed_distribution, context) else: raise ValueError(f"Unsupported data type: {type(data)}") def _encode_text(self, text, transformed_distribution, context): # Encoding of text # Tokenization tokens = self._tokenize_text(text) # Entropy evaluation entropies = self._evaluate_token_entropies(tokens, transformed_distribution) # Determination of adaptive precision precisions = self._determine_adaptive_precisions(entropies) # Adjustment based on resource state resource_state = self.resource_monitor.get_state() adjusted_precisions = self._adjust_precisions_by_resources(precisions, resource_state) # Execution of arithmetic coding encoded_data, encoding_stats = self._arithmetic_encode_tokens(tokens, transformed_distribution, adjusted_precisions) return { 'encoded_data': encoded_data, 'encoding_stats': encoding_stats, 'entropies': entropies, 'precisions': adjusted_precisions } def _tokenize_text(self, text): # Tokenization of text # Implementation omitted return list(text) def _evaluate_token_entropies(self, tokens, transformed_distribution): # Evaluation of token entropies entropies = [] for token in tokens: # Obtaining the index of the token if 'keys' in transformed_distribution: # Dictionary-form distribution if token in transformed_distribution['keys']: idx = transformed_distribution['keys'].index(token) else: idx = -1 # Unknown token else: # Index-form distribution idx = token # Entropy calculation if idx >= 0 and idx < len(transformed_distribution['normalized_distribution']): prob = transformed_distribution['normalized_distribution'][idx] entropy = -np.log2(prob) if prob > 0 else 0 normalized_entropy = min(1.0, entropy / 10.0) # Normalize with a maximum of 10 bits else: normalized_entropy = 1.0 # The unknown token has the maximum entropy entropies.append(normalized_entropy) return entropies def _determine_adaptive_precisions(self, entropies): # Determination of adaptive precision based on entropy precisions = [] for entropy in entropies: if entropy >= self.entropy_threshold_high: # High entropy region has high precision precision = self.max_precision elif entropy <= self.entropy_threshold_low: # Low entropy region has low precision precision = self.min_precision else: # Intermediate region uses linear interpolation normalized_entropy = (entropy - self.entropy_threshold_low) / (self.entropy_threshold_high - self.entropy_threshold_low) precision = int(self.min_precision + normalized_entropy * (self.max_precision - self.min_precision)) precisions.append(precision) return precisions def _adjust_precisions_by_resources(self, precisions, resource_state): # Adjust precision based on resource state adjusted_precisions = precisions.copy() # Adjustment based on CPU usage cpu_usage = resource_state.get('cpu_usage', 0.5) if cpu_usage > 0.8: # Lower precision when CPU usage is high adjusted_precisions = [max(self.min_precision, p - 4) for p in adjusted_precisions] # Adjustment based on memory usage memory_usage = resource_state.get('memory_usage', 0.5) if memory_usage > 0.8: # Lower precision when memory usage is high adjusted_precisions = [max(self.min_precision, p - 2) for p in adjusted_precisions] # Adjustment based on battery level battery_level = resource_state.get('battery_level', 0.5) if battery_level < 0.2: # Lower precision when battery level is low adjusted_precisions = [max(self.min_precision, p - 6) for p in adjusted_precisions] return adjusted_precisions def _arithmetic_encode_tokens(self, tokens, transformed_distribution, precisions):

[0063] # Arithmetic encoding of tokens # Implementation omitted (implement the actual arithmetic encoding algorithm) encoded_data = bytearray([0] * (len(tokens) / / 2 + 1)) # Dummy implementation encoding_stats = { 'original_size': len(tokens), 'encoded_size': len(encoded_data), 'compression_ratio': len(tokens) / len(encoded_data) if len(encoded_data) > 0 else 0 } return encoded_data, encoding_stats def _encode_image(self, image, transformed_distribution, context): # Encoding of image # Implementation omitted return { 'encoded_data': bytearray([0] * (image.size / / 8 + 1)), 'encoding_stats': {}, 'entropies': [], 'precisions': [] } def _encode_audio(self, audio, transformed_distribution, context): # Encoding of audio # Implementation details are omitted return { 'encoded_data': bytearray([0] * (len(audio) / / 4 + 1)), 'encoding_stats': {}, 'entropies': [], 'precisions': [] } def _encode_video(self, video, transformed_distribution, context): # Encoding of video # Implementation details are omitted return { 'encoded_data': bytearray([0] * (sum(frame.size for frame in video) / / 16 + 1)), 'encoding_stats': {}, 'entropies': [], 'precisions': [] } def decode(self, encoded_data, transformed_distribution, data_type, context=None): # Decoding of encoded data # Implementation details are omitted return None ``` 6.4 Implementation of Parallel Processor To achieve high-speed processing of large-scale data, the parallel processor divides the data into multiple chunks and encodes them in parallel. The implementation includes the following processing steps. 1. Data splitting: Split the data into chunks of appropriate size 2. Independent encoding: Encode each chunk independently 3. Context inheritance: Efficiently inherit context information between chunks 4. Result integration: Efficiently integrate the encoding results of each chunk processed in parallel ```python class ParallelProcessor: def __init__(self, config): self.config = config self.chunk_size = config.get('chunk_size', 1024) self.max_workers = config.get('max_workers', multiprocessing.cpu_count()) self.overlap_size = config.get('overlap_size', 64) self.context_inheritance = config.get('context_inheritance', True) def process(self, data, encoder, transformed_distribution): # Parallel processing of data # Processing according to data type if isinstance(data, str): return self._process_text(data, encoder, transformed_distribution) elif isinstance(data, np.ndarray) or isinstance(data, torch.Tensor): if len(data.shape) == 2 or len(data.shape) == 3: return self._process_image(data, encoder, transformed_distribution) elif len(data.shape) == 1: return self._process_audio(data, encoder, transformed_distribution) else: raise ValueError(f"Unsupported data shape: {data.shape}") elif isinstance(data, list) and all(isinstance(x, np.ndarray) for x in data): return self._process_video(data, encoder, transformed_distribution) else: raise ValueError(f"Unsupported data type: {type(data)}") def _process_text(self, text, encoder, transformed_distribution): # Parallel processing of text # Splitting into chunks chunks = self._split_text_into_chunks(text) # Preparation for parallel processing with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_workers) as executor: # Schedule the processing of each chunk future_to_chunk = {} for i, chunk in enumerate(chunks): # Get the context information of the previous chunk if i > 0 and self.context_inheritance: context = self._extract_context_from_chunk(chunks[i-1]) else: context = None # Schedule chunk processing future = executor.submit( self._process_text_chunk, chunk, encoder, transformed_distribution, context, i ) future_to_chunk[future] = i # Collect results results = [] for future in concurrent.futures.as_completed(future_to_chunk): chunk_idx = future_to_chunk[future] try: result = future.result() results.append((chunk_idx, result)) except Exception as e: logger.error(f"Error processing chunk {chunk_idx}: {e}") # Fallback processing in case of error results.append((chunk_idx, self._fallback_process_chunk(chunks[chunk_idx], encoder, transformed_distribution))) # Integration of results sorted_results = sorted(results, key=lambda x: x[0]) return self._merge_text_results(sorted_results, chunks)

[0064] def _split_text_into_chunks(self, text): # Split text into chunks chunks = [] for i in range(0, len(text), self.chunk_size - self.overlap_size): end = min(i + self.chunk_size, len(text)) chunk = text[i:end] chunks.append(chunk) return chunks def _extract_context_from_chunk(self, chunk): # Extract context information from the chunk # Implementation omitted return { 'last_tokens': chunk[-self.overlap_size:] if len(chunk) >= self.overlap_size else chunk } def _process_text_chunk(self, chunk, encoder, transformed_distribution, context, chunk_idx): # Processing of text chunk encoding_result = encoder.encode(chunk, transformed_distribution, context) # Addition of chunk information encoding_result['chunk_idx'] = chunk_idx encoding_result['chunk_size'] = len(chunk) return encoding_result def _fallback_process_chunk(self, chunk, encoder, transformed_distribution): # Fallback processing in case of error # Implementation omitted return { 'encoded_data': bytearray([0] * (len(chunk) / / 4 + 1)), 'encoding_stats': {}, 'error': True } def _merge_text_results(self, sorted_results, chunks): # Integration of text processing results merged_encoded_data = bytearray() merged_stats = { 'original_size': sum(len(chunk) for chunk in chunks), 'encoded_size': 0, 'chunk_count': len(chunks), 'chunk_stats': [] } for chunk_idx, result in sorted_results: # Concatenation of encoded data chunk_data = result['encoded_data'] merged_encoded_data.extend(chunk_data) # Integration of statistical information merged_stats['encoded_size'] += len(chunk_data) merged_stats['chunk_stats'].append({ 'chunk_idx': chunk_idx, 'original_size': result.get('chunk_size', 0), 'encoded_size': len(chunk_data), 'error': result.get('error', False) }) # Calculation of compression ratio merged_stats['compression_ratio'] = merged_stats['original_size'] / merged_stats['encoded_size'] if merged_stats['encoded_size'] > 0 else 0 return { 'encoded_data': merged_encoded_data, 'encoding_stats': merged_stats } def _process_image(self, image, encoder, transformed_distribution): # Parallel processing of the image # Implementation omitted return { 'encoded_data': bytearray([0] * (image.size / / 8 + 1)), 'encoding_stats': {} } def _process_audio(self, audio, encoder, transformed_distribution): # Parallel processing of the audio # Implementation omitted return { 'encoded_data': bytearray([0] * (len(audio) / / 4 + 1)), 'encoding_stats': {} } def _process_video(self, video, encoder, transformed_distribution): # Parallel processing of the video # Implementation omitted return { 'encoded_data': bytearray([0] * (sum(frame.size for frame in video) / / 16 + 1)), 'encoding_stats': {} } ``` 6.5 Implementation of the Error-Tolerant Encoder The error-tolerant encoder realizes encoding with resistance to communication errors. The implementation includes the following processing steps. 1. Importance Evaluation: Evaluate the importance of each part within the data to distinguish important parts from unimportant parts 2. Differential Protection: Apply differential error protection according to the importance 3. Hierarchical Encoding: Divide the data into multiple layers and encode them as a base layer and an extension layer 4. Resynchronization Marker: Periodically insert a resynchronization marker into the encoded stream ```python class ErrorResilientEncoder: def __init__(self, config): self.config = config self.protection_levels = config.get('protection_levels', { 'high': {'redundancy': 0.3, 'ecc_type':'reed_solomon'}, 'medium': {'redundancy': 0.15, 'ecc_type': 'hamming'}, 'low': {'redundancy': 0.05, 'ecc_type': 'parity'} }) self.importance_thresholds = config.get('importance_thresholds', { 'high': 0.8, 'medium': 0.5, 'low': 0.2 }) self.resync_marker_interval = config.get('resync_marker_interval', 1024) self.resync_marker = config.get('resync_marker', bytes([0xFF, 0x00, 0xFF, 0x00])) self.use_layered_coding = config.get('use_layered_coding', True)

[0065] def encode(self, data, importance_scores, encoding_result): # Error-resilient encoding # Determine protection levels based on importance protection_levels = self._determine_protection_levels(importance_scores) # Get the encoded data encoded_data = encoding_result['encoded_data'] # Apply layered coding if self.use_layered_coding: layered_data = self._apply_layered_coding(encoded_data, protection_levels) else: layered_data = None # Apply error correction code protected_data = self._apply_error_correction(encoded_data, protection_levels) # Insert resync markers final_data = self._insert_resync_markers(protected_data) # Add metadata metadata = self._create_metadata(protection_levels, layered_data is not None) return { 'encoded_data': final_data, 'metadata': metadata, 'layered_data': layered_data, 'original_size': len(encoded_data), 'protected_size': len(final_data), 'overhead_ratio': len(final_data) / len(encoded_data) if len(encoded_data) > 0 else 0 } def _determine_protection_levels(self, importance_scores): # Determination of protection levels based on importance if isinstance(importance_scores, dict) and 'overall_importance' in importance_scores: # Use the overall importance score overall_importance = importance_scores['overall_importance'] if overall_importance >= self.importance_thresholds['high']: return ['high'] * len(importance_scores.get('pixel_importance', [])) elif overall_importance >= self.importance_thresholds['medium']: return ['medium'] * len(importance_scores.get('pixel_importance', [])) else: return ['low'] * len(importance_scores.get('pixel_importance', [])) elif isinstance(importance_scores, list): # Use importance scores for each element protection_levels = [] for score in importance_scores: if score >= self.importance_thresholds['high']: protection_levels.append('high') elif score >= self.importance_thresholds['medium']: protection_levels.append('medium') else: protection_levels.append('low') return protection_levels else: # Default is medium protection return ['medium'] * (len(importance_scores) if hasattr(importance_scores, '__len__') else 1) def _apply_layered_coding(self, encoded_data, protection_levels): # Apply layered coding if not self.use_layered_coding: return None # Split into base layer and enhancement layer base_layer_indices = [] enhancement_layer_indices = [] for i, level in enumerate(protection_levels): if level == 'high': base_layer_indices.append(i) else: enhancement_layer_indices.append(i) # Data extraction for each layer base_layer = bytearray() enhancement_layer = bytearray() for i in range(len(encoded_data)): layer_idx = i % len(protection_levels) if layer_idx in base_layer_indices: base_layer.append(encoded_data[i]) else: enhancement_layer.append(encoded_data[i]) # Apply high-level protection to the base layer protected_base_layer = self._apply_error_correction_to_chunk( base_layer, self.protection_levels['high'] ) # Apply low-level protection to the enhancement layer protected_enhancement_layer = self._apply_error_correction_to_chunk( enhancement_layer, self.protection_levels['low'] ) return { 'base_layer': protected_base_layer, 'enhancement_layer': protected_enhancement_layer, 'base_layer_indices': base_layer_indices, 'enhancement_layer_indices': enhancement_layer_indices } def _apply_error_correction(self, encoded_data, protection_levels): # Applying error correction codes # Split the data into chunks chunk_size = 256 # Fixed chunk size chunks = [encoded_data[i:i+chunk_size] for i in range(0, len(encoded_data), chunk_size)] # Determining the protection level for each chunk chunk_protection_levels = [] for i, chunk in enumerate(chunks): # Protection level corresponding to the start of the chunk start_idx = i * chunk_size level_idx = start_idx % len(protection_levels) chunk_protection_levels.append(protection_levels[level_idx])

[0066] # Apply error correction codes to each chunk protected_chunks = [] for i, chunk in enumerate(chunks): level = chunk_protection_levels[i] protection_params = self.protection_levels[level] protected_chunk = self._apply_error_correction_to_chunk(chunk, protection_params) protected_chunks.append(protected_chunk) # Concatenate the protected chunks protected_data = bytearray() for chunk in protected_chunks: protected_data.extend(chunk) return protected_data def _apply_error_correction_to_chunk(self, chunk, protection_params): # Apply error correction codes to the chunk ecc_type = protection_params['ecc_type'] redundancy = protection_params['redundancy'] if ecc_type =='reed_solomon': return self._apply_reed_solomon(chunk, redundancy) elif ecc_type == 'hamming': return self._apply_hamming(chunk, redundancy) elif ecc_type == 'parity': return self._apply_parity(chunk, redundancy) else: logger.warning(f"Unsupported ECC type: {ecc_type}, using parity instead") return self._apply_parity(chunk, redundancy) def _apply_reed_solomon(self, data, redundancy): # Application of Reed - Solomon code # Implementation omitted (actually use a Reed - Solomon encoding library) # Addition of redundant data parity_size = int(len(data) * redundancy) parity = bytearray([0] * parity_size) # Dummy implementation # Concatenation of data and parity protected_data = bytearray(data) protected_data.extend(parity) return protected_data def _apply_hamming(self, data, redundancy): # Application of Hamming code # Implementation omitted return data def _apply_parity(self, data, redundancy): # Application of parity code # Implementation omitted return data def _insert_resync_markers(self, data): # Insert resync markers if self.resync_marker_interval <= 0: return data result = bytearray() for i in range(0, len(data), self.resync_marker_interval): # Get data chunk end = min(i + self.resync_marker_interval, len(data)) chunk = data[i:end] # Add marker and chunk result.extend(self.resync_marker) result.extend(chunk) return result def _create_metadata(self, protection_levels, has_layered_coding): # Create metadata # Distribution of protection levels level_counts = { 'high': protection_levels.count('high'), 'medium': protection_levels.count('medium'), 'low': protection_levels.count('low') } # Protection parameters protection_params = { level: { 'redundancy': params['redundancy'], 'ecc_type': params['ecc_type'] } for level, params in self.protection_levels.items() } return { 'protection_level_distribution': level_counts, 'protection_parameters': protection_params, 'resync_marker_interval': self.resync_marker_interval, 'has_layered_coding': has_layered_coding } def decode(self, protected_data, metadata): # Decoding of protected data # Implementation omitted return None ``` 6.6 Integration of Adaptive Arithmetic Coding Module Integrate the above sub - modules to construct an adaptive arithmetic coding module. The integrated architecture is as follows. ```python class AdaptiveArithmeticCodingModule: def __init__(self, config): self.config = config # Initialization of probability distribution converter self.distribution_transformer = ProbabilityDistributionTransformer( config.get('distribution_transformer_config', {}) ) # Initialization of the adaptive encoder self.adaptive_encoder = AdaptiveEncoder( config.get('adaptive_encoder_config', {}) )

[0067] # Initialization of the parallel processor self.parallel_processor = ParallelProcessor( config.get('parallel_processor_config', {}) ) # Initialization of the error-resilient encoder self.error_resilient_encoder = ErrorResilientEncoder( config.get('error_resilient_encoder_config', {}) ) # Encoding history self.encoding_history = [] def encode(self, data, prediction_distribution, importance_scores=None, context=None): # Encoding of data start_time = time.time() # Transformation of the prediction distribution transformed_distribution = self.distribution_transformer.transform(prediction_distribution) # Encoding by parallel processing parallel_result = self.parallel_processor.process( data, self.adaptive_encoder, transformed_distribution ) # Application of error-resilient encoding if importance_scores is None: # If importance scores are not provided, estimate from the encoder output importance_scores = parallel_result.get('entropies', [0.5]) resilient_result = self.error_resilient_encoder.encode( data, importance_scores, parallel_result ) # Calculation of encoding time encoding_time = time.time() - start_time # Integration of results result = { 'encoded_data': resilient_result['encoded_data'], 'metadata': { 'distribution_metadata': transformed_distribution, 'error_resilience_metadata': resilient_result['metadata'], 'encoding_stats': parallel_result.get('encoding_stats', {}), 'encoding_time': encoding_time }, 'layered_data': resilient_result.get('layered_data'), 'original_size': len(data) if isinstance(data, (bytes, bytearray, str)) else None, 'encoded_size': len(resilient_result['encoded_data']), 'encoding_time': encoding_time } # Calculation of compression ratio if result['original_size'] is not None: result['compression_ratio'] = result['original_size'] / result['encoded_size'] if result['encoded_size'] > 0 else 0 # Update of encoding history self.encoding_history.append({ 'timestamp': time.time(), 'data_type': type(data).__name__, 'original_size': result['original_size'], 'encoded_size': result['encoded_size'], 'compression_ratio': result.get('compression_ratio'), 'encoding_time': encoding_time ) return result def decode(self, encoded_data, metadata): # Decoding of symbolized data start_time = time.time() # Acquisition of metadata distribution_metadata = metadata['distribution_metadata'] error_resilience_metadata = metadata['error_resilience_metadata'] # Application of error resilience decoding error_corrected_data = self.error_resilient_encoder.decode( encoded_data, error_resilience_metadata ) # Inverse transformation of distribution distribution = self.distribution_transformer.inverse_transform( encoded_data, distribution_metadata ) # Application of adaptive decoding decoded_data = self.adaptive_encoder.decode( error_corrected_data, distribution, metadata.get('data_type') ) # Calculation of decoding time decoding_time = time.time() - start_time return { 'decoded_data': decoded_data, 'decoding_time': decoding_time } def get_encoding_statistics(self): # Obtaining encoding statistics if not self.encoding_history: return { 'total_encodings': 0, 'average_compression_ratio': 0, 'average_encoding_time': 0, 'total_original_size': 0, 'total_encoded_size': 0 }

[0068] # Extract only entries with valid compression ratios valid_entries = [entry for entry in self.encoding_history if entry.get('compression_ratio') is not None] if not valid_entries: avg_compression_ratio = 0 else: avg_compression_ratio = sum(entry['compression_ratio'] for entry in valid_entries) / len(valid_entries) avg_encoding_time = sum(entry['encoding_time'] for entry in self.encoding_history) / len(self.encoding_history) total_original_size = sum(entry['original_size'] for entry in self.encoding_history if entry['original_size'] is not None) total_encoded_size = sum(entry['encoded_size'] for entry in self.encoding_history) return { 'total_encodings': len(self.encoding_history), 'average_compression_ratio': avg_compression_ratio, 'average_encoding_time': avg_encoding_time, 'total_original_size': total_original_size, 'total_encoded_size': total_encoded_size, 'overall_compression_ratio': total_original_size / total_encoded_size if total_encoded_size > 0 else 0 } ``` ## 7. System Integration and Usage Method of the Whole System This invention describes a method of integrating each module of the "Adaptive Distributed Compression System Based on Multimodal Understanding" and using it as an actual system. 7.1 System Integration Architecture This system has an architecture that integrates the five aforementioned main modules. The integrated architecture is as follows. ```python class AdaptiveDistributedCompressionSystem: def __init__(self, config): self.config = config # Initialization of the multimodal understanding module self.multimodal_understanding_module = MultimodalUnderstandingModule( config.get('multimodal_understanding_config', {}) ) # Initialization of the adaptive model selection module self.adaptive_model_selection_module = AdaptiveModelSelectionModule( config.get('adaptive_model_selection_config', {}) ) # Initialization of the distributed collaborative compression module self.distributed_collaborative_compression_module = DistributedCollaborativeCompressionModule( config.get('distributed_collaborative_compression_config', {}) ) # Initialization of the knowledge distillation module self.knowledge_distillation_module = KnowledgeDistillationModule( config.get('knowledge_distillation_config', {}) ) # Initialization of the adaptive arithmetic coding module self.adaptive_arithmetic_coding_module = AdaptiveArithmeticCodingModule( config.get('adaptive_arithmetic_coding_config', {}) ) # System state self.compression_history = [] self.session_id = str(uuid.uuid4()) self.session_start_time = time.time() def compress(self, data, data_type=None, context=None): # Compression of data compression_id = str(uuid.uuid4()) start_time = time.time() # Estimation of data type (if not specified) if data_type is None: data_type = self._detect_data_type(data) # Preparation of Context Information if context is None: context = {} context.update({ 'session_id': self.session_id, 'session_time': time.time() - self.session_start_time, 'compression_id': compression_id })

[0069] # Step 1: Multimodal Understanding understanding_result = self.multimodal_understanding_module.process({data_type: data}) # Step 2: Adaptive Model Selection model_selection_result = self.adaptive_model_selection_module.select_model(data, data_type) selected_model = model_selection_result['selected_model'] # Step 3: Distributed Collaborative Compression compression_result = self.distributed_collaborative_compression_module.compress( data, data_type, context ) # Step 4: Adaptive Arithmetic Coding encoding_result = self.adaptive_arithmetic_coding_module.encode( compression_result['differential_info'], compression_result['prediction_confidence'], understanding_result['redundancy_maps'].get(f"{data_type}_importance", None), context ) # Calculation of compression time compression_time = time.time() - start_time # Integration of results result = { 'compression_id': compression_id, 'encoded_data': encoding_result['encoded_data'], 'metadata': { 'data_type': data_type, 'selected_model': selected_model, 'understanding_metadata': understanding_result, 'compression_metadata': compression_result['metadata'], 'encoding_metadata': encoding_result['metadata'], 'context': context }, 'original_size': compression_result['metadata']['original_size'], 'compressed_size': len(encoding_result['encoded_data']), 'compression_ratio': compression_result['metadata']['original_size'] / len(encoding_result['encoded_data']) if len(encoding_result['encoded_data']) > 0 else 0, 'compression_time': compression_time } # Update compression history self.compression_history.append({ 'compression_id': compression_id, 'timestamp': time.time(), 'data_type': data_type, 'original_size': result['original_size'], 'compressed_size': result['compressed_size'], 'compression_ratio': result['compression_ratio'], 'compression_time': compression_time, 'selected_model': selected_model }) return result def decompress(self, compressed_data, metadata): # Decompress compressed data start_time = time.time() # Get metadata data_type = metadata['data_type'] selected_model = metadata['selected_model'] understanding_metadata = metadata['understanding_metadata'] compression_metadata = metadata['compression_metadata'] encoding_metadata = metadata['encoding_metadata'] context = metadata['context'] # Step 1: Adaptive arithmetic decoding decoding_result = self.adaptive_arithmetic_coding_module.decode( compressed_data, encoding_metadata ) # Step 2: Distributed collaborative decompression decompression_result = self.distributed_collaborative_compression_module.decompress( decoding_result['decoded_data'], compression_metadata ) # Calculation of decompression time decompression_time = time.time() - start_time return { 'decompressed_data': decompression_result['reconstructed_data'], 'decompression_time': decompression_time } def _detect_data_type(self, data): # Estimation of data type if isinstance(data, str): return 'text' elif isinstance(data, np.ndarray) or isinstance(data, torch.Tensor): if len(data.shape) == 2 or len(data.shape) == 3: return 'image' elif len(data.shape) == 1: return 'audio' else: return 'unknown' elif isinstance(data, list) and all(isinstance(x, np.ndarray) for x in data): return 'video' elif isinstance(data, dict) and all(k in ['text', 'image', 'audio', 'video'] for k in data.keys()): return'multimodal' else: return 'unknown' def train_custom_model(self, data_type, target_hardware=None, domain=None): # Training of custom model return self.knowledge_distillation_module.distill_model( data_type, target_hardware, domain ) def synchronize_knowledge(self, remote_endpoint): # Knowledge base synchronization return self.distributed_collaborative_compression_module.synchronize_knowledge( remote_endpoint )

[0070] def get_compression_statistics(self): # Obtaining compression statistics if not self.compression_history: return { 'total_compressions': 0, 'average_ratio': 0, 'average_time': 0, 'total_original_size': 0, 'total_compressed_size': 0, 'by_data_type': {} } total_original_size = sum(record['original_size'] for record in self.compression_history) total_compressed_size = sum(record['compressed_size'] for record in self.compression_history) # Statistics by data type by_data_type = {} for record in self.compression_history: data_type = record['data_type'] if data_type not in by_data_type: by_data_type[data_type] = { 'count': 0, 'total_original_size': 0, 'total_compressed_size': 0, 'total_time': 0 } by_data_type[data_type]['count'] += 1 by_data_type[data_type]['total_original_size'] += record['original_size'] by_data_type[data_type]['total_compressed_size'] += record['compressed_size'] by_data_type[data_type]['total_time'] += record['compression_time'] # Calculation of average value for data_type, stats in by_data_type.items(): stats['average_ratio'] = stats['total_original_size'] / stats['total_compressed_size'] if stats['total_compressed_size'] > 0 else 0 stats['average_time'] = stats['total_time'] / stats['count'] if stats['count'] > 0 else 0 return { 'total_compressions': len(self.compression_history), 'average_ratio': total_original_size / total_compressed_size if total_compressed_size > 0 else 0, 'average_time': sum(record['compression_time'] for record in self.compression_history) / len(self.compression_history), 'total_original_size': total_original_size, 'total_compressed_size': total_compressed_size, 'by_data_type': by_data_type } ``` 7.2 System Settings and Initialization To use this system, an appropriate configuration file needs to be prepared and the system needs to be initialized. An example of the configuration file and the initialization method are shown below. ```python def load_config(config_path): # Loading the configuration file with open(config_path, 'r') as f: config = json.load(f) return config def initialize_system(config_path): # Initializing the system config = load_config(config_path) # Generation of the System system = AdaptiveDistributedCompressionSystem(config) # Initialization Log logger.info(f"Adaptive Distributed Compression System initialized with session ID: {system.session_id}") return system ```

[0071] Example of the configuration file (config.json): ```json { "multimodal_understanding_config": { "text_model": "bert-base-uncased", "image_model": "vit-base-patch16-224", "audio_model": "wav2vec2-base-960h", "video_model": "slow_r50", "embedding_dim": 512, "transformer_dim": 768, "num_heads": 12, "num_layers": 6 }, "adaptive_model_selection_config": { "complexity_evaluator_config": { "text_complexity_weights": { "entropy": 0.4, "structure": 0.3, "predictability": 0.3}, "image_complexity_weights": { "entropy": 0.3, "edge_density": 0.3, "texture": 0.2, "predictability": 0.2 } }, "network_monitor_config": { "measurement_interval": 30, "history_length": 10, "bandwidth_weight": 0.4, "latency_weight": 0.4, "packet_loss_weight": 0.2 }, "device_monitor_config": { "measurement_interval": 5, "history_length": 10, "cpu_weight": 0.2, "memory_weight": 0.2, "battery_weight": 0.3, "gpu_weight": 0.2, "thermal_weight": 0.1 }, "model_selector_config": { "objective_weights": { "compression_ratio": 0.4, "processing_time": 0.3, "memory_usage": 0.2, "energy_consumption": 0.1 } } }, "distributed_collaborative_compression_config": { "shared_knowledge_manager_config": { "domain_knowledge_path": "data / domain_knowledge.pkl", "world_knowledge_path": "data / world_knowledge.pkl", "max_history_length": 1000 }, "knowledge_synchronizer_config": { "sync_interval": 3600, "background_sync_enabled": true, "priority_thresholds": { "high": 0.8, "medium": 0.5, "low": 0.2 }, "conflict_resolution_strategy": "timestamp" }, "context_builder_config": { "short_term_memory_size": 10, "long_term_memory_max_items": 1000, "long_term_memory_threshold": 3, "context_decay_factor": 0.95 } }, "knowledge_distillation_config": { "teacher_selector_config": { "available_models": { "text": { "LLaMA3-70B": { "type": "pytorch", "path": "models / llama3-70b" }, "GPT-4": { "type": "api", "path": "openai / gpt-4" } }, "image": { "iGPT-L": { "type": "pytorch", "path": "models / igpt-l" }, "CLIP-ViT-L-14": { "type": "pytorch", "path": "models / clip-vit-l-14" } } }, "evaluation_metrics": { "compression_ratio": 0.6, "processing_time": 0.2, "prediction_accuracy": 0.2 }, "use_ensemble": true }, "student_designer_config": { "resource_constraints": { "mobile_cpu": { "max_parameters": 10000000, "max_model_size": 50000000, "max_memory_usage": 500000000, "max_inference_time": 1.0, "max_power_consumption": 1.0 }, "mobile_gpu": { "max_parameters": 50000000, "max_model_size": 200000000, "max_memory_usage": 2000000000, "max_inference_time": 0.5, "max_power_consumption": 2.0 } } }, "distillation_trainer_config": { "training_config": { "num_epochs": 10, "batch_size": 32, "learning_rate": 0.001, "temperature": 2.0, "alpha": 0.5, "use_curriculum": true } }, "domain_adapter_config": { "domain_data_sources": { "medical": { "type": "file", "path": "data / medical / dataset.json" } , "legal": { "type": "file", "path": "data / legal / dataset.json" } }​},

[0072] "optimization_optimizer_config": { "optimization_config": { "quantization": { "enabled": true, "type": "post_training", "bit_width": 8 }, "pruning": { "enabled": true, "type": "magnitude", "ratio": 0.5 }, "distillation_fine_tuning": { "enabled": true } } } }, "adaptive_arithmetic_coding_config": { "distribution_transformer_config": { "precision": 16, "min_probability": 1e-10, "special_handling_threshold": 1e-6, "use_log_domain": true }, "adaptive_encoder_config": { "precision": 16, "max_precision": 24, "min_precision": 8, "entropy_threshold_high": 0.8, "entropy_threshold_low": 0.3}, "parallel_processor_config": { "chunk_size": 1024, "max_workers": 4, "overlap_size": 64, "context_inheritance": true }, "error_resilient_encoder_config": { "protection_levels": { "high": {"redundancy": 0.3, "ecc_type": "reed_solomon"}, "medium": {"redundancy": 0.15, "ecc_type": "hamming"}, "low": {"redundancy": 0.05, "ecc_type": "parity"} }, "importance_thresholds": { "high": 0.8, "medium": 0.5, "low": 0.2 }, "resync_marker_interval": 1024, "use_layered_coding": true } } } ``` 7.3 System Usage Examples Examples of compressing and decompressing various data formats using this system are shown. # 7.3.1 Compression and Decompression of Text Data ```python def compress_text_example(): # System Initialization system = initialize_system('config.json') # Preparation of Text Data text_data = """ The adaptive distributed compression system based on multimodal understanding is an innovative data compression system that combines large language models (LLMs) and distributed computing. For various data formats such as text, images, audio, and videos, it realizes ultra-high-efficiency reversible compression through collaborative understanding between terminals and adaptive model selection. """ # Compression of Text compression_result = system.compress(text_data, 'text') print(f"Original size: {compression_result['original_size']} bytes") print(f"Compressed size: {compression_result['compressed_size']} bytes") print(f"Compression ratio: {compression_result['compression_ratio']:.2f}x") print(f"Compression time: {compression_result['compression_time']:.3f} seconds") # Saving Compressed Data with open('compressed_text.bin', 'wb') as f: f.write(compression_result['encoded_data']) # Saving Metadata with open('compressed_text_metadata.json', 'w') as f: json.dump(compression_result['metadata'], f, indent=2) # Loading compressed data with open('compressed_text.bin', 'rb') as f: compressed_data = f.read() # Loading metadata with open('compressed_text_metadata.json', 'r') as f: metadata = json.load(f) # Decompressing text decompression_result = system.decompress(compressed_data, metadata) print(f"Decompression time: {decompression_result['decompression_time']:.3f} seconds") print(f"Decompressed text: {decompression_result['decompressed_data'][:100]}...") # Comparing the original text and the decompressed text if text_data == decompression_result['decompressed_data']: print("Decompression successful: texts match") else: print("Decompression error: texts do not match") ```

[0073] # 7.3.2 Image Data Compression and Decompression ```python def compress_image_example(): # System initialization system = initialize_system('config.json') # Load image data image = cv2.imread('example_image.jpg') # Compress the image compression_result = system.compress(image, 'image') print(f"Original size: {compression_result['original_size']} bytes") print(f"Compressed size: {compression_result['compressed_size']} bytes") print(f"Compression ratio: {compression_result['compression_ratio']:.2f}x") print(f"Compression time: {compression_result['compression_time']:.3f} seconds") # Save the compressed data with open('compressed_image.bin', 'wb') as f: f.write(compression_result['encoded_data']) # Save the metadata with open('compressed_image_metadata.json', 'w') as f: json.dump(compression_result['metadata'], f, indent=2) # Loading compressed data with open('compressed_image.bin', 'rb') as f: compressed_data = f.read() # Loading metadata with open('compressed_image_metadata.json', 'r') as f: metadata = json.load(f) # Image decompression decompression_result = system.decompress(compressed_data, metadata) print(f"Decompression time: {decompression_result['decompression_time']:.3f} seconds") # Saving the decompressed image cv2.imwrite('decompressed_image.jpg', decompression_result['decompressed_data']) # Image comparison original_image = cv2.imread('example_image.jpg') decompressed_image = cv2.imread('decompressed_image.jpg') # Calculating PSNR (Peak Signal-to-Noise Ratio) mse = np.mean((original_image - decompressed_image) ** 2) psnr = 10 * np.log10((255 ** 2) / mse) if mse > 0 else 100 print(f"Image quality (PSNR): {psnr:.2f} dB") ``` # 7.3.3 Compression and Decompression of Multimodal Data ```python def compress_multimodal_example(): # System initialization system = initialize_system('config.json') # Preparation of multimodal data presentation_data = { 'text': """ # Adaptive Distributed Compression System Based on Multimodal Understanding ## Overview This system is an innovative data compression system that combines a large language model (LLM) and distributed computing. It is an innovative data compression system that combines a large language model (LLM) and distributed computing. ## Features - Multimodal understanding - Adaptive model selection - Distributed collaborative compression - Knowledge distillation """, 'image': cv2.imread('presentation_slide.jpg') } # Compression of multimodal data compression_result = system.compress(presentation_data,'multimodal') print(f"Original size: {compression_result['original_size']} bytes") print(f"Compressed size: {compression_result['compressed_size']} bytes") print(f"Compression ratio: {compression_result['compression_ratio']:.2f}x") print(f"Compression time: {compression_result['compression_time']:.3f} seconds") # Saving compressed data with open('compressed_presentation.bin', 'wb') as f: f.write(compression_result['encoded_data']) # Saving metadata with open('compressed_presentation_metadata.json', 'w') as f: json.dump(compression_result['metadata'], f, indent = 2) # Loading compressed data with open('compressed_presentation.bin', 'rb') as f: compressed_data = f.read() # Loading metadata with open('compressed_presentation_metadata.json', 'r') as f: metadata = json.load(f) # Unfolding of multimodal data decompression_result = system.decompress(compressed_data, metadata) print(f"Decompression time: {decompression_result['decompression_time']:.3f} seconds") # Verification of the unfolded data decompressed_data = decompression_result['decompressed_data'] print(f"Decompressed text: {decompressed_data['text'][:100]}...") # Saving the unfolded image cv2.imwrite('decompressed_slide.jpg', decompressed_data['image']) ``` # 7.3.4 Training and Use of Domain-Specific Models ```python def train_and_use_domain_model(): # Initialization of the system system = initialize_system('config.json') # Model training for the medical domain training_result = system.train_custom_model( data_type='text', target_hardware='mobile_cpu', domain='medical' ) print(f"Model training completed in {training_result['duration']:.2f} seconds") print(f"Compression stats: {training_result['compression_stats']}") # Saving the trained model model_id = training_result['distillation_id'] model_path = f"models / medical_text_model_{model_id}.pt" torch.save(training_result['model'], model_path) print(f"Model saved to {model_path}") # Preparing medical text data medical_text = """ The patient is a 65-year-old male with a history of hypertension and type 2 diabetes. A 2-cm nodule shadow was observed in the lower lobe of the right lung on chest X-ray. Blood test results show HbA1c of 7.2%, fasting blood glucose of 142 mg / dL, and total cholesterol of 220 mg / dL. """ # Compression using a custom model # (In an actual implementation, a function to register a custom model in the system is required) # Obtaining compression statistics stats = system.get_compression_statistics() print(f"Overall compression statistics: {stats}") ```

[0074] # 7.3.5 Knowledge Synchronization in a Distributed Environment ```python def synchronize_knowledge_example(): # System initialization system = initialize_system('config.json') # Set remote endpoint remote_endpoint = { 'url': 'https: / / remote-system.example.com / sync', 'auth_token': 'example_token' } # Synchronize knowledge base sync_result = system.synchronize_knowledge(remote_endpoint) print(f"Knowledge synchronization result: {sync_result}") # Compression test after synchronization text_data = "This text will be compressed after knowledge synchronization. Shared knowledge should be utilized." compression_result = system.compress(text_data, 'text') print(f"Compression ratio after sync: {compression_result['compression_ratio']:.2f}x") ``` 7.4 System Deployment The method of deploying this system to an actual environment will be described. # 7.4.1 Server-Side Deployment ```python def deploy_server(): # System initialization system = initialize_system('server_config.json') # Create a Flask application app = Flask(__name__) @app.route(' / compress', methods=['POST']) def compress_endpoint(): # Get request data data = request.get_data() data_type = request.headers.get('X-Data-Type', 'text') # Get context information context = { 'client_id': request.headers.get('X-Client-ID'), 'session_id': request.headers.get('X-Session-ID') } # Compress data compression_result = system.compress(data, data_type, context) # Create a response response = make_response(compression_result['encoded_data']) response.headers['X-Compression-ID'] = compression_result['compression_id'] response.headers['X-Original-Size'] = str(compression_result['original_size']) response.headers['X-Compressed-Size'] = str(compression_result['compressed_size']) response.headers['X-Compression-Ratio'] = str(compression_result['compression_ratio']) # Save metadata (in actual implementation, save to database) metadata_storage[compression_result['compression_id']] = compression_result['metadata'] return response @app.route(' / decompress / <compression_id>', methods=['POST']) def decompress_endpoint(compression_id): # Get compressed data compressed_data = request.get_data() # Get metadata if compression_id not in metadata_storage: return jsonify({'error': 'Metadata not found'}), 404 metadata = metadata_storage[compression_id] # Data decompression decompression_result = system.decompress(compressed_data, metadata) # Create response response = make_response(decompression_result['decompressed_data']) response.headers['X-Decompression-Time'] = str(decompression_result['decompression_time']) return response @app.route(' / sync', methods=['POST']) def sync_endpoint(): # Get synchronization data sync_data = request.json # Synchronize knowledge base # (In actual implementation, it is necessary to add a synchronization API to the system) return jsonify({'status':'success'}) # Start the application app.run(host='0.0.0.0', port=5000) ``` # 7.4.2 Client-side Deployment ```python def deploy_client(): # Initialize the system system = initialize_system('client_config.json') # Generation of client ID client_id = str(uuid.uuid4()) def compress_and_send(data, data_type, server_url): # Compression of data compression_result = system.compress(data, data_type) # Sending to the server headers = { 'X-Data-Type': data_type, 'X-Client-ID': client_id, 'X-Session-ID': system.session_id } response = requests.post( f"{server_url} / compress", data=compression_result['encoded_data'], headers=headers ) # Processing of the response compression_id = response.headers.get('X-Compression-ID') ```python # Saving of metadata with open(f"metadata_{compression_id}.json", 'w') as f: json.dump(compression_result['metadata'], f, indent=2) return { 'compression_id': compression_id, 'original_size': int(response.headers.get('X-Original-Size')), 'compressed_size': int(response.headers.get('X-Compressed-Size')), 'compression_ratio': float(response.headers.get('X-Compression-Ratio')) }

[0075] def receive_and_decompress(compression_id, compressed_data, server_url): # Loading metadata try: with open(f"metadata_{compression_id}.json", 'r') as f: metadata = json.load(f) except FileNotFoundError: # If metadata is not local, retrieve it from the server response = requests.get(f"{server_url} / metadata / {compression_id}") if response.status_code != 200: raise ValueError(f"Failed to retrieve metadata for compression ID: {compression_id}") metadata = response.json() # Decompressing data decompression_result = system.decompress(compressed_data, metadata) return decompression_result['decompressed_data'] def synchronize_with_server(server_url): # Synchronization of knowledge with the server sync_result = system.synchronize_knowledge({ 'url': f"{server_url} / sync", 'auth_token': client_id }) return sync_result # Example of a client application def run_client_app(): server_url = "https: / / compression-server.example.com" # Initial synchronization synchronize_with_server(server_url) while True: # User input command = input("Enter command (compress / decompress / sync / exit): ") if command == "exit": break elif command == "compress": data_type = input("Enter data type (text / image / audio / video): ") if data_type == "text": data = input("Enter text to compress: ") result = compress_and_send(data, data_type, server_url) print(f"Compression result: {result}") elif data_type == "image": file_path = input("Enter image file path: ") try: image = cv2.imread(file_path) if image is None: print(f"Failed to load image from {file_path}") continue result =...

Claims

1. A method for compressing data, comprising: analyzing the correlation of data including a plurality of modalities; selecting an optimal compression model based on data characteristics and communication environment; extracting differential information based on shared knowledge between the transmitting side and the receiving side; and compressing the differential information using the selected model. A data compression method characterized by including.

2. In the data compression method according to Claim 1, the plurality of modalities include at least two of text, image, audio, and video, and the analysis of the correlation includes modeling the semantic correspondence between different modalities using a cross-modal attention mechanism. The selection of the optimal compression model is performed by multi-objective optimization based on the complexity of the data, the communication bandwidth, and the resource constraints of the terminal, and the shared knowledge includes at least one of domain-specific knowledge, past communication content, and general world knowledge. A data compression method characterized by this.

3. A data compression system, comprising: a multi-modal understanding module that analyzes the correlation of data including a plurality of modalities; an adaptive model selection module that selects an optimal compression model based on data characteristics and communication environment; and a distributed cooperation that extracts differential information based on shared knowledge between the transmitting side and the receiving side. A data compression system characterized by comprising a compression module, a knowledge distillation module that distills the compression ability of a large-scale model into a lightweight model, and an adaptive arithmetic coding module that compresses differential information using the selected model.