A cloud data distributed storage method and system based on blockchain

Abstract

The present invention belongs to the field of cloud data storage technology and discloses a blockchain-based distributed cloud data storage method and system. The method comprises the following steps: deploying a distributed storage network and a consensus blockchain network in a cloud data center, and constructing a data analysis model, a retrieval tag generation model, and a distributed storage strategy generation model; using the data analysis model to perform data analysis and obtain real-time data analysis results; using the retrieval tag generation model to generate retrieval tags; using the distributed storage strategy generation model to generate distributed storage strategies; using the consensus blockchain network to reach consensus on real-time storage requests; and using the distributed storage network to perform distributed storage of a plurality of encrypted data shards and their real-time retrieval tags. The present invention solves the problems of centralization risk, insufficient data privacy protection, high retrieval difficulty, high storage cost, and poor data storage adaptability in the prior art.

Description

Technical Field

[0001] The present invention belongs to the technical field of cloud data storage, and specifically relates to a cloud data distributed storage method and system based on blockchain. Background Art

[0002] With the development of science and technology and the improvement of data informatization, cloud data storage has become the primary choice for enterprises and government departments due to its high security and high reliability. However, existing technologies still have some significant shortcomings in cloud data storage, including:

[0003] 1) Centralization Risk: Traditional cloud data storage typically relies on a centralized server architecture, which leads to a high risk of single point failure. Once a central server fails, it may cause large-scale data loss or service interruption.

[0004] 2) Insufficient data privacy protection: In a centralized storage model, data owners have limited control over their data. Third-party service providers may face the risk of internal abuse, external leakage, or forced access by government agencies, seriously threatening data privacy.

[0005] 3) High storage costs: In addition to hardware investment, existing cloud storage also requires high maintenance, upgrade, and operating costs. This is particularly true for long-term, large-scale data storage, where the cost issue is even more pronounced.

[0006] 4) Difficulty in retrieval: Existing cloud storage procedures are simple, resulting in a lack of customized retrieval tags for cloud data. This makes subsequent data retrieval difficult and affects user experience.

[0007] 5) Poor data storage adaptability: Most existing cloud storage is based on preset storage strategies and cannot be automatically adjusted according to data characteristics, resulting in limitations in data storage and affecting normal data storage work. Summary of the Invention

[0008] In order to solve the problems of centralization risk, insufficient data privacy protection, difficulty in retrieval, high storage cost and poor data storage adaptability in the existing technology, the purpose of the present invention is to provide a cloud data distributed storage method and system based on blockchain.

[0009] The technical solution adopted in the present invention is:

[0010] A cloud data distributed storage method based on blockchain, comprising the following steps:

[0011] Deploy a distributed storage network and consensus blockchain network in the cloud data center, and build a data analysis model, a retrieval tag generation model, and a distributed storage strategy generation model;

[0012] Use cloud data centers to receive real-time data, and use data analysis models to analyze data based on the real-time data to obtain real-time data analysis results;

[0013] According to the real-time data and the corresponding real-time data analysis results, a retrieval tag generation model is used to generate retrieval tags to obtain real-time retrieval tags;

[0014] Based on the real-time data analysis results, a distributed storage strategy generation model is used to generate a distributed storage strategy to obtain a real-time distributed storage strategy.

[0015] According to the real-time distributed storage strategy, the real-time data is sharded and encrypted to obtain several encrypted data shards and real-time storage requests. The consensus blockchain network is then used to reach consensus on the real-time storage requests.

[0016] If the consensus is successful, the distributed storage network is used to distribute the encrypted data shards and their real-time retrieval tags according to the real-time distributed storage strategy.

[0017] Furthermore, in the cloud data center, a distributed storage network and a consensus blockchain network are deployed, and a data analysis model, a retrieval tag generation model, and a distributed storage strategy generation model are constructed, including the following steps:

[0018] In the cloud data center, identify several data nodes participating in the distributed storage of cloud data and extract historical behavior data of each data node;

[0019] Based on the historical behavior data of each data node, the identity allocation method is used to divide the data nodes into several consensus nodes and several storage nodes;

[0020] Using blockchain technology, several consensus nodes are distributedly connected to obtain a distributed storage network, and several storage nodes are distributedly connected to obtain a consensus blockchain network;

[0021] Collecting a number of historical data, and preprocessing the a number of historical data to obtain a number of preprocessed historical data;

[0022] Based on some pre-processed historical data, a deep learning algorithm is used to build a data analysis model and generate some historical data analysis results;

[0023] Based on a number of historical data and corresponding historical data analysis results, a natural language processing algorithm is used to build a search tag generation model and generate a number of historical search tags;

[0024] Based on the analysis results of several historical data, a reinforcement learning algorithm is used to build a distributed storage strategy generation model, and several historical distributed storage strategy generation experiences are generated.

[0025] Furthermore, based on the historical behavior data of each data node, an identity allocation method is used to divide the data nodes into a number of consensus nodes and a number of storage nodes, including the following steps:

[0026] According to the preset node behavior rules, the consensus behavior evaluation matrix of the data node is generated, and the information entropy of the consensus behavior evaluation matrix is ​​obtained;

[0027] According to the information entropy, the reputation value of each data node is obtained, and the node reputation value update strategy of the data node is set;

[0028] According to the reputation value of the data nodes, several data nodes are sorted in descending order, and the first M data nodes are used as consensus nodes, and the remaining data nodes are used as storage nodes.

[0029] Furthermore, the data analysis model is constructed based on the LSTM-FPN-CNN-DBN algorithm, and the data analysis model includes a first text feature extraction module constructed based on the LSTM algorithm, an image feature extraction module constructed based on the FPN algorithm, an audio feature extraction module constructed based on the CNN algorithm, an attention weight module constructed based on the Attention mechanism, and a data analysis module constructed based on the DBN algorithm. The first text feature extraction module, the image feature extraction module, and the audio feature extraction module are all connected to the attention weight module, and the attention weight module is connected to the data analysis module.

[0030] The retrieval tag generation model is built based on the BERT-LSTM-CRF algorithm, and the retrieval tag generation model includes a multimodal feature extraction module, a second text feature extraction module built based on the LSTM algorithm, and a retrieval tag generation model built based on the CRF algorithm;

[0031] The distributed storage policy generation model is constructed based on the MOPPO-cGAN algorithm, and the distributed storage policy generation model includes a distributed storage policy generation module constructed based on the MOPPO algorithm and an adversarial training module constructed based on the cGAN algorithm, which are connected in sequence. The distributed storage policy generation module is provided with an objective function set, an experience replay pool, an Actor network, a Critic network and an intelligent agent, and the adversarial training module is provided with a generator and a discriminator.

[0032] Furthermore, a cloud data center is used to receive real-time data, and a data analysis model is used to perform data analysis based on the real-time data to obtain real-time data analysis results, including the following steps:

[0033] Using a cloud data center to receive real-time data and pre-process the real-time data to obtain pre-processed real-time data;

[0034] Parsing the preprocessed real-time data to obtain preprocessed real-time image data, preprocessed real-time text data, and preprocessed real-time audio data;

[0035] Extracting a first real-time text data feature of the preprocessed real-time text data using a first text feature extraction module of the data analysis model;

[0036] Using the image feature extraction module of the data analysis model to extract real-time image data features of the pre-processed real-time image data;

[0037] Use the audio feature extraction module of the data analysis model to extract real-time audio data features of the pre-processed real-time audio data;

[0038] According to a preset attention weight value, using the attention weight module of the data analysis model, the first real-time text data feature, the real-time image data feature, and the real-time audio data feature are weightedly fused to obtain a real-time weighted fusion feature;

[0039] According to the real-time weighted fusion features, the data analysis module of the data analysis model is used to perform data analysis and obtain real-time data analysis results.

[0040] Furthermore, based on the real-time data and the corresponding real-time data analysis results, a retrieval tag generation model is used to generate retrieval tags to obtain real-time retrieval tags, including the following steps:

[0041] Using a second text feature extraction module of the retrieval tag generation model, extracting a second real-time text data feature of the real-time data analysis result;

[0042] The multimodal feature extraction module of the retrieval tag generation model is used to extract the real-time key features of the real-time weighted fusion features corresponding to the pre-processed real-time data;

[0043] According to the second real-time text data feature and the real-time key feature, a retrieval tag generation model is used to generate a retrieval tag to obtain a real-time retrieval tag.

[0044] Furthermore, based on the real-time data analysis results, a distributed storage strategy generation model is used to generate a distributed storage strategy to obtain a real-time distributed storage strategy, including the following steps:

[0045] Parsing the real-time data analysis results to obtain a number of real-time data analysis states, and updating the state space of the intelligent agent of the distributed storage strategy generation module in the distributed storage strategy generation model according to the number of real-time data analysis states to obtain an updated state space;

[0046] Randomly extract several historical distributed storage strategy generation experiences from the experience replay pool of the distributed storage strategy generation module, and generate several possible distributed storage actions based on the several historical distributed storage strategy generation experiences;

[0047] According to several possible distributed storage actions, the action space of the intelligent agent of the distributed storage strategy generation module is updated to obtain an updated action space;

[0048] Select a real-time objective function from the objective function set of the distributed storage strategy generation module, and based on the real-time objective function, use the agent to control the critic network to generate the real-time value of all possible distributed storage actions in the updated action space for each real-time data analysis state in the updated state space;

[0049] Based on several real-time values, we use intelligent agents to control the Actor network and generate a probability distribution of all possible distributed storage actions corresponding to each real-time data analysis state.

[0050] The possible distributed storage action with the highest probability distribution in the updated action space is used as the execution distributed storage action of the corresponding real-time data analysis state;

[0051] Integrate the distributed storage actions of all real-time data analysis states in the updated state space to obtain a real-time distributed storage strategy.

[0052] Furthermore, according to the real-time distributed storage strategy, the real-time data is sharded and encrypted to obtain a number of encrypted data shards and real-time storage requests. A consensus blockchain network is then used to reach a consensus on the real-time storage requests, including the following steps:

[0053] According to the real-time data sharding decision in the real-time distributed storage strategy and combined with the shard replica mechanism, the real-time data is sharded to obtain several real-time data shards including replica shards;

[0054] According to the real-time random encryption decision in the real-time distributed storage strategy, a pseudo-random number generator is used to generate a random key, and an encryption seed is generated based on the random key using a key derivation function;

[0055] Based on the encrypted seed, a dynamic encryption algorithm is used to dynamically encrypt several real-time data shards to obtain several encrypted data shards, generate real-time storage requests, and send the real-time storage requests to the consensus blockchain network;

[0056] The consensus node that receives the real-time storage request in the consensus blockchain network is used as the master node, and based on the master node, a consensus algorithm is used to reach a consensus on the real-time storage request.

[0057] Furthermore, if the consensus is successful, a distributed storage network is used to perform distributed storage of several encrypted data shards and their real-time retrieval tags according to the real-time distributed storage strategy, including the following steps:

[0058] If the consensus is successful, several encrypted data shards and their real-time retrieval tags are sent to several storage nodes in the distributed storage network according to the real-time random sending decision of the real-time distributed storage strategy;

[0059] Use all storage nodes to locally store the received encrypted data shards and their real-time retrieval tags, and send real-time storage success information to other storage nodes;

[0060] If the storage node receives a number of real-time storage success messages exceeding a preset number threshold, the distributed storage step is terminated; otherwise, the distributed storage step is continued.

[0061] A blockchain-based cloud data distributed storage system is used to implement a cloud data distributed storage method. The system is set in a cloud data center and includes an initialization unit, a data analysis unit, a retrieval tag generation unit, a strategy generation unit, a data consensus unit, and a distributed storage unit.

[0062] The beneficial effects of the present invention are:

[0063] The present invention discloses a cloud data distributed storage method and system based on blockchain. By deploying a distributed storage network and a consensus blockchain network, the risk of single point failure is reduced, overall security is enhanced, and centralized attacks are resisted. Random encryption and consensus mechanisms are used to ensure that data owners have complete control over data, effectively preventing data leakage and unauthorized access. The distributed storage network can be flexibly expanded without a large amount of hardware investment, adapting to the rapid growth and changing needs of data volume, and reducing hardware and maintenance costs through distributed storage strategies and the use of distributed networks, thereby achieving a more economical data storage solution. Combining the consensus mechanism and the distributed storage strategy, the consistency and high reliability of data between different nodes are guaranteed, reducing the risk of data loss and damage. Automated data analysis and retrieval tag generation are achieved through a data analysis model and a retrieval tag generation model, and customized retrieval tags are set for each data, providing a basis for subsequent data retrieval, thereby improving user experience. The distributed storage strategy generation model is used to adjust the distributed storage strategy according to data characteristics, thereby improving the practicality and efficiency of distributed storage and ensuring the normal operation of data storage.

[0064] Other beneficial effects of the present invention will be further described in the specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1This is a flowchart of the cloud data distributed storage method based on blockchain in the present invention.

[0066] Figure 2 This is a structural block diagram of the cloud data distributed storage system based on blockchain in the present invention. DETAILED DESCRIPTION

[0067] The present invention will be further explained below with reference to the accompanying drawings and specific embodiments.

[0068] Example 1:

[0069] like Figure 1 As shown, this embodiment provides a cloud data distributed storage method based on blockchain, including the following steps:

[0070] S1: Deploy a distributed storage network and consensus blockchain network in a cloud data center, and build a data analysis model, a retrieval tag generation model, and a distributed storage strategy generation model. This includes the following steps:

[0071] S1-1: In the cloud data center, identify several data nodes participating in the distributed storage of cloud data and extract historical behavior data of each data node;

[0072] S1-2: Based on the historical behavior data of each data node, use the identity allocation method to divide the data nodes into consensus nodes and storage nodes, including the following steps:

[0073] S1-2-1: Generate a consensus behavior evaluation matrix for data nodes based on the preset node behavior rules, and obtain the information entropy of the consensus behavior evaluation matrix, including the following steps:

[0074] The preset node behavior rules include defined honest behavior rules, including correct transaction review behavior, accurate identity verification behavior, and legal compliance behavior, and defined malicious behavior rules, including incorrect transaction review behavior, incorrect identity verification behavior, and legal violations;

[0075] The consensus behavior evaluation matrix includes an honest behavior evaluation matrix and a malicious behavior evaluation matrix. Each column in the honest behavior evaluation matrix and the malicious behavior evaluation matrix is ​​normalized to obtain a normalized honest behavior evaluation matrix and a normalized malicious behavior evaluation matrix. Specifically, the critical value method is used for normalization to find the maximum and minimum values ​​of each column, subtract the minimum value from each element, and divide it by the difference between the maximum and minimum values ​​to obtain the normalized matrix:

[0076] The information entropy of each column in the normalized honest behavior evaluation matrix and the normalized malicious behavior evaluation matrix is ​​obtained using the following formula:

[0077]

[0078] Where, E i' is the information entropy of the i'th honest behavior / malicious behavior; i' is the honest behavior / malicious behavior indicator; j' is the evaluation matrix column indicator; n is the total number of honest behaviors / malicious behaviors; p i'j' is the normalized value of the i'th honest behavior / malicious behavior in the j'th column;

[0079] S1-2-2: Based on the information entropy, obtain the reputation value of each data node and set the node reputation value update strategy of the data node, including the following steps:

[0080] S1-2-2-1: Obtain the weight of honest behavior and malicious behavior based on the ratio of information entropy to total information entropy. The smaller the information entropy, the greater the weight. The formula is:

[0081]

[0082] Where w i' is the weight of the i'th honest behavior / malicious behavior;

[0083] S1-2-2-2: Based on the weight of honest behavior, honest behavior is graded and a corresponding reward behavior table is established. Based on the weight of malicious behavior, malicious behavior is graded and a corresponding penalty behavior table is established.

[0084] S1-2-2-3: According to the reward behavior table, obtain the reward value of the data node in the current consensus stage, and according to the penalty behavior table, obtain the penalty value of the data node in the current consensus stage;

[0085] S1-2-2-4: Obtain the corresponding reputation value based on the reward and penalty values ​​of the data node in the current consensus phase. The formula is:

[0086]

[0087] Where, is the reputation value of node i" at iteration number t; is the reward value of honest behavior level L; is the count of integrity behavior level L; is the penalty value of the malicious behavior level L'; is the count of malicious behavior level L'; i" is the indicator of data nodes; t is the indicator of the number of iterations; L is the honest behavior level; L' is the malicious behavior level;

[0088] S1-2-3: Sort the data nodes in descending order based on their reputation values, and use the first M data nodes as consensus nodes, while the remaining data nodes are used as storage nodes.

[0089] S1-3: Using blockchain technology, several consensus nodes are distributedly connected to obtain a distributed storage network, and several storage nodes are distributedly connected to obtain a consensus blockchain network;

[0090] S1-4: collecting a number of historical data, and preprocessing the historical data to obtain a number of preprocessed historical data;

[0091] Preprocessing includes data cleaning, format conversion, and normalization to improve data quality and provide data support for subsequent model construction;

[0092] S1-5: Based on some pre-processed historical data, use deep learning algorithms to build a data analysis model and generate some historical data analysis results;

[0093] The data analysis model is constructed based on the Long Short-Term Memory (LSTM)-Feature Pyramid Networks (FPN)-Convolutional Neural Networks (CNN)-Deep Belief Network (DBN) algorithm, and the data analysis model includes a first text feature extraction module constructed based on the LSTM algorithm, an image feature extraction module constructed based on the FPN algorithm, an audio feature extraction module constructed based on the CNN algorithm, an attention weight module constructed based on the Attention mechanism, and a data analysis module constructed based on the DBN algorithm. The first text feature extraction module, the image feature extraction module, and the audio feature extraction module are all connected to the attention weight module, and the attention weight module is connected to the data analysis module.

[0094] The image feature extraction module extracts hierarchical features from images through a series of convolutional layers, activation functions, and pooling layers, and uses jump connections to fuse feature maps of different levels extracted by CNN. Through upsampling and lateral connections, a feature pyramid is constructed to transfer high-level semantic information to low-level layers, thereby enhancing the semantic expression ability of low-level features. This can effectively combine low-level detail features and high-level semantic features, improve the accuracy of image feature extraction and the expression ability of features; the first text feature extraction module is specifically used to process text data, and uses the characteristics of long short-term memory networks (LSTMs) to capture temporal information and long-term dependencies in texts. It can effectively solve the gradient disappearance problem in traditional recurrent neural networks (RNNs), thereby better extracting deep features from texts; audio The feature extraction module focuses on processing audio data, using convolutional neural networks (CNNs) to extract frequency and timing features from audio. Through local connections and weight sharing mechanisms, it can efficiently capture key information from audio signals. The attention weight module serves as a bridge, connecting the text, image, and audio feature extraction modules with the data analysis module. Using the attention mechanism, it assigns different weights to different types of features, allowing the model to focus more on features that have a greater impact on the analysis results, thereby improving the accuracy of the analysis. The data analysis module is the core of the entire model, responsible for comprehensively analyzing features from different feature extraction modules. As a deep neural network, the deep belief network (DBN) can learn complex representations in the data, thereby achieving more accurate data analysis.

[0095] S1-6: Based on a number of historical data and corresponding historical data analysis results, a natural language processing algorithm is used to construct a search tag generation model and generate a number of historical search tags;

[0096] The retrieval tag generation model is built based on the Bidirectional Encoder Representations from Transformers (BERT)-LSTM-Conditional Random Field (CRF) algorithm, and includes a multimodal feature extraction module, a second text feature extraction module built based on the LSTM algorithm, and a retrieval tag generation model built based on the CRF algorithm.

[0097] By pre-training the weighted fusion features of a large amount of multimodal data, the multimodal feature extraction module can accurately identify the key features in the weighted fusion features for subsequent label prediction. The second text feature extraction module targets the data analysis results. This module uses a long short-term memory network (LSTM) to capture the temporal information and long-term dependencies in the data analysis results. It can effectively handle the sequence problems in the data analysis results and extract deep text features, providing a powerful text representation for subsequent retrieval label generation.

[0098] S1-7: Based on the results of several historical data analyses, a reinforcement learning algorithm is used to construct a distributed storage strategy generation model and generate several historical distributed storage strategy generation experiences.

[0099] The distributed storage policy generation model is based on the Multi-Objective Proximal Policy Optimization (MOPPO)-Conditional Generative Adversarial Network (cGAN) algorithm, and includes a distributed storage policy generation module based on the MOPPO algorithm and an adversarial training module based on the cGAN algorithm, which are connected in sequence. The distributed storage policy generation module is provided with an objective function set, an experience replay pool, an actor network, a critic network, and an intelligent agent, and the adversarial training module is provided with a generator and a discriminator.

[0100] The Actor network of the distributed storage strategy generation module is responsible for outputting the probability distribution of actions that should be taken in a given state. The goal is to learn an optimal strategy, that is, to maximize the long-term cumulative reward. In the continuous action space, the Actor network usually outputs a mean and an optional variance parameter to describe the probability distribution of actions. The Critic network is responsible for evaluating the value of a given state, that is, predicting the expected return that can be obtained by starting from this state and following the current strategy. It usually outputs a scalar value representing the value of the state or the state-action value. The experience replay pool is used to store historical experience for reuse during training. The objective function set includes functions of multiple defined data processing objectives, including minimizing distributed storage costs, minimizing distributed storage response time, and maximizing distributed storage efficiency. The generator of the adversarial training module is used to generate strategies, and the discriminator is used to distinguish between the generated strategies and the optimal strategies. Through this adversarial training process, the distributed storage strategy generation module can learn more effective strategies. At the same time, the adversarial training module helps ensure the diversity and quality of strategies, thereby finding better optimal solutions in multi-objective optimization problems.

[0101] Based on the results of several historical data analyses, a reinforcement learning algorithm is used to build a distributed storage strategy generation model and generate several historical distributed storage strategy generation experiences, including the following steps:

[0102] S1-7-1: Use the MOPPO-GAN algorithm to build an initial distributed storage policy generation model. The initial distributed storage policy generation model includes an initial distributed storage policy generation module and an initial adversarial training module.

[0103] S1-7-2: Set the objective function set, experience replay pool, actor network, critic network, and agent for the initial distributed storage strategy generation module;

[0104] S1-7-3: Use the distributed storage strategy generation problem as the simulation environment for the initial distributed storage strategy generation module, and set the action space and state space for the agent;

[0105] S1-7-4: Based on any objective function in the objective function set and according to a number of historical data analysis results, pre-train the initial distributed storage strategy generation module to obtain a pre-trained distributed storage strategy generation module, and generate a number of historical distributed storage strategies and corresponding historical distributed storage strategy generation experiences;

[0106] S1-7-5: Based on several historical data analysis results and corresponding historical distributed storage strategies, the initial generator of the initial adversarial training module is optimized and trained to obtain an optimized generator, and several generated distributed storage strategies are generated;

[0107] S1-7-6: Based on several historical distributed storage strategies and corresponding generated distributed storage strategies, the initial discriminator of the initial adversarial training module is optimized and trained to obtain an optimized discriminator, and several historical discrimination results are generated;

[0108] S1-7-7: Use the pre-trained distributed storage strategy generation module's Critic network to obtain several rewards for generating distributed storage strategies. Based on these rewards, optimize the pre-trained distributed storage strategy generation module's Actor network to obtain an optimized Actor network.

[0109] S1-7-8: Based on the rewards for generating distributed storage strategies and the corresponding historical judgment results, the critic network of the pre-trained distributed storage strategy generation module is optimized to obtain an optimized critic network;

[0110] S1-7-9: Traverse all objective functions in the objective function set and repeat the above adversarial training steps to obtain an optimized distributed storage strategy generation module with an optimized actor network and an optimized critic network and an optimized adversarial training module with an optimized discriminator and an optimized discriminator;

[0111] S1-7-10: Integrate the optimized distributed storage strategy generation module and the optimized adversarial training module to obtain the final distributed storage strategy generation model, and store several historical distributed storage strategy generation experiences in the experience replay pool;

[0112] S2: Use the cloud data center to receive real-time data, and use the data analysis model to analyze the real-time data to obtain real-time data analysis results, including the following steps:

[0113] S2-1: Using a cloud data center, receiving real-time data, and preprocessing the real-time data to obtain preprocessed real-time data;

[0114] S2-2: parsing the preprocessed real-time data to obtain preprocessed real-time image data, preprocessed real-time text data, and preprocessed real-time audio data;

[0115] S2-3: using a first text feature extraction module of the data analysis model to extract a first real-time text data feature of the preprocessed real-time text data;

[0116] S2-4: using the image feature extraction module of the data analysis model to extract real-time image data features of the pre-processed real-time image data;

[0117] S2-5: Using the audio feature extraction module of the data analysis model, extracting real-time audio data features of the pre-processed real-time audio data;

[0118] S2-6: Based on a preset attention weight value, using the attention weight module of the data analysis model, weightedly fuse the first real-time text data feature, the real-time image data feature, and the real-time audio data feature to obtain a real-time weighted fusion feature;

[0119] S2-7: Based on the real-time weighted fusion features, use the data analysis module of the data analysis model to perform data analysis and obtain real-time data analysis results;

[0120] S3: Based on the real-time data and the corresponding real-time data analysis results, a search tag generation model is used to generate search tags to obtain real-time search tags, including the following steps:

[0121] S3-1: using the second text feature extraction module of the retrieval tag generation model to extract a second real-time text data feature of the real-time data analysis result;

[0122] S3-2: Use the multimodal feature extraction module of the retrieval tag generation model to extract real-time key features of the real-time weighted fusion features corresponding to the preprocessed real-time data;

[0123] S3-3: generating a retrieval tag using a retrieval tag generation model according to the second real-time text data feature and the real-time key feature to obtain a real-time retrieval tag;

[0124] S4: Based on the real-time data analysis results, a distributed storage strategy generation model is used to generate a distributed storage strategy to obtain a real-time distributed storage strategy, including the following steps:

[0125] S4-1: parsing the real-time data analysis results to obtain a number of real-time data analysis states, and updating the state space of the agent of the distributed storage strategy generation module in the distributed storage strategy generation model based on the real-time data analysis states to obtain an updated state space;

[0126] S4-2: Randomly extract several historical distributed storage strategy generation experiences from the experience replay pool of the distributed storage strategy generation module, and generate several possible distributed storage actions based on the several historical distributed storage strategy generation experiences;

[0127] S4-3: updating the action space of the agent of the distributed storage strategy generation module according to several possible distributed storage actions to obtain an updated action space;

[0128] S4-4: Select a real-time objective function from the objective function set of the distributed storage strategy generation module, and based on the real-time objective function, use the agent to control the critic network to generate the real-time value of all possible distributed storage actions in the updated action space for each real-time data analysis state in the updated state space;

[0129] S4-5: Based on several real-time values, use intelligent agents to control the Actor network and generate a probability distribution of all possible distributed storage actions corresponding to each real-time data analysis state;

[0130] S4-6: taking the possible distributed storage action with the highest probability distribution in the updated action space as the execution distributed storage action of the corresponding real-time data analysis state;

[0131] S4-7: Integrate the distributed storage actions of all real-time data analysis states in the updated state space to obtain a real-time distributed storage strategy;

[0132] S5: Based on the real-time distributed storage strategy, the real-time data is sharded and encrypted to obtain several encrypted data shards and real-time storage requests. The consensus blockchain network is then used to reach a consensus on the real-time storage requests, including the following steps:

[0133] S5-1: Based on the real-time data sharding decision in the real-time distributed storage strategy and in combination with the shard replica mechanism, the real-time data is sharded to obtain several real-time data shards including replica shards;

[0134] S5-2: Generate a random key using a pseudo-random number generator based on the real-time random encryption decision in the real-time distributed storage strategy, and generate an encryption seed using a key derivation function based on the random key;

[0135] S5-3: Based on the encryption seed, a dynamic encryption algorithm is used to dynamically encrypt several real-time data shards to obtain several encrypted data shards, generate real-time storage requests, and send the real-time storage requests to the consensus blockchain network;

[0136] S5-4: The consensus node that receives the real-time storage request in the consensus blockchain network is used as the master node. Based on the master node, a consensus algorithm is used to reach a consensus on the real-time storage request, including the following steps:

[0137] S5-4-1: The consensus node that receives the real-time storage request in the consensus blockchain network is used as the master node, and the master node is used to send the real-time storage request to other consensus nodes;

[0138] S5-4-2: Based on the Byzantine Fault Tolerance (IPBFT) consensus algorithm, the master node broadcasts pre-prepared messages to other consensus nodes and verifies the legitimacy of real-time storage requests.

[0139] S5-4-3: If the legitimacy verification passes, the master node broadcasts a preparation message containing the master node's voting information to other consensus nodes and writes the preparation message into the message log;

[0140] S5-4-4: Based on all consensus nodes, confirmation messages are exchanged. If the master node receives confirmation messages exceeding the number threshold, the consensus is successful. The master node converts the real-time storage request and the corresponding real-time retrieval tag into a data block and uploads the data block to the chain. Otherwise, the consensus fails.

[0141] S6: If the consensus is successful, the distributed storage network is used to distribute the encrypted data shards and their real-time retrieval tags according to the real-time distributed storage strategy, including the following steps:

[0142] S6-1: If the consensus is successful, the encrypted data shards and their real-time retrieval tags are sent to several storage nodes in the distributed storage network according to the real-time random sending decision of the real-time distributed storage strategy;

[0143] S6-2: Use all storage nodes to locally store the received encrypted data shards and their real-time retrieval tags, and send real-time storage success information to other storage nodes;

[0144] S6-3: If the storage node receives a number of real-time storage success messages exceeding a preset number threshold, the distributed storage step is terminated; otherwise, the distributed storage step is continued.

[0145] Example 2:

[0146] like Figure 2 As shown, this embodiment provides a cloud data distributed storage system based on blockchain, which is used to implement a cloud data distributed storage method. The system is set in a cloud data center, and the system includes an initialization unit, a data analysis unit, a retrieval tag generation unit, a strategy generation unit, a data consensus unit and a distributed storage unit.

[0147] Initialization unit, used to deploy distributed storage networks and consensus blockchain networks in cloud data centers, and to build data analysis models, retrieval tag generation models, and distributed storage strategy generation models;

[0148] The data analysis unit is used to use the cloud data center to receive real-time data, and perform data analysis based on the real-time data using a data analysis model to obtain real-time data analysis results;

[0149] A retrieval tag generation unit is used to generate retrieval tags using a retrieval tag generation model based on real-time data and corresponding real-time data analysis results to obtain real-time retrieval tags;

[0150] A policy generation unit is used to generate a distributed storage policy based on the real-time data analysis results using a distributed storage policy generation model to obtain a real-time distributed storage policy;

[0151] The data consensus unit is used to shard and encrypt real-time data according to the real-time distributed storage strategy, obtain a number of encrypted data shards and real-time storage requests, and use the consensus blockchain network to reach consensus on the real-time storage requests;

[0152] The distributed storage unit is used to perform distributed storage of several encrypted data shards and their real-time retrieval tags using a distributed storage network according to a real-time distributed storage strategy after consensus is successful.

[0153] The present invention discloses a cloud data distributed storage method and system based on blockchain. By deploying a distributed storage network and a consensus blockchain network, the risk of single point failure is reduced, overall security is enhanced, and centralized attacks are resisted. Random encryption and consensus mechanisms are used to ensure that data owners have complete control over data, effectively preventing data leakage and unauthorized access. The distributed storage network can be flexibly expanded without a large amount of hardware investment, adapting to the rapid growth and changing needs of data volume, and reducing hardware and maintenance costs through distributed storage strategies and the use of distributed networks, thereby achieving a more economical data storage solution. Combining the consensus mechanism and the distributed storage strategy, the consistency and high reliability of data between different nodes are guaranteed, reducing the risk of data loss and damage. Automated data analysis and retrieval tag generation are achieved through a data analysis model and a retrieval tag generation model, and customized retrieval tags are set for each data, providing a basis for subsequent data retrieval, thereby improving user experience. The distributed storage strategy generation model is used to adjust the distributed storage strategy according to data characteristics, thereby improving the practicality and efficiency of distributed storage and ensuring the normal operation of data storage.

[0154] The present invention is not limited to the above optional embodiments. Anyone can derive various other forms of products based on the teachings of the present invention. The above specific embodiments should not be construed as limiting the scope of protection of the present invention. The scope of protection of the present invention shall be based on the scope defined in the claims, and the description can be used to interpret the claims.

Claims

1. A cloud data distributed storage method based on blockchain, characterized by: The steps include: In the cloud data center, identify several data nodes participating in the distributed storage of cloud data and extract historical behavior data of each data node; Based on the historical behavior data of each data node, the identity allocation method is used to divide the data nodes into several consensus nodes and several storage nodes; Using blockchain technology, several consensus nodes are distributedly connected to obtain a distributed storage network, and several storage nodes are distributedly connected to obtain a consensus blockchain network; Build data analysis models, retrieval tag generation models, and distributed storage strategy generation models; Use cloud data centers to receive real-time data, and use data analysis models to analyze data based on the real-time data to obtain real-time data analysis results; According to the real-time data and the corresponding real-time data analysis results, a retrieval tag generation model is used to generate retrieval tags to obtain real-time retrieval tags; Based on the real-time data analysis results, a distributed storage strategy generation model is used to generate a distributed storage strategy to obtain a real-time distributed storage strategy. According to the real-time distributed storage strategy, the real-time data is sharded and encrypted to obtain several encrypted data shards and real-time storage requests. The consensus blockchain network is then used to reach a consensus on the real-time storage requests, including the following steps: According to the real-time data sharding decision in the real-time distributed storage strategy and combined with the shard replica mechanism, the real-time data is sharded to obtain several real-time data shards including replica shards; According to the real-time random encryption decision in the real-time distributed storage strategy, a pseudo-random number generator is used to generate a random key, and an encryption seed is generated based on the random key using a key derivation function; Based on the encrypted seed, a dynamic encryption algorithm is used to dynamically encrypt several real-time data shards to obtain several encrypted data shards, generate real-time storage requests, and send the real-time storage requests to the consensus blockchain network; The consensus node that receives the real-time storage request in the consensus blockchain network is used as the master node, and consensus is reached on the real-time storage request using the consensus algorithm based on the master node; If the consensus is successful, the distributed storage network is used to distribute the encrypted data shards and their real-time retrieval tags according to the real-time distributed storage strategy.

2. The blockchain-based cloud data distributed storage method according to claim 1, characterized in that: Building a data analysis model, a retrieval tag generation model, and a distributed storage strategy generation model includes the following steps: Collecting a number of historical data, and preprocessing the a number of historical data to obtain a number of preprocessed historical data; Based on some pre-processed historical data, a deep learning algorithm is used to build a data analysis model and generate some historical data analysis results; Based on a number of historical data and corresponding historical data analysis results, a natural language processing algorithm is used to build a search tag generation model and generate a number of historical search tags; Based on the analysis results of several historical data, a reinforcement learning algorithm is used to build a distributed storage strategy generation model, and several historical distributed storage strategy generation experiences are generated.

3. The blockchain-based cloud data distributed storage method according to claim 2, characterized in that: Based on the historical behavior data of each data node, the identity allocation method is used to divide the data nodes into consensus nodes and storage nodes, including the following steps: According to the preset node behavior rules, the consensus behavior evaluation matrix of the data node is generated, and the information entropy of the consensus behavior evaluation matrix is ​​obtained; According to the information entropy, the reputation value of each data node is obtained, and the node reputation value update strategy of the data node is set; According to the reputation value of the data nodes, several data nodes are sorted in descending order, and the first M data nodes are used as consensus nodes, and the remaining data nodes are used as storage nodes.

4. The blockchain-based cloud data distributed storage method according to claim 3, characterized in that: The data analysis model is constructed based on the LSTM-FPN-CNN-DBN algorithm, and the data analysis model includes a first text feature extraction module constructed based on the LSTM algorithm, an image feature extraction module constructed based on the FPN algorithm, an audio feature extraction module constructed based on the CNN algorithm, an attention weight module constructed based on the Attention mechanism, and a data analysis module constructed based on the DBN algorithm. The first text feature extraction module, the image feature extraction module, and the audio feature extraction module are all connected to the attention weight module, and the attention weight module is connected to the data analysis module; The retrieval tag generation model is constructed based on the BERT-LSTM-CRF algorithm, and the retrieval tag generation model includes a multimodal feature extraction module, a second text feature extraction module constructed based on the LSTM algorithm, and a retrieval tag generation model constructed based on the CRF algorithm; The distributed storage strategy generation model is constructed based on the MOPPO-cGAN algorithm, and the distributed storage strategy generation model includes a distributed storage strategy generation module constructed based on the MOPPO algorithm and an adversarial training module constructed based on the cGAN algorithm, which are connected in sequence. The distributed storage strategy generation module is provided with an objective function set, an experience replay pool, an Actor network, a Critic network and an intelligent agent, and the adversarial training module is provided with a generator and a discriminator.

5. The cloud data distributed storage method based on blockchain according to claim 4, characterized in that: Using a cloud data center to receive real-time data and using a data analysis model to analyze the real-time data to obtain real-time data analysis results includes the following steps: Using a cloud data center to receive real-time data and pre-process the real-time data to obtain pre-processed real-time data; Parsing the preprocessed real-time data to obtain preprocessed real-time image data, preprocessed real-time text data, and preprocessed real-time audio data; Extracting a first real-time text data feature of the preprocessed real-time text data using a first text feature extraction module of the data analysis model; Using the image feature extraction module of the data analysis model to extract real-time image data features of the pre-processed real-time image data; Use the audio feature extraction module of the data analysis model to extract real-time audio data features of the pre-processed real-time audio data; According to a preset attention weight value, using the attention weight module of the data analysis model, the first real-time text data feature, the real-time image data feature, and the real-time audio data feature are weightedly fused to obtain a real-time weighted fusion feature; According to the real-time weighted fusion features, the data analysis module of the data analysis model is used to perform data analysis and obtain real-time data analysis results.

6. The blockchain-based cloud data distributed storage method according to claim 5, characterized in that: Based on the real-time data and the corresponding real-time data analysis results, a retrieval tag generation model is used to generate retrieval tags to obtain real-time retrieval tags, including the following steps: Using a second text feature extraction module of the retrieval tag generation model, extracting a second real-time text data feature of the real-time data analysis result; The multimodal feature extraction module of the retrieval tag generation model is used to extract the real-time key features of the real-time weighted fusion features corresponding to the pre-processed real-time data; According to the second real-time text data feature and the real-time key feature, a retrieval tag generation model is used to generate a retrieval tag to obtain a real-time retrieval tag.

7. The blockchain-based cloud data distributed storage method according to claim 6, characterized in that: Based on the real-time data analysis results, a distributed storage strategy generation model is used to generate a distributed storage strategy to obtain a real-time distributed storage strategy, including the following steps: Parsing the real-time data analysis results to obtain a number of real-time data analysis states, and updating the state space of the intelligent agent of the distributed storage strategy generation module in the distributed storage strategy generation model according to the number of real-time data analysis states to obtain an updated state space; Randomly extract several historical distributed storage strategy generation experiences from the experience replay pool of the distributed storage strategy generation module, and generate several possible distributed storage actions based on the several historical distributed storage strategy generation experiences; According to several possible distributed storage actions, the action space of the intelligent agent of the distributed storage strategy generation module is updated to obtain an updated action space; Select a real-time objective function from the objective function set of the distributed storage strategy generation module, and based on the real-time objective function, use the agent to control the critic network to generate the real-time value of all possible distributed storage actions in the updated action space for each real-time data analysis state in the updated state space; Based on several real-time values, we use intelligent agents to control the Actor network and generate a probability distribution of all possible distributed storage actions corresponding to each real-time data analysis state. The possible distributed storage action with the highest probability distribution in the updated action space is used as the execution distributed storage action of the corresponding real-time data analysis state; Integrate the distributed storage actions of all real-time data analysis states in the updated state space to obtain a real-time distributed storage strategy.

8. The cloud data distributed storage method based on blockchain according to claim 7, characterized in that: If the consensus is successful, the distributed storage network is used to distribute the encrypted data shards and their real-time retrieval tags according to the real-time distributed storage strategy, including the following steps: If the consensus is successful, several encrypted data shards and their real-time retrieval tags are sent to several storage nodes in the distributed storage network according to the real-time random sending decision of the real-time distributed storage strategy; Use all storage nodes to locally store the received encrypted data shards and their real-time retrieval tags, and send real-time storage success information to other storage nodes; If the storage node receives a number of real-time storage success messages exceeding a preset number threshold, the distributed storage step is terminated; otherwise, the distributed storage step is continued.

9. A cloud data distributed storage system based on blockchain, used to implement the cloud data distributed storage method according to any one of claims 1 to 8, characterized in that: The system is set up in a cloud data center, and the system includes an initialization unit, a data analysis unit, a retrieval tag generation unit, a strategy generation unit, a data consensus unit and a distributed storage unit.

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