Financial Transaction Big Data Processing Method and System Based on Cloud-Edge Integration

Abstract

This application proposes a financial transaction big data processing method and system based on cloud-edge integration. The method includes: obtaining financial transaction data, cleaning and obtaining corresponding standardized data; performing distributed storage, and storing the financially distributed data into a distributed search engine cluster; storing the processed data into a blockchain for data on-chain; validating the data, establishing a blockchain index, and performing visual display according to the query results to determine whether there are transaction errors and perform retrospective queries. By performing data cleaning and establishing an index, this application stores the financially distributed data into a distributed search engine cluster, and combines blockchain technology to achieve secure storage and convenient query of data, effectively improving the efficiency of financial transaction big data processing.

Description

Technical Field

[0001] This application relates to the field of big data computing, and particularly to a method and system for processing financial transaction big data based on cloud-edge integration. Background Art

[0002] Currently in the financial industry, introducing blockchain technology into the financial system can bring huge advantages, making the transaction operations of the financial system more convenient, secure, and fast. At the same time, due to the characteristics of blockchain such as decentralization, data anti-tampering, and transaction anonymity, it can significantly reduce the role of the third party in the financial system, reduce the operating costs of the financial system while improving the efficiency of financial transactions.

[0003] Financial transaction data has characteristics such as real-time, diversity, and variability. At the same time, in financial transactions, various financial entities such as banking institutions and securities companies need to synchronize transaction data in real time. However, due to the uneven technical capabilities of each entity and the influence of network status, data loss, data delay, data errors, etc. may occur during data interaction, affecting the operation of the financial system. And in financial transactions, users hope that financial transaction data is traceable and that transaction information is confidential. How to ensure the traceability of financial transactions and protect transaction information anonymously has become a difficult problem. How to efficiently store, process, and ensure the security and privacy of financial transaction blockchain data is an urgent problem to be solved in the financial encryption market. Summary of the Invention

[0004] This application mainly provides a method and system for processing financial transaction big data based on cloud-edge integration to improve the efficiency and security of financial data processing.

[0005] To solve the above technical problems, one technical solution adopted by this application is: to provide a method for processing financial transaction big data based on cloud-edge integration, including:

[0006] S10: Obtain financial transaction data and clean the financial transaction data to obtain corresponding standardized data;

[0007] S20: Construct a multi-way tree based on the standardized data, generate a tree-like index structure, and determine the query data range based on the tree-like index structure;

[0008] S30: Perform distributed storage on the query results, establish a distributed search engine cluster, and store the financial transaction data after distributed storage into the distributed search engine cluster;

[0009] S40: Chain the processed data and map the chained data to the data layer, network layer, consensus layer, and contract / service layer;

[0010] S50: Verify the data stored distributively, compare the obtained data with the data stored in the blockchain to verify the accuracy of the distributed data source, and update the storage according to the verification result;

[0011] S60: Add the block header data to the blockchain to establish a blockchain index for real-time status query;

[0012] S70: Visualize the query result of the distributively stored data to determine whether there is a transaction error, and label the financial transaction data with transaction errors for retrospective query.

[0013] In a possible implementation manner, the step of constructing a multi-way tree based on the standardized data, generating a tree-like index structure, and determining the query data range based on the tree-like index structure includes:

[0014] S21: Determine the query data range according to the node information in the tree-like index structure, obtain the starting index of the corresponding node according to the node information, then obtain the data range according to the starting index, and generate node data;

[0015] S22: Obtain user operation information to query data, match the label information of the corresponding user according to the operation information, sort the corresponding node data according to the weight of the label information, and visually display the sorted node data.

[0016] In a possible implementation manner, the step of storing the query result distributively, establishing a distributed search engine cluster, and storing the financial transaction data stored distributively into the distributed search engine cluster includes:

[0017] S31: Obtain a data query request, convert the data query request into a search request statement, and distributively store the data into the Hadoop cluster based on the search request statement;

[0018] S32: Split the data and import it into the distributed file system HDFS in batches, store the data into the distributed framework HBase, and establish a distributed search engine cluster;

[0019] S33: Import the data into the distributed search engine cluster, and query and visually display the data through the kibana search engine.

[0020] In a possible implementation manner, the step of uploading the processed data to the blockchain and mapping the uploaded data to the data layer, network layer, consensus layer, and contract / service layer includes:

[0021] S41: Determine a data uploading strategy according to the distributively stored data and perform data encryption;

[0022] S42: Perform a hashing operation on the encrypted data to obtain the corresponding hash signature, and record the hash signature in the block header of the blockchain;

[0023] S43: Determine the blockchain storage location, store the data to be uploaded into the node corresponding to the blockchain storage location, and broadcast the data to be uploaded to other nodes based on the P2P networking protocol;

[0024] S44: Determine the block header data through a consensus algorithm, generate transaction data and perform distributed data storage.

[0025] In a possible implementation, the consensus algorithm is one or several of the POW mechanism, POS mechanism, PBFT mechanism, and DPOS mechanism.

[0026] In a possible implementation, the step of adding the block header data to the blockchain to establish a blockchain index for real-time status query includes:

[0027] S61: Perform a hashing operation on the block header data to generate a hash signature, and query the transaction information of the block according to the hash signature;

[0028] S62: Add the block header data to the blockchain, and determine whether the block header data is correct. If it is correct, add the corresponding block to the blockchain; if it is incorrect, remove the corresponding block from the blockchain.

[0029] In a possible implementation, the step of adding the block header data to the blockchain includes:

[0030] S63: Perform an encryption operation on the block header data to obtain block header data information, perform word segmentation on the block header data information to obtain data information keywords, query data information according to the data information keywords, and determine the data information similarity according to the data information query result;

[0031] S64: Perform clustering analysis based on the data information similarity to obtain similar blocks of the corresponding block, and construct a block relationship network diagram according to the similar blocks;

[0032] S65: Determine the encryption method of the block header data according to the block header data, perform a hashing operation according to the encryption method, perform a hashing operation on the similar blocks to obtain the hash signature of the block, and determine the block header data encryption parameters according to the hash signature of the block. Encrypt according to the block header data encryption parameters, and add the encrypted block header data to the blockchain.

[0033] In a possible implementation manner, the step of performing visual display based on the query result of distributed storage data to determine whether there is a transaction error and performing error marking on the financial transaction data with transaction errors for retrospective query includes:

[0034] S71: According to the query result, determine the data display form, and then obtain data display parameters according to the data display form;

[0035] S72: Convert the data into a data source display table according to the data display parameters, define a data display table based on the data display parameters, and determine the data display rule of the data source display table based on the data display table;

[0036] S73: Preprocess the data, determine the data statistics rule according to the data preprocessing result, substitute the data source display table into the data statistics rule to obtain a data source display result, and define a data display formula according to the data statistics rule; determine the data display result based on the data display formula and the data source display result and perform visual display on the data display result.

[0037] In a possible implementation manner, the step of preprocessing the data, determining the data statistics rule according to the data preprocessing result, substituting the data source display table into the data statistics rule to obtain a data source display result, and defining a data display formula according to the data statistics rule; determining the data display result based on the data display formula and the data source display result and performing visual display on the data display result includes:

[0038] S74: Divide the data display rule into a global data display rule and a local data display rule, obtain a data statistics sub-rule according to the local data display rule, and divide the data source display table into data source display sub-forms;

[0039] S75: Match the data source display sub-form with the data statistics sub-rule based on a matching rule to generate a data source display sub-form query request;

[0040] S76: According to the data source display sub-form query request, query the data source display sub-form based on the distributed search engine cluster, the big data tool set, and the data statistics sub-rule, generate a data source display sub-form display result according to the query result, and substitute the data source display sub-form display result into the data statistics rule to obtain a data source display result.

[0041] To solve the above technical problems, another technical solution adopted by this application is: providing a financial transaction big data processing system based on cloud-edge integration for the above-mentioned financial transaction big data processing method based on cloud-edge integration, including:

[0042] A processing module, configured to obtain financial transaction data, clean the financial transaction data, and convert corresponding data types, data formats, and data dimensions to standardize the financial transaction data;

[0043] A storage module, configured to construct a multi-way tree based on the standardized data, generate a tree-like index structure, query a data range based on the tree-like index structure, and perform distributed storage on query results, establish a distributed search engine cluster, and store the financial data after distributed storage into the distributed search engine cluster;

[0044] An on-chain module, configured to store the processed data into a blockchain for data on-chain, map the on-chain data to a data layer, a network layer, a consensus layer, and a contract / service layer for distributed storage;

[0045] A verification module, configured to verify the data stored in a distributed manner, compare the obtained data with the data stored in the blockchain to verify the accuracy of the distributed data source, and update the storage according to the verification result;

[0046] A query module, configured to add block header data to the blockchain to establish a blockchain index for real-time status query and update storage;

[0047] A display module, configured to perform visual display according to the query results of the data stored in a distributed manner to determine whether there are transaction errors, label the financial transaction data with transaction errors, and perform retrospective query.

[0048] The beneficial effects of this application are as follows: Different from the prior art, this application discloses a method and system for processing financial transaction big data based on cloud-edge integration. By performing data cleaning and establishing an index, the financial data after distributed storage is stored into a distributed search engine cluster, and combined with blockchain technology to achieve secure storage and convenient query of data, effectively improving the efficiency of processing financial transaction big data. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of this application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts, where:

[0050] Figure 1 is a schematic flowchart of a method for processing financial transaction big data based on cloud-edge integration in an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] Next, in combination with the accompanying drawings in the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0052] The terms "first", "second", and "third" in the embodiments of the present application are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first", "second", and "third" may explicitly or implicitly include at least one of such features. In the description of the present application, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0053] Referring to "embodiments" herein means that the specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0054] Please refer to Figure 1 , the embodiments of the present application propose a financial transaction big data processing method based on cloud-edge integration, including:

[0055] S10: Obtain financial transaction data, clean the financial transaction data to obtain corresponding standardized data, and the standardized data includes data type, data format, and data dimension;

[0056] S20: Construct a multi-way tree based on the standardized data, generate a tree-like index structure, and determine the query data range based on the tree-like index structure;

[0057] S30: Perform distributed storage on the query results, establish a distributed search engine cluster, and store the financial transaction data after distributed storage into the distributed search engine cluster;

[0058] S40: Chain the processed data, and map the chained data to the data layer, network layer, consensus layer, and contract / service layer;

[0059] S50: Verify the data stored in the distributed storage, compare the obtained data with the data stored in the blockchain to verify the accuracy of the distributed data source, and update the storage according to the verification result;

[0060] S60: Add the block header data to the blockchain to establish a blockchain index for real-time status query;

[0061] S70: Visualize the query result of the distributed storage data to determine whether there is a transaction error, and label the financial transaction data with transaction errors for backtracking query.

[0062] Specifically, due to the different sources of financial transaction data, it is necessary to clean and standardize the original financial transaction data to ensure data consistency, so as to facilitate subsequent processing according to the same format and rules.

[0063] The cloud is responsible for big data storage and complex calculations, and the edge nodes are responsible for real-time data collection and simple processing, so as to reduce network latency and improve processing efficiency.

[0064] The blockchain is a consortium chain or a public chain, and realizes the certainty of financial transaction data through the mapping of the data layer, network layer, consensus layer and contract / service layer, avoiding data tampering or errors, and improving the verification efficiency through automatic execution of verification.

[0065] The visualization display forms in step S70 include but are not limited to heat maps, time axes, etc., which are used to display transaction data with anomalies or errors and are convenient for annotation. The data with error annotation is associated with the backtracking query of the blockchain to accurately locate the problem link.

[0066] In one embodiment, step S20 further includes:

[0067] S21: Determine the query data range according to the node information in the tree-like index structure, obtain the starting index of the corresponding node according to the node information, then obtain the data range according to the starting index, and generate node data;

[0068] S22: Obtain user operation information to query data, match the corresponding label information of the user according to the operation information, and sort and display the corresponding node data according to the weight of the label information.

[0069] Specifically, in this embodiment, a B+ tree variant is adopted. Only keywords and pointers to subtrees are stored in non-leaf nodes, and no specific data is stored. The specific data is stored in leaf nodes respectively, and the leaf nodes form a linked list through pointers. In this way, the B+ tree can store more keywords and pointers, reduce the height of the tree, achieve a faster traversal speed, and improve the query efficiency.

[0070] User tag information includes user weights, historical query frequencies, etc. The dynamic weights are calculated by the TF-IDF algorithm, and high-frequency data is preferentially displayed.

[0071] In one embodiment, step S30 further includes:

[0072] S31: Obtain a data query request, convert the data query request into a search request statement, and store the data distribution in the Hadoop cluster based on the search request statement;

[0073] S32: Split the data and import it into the distributed file system HDFS in batches, store the data in the distributed framework HBase, and establish a distributed search engine cluster;

[0074] S33: Import the data into the distributed search engine cluster, and query and visually display the data through the kibana search engine.

[0075] Specifically, the Hadoop cluster realizes data parallel processing by building a MapReduce framework, uses the HDFS system to provide fault-tolerant storage, and uses the HBase library for columnar storage and fast retrieval.

[0076] In step S33, the data can be first imported into the elastic distributed file system s3 to realize dynamic management of stored data, and then imported into the distributed search engine cluster.

[0077] In one embodiment, step S40 further includes:

[0078] S41: Determine the data on-chain strategy according to the distributed stored data and perform data encryption;

[0079] S42: Perform a hash operation on the encrypted data to obtain the corresponding hash signature, record the hash signature in the block header of the blockchain, map the on-chain data to the data layer, network layer, consensus layer, and contract / service layer, and perform distributed storage;

[0080] S43: Determine the blockchain storage location, store the on-chain data in the nodes corresponding to the blockchain storage location, and broadcast the on-chain data to other nodes based on the P2P networking protocol;

[0081] S44: Determine the block header data through the consensus algorithm, generate transaction data and perform data distributed storage.

[0082] In one embodiment, the consensus algorithm is one or more of the POW mechanism, the POS mechanism, the PBFT mechanism, and the DPOS mechanism. The process of the consensus algorithm is as follows: The transaction source node initiates a transaction operation request, transmits the transaction data to the consensus node, and after obtaining the transaction data, the consensus node writes it into the transaction pool; Sign the transaction data, obtain the consensus signature and use it as a flag for transaction confirmation; Determine the importance of the data according to the information of the transaction source node, weight the transaction object according to the data importance, and then allocate the corresponding consensus node to execute the transaction operation according to the weight; Determine the transaction execution node through the transaction information, perform the transaction operation according to the transaction execution node and obtain the transaction voucher, and the transaction execution node broadcasts the transaction voucher to the consensus node; The consensus node performs transaction backtracking according to the transaction voucher, obtains the backtracking verification result, and processes the transaction according to the backtracking verification result.

[0083] In one embodiment, step S60 further includes:

[0084] S61: Perform a hash operation on the block header data to generate a hash signature, and query the transaction information of the block according to the hash signature;

[0085] S62: Add the block header data to the blockchain, and determine whether the block header data is correct. If it is correct, add the corresponding block to the blockchain. If it is incorrect, remove the corresponding block from the blockchain.

[0086] In one embodiment, when adding the block header data to the blockchain in step S62, it further includes:

[0087] S63: Perform an encryption operation on the block header data to obtain block header data information, perform word segmentation on the block header data information to obtain data information keywords, query the data information according to the data information keywords, and determine the data information similarity according to the data information query result;

[0088] S64: Perform clustering analysis based on the data information similarity to obtain similar blocks of the corresponding block, and construct a block relationship network diagram according to the similar blocks;

[0089] S65: Determine the encryption method of the block header data according to the block header data, perform a hash operation according to the encryption method, perform a hash operation on the similar blocks to obtain the hash signature of the block, and determine the block header data encryption parameters according to the hash signature of the block. Encrypt according to the block header data encryption parameters, and add the encrypted block header data to the blockchain.

[0090] In one embodiment, step S70 further includes:

[0091] S71: Determine the data display form according to the query result, and then obtain the data display parameters according to the data display form;

[0092] S72: Convert the data into a data source display table according to the data display parameters, generate a confirmation information solicitation form for the data source display form, and based on the confirmation information solicitation form for the data source display form, confirm whether the confirmation information solicitation form for the data source display form needs to be adjusted to form a final version. Then, define the data display table based on the data display parameters, feedback the confirmation information solicitation form for the data source display form to the database, and determine the data display rules for the data source display table based on the data display table;

[0093] S73: Preprocess the data, determine the data statistics rules according to the data preprocessing results, substitute the data source display table into the data statistics rules to obtain the data source display results, and define the data display formula based on the data statistics rules; determine the data display results based on the data display formula and the data source display results and perform visual display on the data display results.

[0094] In one embodiment, step S73 further includes:

[0095] S74: Divide the data display rules into global data display rules and local data display rules, obtain the data statistics sub-rules according to the local data display rules, and divide the data source display table into data source display sub-forms;

[0096] S75: Match the data source display sub-forms with the data statistics sub-rules based on the matching rules to generate a data source display sub-form query request;

[0097] S76: Query the data source display sub-forms based on the data source display sub-form query request, the distributed search engine cluster, the big data toolset, and the data statistics sub-rules, generate a data source display sub-form display result according to the query results, and substitute the data source display sub-form display result into the data statistics rules to obtain the data source display results.

[0098] A financial transaction big data processing system based on cloud-edge integration, which is used for the above-mentioned financial transaction big data processing method based on cloud-edge integration, includes:

[0099] A processing module, which is used to obtain financial transaction data, clean the financial transaction data, and convert the corresponding data types, data formats, and data dimensions to standardize the financial transaction data;

[0100] A storage module, which is used to construct a multi-way tree according to the standardized data, generate a tree-like index structure, query the data range based on the tree-like index structure, and perform distributed storage on the query results, establish a distributed search engine cluster, and store the financial data after distributed storage into the distributed search engine cluster;

[0101] The blockchain module is used to store the processed data into the blockchain, perform data blockchain entry, map the blockchain-entered data to the data layer, network layer, consensus layer, and contract / service layer, and perform distributed storage;

[0102] The verification module is used to verify the data stored in the distributed storage, compare the obtained data with the data stored in the blockchain to verify the accuracy of the distributed data source, and update the storage according to the verification result;

[0103] The query module is used to add the block header data to the blockchain to establish a blockchain index, perform real-time status query, and update the storage;

[0104] The display module is used to perform visual display based on the query result of the distributed storage data to determine whether there is a transaction error, mark the financial transaction data with transaction errors, and perform retrospective query.

[0105] The above are only embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A financial transaction big data processing method based on cloud-edge integration, characterized in that: include: S10: Acquire financial transaction data, and clean the financial transaction data to obtain corresponding standardized data; S20: construct a multi-branch tree according to the standardized data, generate a tree index structure, and determine the query data range based on the tree index structure; S30: Distributed storage is performed on the query results, a distributed search engine cluster is established, and the distributed stored financial transaction data is stored in the distributed search engine cluster; S40: upload the processed data to the chain and map the chain data to the data layer, network layer, consensus layer and contract / service layer; S50: Verify the distributed stored data, compare the acquired data with the data stored in the blockchain to verify the accuracy of the distributed data source, and update the storage according to the verification result; S60: Add block header data to the blockchain to establish a blockchain index for real-time status query; S70: Visually display the query results of the distributed storage data to determine whether there is a transaction error, and mark the financial transaction data with transaction errors for retrospective query; this step also includes: S71: determining a data display form according to the query result, and then obtaining data display parameters according to the data display form; S72: converting the data into a data source display table according to the data display parameters, defining the data display table based on the data display parameters, and determining a data display rule for the data source display table based on the data display table; S74: Divide the data display rule into a global data display rule and a local data display rule, obtain a data statistics sub-rule according to the local data display rule, and divide the data source display table into data source display sub-tables; S75: matching the data source display subform with the data statistics subform based on the matching rule, and generating a data source display subform query request; S76: According to the data source display subform query request, the data source display subform is queried based on the distributed search engine cluster, the big data toolset and the data statistics sub-rules, and a data source display subform display result is generated according to the query result, and the data source display subform display result is substituted into the data statistics rule to obtain the data source display result.

2. The method for processing financial transaction big data based on cloud-edge integration according to claim 1 is characterized in that: The step of constructing a multi-branch tree according to the standardized data, generating a tree index structure, and determining the query data range based on the tree index structure includes: S21: determining a query data range according to the node information in the tree index structure, obtaining a starting index of a corresponding node according to the node information, obtaining a data range according to the starting index, and generating node data; S22: Obtain user operation information to query data, match the tag information of the corresponding user according to the operation information, sort the corresponding node data according to the weight of the tag information, and visualize the sorted node data.

3. The method for processing financial transaction big data based on cloud-edge integration according to claim 1 is characterized in that: The steps of distributively storing the query results, establishing a distributed search engine cluster, and storing the distributed stored financial transaction data in the distributed search engine cluster include: S31: Obtain a data query request, convert the data query request into a search request statement, and distribute and store the data in a Hadoop cluster based on the search request statement; S32: Split the data and import them into the distributed file system HDFS in batches, store the data in the distributed framework HBase, and establish a distributed search engine cluster; S33: Import the data into the distributed search engine cluster, query and visualize the data through the kibana search engine.

4. The method for processing financial transaction big data based on cloud-edge integration according to claim 1 is characterized in that: The steps of uploading the processed data to the chain and mapping the up-chain data to the data layer, network layer, consensus layer and contract / service layer include: S41: Determine the data chain strategy based on the distributed storage data and perform data encryption; S42: Perform a hash operation on the encrypted data to obtain a corresponding hash signature, and record the hash signature in a block header of the blockchain; S43: Determine a blockchain storage location, store the on-chain data in a node corresponding to the blockchain storage location, and broadcast the on-chain data to other nodes based on a P2P networking protocol; S44: Determine the block header data through the consensus algorithm, generate transaction data and perform distributed data storage.

5. The method for processing financial transaction big data based on cloud-edge integration according to claim 4 is characterized in that: The consensus algorithm is one or more of the POW mechanism, POS mechanism, PBFT mechanism, and DPOS mechanism.

6. The method for processing financial transaction big data based on cloud-edge integration according to claim 1 is characterized in that: The step of adding the block header data to the blockchain to establish a blockchain index and perform real-time status query includes: S61: Perform hash operation according to the block header data to generate a hash signature, and query the transaction information of the block according to the hash signature; S62: Add the block header data to the blockchain, and determine whether the block header data is correct. If correct, add the corresponding block to the blockchain; if incorrect, remove the corresponding block from the blockchain.

7. The method for processing financial transaction big data based on cloud-edge integration according to claim 6 is characterized in that: The step of adding the block header data to the blockchain comprises: S63: performing an encryption operation on the block header data to obtain block header data information, performing word segmentation on the block header data information to obtain data information keywords, performing data information query according to the data information keywords, and determining data information similarity according to the data information query result; S64: performing cluster analysis based on the similarity of the data information to obtain similar blocks of the corresponding block, and constructing a block relationship network diagram based on the similar blocks; S65: Determine a block header data encryption method according to the block header data, perform a hash operation according to the encryption method, perform a hash operation on the similar block to obtain a hash signature of the block, determine a block header data encryption parameter according to the hash signature of the block, encrypt according to the block header data encryption parameter, and add the encrypted block header data to the blockchain.

8. A financial transaction big data processing system based on cloud-edge integration, used in the financial transaction big data processing method based on cloud-edge integration as claimed in any one of claims 1 to 7, characterized in that: include: A processing module, used to obtain financial transaction data, clean the financial transaction data, and convert the corresponding data type, data format, and data dimension to standardize the financial transaction data; The storage module is used to construct a multi-branch tree based on standardized data, generate a tree index structure, query the data range based on the tree index structure, and perform distributed storage of the query results, establish a distributed search engine cluster, and store the distributed stored financial data in the distributed search engine cluster; The on-chain module is used to store the processed data in the blockchain, map the on-chain data to the data layer, network layer, consensus layer and contract / service layer for distributed storage; The verification module is used to verify the distributed stored data, compare the acquired data with the data stored in the blockchain to verify the accuracy of the distributed data source, and update the storage according to the verification results; The query module is used to add block header data to the blockchain to establish a blockchain index, perform real-time status query and update storage; The display module is used to perform visual display based on the query results of distributed storage data to determine whether there are transaction errors, mark the financial transaction data with transaction errors, and perform backtracking queries.

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