CN114357000A - Block chain transaction data retrieval system, method, equipment and storage medium - Google Patents
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Abstract
The invention discloses a block chain transaction data retrieval system, a method, equipment and a storage medium, wherein the retrieval system comprises: the block chain monitoring node is used for synchronizing blocks on the block chain and transaction data and sending the data obtained by monitoring to the data processing module by taking the blocks as units; the data processing module is used for receiving block data of different area blocks, respectively processing the different block data and then caching the processed block data into the memory database; the data storage module is used for classifying and screening the data of the memory database stored in the data processing module at regular time according to the structure of the database, and writing the processed structure into the database; and the data query module is used for retrieving data written into the graph database in real time by using the query language of the graph database according to the received query instruction. The invention can quickly carry out real-time relation retrieval on the transactions on the block chain and realize real-time transaction tracing.
Description
Technical Field
The present invention relates to the field of blockchain technologies, and in particular, to a system, a method, a device, and a storage medium for retrieving blockchain transaction data.
Background
Over the past decade, blockchain-based cryptocurrency has evolved into a very mainstream digital financial product; although there are a lot of meaningful landing scenes, many images are brought, due to characteristics such as anonymity, data is difficult to monitor and supervise, and various illegal criminal behaviors are rare.
In the existing regional chain transaction platform, the data query performance is very poor due to the huge data volume of the block chain encryption currency, especially due to the divergent data structure characteristic of the block chain, and the transaction on the block chain, even the address level relation retrieval is difficult to achieve; due to the limitation of performance bottleneck, the existing regional chain transaction platform cannot meet the analysis requirements of service levels, such as address analysis transaction preference, specific situations of capital inflow and outflow, and the like; in addition, because the existing regional chain trading platforms such as etherhouses have higher block trading quantity, the traditional method for analyzing block files or the traditional method cannot meet the requirement of real-time property. Therefore, there is a great need in the market today to introduce tools for deep dissection of data below the surface of cryptocurrency.
Disclosure of Invention
In order to overcome the defects of the prior art, an objective of the present invention is to provide a search system for blockchain transaction data, which can quickly perform real-time relationship search on transactions on a blockchain, and improve the search efficiency.
The second objective of the present invention is to provide a method for analyzing and monitoring block chain transaction data, which can realize real-time transaction tracing.
It is a further object of the present invention to provide an electronic device.
It is a fourth object of the present invention to provide a storage medium.
One of the purposes of the invention is realized by adopting the following technical scheme:
a blockchain transaction data retrieval system, comprising:
the block chain monitoring node is used for synchronizing blocks on the block chain and transaction data and sending the data obtained by monitoring to the data processing module by taking the blocks as units;
the data processing module is used for receiving block data of different area blocks, respectively processing the different block data and then caching the processed block data into the memory database;
the data storage module is used for classifying and screening the data of the memory database stored in the data processing module at regular time according to the structure of the database, and writing the processed structure into the database;
and the data query module is used for retrieving data written into the graph database in real time by using the query language of the graph database according to the received query instruction.
Further, the memory database is a kv database; the data processing module processes different block data, and comprises:
and carrying out block height identification on the block data received from different region blocks, and carrying out hash calculation on the transaction data to obtain a transaction hash value, so that the block height and the transaction hash value are cached into a kv database as keys.
Further, before the data storage module writes the processed structure into the graph database, the method further includes:
and judging whether the graph database is an empty database, and if the current graph database is the empty database, directly writing the processed structure into the graph database by using the initialization mode of the graph database.
Further, still include:
and the service module is connected with the data query module and used for initiating a corresponding query instruction to the data query module according to service requirements, so that the data query module retrieves and obtains a corresponding address and transaction data according to the query instruction.
The second purpose of the invention is realized by adopting the following technical scheme:
a method for analyzing and monitoring block chain transaction data is applied to the block chain transaction data retrieval system, and comprises the following steps:
acquiring a target address, and searching a transaction taking an output or input relation as the target address and a block associated with the transaction by taking the target address as an origin;
setting the searched input or output address of the transaction as the other party of the transaction, and searching the transaction and the block associated with the address by taking the address as an origin;
and aggregating the data use address, the data use address and the relation between the transactions involved in the searching process to obtain the transaction direction so as to realize tracing the transactions.
Further, when conducting transaction tracing, the method further comprises the following steps:
and carrying out transaction sum processing on the transaction data associated with the data use address, and counting and displaying all transaction hash values associated with the data use address.
Further, after obtaining the transaction direction, the method further includes:
and generating and displaying a visual chart, wherein the visual chart comprises a transaction tracing graph, and at least a data use address, transaction data and a transaction direction are marked in the transaction tracing graph.
Further, the visual chart also comprises a transaction statistical chart, and the accumulated transaction amount, the average transaction amount and the accumulated transaction times at different time periods are displayed in the transaction statistical chart.
The third purpose of the invention is realized by adopting the following technical scheme:
an electronic device comprises a processor, a memory and a computer program stored on the memory and capable of running on the processor, wherein the processor implements the method for analyzing and monitoring the blockchain transaction data when executing the computer program.
The fourth purpose of the invention is realized by adopting the following technical scheme:
a storage medium having stored thereon a computer program which, when executed, implements the blockchain transaction data analysis monitoring method described above.
Compared with the prior art, the invention has the beneficial effects that:
the invention can monitor the block data of different block chains, realize the rapid import of massive block chain data, and combine the data retrieval of the graph database, can retrieve the data on the chain in real time at the speed of extremely low response time (millisecond level); based on the method, the transaction information of the address is tracked in real time in combination with the service requirements, and the classified positioning aiming at the address is combined with the behavior habits of some transactions to realize the anti-anonymity target, so that the method can be finally assisted in some service fields of financial research, anti-fraud, anti-money laundering, extirpation and lasso and the like.
Drawings
FIG. 1 is a block diagram of a block chain transaction data retrieval system according to the present invention;
FIG. 2 is a schematic flow chart illustrating a method for analyzing and monitoring blockchain transaction data according to the present invention;
FIG. 3 is a diagram illustrating the tracing of transactions according to the present invention;
FIG. 4 is a transaction flow display diagram showing addresses according to the present invention;
FIG. 5 is a diagram showing a visualization chart according to the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and the detailed description, and it should be noted that any combination of the embodiments or technical features described below can be used to form a new embodiment without conflict.
Example one
The embodiment provides a system for retrieving block chain transaction data, which can realize fast and real-time retrieval of transaction data on a block chain, even address level relation retrieval, and greatly improve data query performance. As shown in fig. 1, the retrieval system of this embodiment specifically includes a block chain monitoring node, a data processing module, a data storage module, a data query module, and a service module.
The block chain monitoring node is compatible with a block chain protocol required to be supported by the system, blocks on different block chains and transaction data are synchronized through the block chain protocol, and the monitored data are sent to the data processing module according to the blocks as units.
And the data processing module is connected with the block chain monitoring node and the data processing module, and after receiving the block data of different area blocks, the data processing module respectively processes the different block data and then caches the different block data in the memory database. The main memory database used in the embodiment is a kv database, and the kv database is a Key-value database, belongs to a distributed storage database, and has the characteristics of high query speed, large data storage amount, and high support for concurrency. In this embodiment, the received different block data is stored in the kv database, and block height identification and transaction hash value calculation need to be performed on the received block data of different area blocks; wherein the block height is only the number of blocks linked on the main chain, i.e. the number of blocks linked on the block chain; when the blockchain snooping node receives a block on the blockchain, the position of the block in the blockchain is dynamically identified, so that the block height is obtained, and the block height can also be used as metadata to be stored in an index database table for quick retrieval.
In the embodiment, the transaction records such as the currency sending address, the currency receiving address, the currency sending time, the currency receiving time, the confirmation number and the like are presented through the transaction hash, specific transaction conditions can be checked through the transaction hash, and the transaction corresponding to each hash is a unique transaction, so that the privacy and the safety of the transaction are ensured.
In this embodiment, the data processing module caches the block height and the transaction hash value as a key to a kv database after the above processing, and the purpose of the data processing module is to abstract and integrate different block data into a set of flow and data set, so as to facilitate subsequent data storage.
The data storage module is configured to classify and screen data of a memory database stored in the data processing module at regular time according to a structure of a graph database, and write a processed structure into the graph database. Because the graph model provides an inherent indexed data structure, it does not need to load or contact irrelevant data for a query of a given condition, which makes it good at handling large amounts of complex, interconnected, low-structured data; the basic storage units of the graph database are: the nodes, the relations and the attributes can present and query the incidence relation among the nodes, so that the relation retrieval requirement in the process of tracing the source of the block chain transaction data is met.
The present embodiment classifies and filters data according to a graph database structure, and the purpose of the present embodiment is to establish an association relationship between data by using divergent data, so that the data is converted into a structure matched with a graph database to be stored in the graph database.
When data is newly added to a graph database, the original data information needs to be counted first, and the relationship between the newly added data and the original data is checked and established, so that the speed of newly adding data to the graph database under the condition of not using an empty database is relatively low. In order to accelerate the import speed of massive block chain data, before writing a processed structure into a graph database, it is necessary to determine whether the graph database is an empty database, and if the current graph database is an empty database, the processed structure is written into the graph database by directly using an initialization mode of the graph database, that is, a database storage file is directly created from source data without any database service such as index, transaction, and the like, so that the data import speed is greatly accelerated, massive data import can be realized at a speed of extremely low response time (millisecond level), the time for retrieving data on a chain is also shortened, and the efficiency is improved.
The data query module integrates and abstracts query syntax of a set of graph database, and combines structures of block chains, so as to facilitate quick retrieval of the block chain structures stored in the graph database. Furthermore, by resolving the frequency and parameters of upper-layer service calling and caching the query result to a certain extent according to the negotiated strategy, better response speed can be obtained under the condition of coping with larger-scale data query.
And the data query module executes corresponding query retrieval operation based on the service requirement of the service module, and the data query module performs real-time retrieval on the data written in the graph database after receiving the query instruction sent by the service module. The service module is shown in an actual application scene of the block chain transaction data retrieval system, and can be applied to service scenes such as fraud prevention, transaction tracing and tracking and the like according to the characteristics such as a chain structure and anonymity of a block chain, so that an acquisition method for the relation between an address and a transaction flow direction is provided for the service scenes.
Example two
The present embodiment provides a method for analyzing and monitoring blockchain transaction data, that is, a blockchain transaction data retrieval system according to the first embodiment is applied in a transaction scenario of transaction tracing, as shown in fig. 2, the method specifically includes the following steps:
step S1: and acquiring a target address, and searching for a transaction taking an output or input relation as the target address and a block associated with the transaction by taking the target address as an origin.
In this embodiment, the target address is an address that a user thinks about to inquire, and the blockchain transaction data retrieval system according to the first embodiment searches out the transaction associated with the target address, where the transaction with the output relationship as the target address refers to the transaction with the receive address as the target address in the transaction process, and the transaction with the input relationship as the target address refers to the transaction with the send address as the target address. Since the blockchain transaction data retrieval system according to the first embodiment performs data retrieval by combining the graph database, address-level relationship retrieval can be realized to determine the transaction associated with the address, and the block associated with the transaction can be searched and obtained to obtain the transaction data corresponding to the transaction.
Step S2: the other party of the transaction is set according to the searched input or output address of the transaction, and the transaction and the block associated with the address are searched by taking the address as the origin.
If the transaction whose output relationship is the target address is searched in step S1, which is equivalent to knowing the receiving address of the transaction, then in step S2, the transaction and the block associated with the address need to be searched by using the input address (i.e. the sending address) of the transaction as the origin; similarly, if the transaction with the input relationship as the target address is searched in step S1, that is, the coin-sending address of the transaction is known, in step S2, the transaction and the block associated with the address are searched with the output address (that is, the coin-receiving address) of the transaction as the origin.
Step S3: and aggregating the data use address, the data use address and the relation between the transactions involved in the searching process to obtain the transaction direction so as to realize tracing the transactions.
Because the input address and the output address of the transaction obtained through the search in steps S1 and S2 can present the transaction direction, the addresses are collectively referred to as data use addresses and serve as a first aggregation condition, the relationship between the transaction and the addresses serves as a second aggregation condition, all related transaction hash values are counted, so that corresponding transaction data are obtained, the transaction data are subjected to transaction amount summation processing, and the transaction data such as a transaction starting point, a transaction end point, transaction amount and the like can be obtained after the data are aggregated, so that the purpose of tracing the transaction is achieved.
After the address and the transaction interested by the user are retrieved, the retrieved result can generate a visual chart to visually display the transaction data (as shown in fig. 3-5) of the address on the chain and the hidden information behind the transaction data, so as to solve the practical problems, such as mining out the clues of illegal activities such as money laundering, fraud and the like for fighting against crimes.
The visual chart comprises a transaction tracing graph, as shown in fig. 3 and 4, at least a data use address, transaction data and a transaction direction are marked in the transaction tracing graph, so that a user can check various transaction data related to a target address on a chain, such as a transaction direction, a transaction amount, a transaction currency-sending address, a transaction currency-receiving address, all transaction hash values related to a transaction and the like through the transaction tracing graph, thereby knowing the specific situation of inflow and outflow of transaction funds and achieving the purpose of transaction tracing. In addition, the visualization chart further includes a transaction statistics chart, as shown in fig. 5, the transaction statistics chart shows the accumulated transaction amount, the average transaction amount, and the accumulated transaction times at different time periods, and is used for analyzing the transaction preference for the address according to the analysis requirement of the service level, so as to meet various service requirements.
According to the method of the embodiment, massive block chain data are quickly imported, data on a chain can be retrieved in real time at a speed of extremely low response time (millisecond level) by combining data retrieval of a database, and based on the data, real-time address tracking transaction information is realized by combining with business requirements, and the classification and positioning aiming at the address are combined with behavior habits of some transactions to realize the anti-anonymity target, so that the method can be finally assisted in some business fields of financial research, anti-fraud, anti-money laundering, extirpation and the like.
EXAMPLE III
The embodiment provides an electronic device, which includes a processor, a memory and a computer program stored on the memory and capable of running on the processor, wherein the processor implements the analysis and monitoring method for blockchain transaction data in the second embodiment when executing the computer program; in addition, the present embodiment also provides a storage medium, on which a computer program is stored, and when the computer program is executed, the method for analyzing and monitoring blockchain transaction data is implemented.
The device and the storage medium in this embodiment are based on two aspects of the same inventive concept, and the method implementation process has been described in detail in the foregoing, so that those skilled in the art can clearly understand the structure and implementation process of the device and the storage medium in this embodiment according to the foregoing description, and for the sake of brevity of the description, details are not repeated here.
The above embodiments are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby, and any insubstantial changes and substitutions made by those skilled in the art based on the present invention are within the protection scope of the present invention.
Claims (10)
1. A blockchain transaction data retrieval system, comprising:
the block chain monitoring node is used for synchronizing blocks on the block chain and transaction data and sending the data obtained by monitoring to the data processing module by taking the blocks as units;
the data processing module is used for receiving block data of different area blocks, respectively processing the different block data and then caching the processed block data into the memory database;
the data storage module is used for classifying and screening the data stored in the memory database at regular time according to the structure of the database, and writing the processed structure into the database;
and the data query module is used for retrieving data written into the graph database in real time by using the query language of the graph database according to the received query instruction.
2. The blockchain transaction data retrieval system of claim 1, wherein the in-memory database is a kv database; the data processing module processes different block data, and comprises:
and carrying out block height identification on the block data received from different region blocks, and carrying out hash calculation on the transaction data to obtain a transaction hash value, so that the block height and the transaction hash value are cached into a kv database as keys.
3. The blockchain transaction data retrieval system of claim 1, wherein prior to the data storage module writing the processed structure to the graph database, further comprising:
and judging whether the graph database is an empty database, and if the current graph database is the empty database, directly writing the processed structure into the graph database by using the initialization mode of the graph database.
4. The blockchain transaction data retrieval system according to any one of claims 1 to 3, further comprising:
and the service module is connected with the data query module and used for initiating a corresponding query instruction to the data query module according to service requirements, so that the data query module retrieves and obtains a corresponding address and transaction data according to the query instruction.
5. A method for analyzing and monitoring blockchain transaction data, which is applied to the blockchain transaction data retrieval system according to any one of claims 1 to 4, comprising:
acquiring a target address, and searching a transaction taking an output or input relation as the target address and a block associated with the transaction by taking the target address as an origin;
setting the searched input or output address of the transaction as the other party of the transaction, and searching the transaction and the block associated with the address by taking the address as an origin;
and aggregating the data use address, the data use address and the relation between the transactions involved in the searching process to obtain the transaction direction so as to realize tracing the transactions.
6. The method for analyzing and monitoring blockchain transaction data according to claim 5, further comprising, during transaction tracing,:
and carrying out transaction sum processing on the transaction data associated with the data use address, and counting and displaying all transaction hash values associated with the data use address.
7. The method for analyzing and monitoring blockchain transaction data according to claim 5, wherein after obtaining the transaction direction, the method further comprises:
and generating and displaying a visual chart, wherein the visual chart comprises a transaction tracing graph, and at least a data use address, transaction data and a transaction direction are marked in the transaction tracing graph.
8. The method as claimed in claim 7, wherein the visual chart further comprises a transaction statistics chart showing the cumulative transaction amount, the average transaction amount and the cumulative transaction times at different time periods.
9. An electronic device comprising a processor, a memory, and a computer program stored on the memory and operable on the processor, wherein the processor executes the computer program to implement the method for analyzing and monitoring blockchain transaction data according to any one of claims 5 to 8.
10. A computer-readable storage medium having stored thereon a computer program which, when executed, implements the method for analysis and monitoring of blockchain transaction data according to any one of claims 5 to 8.
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| CN202210028792.8A CN114357000A (en) | 2022-01-11 | 2022-01-11 | Block chain transaction data retrieval system, method, equipment and storage medium |
| PCT/CN2022/129040 WO2023134274A1 (en) | 2022-01-11 | 2022-11-01 | Block chain transaction data retrieval system, method, device, and storage medium |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114912544A (en) * | 2022-06-06 | 2022-08-16 | 北京百度网讯科技有限公司 | Automatic characteristic engineering model training method and automatic characteristic engineering method |
| CN115049493A (en) * | 2022-06-29 | 2022-09-13 | 北京知帆科技有限公司 | Block chain data tracking method and device and electronic equipment |
| CN115187250A (en) * | 2022-09-09 | 2022-10-14 | 南方科技大学 | Detection method, terminal and storage medium for ether house privacy transaction |
| CN116389163A (en) * | 2023-05-26 | 2023-07-04 | 中科链安(北京)科技有限公司 | Block chain transaction originating node IP tracking method, risk monitoring method and risk monitoring device |
| WO2023134274A1 (en) * | 2022-01-11 | 2023-07-20 | 广州广电运通金融电子股份有限公司 | Block chain transaction data retrieval system, method, device, and storage medium |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107943951B (en) * | 2017-11-24 | 2020-08-11 | 中钞信用卡产业发展有限公司杭州区块链技术研究院 | Method and system for retrieving block chain service information |
| CN108614870B (en) * | 2018-04-18 | 2021-07-30 | 国网湖北省电力有限公司 | Rapid batch warehousing system and method for external data information of power equipment |
| CN110413483B (en) * | 2019-07-30 | 2023-04-07 | 中国工商银行股份有限公司 | Batch operation data monitoring method and device, electronic equipment and storage medium |
| US11281721B2 (en) * | 2019-11-22 | 2022-03-22 | International Business Machines Corporation | Augmenting relational database engines with graph query capability |
| CN112506925A (en) * | 2020-12-01 | 2021-03-16 | 浙商银行股份有限公司 | Data retrieval system and method based on block chain |
| CN114357000A (en) * | 2022-01-11 | 2022-04-15 | 广州广电运通金融电子股份有限公司 | Block chain transaction data retrieval system, method, equipment and storage medium |
-
2022
- 2022-01-11 CN CN202210028792.8A patent/CN114357000A/en active Pending
- 2022-11-01 WO PCT/CN2022/129040 patent/WO2023134274A1/en unknown
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| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2023134274A1 (en) * | 2022-01-11 | 2023-07-20 | 广州广电运通金融电子股份有限公司 | Block chain transaction data retrieval system, method, device, and storage medium |
| CN114912544A (en) * | 2022-06-06 | 2022-08-16 | 北京百度网讯科技有限公司 | Automatic characteristic engineering model training method and automatic characteristic engineering method |
| CN114912544B (en) * | 2022-06-06 | 2023-11-14 | 北京百度网讯科技有限公司 | Training method of automatic feature engineering model and automatic feature engineering method |
| CN115049493A (en) * | 2022-06-29 | 2022-09-13 | 北京知帆科技有限公司 | Block chain data tracking method and device and electronic equipment |
| CN115187250A (en) * | 2022-09-09 | 2022-10-14 | 南方科技大学 | Detection method, terminal and storage medium for ether house privacy transaction |
| CN116389163A (en) * | 2023-05-26 | 2023-07-04 | 中科链安(北京)科技有限公司 | Block chain transaction originating node IP tracking method, risk monitoring method and risk monitoring device |
| CN116389163B (en) * | 2023-05-26 | 2023-08-01 | 中科链安(北京)科技有限公司 | Block chain transaction originating node IP tracking method, risk monitoring method and risk monitoring device |
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