A visual blockchain data storage method and system

Abstract

The present invention discloses a visualized blockchain data storage method and system, which relates to the field of data storage processing. The technical scheme thereof includes: a data acquisition module is used to deploy a lightweight proxy node in each blockchain, and use multi-threading to collect original data on different nodes in parallel; a data storage module uses an automated storage expansion mechanism to design a hierarchical storage architecture, establishes an abstract data model, maps original data of different blockchains into a unified framework, and obtains original data with a unified format; a data integration processing module aggregates the stored original data, obtains an aggregation group, analyzes the correlation between different aggregation groups, and constructs a three-dimensional map of data relations; a visualization display module is used to enable a user to interact with the data stored in the blockchain, takes the three-dimensional map of data relations as the underlying architecture, increases the multi-level visualization of data by the user, provides a user-friendly interface, and realizes visualized storage of blockchain data.

Description

Technical Field

[0001] The present invention relates to the field of data storage processing, and more specifically, to a visual blockchain data storage method and system. Background Art

[0002] The data storage of blockchain is tamper-proof and long-term. As the blockchain network continues to grow, the amount of blockchain data will continue to increase, further increasing the complexity of data on the chain and storage requirements. Especially for multi-blockchain data, each blockchain has a different design and implementation method, and the data format, structure and protocol between blockchains may be different, which increases the difficulty of data processing. In addition, blockchain data contains a variety of complex structured information. The correlation between these data is complex, and direct presentation may confuse users. The design complexity of visualization tools increases.

[0003] As the amount of blockchain data increases, especially for full nodes, the storage cost will increase significantly, resulting in a decrease in the degree of node decentralization. In the visualization process, efficient data query and retrieval technology is required to process large-scale data. As the amount of data increases, the efficiency of data access and processing gradually decreases, resulting in slower response speed and affecting user experience. Blockchain data needs to be constantly synchronized between network nodes. The substantial increase in data volume will increase the burden on network transmission, create bandwidth bottlenecks, and increase latency. The visualization of massive data requires consideration of how to simplify the display to avoid information overload.

[0004] Therefore, based on the above problems, it is necessary to design visual blockchain data storage. Summary of the invention

[0005] In view of the problems existing in the prior art, the purpose of the present invention is to provide a visual blockchain data storage method and system to realize the visual storage of blockchain data.

[0006] To achieve the above purpose, the present invention provides the following technical solution: the visual blockchain data storage system includes:

[0007] Data collection module: used to deploy lightweight proxy nodes in each blockchain and use multi-threading to collect raw data on different nodes in parallel;

[0008] Data storage module: Use the automated storage expansion mechanism to design a layered storage architecture, establish an abstract data model, map the raw data of different blockchains into a unified framework, and obtain the raw data in a unified format;

[0009] Data integration processing module: aggregates the stored raw data, obtains aggregate groups, analyzes the correlation between different aggregate groups, and constructs a three-dimensional map of data relationships;

[0010] Visualization display module: It is used to enable users to interact with the data stored in the blockchain. It uses a three-dimensional map of data relationships as the underlying architecture, increases users' multi-level visualization of data, and provides a user-friendly interface.

[0011] Preferably, the types of the original data include transaction records, smart contracts, block header information, and node status;

[0012] Methods for using multithreading to collect raw data on different nodes in parallel include:

[0013] Select a lightweight proxy node and deploy it to the blockchain;

[0014] Select multi-threading technology, where each thread is responsible for fetching the latest raw data from a node;

[0015] Use a short-term buffering mechanism for continuous acquisition of raw data.

[0016] Preferably, the tiered storage architecture includes a hot storage layer, a cold storage layer and an archive storage layer, the hot storage layer is used to store active data, the cold storage layer is used to store data with low access frequency, and the archive storage layer is used to store data that is no longer accessed for a long time.

[0017] Preferably, the method for designing a hierarchical storage architecture using an automated storage expansion mechanism includes:

[0018] Use monitoring tools to integrate the health status Jk(f), capacity utilization Rl(f), throughput Tu(f), and latency Yc(f) of each storage layer at time point f;

[0019] Calculate the evaluation index of each storage layer at time point f Taking the current time point t as the time base point, calculate the evaluation index at each time point in turn, and obtain the absolute difference between the evaluation index of each time point and the current time point, until the absolute difference of the evaluation index is greater than the absolute threshold, collect the evaluation index of all interval time points between this time point and the time base point, and combine the evaluation index of the time base point to form a p-order data group, where p represents the number of time points extracted from the time base point forward;

[0020] in, and represents the weight coefficient;

[0021] Select d time points forward from the current time t as the time base points, obtain d p-order data groups, and train the fitting model The stopping condition of the fitting model is set as the absolute difference between the output values ​​of two consecutive iterations in o consecutive iterations is less than the fitting threshold;

[0022] Among them, ε(t-1) represents the random error term at the previous time point t-1, αi represents the i-th fitting coefficient, Gv(ti) represents the evaluation index at time point ti, and δ represents the adjustment coefficient;

[0023] Input the evaluation index Gv(t) at the current time point t into the trained fitting model to obtain the Nh(t) value at the time point t. When the Nh(t) value is greater than Q1, use horizontal expansion to increase the storage nodes of the hot storage layer. When the Nh(t) value is greater than Q2, use automated data lifecycle management tools to migrate the data in the cold storage layer to the hot storage layer or the archive storage layer. When the Nh(t) value is greater than Q3, use cloud services to increase the storage nodes of the archive storage layer.

[0024] Among them, Q1, Q2 and Q3 are preset constants.

[0025] Preferably, the abstract data model is established to map the original data of different blockchains into a unified framework. The specific method includes:

[0026] Set the properties of the block including block hash H_b, parent block hash H_pb, block height Q_h, timestamp t_b and transaction record T_b, and use smart contract Z_h, block header information Q_t, and node status J_z as abstract definitions, where the node status is arranged in the order of links in the block;

[0027] Defining an abstract data model includes two layers of mapping, namely attribute mapping and abstract definition mapping. The attribute mapping layer is MX=(H_b, H_pb, Q_h, t_b, T_b), and the abstract definition mapping layer is CX=(Z_h, Q_t, J_z);

[0028] Map the raw data of different blockchains into a unified framework formed by an abstract data model, so that the raw data has a unified standard format.

[0029] Preferably, the method for performing aggregation processing on the original data includes:

[0030] Step A1: Store the collected raw data in a hierarchical storage architecture, then remove irrelevant characters and unify the writing format of the raw data, build a vocabulary based on the set of unique words in the raw data, calculate the TF-IDF value of each word, use each TF-IDF value as a feature of the word, build a blank vector, correspond each blank element in the blank vector to the TF-IDF value of the word in the vocabulary, and then convert each data type in the raw data at the same time point into a feature vector representation;

[0031] Step A2: Collect the feature vectors at each node on the blockchain, and for each data type, extract the maximum Tz of the feature vectors max and minimum Tz min , the feature vector of each data type is divided into K_q blocks to obtain the magnetic center Where Zx(v) represents the magnetic center of the vth block, and v represents the index of the block;

[0032] Step A3: Calculate the distance between each eigenvector and the magnetic center Where Cs(g, v) represents the distance between the eigenvector Tz(g) and the magnetic center Zx(v), Tz(g) represents the g-th eigenvector, and G represents the number of eigenvectors;

[0033] Step A4: Assign each feature vector to the block to which the nearest magnetic center belongs, extract the number of feature vectors in each block, and if the number is less than the evaluation threshold, dissolve the block, and refill the dissolve feature vectors into the blocks to which other magnetic centers belong, thereby adaptively adjusting the preset K_q blocks;

[0034] Step A5: After completing the distribution of feature vectors, update each magnetic center to the mean of all feature vectors in its block, repeat steps A3 to A4 until the preset number of distributions is reached, and stop, treating each block as an aggregation group.

[0035] Preferably, the method for constructing the three-dimensional graph of data relationships includes:

[0036] Construct a blank 3D layer, label the blockchain nodes horizontally, the data types vertically, and the time points vertically;

[0037] Extract the nodes, data types and time points of each aggregation group, and simulate the aggregation group into solid points to fill in the blank three-dimensional layer to form point filling;

[0038] Define the solid points to be connected by directed edges, set attribute values ​​on the directed edges, and set the attribute values ​​according to the formula Wherein, β1 and β2 are used to represent weight coefficients, Px represents the number of times aggregation group Fc_d1 and aggregation group Fc_d2 appear at the same time point, Ph represents the number of times aggregation group Fc_d1 appears at a time point, Pj represents the number of times aggregation group Fc_d2 appears at a time point, Fc_d1∩Fc_d2 represents the overlapping content of aggregation group Fc_d1 and aggregation group Fc_d2, and Fc_d1∪Fc_d2 represents all combined content of aggregation group Fc_d1 and aggregation group Fc_d2;

[0039] Use directed edges to connect any two aggregation groups, calculate the attribute values ​​of the directed edges, and add the attribute values ​​to the directed edges to form a complete three-dimensional graph of data relationships.

[0040] Preferably, the method for the user to interact with the data stored in the blockchain includes:

[0041] Use React.js or Vue.js front-end frameworks to build responsive user interfaces and use visualization libraries to create interactive displays;

[0042] Integrated dashboard tools to customize data display and monitoring.

[0043] Preferably, the three-dimensional graph of data relationships is used as the underlying architecture to increase the multi-level visualization of data for users, and the specific method includes:

[0044] Set up multi-level data display. The macro view is used to display the overall status of the blockchain network; the meso view is used to display detailed information of a block or a node; and the micro view is used to display specific details of a smart contract.

[0045] With the data relationship three-dimensional graph as the underlying architecture, the Neo4j graph database and graph visualization tools are used to display the correlation between the primary color data on each node in the data relationship three-dimensional graph. By clicking on a block or node, you can view the relevant information layer by layer, and use the breadcrumb navigation to quickly switch between different levels.

[0046] A visual blockchain data storage method includes:

[0047] Step G1: Use multi-threading to collect raw data on different nodes in parallel;

[0048] Step G2: Build a tiered storage architecture, including a hot storage layer, a cold storage layer, and an archive storage layer, and use an automated storage expansion mechanism to design the tiered storage architecture;

[0049] Step G3: Establish an abstract data model, map the raw data of different blockchains into a unified framework, and obtain the raw data in a unified format;

[0050] Step G4: Aggregate the stored original data to obtain aggregation groups, build a blank three-dimensional layer, and analyze the correlation between different aggregation groups to obtain a three-dimensional map of data relationships;

[0051] Step G5: View the storage of blockchain data in real time through visual display and user-friendly interface.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] By deploying lightweight proxy nodes in the blockchain and using multi-threading technology to collect raw data from different nodes in parallel, the speed and efficiency of data collection can be greatly improved, especially when multiple nodes generate a large amount of data at the same time, solving the problem of insufficient performance of traditional single-threaded data collection methods. Supporting short-term caching mechanism ensures the continuity and stability of data collection, further improving the reliability of the system.

[0054] The layered architecture of hot storage, cold storage and archive storage can allocate data to different storage layers according to the activity and access frequency of the data. The automatic storage expansion mechanism dynamically adjusts the allocation of storage resources by monitoring the health status, capacity utilization, throughput and latency of the storage layer to ensure the efficiency and stability of system operation.

[0055] By building an abstract data model, the original data of different blockchains are mapped into a unified standard framework, so that data from different blockchains can be processed in a unified format. This abstract processing method makes the system have better compatibility and scalability, and can adapt to the original data types of different blockchains. Unified processing through abstract models reduces the complexity of processing heterogeneous blockchain data.

[0056] By aggregating the raw data, the system can extract valuable aggregation groups from a large amount of raw data and analyze the correlation between different aggregation groups. This aggregation process can not only reduce the amount of data, but also improve the accuracy of data analysis. The calculation and adaptive adjustment mechanism of the magnetic center introduced in the aggregation process can effectively optimize the allocation and adjustment of the aggregation group and ensure the accuracy and rationality of the aggregation results.

[0057] The constructed three-dimensional graph of data relationships allows users to intuitively view the correlation between different nodes, data types and time points. The construction of the three-dimensional graph helps users understand complex data relationships, especially in the display of on-chain data correlation. Using directed edges to connect different aggregation groups and displaying the relationship between different aggregation groups based on calculated attribute values ​​makes data analysis more intuitive. At the same time, the display of the three-dimensional graph can also help discover potential data correlations and patterns.

[0058] The system uses React.js or Vue.js front-end frameworks to provide users with a good interactive experience. A multi-level visual display method is set up, and users can go into different levels to view detailed data as needed, achieving a comprehensive display from the overall link status to the details of a single node or contract. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A structural schematic diagram of a visual blockchain data storage system is proposed for the present invention;

[0060] Figure 2 Schematic diagram of the method applied to the visual blockchain data storage system in the present invention. DETAILED DESCRIPTION

[0061] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0062] Embodiment 1

[0063] Reference Figure 1 and Figure 2 , Example 1 further illustrates a visual blockchain data storage system proposed in the present invention.

[0064] There are some challenges and problems in the visual storage of blockchain data, which are analyzed as follows:

[0065] 1. Huge amount of data

[0066] The data storage of blockchain is tamper-proof and long-term. As the blockchain network continues to grow, the amount of blockchain data will continue to increase, especially on the public chain, where the total amount of blockchain data is very large and difficult to fully present or store. The design characteristics of blockchain determine that its data volume will continue to grow over time. The specific reasons include the decentralization and redundancy of blockchain, the continuous growth of on-chain transactions, the popularity of smart contracts (blockchain allows developers to deploy smart contracts, which further increases the complexity and storage requirements of on-chain data. Information such as state changes and event logs after the execution of smart contracts needs to be recorded) and the growth in the number of users and nodes (with the development of the blockchain ecosystem, the number of users and nodes continues to increase, and the transaction volume and data synchronization requirements are also expanding, resulting in a massive increase in data volume).

[0067] As the amount of blockchain data increases, especially for full nodes, the storage cost will increase significantly, resulting in a decrease in the degree of node decentralization. In the visualization process, efficient data query and retrieval technology is required to process large-scale data. As the amount of data increases, the efficiency of data access and processing gradually decreases, resulting in slower response speed and affecting user experience. Blockchain data needs to be constantly synchronized between network nodes. The substantial increase in data volume will increase the burden on network transmission, create bandwidth bottlenecks, and increase latency. The visualization of massive data requires consideration of how to simplify the display and avoid information overload, while also requiring reasonable abstraction and screening of large-scale data.

[0068] 2. Data Complexity

[0069] Blockchain data contains a variety of complex structured information, such as transaction records, smart contracts, block header information, node status, etc. The correlation between these data is complex, and direct presentation may confuse users. Each block in the blockchain contains multiple transactions, and each transaction contains multiple parts such as transaction input, output, signature, etc., forming a multi-level and multi-dimensional data structure.

[0070] The design complexity of visualization tools has increased: In order to present the multi-level and multi-dimensional data of the blockchain and the complexity of the on-chain data structure, visualization tools need to design complex interactive interfaces and data processing logic, which increases the difficulty of development and system complexity. In addition, displaying too much complex information may cause users to be unable to understand key data or even cause confusion. Therefore, how to extract the most valuable information from complex data and present it concisely has become an important issue.

[0071] 3. Cross-chain data integration

[0072] In a multi-chain environment, each blockchain is designed and implemented differently, and the data formats, structures, and protocols between blockchains may be different. For example, some data structures vary greatly. Therefore, how to integrate data from different blockchains in a unified visual interface is very complicated.

[0073] Cross-chain data integration requires handling the data structures of different blockchains and ensuring that visualization tools can seamlessly support multi-chain data, allowing users to gain a global perspective rather than being limited to the view of a single chain.

[0074] Therefore, how to effectively process and store large-scale blockchain data is a challenge. Visualization tools need to be able to process large amounts of historical data and ensure that performance is not affected when presenting visualizations. Based on the above problems, a visual blockchain data storage method and system are designed. The specific design contents are as follows;

[0075] The visual blockchain data storage system includes:

[0076] Data collection module: used to deploy lightweight proxy nodes in each blockchain and use multi-threading to collect raw data on different nodes in parallel;

[0077] The types of raw data include transaction records, smart contracts, block header information, and node status;

[0078] Transaction records refer to transaction data on each node, including the transaction initiator, recipient, transaction amount, transaction time, handling fee, etc.

[0079] Smart contracts include contract creation, execution, call records, etc., and can extract key information such as contract code, call parameters, and return results;

[0080] The block header information refers to the metadata of the block, including the block number, timestamp, Merkle tree root, previous block hash, difficulty value, random number (nonce), etc.

[0081] Node status refers to the operation status, synchronization status, block progress, activity, etc. of nodes in the network;

[0082] Methods for using multithreading to collect raw data on different nodes in parallel include:

[0083] Select a lightweight proxy node and deploy it to the blockchain; select a suitable light node (light client) according to the different blockchain networks; deploy lightweight nodes in a reliable server or cloud environment (such as AWS EC2, Alibaba Cloud, DigitalOcean, etc.) to ensure that the node can continue to run and stay connected to the blockchain network, monitor the health of the node, and ensure that the node status can be obtained in real time.

[0084] Choose multi-threading technology, where each thread is responsible for fetching the latest raw data from a node. For example, in Python, you can use concurrent.futures.ThreadPoolExecutor to manage multiple threads.

[0085] Use a short-term buffering mechanism for continuous acquisition of raw data.

[0086] In order to optimize the efficiency of data collection and avoid repeatedly grabbing the same block or transaction data, a short-term cache mechanism can be used. When a block or transaction data is successfully collected, it can be temporarily cached for a period of time to quickly respond to subsequent requests.

[0087] For example, Redis can be used to implement short-term caching. Redis is a high-performance in-memory database that is suitable for short-term caching of blockchain data. The captured transaction records, smart contracts, block header information, and node status can be stored in Redis, and an expiration time (TTL) can be set to ensure that the cached data is only retained for a period of time.

[0088] Design a caching strategy:

[0089] To ensure the uniqueness of the cache, set a unique cache key for each data in Redis. For example, block data can use block:<block_height> As a Redis key; transaction data can use tx:<transaction_hash> As a Redis key.

[0090] Set an expiration time TTL (such as 10 or 1 minute) for each cache item to ensure that the data in the cache does not occupy memory space for a long time and that old data can be automatically eliminated when the blockchain data is updated.

[0091] During data collection, if the data of the block already exists in Redis, the cached data is directly returned to avoid repeated crawling; if the data of the block does not exist in the cache, the data is pulled from the proxy node and cached in Redis.

[0092] A short-term cache mechanism is used to avoid repeated fetching of the same data and reduce network burden.

[0093] Data storage module: Use the automated storage expansion mechanism to design a layered storage architecture, establish an abstract data model, map the raw data of different blockchains into a unified framework, and obtain the raw data in a unified format;

[0094] The tiered storage architecture includes a hot storage layer, a cold storage layer, and an archive storage layer. The hot storage layer is used to store active data with a high access frequency to ensure fast response. The cold storage layer is used to store data with a low access frequency, such as historical transactions and block data, which have a low access frequency but still require fast query. The archive storage layer is used to store data that is no longer accessed for a long time, such as block history data that exceeds a certain time.

[0095] Methods for designing a tiered storage architecture using an automated storage expansion mechanism include:

[0096] Use monitoring tools (such as Prometheus or Zabbix) to integrate the health status Jk(f), capacity utilization Rl(f), throughput Tu(f), and latency Yc(f) of each storage layer at time point f;

[0097] Calculate the evaluation index of each storage layer at time point f Taking the current time point t as the time base point, calculate the evaluation index at each time point in turn, and obtain the absolute difference between the evaluation index of each time point and the current time point, until the absolute difference of the evaluation index is greater than the absolute threshold, collect the evaluation index of all interval time points between this time point and the time base point, and combine the evaluation index of the time base point to form a p-order data group, where p represents the number of time points extracted from the time base point forward;

[0098] Among them, the time point f can be the current time point t, the previous time point t-1, and the time point ti, which is a reference to all time point labels.

[0099] in, and Represents the weight coefficient; select d time points forward from the current time t as the time base point, obtain d p-order data groups, and train the fitting model

[0100] The stopping condition of the fitting model is set as the absolute difference between the output values ​​of two consecutive iterations in o consecutive iterations is less than the fitting threshold;

[0101] The absolute threshold and fitting threshold can be set based on experimental data analysis or experience.

[0102] Among them, ε(t-1) represents the random error term at the previous time point t-1, αi represents the i-th fitting coefficient, Gv(ti) represents the evaluation index at time point ti, and δ represents the adjustment coefficient;

[0103] Input the evaluation index Gv(t) at the current time point t into the trained fitting model to obtain the Nh(t) value at the time point t. When the Nh(t) value is greater than Q1, use horizontal expansion to increase the storage nodes of the hot storage layer. When the Nh(t) value is greater than Q2, use automated data lifecycle management tools (such as AWSS3 storage class conversion rules) to migrate data in the cold storage layer to the hot storage layer or archive storage layer. When the Nh(t) value is greater than Q3, use cloud services (such as AWS Glacier, Azure Archive Storage, etc.) to increase the storage nodes of the archive storage layer.

[0104] Among them, Q1, Q2 and Q3 are preset constants.

[0105] When adding nodes, you can use 1 node as the growth step and add one node each time, or you can set the number of nodes to be added by yourself.

[0106] In order to build an abstract data model and map the raw data of different blockchains into a unified framework, it is necessary to define a common data structure and eliminate the differences between blockchains through a mapping method.

[0107] Establish an abstract data model to map the raw data of different blockchains into a unified framework. The specific methods include:

[0108] Definition: Block is the basic unit of storage in blockchain. Each block is linked to the previous block to form a blockchain. The properties of the block are set, including block hash H_b, parent block hash H_pb, block height Q_h, timestamp t_b and transaction record T_b (transaction list). Smart contract Z_h, block header information Q_t, and node status J_z are used as abstract definitions. Among them, the node status is arranged in the order of links in the block. The node status can predefine the status category, such as normal idle, normal working, use warning, use failure, etc.

[0109] Defining an abstract data model includes two layers of mapping, namely attribute mapping and abstract definition mapping. The attribute mapping layer is MX=(H_b, H_pb, Q_h, t_b, T_b), and the abstract definition mapping layer is CX=(Z_h, Q_t, J_z);

[0110] Map the raw data of different blockchains into a unified framework formed by an abstract data model, so that the raw data has a unified standard format.

[0111] For example, for the field:

[0112] block_hash,previous_block_hash,height,timestamp,tx[];

[0113] Mapping to the attribute map layer:

[0114] (H_b) = block_hash;

[0115] (H_pb)=previous_block_hash;

[0116] (Q_h) = height;

[0117] (t_b) = timestamp;

[0118] (T_b) = tx[] (each item in the transaction record is a transaction);

[0119] MX=

[0120] (block_hash,previous_block_hash,height,timestamp,tx[]).

[0121] Data integration processing module: aggregates the stored raw data, obtains aggregate groups, analyzes the correlation between different aggregate groups, and constructs a three-dimensional map of data relationships;

[0122] Methods for aggregating raw data include:

[0123] Step A1: Store the collected raw data in a hierarchical storage architecture, and then remove irrelevant characters and unify the writing format of the raw data. Removing irrelevant characters means removing punctuation marks, special characters, etc. in the text for unified processing. Unifying the writing format means converting all texts to the same writing standard to ensure that the same words are not considered different due to different capitalization when compared; construct a vocabulary based on the set of unique words in the raw data, regard each word in the text as a feature, represent the text through these features, list all the words that appear in the text, and form a vocabulary. For example, suppose our vocabulary is ["quick", "brown", "fox", "jumps", "over", "lazy", "dog"], calculate the TF-IDF value of each word, use each TF-IDF value as a feature of the word, construct a blank vector, and correspond each blank element in the blank vector to the TF-IDF value of the word in the vocabulary. In this way, the text can be represented by a vector, and each data type in the original data at the same time point can be converted into a feature vector representation, and each paragraph of text can be represented as a vector. Each position of the vector corresponds to a word in the vocabulary, and the frequency of the word in the text represents the value of the position.

[0124] The TF-IDF value of each word is the product of TF and IDF. The term frequency (TF) indicates the frequency of each word in the document. The formula is: Inverse Document Frequency (IDF) is used to measure the rarity of a word in all documents. The formula is: W_z is the total number of documents.

[0125] Step A2: Collect the feature vectors at each node on the blockchain, and for each data type, extract the maximum Tz of the feature vectors max and minimum Tz min , the feature vector of each data type is divided into K_q blocks to obtain the magnetic center Where Zx(v) represents the magnetic center of the vth block, and v represents the index of the block;

[0126] Step A3: Calculate the distance between each eigenvector and the magnetic center Where Cs(g, v) represents the distance between the eigenvector Tz(g) and the magnetic center Zx(v), Tz(g) represents the g-th eigenvector, and G represents the number of eigenvectors;

[0127] Step A4: Assign each feature vector to the block to which the nearest magnetic center belongs, extract the number of feature vectors in each block, and if the number is less than the evaluation threshold, dissolve the block, and refill the dissolve feature vectors into the blocks to which other magnetic centers belong, thereby adaptively adjusting the preset K_q blocks;

[0128] Step A5: After completing the distribution of feature vectors, update each magnetic center to the mean of all feature vectors in its block, repeat steps A3 to A4 until the preset number of distributions is reached, and stop, treating each block as an aggregation group.

[0129] The number of allocations can also be regarded as the number of iterations. The preset number of iterations should be large enough to ensure that all eigenvectors can converge and group data with similar correlations together to facilitate subsequent data analysis between blocks.

[0130] The method for constructing a three-dimensional graph of data relations includes:

[0131] Construct a blank 3D layer, label the blockchain nodes horizontally, the data types vertically, and the time points vertically;

[0132] Extract the nodes, data types and time points of each aggregation group, and simulate the aggregation group into solid points to fill in the blank three-dimensional layer to form point filling;

[0133] Define the solid points to be connected by directed edges, set attribute values ​​on the directed edges, and set the attribute values ​​according to the formula Wherein, β1 and β2 are used to represent weight coefficients, Px represents the number of times aggregation group Fc_d1 and aggregation group Fc_d2 appear at the same time point, Ph represents the number of times aggregation group Fc_d1 appears at a time point, Pj represents the number of times aggregation group Fc_d2 appears at a time point, Fc_d1∩Fc_d2 represents the overlapping content of aggregation group Fc_d1 and aggregation group Fc_d2, and Fc_d1∪Fc_d2 represents all combined content of aggregation group Fc_d1 and aggregation group Fc_d2;

[0134] Use directed edges to connect any two aggregation groups, calculate the attribute values ​​of the directed edges, and add the attribute values ​​to the directed edges to form a complete three-dimensional graph of data relationships.

[0135] Building a knowledge graph between data sets is a complex data analysis task that extracts entities and their relationships from the data and organizes and displays them in the form of a graph.

[0136] Visualization display module: used to enable users to interact with the data stored in the blockchain, with a three-dimensional map of data relationships as the underlying architecture, to increase users' multi-level visualization of data and provide a user-friendly interface;

[0137] Methods for users to interact with data stored in the blockchain include:

[0138] Use React.js or Vue.js front-end frameworks to build responsive user interfaces and visualization libraries (D3.js, ECharts, or Three.js) to create interactive displays (e.g., charts, network diagrams, 3D views, etc.);

[0139] Integrate dashboard tools (such as Grafana, Tableau, etc.) to customize data display and monitoring.

[0140] With the three-dimensional graph of data relationships as the underlying architecture, users can gain multi-level visualization of data. Specific methods include:

[0141] Set up multi-level data display. The macro view is used to display the overall status of the blockchain network (such as the transaction volume of the entire network, block speed, node distribution, on-chain asset flow, etc.); the meso view is used to display detailed information of a block or a node (such as the number of transactions contained in the block, basic information of each transaction, sending and receiving addresses, etc.); the micro view is used to display the specific details of a smart contract (such as the input and output of the transaction, the parameters and results of the contract call);

[0142] With the data relationship three-dimensional graph as the underlying architecture, the Neo4j graph database and graph visualization tools are used to display the correlation between the primary color data on each node in the data relationship three-dimensional graph. By clicking on a block or node, you can view the relevant information layer by layer, and use the breadcrumb navigation to quickly switch between different levels.

[0143] Integrate OAuth2.0, JWT and other authentication mechanisms to ensure the authenticity of user identity and the correctness of access rights; design a detailed role permission model so that different user roles have different data access rights; implement interactive operations such as data filtering, searching, dragging, and zooming to enhance user experience; support exporting visualized data to PDF, Excel and other formats to facilitate offline analysis and sharing by users.

[0144] Data privacy protection ensures that sensitive data is not accessed by unauthorized users; efficient permission management mechanism supports dynamic permission adjustment and auditing.

[0145] Embodiment 2

[0146] Reference Figure 1 and Figure 2, Example 2 further illustrates a visual blockchain data storage system proposed by the present invention.

[0147] The visual blockchain data storage method comprises the following steps:

[0148] Step G1: Use multi-threading to collect raw data on different nodes in parallel;

[0149] Step G2: Build a tiered storage architecture, including a hot storage layer, a cold storage layer, and an archive storage layer, and use an automated storage expansion mechanism to design the tiered storage architecture;

[0150] Step G3: Establish an abstract data model, map the raw data of different blockchains into a unified framework, and obtain the raw data in a unified format;

[0151] Step G4: Aggregate the stored original data to obtain aggregation groups, build a blank three-dimensional layer, and analyze the correlation between different aggregation groups to obtain a three-dimensional map of data relationships;

[0152] Step G5: View the storage of blockchain data in real time through visual display and user-friendly interface.

[0153] The visual blockchain data storage system is applied to a visual blockchain data storage method and includes:

[0154] Data collection module: used to deploy lightweight proxy nodes in each blockchain and use multi-threading to collect raw data on different nodes in parallel;

[0155] Data storage module: Use the automated storage expansion mechanism to design a layered storage architecture, establish an abstract data model, map the raw data of different blockchains into a unified framework, and obtain the raw data in a unified format;

[0156] Data integration processing module: aggregates the stored raw data, obtains aggregate groups, analyzes the correlation between different aggregate groups, and constructs a three-dimensional map of data relationships;

[0157] Visualization display module: used to enable users to interact with the data stored in the blockchain, with a three-dimensional map of data relationships as the underlying architecture, to increase users' multi-level visualization of data and provide a user-friendly interface;

[0158] The modules are connected to each other via wired and / or wireless means.

[0159] In addition, according to the implementation of the present application, the process described in the figure of a visual blockchain data storage system can be implemented as a computer software program. For example, the present application provides a non-transitory machine-readable storage medium, which stores machine-readable instructions, and the machine-readable instructions can be executed by a processor to execute instructions corresponding to the method steps provided in the present application. Of course, the architecture shown in the figure of a visual blockchain data storage system is only exemplary. When implementing different devices, adaptive selection or adjustment can be made according to actual needs.

[0160] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0161] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technical users in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A visual blockchain data storage system, characterized in that: The visual blockchain data storage system includes: Data collection module: used to deploy lightweight proxy nodes in each blockchain and use multi-threading to collect raw data on different nodes in parallel; Data storage module: Use the automated storage expansion mechanism to design a layered storage architecture, establish an abstract data model, map the raw data of different blockchains into a unified framework, and obtain the raw data in a unified format; The tiered storage architecture includes a hot storage layer, a cold storage layer, and an archive storage layer. The hot storage layer is used to store active data, the cold storage layer is used to store data with low access frequency, and the archive storage layer is used to store data that is no longer accessed for a long time. The method for designing a tiered storage architecture using an automated storage expansion mechanism includes: Use monitoring tools to integrate the health status Jk(f), capacity utilization Rl(f), throughput Tu(f), and latency Yc(f) of each storage layer at time point f; Calculate the evaluation index of each storage layer at time point f in, and represents the weight coefficient; Taking the current time point t as the time base point, calculate the evaluation index at each time point in turn, and obtain the absolute difference between the evaluation index of each time point and the current time point, until the absolute difference of the evaluation index is greater than the absolute threshold, collect the evaluation index of all interval time points between this time point and the time base point, and combine the evaluation index of the time base point to form a p-order data group, where p represents the number of time points extracted from the time base point forward; Select d time points forward from the current time t as the time base points, obtain d p-order data groups, and train the fitting model The stopping condition of the fitting model is set as the absolute difference between the output values ​​of two consecutive iterations in o consecutive iterations is less than the fitting threshold; Among them, ε(t-1) represents the random error term at the previous time point t-1, αi represents the i-th fitting coefficient, Gv(ti) represents the evaluation index at time point ti, and δ represents the adjustment coefficient; Input the evaluation index Gv(t) at the current time point t into the trained fitting model to obtain the Nh(t) value at the time point t. When the Nh(t) value is greater than Q1, use horizontal expansion to increase the storage nodes of the hot storage layer. When the Nh(t) value is greater than Q2, use automated data lifecycle management tools to migrate the data in the cold storage layer to the hot storage layer or the archive storage layer. When the Nh(t) value is greater than Q3, use cloud services to increase the storage nodes of the archive storage layer. Among them, Q1, Q2 and Q3 are preset constants; Data integration processing module: aggregates the stored raw data, obtains aggregate groups, analyzes the correlation between different aggregate groups, and constructs a three-dimensional map of data relationships; Visualization display module: used to enable users to interact with the data stored in the blockchain, with a three-dimensional map of data relationships as the underlying architecture, to increase users' multi-level visualization of data and provide a user-friendly interface; The method for constructing the three-dimensional graph of data relations comprises: Construct a blank 3D layer, label the blockchain nodes horizontally, the data types vertically, and the time points vertically; Extract the nodes, data types and time points of each aggregation group, and simulate the aggregation group into solid points to fill in the blank three-dimensional layer to form point filling; Define the solid points to be connected by directed edges, set attribute values ​​on the directed edges, and set the attribute values ​​according to the formula Wherein, β1 and β2 are used to represent weight coefficients, Px represents the number of times aggregation group Fc_d1 and aggregation group Fc_d2 appear at the same time point, Ph represents the number of times aggregation group Fc_d1 appears at a time point, Pj represents the number of times aggregation group Fc_d2 appears at a time point, Fc_d1∩Fc_d2 represents the overlapping content of aggregation group Fc_d1 and aggregation group Fc_d2, and Fc_d1∪Fc_d2 represents all combined content of aggregation group Fc_d1 and aggregation group Fc_d2; Use directed edges to connect any two aggregation groups, calculate the attribute values ​​of the directed edges, and add the attribute values ​​to the directed edges to form a complete three-dimensional graph of data relationships.

2. The visual blockchain data storage system according to claim 1, characterized in that: The types of raw data include transaction records, smart contracts, block header information, and node status; Methods for using multithreading to collect raw data on different nodes in parallel include: Select a lightweight proxy node and deploy it to the blockchain; Select multi-threading technology, where each thread is responsible for fetching the latest raw data from a node; Use a short-term buffering mechanism for continuous acquisition of raw data.

3. The visual blockchain data storage system according to claim 2 is characterized in that: The abstract data model is established to map the original data of different blockchains into a unified framework. The specific method includes: Set the properties of the block including block hash H_b, parent block hash H_pb, block height Q_h, timestamp t_b and transaction record T_b, and use smart contract Z_h, block header information Q_t, and node status J_z as abstract definitions, where the node status is arranged in the order of links in the block; Defining an abstract data model includes two layers of mapping, namely attribute mapping and abstract definition mapping. The attribute mapping layer is MX=(H_b, H_pb, Q_h, t_b, T_b), and the abstract definition mapping layer is CX=(Z_h, Q_t, J_z); Map the raw data of different blockchains into a unified framework formed by an abstract data model, so that the raw data has a unified standard format.

4. The visual blockchain data storage system according to claim 3 is characterized in that: The method for performing aggregation processing on the raw data includes: Step A1: Store the collected raw data in a hierarchical storage architecture, then remove irrelevant characters and unify the writing format of the raw data, build a vocabulary based on the set of unique words in the raw data, calculate the TF-IDF value of each word, use each TF-IDF value as a feature of the word, build a blank vector, correspond each blank element in the blank vector to the TF-IDF value of the word in the vocabulary, and then convert each data type in the raw data at the same time point into a feature vector representation; Step A2: Collect the feature vectors at each node on the blockchain, and for each data type, extract the maximum Tz of the feature vectors max and minimum Tz min , the feature vector of each data type is divided into K_q blocks to obtain the magnetic center Where Zx(v) represents the magnetic center of the vth block, and v represents the index of the block; Step A3: Calculate the distance between each eigenvector and the magnetic center Where Cs(g, v) represents the distance between the eigenvector Tz(g) and the magnetic center Zx(v), Tz(g) represents the g-th eigenvector, and G represents the number of eigenvectors; Step A4: Assign each feature vector to the block to which the nearest magnetic center belongs, extract the number of feature vectors in each block, and if the number is less than the evaluation threshold, dissolve the block, and refill the dissolve feature vectors into the blocks to which other magnetic centers belong, thereby adaptively adjusting the preset K_q blocks; Step A5: After completing the distribution of feature vectors, update each magnetic center to the mean of all feature vectors in its block, repeat steps A3 to A4 until the preset number of distributions is reached, and stop, treating each block as an aggregation group.

5. The visual blockchain data storage system according to claim 4 is characterized in that: The method for the user to interact with the data stored in the blockchain includes: Use React.js or Vue.js front-end frameworks to build responsive user interfaces and use visualization libraries to create interactive displays; Integrated dashboard tools to customize data display and monitoring.

6. The visual blockchain data storage system according to claim 5 is characterized in that: The three-dimensional graph of data relationships is used as the underlying architecture to increase the multi-level visualization of data for users. The specific methods include: Set up multi-level data display. The macro view is used to display the overall status of the blockchain network; the meso view is used to display detailed information of a block or a node; and the micro view is used to display specific details of a smart contract. With the data relationship three-dimensional graph as the underlying architecture, the Neo4j graph database and graph visualization tools are used to display the correlation between the primary color data on each node in the data relationship three-dimensional graph. By clicking on a block or node, you can view the relevant information layer by layer, and use the breadcrumb navigation to quickly switch between different levels.

7. A visual blockchain data storage method, applied to a visual blockchain data storage system according to any one of claims 1 to 6, characterized in that: The visual blockchain data storage method comprises: Step G1: Use multi-threading to collect raw data on different nodes in parallel; Step G2: Build a tiered storage architecture, including a hot storage layer, a cold storage layer, and an archive storage layer, and use an automated storage expansion mechanism to design the tiered storage architecture; Step G3: Establish an abstract data model, map the raw data of different blockchains into a unified framework, and obtain the raw data in a unified format; Step G4: Aggregate the stored original data to obtain aggregation groups, build a blank three-dimensional layer, and analyze the correlation between different aggregation groups to obtain a three-dimensional map of data relationships; Step G5: View the storage of blockchain data in real time through visual display and user-friendly interface.

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