Blockchain data management method, device and electronic device based on artificial intelligence

Abstract

The present application provides a blockchain data management method, device and electronic device based on artificial intelligence, which relates to the technical field of blockchain. The method includes: obtaining a data set to be stored, which includes multiple data to be stored; classifying and identifying each data to be stored through artificial intelligence, and determining the data category corresponding to each data to be stored; and determining the synchronization strategy corresponding to each data category; according to the synchronization strategy, the data to be stored corresponding to the data category is sent to the corresponding synchronization node for synchronization. The present application can realize data synchronization according to different strategies based on the actual situation of transaction data.

Description

Technical Field

[0001] The present application relates to the technical field of blockchain, and specifically to a blockchain data management method, device and electronic device based on artificial intelligence. Background Art

[0002] Blockchain is a distributed database technology. The basic idea is to use cryptographic methods to ensure the immutability and security of data by distributing and storing data among multiple participants (nodes). Data is linked together in the form of blocks. Each block contains a batch of transaction records and is connected to the previous block through a hash function to form a growing chain. The decentralized nature of blockchain means that there is no single control authority. All participants have copies and use consensus algorithms to verify and synchronize data. Smart contracts are automated programs on the blockchain that can execute contract terms according to predetermined conditions. Blockchain technology is widely used in cryptocurrencies as well as other fields such as finance, supply chain management, healthcare, etc. to improve the transparency, security and traceability of data.

[0003] In the blockchain, nodes are participants in the network, responsible for maintaining, verifying and disseminating transaction data. Each node stores a complete copy of the blockchain and participates in the creation and verification process of blocks through a consensus algorithm. Nodes can be miners (responsible for packaging transactions to form new blocks and competing for the right to record accounts by solving mathematical problems), full nodes (storing the data of the entire blockchain) or light nodes (only storing part of the blockchain data). Through peer-to-peer network communication, transaction transmission and consensus are achieved between nodes, ensuring the decentralized storage and security of data, thereby building a decentralized, transparent and tamper-proof blockchain system.

[0004] When storing transaction-related data in a blockchain system, it is crucial to determine the full node synchronization strategy and light node synchronization strategy for transaction data. If transaction data is synchronized on a full node, the complete content of the transaction data will be synchronized, thus having higher integrity and security. However, due to the large amount of synchronized data, the efficiency of data synchronization is relatively low. If transaction data is synchronized on a light node, data synchronization can be performed more quickly by only obtaining block headers and key information, and less storage and computing resources are occupied, but the integrity of the transaction data is poor. Therefore, a method is needed to synchronize data according to different strategies based on the actual situation of the transaction data. Summary of the invention

[0005] The present application provides a blockchain data management method, device and electronic device based on artificial intelligence, which can realize data synchronization according to different strategies based on the actual situation of transaction data.

[0006] In a first aspect of the present application, a blockchain data management method based on artificial intelligence is provided, the method comprising:

[0007] Acquire a data set to be stored, wherein the data set to be stored includes a plurality of data to be stored;

[0008] Classify and identify each of the data to be stored, and determine the data category corresponding to each of the data to be stored;

[0009] Determine a synchronization strategy corresponding to each of the data categories;

[0010] According to the synchronization strategy, the data to be stored corresponding to the data category is sent to the corresponding synchronization node for synchronization.

[0011] By adopting the above technical solution, by obtaining the data set to be stored, each data to be stored is classified and identified, the data category is determined, and the corresponding synchronization strategy is determined accordingly, and finally the data to be stored corresponding to the different data categories are sent to the corresponding synchronization node for synchronization. This application effectively combines data classification and dynamic determination of synchronization strategy, and can flexibly adjust the synchronization strategy according to actual conditions to adapt to the characteristics and synchronization requirements of different data categories. Through the flexible coordination of classification identification and strategy, the synchronization of different transaction data can be handled more intelligently, the synchronization efficiency can be improved, the integrity of the data can be ensured, and the burden of data storage can be reduced at the same time, realizing a more intelligent and efficient data synchronization process.

[0012] Optionally, before classifying and identifying each of the data to be stored and determining the data category corresponding to each of the data to be stored, the method includes:

[0013] Acquire first data to be stored from the plurality of data to be stored, wherein the first data to be stored is any one of the plurality of data to be stored;

[0014] Determine a first key feature from the first data to be stored, where the first key feature is any one of a plurality of key features in the first data to be stored;

[0015] Matching the first key feature with a plurality of pre-stored key features, where the plurality of pre-stored key features are a plurality of key features of pre-stored data;

[0016] If it is determined that there is a second key feature that is the same as the first key feature among the plurality of pre-stored key features, obtaining a data tag corresponding to the second key feature, the data tag being the data tag of the pre-stored data corresponding to the second key feature;

[0017] A mapping relationship between the data tag and the first data to be stored is established.

[0018] By adopting the above technical solution, by extracting the first key feature of the first data to be stored, matching it with the key feature of the pre-stored data, and obtaining the data label corresponding to the second key feature that successfully matches, the classification and identification of the data to be stored is realized. By matching the key features, the data to be stored is associated with the category of the pre-stored data, thereby determining the data category corresponding to the data to be stored. After establishing the mapping relationship between the data label and the data to be stored, the synchronization strategy can be dynamically adjusted according to actual business needs to ensure that the appropriate synchronization method is used for different categories of data, thereby improving synchronization efficiency, ensuring data consistency, and better meeting the special needs of different categories of data during the data synchronization process, with more intelligent and flexible data synchronization management capabilities.

[0019] Optionally, the classifying and identifying each of the data to be stored to determine the data category corresponding to each of the data to be stored specifically includes:

[0020] Identify the first data to be stored, and determine the data tag corresponding to the first data to be stored;

[0021] According to a preset corresponding relationship, a first category corresponding to the data tag is determined, where the preset corresponding relationship is a corresponding relationship between the data tag and the data category, and the first category is any one of a plurality of data categories.

[0022] By adopting the above technical solution, the first data to be stored is identified, its corresponding data label is determined, and then the data label is mapped to the first category according to the preset corresponding relationship, thereby realizing the classification and identification of the data to be stored. This process can quickly and accurately classify each data to be stored into one of the predefined data categories, providing a basis for the formulation of data management and synchronization strategies. Through the effective classification of data, different synchronization strategies can be more intelligently adopted according to actual business needs to ensure that different categories of data are processed in a personalized manner, thereby improving data synchronization efficiency and ensuring data integrity and consistency. This technical solution effectively reduces the complexity of data management and provides more flexible and intelligent data processing capabilities.

[0023] Optionally, before classifying and identifying each of the data to be stored and determining the data category corresponding to each of the data to be stored, the method further includes:

[0024] determining a plurality of key features from the first data to be stored;

[0025] According to the multiple key features and the multiple pre-stored key features, a first similarity between the first to-be-stored data and the pre-stored data is calculated, specifically by the following formula:

[0026] ;

[0027] Where C is the first similarity, x i is the i-th key feature, y i is the i-th pre-stored key feature;

[0028] Determine whether the first similarity is greater than or equal to a preset first threshold, and if the first similarity is greater than or equal to the preset first threshold, obtain the data label of the pre-stored data;

[0029] A mapping relationship between the data tag and the first data to be stored is established.

[0030] By adopting the above technical solution, before classifying and identifying the data to be stored, a more refined data classification process is achieved by extracting multiple key features and calculating the similarity between the first data to be stored and the pre-stored data. Through similarity calculation, the feature similarity between the data to be stored and the pre-stored data can be compared more comprehensively, rather than relying solely on the matching of a single key feature. This method can control the strictness of classification by flexibly adjusting the similarity threshold, making it more adaptable and adjustable. By establishing a mapping relationship between the data label and the first data to be stored, the data category can be determined more carefully for each data, thereby further improving the personalized management of the synchronization strategy on the basis of classification and identification, and ensuring that the data is synchronized more reasonably and accurately according to its characteristics.

[0031] Optionally, after identifying the first data to be stored and determining the data tag corresponding to the first data to be stored, the method further includes:

[0032] Determining a second similarity between the first data to be stored and the second data to be stored according to a plurality of key features of the first data to be stored and a plurality of key features of the second data to be stored, wherein the second data to be stored is any one of the plurality of data to be stored except the first data to be stored;

[0033] Determine whether the second similarity is greater than or equal to a preset second threshold, and if the second similarity is greater than or equal to the preset second threshold, determine the data label of the first data to be stored;

[0034] A mapping relationship between the data tag and the second data to be stored is established.

[0035] By adopting the above technical solution, after determining the data label of the first data to be stored, a more detailed data similarity comparison is achieved by calculating the second similarity between the first data to be stored and other data to be stored. By comparing multiple key features, the method provides a more comprehensive similarity measurement, rather than relying solely on the matching of a single key feature. By judging whether the second similarity is greater than or equal to the preset second threshold, the data label can be determined based on the similarity between different data, thereby further refining and optimizing the classification results of the data. After establishing a mapping relationship between the data label and the second data to be stored, the correlation between the data can be considered more comprehensively, ensuring that similar data is processed consistently in the synchronization strategy, and further improving the accuracy and intelligence of the synchronization. The effect of this technical solution is to enhance the comprehensive measurement of data similarity, provide smarter and more flexible data management and synchronization strategies, so as to better meet the complex relationship between different data in practical applications.

[0036] Optionally, the determining of the synchronization strategy corresponding to each of the data categories specifically includes:

[0037] Determine that a first data category among the multiple data categories corresponds to a first synchronization strategy, the first data category is any one of the multiple data categories, and the first synchronization strategy is to synchronize complete data in all nodes of the blockchain system;

[0038] And determine that a second data category among the multiple data categories corresponds to a second synchronization strategy, where the second data category is any data category among the multiple data categories except the first data category, and the second synchronization strategy is to synchronize data in the light node of the blockchain system.

[0039] By adopting the above technical solution and determining the synchronization strategy corresponding to each data category, different synchronization methods can be adopted according to the characteristics of different data categories when synchronizing data. Specifically, for the first data category among multiple data categories, it is determined to adopt the first synchronization strategy, that is, to synchronize the complete data in the full node of the blockchain. This helps to ensure the integrity and consistency of key data and is suitable for data categories that require comprehensive synchronization. For the second data category among multiple data categories, it is determined to adopt the second synchronization strategy, that is, to synchronize data in the light node of the blockchain. This lightweight synchronization method is suitable for situations where the amount of data is large but does not require full synchronization, which improves flexibility and efficiency. The effect of this technical solution is to select appropriate synchronization strategies according to the different needs of data categories, so as to more intelligently manage and synchronize data in the blockchain, and improve the flexibility and efficiency of synchronization.

[0040] Optionally, sending the to-be-stored data corresponding to the data category to a corresponding synchronization node for synchronization according to the synchronization strategy specifically includes:

[0041] Determine, among a plurality of data to be stored, a plurality of first data corresponding to the first data category, and a plurality of second data corresponding to the second data category;

[0042] Determine the full node among the plurality of synchronization nodes according to the first synchronization strategy;

[0043] According to the second synchronization strategy, determining the light node among the plurality of synchronization nodes;

[0044] Send each of the first data to each of the full nodes, and send each of the second data to each of the light nodes.

[0045] By adopting the above technical solution, by sending the data to be stored corresponding to the data category determined according to the synchronization strategy to the corresponding synchronization node, differentiated processing of different data categories is achieved. Specifically, for the first data category, multiple full nodes are determined according to the first synchronization strategy to ensure that the data of this category is fully synchronized to the full node of the blockchain to maintain the integrity and consistency of the data. For the second data category, multiple light nodes are determined according to the second synchronization strategy, and the data of this category is synchronized to the light node of the blockchain in a lightweight manner to achieve more efficient synchronization operations. The effect of this technical solution is that it can intelligently select synchronization nodes according to the synchronization strategy, adapt to the synchronization requirements of different data categories, improve the accuracy and efficiency of synchronization operations, and ensure the reasonable management and synchronization of data in the blockchain.

[0046] In a second aspect of the present application, a blockchain data management device based on artificial intelligence is provided, including an acquisition module, an identification module, a synchronization module and a sending module, wherein:

[0047] The acquisition module is used to acquire a data set to be stored, wherein the data set to be stored includes a plurality of data to be stored;

[0048] The identification module is used to classify and identify each of the data to be stored, and determine the data category corresponding to each of the data to be stored;

[0049] The synchronization module is used to determine the synchronization strategy corresponding to each of the data categories;

[0050] The sending module is used to send the to-be-stored data corresponding to the data category to the corresponding synchronization node for synchronization according to the synchronization strategy.

[0051] Optionally, the acquisition module is used to acquire first data to be stored from the multiple data to be stored, and the first data to be stored is any one of the multiple data to be stored;

[0052] The identification module is used to determine a first key feature from the first data to be stored, where the first key feature is any one of a plurality of key features in the first data to be stored;

[0053] The identification module is used to match the first key feature with a plurality of pre-stored key features, where the plurality of pre-stored key features are a plurality of key features of pre-stored data;

[0054] The acquisition module is used for acquiring a data tag corresponding to a second key feature if it is determined that there is a second key feature that is the same as the first key feature among the plurality of pre-stored key features, wherein the data tag is a data tag of the pre-stored data corresponding to the second key feature;

[0055] The synchronization module is used to establish a mapping relationship between the data tag and the first data to be stored.

[0056] Optionally, the identification module is used to identify the first data to be stored and determine the data tag corresponding to the first data to be stored;

[0057] The identification module is used to determine the first category corresponding to the data tag according to a preset corresponding relationship, where the preset corresponding relationship is a corresponding relationship between the data tag and the data category, and the first category is any one of multiple data categories.

[0058] Optionally, the identification module is used to determine a plurality of key features from the first data to be stored;

[0059] The identification module is used to calculate the first similarity between the first to-be-stored data and the pre-stored data according to the multiple key features and the multiple pre-stored key features, specifically by the following formula:

[0060] ;

[0061] Where C is the first similarity, x i is the i-th key feature, y i is the i-th pre-stored key feature;

[0062] The identification module is used to determine whether the first similarity is greater than or equal to a preset first threshold, and if the first similarity is greater than or equal to the preset first threshold, obtain the data label of the pre-stored data;

[0063] The synchronization module is used to establish a mapping relationship between the data tag and the first data to be stored.

[0064] Optionally, the identification module is used to determine a second similarity between the first data to be stored and the second data to be stored according to multiple key features of the first data to be stored and multiple key features of the second data to be stored, wherein the second data to be stored is any one of the multiple data to be stored except the first data to be stored;

[0065] The identification module is used to determine whether the second similarity is greater than or equal to a preset second threshold, and if the second similarity is greater than or equal to the preset second threshold, determine the data label of the first data to be stored;

[0066] The synchronization module is used to establish a mapping relationship between the data tag and the second data to be stored.

[0067] Optionally, the identification module is used to determine that a first data category among the multiple data categories corresponds to a first synchronization strategy, the first data category is any data category among the multiple data categories, and the first synchronization strategy is to synchronize complete data in all nodes of the blockchain system;

[0068] The identification module is used to determine that a second data category among multiple data categories corresponds to a second synchronization strategy, where the second data category is any data category among the multiple data categories except the first data category, and the second synchronization strategy is to synchronize data in the light node of the blockchain system.

[0069] Optionally, the identification module is used to determine, among a plurality of data to be stored, a plurality of first data corresponding to the first data category, and a plurality of second data corresponding to the second data category;

[0070] The synchronization module is used to determine the full node among the plurality of synchronization nodes according to the first synchronization strategy;

[0071] The synchronization module is used to determine the light node among the plurality of synchronization nodes according to the second synchronization strategy;

[0072] The sending module is used to send each of the first data to each of the full nodes, and to send each of the second data to each of the light nodes.

[0073] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes any one of the methods described above.

[0074] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.

[0075] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0076] By obtaining the data set to be stored, classifying and identifying each data to be stored, determining the data category, and determining the corresponding synchronization strategy accordingly, the data to be stored corresponding to the different data categories are finally sent to the corresponding synchronization node for synchronization. This application effectively combines data classification and dynamic determination of synchronization strategy, and can flexibly adjust the synchronization strategy according to actual conditions to adapt to the characteristics and synchronization requirements of different data categories. Through the flexible coordination of classification identification and strategy, the synchronization of different transaction data can be handled more intelligently, the synchronization efficiency can be improved, the integrity of the data can be ensured, and the burden of data storage can be reduced at the same time, realizing a more intelligent and efficient data synchronization process. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a flowchart of the blockchain data management method based on artificial intelligence disclosed in the embodiment of the present application;

[0078] Figure 2 It is a schematic diagram of an application scenario of the blockchain data management method based on artificial intelligence disclosed in the embodiment of the present application;

[0079] Figure 3 It is a structural diagram of an artificial intelligence-based blockchain data management device disclosed in an embodiment of the present application;

[0080] Figure 4 It is a structural schematic diagram of an electronic device disclosed in an embodiment of the present application.

[0081] Explanation of the reference numerals: 201, server; 202, synchronization node; 301, acquisition module; 302, identification module; 303, synchronization module; 304, sending module; 401, processor; 402, communication bus; 403, user interface; 404, network interface; 405, memory. DETAILED DESCRIPTION

[0082] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0083] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0084] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0085] Blockchain is a distributed database technology. The basic idea is to use cryptographic methods to ensure the immutability and security of data by distributing and storing data among multiple participants (nodes). Data is linked together in the form of blocks. Each block contains a batch of transaction records and is connected to the previous block through a hash function to form a growing chain. The decentralized nature of blockchain means that there is no single control authority. All participants have copies and use consensus algorithms to verify and synchronize data. Smart contracts are automated programs on the blockchain that can execute contract terms according to predetermined conditions. Blockchain technology is widely used in cryptocurrencies as well as other fields such as finance, supply chain management, healthcare, etc. to improve the transparency, security and traceability of data.

[0086] In the blockchain, nodes are participants in the network, responsible for maintaining, verifying and disseminating transaction data. Each node stores a complete copy of the blockchain and participates in the creation and verification process of blocks through a consensus algorithm. Nodes can be miners (responsible for packaging transactions to form new blocks and competing for the right to record accounts by solving mathematical problems), full nodes (storing the data of the entire blockchain) or light nodes (only storing part of the blockchain data). Through peer-to-peer network communication, transaction transmission and consensus are achieved between nodes, ensuring the decentralized storage and security of data, thereby building a decentralized, transparent and tamper-proof blockchain system.

[0087] When storing transaction-related data in a blockchain system, it is crucial to determine the full node synchronization strategy and light node synchronization strategy for transaction data. If transaction data is synchronized on a full node, the complete content of the transaction data will be synchronized, thus having higher integrity and security. However, due to the large amount of synchronized data, the efficiency of data synchronization is relatively low. If transaction data is synchronized on a light node, data synchronization can be performed more quickly by only obtaining block headers and key information, and less storage and computing resources are occupied, but the integrity of transaction data is poor. Therefore, a method is needed to achieve synchronization according to different strategies based on the actual situation of transaction data.

[0088] This embodiment discloses a blockchain data management method based on artificial intelligence, referring to Figure 1 , including the following steps S110-S140:

[0089] The blockchain data management method based on artificial intelligence disclosed in the embodiment of the present application is applied to server 201, which includes but is not limited to electronic devices such as mobile phones, tablet computers, wearable devices, PCs (Personal Computers), etc., and can also be a background server 201 running the blockchain data management method based on artificial intelligence. Server 201 can be implemented as an independent server 201 or a server 201 cluster composed of multiple servers 201. Figure 2 , the server 201 is connected to the blockchain system in communication. In the blockchain system, multiple synchronization nodes 202 are interconnected. The synchronization nodes 202 are participants in the distributed network and are responsible for maintaining the security, consistency and decentralization of the blockchain network. Each synchronization node 202 is a computer in the network, which can be a personal computer, server 201 or a dedicated hardware device. The synchronization nodes 202 are connected to each other through a point-to-point communication protocol to form a decentralized network.

[0090] S110, obtaining a data set to be stored.

[0091] When a user needs to store a large amount of data in the blockchain system, the server 201 obtains the data set to be stored that the user needs to store, and the data set to be stored includes multiple data to be stored and establishes a connection with the blockchain system. For example, when all data involved in a transaction need to be stored in the blockchain system, the data set to be stored includes the transaction initiator, transaction time, transaction location, transaction equipment, etc.

[0092] S120, classify and identify each data to be stored, and determine the data category corresponding to each data to be stored.

[0093] After obtaining multiple data to be stored included in the data set to be stored, artificial intelligence (AI) is used to classify and identify the data to be stored. Specifically, any one of the multiple data to be stored, the first stored data, is used as an example. For the selected first data to be stored, one or more key features are determined. These key features are important attributes used to describe and match data. For example, if the data to be stored is the transaction time, the key feature is the time format of the data to be stored. If the data to be stored is the transaction location, the key feature is the coordinate information of the data to be stored.

[0094] The feature extraction algorithm may be applied, and the convolutional neural network (CNN) or other feature extraction methods in deep learning may be used to extract key features from the first data to be stored. Specifically, after preprocessing the original data including noise removal, missing value filling, data standardization, etc., it is necessary to select an appropriate feature extraction method according to the data type and task requirements. In image data, the convolution layer can be used to extract the texture and shape features of the image; in text data, the bag-of-words model or word embedding technology can be used to extract the semantic information of words; in numerical data, statistical methods such as mean, variance, etc. can be used for feature extraction. Then, the selected feature extraction method is applied to the first data to be stored to obtain the corresponding feature representation. In deep learning, this may involve the use of a pre-trained neural network model, such as using a pre-trained convolutional neural network (CNN) to extract image features in an image recognition task. Due to the variety of data types, the specific feature extraction method is not described here, but in general, feature extraction is performed from the first data to be stored to obtain multiple key features corresponding to it.

[0095] The first key feature is compared and matched with a plurality of pre-stored key features through a corresponding similarity calculation algorithm and a matching algorithm. These pre-stored key features may be key features marked with various transaction data already in the database. If the match is successful, that is, the first key feature matches a feature in the pre-stored key features, then the second key feature corresponding to the same pre-stored key feature is determined.

[0096] In this step, the focus is on the selection of similarity calculation algorithms and matching algorithms. Available algorithms include but are not limited to Euclidean distance, Manhattan distance, Jaccard Similarity Coefficient or Pearson Correlation Coefficient. The selection of an appropriate similarity measurement method usually depends on the nature of the data and the requirements of the specific task. For example, the Euclidean distance algorithm is simple and intuitive, easy to understand and implement. It is applicable to various data types, including numerical and non-numerical data. However, it is sensitive to data dimensions and scales, and feature scaling may be required to process features of different dimensions and scales. The Jaccard similarity coefficient is suitable for similarity calculation of key features of text data types.

[0097] Therefore, after extracting the key features from the data to be stored, the machine learning algorithm or deep learning model is used to train according to the problem requirements. For example, if the problem needs to emphasize semantic similarity, an embedding model based on deep learning can be selected for training. And evaluate the performance of different similarity algorithms on a given problem, including conducting experiments on training and test data sets to compare performance indicators such as accuracy, precision, and recall of the algorithms. Based on the performance evaluation results and feature analysis, an intelligent system can be established to automatically select the similarity algorithm that best suits the current needs using an algorithm selection strategy. In order to achieve different application scenarios, this intelligent system can select the most suitable similarity algorithm based on real-time key features to achieve a more flexible and intelligent matching process. For example, if the key feature of the data is text, you can consider using a similarity algorithm based on word embedding (such as Word2Vec or BERT); if it is an image feature, you can consider using a convolutional neural network to extract the feature and then apply cosine similarity.

[0098] Then, a data tag corresponding to the second key feature is obtained, and the second key feature is the key feature of the pre-stored data. The data tag is a computer-readable type mark of the data, including transaction timestamp, transaction location, transaction participants, transaction amount, transaction type, payment method, etc. A mapping relationship is established between the obtained data tag and the first data to be stored. This can be adding a data tag to the first data to be stored in the database, or using this mapping relationship in other forms of storage or processing.

[0099] The server realizes classification and identification of the data to be stored by extracting the first key feature of the first data to be stored, matching it with the key feature of the pre-stored data through artificial intelligence, and obtaining the data label corresponding to the second key feature that successfully matches. By matching the key features, the data to be stored is associated with the category of the pre-stored data, thereby determining the data category corresponding to the data to be stored. After the mapping relationship between the data label and the data to be stored is established, artificial intelligence can further dynamically adjust the synchronization strategy according to actual business needs to ensure that the appropriate synchronization method is used for different categories of data, thereby improving synchronization efficiency, ensuring data consistency, and better meeting the special needs of different categories of data during the data synchronization process, with more intelligent and flexible data synchronization management capabilities.

[0100] Furthermore, a label can be added to the first data to be stored by calculating the first similarity between the first data to be stored and the pre-stored data. Multiple key features of the first data to be stored and multiple pre-stored key features of the pre-stored data are determined respectively. Then, based on the multiple key features and the multiple pre-stored key features, the first similarity between the first data to be stored and the pre-stored data is calculated, specifically by the following formula:

[0101] ;

[0102] Among them, C is the first similarity, x i is the i-th key feature, y i is the i-th pre-stored key feature;

[0103] Through the above formula, the calculated first similarity can measure the degree of linear relationship between the first data to be stored and the pre-stored data, and the numerator measures the similarity change trend of the two data relative to their mean. When the numerator is positive, it means that the similarity trends of the two data are the same, that is, when one increases, the other also increases; when the numerator is negative, it means that the similarity trends of the two data are opposite, that is, when one increases, the other decreases. The denominator is used to measure the degree of dispersion of each key feature in the data relative to the mean. The larger the standard deviation, the greater the fluctuation of the key feature relative to the mean, and the wider the data dispersion.

[0104] Then determine whether the first similarity between the first data to be stored and the pre-stored data is greater than or equal to the preset first threshold. In this embodiment, the preset first threshold can be adjusted according to the actual situation, and this application does not make specific restrictions. If the first similarity is greater than or equal to the preset first threshold, it indicates that the similarity between the first data to be stored and the pre-stored data is high, and the data label of the pre-stored data is obtained, and a mapping relationship is established between the obtained data label and the first data to be stored. On the contrary, if the first similarity is less than the preset first threshold, the mapping relationship between the obtained data label and the first data to be stored cannot be established.

[0105] Before classifying and identifying the data to be stored, a more refined data classification process is achieved by extracting multiple key features and calculating the similarity between the first data to be stored and the pre-stored data through a similarity algorithm or a related model. Through similarity calculation, the feature similarity between the data to be stored and the pre-stored data can be compared more comprehensively, rather than relying solely on the matching of a single key feature. This method can flexibly adjust the similarity threshold through artificial intelligence to control the strictness of the classification, making it more adaptable and adjustable. By establishing a mapping relationship between the data label and the first data to be stored, the data category can be determined more carefully for each data, thereby further improving the personalized management of the synchronization strategy on the basis of classification and identification, ensuring that the data is synchronized more reasonably and accurately according to its characteristics.

[0106] Furthermore, multiple data to be stored can be compared in pairs through artificial intelligence to determine the similarity of any two data to be stored. If the similarity between the two is high, the data label of one of the data to be stored is directly set as the label of the other data to be stored. Specifically refer to the above steps, according to multiple key features of the first data to be stored and multiple key features of the second data to be stored, calculate the second similarity between the first data to be stored and the second data to be stored. Compare the calculated second similarity with the preset second threshold. In this embodiment, the preset second threshold can be adjusted according to actual conditions, and this application does not make specific limitations. If the second similarity is greater than or equal to the preset second threshold, it is considered that the two data are similar in key features. Establish a mapping relationship between the determined data label and the second data to be stored. This can be a simple mapping table, database record or other data structure to associate the data label with the corresponding second data to be stored.

[0107] After determining the data label of the first data to be stored, the server realizes a more detailed data similarity comparison by calculating the second similarity between the first data to be stored and the other data to be stored. By comparing multiple key features, the method provides a more comprehensive similarity measurement, rather than relying solely on the matching of a single key feature. By judging whether the second similarity is greater than or equal to the preset second threshold, the data label can be determined according to the similarity between different data, thereby further refining and optimizing the classification results of the data. After establishing the mapping relationship between the data label and the second data to be stored, the correlation between the data can be considered more comprehensively, ensuring that similar data is processed consistently in the synchronization strategy, and further improving the accuracy and intelligence of synchronization. The effect of this technical solution is to enhance the comprehensive measurement of data similarity, provide smarter and more flexible data management and synchronization strategies, so as to better meet the complex relationship between different data in practical applications. In short, a method similar to supervised learning can be used to train the classification model using a data set of known categories. This enables artificial intelligence to have the ability to automatically classify new data by learning previous data.

[0108] S130: Determine the synchronization strategy corresponding to each data category.

[0109] After labeling each data to be stored in the data set to be stored, when performing data classification and identification, artificial intelligence is used to identify the first data to be stored, and the data label contained therein is identified to obtain the data label corresponding to the first data to be stored. Then a corresponding relationship is formulated or preset to associate the data label with the data category. This can be a pre-defined mapping table, rule set, or a corresponding relationship learned through a machine learning algorithm. According to the preset corresponding relationship, the first category corresponding to the data label is determined. The first category is any one of a plurality of data categories, and the data category is used to obtain a category after classifying the data according to the data label. In the present application, the data category preferably includes two data categories, a first data category and a second data category. The first data category and the second data category correspond to different data synchronization strategies.

[0110] By identifying the first data to be stored, determining its corresponding data tag, and then mapping the data tag to the first category according to the preset corresponding relationship, the classification and identification of the data to be stored is achieved. This process can quickly and accurately classify each data to be stored into one of the predefined data categories, providing a basis for the formulation of data management and synchronization strategies. Through the effective classification of data, different synchronization strategies can be more intelligently adopted according to actual business needs to ensure that different categories of data are processed in a personalized manner, thereby improving data synchronization efficiency and ensuring data integrity and consistency. This technical solution effectively reduces the complexity of data management and provides more flexible and intelligent data processing capabilities.

[0111] The first synchronization strategy corresponding to the first data category is that the complete data needs to be synchronized in all full nodes of the blockchain system. The data to be stored corresponding to this data category is relatively important transaction data, which needs to be synchronized in all full nodes of the blockchain system to ensure the integrity and security of the data. For example, the data to be stored corresponding to the first data category may include the transaction amount, because it directly affects the financial impact and risk of the transaction. It may also include the transaction timestamp, because it is important for monitoring market behavior, analyzing trends and time sensitivity. The second synchronization strategy corresponding to the second data category is that the data needs to be synchronized in all light nodes of the blockchain system. The data to be stored corresponding to this data category is relatively less important transaction data, which needs to be synchronized in all light nodes of the blockchain system to ensure the rate of data synchronization. For example, the data to be stored corresponding to the second data category may include hardware and device information involved in the transaction. Unless there are specific information security or compliance requirements, specific hardware and device information may not be the primary concern.

[0112] By determining the synchronization strategy corresponding to each data category, different synchronization methods can be used according to the characteristics of different data categories when synchronizing data. Specifically, for the first data category among multiple data categories, it is determined to adopt the first synchronization strategy, that is, to synchronize the complete data in the full node of the blockchain. This helps to ensure the integrity and consistency of key data and is suitable for data categories that require comprehensive synchronization. For the second data category among multiple data categories, it is determined to adopt the second synchronization strategy, that is, to synchronize data in the light node of the blockchain. This lightweight synchronization method is suitable for situations where the amount of data is large but does not require full synchronization, which improves flexibility and efficiency. The effect of this technical solution is to select appropriate synchronization strategies according to the different needs of data categories, so as to more intelligently manage and synchronize data in the blockchain, and improve the flexibility and efficiency of synchronization.

[0113] S140: According to the synchronization strategy, the data to be stored corresponding to the data category is sent to the corresponding synchronization node 202 for synchronization.

[0114] According to the previous mapping relationship, multiple first data corresponding to the first data category and multiple second data corresponding to the second data category are determined among the multiple data to be stored. Then, according to the first synchronization strategy, multiple full nodes included in the multiple synchronization nodes 202 are determined. These full nodes are the main nodes in the blockchain system, which are used to store the complete first data. And according to the second synchronization strategy, multiple light nodes included in the multiple synchronization nodes 202 are determined. These light nodes are auxiliary nodes in the blockchain system, which are used to store lightweight data or provide partial access to data. Finally, the first data is sent to the corresponding full node through network communication protocols such as HTTP and P2P. And the second data is sent to the corresponding light node. Each full node receives and stores the corresponding first data to ensure integrity and consistency. Each light node receives and stores the corresponding second data, and the amount of data stored is determined according to the storage capacity and needs of the light node.

[0115] By sending the data to be stored corresponding to the data category determined according to the synchronization strategy to the corresponding synchronization node 202, differentiated processing of different data categories is achieved. Specifically, for the first data category, multiple full nodes are determined according to the first synchronization strategy to ensure that the data of this category is fully synchronized to the full node of the blockchain to maintain the integrity and consistency of the data. For the second data category, multiple light nodes are determined according to the second synchronization strategy, and the data of this category is synchronized to the light node of the blockchain in a lightweight manner to achieve more efficient synchronization operations. The effect of this technical solution is that it can intelligently select the synchronization node 202 according to the synchronization strategy, adapt to the synchronization requirements of different data categories, improve the accuracy and efficiency of the synchronization operation, and ensure the reasonable management and synchronization processing of data in the blockchain.

[0116] By acquiring the data set to be stored, and combining artificial intelligence to classify and identify each data to be stored, the data category is determined, and the corresponding synchronization strategy is determined accordingly, and finally the data to be stored corresponding to the different data categories are sent to the corresponding synchronization node 202 for synchronization. The present application effectively combines data classification and dynamic determination of synchronization strategy, and can flexibly adjust the synchronization strategy according to actual conditions to adapt to the characteristics and synchronization requirements of different data categories. Through the flexible coordination of classification identification and strategy, the synchronization of different transaction data can be handled more intelligently, the synchronization efficiency can be improved, the integrity of the data can be ensured, and the burden of data storage can be reduced at the same time, realizing a more intelligent and efficient data synchronization process.

[0117] This embodiment also discloses a blockchain data management device based on artificial intelligence, referring to Figure 3 , including an acquisition module 301, an identification module 302, a synchronization module 303 and a sending module 304, wherein:

[0118] The acquisition module 301 is used to acquire a data set to be stored, where the data set to be stored includes a plurality of data to be stored.

[0119] The identification module 302 is used to classify and identify each data to be stored, and determine the data category corresponding to each data to be stored.

[0120] The synchronization module 303 is used to determine the synchronization strategy corresponding to each data category.

[0121] The sending module 304 is used to send the to-be-stored data corresponding to the data category to the corresponding synchronization node 202 for synchronization according to the synchronization strategy.

[0122] In a possible implementation, the acquisition module 301 is used to acquire first data to be stored from multiple data to be stored, where the first data to be stored is any one of the multiple data to be stored.

[0123] The identification module 302 is used to determine a first key feature from the first data to be stored, where the first key feature is any key feature among multiple key features in the first data to be stored.

[0124] The identification module 302 is used to match the first key feature with a plurality of pre-stored key features, where the plurality of pre-stored key features are a plurality of key features of pre-stored data.

[0125] The acquisition module 301 is used to acquire a data tag corresponding to the second key feature if it is determined that there is a second key feature that is the same as the first key feature among the multiple pre-stored key features, and the data tag is the data tag of the pre-stored data corresponding to the second key feature.

[0126] The synchronization module 303 is used to establish a mapping relationship between the data tag and the first data to be stored.

[0127] In a possible implementation, the identification module 302 is configured to identify the first data to be stored and determine a data tag corresponding to the first data to be stored.

[0128] The identification module 302 is used to determine the first category corresponding to the data tag according to a preset corresponding relationship. The preset corresponding relationship is the corresponding relationship between the data tag and the data category. The first category is any one of the multiple data categories.

[0129] In a possible implementation, the identification module 302 is configured to determine a plurality of key features from the first data to be stored.

[0130] The identification module 302 is used to calculate the first similarity between the first to-be-stored data and the pre-stored data according to the multiple key features and the multiple pre-stored key features, specifically by the following formula:

[0131] ;

[0132] Among them, C is the first similarity, x i is the i-th key feature, y i is the i-th pre-stored key feature.

[0133] The identification module 302 is used to determine whether the first similarity is greater than or equal to a preset first threshold, and if the first similarity is greater than or equal to the preset first threshold, obtain the data label of the pre-stored data.

[0134] The synchronization module 303 is used to establish a mapping relationship between the data tag and the first data to be stored.

[0135] In a possible implementation, the identification module 302 is used to determine a second similarity between the first data to be stored and the second data to be stored based on multiple key features of the first data to be stored and multiple key features of the second data to be stored, where the second data to be stored is any one of the multiple data to be stored except the first data to be stored.

[0136] The identification module 302 is used to determine whether the second similarity is greater than or equal to a preset second threshold, and if the second similarity is greater than or equal to the preset second threshold, determine the data label of the first data to be stored.

[0137] The synchronization module 303 is used to establish a mapping relationship between the data tag and the second data to be stored.

[0138] In a possible implementation, the identification module 302 is used to determine that a first data category among multiple data categories corresponds to a first synchronization strategy, the first data category is any data category among the multiple data categories, and the first synchronization strategy is to synchronize complete data in all nodes of the blockchain system.

[0139] The identification module 302 is used to determine that a second data category among multiple data categories corresponds to a second synchronization strategy, where the second data category is any data category among the multiple data categories except the first data category, and the second synchronization strategy is to synchronize data in a light node of the blockchain system.

[0140] In a possible implementation, the identification module 302 is used to determine, among a plurality of data to be stored, a plurality of first data corresponding to a first data category and a plurality of second data corresponding to a second data category.

[0141] The synchronization module 303 is used to determine the full nodes among the multiple synchronization nodes 202 according to the first synchronization strategy.

[0142] The synchronization module 303 is used to determine a light node among the multiple synchronization nodes 202 according to the second synchronization strategy.

[0143] The sending module 304 is used to send each first data to each full node, and send each second data to each light node.

[0144] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0145] This embodiment also discloses an electronic device, referring to Figure 4 The electronic device may include: at least one processor 401 , at least one communication bus 402 , a user interface 403 , a network interface 404 , and at least one memory 405 .

[0146] The communication bus 402 is used to realize the connection and communication between these components.

[0147] The user interface 403 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 403 may also include a standard wired interface and a wireless interface.

[0148] The network interface 404 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0149] Among them, the processor 401 may include one or more processing cores. The processor 401 uses various interfaces and lines to connect various parts within the entire server 201, and executes various functions and processes data of the server 201 by running or executing instructions, programs, code sets or instruction sets stored in the memory 405, and calling data stored in the memory 405. Optionally, the processor 401 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (ProgrammableLogic Array, PLA). The processor 401 can integrate one or a combination of a central processing unit 401 (Central Processing Unit, CPU), an image processor 401 (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 401, and can be implemented separately through a chip.

[0150] Among them, the memory 405 may include a random access memory 405 (Random Access Memory, RAM), and may also include a read-only memory 405 (Read-Only Memory). Optionally, the memory 405 includes a non-transitory computer-readable storage medium. The memory 405 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 405 may also be at least one storage device located away from the aforementioned processor 401. As shown in the figure, the memory 405 as a computer storage medium may include an operating system, a network communication module, a user interface 403 module, and an application program of a blockchain data management method based on artificial intelligence.

[0151] exist Figure 4In the electronic device shown, the user interface 403 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 401 can be used to call the application program of the blockchain data management method based on artificial intelligence stored in the memory 405. When executed by one or more processors 401, the electronic device executes one or more methods in the above embodiments.

[0152] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0153] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0154] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0155] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0156] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0157] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory 405. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory 405 and includes several instructions for a computer device (which can be a personal computer, server 201 or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory 405 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0158] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. The blockchain data management method based on artificial intelligence is characterized by: The method comprises: Acquire a data set to be stored, wherein the data set to be stored includes a plurality of data to be stored, and the data set to be stored includes a transaction initiator, a transaction time, a transaction location, and a transaction device; Classify and identify each of the data to be stored, and determine the data category corresponding to each of the data to be stored; Determine a synchronization strategy corresponding to each of the data categories; According to the synchronization strategy, the to-be-stored data corresponding to the data category is sent to the corresponding synchronization node for synchronization; Before classifying and identifying each of the data to be stored and determining the data category corresponding to each of the data to be stored, the method includes: Acquire first data to be stored from the plurality of data to be stored, wherein the first data to be stored is any one of the plurality of data to be stored; Determine a first key feature from the first data to be stored, where the first key feature is any one of a plurality of key features in the first data to be stored; Matching the first key feature with a plurality of pre-stored key features, where the plurality of pre-stored key features are a plurality of key features of pre-stored data; If it is determined that there is a second key feature that is the same as the first key feature among the plurality of pre-stored key features, obtaining a data tag corresponding to the second key feature, the data tag being the data tag of the pre-stored data corresponding to the second key feature; Establishing a mapping relationship between the data tag and the first data to be stored; The classifying and identifying each of the data to be stored to determine the data category corresponding to each of the data to be stored specifically includes: Identify the first data to be stored, and determine the data tag corresponding to the first data to be stored; Determine a first category corresponding to the data tag according to a preset corresponding relationship, wherein the preset corresponding relationship is a corresponding relationship between the data tag and the data category, and the first category is any one of the multiple data categories; Before classifying and identifying each of the data to be stored and determining the data category corresponding to each of the data to be stored, the method further includes: determining a plurality of key features from the first data to be stored; According to the multiple key features and the multiple pre-stored key features, a first similarity between the first to-be-stored data and the pre-stored data is calculated, specifically by the following formula: ； Where C is the first similarity, x i is the i-th key feature, y i is the i-th pre-stored key feature; Determine whether the first similarity is greater than or equal to a preset first threshold, and if the first similarity is greater than or equal to the preset first threshold, obtain the data label of the pre-stored data; A mapping relationship between the data tag and the first data to be stored is established.

2. The blockchain data management method based on artificial intelligence according to claim 1 is characterized in that: After identifying the first data to be stored and determining the data tag corresponding to the first data to be stored, the method further includes: Determining a second similarity between the first data to be stored and the second data to be stored according to a plurality of key features of the first data to be stored and a plurality of key features of the second data to be stored, wherein the second data to be stored is any one of the plurality of data to be stored except the first data to be stored; Determine whether the second similarity is greater than or equal to a preset second threshold, and if the second similarity is greater than or equal to the preset second threshold, determine the data label of the first data to be stored; A mapping relationship between the data tag and the second data to be stored is established.

3. The blockchain data management method based on artificial intelligence according to claim 1 is characterized in that: The step of determining the synchronization strategy corresponding to each of the data categories specifically includes: Determine that a first data category among the multiple data categories corresponds to a first synchronization strategy, the first data category is any one of the multiple data categories, and the first synchronization strategy is to synchronize complete data in all nodes of the blockchain system; And determine that a second data category among the multiple data categories corresponds to a second synchronization strategy, where the second data category is any data category among the multiple data categories except the first data category, and the second synchronization strategy is to synchronize data in the light node of the blockchain system.

4. The blockchain data management method based on artificial intelligence according to claim 3 is characterized in that: The step of sending the to-be-stored data corresponding to the data category to the corresponding synchronization node for synchronization according to the synchronization strategy specifically includes: Determine, among a plurality of data to be stored, a plurality of first data corresponding to the first data category, and a plurality of second data corresponding to the second data category; Determine the full node among the plurality of synchronization nodes according to the first synchronization strategy; According to the second synchronization strategy, determining the light node among the plurality of synchronization nodes; Send each of the first data to each of the full nodes, and send each of the second data to each of the light nodes.

5. The blockchain data management device based on artificial intelligence is characterized by: The system comprises an acquisition module (301), an identification module (302), a synchronization module (303) and a sending module (304), wherein: The acquisition module (301) is used to acquire a data set to be stored, wherein the data set to be stored includes a plurality of data to be stored; The identification module (302) is used to classify and identify each of the data to be stored, and determine the data category corresponding to each of the data to be stored; The synchronization module (303) is used to determine the synchronization strategy corresponding to each of the data categories; The sending module (304) is used to send the to-be-stored data corresponding to the data category to the corresponding synchronization node for synchronization according to the synchronization strategy; The identification module (302) is also used for classifying and identifying each of the data to be stored and determining the data category corresponding to each of the data to be stored, the method comprising: obtaining a first data to be stored from a plurality of the data to be stored, the first data to be stored being any one of the plurality of the data to be stored; determining a first key feature from the first data to be stored, the first key feature being any one of the plurality of key features in the first data to be stored; matching the first key feature with a plurality of pre-stored key features, the plurality of pre-stored key features being a plurality of key features of the pre-stored data; if it is determined that a second key feature identical to the first key feature exists in the plurality of the pre-stored key features, obtaining a data tag corresponding to the second key feature, the data tag being the data tag of the pre-stored data corresponding to the second key feature; and establishing a mapping relationship between the data tag and the first data to be stored; The identification module (302) is further used for classifying and identifying each of the data to be stored, and determining the data category corresponding to each of the data to be stored, specifically comprising: identifying the first data to be stored, and determining the data label corresponding to the first data to be stored; determining the first category corresponding to the data label according to a preset corresponding relationship, wherein the preset corresponding relationship is a corresponding relationship between the data label and the data category, and the first category is any one of a plurality of data categories; The identification module (302) is further configured to, before classifying and identifying each of the data to be stored and determining the data category corresponding to each of the data to be stored, the method further comprises: determining a plurality of key features from the first data to be stored; and calculating a first similarity between the first data to be stored and the pre-stored data based on the plurality of key features and the plurality of pre-stored key features, specifically by the following formula: ； Where C is the first similarity, x i is the i-th key feature, y i is the i-th pre-stored key feature; determines whether the first similarity is greater than or equal to a preset first threshold, and if the first similarity is greater than or equal to the preset first threshold, obtains the data label of the pre-stored data; establishes a mapping relationship between the data label and the first data to be stored.

6. An electronic device, characterized in that: The electronic device comprises a processor (401), a memory (405), a user interface (403) and a network interface (404), wherein the memory (405) is used to store instructions, the user interface (403) and the network interface (404) are both used to communicate with other devices, and the processor (401) is used to execute the instructions stored in the memory (405) so that the electronic device executes the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 4 is performed.

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