Block chain-based accounting book management method, electronic device and readable storage medium

Abstract

The invention relates to an accounting book management method based on a block chain, an electronic device and a readable storage medium. The method comprises the steps of obtaining original transaction data of a target account book; mapping the original transaction data based on a standardized subject table of a preset block chain to obtain mapping transaction data of the target account book; performing transaction level comparison on the mapping transaction data based on the block chain to obtain a comparison report of the target account book; correcting the target account book based on the difference points in the comparison report to obtain a corrected account book of the target account book; performing multi-dimensional verification on the corrected account book to obtain a verification report of the target account book; and carrying out the correction account book association on the verification report, and inputting the association result into the block chain, thereby solving the problems of no uniform standard of data, difficulty in accurately identifying differences, single verification dimension and difficulty in subsequent tracing in traditional block chain accounting book management.

Description

Technical Field

[0001] This invention relates to the field of ledger management technology, and in particular to a blockchain-based accounting ledger management method, electronic device, and readable storage medium. Background Technology

[0002] Current blockchain-based accounting ledger management technologies primarily involve directly uploading transaction data from accounting ledgers to blockchain nodes for storage, relying solely on the immutability of blockchain for basic data preservation. In the data processing stage, manual or simple procedures are typically used to classify and organize the raw transaction data, lacking a unified, standardized account mapping mechanism. Furthermore, most data verification remains at the ledger level, failing to delve into precise comparisons at the transaction level. Subsequent corrections of ledger discrepancies are often done manually, focusing only on single-dimensional adjustments. Verification often revolves solely around data consistency, neglecting multi-dimensional verification incorporating historical anomalies and business / financial logic.

[0003] However, existing blockchain-based accounting ledger management technologies lack a pre-defined standardized chart of accounts for blockchain, making it impossible to accurately map original transaction data and easily leading to data conflicts; they do not combine the immutability of blockchain for transaction-level comparison, making it difficult to accurately identify ledger differences; they do not perform multi-dimensional verification of corrected ledgers by combining historical abnormal transaction characteristics and account transmission rules, and they do not link verification reports with corrected ledgers into the chain, making subsequent traceability difficult and seriously affecting the accuracy and reliability of ledger management. Summary of the Invention

[0004] This disclosure provides a blockchain-based accounting ledger management method, electronic device, and readable storage medium.

[0005] Firstly, this disclosure provides a blockchain-based accounting ledger management method, including: S1: Obtain the raw transaction data of the target ledger; S2: The original transaction data is mapped based on a pre-defined standardized chart of accounts for the blockchain to obtain the mapped transaction data of the target ledger; S3: Based on the blockchain, perform transaction-level comparison of the mapped transaction data to obtain a comparison report of the target ledger; S4: Based on the differences in the comparison report, the target ledger is corrected to obtain the corrected ledger of the target ledger; S5: Perform multi-dimensional verification on the revised ledger to obtain a verification report of the target ledger; S6: Associate the verification report with the corrected ledger and input the association result into the blockchain.

[0006] In a preferred embodiment, obtaining the raw transaction data of the target ledger includes: Establish communication connections with the distributed nodes of the target ledger to obtain the original block data of the distributed nodes; The transaction fields of the original block data are deconstructed to obtain the original transaction data of the original block data set.

[0007] In a preferred embodiment, the standardized chart of accounts based on a preset blockchain maps the original transaction data to obtain the mapped transaction data of the target ledger, including: Based on the standardized chart of accounts, discrete deviation marking is performed on the original transaction data to obtain conflict identification data of the original transaction data; Based on the tree-like hierarchical structure of the standardized chart of accounts, dynamic account matching is performed on the conflict identification data to obtain the matching data of the original transaction data; The matching data is standardized and verified based on the historical rule summary of the blockchain to obtain the mapping transaction data of the target ledger.

[0008] In a preferred embodiment, the step of performing transaction-level comparison of the mapped transaction data based on the blockchain to obtain a comparison report of the target ledger includes: Based on the transaction records of the blockchain, the baseline state of the mapped transaction data is extracted to obtain the baseline state data of the mapped transaction data; Based on the baseline state data, attribute differences are identified in the target ledger to obtain the attribute difference features of the target ledger; A comparison report of the target ledger is generated based on the attribute difference features.

[0009] In a preferred embodiment, the step of correcting the target ledger based on the differences in the comparison report to obtain a corrected ledger of the target ledger includes: Extract the differences from the comparison report to obtain the micro-difference regions in the comparison report; Based on the micro-difference region, the mapped transaction data is context-corrected to obtain the corrected mapped data of the mapped transaction data. Based on the corrected mapping data, the original data of the target ledger is overwritten with differences to obtain the corrected ledger of the target ledger.

[0010] In a preferred embodiment, the step of performing multi-dimensional verification on the corrected ledger to obtain a verification report for the target ledger includes: Based on the historical abnormal transaction feature database of the blockchain, the risk pattern of the corrected ledger is reverse-matched to obtain the risk residual matching set of the corrected ledger. Based on the inter-account transmission rules of the blockchain, the business and financial logic consistency of the revised ledger is verified to obtain the logical deviation area of ​​the revised ledger. Based on the risk residual matching set and the logical deviation region, the corrected ledger is cross-validated in multiple dimensions to obtain a validation report of the target ledger.

[0011] In a preferred embodiment, the risk pattern reverse matching of the corrected ledger based on the historical abnormal transaction feature database stored in the blockchain to obtain the risk residual matching set of the corrected ledger includes: Based on the historical abnormal transaction feature library, the transaction records of the correction ledger are traversed to obtain the abnormal transaction candidate set of the correction ledger; Based on the transaction weight rules of the historical abnormal transaction feature library, the abnormal transaction candidate set is classified into risk probabilities to obtain the risk classification features of the abnormal transaction candidate set. Risk residual aggregation is performed on the risk classification features to obtain the risk residual matching set of the revised ledger.

[0012] In a preferred embodiment, the step of associating the verification report with the corrected ledger and inputting the association result into the blockchain includes: Combine the verification report and the correction ledger, and bind the binding summary record of the correction ledger; Based on the smart contract address of the blockchain, the binding summary record is encapsulated into a blockchain transaction to obtain the audit transaction package of the corrected ledger; Based on the audit transaction package, the change history of the revised ledger is associated with anchor points, and the association result of the revised ledger is obtained.

[0013] To address the aforementioned issues, the present invention also provides a blockchain-based electronic accounting ledger management device, comprising a memory, a processor, and a computer program stored in the memory.

[0014] To address the aforementioned issues, the present invention also provides a blockchain-based readable storage medium for accounting ledger management, on which a computer program is stored.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This solution constructs a unified accounting data mapping benchmark by pre-setting a standardized blockchain chart of accounts. First, it marks the original transaction data with discrete deviations to accurately identify conflicting data such as incorrect account names and missing codes. Then, it completes dynamic account matching based on the hierarchical tree structure of the chart of accounts and performs standardized verification by combining the historical rule summary of the blockchain. This effectively solves the problem of data lacking unified standards and prone to conflict in traditional management, and ensures that the mapped transaction data fully conforms to accounting standards.

[0016] Meanwhile, it breaks through the limitations of traditional ledger-level verification and delves into the transaction-level dimension. Based on the immutable transaction records of the blockchain, it extracts benchmark state data, accurately identifies the attribute differences of the target ledger, and generates a detailed comparison report, providing a clear basis for subsequent corrections. The entire process from data mapping to difference identification ensures the accuracy of ledger data. Attached Figure Description

[0017] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings: Figure 1 The flowchart of a blockchain-based accounting ledger management method according to Embodiment 1 of the present invention is shown. Figure 2 The diagram shows the composition of an electronic device for implementing a blockchain-based accounting ledger management method according to Embodiment 2 of the present invention. Detailed Implementation

[0018] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.

[0019] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0020] Example 1 Figure 1 This is a flowchart illustrating a blockchain-based accounting ledger management method provided in an embodiment of this disclosure. Figure 1 As shown, a smart device control method includes: S1: Obtain the raw transaction data of the target ledger; In this embodiment of the invention: obtaining the original transaction data of the target ledger includes: Establish communication connections with the distributed nodes of the target ledger to obtain the original block data of the distributed nodes; The transaction fields of the original block data are deconstructed to obtain the original transaction data of the original block data set.

[0021] Specifically, target ledgers are specific ledger objects that need to be managed in accounting. They contain records of economic transactions of an enterprise or organization within a certain period. These records reflect its financial income and expenditure and are the core basis for accounting and management.

[0022] Distributed nodes are components of a blockchain network, located in different physical locations. Each node stores complete or partial data of the blockchain, and the nodes communicate and synchronize data through the network to jointly maintain the normal operation of the blockchain. In accounting ledger management scenarios, these nodes are used to store and transmit block data related to the target ledger.

[0023] Raw block data is unprocessed block data containing transaction information obtained from distributed nodes. In addition to the transaction records of the target ledger, each block data may also contain information such as the block index, timestamp, and hash value. This information is used to ensure the integrity and immutability of the block data.

[0024] Transaction fields are the specific components that make up a transaction record in the original block data. The composition of fields may differ for different types of transactions. For example, in a commodity sales transaction, transaction fields may include transaction number, transaction date, information of both parties to the transaction, commodity name, quantity, unit price, amount, etc. These fields together describe the details of a transaction.

[0025] Raw transaction data is a set of data that directly reflects the economic transaction substance of the target ledger after the transaction fields of the raw block data are deconstructed. It removes non-core transaction information such as block indexes and hash values ​​from the raw block data and retains only the transaction content related to accounting, providing basic data for subsequent accounting processing.

[0026] Furthermore, firstly, for the target ledger that needs to be managed, a communication connection is established with the distributed blockchain nodes that store the relevant data of the ledger. The raw block data containing the target ledger's transaction information and related auxiliary information is obtained from these nodes distributed in different physical locations through network transmission. Next, the obtained raw block data is processed. According to the composition logic of accounting transaction records, the various transaction fields describing the specific details of the transactions are decomposed. Then, the raw transaction data containing only the core content of the target ledger's economic transactions is filtered and integrated to obtain the raw transaction data. This provides accurate and accounting-processing-compliant basic data for subsequent blockchain-based accounting ledger management operations, such as data mapping and comparison.

[0027] S2: The original transaction data is mapped based on a pre-defined standardized chart of accounts for the blockchain to obtain the mapped transaction data of the target ledger; In this embodiment of the invention: the mapping of the original transaction data to the target ledger using a pre-defined standardized chart of accounts based on a blockchain, to obtain the mapped transaction data, includes: Based on the standardized chart of accounts, discrete deviation marking is performed on the original transaction data to obtain conflict identification data of the original transaction data; Based on the tree-like hierarchical structure of the standardized chart of accounts, dynamic account matching is performed on the conflict identification data to obtain the matching data of the original transaction data; The matching data is standardized and verified based on the historical rule summary of the blockchain to obtain the mapping transaction data of the target ledger.

[0028] Specifically, blockchain is a decentralized distributed ledger technology system with characteristics such as immutability, decentralized storage, and traceability. It is used to store associated results, ensuring the security, authenticity, and immutability of the associated results, and providing a reliable data environment for subsequent ledger tracing and auditing.

[0029] The standardized chart of accounts is a unified account system that is pre-set in the blockchain system and conforms to the accounting ledger management standards. It includes fixed attributes such as accounting account names, codes, accounting scope, and hierarchical relationships. It is used as a benchmark standard for mapping original transaction data. For example, bank deposits and accounts receivable under asset accounts, and short-term loans and accounts payable under liability accounts are all included in the chart of accounts and clearly defined.

[0030] Conflict identification data is generated by comparing the original transaction data with the standardized chart of accounts one by one, and marking the parts of the original transaction data that are inconsistent or mismatched with the standardized chart of accounts. Inconsistencies or mismatches include incorrect account names, missing account codes, and accounting scopes exceeding the standard in the original transaction data. For example, if accounts payable are mistakenly written as goods payable in the original transaction data, this part of the data will be marked as conflict identification data after comparison.

[0031] The tree-like hierarchical structure of a standardized chart of accounts is the organizational form of a standardized chart of accounts. It presents the hierarchical relationship between accounts in a tree structure, extending from the top-level first-level accounts to the second-level accounts, third-level accounts, and so on. For example, assets are the first-level accounts, which include current assets and non-current assets as second-level accounts. Current assets further include cash, cash equivalents, accounts receivable, and other third-level accounts. This structure can clearly reflect the subordinate and classification relationships between accounts.

[0032] Matching data is generated by dynamically retrieving the most suitable standard account and corresponding account from conflict identifier data in the blockchain system according to the tree-like hierarchical structure of the standardized account table. The data is then associated with the standard account and account to form the matching data. For example, if the account in a conflict identifier data is accounts payable, the standard account of liabilities-current liabilities-accounts payable and its corresponding account can be matched through the tree-like hierarchical structure search, and the matching data is formed after association.

[0033] The historical rule summary of a blockchain is a collection of standardized processing rules formed during the past accounting ledger management process and stored in the blockchain. It includes standards for mapping historical transaction data, rules for matching conflicting data, and methods for data verification. Due to the immutable nature of blockchain, the historical rule summary is authentic and traceable. For example, when processing conflicting data such as non-standard descriptions of account names in the past, the rule of prioritizing matching the standard account corresponding to the core meaning of the account was adopted, and this rule was recorded in the historical rule summary.

[0034] The mapping transaction data of the target ledger is generated by comparing and verifying the matching data with the historical rule summary of the blockchain. After confirming that the matching data conforms to the historical standardized processing rules, the transaction data is fully adapted to the standardized chart of accounts. This data can be directly used for subsequent comparison and correction of accounting ledgers. For example, after verification by the historical rule summary, if a certain matching data conforms to the rule that the matching of past accounts should prioritize the consistency of the core meaning, the data becomes the mapping transaction data after the verification is passed.

[0035] Furthermore, firstly, in the blockchain-based accounting ledger management system, the system retrieves a pre-defined standardized chart of accounts and the acquired original transaction data. The system automatically compares each piece of information in the original transaction data with the corresponding attribute in the standardized chart of accounts. When inconsistencies with the standardized chart of accounts are detected in the original transaction data, such as incorrect account names, missing codes, or inconsistent accounting scopes, the system will specifically mark that part of the data, and finally generate conflict label data containing all conflicting data and corresponding labels, thus completing the discrete deviation marking operation of the original transaction data.

[0036] Secondly, the system calls the tree-like hierarchical structure of the standardized chart of accounts and dynamically searches for each conflict record in the conflict identification data according to the order from the top to the bottom of the tree hierarchy. During the search, the system combines the core information of the conflict data (such as transaction type, range of amount involved, etc.) to find the most suitable standard account and corresponding account in the tree hierarchy. After finding a match, the system automatically associates and binds the conflict identification data with the standard account and account, completes the dynamic account matching operation, and generates matching data.

[0037] Finally, the system extracts historical rule summaries from the blockchain and comprehensively compares and verifies the matching data obtained in the second step with the various standardized processing rules in the historical rule summaries. The verification includes whether the subject association of the matching data conforms to the historical matching rules, whether the data format follows the historical standardized format, and whether the accounting logic is consistent with the historical verification logic. If the verification passes, it means that the matching data has fully met the standardized requirements of blockchain accounting ledger management. At this time, the system determines the matching data as the mapping transaction data of the target ledger. If the verification fails, it returns to the previous step to re-perform dynamic account matching until mapping transaction data that conforms to the historical rule summaries is generated, thus completing the standardized verification operation.

[0038] S3: Based on the blockchain, perform transaction-level comparison of the mapped transaction data to obtain a comparison report of the target ledger; In this embodiment of the invention: the step of performing transaction-level comparison of the mapped transaction data based on the blockchain to obtain a comparison report of the target ledger includes: Based on the transaction records of the blockchain, the baseline state of the mapped transaction data is extracted to obtain the baseline state data of the mapped transaction data; Based on the baseline state data, attribute differences are identified in the target ledger to obtain the attribute difference features of the target ledger; A comparison report of the target ledger is generated based on the attribute difference features.

[0039] Specifically, blockchain transaction records are collections of transaction information stored in the distributed nodes of the blockchain, which are encrypted and verified, and cannot be unilaterally modified or deleted once written. These records are ensured to be authentic and consistent through the blockchain's consensus mechanism, and cover details of all legal accounting transactions in history, including key information such as transaction amount, transaction object, transaction time, and related accounts.

[0040] The mapped transaction data is based on a pre-set blockchain standardized chart of accounts. After performing discrete deviation marking, dynamic account matching, and standardized verification on the original transaction data of the target ledger, the data is aligned with the blockchain standard chart of accounts. This data has eliminated the parts of the original transaction data that conflict with the standard chart of accounts and has the basic format and logical consistency for comparison with blockchain data.

[0041] The baseline status data is transaction status information extracted by matching and analyzing the mapped transaction data with the immutable transaction records on the blockchain. It can be used as a benchmark for subsequent difference comparison. It includes the compliant transaction characteristics, standard attribute values ​​and status parameters under normal business logic corresponding to the mapped transaction data in the blockchain, such as the standard accounts receivable account code, amount accounting rules and accounting time requirements corresponding to a certain sales transaction.

[0042] Attribute difference features are identified by comparing the attribute information of each transaction in the target ledger with the corresponding attributes in the baseline status data one by one. Inconsistent features are identified by comparing the attribute information of each transaction in the target ledger with the corresponding attributes in the baseline status data. For example, the account code of a purchase transaction in the target ledger is inconsistent with the standard code in the baseline status data, or the amount of an expense expenditure deviates from the approved amount in the baseline status data. These features are recorded in a structured form, clearly specifying the location, type and specific value of the difference.

[0043] The comparison report is a structured document that presents the comparison results between the target ledger and the blockchain data. In addition to a detailed description of the attribute differences, it also marks the transaction number, accounting period, accounting subject involved, and scope of impact of the differences. It may also include a preliminary analysis of the causes of the differences, providing a clear basis for subsequent corrections to the target ledger.

[0044] Furthermore, firstly, the system retrieves immutable transaction records from the distributed nodes of the blockchain. Through the decentralized storage characteristics of the blockchain, it ensures that the obtained records are authentic and have not been tampered with. The system then matches the mapped transaction data with the retrieved immutable blockchain transaction records one by one. Based on the unique identifier of the transaction, the system associates the corresponding record with the transaction data, extracts features from the successfully matched transaction data, and filters out the standard status parameters of the mapped transaction data under the blockchain compliance system, including account attribution, amount accounting logic, business process nodes, etc. The extracted standard status parameters are organized into structured benchmark status data, thus completing the benchmark data construction process.

[0045] Secondly, the system loads all transaction data from the target ledger and preprocesses the data according to accounting subject classification and transaction time sequence to facilitate attribute comparison on a transaction-by-transaction basis. It then calls the baseline state data generated in sub-step 1 and compares the attributes of each transaction in the target ledger with the attributes of the corresponding transaction in the baseline state data one by one. During the comparison process, if any inconsistency is found between the transaction attributes of the target ledger and the baseline state data, the difference is marked, and the specific content and location of the difference are recorded. Finally, all marked difference information is summarized and classified, and attribute difference features with common and specific characteristics are extracted to form a set of difference features.

[0046] Finally, the attribute difference feature set is cleaned and structured to remove duplicate or invalid difference records, ensuring the accuracy and completeness of the difference information. According to the preset report template, the transaction details, specific content of the difference, and impact assessment of the difference are filled into the corresponding modules. At this time, the system will prioritize the differences according to their type and severity, for example, placing differences involving significant monetary deviations or compliance risks at the top, and supplementing the preliminary speculation on the causes of the differences, generating a complete target ledger comparison report. This report can be directly used for subsequent target ledger correction operations.

[0047] S4: Based on the differences in the comparison report, the target ledger is corrected to obtain the corrected ledger of the target ledger; In this embodiment of the invention: the step of correcting the target ledger based on the differences in the comparison report to obtain the corrected ledger of the target ledger includes: Extract the differences from the comparison report to obtain the micro-difference regions in the comparison report; Based on the micro-difference region, the mapped transaction data is context-corrected to obtain the corrected mapped data of the mapped transaction data. Based on the corrected mapping data, the original data of the target ledger is overwritten with differences to obtain the corrected ledger of the target ledger.

[0048] Specifically, the discrepancies in the comparison report are the specific information recorded in the report that is inconsistent between the original data of the target ledger and the standard transaction data of the blockchain. For example, there may be errors in the account coding, the amount calculation, or the transaction time entry for a certain transaction.

[0049] Micro-difference areas are sets of differences with clear boundaries and specific content, formed by extracting all differences from the comparison report and classifying them according to transaction dimensions, account categories, or difference types. They can accurately locate the specific location and related data of each difference in the target ledger.

[0050] The mapped transaction data is obtained by performing discrete deviation marking, dynamic account matching, and standardized verification on the original transaction data of the target ledger based on a pre-set blockchain standardized chart of accounts. It has been aligned with the blockchain standard chart of accounts and has a basic compliant format.

[0051] Corrected mapping data is transaction data obtained by contextually correcting the mapped transaction data, eliminating the deviations corresponding to micro-difference areas. It not only corrects the differences themselves, but also ensures the consistency of the corrected data with surrounding related transactions and account logic, and complies with blockchain transaction standards.

[0052] Difference coverage is the process of replacing the corresponding content in the original data with the parts of the corrected mapping data that differ from the original data in the target ledger. It only covers the fields with discrepancies, while retaining the parts of the original data that are indistinguishable and compliant, thus ensuring the accuracy of the data correction.

[0053] The corrected ledger is a ledger obtained by covering the differences in the original data of the target ledger, eliminating all identified biases. Its data not only meets the requirements of the blockchain standardized chart of accounts, but also maintains the integrity and consistency of its own transaction logic, and can enter the subsequent multi-dimensional verification stage.

[0054] Furthermore, the system uses a preset difference point identification algorithm to traverse all records in the comparison report and filter out the information items marked as differences. These items are the difference points. The system classifies and integrates the extracted difference points according to the transaction flow order and accounting subject level of the target ledger. Difference points belonging to the same transaction, the same subject, or the same type are grouped together, and the ledger data range corresponding to each group of differences is clarified, ultimately forming micro-difference areas, which provide a positioning basis for subsequent accurate correction.

[0055] Secondly, through a data association algorithm, each difference in the micro-difference region is matched with the corresponding transaction record in the mapped transaction data to locate the specific field in the mapped transaction data that needs to be corrected. Then, the system retrieves the context information of the transaction corresponding to the difference from the blockchain node, including the related transaction records, the transmission rules of the subjects involved, and the processing rules of similar transactions in the blockchain history. Based on this information, the system determines the cause of the difference and the reasonable direction of correction.

[0056] Subsequently, the system adjusts the fields with discrepancies in the mapped transaction data according to the requirements and historical rules of the blockchain standardized chart of accounts. For example, it corrects incorrect account codes, calibrates the amount values ​​with discrepancies, and completes missing transaction information. At the same time, it verifies whether the corrected data is consistent with the surrounding related transactions and account logic. If there is a conflict, it readjusts the data. After all the discrepancies are corrected and the logic is confirmed to be consistent, the corrected mapping data is generated and stored in a temporary processing node for further verification.

[0057] Finally, the system retrieves the original data and corrected mapping data of the target ledger. Using a field-level comparison algorithm, it compares the transaction records of the two data one by one to identify the fields in the original data that differ from the corrected mapping data. After determining the scope that needs to be covered, the system replaces the different fields with the corrected fields and retains the fields without differences. The corrected fields in the corrected mapping data are then overwritten with the corresponding deviation fields in the original data of the target ledger. During the overwriting process, the system records the operation log of each overwriting operation in real time, including the original value before overwriting, the corrected value after overwriting, and the operation time. This log will be stored synchronously on the blockchain node for traceability.

[0058] After the overlay is completed, the system performs integrity verification on the newly generated ledger data to ensure that all transaction records are complete and fields are free of null values. At the same time, it verifies the data's conformity with the blockchain's standardized chart of accounts. Once it confirms that there are no deviations, the ledger is designated as the corrected ledger of the target ledger and stored on the designated node of the blockchain, thus completing the correction process.

[0059] S5: Perform multi-dimensional verification on the revised ledger to obtain a verification report of the target ledger; In this embodiment of the invention: the step of performing multi-dimensional verification on the corrected ledger to obtain a verification report of the target ledger includes: Based on the historical abnormal transaction feature database of the blockchain, the risk pattern of the corrected ledger is reverse-matched to obtain the risk residual matching set of the corrected ledger. Based on the inter-account transmission rules of the blockchain, the business and financial logic consistency of the revised ledger is verified to obtain the logical deviation area of ​​the revised ledger. Based on the risk residual matching set and the logical deviation region, the corrected ledger is cross-validated in multiple dimensions to obtain a validation report of the target ledger.

[0060] Specifically, the blockchain's historical abnormal transaction feature database is a collection of encrypted and tamper-proof historical abnormal transaction information stored in the distributed nodes of the blockchain. It includes abnormal transaction features identified in past accounting ledger management, such as abnormal amount ranges, non-compliant transaction objects, abnormal transaction frequency, and transaction processes that do not conform to normal business and financial logic. These features are written after being verified by the blockchain consensus mechanism and can serve as a benchmark for risk identification.

[0061] The risk residual matching set is a set of transactions that still have residual risk characteristics identified from the corrected ledger through a risk pattern reverse matching operation. It includes information such as the transaction number, risk characteristic type, and risk matching degree of each residual risk transaction, which is used for subsequent cross-validation.

[0062] The inter-account transmission rules of blockchain are pre-set in the blockchain system and conform to the accounting standards and business logic. They clarify the numerical transmission relationship between different accounting accounts, the order of account changes corresponding to business processes, and the reconciliation relationship of account balances.

[0063] Business and financial logic consistency verification is an operation that checks the degree of matching between changes in accounting subjects involved in transactions in the correction ledger and the actual business process and the rules for transmission between subjects. The core is to verify whether the logic of business occurrence and subject change is smooth and whether the data transmission between subjects conforms to the preset rules, so as to ensure that accounting records are consistent with business substance.

[0064] The logical deviation area is a transaction area identified through business and financial logic consistency verification that contains errors in the logical transmission of accounts or mismatches in business and financial logic in the corrective ledger. It includes information such as the scope of the deviation, the type of deviation, and the specific manifestation of the deviation.

[0065] The target ledger's verification report is a structured document presenting the multi-dimensional verification results of the corrected ledger. It includes a summary analysis of the risk residual matching set, a detailed description of the logical deviation area, cross-validation conclusions, and improvement suggestions for residual risks and logical deviations. It is used to reflect the final quality of the target ledger after correction and to provide a basis for subsequent on-chain association.

[0066] Furthermore, firstly, the original transaction data of the target ledger is obtained by establishing a communication connection with the distributed nodes of the target ledger to acquire the original block data, and then the transaction fields are deconstructed to extract the original transaction data. Next, based on a pre-defined blockchain standardized chart of accounts, the original transaction data is first marked with discrete deviations to obtain conflict identification data, and then the accounts are dynamically matched to generate matching data by combining the tree-like hierarchical structure of the chart of accounts. Finally, the mapped transaction data is obtained through verification using the blockchain historical rule summary.

[0067] Then, baseline state data for mapping transaction data is extracted from blockchain transaction records. Based on this, attribute differences are identified in the target ledger, and a comparison report is generated. According to the differences in the report, micro-difference areas are first extracted, and the mapping transaction data is corrected accordingly to obtain corrected mapping data. Finally, the corrected mapping data is used to overwrite the original data of the target ledger to form a corrected ledger.

[0068] Finally, the revised ledger is traversed using a historical abnormal transaction feature database of the blockchain. A risk residual matching set is obtained through risk probability grading and aggregation. The revised ledger is then verified according to the inter-account transmission rules of the blockchain to identify logical deviation areas. A verification report is generated by combining these two methods for multi-dimensional cross-verification of the revised ledger. The report is then bound to the revised ledger to generate a binding summary record. After blockchain transaction encapsulation and association anchor writing, the association result is input into the blockchain.

[0069] In this embodiment of the invention: the risk pattern reverse matching of the corrected ledger based on the historical abnormal transaction feature database stored in the blockchain to obtain the risk residual matching set of the corrected ledger includes: Based on the historical abnormal transaction feature library, the transaction records of the correction ledger are traversed to obtain the abnormal transaction candidate set of the correction ledger; Based on the transaction weight rules of the historical abnormal transaction feature library, the abnormal transaction candidate set is classified into risk probabilities to obtain the risk classification features of the abnormal transaction candidate set. Risk residual aggregation is performed on the risk classification features to obtain the risk residual matching set of the revised ledger.

[0070] Specifically, the abnormal transaction candidate set is a set of transaction records that show preliminary signs of matching abnormal transaction characteristics by comparing the transaction records of the revised ledger with the historical abnormal transaction feature database. These transaction records have not yet been determined to be clearly abnormal, but are only used as candidates for subsequent risk classification.

[0071] The transaction weighting rule is a pre-set rule system in the historical abnormal transaction feature database used to measure the risk level of each transaction in the abnormal transaction candidate set. The rule will assign corresponding weights to different features based on factors such as the severity, frequency, and scope of impact of the abnormal transaction features. For example, the abnormal feature involving large amounts of funds and no reasonable business background has a higher weight than the abnormal feature involving small amounts and sporadic abnormality.

[0072] Risk grading features are feature information formed by calculating the weight of the abnormal features of each transaction in the abnormal transaction candidate set according to the transaction weight rules, and then determining the risk probability level of each transaction. This feature clarifies the risk level of each candidate transaction and the basis for the corresponding weight calculation.

[0073] Furthermore, in blockchain accounting ledger management, the historical abnormal transaction feature database and correction ledger of the blockchain node are retrieved first. Each record in the correction ledger is analyzed in the order of transactions. The key information is compared with the abnormal features in the feature database to screen out the transactions that initially match and organize them into an abnormal transaction candidate set.

[0074] Next, the transaction weight rules of the feature library are extracted, each transaction in the candidate set is processed, the matching abnormal features are identified and the risk probability is calculated, the risk level is determined according to the preset standard, and the transaction information, features, probability and level are integrated to form a risk classification feature set.

[0075] Then, the risk classification feature set is loaded, invalid records are preprocessed and removed, and the data is classified according to risk level and anomaly type. The number and amount of each type of transaction are statistically analyzed, the core risk description is extracted, and the results are integrated to form a risk residual matching set. This provides a risk basis for multi-dimensional verification of the corrected ledger. The entire process relies on the immutability of blockchain to ensure the authenticity and traceability of data and to ensure accurate and compliant operation.

[0076] S6: Associate the verification report with the corrected ledger and input the association result into the blockchain.

[0077] In this embodiment of the invention: the step of associating the verification report with the corrected ledger and inputting the association result into the blockchain includes: Combine the verification report and the correction ledger, and bind the binding summary record of the correction ledger; Based on the smart contract address of the blockchain, the binding summary record is encapsulated into a blockchain transaction to obtain the audit transaction package of the corrected ledger; Based on the audit transaction package, the change history of the revised ledger is associated with anchor points, and the association result of the revised ledger is obtained.

[0078] Specifically, the binding summary record is a record formed by extracting and binding key information from the correction ledger in conjunction with the verification report and the correction ledger. It is used to condense the core information related to the correction ledger and the verification report, and to provide basic data for subsequent transaction encapsulation.

[0079] The smart contract address in a blockchain is a unique identifier for a smart contract within the blockchain network. It is used to locate the corresponding smart contract and pre-defines the rules and procedures for auditing transaction processing, providing compliance and procedural basis for the encapsulation of blockchain transactions bound to summary records.

[0080] An audit transaction package is a data packet formed by encapsulating the binding summary record according to the blockchain transaction format and smart contract rules based on the smart contract address of the blockchain. It contains encrypted information of the binding summary record, transaction processing instructions, smart contract call information, etc., and can be transmitted and processed in the blockchain network.

[0081] The change history of the revised ledger is a collection of records of all change operations from the original ledger to the completion of the revision. This includes information such as the time, content, and operator of each operation, such as difference point extraction, mapping transaction data correction, and original data difference overwriting. It is used to trace the formation process of the revised ledger.

[0082] The linking anchor is an identifier generated based on key information in the audit transaction package. It is used to link the history of changes to the corrected ledger with the audit transaction package. It is unique and traceable. Through this identifier, the correspondence between the history of changes to the corrected ledger and the audit transaction package can be quickly located in the blockchain.

[0083] The association result is a complete data set formed after the change history of the revised ledger is written with the association anchor point based on the audit transaction package. This set contains the revised ledger, verification report, binding summary record, audit transaction package, and change history association information. This set can be directly input into the blockchain for storage.

[0084] Furthermore, firstly, staff or the system retrieves the previously generated verification report and correction ledger, integrates the key information from both, extracts the core transaction data and correction records from the correction ledger and the key verification conclusions from the verification report, forms a binding summary record, and completes the initial association between the verification report and the correction ledger.

[0085] Next, obtain the smart contract address in the blockchain that is related to the accounting ledger audit transaction. Based on the smart contract rules corresponding to the address, encapsulate the binding summary record according to the standard format of blockchain transactions, supplement the instructions and encrypted information required for transaction processing, and generate the audit transaction package.

[0086] Finally, a correlation anchor is generated based on the key data in the audit transaction package. This correlation anchor is written into the change history of the correction ledger, so that the change history of the correction ledger and the audit transaction package are uniquely associated through the correlation anchor. This results in a correlation result that includes the correction ledger, verification report, binding summary record, audit transaction package and change history correlation information. This correlation result is then uploaded to the blockchain to complete the storage of the correlation result on the blockchain.

[0087] Example 2 like Figure 2 As shown, this embodiment also provides a blockchain-based electronic accounting ledger management device. The electronic device may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13. It may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a blockchain-based accounting ledger management program.

[0088] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., executing blockchain-based accounting ledger management programs) and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0089] The memory 11 includes at least one type of medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 11 can include both internal and external storage units of the electronic device. The memory 11 can be used not only to store application software and various types of data installed on the electronic device, such as the code for a program to determine the moisture content of saline soil, but also to temporarily store data that has been output or will be output.

[0090] The communication bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0091] The communication interface 13 is used for communication between the aforementioned electronic device and other electronic devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, Bluetooth interface, etc.), typically used to establish communication connections between the electronic device and other electronic devices. The user interface may be a display, an input unit (such as a keyboard), or optionally, a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device and to display a visual user interface.

[0092] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0093] For example, although not shown, the electronic device may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0094] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0095] The blockchain-based accounting ledger management program stored in the memory 11 of the electronic device is a combination of multiple instructions that, when run in the processor 10, can achieve the following: S1: Obtain the raw transaction data of the target ledger; S2: The original transaction data is mapped based on a pre-defined standardized chart of accounts for the blockchain to obtain the mapped transaction data of the target ledger; S3: Based on the blockchain, perform transaction-level comparison of the mapped transaction data to obtain a comparison report of the target ledger; S4: Based on the differences in the comparison report, the target ledger is corrected to obtain the corrected ledger of the target ledger; S5: Perform multi-dimensional verification on the revised ledger to obtain a verification report of the target ledger; S6: Associate the verification report with the corrected ledger and input the association result into the blockchain.

[0096] Specifically, the specific implementation method of the processor 10 for the above instructions can be referred to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, and will not be repeated here.

[0097] Furthermore, if the modules / units integrated into the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a medium. The medium can be volatile or non-volatile. For example, the medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0098] In the several embodiments provided by this invention, it should be understood that the disclosed electronic devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0099] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0100] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0101] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0102] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A blockchain-based accounting ledger management method, characterized in that, The method includes: S1: Obtain the raw transaction data of the target ledger; S2: The original transaction data is mapped based on a pre-defined standardized chart of accounts for the blockchain to obtain the mapped transaction data of the target ledger; S3: Based on the blockchain, perform transaction-level comparison of the mapped transaction data to obtain a comparison report of the target ledger; S4: Based on the differences in the comparison report, the target ledger is corrected to obtain the corrected ledger of the target ledger; S5: Perform multi-dimensional verification on the revised ledger to obtain a verification report of the target ledger; S6: Associate the verification report with the corrected ledger and input the association result into the blockchain.

2. The blockchain-based accounting ledger management method as described in claim 1, characterized in that, The acquisition of the raw transaction data of the target ledger includes: Establish communication connections with the distributed nodes of the target ledger to obtain the original block data of the distributed nodes; The transaction fields of the original block data are deconstructed to obtain the original transaction data of the original block data set.

3. The blockchain-based accounting ledger management method as described in claim 1, characterized in that, The standardized chart of accounts based on a preset blockchain maps the original transaction data to obtain the mapped transaction data of the target ledger, including: Based on the standardized chart of accounts, discrete deviation marking is performed on the original transaction data to obtain conflict identification data of the original transaction data; Based on the tree-like hierarchical structure of the standardized chart of accounts, dynamic account matching is performed on the conflict identification data to obtain the matching data of the original transaction data; The matching data is standardized and verified based on the historical rule summary of the blockchain to obtain the mapping transaction data of the target ledger.

4. The blockchain-based accounting ledger management method as described in claim 1, characterized in that, The step of performing a transaction-level comparison of the mapped transaction data based on the blockchain to obtain a comparison report of the target ledger includes: Based on the transaction records of the blockchain, the baseline state of the mapped transaction data is extracted to obtain the baseline state data of the mapped transaction data; Based on the baseline state data, attribute differences are identified in the target ledger to obtain the attribute difference features of the target ledger; A comparison report of the target ledger is generated based on the attribute difference features.

5. The blockchain-based accounting ledger management method as described in claim 1, characterized in that, The step of correcting the target ledger based on the differences in the comparison report to obtain the corrected ledger of the target ledger includes: Extract the differences from the comparison report to obtain the micro-difference regions in the comparison report; Based on the micro-difference region, the mapped transaction data is context-corrected to obtain the corrected mapped data of the mapped transaction data. Based on the corrected mapping data, the original data of the target ledger is overwritten with differences to obtain the corrected ledger of the target ledger.

6. The blockchain-based accounting ledger management method as described in claim 1, characterized in that, The multi-dimensional verification of the revised ledger to obtain a verification report of the target ledger includes: Based on the historical abnormal transaction feature database of the blockchain, the risk pattern of the corrected ledger is reverse-matched to obtain the risk residual matching set of the corrected ledger. Based on the inter-account transmission rules of the blockchain, the business and financial logic consistency of the revised ledger is verified to obtain the logical deviation area of ​​the revised ledger. Based on the risk residual matching set and the logical deviation region, the corrected ledger is cross-validated in multiple dimensions to obtain a validation report of the target ledger.

7. The blockchain-based accounting ledger management method as described in claim 6, characterized in that, The risk pattern reverse matching of the corrected ledger based on the historical abnormal transaction feature database stored in the blockchain is used to obtain the risk residual matching set of the corrected ledger, including: Based on the historical abnormal transaction feature library, the transaction records of the correction ledger are traversed to obtain the abnormal transaction candidate set of the correction ledger; Based on the transaction weight rules of the historical abnormal transaction feature library, the abnormal transaction candidate set is classified into risk probabilities to obtain the risk classification features of the abnormal transaction candidate set. Risk residual aggregation is performed on the risk classification features to obtain the risk residual matching set of the revised ledger.

8. The blockchain-based accounting ledger management method as described in claim 1, characterized in that, The step of associating the verification report with the corrected ledger and inputting the association result into the blockchain includes: Combine the verification report and the correction ledger, and bind the binding summary record of the correction ledger; Based on the smart contract address of the blockchain, the binding summary record is encapsulated into a blockchain transaction to obtain the audit transaction package of the corrected ledger; Based on the audit transaction package, the change history of the revised ledger is associated with anchor points, and the association result of the revised ledger is obtained.

9. A blockchain-based electronic accounting ledger management device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 8.

10. A blockchain-based readable storage medium for accounting ledger management, wherein a computer program is stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 8.

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