Deep learning driven data processing and compression method and system

Abstract

The invention provides a deep learning-driven data processing and compression method and system, and belongs to the technical field of cloud computing, and the deep learning-driven data processing and compression method and system comprises a data acquisition and preprocessing module which is responsible for obtaining original data from different sources, including images, videos, audios or sensor data and the like; the data is subjected to preliminary cleaning, format conversion and standardization processing, it is ensured that the data quality meets subsequent processing requirements, the system can dynamically adjust a compression strategy and model parameters according to real-time changes of data content through the optimization and self-adaptive control module, it is ensured that the optimal compression effect and the resource utilization rate can be achieved in different scenes, and the system reliability is improved. The module also realizes model compression and optimization technologies, such as quantification, network pruning and knowledge distillation, which effectively reduce the memory usage and calculation cost of the model, and further improve the efficiency of data processing and compression.

Description

Technical Field

[0001] The present invention belongs to the technical field of cloud computing, and in particular relates to a deep learning driven data processing and compression method and system. Background Art

[0002] In the current era of rapid development of information technology, the amount of data is growing explosively, especially multimedia data such as images, videos, audio, and data generated by various sensors. They face huge challenges in transmission, storage, and processing. Traditional data processing and compression methods are often based on fixed algorithms, lacking intelligence and adaptability, and are difficult to effectively cope with complex and changeable data characteristics and the growing data scale. Especially in quality-sensitive application scenarios, such as high-definition video transmission, real-time speech recognition, etc., how to achieve efficient compression while ensuring data quality has become a key issue to be solved. In addition, with the rise of deep learning technology, the demand for model storage and computing resources is increasing, which requires data processing and compression technology to not only optimize the data itself, but also consider the lightweight and efficiency improvement of the deep learning model itself.

[0003] Therefore, based on the above technical problems, it is necessary to design a deep learning-driven data processing and compression method and system, aiming to realize intelligent processing and efficient compression of data by integrating the powerful learning ability of deep learning and the advantages of automated feature extraction. The system innovatively integrates multiple modules such as data acquisition preprocessing, deep learning core processing, optimization and adaptive control, service quality assurance monitoring, and interface and application integration to form an end-to-end solution. By automatically learning the key features in the data and performing intelligent encoding, the system can significantly reduce the data volume while maintaining important information in the data. In addition, it can also dynamically adjust the compression strategy according to the data content and application scenario to ensure that the best compression effect and resource utilization efficiency can be achieved under different requirements. This solution is also committed to the optimization and lightweight of the model itself. Through a series of advanced technologies such as quantization, pruning, knowledge distillation, etc., the memory occupancy and computing cost of the model are reduced, thereby indirectly improving the efficiency of data processing and compression. In summary, the present invention not only solves the problem of data processing and compression efficiency, but also takes into account the optimization of data quality and resource utilization, meeting the needs of diverse application scenarios. Summary of the invention

[0004] The purpose of the present invention is to provide a deep learning driven data processing and compression method and system, aiming to solve the problem of how to use deep learning technology to realize intelligent processing and efficient compression of data under the premise of ensuring data quality and efficient use of resources in the prior art. Specifically, it integrates the feature extraction capability of deep learning, automatically identifies and extracts key information of data, uses intelligent coding to reduce data volume, and dynamically adjusts the compression strategy to adapt to different content and scenario requirements. It also optimizes the model to reduce resource consumption, and ultimately achieves the goal of improving data processing and compression efficiency while ensuring data quality and adapting to a variety of application scenarios.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A deep learning driven data processing and compression method and system, characterized in that it comprises the following steps:

[0007] Deep learning driven data processing and compression method and system:

[0008] Data acquisition and preprocessing module: It is responsible for obtaining raw data from different sources, including images, videos, audio or sensor data, and performing preliminary data cleaning, format conversion and standardization to ensure that the data quality meets the subsequent processing requirements;

[0009] Deep learning core processing module: It contains multiple submodules, such as feature extraction, data encoding, compression and decompression, etc. It uses deep learning models (such as CNNs, RNNs, GANs, etc.) to perform intelligent data processing and achieve efficient compression by learning data features;

[0010] Optimization and adaptive control module: It monitors the system operation status and dynamically adjusts the compression strategy and model parameters according to the characteristics of the data flow to achieve the best compression effect and resource utilization, and implements model compression and optimization technologies such as quantization, pruning, knowledge distillation, etc. to reduce computing and storage requirements;

[0011] Service quality assurance and monitoring module: ensures quality control during data processing and compression, evaluates the distortion and recovery quality of data before and after compression, and provides system performance monitoring, including latency, throughput, resource usage, etc., to ensure stable system operation;

[0012] Interface and application integration module: It provides an external interface, allowing other systems or applications to integrate and use data processing and compression services, and supports customized requirements for various application scenarios, such as cloud storage, edge computing, real-time transmission, etc.

[0013] As a preferred solution of the present invention, the compression method mainly includes the following steps and strategies:

[0014] S1. Feature learning and extraction: Use deep neural networks (such as convolutional neural networks, recurrent neural networks, etc.) to automatically learn key features in the data. These features are often highly abstract and generalized from the original data, and can remove redundant information and retain only the most critical parts for understanding the meaning of the data;

[0015] S2, data encoding: Convert the learned features into a more compact encoding form. This step may involve quantization (mapping continuous values ​​into discrete sets to reduce the number of bits represented), vector quantization (mapping high-dimensional feature space into a low-dimensional codebook), or entropy coding (such as Huffman coding, which assigns codes of different lengths according to the frequency of feature occurrence, and high-frequency features are represented by shorter codes);

[0016] S3, Adaptive compression strategy: Dynamically adjust compression parameters according to the characteristics of data content. For example, a higher compression ratio can be used for smooth areas in the image, while the compression level can be appropriately reduced for areas with rich details to maintain visual quality;

[0017] S4, Mixed lossy and lossless compression: Combining lossy compression (sacrificing some data accuracy in exchange for a higher compression ratio) and lossless compression (ensuring complete data recovery, but with a relatively low compression ratio), flexibly select or combine them according to the needs of the application scenario;

[0018] S5. Application of generative models: Generative models such as generative adversarial networks (GANs) and variational autoencoders (VAEs) are used to reconstruct data. These models capture the statistical laws of data during compression, thereby generating new samples close to the original data when decompressed.

[0019] S6. Model optimization and lightweighting: compress the deep learning model itself, such as reducing the number of neurons through network pruning, quantizing weight parameters to reduce accuracy, or transferring knowledge from a large model to a small model through knowledge distillation, reducing the model's memory usage and computing cost, and indirectly promoting the efficiency of data processing and compression.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] 1. In this solution, the system uses deep learning models (such as convolutional neural networks CNNs, recurrent neural networks RNNs, generative adversarial networks GANs, etc.) for feature learning and extraction, automatically identifying and extracting key information from the data. This process highly abstracts and summarizes the data, removes redundant information, and retains the core meaning of the data. The subsequent data encoding step uses quantization, vector quantization, entropy coding and other technologies to convert these features into a more compact encoding form, significantly reducing the data volume while maintaining the important content of the data. Through the optimization and adaptive control module, the system can dynamically adjust the compression strategy and model parameters according to the real-time changes in the data content to ensure the best compression effect and resource utilization in different scenarios. The module also implements model compression and optimization technologies, such as quantization, network pruning, and knowledge distillation. These technologies effectively reduce the memory usage and computing cost of the model, and further improve the efficiency of data processing and compression.

[0022] 2. In this solution, the service quality assurance and monitoring module ensures quality control during data processing and compression. By evaluating the data distortion and recovery quality before and after compression, it provides system performance monitoring, including indicators such as latency, throughput, and resource usage, to ensure the stable operation of the system. It is suitable for quality-sensitive application scenarios such as high-definition video transmission and real-time speech recognition. The interface and application integration module provides an external interface, allowing other systems or applications to integrate data processing and compression services, meeting the customized requirements of various application scenarios such as cloud storage, edge computing, and real-time transmission, and improving the practicality and market adaptability of the system.

[0023] 3. In this solution, the system has the ability of online learning and can adjust model parameters and compression strategies according to real-time feedback to achieve self-optimization. At the same time, model lifecycle management ensures automatic updating and performance tracking of model versions, maintaining the optimal configuration of the system, which is crucial in a rapidly changing data environment. This solution not only improves the efficiency of data processing and compression at the technical level, but also optimizes data quality and resource utilization, meeting the needs of diversified application scenarios. Through the intelligent processing of deep learning, data processing and compression are automated, intelligent and efficient, promoting the development of cloud computing technology, especially in resource-limited environments, such as mobile devices and edge computing scenarios. The lightweight design of this solution is of great significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0025] Figure 1 It is a supply chain process distribution diagram of the present invention; DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Example 1

[0028] See also Figure 1 , the present invention provides the following technical solutions:

[0029] A deep learning driven data processing and compression method and system, characterized in that it comprises the following steps:

[0030] Deep learning driven data processing and compression method and system:

[0031] Data acquisition and preprocessing module: It is responsible for obtaining raw data from different sources, including images, videos, audio or sensor data, and performing preliminary data cleaning, format conversion and standardization to ensure that the data quality meets the subsequent processing requirements;

[0032] Deep learning core processing module: It contains multiple submodules, such as feature extraction, data encoding, compression and decompression, etc. It uses deep learning models (such as CNNs, RNNs, GANs, etc.) to perform intelligent data processing and achieve efficient compression by learning data features;

[0033] Optimization and adaptive control module: It monitors the system operation status and dynamically adjusts the compression strategy and model parameters according to the characteristics of the data flow to achieve the best compression effect and resource utilization, and implements model compression and optimization technologies such as quantization, pruning, knowledge distillation, etc. to reduce computing and storage requirements;

[0034] Service quality assurance and monitoring module: ensures quality control during data processing and compression, evaluates the distortion and recovery quality of data before and after compression, and provides system performance monitoring, including latency, throughput, resource usage, etc., to ensure stable system operation;

[0035] Interface and application integration module: It provides an external interface, allowing other systems or applications to integrate and use data processing and compression services, and supports customized requirements for various application scenarios, such as cloud storage, edge computing, real-time transmission, etc.

[0036] In a specific embodiment of the present invention, the deepening process of the data acquisition and preprocessing module is as follows:

[0037] Intelligent perception and screening: Use machine learning algorithms to automatically identify and screen out valid data and remove irrelevant or noisy information, such as automatically detecting and removing still frames in video streams or filtering background noise in audio data;

[0038] Dynamic data adaptation: Dynamically adjust preprocessing algorithms based on data type and source, such as applying different denoising and enhancement strategies to images of different resolutions, or smoothing sensor data before time series analysis;

[0039] Metadata management: Generate and maintain metadata to record the contextual information, processing history, and quality indicators of raw data to facilitate subsequent retrieval, auditing, and compliance checks;

[0040] Deepening process of deep learning core processing module:

[0041] Multimodal feature fusion: For composite data (such as image + text, video + audio), a multimodal deep learning model is designed to fuse features of different modalities, thereby improving compression efficiency while retaining more semantic information.

[0042] Adaptive feature selection: using technologies such as autoencoders and attention mechanisms to adaptively select the most important features for encoding, reduce redundancy, and optimize compression ratios;

[0043] Hierarchical compression strategy: hierarchical compression is implemented in deep learning models, with different compression rates applied to features at different levels. High-level features (semantic information) are compressed with higher fidelity, while low-level features (detail information) can sacrifice quality appropriately in exchange for a higher compression ratio.

[0044] Optimization and deepening of adaptive control module:

[0045] Online learning and adjustment: The system has the ability to learn online and dynamically fine-tune model parameters and compression strategies based on real-time feedback of compression efficiency and data quality indicators to achieve continuous optimization.

[0046] Resource scheduling and load balancing: intelligently schedule computing resources based on the current system resource status and task queue, balance the workload between modules, and ensure efficient operation;

[0047] Model lifecycle management: Automatically manage model versions, support seamless switching between old model retirement and new model deployment, and track model performance to ensure that the optimal model configuration is always used;

[0048] Deepening process of service quality assurance and monitoring module:

[0049] Intelligent prediction and prevention: Apply machine learning prediction models to identify potential risks of service quality degradation in advance, such as predicting resource bottlenecks during peak hours, and taking preventive measures in a timely manner;

[0050] Fine-grained quality assessment: It not only assesses the overall data quality, but also provides fine-grained quality indicators, such as the peak signal-to-noise ratio (PSNR) of images and the VMAF (video multi-method assessment fusion) score of videos, to ensure that the compressed data meets the requirements of specific application scenarios.

[0051] Dynamic threshold adjustment: Dynamically adjust the quality control threshold based on historical data and real-time data analysis to ensure the best user experience under resource constraints;

[0052] Deepening process of interface and application integration module:

[0053] Standardization and protocol adaptation: Provide data exchange formats and communication protocols that meet industry standards, support multiple interface modes such as RESTfulAPI and gRPC, and ensure broad compatibility with other systems or platforms;

[0054] Security and privacy protection: Integrate encryption algorithms and access control mechanisms to ensure security during data transmission and storage, comply with international privacy regulations such as GDPR, and provide data desensitization and anonymization options;

[0055] Customized service templates: Develop configurable service templates to allow users to quickly define data processing and compression processes based on specific application scenarios, reduce customized development costs, and improve flexibility and responsiveness.

[0056] For details, please refer to Figure 1 ,The compression method mainly includes the following steps and strategies:

[0057] S1. Feature learning and extraction: Use deep neural networks (such as convolutional neural networks, recurrent neural networks, etc.) to automatically learn key features in the data. These features are often highly abstract and generalized from the original data, and can remove redundant information and retain only the most critical parts for understanding the meaning of the data;

[0058] S2, data encoding: Convert the learned features into a more compact encoding form. This step may involve quantization (mapping continuous values ​​into discrete sets to reduce the number of bits represented), vector quantization (mapping high-dimensional feature space into a low-dimensional codebook), or entropy coding (such as Huffman coding, which assigns codes of different lengths according to the frequency of feature occurrence, and high-frequency features are represented by shorter codes);

[0059] S3, Adaptive compression strategy: Dynamically adjust compression parameters according to the characteristics of data content. For example, a higher compression ratio can be used for smooth areas in the image, while the compression level can be appropriately reduced for areas with rich details to maintain visual quality;

[0060] S4, Mixed lossy and lossless compression: Combining lossy compression (sacrificing some data accuracy in exchange for a higher compression ratio) and lossless compression (ensuring complete data recovery, but with a relatively low compression ratio), flexibly select or combine them according to the needs of the application scenario;

[0061] S5. Application of generative models: Generative models such as generative adversarial networks (GANs) and variational autoencoders (VAEs) are used to reconstruct data. These models capture the statistical laws of data during compression, thereby generating new samples close to the original data when decompressed.

[0062] S6. Model optimization and lightweighting: compress the deep learning model itself, such as reducing the number of neurons through network pruning, quantizing weight parameters to reduce accuracy, or transferring knowledge from a large model to a small model through knowledge distillation, reducing the model's memory usage and computing cost, and indirectly promoting the efficiency of data processing and compression.

[0063] In this embodiment: Improving compression efficiency and quality: Feature learning and extraction and adaptive compression strategies can intelligently select and optimize compression methods for different types of data content, which can not only greatly reduce the volume of data storage and transmission, but also maintain the key information and quality of the data as much as possible, especially in visual and auditory data processing, and can effectively balance compression rate and fidelity;

[0064] Flexibility and customization: Through the mixed use of lossy and lossless compression, the compression strategy can be flexibly adjusted according to the specific needs of different application scenarios to meet the different emphases on data integrity, transmission speed or storage space, so that the process can play the best performance in a variety of scenarios;

[0065] Enhanced data understanding and re-creation: Generative models (such as GANs and VAEs) are used in the compression and decompression process to not only reduce the size of data, but also further explore and utilize the statistical structure and patterns within the data, so that the decompressed data can even approach or simulate the characteristics of the original data in some cases, which is of great value for applications such as content generation and data analysis;

[0066] Model and resource optimization: Model optimization and lightweight strategies directly reduce the operating burden of deep learning models, reduce hardware resource requirements, and accelerate the data processing process, which is especially important for resource-constrained environments such as mobile devices and edge computing; at the same time, this also promotes the popularization and application scope of algorithms, enabling complex data processing and compression technologies to be implemented on a wider range of platforms.

[0067] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A deep learning driven data processing and compression method and system, characterized in that: The steps include: Deep learning driven data processing and compression method and system: Data acquisition and preprocessing module: It is responsible for obtaining raw data from different sources, including images, videos, audio or sensor data, and performing preliminary data cleaning, format conversion and standardization to ensure that the data quality meets the subsequent processing requirements; Deep learning core processing module: It contains multiple submodules, such as feature extraction, data encoding, compression and decompression, etc. It uses deep learning models (such as CNNs, RNNs, GANs, etc.) to perform intelligent data processing and achieve efficient compression by learning data features; Optimization and adaptive control module: It monitors the system operation status and dynamically adjusts the compression strategy and model parameters according to the characteristics of the data flow to achieve the best compression effect and resource utilization, and implements model compression and optimization technologies such as quantization, pruning, knowledge distillation, etc. to reduce computing and storage requirements; Service quality assurance and monitoring module: ensures quality control during data processing and compression, evaluates the distortion and recovery quality of data before and after compression, and provides system performance monitoring, including latency, throughput, resource usage, etc., to ensure stable system operation; Interface and application integration module: It provides an external interface, allowing other systems or applications to integrate and use data processing and compression services, and supports customized requirements for various application scenarios, such as cloud storage, edge computing, real-time transmission, etc.

2. The deep learning driven data processing and compression method and system according to claim 1, characterized in that: The compression method mainly includes the following steps and strategies: S1. Feature learning and extraction: Use deep neural networks (such as convolutional neural networks, recurrent neural networks, etc.) to automatically learn key features in the data. These features are often highly abstract and generalized from the original data, and can remove redundant information and retain only the most critical parts for understanding the meaning of the data; S2, data encoding: Convert the learned features into a more compact encoding form. This step may involve quantization (mapping continuous values ​​into discrete sets to reduce the number of bits represented), vector quantization (mapping high-dimensional feature space into a low-dimensional codebook), or entropy coding (such as Huffman coding, which assigns codes of different lengths according to the frequency of feature occurrence, and high-frequency features are represented by shorter codes); S3, Adaptive compression strategy: Dynamically adjust compression parameters according to the characteristics of data content. For example, a higher compression ratio can be used for smooth areas in the image, while the compression level can be appropriately reduced for areas with rich details to maintain visual quality; S4, Mixed lossy and lossless compression: Combining lossy compression (sacrificing some data accuracy in exchange for a higher compression ratio) and lossless compression (ensuring complete data recovery, but with a relatively low compression ratio), flexibly select or combine them according to the needs of the application scenario; S5. Application of generative models: Generative models such as generative adversarial networks (GANs) and variational autoencoders (VAEs) are used to reconstruct data. These models capture the statistical laws of data during compression, thereby generating new samples close to the original data when decompressed. S6. Model optimization and lightweighting: compress the deep learning model itself, such as reducing the number of neurons through network pruning, quantizing weight parameters to reduce accuracy, or transferring knowledge from a large model to a small model through knowledge distillation, reducing the model's memory usage and computing cost, and indirectly promoting the efficiency of data processing and compression.

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