A method and system for improving blockchain data processing efficiency

Abstract

The present invention relates to the field of data storage technology, and more specifically, to a method and system for improving the efficiency of blockchain data processing. The method comprises: obtaining transaction information from blockchain data, extracting keywords to determine transaction keywords; analyzing frequency characteristics of transaction keywords to determine a co-occurrence frequency index; constructing a transaction user network; determining the business relevance between the two parties to a transaction in the transaction user network, and dividing the network into different communities; determining a transaction frequency index for the target user based on the business relevance between the target user and other transaction users in the same community and the transaction amount of each transaction; determining a priority index for the community based on the transaction frequency indexes of all transaction users in the same community; and classifying and storing transaction information from different communities based on the values ​​of the priority indexes of all communities. Embodiments of the present invention can improve the reliability of blockchain data storage strategies and enhance data processing efficiency.

Description

Technical Field

[0001] The present invention relates to the field of data storage technology, and in particular to a method and system for improving blockchain data processing efficiency. Background Art

[0002] Blockchain technology is a distributed ledger that uses encryption algorithms to ensure data security and immutability. In a blockchain, data is typically stored in blocks, each containing a certain number of transaction records and linked to the previous block, forming a chain structure. With the rapid development and widespread application of blockchain technology, the amount of data in blockchain networks has increased dramatically, placing higher demands on the data processing and storage capabilities of blockchain nodes.

[0003] Existing technologies typically store blockchain transaction information by using search keywords and storing it in a single database. Due to the large volume of blockchain data and its complex data sources, this approach, based solely on pre-set search keywords, can lead to inefficient data retrieval, verification, and synchronization. This can lead to performance bottlenecks, especially when processing high-concurrency requests. This means existing blockchain data storage strategies lack reliability and efficiency. Summary of the Invention

[0004] In order to solve the technical problems of low reliability and insufficient processing efficiency of existing blockchain data storage strategies, the present invention provides a method and system for improving blockchain data processing efficiency. The technical solutions adopted are as follows:

[0005] The present invention proposes a method for improving blockchain data processing efficiency, the method comprising:

[0006] Obtaining transaction information from blockchain data, performing keyword extraction on the transaction information, and determining transaction keywords for each transaction record data;

[0007] Selecting any transaction record data as target transaction data; determining a co-occurrence frequency index of the transaction keywords in the target transaction data based on the frequency characteristics of the transaction keywords in all transaction record data;

[0008] Constructing a transaction user network based on the transaction parties in all transaction record data; within the transaction user network, determining the business relevance of the transaction parties based on the frequency of the transaction record data corresponding to the target transaction data and the co-occurrence frequency index of each co-occurrence;

[0009] Divide transaction users into different communities based on the degree of business association; take any transaction user in any community as a target user, and determine the target user's transaction frequency index based on the target user's business association with other transaction users in the same community and the transaction amount of each transaction; and determine the priority index of the community based on the transaction frequency indexes of all transaction users in the same community;

[0010] The transaction information of different communities is classified and stored according to the values ​​of the priority indicators of all communities.

[0011] Furthermore, the determining of transaction keywords for each transaction record data includes:

[0012] Obtain the source code of the smart contract corresponding to each transaction record data, and obtain transaction keywords from the source code based on a keyword extraction algorithm.

[0013] Furthermore, the method for obtaining the co-occurrence frequency index of transaction keywords in the target transaction data includes:

[0014] Determine the frequency of occurrence of each transaction keyword and construct a frequent item set of transaction keywords in all transaction record data based on the FP-Growth algorithm;

[0015] The intersection of all transaction keywords and frequent item sets corresponding to the target transaction data is used as the frequent words of the target transaction data;

[0016] The proportion of the occurrence frequency of the frequent words in the target transaction data in the frequent item set is used as the co-occurrence frequency index of the transaction keywords in the target transaction data.

[0017] Furthermore, the method for obtaining the degree of business relevance between the two parties to the transaction includes:

[0018] Determine the mean of the collinearity frequency index of each transaction between the two parties as the first correlation coefficient;

[0019] Normalize the number of transactions by the maximum and minimum values ​​to obtain the second correlation coefficient;

[0020] The business correlation degree between the two parties to the transaction is determined according to the first correlation coefficient and the second correlation coefficient, wherein the first correlation coefficient and the second correlation coefficient are both positively correlated with the business correlation degree.

[0021] Furthermore, determining the business correlation between the two parties to the transaction based on the first correlation coefficient and the second correlation coefficient includes:

[0022] The product of the first association coefficient and the second association coefficient is calculated as the service association degree.

[0023] Furthermore, transaction users are divided into different communities according to the degree of business relevance, including:

[0024] Based on the degree of business association, the SLPA community algorithm is used to divide all transaction users into communities, resulting in multiple different communities.

[0025] Furthermore, the method for obtaining the target user's transaction frequency index includes:

[0026] Normalize the maximum and minimum transaction amounts of each transaction to obtain the amount impact index;

[0027] Calculate the product of the business relevance and amount impact index of the target user in each transaction as the single frequency coefficient of the corresponding transaction;

[0028] The sum of the single frequency coefficients of all transactions in which the target user participates is used as the transaction frequency index of the target user.

[0029] Furthermore, the priority index of the community is determined based on the transaction frequency index of all transaction users in the same community, including:

[0030] The average of the transaction frequency indexes of all transaction users in the same community is calculated and normalized as the priority index of the community.

[0031] Furthermore, the transaction information of different communities is classified and stored according to the values ​​of the priority indicators of all communities, including:

[0032] Communities whose priority index is greater than a preset priority threshold are classified as Category 1 communities, and others as Category 2 communities;

[0033] Different databases are used to store data corresponding to the first type of community and data corresponding to the second type of community, respectively. The database storing data corresponding to the first type of community has a higher call speed than the database storing data corresponding to the second type of community.

[0034] On the other hand, the present invention also provides a system for improving the efficiency of blockchain data processing, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the aforementioned methods when executing the computer program.

[0035] The present invention has the following beneficial effects:

[0036] The present invention obtains transaction information from blockchain data, extracts keywords from the transaction information, and determines transaction keywords for each transaction record. Then, based on the frequency characteristics of the transaction keywords in all transaction records, it determines a co-occurrence frequency index for the target transaction data. The co-occurrence frequency index effectively calculates the specific numerical value of the co-occurrence frequency and accurately determines the contribution characteristics of the transaction keywords. By constructing a transaction user network, the business relevance between the two transaction parties corresponding to the target transaction data and the co-occurrence frequency index of each co-occurrence is determined. A detailed analysis of the business relevance is performed on a per-transaction user basis, and subsequent community division is achieved based on the relevance of different transaction users. A transaction frequency index for the target user is determined based on the business relevance between the target user and other transaction users in the same community and the transaction amount of each transaction. This means that transaction characteristics are analyzed at the transaction user level to determine a more accurate and effective transaction frequency index. Subsequently, a community priority index can be determined based on the transaction frequency indexes of all transaction users within the same community. Transaction information from different communities is then categorized and stored based on the values ​​of the priority indexes for all communities. In the embodiment of the present invention, different transaction users are divided into communities, and distributed data storage is achieved between blockchains based on the community division effect. This can facilitate the call, retrieval and verification of transaction information data in the blockchain, avoid occupying the transmission channel of the same database in the case of high concurrency requests, improve the reliability of the blockchain data storage strategy, and enhance data processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 A flow chart of a method for improving blockchain data processing efficiency provided by one embodiment of the present invention;

[0039] Figure 2 A structural diagram of a system for improving blockchain data processing efficiency, provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0040] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a method and system for improving blockchain data processing efficiency, including its specific implementation, structure, features, and effectiveness. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0041] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0042] The following describes in detail a method for improving blockchain data processing efficiency provided by the present invention with reference to the accompanying drawings.

[0043] See also Figure 1 , which shows a flow chart of a method for improving blockchain data processing efficiency provided by one embodiment of the present invention, the method comprising:

[0044] S101: Acquire transaction information in blockchain data, extract keywords from the transaction information, and determine transaction keywords for each transaction record data.

[0045] The application scenario of the embodiments of the present invention is the storage of blockchain data. Blockchain technology is a distributed ledger technology that uses encryption algorithms to ensure data security and immutability. In a blockchain, data is typically stored in blocks. Each block contains a certain number of transaction records and is connected to the previous block to form a chain structure. The blockchain data corresponding to the embodiments of the present invention is specifically the data of smart contracts in the blockchain.

[0046] It is understandable that blockchain data storage affects the call of blockchain data, which in turn affects data processing efficiency. Traditional blockchain nodes usually store all data in a single database, resulting in inefficient data retrieval, verification and synchronization processes, especially when processing high-concurrency requests, which easily leads to performance bottlenecks.

[0047] Therefore, the embodiment of the present invention analyzes the transaction characteristics of different users in the blockchain and proposes a distributed storage strategy to facilitate data call, retrieval and verification. It can avoid occupying the transmission channel of the same database in the case of high concurrency requests and enhance data processing efficiency.

[0048] Transaction information refers to the transaction-related data recorded in the blockchain. Transaction information includes the information of both parties to the transaction, the transaction amount, a timestamp, and additional transaction information (such as smart contract information).

[0049] Among them, the transaction keywords can be specifically, for example, the type of transaction, some key features of the transaction goods, etc. In the embodiment of the present invention, the transaction keywords can be obtained through the contract information of the smart contract.

[0050] Furthermore, in some embodiments of the present invention, determining the transaction keywords of each transaction record data includes: obtaining the source code of the smart contract corresponding to each transaction record data, and obtaining the transaction keywords from the source code based on a keyword extraction algorithm.

[0051] The keyword extraction algorithm in the embodiments of the present invention can specifically be, for example, a regular expression and a split function. It should be noted that the source code of the smart contract is obtained from each transaction message. The source code contains function definitions and event declarations. Function and event names typically contain key information about business operations. Because function and event names may contain underscores (_), camelCase, or other special characters, regular expressions and a split function can be used to extract transaction keywords from these functions and events.

[0052] Of course, in other embodiments of the present invention, the acquisition of transaction keywords may also be achieved based on other existing keyword algorithms, such as the TF-IDF algorithm.

[0053] After determining the transaction keywords, the embodiment of the present invention can implement business association analysis based on transaction information such as the frequency characteristics and amount of the transaction keywords in different transaction record data. The specific analysis process is described in subsequent embodiments.

[0054] S102: Select any transaction record data as target transaction data; determine the co-occurrence frequency index of the transaction keywords in the target transaction data based on the frequency characteristics of the transaction keywords in all transaction record data.

[0055] To facilitate analysis, the embodiment of the present invention uses any transaction record data as target transaction data, and then performs specific analysis on the target transaction data.

[0056] During the specific analysis process, in order to determine the importance of the target transaction data itself, the embodiment of the present invention uses the frequency of occurrence as an important analysis indicator. That is, the more times the corresponding transaction keyword appears in all transaction records, the more likely it is that the two parties to the transaction will continue to conduct transactions of the same or similar types containing the transaction keyword. Therefore, the analysis of transaction keywords can be achieved based on the co-occurrence frequency indicator.

[0057] Furthermore, in some embodiments of the present invention, a method for obtaining co-occurrence frequency indicators of transaction keywords in target transaction data includes: determining the occurrence frequency of each transaction keyword, and constructing a frequent item set of the transaction keywords in all transaction record data based on the FP-Growth algorithm; taking the intersection of all transaction keywords corresponding to the target transaction data and the frequent item set as the frequent words of the target transaction data; and taking the proportion of the occurrence frequency of frequent words in the target transaction data in the frequent item set as the co-occurrence frequency indicator of the transaction keywords in the target transaction data.

[0058] The FP-Growth algorithm is a data mining algorithm used to discover frequent itemsets in a dataset. A frequent itemset is a collection of frequently occurring items in a dataset, specifically, a collection of frequently occurring transaction keywords.

[0059] Therefore, the intersection of all transaction keywords corresponding to the target transaction data and the frequent item sets is the transaction keywords that appear more frequently in the target transaction data. In the embodiment of the present invention, they are called frequent words. The higher the probability of occurrence of a frequent word, the more frequent the transaction items corresponding to the target transaction data, which means that there is a greater possibility that it will be taken out for data analysis and comparison in the future.

[0060] The frequency ratio of the frequent words in the target transaction data in the embodiment of the present invention is the ratio of the frequency of the frequent words in the target transaction data to the corresponding frequencies of all transaction keywords in the frequent item set. The ratio is used as the frequency ratio. The higher the value, the higher the probability that the corresponding frequent word appears in the overall transaction list. Further, the more frequent the transaction type, the larger the value of the co-occurrence frequency index.

[0061] It should be noted that the co-occurrence frequency index is a numerical representation of the co-occurrence frequency, which represents the frequency information of the transaction keywords in the corresponding target transaction data in the entire blockchain. The larger the value, the more repeated the transaction keywords in the target transaction data, further indicating the frequent occurrence characteristics of the target transaction data itself.

[0062] Of course, in other embodiments of the present invention, a co-occurrence frequency algorithm may also be used to implement the specific calculation of the co-occurrence frequency index, and there is no limitation to this.

[0063] S103: Construct a transaction user network based on the transaction parties in all transaction record data; in the transaction user network, determine the business association degree of the corresponding transaction parties based on the frequency of the transaction record data of the transaction parties corresponding to the target transaction data and the co-occurrence frequency index of each transaction.

[0064] Because transaction records contain corresponding transacting parties and transacted parties, embodiments of the present invention analyze both transacting parties as individuals. These individuals are networked together, characterized by the transactional activity between the two parties. This constructs a comprehensive transacting user network based on the transacting parties in each transaction record.

[0065] Among them, the business correlation degree represents the business correlation characteristics of the corresponding transaction parties. For example, the higher the correlation degree between the two parties conducting the same type of transactions, the higher the correlation degree. Therefore, the embodiment of the present invention can determine the business correlation degree of the corresponding transaction parties based on the frequency of the transaction record data of the transaction parties corresponding to the target transaction data and the co-occurrence frequency index of each time.

[0066] Furthermore, in some embodiments of the present invention, a method for obtaining the degree of business association between the two parties to a transaction includes: determining the mean of the collinearity frequency index of each transaction between the two parties to the transaction as a first correlation coefficient; normalizing the number of transactions by maximum and minimum values ​​to obtain a second correlation coefficient; and determining the degree of business association between the two parties to the transaction based on the first correlation coefficient and the second correlation coefficient, wherein the first correlation coefficient and the second correlation coefficient are both positively correlated with the degree of business association.

[0067] Among them, the first correlation coefficient represents the characteristics of the collinear frequency index, that is, the similarity of the transaction keywords. It should be noted that since the two parties to the transaction belong to the same transaction record data, their corresponding transaction keywords are the same or similar. Therefore, directly using the mean of the collinear frequency index can accurately represent the similarity of the corresponding transaction keywords.

[0068] The second correlation coefficient represents the frequency of transactions, that is, the more transactions the two parties have, the larger the corresponding second correlation coefficient value.

[0069] In combination with the above analysis, in the embodiment of the present invention, the first correlation coefficient and the second correlation coefficient can be combined to analyze the business correlation between the two parties of the transaction and obtain the degree of business correlation.

[0070] Furthermore, in some embodiments of the present invention, determining the business correlation degree between the two transaction parties based on the first correlation coefficient and the second correlation coefficient includes: calculating the product of the first correlation coefficient and the second correlation coefficient as the business correlation degree.

[0071] In this embodiment of the present invention, since the first correlation coefficient characterizes the collinear frequency index, a larger first correlation coefficient indicates a higher degree of similarity between the corresponding transaction keywords, while a larger second correlation coefficient indicates a greater number of transactions between the two parties. Therefore, both the first and second correlation coefficients are positively correlated with the degree of business association. A positive correlation, in other words, indicates that the dependent variable increases as the independent variable increases. Therefore, in this embodiment of the present invention, the product of the first and second correlation coefficients can be calculated as the degree of business association.

[0072] Of course, in other embodiments of the present invention, the sum of the first correlation coefficient and the second correlation coefficient may be calculated as the service correlation degree. In other embodiments of the present invention, to unify the value range of the service correlation degree, the product or sum of the first correlation coefficient and the second correlation coefficient may be normalized to the maximum and minimum values, and this is not limited.

[0073] S104: Divide transaction users into different communities based on the degree of business relevance; take any transaction user in any community as a target user, and determine the target user's transaction frequency index based on the target user's business relevance with other transaction users in the same community and the transaction amount of each transaction; determine the community's priority index based on the transaction frequency indexes of all transaction users in the same community.

[0074] It should be noted that since the transaction list stores the information of both parties to the transaction, a connection relationship can be established based on the information of both parties to the transaction in each transaction record. Since the business correlation between the two parties to the transaction is relatively strong, the business correlation between the two parties can be determined based on the business keywords between the two parties to the transaction, and community classification can be performed based on the business correlation of the connection to determine different business groups.

[0075] It's important to note that different business types have different requirements for blockchain data. For example, financial users need to monitor market changes in real time, and financial transactions must be executed quickly. Any delay will result in lost trading opportunities. Therefore, financial users must process transaction requests quickly, placing high demands on database response speed and performance. Therefore, financial services have a higher data priority than other businesses. Some users, on the other hand, do not require sensitive data changes but require higher security. Therefore, different communities need to be divided according to different business requirements.

[0076] Furthermore, in some embodiments of the present invention, transaction users are divided into different communities according to the degree of business association, including: using the SLPA community algorithm to divide all transaction users into communities based on different business association degrees to obtain multiple different communities.

[0077] Among them, the SLPA community algorithm is an algorithm well known to those skilled in the art, which can divide corresponding multiple elements into different communities. In the embodiment of the present invention, the business correlation degree is used as the division standard.

[0078] In other embodiments of the present invention, other partitioning algorithms may also be used, such as building an association network based on the degree of business association and performing community partitioning based on the association network, or using a density clustering algorithm to implement community partitioning, which is not limited.

[0079] In order to facilitate specific analysis of the community, in the embodiment of the present invention, any transaction user in any community can be used as a target user, and individual analysis of the target user can be implemented.

[0080] Furthermore, in some embodiments of the present invention, a method for obtaining a target user's transaction frequency index includes: normalizing the transaction amount of each transaction by the maximum and minimum values ​​to obtain an amount influence index; calculating the product of the business correlation degree between the two parties in each transaction of the target user and the amount influence index as the single frequency coefficient of the corresponding transaction; and taking the sum of the single frequency coefficients of all transactions in which the target user participated as the target user's transaction frequency index.

[0081] The amount impact index represents the corresponding transaction amount. Larger amounts indicate greater data sensitivity and higher security requirements, and therefore warrant higher priority analysis. Therefore, in this embodiment of the present invention, the product of the business relevance and the amount impact index can be calculated as the single transaction frequency coefficient, which indicates the importance of a single transaction.

[0082] In an embodiment of the present invention, the sum of the single frequency coefficients of all transactions is used as the transaction frequency index of the target user, which represents important information about the target user itself. The larger the value of the transaction frequency index of the target user, the more important the target user is in all transactions, the more frequent the transactions, and the higher the number of times the transaction type occurs.

[0083] Based on this, in an embodiment of the present invention, a specific analysis can be performed on all transaction users in the same community. Furthermore, in some embodiments of the present invention, a priority index of the community is determined based on the transaction frequency index of all transaction users in the same community, including: calculating the mean of the transaction frequency index of all transaction users in the same community, and normalizing it as the priority index of the community.

[0084] By normalizing the average of the transaction frequency indicators of all transaction users in the same community and using it as the community priority indicator, we can conduct a specific analysis of the transaction needs of transaction users in the community and determine the priority indicator. The larger the priority indicator, the more frequent the transaction process in the corresponding community, and the greater the requirements for transaction amount, transaction timeliness, etc.

[0085] S105: Transaction information of different communities is classified and stored according to the values ​​of the priority indicators of all communities.

[0086] Combined with the analysis process of the above steps, the embodiments of the present invention can realize classified storage of the community, thereby realizing distributed storage of data, avoiding direct storage in the same database, which leads to low data call efficiency. At the same time, classified storage can also realize different hardware configurations according to community needs.

[0087] Furthermore, in some embodiments of the present invention, transaction information of different communities is classified and stored according to the numerical values ​​of the priority indicators of all communities, including: treating communities with priority indicators greater than a preset priority threshold as Class I communities, and others as Class II communities; using different databases to store data corresponding to Class I communities and data corresponding to Class II communities, respectively, wherein the call speed of the database storing data corresponding to Class I communities is higher than that of the database storing data corresponding to Class II communities.

[0088] That is to say, by dividing all communities into two categories, classified storage is achieved. Of course, in other embodiments of the present invention, the type of division can also be adjusted according to the actual software and hardware conditions, and there is no limitation on this.

[0089] Among them, the preset priority threshold is the threshold value of the priority index. In some embodiments of the present invention, the preset priority threshold can be specifically 0.75, for example, that is, communities with priority indicators greater than 0.75 are classified as Class I communities, and others are classified as Class II communities. There is no restriction on this.

[0090] The present invention obtains transaction information from blockchain data, extracts keywords from the transaction information, and determines transaction keywords for each transaction record. Then, based on the frequency characteristics of the transaction keywords in all transaction records, it determines a co-occurrence frequency index for the target transaction keywords in the target transaction data. The co-occurrence frequency index enables effective co-occurrence frequency analysis and accurately determines the contribution characteristics of the transaction keywords. A transaction user network is constructed based on the transaction parties in all transaction records. Within the transaction user network, the business relevance between the two transaction parties is determined based on the frequency of the transaction records corresponding to the target transaction data and the co-occurrence frequency index for each co-occurrence. A detailed analysis of the business relevance is performed on a per-transaction user basis, and subsequent community division is performed based on the relevance of different transaction users. A transaction frequency index for the target user is determined based on the business relevance between the target user and other transaction users in the same community and the transaction amount of each transaction. This means that transaction characteristics are analyzed at the transaction user level to determine a more accurate and effective transaction frequency index. Subsequently, a community priority index can be determined based on the transaction frequency indexes of all transaction users within the same community. Transaction information from different communities is then categorized and stored based on the values ​​of the priority indexes for all communities. In the embodiment of the present invention, different transaction users are divided into communities, and distributed data storage is achieved between blockchains based on the community division results. This can facilitate data call, retrieval, and verification, avoid occupying the transmission channel of the same database during high-concurrency requests, and enhance data processing efficiency.

[0091] This embodiment also provides a system for improving the efficiency of blockchain data processing. Figure 2 , Figure 2 This is a structural diagram of a system for improving blockchain data processing efficiency provided by one embodiment of the present invention. The system 600 includes a memory 601, a processor 602, and a computer program 603 stored in the memory 601 and executable on the processor 602. When the processor 602 executes the computer program 603, the steps of the aforementioned method for improving blockchain data processing efficiency are implemented.

[0092] This embodiment also provides a computer-readable storage medium, which stores computer program code. When the computer program code is executed on a computer, the computer executes the above-mentioned related method steps to implement a method for improving blockchain data processing efficiency provided in the above embodiment.

[0093] This embodiment also provides a computer program product. When the computer program product is run on a computer, it causes the computer to execute the above-mentioned related steps to implement a method for improving blockchain data processing efficiency provided by the above embodiment.

[0094] Among them, the system, computer-readable storage medium or computer program product provided in this embodiment is used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, and will not be repeated here.

[0095] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0096] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for improving blockchain data processing efficiency, characterized in that: The method comprises: Obtaining transaction information from blockchain data, performing keyword extraction on the transaction information, and determining transaction keywords for each transaction record data; Selecting any transaction record data as target transaction data; determining a co-occurrence frequency index of the transaction keywords in the target transaction data based on the frequency characteristics of the transaction keywords in all transaction record data; A transaction user network is constructed based on the transaction parties in all transaction record data. In the transaction user network, the business correlation between the two transaction parties is determined based on the frequency of the transaction record data corresponding to the target transaction data and the co-occurrence frequency index of each transaction. Divide transaction users into different communities based on the degree of business relevance. Take any transaction user in any community as the target user and determine the target user's transaction frequency index based on the target user's business relevance with other transaction users in the community and the transaction amount of each transaction. Determine the community's priority index based on the transaction frequency index of all transaction users in the same community. Transaction information of different communities is classified and stored according to the values ​​of priority indicators of all communities; Transaction information of different communities is classified and stored according to the values ​​of priority indicators of all communities, including: Communities with priority indicators greater than the preset priority threshold are classified as Category 1 communities, and others as Category 2 communities; Different databases are used to store data corresponding to the first type of community and data corresponding to the second type of community, wherein the database storing data corresponding to the first type of community has a higher call speed than the database storing data corresponding to the second type of community; Methods for obtaining target users' transaction frequency indicators include: Normalize the maximum and minimum transaction amounts of each transaction to obtain the amount impact index; Calculate the product of the business relevance and amount impact index of the target user in each transaction as the single frequency coefficient of the corresponding transaction; The sum of the single frequency coefficients of all transactions in which the target user participated is used as the target user's transaction frequency index.

2. A method for improving blockchain data processing efficiency according to claim 1, characterized in that: Determine the transaction keywords for each transaction record data, including: Obtain the source code of the smart contract corresponding to each transaction record data, and obtain transaction keywords from the source code based on the keyword extraction algorithm.

3. A method for improving blockchain data processing efficiency according to claim 1, characterized in that: A method for obtaining co-occurrence frequency indicators of transaction keywords in target transaction data includes: Determine the frequency of occurrence of each transaction keyword and construct a frequent item set of transaction keywords in all transaction record data based on the FP-Growth algorithm; The intersection of all transaction keywords and frequent item sets corresponding to the target transaction data is taken as the frequent words of the target transaction data; The proportion of the frequency of frequent words in the target transaction data in the frequent item set is used as the co-occurrence frequency indicator of transaction keywords in the target transaction data.

4. A method for improving blockchain data processing efficiency according to claim 1, characterized in that: Methods for obtaining the degree of business relevance between the two parties to a transaction include: Determine the mean of the collinearity frequency index of each transaction between the two parties as the first correlation coefficient; The second correlation coefficient is obtained by normalizing the number of transactions to the maximum and minimum values; The business correlation degree between the two transaction parties is determined according to the first correlation coefficient and the second correlation coefficient, wherein both the first correlation coefficient and the second correlation coefficient are positively correlated with the business correlation degree.

5. A method for improving blockchain data processing efficiency according to claim 4, characterized in that: Determining the degree of business correlation between the two parties of the transaction based on the first correlation coefficient and the second correlation coefficient includes: The product of the first correlation coefficient and the second correlation coefficient is calculated as the business correlation degree.

6. A method for improving blockchain data processing efficiency according to claim 1, characterized in that: Transaction users are divided into different communities based on the degree of business relevance, including: Based on the degree of business association, the SLPA community algorithm is used to divide all transaction users into communities, resulting in multiple different communities.

7. A method for improving blockchain data processing efficiency according to claim 1, characterized in that: Determine the community's priority indicators based on the transaction frequency indicators of all trading users in the same community, including: Calculate the average transaction frequency index of all transaction users in the same community and normalize it as the priority index of the community.

8. A system for improving blockchain data processing efficiency, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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