Financial transaction big data processing method and system based on cloud-side integration

Abstract

The invention provides a financial transaction big data processing method and system based on cloud-side integration, and the method comprises the steps: obtaining financial transaction data, cleaning the financial transaction data, and obtaining corresponding standardized data; carrying out distributed storage, and storing the financial data subjected to distributed storage into a distributed search engine cluster; storing the processed data in a block chain, and performing data chaining; and verifying the data, establishing a block chain index, and performing visual display according to a query result to judge whether a transaction error exists or not and performing backtracking query. Through data cleaning and index establishment, the financial data subjected to distributed storage are stored in the distributed search engine cluster, and secure storage and convenient query of the data are realized in combination with a block chain technology, so that the financial transaction big data processing efficiency is effectively improved.

Description

Technical Field

[0001] The present application relates to the field of big data computing, and in particular 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, safer and faster. At the same time, due to the characteristics of decentralization, data tamper-proofing and transaction anonymity, blockchain can greatly reduce the role of third parties in the financial system, reduce the operating costs of the financial system and improve the efficiency of financial transactions.

[0003] Financial transaction data has the characteristics of 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 transaction data interaction, affecting the operation of the financial system. In addition, in financial transactions, users hope that financial transaction data is traceable and 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 financial transaction big data processing method and system based on cloud-edge integration to improve the efficiency and security of financial data processing.

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

[0006] In a possible implementation, the step of constructing a multi-branch tree according to the standardized data, generating a tree index structure, and determining a 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.

[0007] In a possible implementation, 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.

[0008] In a possible implementation, the step of uploading the processed data to a blockchain and mapping the uplinked data to a data layer, a network layer, a consensus layer, and a contract / service layer includes: 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.

[0009] In a possible implementation, the consensus algorithm is one or more of a POW mechanism, a POS mechanism, a PBFT mechanism, and a DPOS mechanism.

[0010] In a possible implementation, the step of adding 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.

[0011] In a possible implementation, the step of adding the block header data to the blockchain includes: 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.

[0012] In a possible implementation, the step of visually displaying the query results based on the distributed storage data to determine whether there is a transaction error, and marking the financial transaction data with transaction errors for retrospective query 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; S73: Preprocess the data, determine data statistical rules according to the data preprocessing results, substitute the data source display table into the data statistical rules to obtain the data source display results, and define a data display formula according to the data statistical rules; determine the data display results based on the data display formula and the data source display results and visualize the data display results.

[0013] In a possible implementation, data is preprocessed, a data statistical rule is determined according to the data preprocessing result, the data source display table is substituted into the data statistical rule to obtain a data source display result, and a data display formula is defined according to the data statistical rule; the step of determining a data display result based on the data display formula and the data source display result and visually displaying the data display result includes: 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.

[0014] In order to solve the above technical problems, another technical solution adopted by the present application is to provide a financial transaction big data processing system based on cloud-edge integration, which is used in the above-mentioned financial transaction big data processing method based on cloud-edge integration, including: 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.

[0015] The beneficial effects of the present application are: different from the prior art, the present application discloses a financial transaction big data processing method and system based on cloud-edge integration, which performs data cleaning and indexing, stores the distributedly stored financial data in 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work, among which: Figure 1 It is a flow chart of a method for processing financial transaction big data based on cloud-edge integration in one embodiment of the present application. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] The terms "first", "second", "third" in the embodiments of the present application are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Thus, the features defined as "first", "second", "third" can expressly or implicitly include at least one of the features. In the description of the present application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0019] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0020] See also Figure 1 The embodiment of the present application proposes a method for processing financial transaction big data based on cloud-edge integration, including: S10: Acquire financial transaction data, and clean the financial transaction data to obtain corresponding standardized data, where the standardized data includes data type, data format, and data dimension; 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: Distribute and store the query results, establish a distributed search engine cluster, and store the distributed stored financial transaction data 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 backtracking query.

[0021] Specifically, due to the different sources of financial transaction data, the original financial transaction data needs to be cleaned and standardized to ensure data consistency so that subsequent processing can be carried out in accordance with the same format and rules.

[0022] The cloud is responsible for big data storage and complex computing, while the edge nodes are responsible for real-time data collection and simple processing, thereby reducing network latency and improving processing efficiency.

[0023] Blockchain is a consortium chain or a public chain. It achieves the certainty of financial transaction data through mapping the data layer, network layer, consensus layer and contract / service layer, avoids data tampering or errors, and improves verification efficiency by automatically executing verification.

[0024] The visual display forms in step S70 include but are not limited to heat maps, timelines, etc., which are used to display abnormal or erroneous transaction data and facilitate labeling. After the erroneously labeled data is linked, a backtracking query of the blockchain is triggered to accurately locate the problem link.

[0025] In one embodiment, step S20 further includes: S21: determining the query data range according to the node information in the tree index structure, obtaining the starting index of the corresponding node according to the node information, then obtaining the data range according to the starting index, and generating node data; S22: Obtain user operation information to query data, match the corresponding user's tag information according to the operation information, and sort and display the corresponding node data according to the weight of the tag information.

[0026] Specifically, a B+ tree variant is adopted in this embodiment, which only stores keywords and pointers to subtrees in non-leaf nodes, but does not store specific data. The specific data is stored in leaf nodes respectively, and a linked list is formed between leaf nodes through pointers. In this way, the B+ tree can store more keywords and pointers, reduce the height of the tree, achieve faster traversal speed, and improve query efficiency.

[0027] User tag information includes user weight, historical query frequency, etc. The dynamic weight is calculated through the TF-IDF algorithm, and high-frequency data is displayed first.

[0028] In one embodiment, step S30 further includes: 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 data into the distributed search engine cluster, query and visualize the data through the kibana search engine.

[0029] Specifically, the Hadoop cluster implements parallel data 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.

[0030] In step S33, the data may be first imported into the elastic distributed file system s3 to implement dynamic management of the stored data, and then imported into the distributed search engine cluster.

[0031] In one embodiment, step S40 further includes: S41: Determine the data chain strategy based on the distributed storage data and perform data encryption; S42: Perform hash operation on the encrypted data to obtain the corresponding hash signature, record the hash signature in the block header of the blockchain, and map the on-chain data to the data layer, network layer, consensus layer, and contract / service layer for distributed storage; S43: Determine the blockchain storage location, store the on-chain data in the node corresponding to the blockchain storage location, and broadcast the on-chain data to other nodes based on the P2P networking protocol; S44: Determine the block header data through the consensus algorithm, generate transaction data and perform distributed data storage.

[0032] In one embodiment, the consensus algorithm is one or more of the POW mechanism, POS mechanism, PBFT mechanism, and 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 the consensus node obtains the transaction data and writes it into the transaction pool; the transaction data is signed, the consensus signature is obtained and used as a mark of transaction confirmation; the importance of the data is determined according to the information of the transaction source node, the transaction object is weighted according to the importance of the data, and then the corresponding consensus node is assigned to perform the transaction operation according to the weight; the transaction execution node is determined through the transaction information, the transaction operation is performed according to the transaction execution node and the transaction voucher is obtained, 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 performs transaction processing according to the backtracking verification result.

[0033] In one embodiment, step S60 further 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 it is correct, add the corresponding block to the blockchain; if it is incorrect, remove the corresponding block from the blockchain.

[0034] In one embodiment, the step S62 of adding the block header data to the blockchain further includes: S63: Perform an encryption operation on the block header data, obtain the block header data information, perform word segmentation on the block header data information, obtain data information keywords, perform data information query according to the data information keywords, and determine the data information similarity according to the data information query result; S64: performing cluster analysis based on the similarity of data information to obtain similar blocks of the corresponding block, and constructing a block relationship network diagram based on the similar blocks; S65: Determine the block header data encryption method according to the block header data, perform hash operation according to the encryption method, perform hash operation on similar blocks to obtain the hash signature of the block, determine the 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.

[0035] In one embodiment, step S70 further includes: S71: Determine a data display format according to the query result, and then obtain data display parameters according to the data display format; S72: converting the data into a data source display table according to the data display parameters, generating data source display form solicitation confirmation information, confirming whether the data source display form solicitation confirmation information needs to be adjusted according to the data source display form solicitation confirmation information to form a final version, and then defining the data display table based on the data display parameters, feeding back the data display form solicitation confirmation information to the database, and determining the data display rules of the data source display table based on the data display table; S73: Preprocess the data, determine the data statistical rules according to the data preprocessing results, substitute the data source display table into the data statistical rules to obtain the data source display results, and define the data display formula according to the data statistical rules; determine the data display results based on the data display formula and the data source display results and visualize the data display results.

[0036] In one embodiment, step S73 further includes: S74: Divide the data display rules into global data display rules and local data display rules, obtain data statistics sub-rules according to the local data display rules, and divide the data source display table into data source display sub-forms; 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 tool set and the data statistics sub-rules, and the data source display subform display results are generated according to the query results, and the data source display subform display results are substituted into the data statistics rules to obtain the data source display results.

[0037] The financial transaction big data processing system based on cloud-edge integration is used for the above-mentioned financial transaction big data processing method based on cloud-edge integration, including: A processing module 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; 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.

[0038] The above descriptions are merely embodiments of the present application and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also 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 backtracking query.

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. The method for processing financial transaction big data based on cloud-edge integration according to claim 1 is characterized in that: The step of visually displaying the query results of the distributed storage data to determine whether there is a transaction error and marking the financial transaction data with transaction errors for retrospective query 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; S73: Preprocess the data, determine data statistical rules according to the data preprocessing results, substitute the data source display table into the data statistical rules to obtain the data source display results, and define a data display formula according to the data statistical rules; determine the data display results based on the data display formula and the data source display results and visualize the data display results.

9. The method for processing financial transaction big data based on cloud-edge integration according to claim 8 is characterized in that: Preprocess the data, determine data statistical rules according to the data preprocessing results, substitute the data source display table into the data statistical rules to obtain the data source display results, and define the data display formula according to the data statistical rules; The step of determining a data display result based on the data display formula and the data source display result and visually displaying the data display result includes: 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.

10. 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 9, 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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