CN111339102A - Financial record information accurate and trusted storage method based on block chain - Google Patents
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Abstract
The invention discloses a block chain-based financial record information accurate and reliable storage method, which is used for performing multi-level and multi-granularity decomposition on financial record information through a rough set theory based on a set theory method and realizing accurate multi-level and multi-granularity reliable storage of the financial record information through an MPT (message passing through) tree of a block chain and a corresponding hash value technology. Based on the rough set theory and technology, the method gives consideration to different granularities of financial information, and realizes multi-granularity storage of financial record information; based on the block chain MPT tree technology, hierarchical credible storage among different granularities of financial record information is realized; the trusted storage of leaf nodes and the trusted storage of branch nodes of the block chain MPT are both considered, so that the accurate storage of multi-level and multi-granularity financial record information is realized; and considering the block chain structural hash value and the content hash value, and realizing the trusted storage of the multi-level and multi-granularity financial record information.
Description
Technical Field
The invention relates to the field of financial data management, in particular to a block chain-based financial record information accurate and trusted storage method.
Background
How to strengthen the safe reliability of financial record information in data such as accounting documents, financial statements and the like, the source of the financial record information can be checked, the responsibility can be studied, and more accurate positioning and storage of the layered financial record information are realized, which is a difficult problem to be solved urgently.
The block chain is a distributed storage architecture combining technologies such as point-to-point transmission, a consensus mechanism and a Hash algorithm, and has the characteristics of decentralization, anonymity, non-tamper property, traceability and the like. Some scholars have studied the application of trusted storage of blockchains in financial information management. Tapscott (2016) recognizes that accurate and efficient tracking of historical data by blockchains helps to more clearly delineate the financial status and overall performance of a company, and also helps people to notice ignored accounts and details. Watson (2017) considers that the blockchain increases the real-time dimension of tracking financial information, and can reduce the subjective judgment space of accountants to the greatest extent. Sun Yue \29856 (2017) establishes a distributed financial system on the private chain by simulation, all accounts stored on the chain cannot be tampered, and the access is limited based on the cryptography technology. It can be seen that scholars at home and abroad think that the trusted storage of the block chain can make the financial information more convenient for management and supervision. At present, the existing research directly stores accounting data such as certificates and reports in a block chain in a file form, and does not finely classify and store information in the accounting data.
Some researchers have studied the granularity of corporate financial data. Bryan (2003) earlier proposed the term "granularity" of financial reporting data, who proposed that periodic reports and ad hoc announcements should employ different granularity classification criteria. The euler level (2010) proposes that the current financial data classification standard needs to face the problem of 'granularity', and if the granularity is too coarse, the information is lost, and if the granularity is too fine, the information comparability is lost. Zhang Tianxi (2011) provides a financial information granularity measurement model and a classification standard element selection model, and provides a new thought and a more effective tool for a financial report theory. Stannson (2011) proposes that the economic business process and the financial process can be better fused by analyzing the granularity of the financial data along with the accumulation of the financial data and the development of the analysis requirement. The scholars study the granularity problem of the financial data from different aspects, but the granularity problem is not completely expressed through a mathematical model, and the layering and the structuring of the financial data cannot be well embodied.
The rough set theory is one of the main methods for studying the particle size problem, and Pawlak Z (1991) et al define the particle structure in the rough set theory using the method of set theory. And G.P.Lin (2015) provides a multi-source information system on the basis, and the fusion problem of different information source data is processed by using a rough set theory. The rough set theory based on the set theory method can completely and accurately express the grain structure model, is very suitable for researching the multi-attribute and multi-level problems in practice, and has strong application value.
Disclosure of Invention
In order to solve the problems, the invention provides a block chain-based financial record information accurate and reliable storage method, and the constructed multi-level and multi-granularity financial record information reliable storage area block chain MPT model can realize the hierarchical and multi-granularity accurate storage and query of financial record information; the structural hash value and the content hash value are fused, and the efficiency of block chain storage and retrieval of the financial record information can be further improved.
In order to achieve the purpose, the invention adopts the technical scheme that:
a block chain-based financial record information accurate and reliable storage method is characterized in that multi-level and multi-granularity decomposition is carried out on financial record information through a rough set theory based on a set theory method, and accurate multi-level and multi-granularity reliable storage of the financial record information is achieved through an MPT tree of a block chain and a corresponding hash value technology.
Further, the method specifically comprises the following steps:
s1 multilevel multi-granularity decomposition of financial record information based on rough set
S11, introducing a rough set theory into the construction of a grain structure model of financial record information, firstly, carrying out granularity analysis on data of the balance sheet, defining a set of each piece of data in the balance sheet by using a domain U, wherein an attribute set A is { a 1: primary subject name, a 2: name of secondary subject, a 3: third-level subject names }, f is a corresponding relation between each piece of data and attributes, namely, the subject names of the levels to which the data belong, and the value ranges of the attributes are respectively: va1 { asset, liability, owner's equity }, Va2 { flowing asset, non-flowing asset, flowing liability, non-flowing liability, real income capital, capital equity, earnings, unallocated profit }, Va3 { monetary funds, accounts receivable, inventory, accounts payable … };
s12, defining three different equivalence classes according to the order of the granularity from small to large:
1) b ═ a1, a2, a3 in the unrecognizable relationship r (B), that is, data in the balance sheet are divided based on the names of the first, second and third-level subjects, and the corresponding data in each third-level subject is an equivalent class (particle), in this case, the granularity of the data is minimum, the refinement degree is highest, but the integration degree is lowest, and the comparability of the data is also lowest because the difference set by the finer-level subjects among different enterprises is larger;
2) b ═ a1, a2}, namely, data in the balance sheet of the assets are divided according to the names of the primary subject and the secondary subject, the corresponding data under each secondary subject is an equivalent class (particle), the granularity of the data is moderate in this case, and the refinement degree, the comprehensive degree and the comparability of the data are moderate;
3) b ═ a1, namely, the data in the balance sheet is divided according to the names of the primary subjects, the corresponding data under each primary subject is an equivalence class (particle), in this case, the granularity of the data is the largest, the refinement degree of the data is the lowest, but the comprehensive degree and comparability are the highest.
S13, carrying out different types of division on data in the balance sheet according to the granularity to form different grain layers, and combining the different grain layers to construct a multi-layer grain structure model; carrying out granularity analysis on the data of the profit list and the cash flow table by the same method, and respectively constructing a multi-layer grain structure model;
s2, multi-level and multi-granularity precision credible financial record information storage based on block chain MPT tree and hash value
Combining the block chain MPT tree with a multi-level and multi-granularity structural model of financial record information to construct an MPT tree of the financial report data of the enterprise, wherein the MPT tree is used for storing the financial report data of the enterprise; the multi-level tree structure of the MPT tree corresponds to the multi-level and multi-granularity structure of the financial indexes, and the financial indexes can be stored in corresponding nodes of the MPT tree respectively.
The invention has the following beneficial effects:
(1) based on a rough set theory and technology, different granularities of financial information are considered, and multi-granularity storage of financial record information is realized;
(2) based on the block chain MPT tree technology, hierarchical credible storage among different granularities of financial record information is realized;
(3) the trusted storage of leaf nodes and the trusted storage of branch nodes of the block chain MPT are both considered, so that the accurate storage of multi-level and multi-granularity financial record information is realized;
(4) and considering the block chain structural hash value and the content hash value, and realizing the trusted storage of the multi-level and multi-granularity financial record information.
Drawings
FIG. 1 is a model of the structure of a balance portfolio.
FIG. 2 is a model of profit granule structure in an embodiment of the present invention.
FIG. 3 is a structural model of a cash flow meter in an embodiment of the present invention.
Fig. 4 is a diagram of an MPT tree structure according to an embodiment of the present invention.
FIG. 5 is a block diagram of the MPT tree data portion of the enterprise A financial reporting data in an embodiment of the present invention.
FIG. 6 is a block diagram of the MPT tree content hash section of the enterprise A financial reporting data in an embodiment of the present invention.
FIG. 7 is a block diagram of the MPT tree structure hash portion of the enterprise A financial reporting data in an embodiment of the present invention.
Fig. 8 shows the main architecture of the blockchain platform according to the embodiment of the present invention.
Fig. 9 illustrates the operation of the POW consensus mechanism in the embodiment of the present invention.
Fig. 10 is a distributed network connection in an embodiment of the invention.
FIG. 11 is a financial information entry interface in an embodiment of the invention.
FIG. 12 is a block information of different node displays according to an embodiment of the present invention.
Fig. 13 is an MPT tree data query interface in an embodiment of the invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications can be made by persons skilled in the art without departing from the spirit of the invention. All falling within the scope of the present invention.
The embodiment of the invention provides a financial record information accurate and reliable storage method based on a block chain. Specifically, the method comprises the following steps:
(1) multi-level multi-granularity decomposition of financial record information based on rough set
Introducing a rough set theory into the construction of a grain structure model of financial record information, firstly, carrying out granularity analysis on data of a balance sheet, defining a set of each piece of data in the balance sheet by using a domain U, and setting an attribute set A as { a 1: primary subject name, a 2: name of secondary subject, a 3: third-level subject names }, f is a corresponding relation between each piece of data and attributes, namely, the subject names of the levels to which the data belong, and the value ranges of the attributes are respectively: va1 ═ asset, liability, owner's equity, Va2 ═ mobile asset, non-mobile asset, mobile liability, non-mobile liability, real income capital, capital equity, earnings, unallocated profit, Va3 ═ monetary capital, accounts receivable, inventory, accounts payable ….
Three different equivalence classes are defined next in order of the particle size from small to large: 1) b ═ a1, a2, a3 in the unrecognizable relationship r (B), i.e. data in the balance sheet are divided based on the names of the first, second and third-level subjects, and the corresponding data under each third-level subject is an equivalence class (particle). In this case, the data has the smallest granularity, the highest refinement, but the lowest integration. Meanwhile, the more detailed account set differences among different enterprises are larger, so that the comparability of the data is the lowest; 2) b ═ a1, a2, i.e. data in the balance sheet are divided according to the names of the primary and secondary subjects, and the corresponding data under each secondary subject is an equivalence class (particle). The granularity of the data is moderate in the condition, and the refinement degree, the comprehensive degree and the comparability of the data are moderate; 3) b ═ a1, that is, data in the balance sheet is divided according to the names of the primary subjects, and the corresponding data under each primary subject is an equivalent class (particle). In this case, the granularity of the data is the largest, the refinement of the data is the lowest, but the comprehension and comparability are the highest.
After different types of data in the balance sheet are divided according to the granularity, different grain layers are formed, wherein the primary subject granularity is the largest, the secondary subject granularity is moderate, and the tertiary subject granularity is the smallest; different grain layers are combined to construct a multi-layer grain structure model, as shown in fig. 1 (in order to save storage space, the names of tables and subjects are coded by English letters). By constructing the multilayer grain structure model, the refinement degree of the data is improved, the comprehensive degree and comparability of the data are ensured, and the quality of information contained in the balance sheet is improved.
The data of the profit sheet and the cash flow sheet are subjected to granularity analysis in the same way, and a multi-layer grain structure model is respectively constructed, as shown in fig. 2 and 3.
(2) Financial record information multilevel multi-granularity precision trusted storage based on block chain MPT tree and hash value
The hash algorithm is a function for mapping plaintext with any length into encrypted information with fixed length, two plaintext sections with different contents are difficult to find, and hash values of the two plaintext sections are consistent, so that the hash value has collision resistance, and the hash algorithm is mainly used for guaranteeing the safety of the information and improving the utilization rate of a data storage space. The MPT tree is a tree-shaped data structure established by a hash algorithm and is used for storing transaction information, states and corresponding state changes of accounts in a blockchain platform such as an Ethern. In the MPT tree, several hash values of the same branch are subjected to hash operation to generate a 'sub-hash', so that a smaller number of new first-level hashes are obtained, and the operations are analogized upwards in this way, and finally an inverted tree is formed. The specific structure of the MPT tree is shown in fig. 4, which mainly includes three nodes: leaf nodes (leaf nodes), extension nodes (extension nodes), and branch nodes (branch nodes), the key of each type of node representing the real path from the tree root to the node, the values of the extension and leaf nodes being the stored data, and the value of the branch node representing the node pointed to. In addition, a prefix (prefix) is contained in front of each of the extension node and the leaf node and is used for distinguishing the parity of the two nodes and the node key. The MPT tree carries out accurate and reliable storage on multi-level and multi-granularity financial record information with correlation in a layered Hash operation mode, finally generates a root Hash, and can safely store and verify data in the whole MPT tree through the root Hash.
The invention combines the MPT tree with the multi-level and multi-granularity structural model of the financial record information to construct the MPT tree of the financial report data of the enterprise, which is used for storing the financial report data of the enterprise. Taking enterprise a as an example, fig. 5 is an actual data part of the MPT tree of the financial statement data of enterprise a, a key of each node represents an enterprise, a year, a subject code and an amount code, and values of the extended nodes and leaf nodes store corresponding amounts. FIG. 6 is a content hash portion of the MPT tree of the enterprise A financial reporting data, where each hash value corresponds to an actual piece of data stored in the MPT tree. Fig. 7 is a structural hash part of the MPT tree of the financial statement data of the enterprise a, the bottom layer of the tree is the hash value of the leaf node, hash values on the same branch are subjected to hash operation, and so on, and finally the root hash of the MPT tree is generated. In fig. 5, 6 and 7, 8 pieces of financial statement data of the enterprise a are stored together, and the corresponding enterprise name, report year, report name, subject name and amount are shown in table 1.
TABLE 1 Enterprise A financial statement data
This concrete implementation simulation sets up a block chain platform of enterprise financial information management, realizes the multistage granularity storage of the accurate of financial statement data, then simulates and snatchs relevant data, verifies the model through the experiment.
(1) Block chain platform construction
In the specific implementation, a Go language is used for simulating and building a block chain platform in a Linux system, the main architecture is as shown in fig. 8, and a data layer mainly stores related information through a block and an MPT tree; in a network layer, a distributed network consisting of 6 nodes is included, any nodes are connected with each other and can carry out information transmission, and all the nodes achieve information consistency through a POW (proof of office) consensus mechanism; the application layer comprises accurate storage and query of financial statement data and tracing of financial information.
The main functions implemented by the platform include the following 4 parts:
1) and (3) generation of a block: the financial staff selects the entered financial information category (original voucher/bookkeeping voucher/financial statement) and then fills in the corresponding financial information content. And when information is recorded once, the system background automatically operates a POW consensus mechanism and generates a new block, the new block is connected with the existing block chain to form a new block chain, the block chain information of all the nodes is updated, and finally the new block chain is stored in the BoltDB database after being serialized.
2) Updating the MPT tree: and according to the financial statement information input by financial staff, taking the enterprise name, the statement year and the subject code as keys, and inserting the data which is subjected to RLP coding as value into the existing MPT tree. And the new branch and the original MPT tree are subjected to Hash calculation to generate new root Hash, so that the MPT tree is updated. In addition, the MPT trees are stored in a levelDB database through serialization processing.
3) Query of data in MPT tree: the method comprises the steps of firstly inputting an MPT root stored in a block, reading an existing MPT tree in a levelDB database, then inputting an enterprise name, a report year and a subject code, and automatically inquiring corresponding financial report data in the MPT tree by taking the input information as a key by a system background.
Examples
In this embodiment, 50 financial statement data of 5 listed companies such as Guizhou Maotai and Grey land property in 2018 are captured from the east wealth network, 30 transfer checks, debit notes, special invoices for value-added tax and other original vouchers and 20 related information of bookkeeping vouchers are simulated and sequentially recorded in a block chain platform, and the model is verified through MPT tree data query and block information tracing, wherein the specific process is as follows:
a) the block chain platform is opened, and the system automatically runs the POW consensus mechanism, i.e., mine digging, to generate the created blocks of the block chain, as shown in fig. 9. And at the same time, 6 nodes are successfully connected to form a distributed network, as shown in fig. 10.
b) All financial information is sequentially entered into the system, and an entry interface is shown in fig. 11. The system sequentially generates 100 corresponding blocks, and the block information displayed by each node in the distributed network is completely the same, as shown in fig. 12, the main content includes a block Index (Index), a block Hash (Hash), a TimeStamp (TimeStamp), a financial information category (Class), and an encrypted financial information content (Data).
c) And 4 groups of data of subjects with different levels in the same enterprise, same year and same report are selected from the input financial report data, relevant information is input to be inquired in the MPT tree, the system returns corresponding data, and the inquiry interface is shown in figure 13. All query results are shown in table 2.
TABLE 2MPT Tree data query results
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (2)
1. A financial record information accurate and credible storage method based on a block chain is characterized in that: the method comprises the steps of performing multilevel and multi-granularity decomposition on financial record information through a rough set theory based on a set theory method, and realizing accurate multilevel and multi-granularity credible storage of the financial record information through an MPT tree of a block chain and a corresponding hash value technology.
2. The method for accurate and trusted storage of block chain-based financial record information according to claim 1, wherein: the method specifically comprises the following steps:
s1 multilevel multi-granularity decomposition of financial record information based on rough set
S11, introducing a rough set theory into the construction of a grain structure model of financial record information, firstly, carrying out granularity analysis on data of the balance sheet, defining a set of each piece of data in the balance sheet by using a domain U, wherein an attribute set A is { a 1: primary subject name, a 2: name of secondary subject, a 3: third-level subject names }, f is a corresponding relation between each piece of data and attributes, namely, the subject names of the levels to which the data belong, and the value ranges of the attributes are respectively: va1 { asset, liability, owner's equity }, Va2 { flowing asset, non-flowing asset, flowing liability, non-flowing liability, real income capital, capital equity, earnings, unallocated profit }, Va3 { monetary funds, accounts receivable, inventory, accounts payable … };
s12, defining three different equivalence classes according to the order of the granularity from small to large:
1) b ═ a1, a2, a3 in the unrecognizable relationship r (B), that is, data in the balance sheet are divided based on the names of the first, second and third-level subjects, and the corresponding data in each third-level subject is an equivalent class (particle), in this case, the granularity of the data is minimum, the refinement degree is highest, but the integration degree is lowest, and the comparability of the data is also lowest because the difference set by the finer-level subjects among different enterprises is larger;
2) b ═ a1, a2}, namely, data in the balance sheet of the assets are divided according to the names of the primary subject and the secondary subject, the corresponding data under each secondary subject is an equivalent class (particle), the granularity of the data is moderate in this case, and the refinement degree, the comprehensive degree and the comparability of the data are moderate;
3) b ═ a1, namely dividing the data in the balance sheet according to the names of primary subjects, wherein the corresponding data under each primary subject is an equivalent class (particle), in this case, the granularity of the data is the largest, the refinement degree of the data is the lowest, but the comprehensive degree and comparability are the highest;
s13, carrying out different types of division on data in the balance sheet according to the granularity to form different grain layers, and combining the different grain layers to construct a multi-layer grain structure model; carrying out granularity analysis on the data of the profit list and the cash flow table by the same method, and respectively constructing a multi-layer grain structure model;
s2, multi-level and multi-granularity precision credible financial record information storage based on block chain MPT tree and hash value
Combining the block chain MPT tree with the multi-level and multi-granularity structural model of the financial record information to construct the MPT tree of the financial report data of the enterprise, which is used for storing the financial report data of the enterprise.
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