A Blockchain-Based Trade Data Management Method and System

Abstract

The present application provides a blockchain-based trade data management method and system, which relates to the technical field of trade data management, and obtains trade data; determines the data point distance between each transaction node through the trade data, and constructs a spacing difference matrix of the trade data from the data point distance; extracts trade records between different transaction nodes from the trade data, determines the correlation dependency between each transaction node through the trade records between different transaction nodes, and obtains trade dependency relationships based on all the correlation dependencies; uses the spacing difference matrix and the trade dependency relationships to decouple the trade data from node domains, obtains multiple trade node domains, and determines the intra-domain fuzzy factor of each trade node domain; classifies and stores the trade records in each trade node domain based on the corresponding intra-domain fuzzy factor. The present application can classify and store the trade records based on the dependency relationships between transaction nodes to improve the storage efficiency of the trade data management system.

Description

Technical Field

[0001] The present application relates to the technical field of trade data management, and more specifically, to a blockchain-based trade data management method and system. Background Art

[0002] Trade data management based on blockchain is a systematic method that uses distributed ledgers, encrypted storage and smart contract mechanisms of blockchain technology to achieve secure storage, transparent sharing and automated execution of trade data. The core idea is to connect various trade participants (such as suppliers, manufacturers, logistics providers, distributors and customers) through the blockchain network to achieve full-process digitization, immutability and real-time sharing of trade information, thereby solving problems such as information asymmetry, data tampering and inefficiency in traditional trade. The decentralized nature of blockchain allows participants to verify and record all transaction data through a consensus mechanism, greatly improving the credibility and security of the entire system. In addition, through smart contract technology, the terms and conditions in the trade agreement can be automatically executed, thereby reducing human intervention and improving the automation and efficiency of the entire trade process.

[0003] The current mainstream blockchain technology has performance bottlenecks in transaction throughput and data storage, making it difficult to meet the real-time processing requirements of high-frequency and large-scale data volumes. In addition, trade data usually involves multi-level complex relationships, which affects the maintenance and query efficiency of trade data. When storing trade data in engineering projects, the redundancy and complexity of trade data lead to low storage efficiency when classifying and storing trade data. Summary of the invention

[0004] The present application provides a blockchain-based trade data management method and system, which can classify and store trade records according to the dependencies between transaction nodes to improve the storage efficiency of the trade data management system.

[0005] In a first aspect, the present application provides a blockchain-based trade data management method, the management method comprising the following steps:

[0006] Obtain the trade records of all transaction nodes in the target blockchain network and obtain trade data;

[0007] Determine the data point distances between each transaction node through the trade data, and construct a spacing difference matrix of the trade data based on the data point distances between each transaction node;

[0008] Extracting trade records between different transaction nodes from the trade data, determining the correlation dependency between the transaction nodes through the trade records between the different transaction nodes, and obtaining the trade dependency relationship between the transaction nodes in the target blockchain network according to the correlation dependency between the transaction nodes;

[0009] Decoupling the trade data by node domains using the spacing difference matrix and the trade dependency to obtain a plurality of trade node domains, and then determining a fuzzy factor within each trade node domain;

[0010] The trade records in each trade node domain are classified and stored according to the corresponding intra-domain fuzzy factors.

[0011] In this embodiment, determining the data point distance between each transaction node through the trade data specifically includes:

[0012] Obtaining a trade record corresponding to each transaction node in the trade data;

[0013] The data point distances between each transaction node are determined using the trade records corresponding to each transaction node.

[0014] In this embodiment, constructing the spacing difference matrix of the trade data from the data point distances between the various transaction nodes is to use a matrix composed of all data point distances as the spacing difference matrix of the trade data.

[0015] In this embodiment, determining the correlation dependency between different transaction nodes through the trade records between different transaction nodes specifically includes:

[0016] For each transaction node, the trade frequency between the transaction node and other transaction nodes is obtained according to the trade records between the transaction node and other transaction nodes;

[0017] The correlation dependency between the transaction node and other transaction nodes is determined by the trade frequency between the transaction node and other transaction nodes, and then the correlation dependency between each transaction node is obtained.

[0018] In this embodiment, obtaining the trade dependency relationship of the transaction nodes in the target blockchain network according to the correlation dependency between the transaction nodes specifically includes:

[0019] Two transaction nodes with a correlation dependency higher than a set threshold are regarded as strong transaction node pairs, and then all strong transaction node pairs are obtained;

[0020] Two transaction nodes whose correlation dependency is lower than the set threshold are regarded as weak transaction node pairs, and then all weak transaction node pairs are obtained;

[0021] The trade dependency relationship of transaction nodes in the target blockchain network is constructed based on the dependency characteristics of each strong transaction node pair and the dependency characteristics of each weak transaction node pair.

[0022] In this embodiment, the trade data is decoupled by node domains using the spacing difference matrix and the trade dependency relationship, and multiple trade node domains are obtained, specifically including:

[0023] Dividing all transaction nodes based on the spacing difference matrix and the trade dependency relationship, thereby obtaining a plurality of transaction node clusters;

[0024] The trade data is node-domain decoupled according to all transaction node clusters to obtain multiple trade node domains.

[0025] In this embodiment, determining the intra-domain fuzzy factor of each trade node domain specifically includes:

[0026] For each trade node domain, determining the total number of trade records in the trade node domain;

[0027] Get the domain center of the trade node domain;

[0028] Determine the internal accumulation coefficient of the trade node domain;

[0029] The intra-domain fuzzy factor of the trade node domain is determined by the total number of trade records, the domain center and the inner accumulation coefficient, and then the intra-domain fuzzy factor of each trade node domain is obtained.

[0030] In this embodiment, the inner clustering coefficient of the trade node domain is determined by taking the average of the distances between all trade records in the trade node domain and the domain center as the inner clustering coefficient of the trade node domain.

[0031] In this embodiment, classifying and storing the trade records in each trade node domain according to the corresponding intra-domain fuzzy factor specifically includes:

[0032] The trade node domain whose fuzzy factor in the domain is less than the fuzzy decision value is used as the trade node domain to be stored, thereby obtaining multiple trade node domains to be stored;

[0033] The trade records in each trade node domain to be stored are stored as a class respectively.

[0034] In a second aspect, the present application provides a blockchain-based trade data management system for executing a blockchain-based trade data management method, the management system comprising:

[0035] The trade data acquisition module is used to obtain the trade records of all transaction nodes in the target blockchain network and obtain trade data;

[0036] A difference matrix determination module, used to determine the data point distances between each transaction node through the trade data, and construct a spacing difference matrix of the trade data based on the data point distances between each transaction node;

[0037] A dependency extraction module is used to extract trade records between different transaction nodes from the trade data, determine the correlation dependency between each transaction node through the trade records between different transaction nodes, and obtain the trade dependency of the transaction nodes in the target blockchain network according to the correlation dependency between each transaction node;

[0038] A fuzzy factor determination module, configured to perform node domain decoupling on the trade data using the spacing difference matrix and the trade dependency relationship to obtain a plurality of trade node domains, and then determine the intra-domain fuzzy factor of each trade node domain;

[0039] The data classification storage module is used to classify and store the trade records in each trade node domain according to the corresponding intra-domain fuzzy factors.

[0040] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0041] The trade data is obtained by acquiring the trade records of all transaction nodes in the target blockchain network; the data point distances between each transaction node are determined through the trade data, and the spacing difference matrix of the trade data is constructed by the data point distances between each transaction node; the trade records between different transaction nodes are extracted from the trade data, and the correlation dependency between each transaction node is determined through the trade records between different transaction nodes, and the trade dependency relationship of the transaction nodes in the target blockchain network is obtained according to the correlation dependency between each transaction node; the trade data is decoupled by node domain using the spacing difference matrix and the trade dependency relationship to obtain multiple trade node domains, and then the intra-domain fuzzy factor of each trade node domain is determined; the trade records in each trade node domain are classified and stored according to the corresponding intra-domain fuzzy factor.

[0042] It can be seen that in the present application, trade records can be classified and stored according to the dependencies between transaction nodes; wherein, the differences between each transaction node are reflected by the data point distance, and by constructing a spacing difference matrix, the relationship strength between transaction nodes can be more intuitively understood; then, by analyzing the trade dependencies, the transaction nodes that are critical to the entire transaction network can be identified, redundant data can be identified and eliminated, and duplicate storage can be reduced; further, through node domain decoupling, trade records can be divided into different node domains according to similarities and dependencies. This structured data storage method makes subsequent data analysis and management more efficient, and the intra-domain fuzzy factor can quantify the degree of dispersion between trade records in the trade node domain; finally, the trade node domain to be stored is screened out according to the intra-domain fuzzy factor, and a trade node domain to be stored is stored as a separate class, so that the trade records in each category are more similar, the accuracy of trade data classification is improved, and the classification storage efficiency is optimized.

[0043] In summary, the technical solution adopted in this application can classify and store trade records according to the dependencies between transaction nodes to improve the storage efficiency of the trade data management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0045] Figure 1 is a flowchart of a blockchain-based trade data management method provided by this application;

[0046] Figure 2 is an exemplary flow chart for determining the relevant dependency provided by the present application;

[0047] Figure 3 is an exemplary flow chart for determining a trade node domain according to the present application;

[0048] Figure 4 This is a module structure diagram of the blockchain-based trade data management system provided in this application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] The embodiment of the present application provides a trade data management method and system based on blockchain, the core of which is to obtain the trade records of all transaction nodes in the target blockchain network to obtain trade data; determine the data point distance between each transaction node through the trade data, and construct the spacing difference matrix of the trade data from the data point distance between each transaction node; extract the trade records between different transaction nodes from the trade data, determine the correlation dependency between each transaction node through the trade records between different transaction nodes, and obtain the trade dependency relationship of the transaction nodes in the target blockchain network according to the correlation dependency between each transaction node; use the spacing difference matrix and the trade dependency relationship to decouple the trade data from the node domain to obtain multiple trade node domains, and then determine the intra-domain fuzzy factor of each trade node domain; classify and store the trade records in each trade node domain according to the corresponding intra-domain fuzzy factor. The above scheme can be used to classify and store trade records according to the dependency relationship between transaction nodes to improve the storage efficiency of the trade data management system.

[0051] Embodiment 1

[0052] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 As shown in the figure, this figure is an exemplary flow chart of a trade data management method based on blockchain according to this embodiment of the present application, and the management method includes the following steps:

[0053] In step S1, the trade records of all transaction nodes in the target blockchain network are obtained to obtain trade data.

[0054] In the specific implementation, first, ensure that access permission is obtained to the target blockchain network; then, configure the corresponding client tool according to the target blockchain platform used, and this client will serve as the interface for data query; finally, the transaction node information in the target blockchain network can be queried through the query interface, so that all trade records can be obtained from each transaction node, and the data consisting of all trade records can be combined as trade data.

[0055] In step S2, the data point distances between the various transaction nodes are determined through the trade data, and a spacing difference matrix of the trade data is constructed based on the data point distances between the various transaction nodes.

[0056] In this embodiment, the data point distance between each transaction node can be determined by the trade data in the following manner, namely:

[0057] Obtaining a trade record corresponding to each transaction node in the trade data;

[0058] The data point distances between each transaction node are determined using the trade records corresponding to each transaction node.

[0059] In the specific implementation, first, the trade records corresponding to each transaction node can be obtained by traversing the trade data; then, the trade records corresponding to each transaction node can be used to determine the data point distance between each transaction node, that is, the distance between trade records is measured by the Euclidean measurement method, and the distance is used as the data point distance between the corresponding transaction nodes. The data point distance is used to represent the degree of difference between the transaction nodes. The data point distance between each transaction node can be obtained in the above manner.

[0060] In this embodiment, constructing the spacing difference matrix of the trade data from the data point distances between the various transaction nodes is to use a matrix composed of all data point distances as the spacing difference matrix of the trade data.

[0061] It should be noted that the data point distance reflects the differences between various transaction nodes. By constructing the distance difference matrix, we can more intuitively understand the strength of the relationship between transaction nodes. For example, a small data point distance between two transaction nodes indicates that their transaction relationship is close, while a large data point distance indicates that there are fewer transactions or no direct connection between them, which is very useful for analyzing the dependency relationship between transaction nodes.

[0062] In step S3, the trade records between different transaction nodes are extracted from the trade data, the correlation dependency between each transaction node is determined through the trade records between different transaction nodes, and the trade dependency relationship of the transaction nodes in the target blockchain network is obtained according to the correlation dependency between each transaction node.

[0063] In specific implementation, when extracting trade records between different transaction nodes in trade data, the trade records between different transaction nodes in the target blockchain network can be obtained by traversing the trade data.

[0064] Preferably, in this embodiment, reference Figure 2As shown, this figure is an exemplary flow chart of determining the relevant dependency in an embodiment of the present application. In this embodiment, the relevant dependency between each transaction node is determined by the trade records between different transaction nodes, which can be implemented by the following steps:

[0065] First, in step S31, for each transaction node, the trade frequency between the transaction node and other transaction nodes is obtained according to the trade records between the transaction node and other transaction nodes;

[0066] Then, in step S32, the correlation dependency between the transaction node and other transaction nodes is determined according to the trade frequency between the transaction node and other transaction nodes, and then the correlation dependency between each transaction node is obtained.

[0067] In the specific implementation, first, for each transaction node, the trade frequency between the transaction node and other transaction nodes can be obtained by traversing the trade records between the transaction node and other transaction nodes, and the trade frequency refers to the number of trades between the selected transaction node and other transaction nodes; then, the correlation dependency between the transaction node and other transaction nodes can be determined by the trade frequency between the transaction node and other transaction nodes, and all trade frequencies of the transaction node can be counted. It should be noted that the trade of the transaction node can be trade within the node or trade between nodes, so the ratio of the trade frequency between the transaction node and other transaction nodes to all trade frequencies of the transaction node is used as the correlation dependency between the transaction node and other transaction nodes. The correlation dependency is used to represent the degree of correlation dependency between transaction nodes. The correlation dependency between each transaction node can be obtained in the above manner.

[0068] In this embodiment, the trade dependency relationship of the transaction nodes in the target blockchain network can be obtained according to the correlation dependency between the transaction nodes in the target blockchain network in the following manner, namely:

[0069] Two transaction nodes with a correlation dependency higher than a set threshold are regarded as strong transaction node pairs, and then all strong transaction node pairs are obtained;

[0070] Two transaction nodes whose correlation dependency is lower than the set threshold are regarded as weak transaction node pairs, and then all weak transaction node pairs are obtained;

[0071] The trade dependency relationship of transaction nodes in the target blockchain network is constructed based on the dependency characteristics of each strong transaction node pair and the dependency characteristics of each weak transaction node pair.

[0072] In the specific implementation, first, a threshold of correlation dependency needs to be defined. This threshold is used to distinguish strong transaction node pairs from weak transaction node pairs. The selection of the threshold can be based on experience, historical data analysis, or determined by statistical methods (such as percentiles), which is not limited here; then, two transaction nodes with correlation dependency higher than the set threshold are regarded as strong transaction node pairs, thereby obtaining all strong transaction node pairs, and two transaction nodes with correlation dependency lower than the set threshold are regarded as weak transaction node pairs, thereby obtaining all weak transaction node pairs; finally, according to the dependency characteristics of strong transaction node pairs and weak transaction node pairs, the target area is constructed. The trade dependency relationship of transaction nodes in the block chain network can take the trade frequency between transaction nodes as the dependency feature of the corresponding transaction node pair, and the dependency features of each strong transaction node pair and the dependency features of each weak transaction node pair can be obtained, thereby integrating the dependency relationship models of strong transaction node pairs and weak transaction node pairs to form a comprehensive trade dependency graph. The trade dependency graph can include strong dependency edges and weak dependency edges, and the weights of the edges are expressed by the relevant dependency degree. Among them, the strong dependency edges represent the relationship between strong transaction node pairs, and the weak dependency edges represent the relationship between weak transaction node pairs, showing the fragility of their dependency relationships.

[0073] It should be noted that by analyzing trade dependencies, it is possible to identify transaction nodes that are critical to the entire transaction network, and to identify those transaction nodes with excessively high or low correlation dependencies. Transaction nodes with excessively high correlation dependencies may have concentration risks, while transaction nodes with excessively low correlation dependencies may be overlooked in transactions. By identifying the dependencies of transaction nodes, redundant data can be identified and eliminated, reducing duplicate storage.

[0074] In step S4, the trade data is node-domain decoupled using the spacing difference matrix and the trade dependency relationship to obtain a plurality of trade node domains, and then the intra-domain fuzzy factor of each trade node domain is determined.

[0075] Preferably, in this embodiment, reference Figure 3 As shown, this figure is an exemplary flow chart of determining a trade node domain in an embodiment of the present application. In this embodiment, the trade data is decoupled by node domain using the spacing difference matrix and the trade dependency relationship, and multiple trade node domains are obtained, which can be specifically implemented by the following steps:

[0076] First, in step S41, all transaction nodes are divided based on the distance difference matrix and the trade dependency relationship, thereby obtaining a plurality of transaction node clusters;

[0077] Then, in step S42, the trade data is node-domain decoupled according to all transaction node clusters to obtain a plurality of trade node domains.

[0078] In the specific implementation, first, all transaction nodes can be divided based on the spacing difference matrix and the trade dependency, that is, for each transaction node, the corresponding data point distance and the related dependency of the transaction node are obtained in the spacing difference matrix and the trade dependency, so that the data point distance and the related dependency are combined to construct a feature representation vector to represent each transaction node. The feature representation vector of each transaction node can be obtained in the above manner; then, the feature representation vector of each transaction node is input into the clustering algorithm for division, so that multiple transaction node clusters can be obtained, and the transaction nodes in the transaction node clusters have high similarity and dependency; finally, the trade data can be node-decoupled according to all transaction node clusters to obtain multiple trade node domains, that is, the original trade data is assigned to the corresponding transaction nodes, so as to obtain multiple trade node domains that are independent of each other, that is, the decoupling process, and the trade records in each trade node domain have high similarity. It should be noted that through node domain decoupling, multiple independent trade node domains are obtained, and data classification can be more refined, which improves the precision and classification accuracy of trade data. When classified storage, a suitable data structure and storage method can be selected according to the characteristics of each trade node domain, which improves storage efficiency.

[0079] In this embodiment, the intra-domain fuzzy factor of each trade node domain may be determined in the following manner, namely:

[0080] For each trade node domain, determining the total number of trade records in the trade node domain;

[0081] Get the domain center of the trade node domain;

[0082] Determine the internal accumulation coefficient of the trade node domain;

[0083] The intra-domain fuzzy factor of the trade node domain is determined by the total number of trade records, the domain center and the inner accumulation coefficient, and then the intra-domain fuzzy factor of each trade node domain is obtained.

[0084] In specific implementation, first, for each trade node domain, the total number of trade records in the trade node domain can be obtained by traversal, and the mean of the feature representation vectors of all transaction nodes in the trade node domain can be calculated, so that the result is used as the domain center of the trade node domain; then, the inner accumulation coefficient of the trade node domain can be determined, that is, the mean of the distance between all trade records in the trade node domain and the domain center is used as the inner accumulation coefficient of the trade node domain. The inner accumulation coefficient represents the degree of convergence of all trade records in the trade node domain to the domain center. The larger the inner accumulation coefficient, the closer all trade records are to the domain center; finally, the inner domain fuzzy factor of the trade node domain can be determined by the total number of trade records, the domain center and the inner accumulation coefficient. The inner domain fuzzy factor represents the degree of dispersion of all trade records in the trade node domain. In actual implementation, the inner domain fuzzy factor can be determined according to the following formula:

[0085]

[0086] in, represents the intra-domain fuzzy factor of the k-th trade node domain, represents the total number of trade records in the kth trade node domain, represents the tth trade record in the kth trade node domain, represents the domain center of the kth trade node domain, represents the inner accumulation coefficient of the kth trade node domain. The inner fuzzy factor of each trade node domain can be obtained by the above method.

[0087] It should be noted that through node domain decoupling, trade records can be divided into different node domains according to similarities and dependencies. This structured data storage method makes subsequent data analysis and management more efficient, and the intra-domain fuzzy factor can quantify the degree of dispersion between trade records in the trade node domain. Decision makers can formulate more accurate strategies based on actual data.

[0088] In step S5, the trade records in each trade node domain are classified and stored according to the corresponding intra-domain fuzzy factors.

[0089] In this embodiment, the trade records in each trade node domain are classified and stored according to the corresponding intra-domain fuzzy factors in the following manner, namely:

[0090] The trade node domain whose fuzzy factor in the domain is less than the fuzzy decision value is used as the trade node domain to be stored, thereby obtaining multiple trade node domains to be stored;

[0091] The trade records in each trade node domain to be stored are stored as a class respectively.

[0092] In the specific implementation, first, a fuzzy decision value needs to be set. The fuzzy decision value is used to determine which trade node domains have a lower intra-domain fuzzy factor and are therefore suitable for storage. The fuzzy decision value can be determined based on historical data analysis, industry standards, or empirical rules; then, the trade node domains whose intra-domain fuzzy factors are less than the fuzzy decision value are used as trade node domains to be stored, thereby obtaining multiple trade node domains to be stored; finally, the trade records in each trade node domain to be stored are stored as a class.

[0093] It should be noted that the trade node domain to be stored is screened out according to the fuzzy factor within the domain, and a trade node domain to be stored is stored as a separate class, so that the trade records in each category are more similar, which improves the accuracy of trade data classification and optimizes the classification storage efficiency.

[0094] It can be seen that in the present application, trade records can be classified and stored according to the dependencies between transaction nodes; wherein, the differences between each transaction node are reflected by the data point distance, and by constructing a spacing difference matrix, the relationship strength between transaction nodes can be more intuitively understood; then, by analyzing the trade dependencies, the transaction nodes that are critical to the entire transaction network can be identified, redundant data can be identified and eliminated, and duplicate storage can be reduced; further, through node domain decoupling, trade records can be divided into different node domains according to similarities and dependencies. This structured data storage method makes subsequent data analysis and management more efficient, and the intra-domain fuzzy factor can quantify the degree of dispersion between trade records in the trade node domain; finally, the trade node domain to be stored is screened out according to the intra-domain fuzzy factor, and a trade node domain to be stored is stored as a separate class, so that the trade records in each category are more similar, the accuracy of trade data classification is improved, and the classification storage efficiency is optimized.

[0095] In summary, the technical solution adopted in this application can classify and store trade records according to the dependencies between transaction nodes to improve the storage efficiency of the trade data management system.

[0096] Embodiment 2

[0097] This application provides a trade data management system based on blockchain, referring to Figure 4 As shown, this figure is a schematic diagram of a management system according to this embodiment of the present application, and the management system includes:

[0098] The trade data acquisition module 100 is used to acquire the trade records of all transaction nodes in the target blockchain network to obtain trade data;

[0099] A difference matrix determination module 200 is used to determine the data point distances between each transaction node through the trade data, and construct a spacing difference matrix of the trade data based on the data point distances between each transaction node;

[0100] A dependency extraction module 300 is used to extract trade records between different transaction nodes from the trade data, determine the correlation dependency between each transaction node through the trade records between different transaction nodes, and obtain the trade dependency of the transaction nodes in the target blockchain network according to the correlation dependency between each transaction node;

[0101] A fuzzy factor determination module 400, configured to perform node domain decoupling on the trade data using the spacing difference matrix and the trade dependency relationship to obtain a plurality of trade node domains, and then determine the intra-domain fuzzy factor of each trade node domain;

[0102] The data classification storage module 500 is used to classify and store the trade records in each trade node domain according to the corresponding intra-domain fuzzy factor.

[0103] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0104] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, the storage medium including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically-erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0105] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. A trade data management method based on blockchain, characterized in that: The management method comprises the following steps: Obtain the trade records of all transaction nodes in the target blockchain network and obtain trade data; Determine the data point distances between each transaction node through the trade data, and construct a spacing difference matrix of the trade data based on the data point distances between each transaction node; Extracting trade records between different transaction nodes from the trade data, determining the correlation dependency between the transaction nodes through the trade records between the different transaction nodes, and obtaining the trade dependency relationship between the transaction nodes in the target blockchain network according to the correlation dependency between the transaction nodes; Decoupling the trade data by node domains using the spacing difference matrix and the trade dependency to obtain a plurality of trade node domains, and then determining a fuzzy factor within each trade node domain; Classify and store the trade records in each trade node domain according to the corresponding intra-domain fuzzy factors; Among them, the relevant dependencies between various transaction nodes are determined through the trade records between different transaction nodes, specifically including: For each transaction node, the trade frequency between the transaction node and other transaction nodes is obtained according to the trade records between the transaction node and other transaction nodes; Determine the correlation dependency between the transaction node and other transaction nodes by the trade frequency between the transaction node and other transaction nodes, and then obtain the correlation dependency between each transaction node, wherein the ratio of the trade frequency between the transaction node and other transaction nodes to all trade frequencies of the transaction node is taken as the correlation dependency between the transaction node and other transaction nodes; Among them, the trade dependency relationship of the transaction nodes in the target blockchain network obtained according to the correlation dependency between each transaction node specifically includes: Two transaction nodes with a correlation dependency higher than a set threshold are regarded as strong transaction node pairs, and then all strong transaction node pairs are obtained; Two transaction nodes whose correlation dependency is lower than the set threshold are regarded as weak transaction node pairs, and then all weak transaction node pairs are obtained; The trade dependency relationship of the transaction nodes in the target blockchain network is constructed based on the dependency characteristics of each strong transaction node pair and the dependency characteristics of each weak transaction node pair, wherein the trade frequency between the transaction nodes is used as the dependency characteristic of the corresponding transaction node pair.

2. A blockchain-based trade data management method as claimed in claim 1, characterized in that: Determining the data point distance between each transaction node through the trade data specifically includes: Obtaining a trade record corresponding to each transaction node in the trade data; The data point distances between each transaction node are determined using the trade records corresponding to each transaction node.

3. A blockchain-based trade data management method as claimed in claim 1, characterized in that: Constructing the spacing difference matrix of the trade data from the data point distances between the various transaction nodes is to use the matrix composed of all data point distances as the spacing difference matrix of the trade data.

4. A blockchain-based trade data management method as claimed in claim 1, characterized in that: The trade data is decoupled by node domains using the spacing difference matrix and the trade dependency relationship, and a plurality of trade node domains are obtained, specifically including: Dividing all transaction nodes based on the spacing difference matrix and the trade dependency relationship, thereby obtaining a plurality of transaction node clusters; The trade data is node-domain decoupled according to all transaction node clusters to obtain multiple trade node domains.

5. A blockchain-based trade data management method as claimed in claim 1, characterized in that: The domain fuzzy factors for determining each trade node domain specifically include: For each trade node domain, determining the total number of trade records in the trade node domain; Get the domain center of the trade node domain; Determine the internal accumulation coefficient of the trade node domain; The intra-domain fuzzy factor of the trade node domain is determined by the total number of trade records, the domain center and the inner accumulation coefficient, and then the intra-domain fuzzy factor of each trade node domain is obtained.

6. A blockchain-based trade data management method as claimed in claim 5, characterized in that: Determining the inner clustering coefficient of the trade node domain is to use the mean of the distances between all trade records in the trade node domain and the domain center as the inner clustering coefficient of the trade node domain.

7. A blockchain-based trade data management method as claimed in claim 1, characterized in that: Classification and storage of trade records in each trade node domain according to the corresponding domain fuzzy factors specifically include: The trade node domain whose fuzzy factor in the domain is less than the fuzzy decision value is used as the trade node domain to be stored, thereby obtaining multiple trade node domains to be stored; The trade records in each trade node domain to be stored are stored as a class respectively.

8. A blockchain-based trade data management system, used to execute a blockchain-based trade data management method as claimed in any one of claims 1 to 7, characterized in that: The management system comprises: The trade data acquisition module is used to obtain the trade records of all transaction nodes in the target blockchain network and obtain trade data; A difference matrix determination module, used to determine the data point distances between each transaction node through the trade data, and construct a spacing difference matrix of the trade data based on the data point distances between each transaction node; A dependency extraction module is used to extract trade records between different transaction nodes from the trade data, determine the correlation dependency between each transaction node through the trade records between different transaction nodes, and obtain the trade dependency of the transaction nodes in the target blockchain network according to the correlation dependency between each transaction node; A fuzzy factor determination module, configured to perform node domain decoupling on the trade data using the spacing difference matrix and the trade dependency relationship to obtain a plurality of trade node domains, and then determine the intra-domain fuzzy factor of each trade node domain; The data classification storage module is used to classify and store the trade records in each trade node domain according to the corresponding intra-domain fuzzy factors.

Images