Security management method and system for lightweight block chain data

Abstract

The invention provides a security management method and system for lightweight block chain data, and relates to the technical field of data processing, and the method comprises the steps: carrying out the preprocessing of original transaction data on a block chain, obtaining processed data, carrying out the lossless compression of the processed data, and obtaining compressed data; according to the compressed data, constructing a distributed data processing architecture based on a block chain, and automatically executing data verification, screening and preliminary analysis tasks by using an intelligent contract to obtain a data set after preliminary analysis; and carrying out encryption processing on a sensitive part in the data set after preliminary analysis, and carrying out data statistics and mode identification to generate encrypted data. According to the method, efficient and safe management and rapid recovery of the block chain data are realized through strategies such as lightweight processing, intelligent contract automation, encryption protection, real-time monitoring and automatic optimization, and the efficiency and safety of data processing are effectively improved.

Description

Technical Field

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

[0002] When faced with large-scale transaction data, traditional blockchain data management technologies may cause data processing speed to decrease and affect the real-time response capability of the system due to the need to perform complex data verification, storage and transmission operations. Although these methods guarantee data security to a certain extent, there is still room for improvement in efficient data processing.

[0003] For example, traditional blockchain data management technology may have a certain lag in security monitoring. Due to the complexity of data processing and verification processes, the system may not be able to identify and respond to all abnormal behaviors and potential security threats in real time, thus providing opportunities for malicious nodes to take advantage.

[0004] As blockchain application scenarios continue to expand, data security management needs are also changing. However, traditional methods lack sufficient flexibility and scalability, and it is difficult to quickly adapt to new security threats and challenges. This may cause the system to be overwhelmed when facing new attacks. In terms of privacy protection, it relies too much on a single encryption technology or privacy protection strategy. This singleness may lead to unsatisfactory privacy protection effects in certain specific scenarios, and cannot fully meet users' diverse needs for data privacy. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a lightweight blockchain data security management method and system, which improves the speed and efficiency of data processing.

[0006] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0007] In a first aspect, a method for securely managing lightweight blockchain data is provided, the method comprising:

[0008] Preprocess the original transaction data on the blockchain to obtain processed data, and losslessly compress the processed data to obtain compressed data;

[0009] Based on the compressed data, a distributed data processing architecture based on blockchain is constructed, and smart contracts are used to automatically perform data verification, screening and preliminary analysis tasks to obtain a preliminary analysis data set;

[0010] Encrypt the sensitive parts of the data set after preliminary analysis, perform data statistics and pattern recognition, and generate encrypted data;

[0011] Based on the encrypted data, a security monitoring model is built to analyze blockchain transaction data in real time, identify abnormal behaviors and potential security threats, and automatically optimize and adjust monitoring plans and parameters based on the monitored data characteristics and behavior patterns;

[0012] Establish data recovery and disaster backup plans to restore data from backup nodes when the security monitoring model detects data loss or damage.

[0013] Furthermore, the original transaction data on the blockchain is preprocessed to obtain processed data, and the processed data is losslessly compressed to obtain compressed data, including:

[0014] Obtain the original blockchain transaction data through the blockchain network nodes. Each block contains transaction records and timestamps.

[0015] Perform pre-processing operations on the original blockchain transaction data, including data cleaning and data formatting, to obtain processed data;

[0016] Based on the processed data, initialize an empty dictionary to store the string patterns that appear during the compression process;

[0017] Scan the processed data, read one character at a time, and search for the final matching string in the initialized dictionary; if a matching string is found, replace the original string with the position and matching length of the corresponding matching string in the dictionary to form a compressed code; if there is no matching string, output the current character;

[0018] After each replacement is completed, the new string pattern is added to the dictionary and the dictionary is updated;

[0019] Scan the remaining processed data, repeat the character reading, string matching, replacement and encoding and dictionary updating process, and when the processed data is scanned and processed, combine the codes and characters output during the compression process to obtain the compressed data.

[0020] Furthermore, based on the compressed data, a distributed data processing architecture based on blockchain is constructed, and smart contracts are used to automatically perform data verification, screening and preliminary analysis tasks to obtain a preliminary analysis data set, including:

[0021] Store the compressed data in a specific storage area on the blockchain and determine the number and configuration of nodes in the distributed data processing architecture;

[0022] A corresponding number of data processing nodes are configured on the blockchain, each of which has data decompression, processing and analysis functions, and distributes the compressed data stored in a specific area to each data processing node; a preset smart contract is configured on the blockchain, and the smart contract includes data verification, screening and preliminary analysis logic;

[0023] Initialize the smart contract, set parameters, status and trigger conditions, including specific time and completion of data reception. When the trigger conditions are met, the blockchain network automatically executes on each data processing node, decompresses the compressed data and obtains the decompressed data;

[0024] Verify the decompressed data. After the verification is passed, filter the information that meets the requirements from the decompressed data to obtain the filtered data.

[0025] Conduct preliminary analysis on the screened data, including calculating statistical indicators and identifying data patterns, and obtain preliminary analysis results, including statistical indicators, data patterns, outliers, data quality assessment and business indicator information;

[0026] Integrate statistical indicators, data patterns, outliers, data quality assessments, and business indicator information to obtain a preliminary analysis data set.

[0027] Furthermore, the sensitive parts of the data set after the preliminary analysis are encrypted, and data statistics and pattern recognition are performed to generate encrypted data, including:

[0028] Check the data set after preliminary analysis item by item and filter out sensitive data according to the preset sensitive data identification standards;

[0029] Encrypt sensitive data to generate encrypted sensitive data, and replace the corresponding part in the original data set with the encrypted sensitive data to construct a partially encrypted data set;

[0030] Re-count some encrypted data sets, calculate new statistical indicators, and update data distribution information;

[0031] Analyze partially encrypted data sets to identify new patterns, association rules, or anomalous behaviors in the data;

[0032] Integrate statistical indicators, updated data distribution information, and new patterns, association rules, or abnormal behavior information in the identified data into partially encrypted data sets to generate encrypted data that contains the key information and statistical features of the original data.

[0033] Furthermore, some encrypted data sets are analyzed to identify new patterns, association rules or abnormal behaviors in the data, including:

[0034] Extract feature variables from part of the encrypted data set, including non-encrypted features and representative features of processed encrypted data;

[0035] According to the characteristics of the feature variables and the analysis objectives, the cosine similarity is used to calculate the similarity between the feature vectors in the partially encrypted data set;

[0036] According to the similarity between each feature vector, cluster analysis is performed on some encrypted data sets. In the clustering process, data points are divided into different clusters according to the similarity of each feature vector, and the center point of the cluster and the statistical information of the data within the cluster are determined to obtain the clustering results, including the division of clusters, the center point of the cluster, and the statistical information of the data within the cluster;

[0037] Analyze the clustering results to identify new patterns, association rules or abnormal behaviors in the data; new patterns are manifested as associations between data features within a specific cluster; association rules are obtained by analyzing the dependencies between data within a cluster; abnormal behaviors are manifested as clusters that are different from data points.

[0038] Furthermore, based on the encrypted data, a security monitoring model is constructed to analyze blockchain transaction data in real time, identify abnormal behaviors and potential security threats, and automatically optimize and adjust monitoring plans and parameters based on the monitored data characteristics and behavior patterns, including:

[0039] Based on the encrypted data, determine the features related to anomaly detection and security threat identification, including transaction frequency, transaction amount distribution, and transaction pattern, to construct a feature representation set for encrypted data;

[0040] Determine a security monitoring model according to the feature representation set, and train the security monitoring model using the historical encrypted data and the corresponding feature representation set to obtain a trained security monitoring model;

[0041] The encrypted blockchain transaction data collected in real time is converted into a feature representation set and input into the trained security monitoring model for real-time analysis. By comparing the difference between the current transaction behavior and the normal transaction pattern, the abnormal behavior that is inconsistent with the normal transaction behavior is identified; based on the abnormal behavior, potential security threats are analyzed, including fraudulent transactions and money laundering activities, and identification results containing abnormal behavior and potential security threats are generated;

[0042] Based on the identification results, the monitoring plan is adjusted, including adding new features, optimizing safety monitoring model parameters, and changing the monitoring frequency.

[0043] Second, a lightweight blockchain data security management system includes:

[0044] The acquisition module is used to pre-process the original transaction data on the blockchain to obtain processed data, and to losslessly compress the processed data to obtain compressed data; based on the compressed data, a distributed data processing architecture based on the blockchain is constructed, and smart contracts are used to automatically perform data verification, screening and preliminary analysis tasks to obtain a preliminary analysis data set; the sensitive parts of the preliminary analysis data set are encrypted, and data statistics and pattern recognition are performed to generate encrypted data;

[0045] The processing module is used to build a security monitoring model based on the encrypted data to analyze blockchain transaction data in real time, identify abnormal behaviors and potential security threats, and automatically optimize and adjust monitoring plans and parameters based on the monitored data characteristics and behavior patterns; establish data recovery and disaster backup plans, and restore data from backup nodes when the security monitoring model detects data loss or damage.

[0046] According to a third aspect, a computing device includes:

[0047] one or more processors;

[0048] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0049] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0050] The above solution of the present invention includes at least the following beneficial effects:

[0051] By preprocessing and losslessly compressing the original transaction data on the blockchain, the burden of data storage and transmission is effectively reduced. This step not only reduces the consumption of system resources, but also improves the speed and efficiency of data processing, allowing the system to respond to transaction requests and query requirements more quickly. Build a distributed data processing architecture based on blockchain, and use smart contracts to automatically perform data verification, screening, and preliminary analysis tasks. This decentralized processing method not only improves the automation of data processing, but also enhances the robustness of the system, allowing the system to remain operational when a single node fails.

[0052] The sensitive parts of the data set after preliminary analysis are encrypted to ensure the security of the data during transmission and storage. This measure effectively prevents data leakage and illegal access and protects the privacy rights of users. A security monitoring model is built to analyze blockchain transaction data in real time, which can promptly detect and identify abnormal behaviors and potential security threats. By automatically optimizing and adjusting monitoring plans and parameters, the system can continuously adapt to new security threats and challenges and improve the overall security protection capabilities. A data recovery and disaster backup plan is established, and when the security monitoring model detects data loss or damage, the data can be quickly restored from the backup node. This measure ensures the integrity and availability of data and reduces the risks and losses caused by data loss or damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1It is a flow chart of a method for secure management of lightweight blockchain data provided by an embodiment of the present invention.

[0054] Figure 2 It is a schematic diagram of a lightweight blockchain data security management system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0056] like Figure 1 As shown, an embodiment of the present invention proposes a method for secure management of lightweight blockchain data, the method comprising the following steps:

[0057] Step 11, pre-processing the original transaction data on the blockchain to obtain processed data, and losslessly compressing the processed data to obtain compressed data;

[0058] Step 12: Based on the compressed data, a distributed data processing architecture based on blockchain is constructed, and smart contracts are used to automatically perform data verification, screening and preliminary analysis tasks to obtain a preliminary analysis data set;

[0059] Step 13, encrypting the sensitive parts of the data set after preliminary analysis, and performing data statistics and pattern recognition to generate encrypted data;

[0060] Step 14: Based on the encrypted data, a security monitoring model is constructed to analyze blockchain transaction data in real time, identify abnormal behaviors and potential security threats, and automatically optimize and adjust monitoring plans and parameters based on the monitored data characteristics and behavior patterns;

[0061] Step 15: Establish a data recovery and disaster backup plan. When the security monitoring model detects data loss or damage, restore the data from the backup node.

[0062] In the embodiment of the present invention, the volume of the original transaction data is effectively reduced through preprocessing and lossless compression, which reduces the storage burden of the blockchain node and enables the system to manage large-scale data more efficiently. The compressed data occupies less bandwidth during transmission, speeds up data transmission, reduces network latency, and improves user experience.

[0063] The distributed data processing architecture can fully utilize the computing power of multiple nodes, process data in parallel, and significantly improve data processing efficiency. The automatic execution of smart contracts ensures the fairness and transparency of the data verification, screening, and preliminary analysis process, and enhances the credibility of the data. Automated processing reduces manual intervention and labor costs, while improving the accuracy and consistency of data processing.

[0064] Encryption of sensitive data ensures the security of data during storage and transmission, and effectively protects user privacy. Encrypted data can still be used for data statistics and pattern recognition, providing strong support for subsequent security monitoring and data analysis.

[0065] The security monitoring model can analyze transaction data in real time, detect abnormal behaviors and potential security threats in a timely manner, and provide the system with timely warning and response mechanisms. The security monitoring model can automatically optimize and adjust the monitoring plan and parameters based on the monitored data characteristics and behavior patterns to ensure that the system always maintains the best security status.

[0066] The data recovery and disaster backup solution ensures that even in the event of data loss or damage, data can be quickly restored from the backup node, ensuring data integrity and availability. The backup mechanism enhances the system's fault tolerance, allowing the system to quickly recover and continue to operate normally in the face of unexpected situations.

[0067] In a preferred embodiment of the present invention, the above step 11, pre-processing the original transaction data on the blockchain to obtain processed data, and losslessly compressing the processed data to obtain compressed data, may include:

[0068] Step 111, obtaining original blockchain transaction data through blockchain network nodes, each block contains transaction records and timestamps;

[0069] Step 112, performing preprocessing operations on the original blockchain transaction data, including data cleaning and data formatting, to obtain processed data;

[0070] Step 113, initializing an empty dictionary based on the processed data to store the string patterns that appear during the compression process;

[0071] Step 114, scanning the processed data, reading one character at a time, and searching the initialized dictionary for the final matching string; if a matching string is found, the original string is replaced with the position and matching length of the corresponding matching string in the dictionary to form a compressed code; if there is no matching string, the current character is output;

[0072] Step 115, after each replacement is completed, the new string pattern is added to the dictionary and the dictionary is updated;

[0073] Step 116, scan the remaining processed data, repeat the character reading, string matching, replacement and encoding and dictionary updating process, when the processed data is scanned and processed, combine the code and characters output during the compression process to obtain compressed data.

[0074] In an embodiment of the present invention, in step 111, a set of blockchain network nodes is provided, each node storing a part or all of the blockchain data. By accessing these nodes, the original blockchain transaction data can be obtained. Each block contains a transaction record and a timestamp.

[0075] Step 112, clean the original blockchain transaction data to remove invalid or redundant data, such as null values, duplicate records, etc. Then format the data, arrange the transaction records and timestamps in a predetermined format, and obtain processed data.

[0076] Step 113, initialize an empty dictionary for storing string patterns and their corresponding codes that appear during compression. The dictionary key is the string pattern, and the value is the unique code of the pattern in the dictionary (the insertion order of the string pattern in the dictionary is used as the code).

[0077] Step 114, set the current scanned position as p, and initialize p = 0. Read a character from the processed data each time, denoted as c. Search the dictionary for the longest string pattern with c as the prefix, denoted as m.

[0078] Step 115: If a matching string m is found, the code and matching length of the corresponding matching string in the dictionary are used to replace the original string to form a compressed code. That is, the code and matching length of the corresponding matching string in the dictionary are output. If there is no matching string, the current character c is directly output.

[0079] Step 116, after each replacement is completed, the new string pattern (i.e., the currently scanned character or character combination) is added to the dictionary and the dictionary is updated. The scanning position is updated, and the remaining processed data is scanned continuously, and the character reading, string matching, replacement and encoding and dictionary updating process are repeated. When the processed data is scanned and processed, the code and characters output during the compression process are combined to obtain the compressed data.

[0080] Assume that the original blockchain transaction data is cleaned and formatted to obtain processed data R = "abcdabc".

[0081] Initialize dictionary d = {};

[0082] The scanning and compression operations from step 113 to step 116 are as follows:

[0083] Read 'a', no match, output 'a', update dictionary d = {'a': 1};

[0084] Read 'b', no match, output 'b', update dictionary d = {'a': 1, 'b': 2};

[0085] Read 'c', no match, output 'c', update dictionary d = {a': 1, b': 2, c': 3};

[0086] Read 'd': no ​​match, output 'd', update dictionary d = {a': 1, b': 2, c': 3, d': 4}.

[0087] Read 'a': there is a match ('a'), output (1, 1), and do not update the dictionary;

[0088] Read 'b': There is a match ('b'), output (2, 1), and do not update the dictionary;

[0089] Read 'c': There is a match ('c'), output (3, 1), and the dictionary is not updated.

[0090] Get the compressed data, D = [a ′,′ b ′ ,c ′ c ′ d ′ ,(1,1),(2,1),(3,1)].

[0091] Obtaining data directly from blockchain network nodes can ensure that the source of the data is reliable and cannot be tampered with. The latest transaction data can be obtained in a timely manner, providing a basis for subsequent data processing and analysis. Through data cleaning, invalid, redundant or erroneous data can be removed to improve data quality. Data formatting enables data from different sources or formats to be unified. Through lossless compression algorithms, repeated or predictable parts of processed data are represented by shorter codes, thereby reducing the size of the data. Compressed data takes up less storage space, reducing storage costs. Compressed data requires less time during transmission, which increases the speed of data transmission. Because it is lossless compression, the compressed data can be completely restored to the original data without losing any information. During the compression process, the dictionary is continuously updated as new string patterns appear, allowing the compression algorithm to adapt to different data characteristics and improve compression efficiency.

[0092] In a preferred embodiment of the present invention, the above step 12, based on the compressed data, constructs a distributed data processing architecture based on blockchain, and uses smart contracts to automatically perform data verification, screening and preliminary analysis tasks to obtain a preliminary analysis data set, which may include:

[0093] Step 122, storing the compressed data in a specific storage area on the blockchain, and determining the number and configuration of nodes of the distributed data processing architecture;

[0094] Step 123, configuring a corresponding number of data processing nodes on the blockchain, each node having data decompression, processing and analysis functions, and distributing the compressed data stored in a specific area to each data processing node; configuring a preset smart contract on the blockchain, the smart contract including data verification, screening and preliminary analysis logic;

[0095] Step 124, initialize the smart contract, set parameters, status and trigger conditions, including specific time, completion of data reception, and when the trigger conditions are met, the blockchain network automatically executes on each data processing node, decompresses the compressed data, and obtains the decompressed data;

[0096] Step 125, verifying the decompressed data, and after the verification is passed, filtering the information that meets the requirements from the decompressed data to obtain filtered data;

[0097] Step 126, performing preliminary analysis on the screened data, including calculating statistical indicators, identifying data patterns, and obtaining preliminary analysis results, including statistical indicators, data patterns, outliers, data quality assessments, and business indicator information;

[0098] Integrate statistical indicators, data patterns, outliers, data quality assessments, and business indicator information to obtain a preliminary analysis data set.

[0099] In an embodiment of the present invention, in step 122, the compressed data is stored in a specific storage area on the blockchain. The number of nodes and configuration parameters (such as CPU, memory, storage, etc.) of the distributed data processing architecture are determined based on the data scale, processing requirements, and network performance.

[0100] Step 123, configure data processing nodes on the blockchain, each node has data decompression, processing and analysis functions. Distribute storage areas to each data processing node so that they can access compressed data. Design and deploy smart contracts that include data verification, screening and preliminary analysis logic. The triggering conditions of the smart contract include specific time and data reception completion events.

[0101] Step 124, set the parameters and status of the smart contract, including storage address, number of nodes, specific time, etc. The initialization status is "waiting for trigger". When the blockchain time reaches a specific time or all data processing nodes report that data reception is complete, the smart contract execution is triggered. The smart contract automatically performs decompression operations on each data processing node to obtain decompressed data.

[0102] Step 125 verifies the decompressed data to check the integrity, consistency and validity of the data. If the verification is passed, continue; otherwise, record an error and terminate the process. Filter the information that meets the requirements from the decompressed data to obtain the filtered data. The filtering conditions are customized according to business needs, such as specific field values, data ranges, etc.

[0103] Step 126, perform preliminary analysis on the screened data, including calculating statistical indicators (such as mean, standard deviation), identifying data patterns (such as trends, periodicity), detecting outliers, etc. Perform data quality assessment, such as completeness, accuracy, and timeliness assessment. Extract business indicator information, such as transaction volume, user activity, etc. Integrate statistical indicators, data patterns, outliers, data quality assessments, and business indicator information to obtain a preliminary analysis data set.

[0104] Suppose there is a transaction data set on the blockchain, which contains information such as transaction amount, transaction time, and addresses of both parties to the transaction. The compressed transaction data is stored in a specific storage area, and it is determined that 5 data processing nodes are required. Configure these 5 nodes on the blockchain and deploy a smart contract, which contains logic such as verifying the validity of transaction data, screening transactions with amounts greater than 1000, calculating the average transaction amount, and detecting abnormal transactions. Set the smart contract to automatically trigger at dawn every day, or immediately after all nodes receive the data. When the contract is executed, the node will decompress the data and process it. After the data is decompressed, the contract will verify the integrity of the data (such as checking the hash value) and screen out transactions with amounts greater than 1000. Perform preliminary analysis on the screened data, such as calculating the average transaction amount, identifying the time trend of the transaction amount, detecting abnormally high transaction amounts, etc., and integrate these analysis results into a preliminary analysis data set.

[0105] The immutability of blockchain is used to ensure the safe storage of compressed data. By properly configuring nodes, effective allocation and utilization of resources can be achieved, and processing efficiency can be improved. A corresponding number of data processing nodes are configured on the blockchain, and each node has the functions of data decompression, processing and analysis. The compressed data is distributed to each data processing node to ensure that each node can obtain a complete data set. Processing data through distributed nodes improves processing speed and reliability and reduces the risk of single point failure. The introduction of smart contracts realizes the automation of data processing and reduces human intervention and errors. Trigger conditions can be set according to actual needs to achieve flexibility and timeliness of data processing. The automatic execution of smart contracts simplifies the data decompression process and improves processing efficiency. Through verification and screening, ensure that the processed data is of high quality and meets the requirements. Accurately filter information according to business needs to improve the pertinence and practicality of data. Automatically perform preliminary analysis through smart contracts to improve the efficiency and accuracy of data analysis.

[0106] In a preferred embodiment of the present invention, the above step 13, encrypting the sensitive part of the data set after preliminary analysis, performing data statistics and pattern recognition, and generating encrypted data, may include:

[0107] Step 131, check the data set after preliminary analysis item by item, and filter sensitive data according to preset sensitive data identification standards;

[0108] Step 132, encrypt the sensitive data to generate encrypted sensitive data, and replace the corresponding part in the original data set with the encrypted sensitive data to construct a partially encrypted data set;

[0109] Step 133, re-statisticing the partially encrypted data set, calculating new statistical indicators, and updating data distribution information;

[0110] Step 134, analyzing the partially encrypted data set to identify new patterns, association rules, or abnormal behaviors in the data;

[0111] Step 135, integrating the statistical indicators, updated data distribution information and the identified new patterns, association rules or abnormal behavior information in the data into the partially encrypted data set to generate encrypted data containing the key information and statistical features of the original data.

[0112] In the embodiment of the present invention, step 131 presets sensitive data identification standards according to business requirements, such as data items containing personal privacy information, business secrets, financial data, etc. Check each data item in the data set after preliminary analysis to determine whether it is sensitive data. Filter out data items that meet the sensitive data identification standards to form a sensitive data set.

[0113] Assume that after preliminary analysis, the data set contains information such as user ID, name, age, income, purchase history, etc. According to the sensitive data identification standard, name, income, and purchase history are considered sensitive data, so these fields will be screened out to form a sensitive data set.

[0114] Step 132: Select an encryption algorithm, such as AES, based on the security requirements of the data. Encrypt each item of data in the sensitive data set to generate encrypted sensitive data. Replace the corresponding part of the original data set with the encrypted sensitive data to construct a partially encrypted data set.

[0115] Step 133, according to the data characteristics and analysis requirements, select a statistical method, such as mean, perform statistics on the encrypted data set again, calculate new statistical indicators, update the data distribution information according to the new statistical indicators, and form an updated data distribution description.

[0116] Step 134, select a data analysis method, such as cluster analysis, based on the data characteristics and analysis objectives. Analyze the partially encrypted data set to identify new patterns, association rules, or abnormal behaviors in the data. Extract the new patterns, association rules, or abnormal behavior information obtained from the analysis to form an analysis result set.

[0117] Step 135: Integrate the statistical indicators, updated data distribution information and analysis result set into the partially encrypted data set. The integrated data set is formatted to generate encrypted data, which contains the key information and statistical features of the original data and protects the security of sensitive data.

[0118] By checking the data set item by item and screening sensitive data, it can be ensured that the subsequent encryption processing is targeted at the information that really needs to be protected, thereby improving the security of the data. Screening sensitive data helps to clarify the focus of data protection. Encrypting sensitive data can effectively prevent unauthorized access and leakage, and protect personal privacy and business secrets. The encrypted sensitive data replaces the corresponding part in the original data set to construct a partially encrypted data set, which not only protects sensitive information but also maintains the integrity of the data. Re-statisticing the partially encrypted data set can reflect the impact of encryption processing on data distribution and ensure the accuracy and timeliness of statistical indicators. The updated data distribution information provides a reliable basis for subsequent data analysis and pattern recognition. Analyzing the partially encrypted data set can identify new patterns, association rules or abnormal behaviors in the data, providing strong support for business decisions. By mining the hidden laws in the data, the value of the data can be enhanced. The statistical indicators, updated data distribution information and the identified new patterns, association rules or abnormal behavior information in the data are integrated into the partially encrypted data set to generate encrypted data, realizing the comprehensive display of information. The encrypted data contains both the key information and statistical characteristics of the original data and protects the security of sensitive data.

[0119] In another preferred embodiment of the present invention, the above step 134, analyzing the partially encrypted data set to identify new patterns, association rules or abnormal behaviors in the data, may include:

[0120] Step 1344, extracting feature variables from the partially encrypted data set, including non-encrypted features and representative features of the processed encrypted data;

[0121] Step 1345, using cosine similarity to calculate the similarity between each feature vector in the partially encrypted data set according to the characteristics of the feature variable and the analysis target;

[0122] Step 1346, performing cluster analysis on the partial encrypted data set according to the similarity between the feature vectors. In the clustering process, the data points are divided into different clusters according to the similarity between the feature vectors, and the center point of the cluster and the statistical information of the data within the cluster are determined to obtain the clustering results, including the division of the cluster, the center point of the cluster, and the statistical information of the data within the cluster;

[0123] Step 1347, analyze the clustering results to identify new patterns, association rules or abnormal behaviors in the data; wherein new patterns are manifested as associations between data features within a specific cluster; association rules are obtained by analyzing the dependencies between data within the cluster; and abnormal behaviors are manifested as clusters that are different from data points.

[0124] In an embodiment of the present invention, step 1344 extracts the identified non-encrypted fields from the data set to form a new data set, namely, a non-encrypted data set. This data set contains only unencrypted fields and their corresponding values. The fields in the non-encrypted data set are used as feature variables to construct a non-encrypted feature set. This feature set is a list or set that contains the names of all non-encrypted fields. In the process of reviewing the data set, it is also determined which fields are encrypted based on the annotations, instructions or data dictionary of the fields. These fields usually contain sensitive information, such as user passwords, transaction amounts, personal identity information, etc. According to the nature of the encrypted data and the analysis objectives, the principal component analysis (PCA) method is selected. The encrypted data is used as input data and the PCA algorithm is applied for feature extraction. The covariance matrix of the encrypted data is calculated, and the principal components are extracted by solving the eigenvalues ​​and eigenvectors. The calculation formula of the covariance matrix is: Among them, C hj represents the covariance between the hth feature and the jth feature in the data set; M represents the number of samples in the data set; k represents the index variable; X[k,h] represents the central value of the kth sample on the hth feature; X[k,j] represents the central value of the kth sample on the jth feature. These principal components are the representative features of the encrypted data. Based on the results of PCA, the first k principal components (the value of k is determined based on the contribution rate of the eigenvalues) are selected as the representative features of the encrypted data, and an encrypted data feature set is constructed. This feature set is a list or set that contains the names or numbers of all selected principal components. The non-encrypted feature set and the encrypted data feature set are combined to form a complete feature variable set. This feature variable set contains the names or numbers of all feature variables used for analysis.

[0125] Step 1345, for each data point in the data set, construct a feature vector based on its feature variable set. According to the characteristics of the feature variables and the analysis objectives, cosine similarity is selected as the similarity measurement method. The similarity between each feature vector is calculated using the cosine similarity formula. Select an appropriate clustering algorithm (such as K-means) and initialize clustering parameters (such as the number of clusters, thresholds, etc.). Based on the similarity between each feature vector, cluster analysis is performed on the partial encrypted data set. During the clustering process, the data points are divided into different clusters according to the size of the similarity of each feature vector, and the center point of each cluster and the statistical information of the data within the cluster (such as the mean, variance, etc.) are determined. The clustering results include the division of clusters, the center point of the cluster, and the statistical information of the data within the cluster. By observing the data features within a specific cluster, new patterns in the data are identified, and these new patterns are manifested as correlations between the data features within a specific cluster. Use an association rule mining algorithm (such as Apriori) to analyze the dependencies between the data within the cluster and obtain association rules. Specifically:

[0126] Define the minimum support threshold required for an itemset to be considered a frequent itemset. Support indicates how often an itemset appears in all transactions. Define the minimum confidence threshold required for an association rule to be considered a strong rule. Generate all possible itemsets from the data in the cluster as candidate itemsets. Traverse each transaction in the cluster and calculate the support of each candidate itemset as Among them, u(Q) represents the support of item set Q, which is a value between 0 and 1, reflecting the frequency of item set Q appearing in the cluster; = represents the number of transactions in cluster V that contain item set Q as a subset; V represents the total number of transactions in the cluster; T represents a specific transaction. Remove candidate item sets with support lower than the minimum support and retain frequent item sets. Generate all possible association rules from frequent item sets. Compare the data features of each cluster and identify clusters that are different from most data points. These clusters represent abnormal behaviors or abnormal data points.

[0127] Assume that part of the encrypted data set contains the user's shopping behavior data, where the non-encrypted features include the user's age, gender, and shopping time. The encrypted data features are representative features extracted from the user's shopping records through principal component analysis. Age, gender, shopping time, and encrypted shopping record features are extracted from the data set. The similarity between the feature vectors of each user is calculated using cosine similarity. The K-means algorithm is used to cluster the users, and the data points are divided into different clusters according to the similarity, such as the young female user cluster and the middle-aged male user cluster. It is found that the proportion of young female users who buy cosmetics and clothing is relatively high in the cluster, forming a new shopping pattern. In the middle-aged male user cluster, users who buy electronic products and sporting goods also buy alcoholic products, forming an association rule. It is found that the shopping time of users in one cluster is different from that of other clusters, mainly concentrated in the late night, which represents an abnormal shopping behavior.

[0128] Cluster analysis can reveal new patterns hidden in the data, which may represent new trends, new laws or new associations in the data, providing strong support for data-driven decision-making. Mining association rules helps to understand the dependencies in the data, thereby discovering the potential connections between different variables and providing a new perspective for data analysis and prediction. Timely detection of abnormal behavior is crucial in areas such as risk prevention and control and fraud identification. Cluster analysis can accurately identify abnormal points or abnormal clusters in the data, providing a strong basis for timely response measures. By analyzing some encrypted data sets, we can not only mine the intrinsic value of the data, but also provide a scientific basis for the further use and protection of the data, thereby enhancing the overall value of the data.

[0129] In another preferred embodiment of the present invention, the calculation formula of cosine similarity is:

[0130]

[0131] Where cos(A, B) represents the cosine similarity between feature vector A and feature vector B; n represents the number of feature vectors; i represents the index variable; w i，A represents the weight of feature vector A on the i-th feature; f i represents the value of the feature vector A on the i-th feature; w i，B represents the weight of feature vector B on the i-th feature; s i Represents the value of vector B on the i-th feature.

[0132] In the embodiment of the present invention, the feature vectors A and B are defined to have the same dimension, that is, to contain the same number of features. For each feature vector, obtain its weight w on each feature. i，A and w i，B and the corresponding eigenvalue fi and i Initialize the accumulators, where the numerator accumulator is used to accumulate the sum of the numerator part, with an initial value of 0. The denominator A accumulator is used to accumulate the sum of the squares of the denominator part of the A vector, with an initial value of 0. The denominator B accumulator is used to accumulate the sum of the squares of the denominator part of the B vector, with an initial value of 0. For each feature i (from 1 to n), calculate the weighted value w of the A vector i，A ·f i , represents the weighted eigenvalue of the eigenvector A on the i-th feature. Calculate the weighted value of the B vector, w i.B ·s i , represents the weighted eigenvalue of eigenvector B on the i-th feature. Multiply the weighted value of vector A by the weighted value of vector B and add the result to the numerator accumulator. Calculate the square of the weighted value of vector A and add the result to the denominator A accumulator. Calculate the square of the weighted value of vector B and add the result to the denominator B accumulator.

[0133] Take the square root of the value of the denominator A accumulator to get the modulus (or length) of vector A. Take the square root of the value of the denominator B accumulator to get the modulus (or length) of vector B. The value of the numerator accumulator is the numerator in the formula. The product of the modulus of vector A and the modulus of vector B is the denominator in the formula. Divide the numerator by the denominator to get the value of cosine similarity, cos(A, B).

[0134] The value of cosine similarity is a number between -1 and 1. A value close to 1 means that the two vectors are similar, a value close to -1 means that the two vectors are not similar, and a value close to 0 means that the two vectors have no obvious similarity.

[0135] The feature weight w in the formula i，A and w i，B , which makes it possible to adjust the contribution of a feature in the similarity calculation based on its importance. This is particularly useful for dealing with datasets with features of different importance. Cosine similarity performs well in high-dimensional spaces and is not affected by the curse of dimensionality. It focuses on the direction of the vector rather than the length, so it can accurately reflect the similarity between vectors even in high-dimensional data. Since cosine similarity measures similarity by calculating the angle between vectors, it is not affected by the dimension of the eigenvalue. This allows comparisons between features of different dimensions.

[0136] In a preferred embodiment of the present invention, the above step 14, based on the encrypted data, constructs a security monitoring model to analyze blockchain transaction data in real time, identify abnormal behaviors and potential security threats, and automatically optimizes and adjusts monitoring plans and parameters based on the monitored data characteristics and behavior patterns, which may include:

[0137] Step 141, determining features related to anomaly detection and security threat identification based on the encrypted data, including transaction frequency, transaction amount distribution, and transaction pattern, to construct a feature representation set of the encrypted data;

[0138] Step 142, determining a security monitoring model according to the feature representation set, and training the security monitoring model using the historical encrypted data and the corresponding feature representation set to obtain a trained security monitoring model;

[0139] Step 143, converting the encrypted blockchain transaction data collected in real time into a feature representation set form and inputting it into the trained security monitoring model for real-time analysis, identifying abnormal behaviors that are inconsistent with normal transaction behaviors by comparing the differences between current transaction behaviors and normal transaction patterns; analyzing potential security threats based on abnormal behaviors, including fraudulent transactions and money laundering activities, and generating identification results including abnormal behaviors and potential security threats;

[0140] Step 144, adjusting the monitoring plan according to the identification results, including adding new features, optimizing safety monitoring model parameters, and changing the monitoring frequency.

[0141] In the embodiment of the present invention, in step 141, features related to anomaly detection and security threat identification are determined based on domain knowledge and historical data analysis. Statistical methods (such as mean, variance, standard deviation) and machine learning techniques (such as feature importance scoring) are used to evaluate the effectiveness of features. The selected features include transaction frequency, transaction amount distribution, and transaction pattern. The selected features are combined into a feature vector to represent each transaction or transaction set.

[0142] Step 142, determine the security monitoring model according to the characteristics of blockchain transaction data and the needs of security monitoring. Assume that support vector machine SVM is selected as the security monitoring model. Extract features from historical encrypted data and construct a feature representation set. Ensure that the training data set contains a sufficient number of normal transaction samples and known abnormal transaction samples (such as fraudulent transactions, money laundering activities). Use the training data set to train the SVM model to obtain a trained security monitoring model. Use cross-validation (such as k-fold cross-validation) to evaluate the performance of the model. Calculate indicators such as accuracy, recall, and precision.

[0143] Step 143, collect encrypted blockchain transaction data in real time. Extract features for each transaction data and construct a new feature vector. Input the new feature vector into the trained security monitoring model for real-time analysis, and output the probability that the current transaction behavior is normal or abnormal. Compare the probability with the preset threshold (such as 0.5) to determine whether the transaction is normal. If the probability > 0.5, the transaction is considered abnormal; otherwise, the transaction is considered normal. Based on the characteristics and patterns of abnormal behavior, further analyze potential security threats, such as fraudulent transactions, money laundering activities, etc. The identified abnormal behavior and potential security threats are presented in the form of reports or alerts. The report contains detailed information of the abnormal transaction, identification time, potential threat type, etc.

[0144] Step 144, conduct a detailed analysis of the identification results and count the types, frequencies, and distributions of abnormal behaviors. Analyze the performance of the model on different types of transactions to identify possible weaknesses or room for improvement. Based on the identification results, add new features to capture more abnormal behavior patterns. Evaluate the effectiveness of existing features and remove redundant or invalid features to simplify the model and improve efficiency. Use grid search to find the final parameter combination in the parameter space. Cross-validate each parameter combination to evaluate model performance. Adjust the frequency and granularity of monitoring based on the frequency and severity of abnormal behavior. For example, increase the monitoring frequency (such as monitoring once a minute) when abnormal behavior occurs frequently, and reduce the monitoring frequency (such as monitoring once an hour) under normal circumstances to save resources. Apply the adjusted feature set, model parameters, and monitoring frequency to the security monitoring system. Continuously monitor model performance and make further adjustments and optimizations as needed.

[0145] Carefully selected features can more effectively capture abnormal patterns and security threats in transaction data, improving the accuracy and efficiency of the model. A well-trained security monitoring model can accurately identify abnormal behaviors and security threats in transaction data in real time, providing strong support for timely response measures. By analyzing transaction data in real time, abnormal behaviors and security threats can be discovered and reported in a timely manner, providing strong protection for the safe operation of blockchain systems. By automatically optimizing and adjusting monitoring plans and parameters, the accuracy and efficiency of security monitoring models can be continuously improved, adapting to the ever-changing security threat environment, and providing strong support for the long-term safe operation of blockchain systems. At the same time, by reducing unnecessary monitoring and feature processing, computing resources can be saved and operating costs can be reduced.

[0146] In a preferred embodiment of the present invention, the above step 15, establishing a data recovery and disaster backup plan, when the security monitoring model detects data loss or damage, recovering data from the backup node, may include:

[0147] In an embodiment of the present invention, a monitoring frequency is set (such as every hour, every day), and criteria for determining data loss or damage are defined (such as file size changes, checksum mismatches, etc.). When an anomaly is detected, the model triggers an alarm. Full backup is combined with incremental backup, and data is backed up to backup nodes regularly (such as full backup every day, incremental backup every hour). Select backup nodes that are geographically dispersed and have a stable network to ensure that at least one node is available when a disaster occurs. Backup data should be stored in encrypted form, and strict access rights should be set to ensure data security.

[0148] Write an automated recovery script to automatically retrieve and restore data from the backup node when receiving an alarm from the security monitoring model. Perform recovery tests regularly to ensure the effectiveness of the recovery script and verify the integrity of the backup data. Set recovery priorities based on business importance to ensure that critical business data is restored first.

[0149] The security monitoring model is integrated with the backup system. When data loss or damage is detected, the backup data verification and recovery process is automatically triggered. All monitoring, backup, and recovery operation logs are recorded. Training is provided to relevant personnel (such as administrators and operation and maintenance personnel) on data security, backup and recovery, etc. Regular emergency drills are conducted for data loss or damage to verify the integrity and effectiveness of the plan. Continuously monitor the performance of the backup and recovery process, evaluate the effectiveness of the plan, and promptly identify and resolve problems. With the development of technology, the security monitoring model, backup technology, and recovery mechanism are continuously updated to improve data security and recovery efficiency.

[0150] like Figure 2 As shown, an embodiment of the present invention also provides a lightweight blockchain data security management system, including:

[0151] The acquisition module is used to pre-process the original transaction data on the blockchain to obtain processed data, and to losslessly compress the processed data to obtain compressed data; based on the compressed data, a distributed data processing architecture based on the blockchain is constructed, and smart contracts are used to automatically perform data verification, screening and preliminary analysis tasks to obtain a preliminary analysis data set; the sensitive parts of the preliminary analysis data set are encrypted, and data statistics and pattern recognition are performed to generate encrypted data;

[0152] The processing module is used to build a security monitoring model based on the encrypted data to analyze blockchain transaction data in real time, identify abnormal behaviors and potential security threats, and automatically optimize and adjust monitoring plans and parameters based on the monitored data characteristics and behavior patterns; establish data recovery and disaster backup plans, and restore data from backup nodes when the security monitoring model detects data loss or damage.

[0153] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0154] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0155] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for secure management of lightweight blockchain data, characterized in that: The method comprises: Preprocess the original transaction data on the blockchain to obtain processed data, and losslessly compress the processed data to obtain compressed data; Based on the compressed data, a distributed data processing architecture based on blockchain is constructed, and smart contracts are used to automatically perform data verification, screening and preliminary analysis tasks to obtain a preliminary analysis data set; Encrypt the sensitive parts of the data set after preliminary analysis, perform data statistics and pattern recognition, and generate encrypted data; Based on the encrypted data, a security monitoring model is built to analyze blockchain transaction data in real time, identify abnormal behaviors and potential security threats, and automatically optimize and adjust monitoring plans and parameters based on the monitored data characteristics and behavior patterns; Establish data recovery and disaster backup plans to restore data from backup nodes when the security monitoring model detects data loss or damage.

2. The method for secure management of lightweight blockchain data according to claim 1, characterized in that: Preprocess the original transaction data on the blockchain to obtain processed data, and losslessly compress the processed data to obtain compressed data, including: Obtain the original blockchain transaction data through the blockchain network nodes. Each block contains transaction records and timestamps. Perform pre-processing operations on the original blockchain transaction data, including data cleaning and data formatting, to obtain processed data; Based on the processed data, initialize an empty dictionary to store the string patterns that appear during the compression process; Scan the processed data, read one character at a time, and search for the final matching string in the initialized dictionary; if a matching string is found, replace the original string with the position and matching length of the corresponding matching string in the dictionary to form a compressed code; if there is no matching string, output the current character; After each replacement is completed, the new string pattern is added to the dictionary and the dictionary is updated; Scan the remaining processed data, repeat the character reading, string matching, replacement and encoding and dictionary updating process, and when the processed data is scanned and processed, combine the codes and characters output during the compression process to obtain the compressed data.

3. The method for secure management of lightweight blockchain data according to claim 2, characterized in that: Based on the compressed data, a distributed data processing architecture based on blockchain is constructed, and smart contracts are used to automatically perform data verification, screening and preliminary analysis tasks to obtain a preliminary analysis data set, including: Store the compressed data in a specific storage area on the blockchain and determine the number and configuration of nodes in the distributed data processing architecture; A corresponding number of data processing nodes are configured on the blockchain, each of which has data decompression, processing and analysis functions, and distributes the compressed data stored in a specific area to each data processing node; a preset smart contract is configured on the blockchain, and the smart contract includes data verification, screening and preliminary analysis logic; Initialize the smart contract, set parameters, status and trigger conditions, including specific time and completion of data reception. When the trigger conditions are met, the blockchain network automatically executes on each data processing node, decompresses the compressed data and obtains the decompressed data; Verify the decompressed data. After the verification is passed, filter the information that meets the requirements from the decompressed data to obtain the filtered data. Conduct preliminary analysis on the screened data, including calculating statistical indicators and identifying data patterns, and obtain preliminary analysis results, including statistical indicators, data patterns, outliers, data quality assessment and business indicator information; Integrate statistical indicators, data patterns, outliers, data quality assessments, and business indicator information to obtain a preliminary analysis data set.

4. The method for secure management of lightweight blockchain data according to claim 3 is characterized in that: The sensitive parts of the data set after preliminary analysis are encrypted, and data statistics and pattern recognition are performed to generate encrypted data, including: Check the data set after preliminary analysis item by item and filter out sensitive data according to the preset sensitive data identification standards; Encrypt sensitive data to generate encrypted sensitive data, and replace the corresponding part in the original data set with the encrypted sensitive data to construct a partially encrypted data set; Re-count some encrypted data sets, calculate new statistical indicators, and update data distribution information; Analyze partially encrypted data sets to identify new patterns, association rules, or anomalous behaviors in the data; Integrate statistical indicators, updated data distribution information, and new patterns, association rules, or abnormal behavior information in the identified data into partially encrypted data sets to generate encrypted data that contains the key information and statistical features of the original data.

5. The method for secure management of lightweight blockchain data according to claim 4 is characterized in that: Analyze portions of encrypted data sets to identify new patterns, associations, or anomalous behaviors in the data, including: Extract feature variables from part of the encrypted data set, including non-encrypted features and representative features of processed encrypted data; According to the characteristics of the feature variables and the analysis objectives, the cosine similarity is used to calculate the similarity between the feature vectors in the partially encrypted data set; According to the similarity between each feature vector, cluster analysis is performed on some encrypted data sets. During the clustering process, the data points are divided into different clusters according to the size of the similarity of each feature vector, and the center point of the cluster and the statistical information of the data within the cluster are determined to obtain the clustering results, including the division of clusters, the center point of the cluster, and the statistical information of the data within the cluster.

6. The method for secure management of lightweight blockchain data according to claim 5 is characterized in that: Analyze portions of encrypted data sets to identify new patterns, associations, or anomalous behaviors in the data, including: Analyze the clustering results to identify new patterns, association rules or abnormal behaviors in the data; new patterns are manifested as associations between data features within a specific cluster; association rules are obtained by analyzing the dependencies between data within a cluster; abnormal behaviors are manifested as clusters that are different from data points.

7. The method for secure management of lightweight blockchain data according to claim 6 is characterized in that: Based on the encrypted data, a security monitoring model is built to analyze blockchain transaction data in real time, identify abnormal behaviors and potential security threats, and automatically optimize and adjust monitoring plans and parameters based on the monitored data characteristics and behavior patterns, including: Based on the encrypted data, determine the features related to anomaly detection and security threat identification, including transaction frequency, transaction amount distribution, and transaction pattern, to construct a feature representation set for encrypted data; Determine a security monitoring model according to the feature representation set, and train the security monitoring model using the historical encrypted data and the corresponding feature representation set to obtain a trained security monitoring model; The encrypted blockchain transaction data collected in real time is converted into a feature representation set and input into the trained security monitoring model for real-time analysis. By comparing the difference between the current transaction behavior and the normal transaction pattern, the abnormal behavior that is inconsistent with the normal transaction behavior is identified; based on the abnormal behavior, potential security threats are analyzed, including fraudulent transactions and money laundering activities, and identification results containing abnormal behavior and potential security threats are generated; Based on the identification results, the monitoring plan is adjusted, including adding new features, optimizing safety monitoring model parameters, and changing the monitoring frequency.

8. A lightweight blockchain data security management system, the system implementing the method as described in any one of claims 1 to 7, characterized in that: include: The acquisition module is used to pre-process the original transaction data on the blockchain to obtain processed data, and to losslessly compress the processed data to obtain compressed data; Based on the compressed data, a distributed data processing architecture based on blockchain is constructed, and smart contracts are used to automatically perform data verification, screening and preliminary analysis tasks to obtain a preliminary analysis data set; Encrypt the sensitive parts of the data set after preliminary analysis, perform data statistics and pattern recognition, and generate encrypted data; The processing module is used to build a security monitoring model based on the encrypted data to analyze blockchain transaction data in real time, identify abnormal behaviors and potential security threats, and automatically optimize and adjust monitoring plans and parameters based on the monitored data characteristics and behavior patterns; Establish data recovery and disaster backup plans to restore data from backup nodes when the security monitoring model detects data loss or damage.

9. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

Images