Cross validation processing method and device for internal transaction

Abstract

The invention discloses a cross validation processing method and device for internal transactions, and relates to the field of big data, and the method comprises the steps: extracting various types of transaction feature vectors from internal transaction information which is not subjected to cross validation to form judgment condition matrix aggregation; when it is determined that cross validation between the internal transactions recorded by the at least two transaction parties is passed according to the aggregation, obtaining the internal transaction passing preliminary cross validation; calling a data source image of the internal transaction passing the preliminary verification from a source system database according to the identifier of the transaction passing the preliminary cross verification; after key elements are extracted from the image, a cloud image measurement transaction element item set is formed, the cloud image measurement transaction element item sets of the internal transactions recorded by at least two transaction parties are matched, and if the matching is passed, the internal transactions passing the cloud image verification preliminary cross verification are marked and then included in a marked internal transaction database. According to the invention, the accuracy and efficiency of cross validation processing of internal transactions can be improved.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a cross-verification processing method and device for internal transactions. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the invention recited in the claims. No description herein is admitted to be prior art by inclusion in this section.

[0003] Internal transactions refer to the transfer of resources, services, obligations or other interests between the various levels of the enterprise and its subsidiaries or between subsidiaries. Internal transactions include entities based on dependencies such as resources, services, obligations or other interests: natural persons, legal persons or unincorporated organizations. Based on internal transactions, risky transactions with associated relationships such as resources, services, obligations or other interests can be identified.

[0004] According to regulatory requirements, all levels of corporate institutions and subsidiaries need to identify, report and manage internal transactions in their units, and uniformly disclose information in accordance with consolidated management requirements. Therefore, efficient and accurate verification of internal transactions is crucial. In existing technologies, verification of internal transactions mainly adopts the following combination of automatic data collection and manual identification.

[0005] After the system automatically extracts the transaction data, it determines that it is an internal transaction through the counterparty. If the system does not automatically extract the transaction, it will manually identify and supplement the internal transaction. Due to defects and problems in the system data acquisition and manual identification, the transaction data is not fully identified, and there will be misreporting and omissions when the enterprise's institutions and subsidiaries at all levels identify and report internal transactions. Therefore, in order to ensure the completeness and accuracy of internal transaction data, the enterprise's institutions and subsidiaries at all levels will push their own reported internal transactions to the counterparty, and the counterparty will determine whether a certain internal transaction pushed has been synchronously reported by the unit and whether the reported content is consistent. If the counterparty determines that a certain internal transaction pushed has been fully and accurately reported by the unit, the counterparty can match and check the cross-verification function of the internal transaction that has occurred in the system, indicating that the reported content of both parties to the transaction is consistent. If the counterparty cannot match and check, it means that at least one of the two parties to the transaction has misreported or missed in the identification and reporting of internal transactions, and needs to be verified and re-reported.

[0006] Under current circumstances, the work of pushing internal transactions to corresponding institutions or subsidiaries relies on manual processing every six months. Not only is the workload of sorting and issuing large amounts of data, but human errors may also occur, and even the timeliness of reporting cannot be guaranteed.

[0007] Therefore, the existing internal transaction cross-verification method is highly dependent on manual processing. Since manual cross-verification is labor-intensive, inefficient, and highly dependent on the professionalism and sense of responsibility of the handlers themselves, it is easy to have missed matches, matching errors, etc. Summary of the invention

[0008] An embodiment of the present invention provides a cross-validation processing method for internal transactions, which is used to improve the accuracy and efficiency of cross-validation processing for internal transactions. The method includes:

[0009] Acquire internal transaction information that has not completed cross-validation from an internal transaction database; the internal transaction database is an internal transaction database pre-established based on internal transaction information acquired from multiple source system databases;

[0010] Extracting multiple types of transaction feature vectors from the internal transaction information that has not completed cross-validation to form a judgment condition matrix aggregation;

[0011] When it is determined that the cross-validation between the internal transactions recorded by at least two transaction parties has passed according to the judgment condition matrix aggregation, the internal transactions that have passed the preliminary cross-validation are obtained;

[0012] According to the identifier of the internal transaction that has passed the preliminary cross-verification, the data source image of the internal transaction that has passed the preliminary cross-verification is retrieved from the source system database;

[0013] After extracting key elements from the data source image, a cloud image measurement transaction element item set is formed, and the cloud image measurement transaction element item sets corresponding to the internal transactions recorded by at least two transaction parties are matched. If the match is successful, the internal transaction that has passed the preliminary cross-validation of the cloud image verification is obtained;

[0014] The internal transactions that have passed the preliminary cross-validation are marked as internal transactions that have completed cross-validation and included in the marked internal transaction database.

[0015] The embodiment of the present invention further provides a cross-validation processing device for internal transactions, which is used to improve the accuracy and efficiency of cross-validation processing for internal transactions. The device includes:

[0016] An acquisition unit is used to acquire the internal transaction information that has not completed cross-validation from an internal transaction database; the internal transaction database is an internal transaction database pre-established based on the internal transaction information acquired from multiple source system databases;

[0017] A matrix aggregation construction unit is used to extract multiple types of transaction feature vectors from the internal transaction information that has not completed cross-validation to form a judgment condition matrix aggregation;

[0018] A preliminary cross-validation unit, configured to obtain an internal transaction that has passed the preliminary cross-validation when it is determined that the cross-validation between the internal transactions recorded by at least two transaction parties has passed according to the judgment condition matrix aggregation;

[0019] The backtracking image unit is used to retrieve the data source image of the internal transaction that has passed the preliminary cross-verification from the source system database according to the identifier of the internal transaction that has passed the preliminary cross-verification;

[0020] A cloud image measurement processing unit is used to extract key elements from the data source image to form a cloud image measurement transaction element item set, match the cloud image measurement transaction element item sets corresponding to the internal transactions recorded by at least two transaction parties, and if the match is successful, obtain the internal transaction that has passed the preliminary cross-validation of the cloud image verification;

[0021] The cross-validation unit is optimized to mark the internal transactions that have passed the preliminary cross-validation as internal transactions that have completed the cross-validation and include them in the marked internal transaction database.

[0022] An embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned cross-verification processing method for internal transactions when executing the computer program.

[0023] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the cross-verification processing method for internal transactions is implemented.

[0024] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the cross-verification processing method for internal transactions is implemented.

[0025] Compared with the technical solution in the prior art that combines automatic data acquisition and manual identification to perform internal transaction cross-validation, which has low efficiency and accuracy, the cross-validation processing solution for internal transactions provided by the embodiment of the present invention has the following beneficial technical effects: an internal transaction database is pre-established from internal transaction information obtained from multiple source system databases, and internal transaction information that has not completed cross-validation is obtained from the internal transaction database; multiple types of transaction feature vectors are extracted from the internal transaction information that has not completed cross-validation to form a judgment condition matrix aggregation; based on the judgment condition matrix aggregation and the key elements extracted from the data source image, a cloud image metering transaction element item set is formed to perform cross-validation of internal transactions, which can realize efficient and accurate cross-validation of internal transactions, and improve the accuracy and efficiency of cross-validation processing of internal transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0027] Figure 1 Schematic diagram of the process flow of the cross-verification processing method for internal transactions in an embodiment of the present invention;

[0028] Figure 2 A schematic diagram of a flow chart of a cross-verification processing method for internal transactions in another embodiment of the present invention;

[0029] Figure 3 Schematic diagram of the pretreatment method in an embodiment of the present invention;

[0030] Figure 4 It is a flowchart of a processing method for marking judgment and table output in an embodiment of the present invention;

[0031] Figure 5 A schematic diagram of a flow chart of a cross-verification processing method for internal transactions in another embodiment of the present invention;

[0032] Figure 6 A schematic diagram of a flow chart of a cross-verification processing method for internal transactions in another embodiment of the present invention;

[0033] Figure 7 A schematic diagram of the process of the preliminary cross-validation and cloud graph verification processing method in an embodiment of the present invention;

[0034] Figure 8 It is a flowchart of a cloud graph restoration processing method for internal transactions in an embodiment of the present invention;

[0035] Fig. 9 It is a schematic diagram of the structure of the cross-verification processing device for internal transactions in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0037] The acquisition, transmission, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0038] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0039] Figure 1 FIG. 1 is a flow chart of a cross-validation processing method for internal transactions in an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0040] Step 101: Acquire internal transaction information that has not completed cross-validation from an internal transaction database; the internal transaction database is an internal transaction database pre-established based on internal transaction information acquired from multiple source system databases;

[0041] Step 102: extracting multiple types of transaction feature vectors from the internal transaction information that has not completed cross-validation to form a judgment condition matrix aggregation;

[0042] Step 103: when it is determined that the cross-validation between the internal transactions recorded by at least two transaction parties has passed according to the judgment condition matrix aggregation, the internal transactions that have passed the preliminary cross-validation are obtained;

[0043] Step 104: according to the identifier of the internal transaction that has passed the preliminary cross-verification, retrieve the data source image of the internal transaction that has passed the preliminary cross-verification from the source system database;

[0044] Step 105: extract key elements from the data source image to form a cloud image measurement transaction element item set, match the cloud image measurement transaction element item sets corresponding to the internal transactions recorded by at least two transaction parties, and if the match succeeds, obtain the internal transaction that has passed the preliminary cross-validation of the cloud image verification;

[0045] Step 106: Mark the internal transactions that have passed the preliminary cross-validation as internal transactions that have completed cross-validation and include them in the marked internal transaction database.

[0046] Compared with the technical solutions in the prior art that combine automatic data acquisition and manual identification to perform internal transaction cross-validation, which have low efficiency and accuracy, the beneficial technical effects of the internal transaction cross-validation processing method provided by the embodiment of the present invention are: pre-establishing an internal transaction database from internal transaction information obtained from multiple source system databases, obtaining internal transaction information that has not completed cross-validation from the internal transaction database; extracting multiple types of transaction feature vectors from the internal transaction information that has not completed cross-validation to form a judgment condition matrix aggregation; performing cross-validation of internal transactions based on the judgment condition matrix aggregation and extracting key elements from the data source image to form a cloud image metering transaction element item set, which can achieve efficient and accurate cross-validation of internal transactions and improve the accuracy and efficiency of cross-validation processing of internal transactions. Figures 2 to 9 The cross-validation processing method for the internal transaction is introduced in detail.

[0047] In order to facilitate understanding of the overall concept of the present invention, first, the overall solution of the embodiment of the present invention is introduced.

[0048] In the preprocessing step of the embodiment of the present invention, Figure 2 The steps implemented in the "preprocessing module" are: Figure 2 FIG. 1 is a flow chart of a cross-validation processing method for internal transactions in another embodiment of the present invention. Figure 2 After obtaining relatively complete internal transactions, the databases are stored in layers and databases according to different transaction types, and then aggregated into an internal transaction database, such as the "A institution's accounting system, business system and other source system databases" and the "B subsidiary's accounting system, business and other source system databases" shown in Figure 2 The "internal transaction database" shown in the figure is an internal transaction database pre-established based on internal transaction information obtained from multiple source system databases, and internal transaction identification is regularly rechecked to improve the efficiency of subsequent cross-validation work and the accuracy of matching. This pre-processing step can be performed as follows Figure 3 The preprocessing module process shown is implemented. Figure 3 Schematic diagram of the pretreatment method in an embodiment of the present invention.

[0049] In the above step 101, when obtaining the internal transaction information that has not completed cross-validation from the internal transaction database, firstly, Figure 2 The "marking judgment module" shown in FIG. 1 judges whether the acquired internal transaction is an unmarked transaction, that is, whether it is an internal transaction that has not completed cross-validation. This step 101 can be performed as follows: Figure 4 The marking judgment module process shown is implemented. Figure 4 The figure is a flowchart of a method for processing marking judgment and table output in an embodiment of the present invention.

[0050] The above steps 102 and 103 can be performed as follows Figure 2 The cross-validation module shown is used to realize this, that is, through the judgment condition matrix aggregation algorithm, the system automatically determines whether the cross-validation between internal transactions is passed, which can greatly reduce the workload of the existing system combining data acquisition and manual cross-validation of internal transactions and improve matching efficiency.

[0051] The above steps 104 and 105 can be performed as follows: Figure 2 The data cloud graph verification module shown is used to achieve this, that is, by tracing back the image information of the source system, extracting key elements for re-matching to verify whether the cross-validation result is correct, which can improve the matching accuracy, reduce the workload of manual inspection, and improve the efficiency of internal transaction cross-validation.

[0052] The above step 106 is as follows Figure 2 "Verification passed, marking" is shown.

[0053] In order to facilitate understanding of the present invention, it is described in detail below with reference to examples.

[0054] 1) The various levels of the enterprise and its subsidiaries, from the perspective of each of their own transaction entities, capture internal transaction data from multiple source system databases, such as financial accounting systems, front-end business systems, and other source systems (corporate credit systems, financial market systems, interbank asset management systems, financial systems, deposit systems, and centralized procurement systems), and aggregate them into a source database. Figure 3 As shown in the figure, the source database executes the preset preprocessing module, that is, after obtaining relatively complete internal transactions through algorithms such as cleaning non-internal transactions, radar measurement, and neural network model supplementary factor items, it is stored in layers and databases according to different transaction types, and aggregated to form an internal transaction database. The purpose of preprocessing is to improve the efficiency of subsequent cross-validation work.

[0055] In addition, the preprocessing module can also be used to regularly recheck transaction data in the source database that has not been identified as internal transactions to determine whether the previous identification results are incorrect, thereby improving the accuracy of internal transaction cross-matching.

[0056] 2) If Figure 4 As shown, the marking judgment module screens, sorts and marks the internal transactions in the internal transaction database on a daily basis. If the screening finds that a certain internal transaction has been marked, that is, the internal transaction has completed cross-verification, then the transaction will enter the marked internal transaction database and wait for further processing by the subsequent consolidated data output disclosure module; if the screening finds that a certain internal transaction has not been marked, that is, the internal transaction has not completed cross-verification, then the transaction will execute the cross-verification module.

[0057] 3) The connotation of the cross-validation module is: extract 9 types of transaction feature vectors from the unlabeled internal transaction information, which can be respectively: vector 1: amount vector (including transaction contract amount, transaction amount, transaction balance), vector 2: price vector (transaction price-amount, transaction price-non-amount), vector 3: transaction debit and credit direction vector, vector 4: transaction date difference vector, vector 5: transaction contract number vector, vector 6: transaction summary vector, vector 7: COA account number vector, vector 8: transaction subject vector, vector 9: currency vector (including transaction currency, transaction price currency). The 9 types of vectors form matrices in a variety of different combinations, and form the first layer of matrix groups after disambiguation. In the first layer of matrix groups, each 2 matrices are respectively used to form a new matrix according to matrix operations (operation methods include addition, subtraction, multiplication, transposition, conjugation, conjugate transposition, etc.), and the second layer of matrix groups are formed after disambiguation. By analogy, after the multi-layer matrix group is aggregated and disambiguated, it forms the judgment condition matrix aggregation, which is the cross-validation module. In specific implementation, the disambiguation processing in the embodiment of the present invention refers to eliminating text that cannot be interpreted or understood by the machine and resolving language ambiguity, that is, performing multi-level language analysis on the matrix to handle ambiguity, improving the accuracy of aggregation of the judgment condition matrix, and further improving the accuracy of subsequent internal transaction cross-validation.

[0058] From the above, it can be seen that in one embodiment, Figure 5 FIG. 1 is a flow chart of a cross-verification processing method for internal transactions in another embodiment of the present invention. Figure 5 As shown, extracting multiple types of transaction feature vectors from the internal transaction information that has not completed cross-validation to form a judgment condition matrix aggregation may include the following steps:

[0059] Step 201: extracting multiple types of transaction feature vectors from internal transaction information that has not completed cross-validation;

[0060] Step 202: multiple types of transaction feature vectors are combined in different ways to form a matrix;

[0061] Step 203: The matrices formed by different combinations are subjected to disambiguation processing to form a first-layer matrix group;

[0062] Step 204: Using the first layer matrix group as the initial layer matrix group, cyclically execute the following steps of forming the judgment condition matrix aggregation, and perform the following operations in each cycle:

[0063] Step 2041: Obtain the current periodic layer matrix group;

[0064] Step 2042: construct a new matrix using every two matrices in the current periodic layer matrix group according to matrix operations, and after disambiguation processing, multiple new matrices constitute the next periodic layer matrix group;

[0065] Step 2043: When the next period layer matrix group does not meet the preset termination condition, the next period layer matrix group is used as the current period layer matrix group to perform the next period operation until the next period layer matrix group meets the preset termination condition, and the judgment condition matrix aggregation is obtained.

[0066] In specific implementation, the above-mentioned implementation method of constituting the aggregation of the judgment condition matrix can further improve the efficiency and accuracy of cross-validation.

[0067] The cross-validation module is run for unmarked internal transactions. If the cross-validation is judged to be passed, it means that the two or more internal transactions have been preliminarily matched. The data cloud map verification module is continued to be run to verify whether the secondary match is passed. The cross-validation module is run for unmarked internal transactions. If the cross-validation is judged to be failed, it means that the two or more internal transactions have been preliminarily matched. The data cloud map restoration module is run instead.

[0068] 4) The connotation of the data cloud map verification module is: the internal transactions that have passed the cross-verification are traced back to the source systems such as the financial accounting system or the front-end business system according to the invoice number or business voucher number, and the data source images (including invoice images, business voucher images, contract images, etc.) are retrieved. The source images are analyzed by cloud computing algorithms, and the key fields (elements) are extracted to form a cloud image measurement transaction element item set. The cloud image measurement transaction element item set is matched again to verify whether the previous cross-verification is accurate.

[0069] In the specific implementation, in order to facilitate understanding of how to implement it, an example is given below for the above-mentioned cloud computing algorithm and cloud image metering transaction element item set: the data source image is abstracted into a cloud virtual resource vector, and the element set formed by extracting key elements from the cloud virtual resource vector is a cloud image metering transaction element item set, which can improve resource utilization, thereby improving the internal performance of the computer running the internal transaction cross-validation method of the present application, improving operating efficiency, and further improving the efficiency and accuracy of internal transaction cross-validation.

[0070] If the internal transaction that has passed the cross-validation passes the data cloud map verification module during re-verification, it means that the internal transaction is matched again and the confidence level of the correct match is high. The internal transaction can be marked and included in the marked internal transaction database, waiting for further processing by the subsequent consolidated data output disclosure module. If the internal transaction that has passed the cross-validation fails to pass the data cloud map verification module during re-verification, it means that the initial matching result is incorrect, and the internal transaction will be run through the data cloud map restoration module instead.

[0071] 5) Run the consolidated data output and disclosure module for the marked internal transactions included in the marked internal transaction database through step 2) or 4). The meaning of the consolidated data output and disclosure module is: after screening and extracting all the marked internal transactions with non-zero debit amounts, form unilateral consolidated data, which will be used to submit the group's consolidated management information to the regulatory authorities and disclose the group's related information to the outside. The consolidated data formed after running the consolidated data output and disclosure module can eliminate the problems of duplicate data submission and duplicate disclosure, and improve the accuracy of the consolidated data.

[0072] 6) For internal transactions that fail to pass step 3) or 4), run the data cloud map restoration module. The connotation of the data cloud map restoration module is: internal transactions are traced back to the source system such as the financial accounting system or the front-end business system according to the invoice number or business voucher number, and the data source image (including invoice image, business voucher image, contract image, etc.) is retrieved. The source image is analyzed through cloud computing and other algorithms to extract key elements (which may include: counterparty, counterparty group, transaction contract number, transaction contract amount, transaction amount, transaction balance, transaction price-amount, transaction price-non-amount, transaction date, transaction subject, transaction summary, COA account number, transaction currency, transaction price currency, etc.) and then build the cloud image measurement transaction element knowledge graph. By running the regression function, the edge features of the cloud image measurement transaction element knowledge graph can be restored to "regression internal transaction", and three matching and collision operations are performed in sequence. For details of the three matching and collision operations, see Figure 8 introduce.

[0073] The first matching collision is based on the internal transactions of Institution A (the first transaction party, the transaction initiator) that have not passed the cross-validation module and / or the data cloud map verification module, and the internal transactions of Subsidiary B (the second transaction party, the transaction counterparty) that have not passed the cross-validation module or the data cloud map verification module are run. The "regressive internal transactions" formed by the data cloud map restoration module are matched and collided with a certain logic (which can be random logic) to test whether the matching collision passes. If the matching collision passes, the human-machine training module is run; if the matching collision fails, the second matching collision is run.

[0074] The second matching collision is based on the internal transactions of subsidiary B that have not passed the cross-validation module or the data cloud map verification module, and performs a certain logic (can be random logic) matching collision with the internal transactions of institution A that have not passed the cross-validation module or the data cloud map verification module and the "regression internal transactions" restored after running the data cloud map restoration module to test whether the matching collision passes. If the matching collision passes, the human-machine training module is run; if the matching collision fails, the third matching collision is run.

[0075] The third matching collision is based on the "returning internal transactions" of Institution A that have not passed the cross-validation module or the data cloud map verification module, which are restored after the data cloud map restoration module is run. The matching collision is performed with the "returning internal transactions" of Subsidiary B that have not passed the cross-validation module or the data cloud map verification module, which are restored after the data cloud map restoration module is run. The matching collision is performed with a certain logic (which can be random logic) to test whether the matching collision is passed. If the matching collision is passed, the human-machine training module is run; if the matching collision is not passed, the internal transaction is temporarily stored in the retained internal transaction database.

[0076] From the above, it can be seen that in one embodiment, Figure 6 FIG. 1 is a flow chart of a cross-verification processing method for internal transactions in another embodiment of the present invention. Figure 6 As shown, the cross-validation processing method for the above internal transactions may also include the following cloud map restoration and matching operation steps for internal transactions that fail the preliminary cross-validation (internal transactions that fail the cross-validation module) and for internal transactions that fail the preliminary cross-validation (internal transactions that fail the data cloud map verification module):

[0077] Step 301: when it is determined that the cross-validation between the internal transactions recorded by at least two transaction parties fails according to the judgment condition matrix aggregation, the internal transactions that fail the preliminary cross-validation are obtained;

[0078] Step 302: when the cloud image metering transaction element item set corresponding to the internal transactions recorded by at least two transaction parties fails to match, the internal transactions that fail the preliminary cross-verification are obtained;

[0079] Step 303: for the internal transactions belonging to the transaction initiator that have not passed the preliminary cross-validation and the internal transactions that have not passed the preliminary cross-validation, retrieve the data source image of each transaction initiator of the internal transactions belonging to the transaction initiator that have not passed the preliminary cross-validation and the internal transactions that have not passed the preliminary cross-validation from the source system database;

[0080] Step 304: for the internal transactions belonging to the transaction counterparties that have not passed the preliminary cross-validation and the internal transactions that have not passed the preliminary cross-validation, retrieve the data source image of each transaction counterparty of the internal transactions belonging to the transaction counterparties that have not passed the preliminary cross-validation and the internal transactions that have not passed the preliminary cross-validation from the source system database;

[0081] Step 305: extract key elements from the data source image of each transaction initiator and construct a knowledge graph of cloud image measurement transaction elements of each transaction initiator;

[0082] Step 306: extracting key elements from the data source image of each transaction counterparty and constructing a knowledge graph of cloud image measurement transaction elements of each transaction counterparty;

[0083] Step 307: restore each edge feature of the cloud image measurement transaction element item knowledge graph of each transaction initiator to the regressed internal transaction of each transaction initiator;

[0084] Step 308: Restore each edge feature of the cloud image measurement transaction element item knowledge graph of each transaction counterparty to the regressed internal transaction of each transaction counterparty;

[0085] Step 309: Based on the internal transactions that failed the initial cross-validation and the internal transactions that failed the verification initial cross-validation belonging to the transaction initiator, the internal transactions that failed the initial cross-validation and the internal transactions that failed the verification initial cross-validation belonging to the transaction counterparty, the regressed internal transactions of each transaction initiator, and the regressed internal transactions of each transaction counterparty, matching processing is performed to obtain the internal transactions that finally passed the cross-validation.

[0086] In specific implementation, the cloud graph restoration and matching operations for internal transactions that failed the initial cross-validation and the verification of internal transactions that failed the initial cross-validation can further improve the accuracy of internal transaction cross-validation.

[0087] As can be seen from the above, in one embodiment, according to the internal transactions that failed the initial cross-validation and the internal transactions that failed the initial cross-validation of the transaction initiator, the internal transactions that failed the initial cross-validation and the internal transactions that failed the initial cross-validation of the transaction counterparty, the regressed internal transactions of each transaction initiator, and the regressed internal transactions of each transaction counterparty, matching processing is performed to obtain the internal transactions that finally passed the cross-validation, which may include:

[0088] Based on the internal transactions that failed the initial cross-validation and the internal transactions that failed the initial cross-validation belonging to the transaction initiator, the first matching is performed with the regressed internal transactions of each transaction counterparty. When the first matching is successful, the internal transactions that finally passed the cross-validation are obtained.

[0089] In specific implementation, the above-mentioned first matching operation for internal transactions that failed the initial cross-validation and the verification of internal transactions that failed the initial cross-validation can further improve the accuracy of internal transaction cross-validation.

[0090] As can be seen from the above, in one embodiment, the cross-verification processing method for internal transactions may further include:

[0091] When the first matching fails, the internal transactions that failed the initial cross-validation and the internal transactions that failed the initial cross-validation belonging to the counterparty are used as the basis for a second matching with the regressed internal transactions of each transaction initiator. When the second matching passes, the internal transactions that finally passed the cross-validation are obtained.

[0092] In specific implementation, the above second matching operation for internal transactions that failed the initial cross-validation and verifying internal transactions that failed the initial cross-validation can further improve the accuracy of internal transaction cross-validation.

[0093] From the above, it can be seen that in one embodiment, the cross-verification processing method of the internal transaction can also include:

[0094] When the second matching fails, the regressed internal transaction of each transaction initiator is used as a benchmark, and a third matching is performed with the regressed internal transaction of each transaction counterparty. When the third matching passes, the final cross-validated internal transaction is obtained.

[0095] In specific implementation, the third matching operation for internal transactions that failed the initial cross-validation and the verification of internal transactions that failed the initial cross-validation can further improve the accuracy of internal transaction cross-validation.

[0096] In specific implementation, steps 301 to 309 can be performed as follows: Figure 2 The data cloud map restoration module shown in the figure is used to achieve this, that is, by tracing back the image information of the source system, extracting key elements and constructing a cloud image measurement transaction element knowledge graph, and running the preset regression function to match and collide internal transactions that have not passed cross-validation or cloud map verification to increase the matching success rate.

[0097] The stranded internal transaction database is judged once a day, and the judgment condition is "Is the data loading date of the stranded internal transaction database close to the reporting and supervision deadline?" If it is judged to be close, a risk reminder will be pushed to the two trading entities of the stranded internal transaction, and the reminder content will be "The internal transaction with contract number XXX between your company and XXX counterparty is suspected of misreporting / omission, and the automatic cross-validation has not been completed within the XXX time limit. Please deal with it in time"; if it is judged not to be close, the stranded transaction will be merged into the internal transaction that failed to pass the cross-validation module or the data cloud map verification module on the next day, and the data cloud map restoration module will be repeatedly run until the matching collision is passed and the human-computer training module is run, or the data loading date of the stranded internal transaction database is close to the reporting and supervision deadline, and a risk reminder will be pushed.

[0098] As can be seen from the above, in one embodiment, the cross-verification processing method of the internal transaction may further include: when the third matching fails, temporarily storing the internal transaction in a retained internal transaction database;

[0099] The database of stranded internal transactions is tested once a day. When it is detected that the data loading date of the database is approaching the reporting and supervision deadline, risk warning information is pushed to both transaction entities of the stranded internal transaction.

[0100] During specific implementation, the above specific implementation method for handling retained internal transactions can further improve the accuracy of internal transaction cross-verification.

[0101] From the above, it can be seen that in one embodiment, the cross-validation processing method of the above internal transactions may also include: when it is detected that the data loading date of the retained internal transaction database is not close to the reporting and supervision deadline date, performing a cloud map restoration and matching operation of the internal transaction on the next day for the retained internal transaction.

[0102] For specific implementation, please refer to the above steps for detecting and storing stranded transactions. Figure 2 Implementation plan for the stranded internal transaction database in.

[0103] 7) For internal transactions that have passed the matching collision, the human-machine training module is run, that is, the matching collision is manually checked to see if it is accurate. If the human-machine training is passed, it means that the matching collision in the data cloud map restoration module is accurate, and the confidence that the internal transaction matching is correct is high. The internal transaction can be marked and included in the marked internal transaction database, waiting for further processing by the subsequent consolidated data output disclosure module; if the human-machine training is not passed, it means that the matching collision in the data cloud map restoration module is inaccurate, and the inspection results are fed back to the system, which automatically initiates optimization, and the internal transactions that have not passed the human-machine training are temporarily stored in the retained internal transaction database, waiting for the data cloud map restoration module to be run again the next day.

[0104] From the above, it can be seen that in one embodiment, the cross-validation processing method of the internal transaction may further include: if the match is successful, sending the internal transaction that has passed the match to the terminal of the business personnel to verify the matching result.

[0105] In specific implementation, this step can be performed as follows Figure 2 The human-machine training module shown is implemented, that is, the matching collision is manually checked to see if it is accurate, and the test results are fed back to the system to improve the rationality and accuracy of the system's automatic matching parameter settings.

[0106] As can be seen from the above, in one embodiment, the cross-verification processing method for internal transactions may further include:

[0107] The marked internal transactions included in the marked internal transaction database will be formed into consolidated data and pushed to the regulatory agency.

[0108] In specific implementation, this step can be performed as follows Figure 2 The consolidated data output and disclosure module shown in the figure is used to realize the consolidated data. The consolidated data output and disclosure module is to screen and extract unilateral transactions from all matched and marked internal transactions to form consolidated data, which is used to subsequently submit group consolidated management information to regulatory agencies and disclose group-related information to the outside world. The consolidated data formed after running the consolidated data output and disclosure module can eliminate the problems of repeated data submission and disclosure, improve the accuracy of consolidated data, and reduce the difficulty of purely manual matching of transactions and compilation of consolidated data.

[0109] In the specific implementation, the specific implementation plans of the above cross-validation module, data cloud map verification module, and human-machine training module are detailed in Figure 7 As shown, Figure 7 Schematic diagram of the process of preliminary cross-validation and cloud map verification processing method in the embodiment of the present invention. Figure 8 As shown, Figure 8 It is a flowchart of a cloud graph restoration processing method for internal transactions in an embodiment of the present invention.

[0110] 8) Parameterized dynamic adjustment:

[0111] Dynamically adjust the extreme value of vector 4 (transaction date difference vector) in the judgment condition matrix aggregation of the cross-validation module according to the maximum transaction date difference between two random transactions retained in the internal transaction database; that is, in one embodiment, the cross-validation processing method of the above internal transactions may also include: dynamically adjust the extreme value of the transaction date difference vector in the judgment condition matrix aggregation according to the maximum transaction date difference between two random transactions retained in the internal transaction database, which can further improve the accuracy of internal transaction cross-validation.

[0112] A normal distribution graph is dynamically prepared based on the transaction date differences of the matched marked transactions, and the date difference distribution probability is fed back in sequence from large to small as the priority choice of vector 4 (transaction date difference vector) in the judgment condition matrix aggregation of the cross-validation module. That is, in one embodiment, the cross-validation processing method of the above-mentioned internal transactions may also include: dynamically preparing a normal distribution graph based on the transaction date differences of the internal transactions that have completed cross-validation, and feeding back in sequence from large to small as the priority choice of the transaction date difference vector in the judgment condition matrix aggregation according to the distribution probability of the date difference in the normal distribution graph, which can further improve the accuracy of internal transaction cross-validation.

[0113] The embodiment of the present invention also provides a cross-verification processing device for internal transactions, as described in the following embodiment. Since the principle of solving the problem by the device is similar to the cross-verification processing method for internal transactions, the implementation of the device can refer to the implementation of the cross-verification processing method for internal transactions, and the repeated parts will not be repeated.

[0114] Fig. 9 FIG. 1 is a schematic diagram of the structure of a cross-verification processing device for internal transactions in an embodiment of the present invention. Fig. 9 As shown, the device comprises:

[0115] An acquisition unit 01 is used to acquire internal transaction information that has not completed cross-validation from an internal transaction database; the internal transaction database is an internal transaction database pre-established based on internal transaction information acquired from multiple source system databases;

[0116] Matrix aggregation construction unit 02, used to extract multiple types of transaction feature vectors from the internal transaction information that has not completed cross-validation to form a judgment condition matrix aggregation;

[0117] A preliminary cross-validation unit 03, configured to obtain an internal transaction that has passed the preliminary cross-validation when it is determined that the cross-validation between the internal transactions recorded by at least two transaction parties has passed according to the judgment condition matrix aggregation;

[0118] The backtracking image unit 04 is used to retrieve the data source image of the internal transaction that has passed the preliminary cross-verification from the source system database according to the identifier of the internal transaction that has passed the preliminary cross-verification;

[0119] The cloud image measurement processing unit 05 is used to extract key elements from the data source image to form a cloud image measurement transaction element item set, match the cloud image measurement transaction element item sets corresponding to the internal transactions recorded by at least two transaction parties, and if the match is successful, obtain the internal transaction that has passed the preliminary cross-validation of the cloud image verification;

[0120] The optimized cross-validation unit 06 is used to mark the internal transactions that have passed the preliminary cross-validation as internal transactions that have completed the cross-validation and then include them into the marked internal transaction database.

[0121] In one embodiment, the matrix aggregation construction unit is specifically used for:

[0122] Extracting various types of transaction feature vectors from internal transaction information that has not completed cross-validation;

[0123] Multiple types of transaction feature vectors are combined into matrices in different ways;

[0124] The matrices formed by different combinations are processed by disambiguation to form the first-layer matrix group;

[0125] The first layer matrix group is used as the initial layer matrix group, and the following steps of forming the judgment condition matrix aggregation are cyclically executed. The following operations are performed in each cycle:

[0126] Get the current periodic layer matrix group;

[0127] Each two matrices in the current periodic layer matrix group are used to form a new matrix according to matrix operations, and multiple new matrices are processed by disambiguation to form the next periodic layer matrix group;

[0128] When the next period layer matrix group does not meet the preset termination condition, the next period layer matrix group is used as the current period layer matrix group to perform the next period operation until the next period layer matrix group meets the preset termination condition, and the judgment condition matrix aggregation is obtained.

[0129] In one embodiment, the cross-validation processing device for internal transactions may further include a cloud graph restoration unit for performing the following cloud graph restoration and matching operations for internal transactions that have failed the preliminary cross-validation and for verifying internal transactions that have failed the preliminary cross-validation:

[0130] When it is determined that the cross-validation between the internal transactions recorded by at least two transaction parties fails according to the judgment condition matrix aggregation, the internal transactions that fail the preliminary cross-validation are obtained;

[0131] When the cloud image measurement transaction element item set corresponding to the internal transactions recorded by at least two transaction parties fails to match, the internal transaction that fails the preliminary cross-verification is obtained;

[0132] For the internal transactions that failed the initial cross-verification and the internal transactions that failed the initial cross-verification belonging to the transaction initiator, retrieve the data source image of each transaction initiator of the internal transactions that failed the initial cross-verification and the internal transactions that failed the initial cross-verification from the source system database;

[0133] For the internal transactions belonging to the transaction counterparties that have not passed the preliminary cross-validation and the internal transactions that have not passed the preliminary cross-validation, retrieve the data source image of each transaction counterparty of the internal transactions belonging to the transaction counterparties that have not passed the preliminary cross-validation and the internal transactions that have not passed the preliminary cross-validation from the source system database;

[0134] After extracting key elements from the data source image of each transaction initiator, a knowledge graph of cloud image measurement transaction elements of each transaction initiator is constructed;

[0135] After extracting key elements from the data source image of each transaction counterparty, a knowledge graph of cloud image measurement transaction elements of each transaction counterparty is constructed;

[0136] Restore each edge feature of the cloud image measurement transaction element knowledge graph of each transaction initiator to the regressed internal transaction of each transaction initiator;

[0137] Restore the edge features of the knowledge graph of cloud image measurement transaction elements of each transaction counterparty to the regressed internal transaction of each transaction counterparty;

[0138] Based on the internal transactions that failed the initial cross-validation and the internal transactions that failed the initial cross-validation belonging to the transaction initiator, the internal transactions that failed the initial cross-validation and the internal transactions that failed the initial cross-validation belonging to the transaction counterparty, the regressed internal transactions of each transaction initiator, and the regressed internal transactions of each transaction counterparty, matching processing is performed to obtain the internal transactions that finally passed the cross-validation.

[0139] In one embodiment, according to the internal transactions that failed the initial cross-validation and the internal transactions that failed the initial cross-validation of the transaction initiator, the internal transactions that failed the initial cross-validation and the internal transactions that failed the initial cross-validation of the transaction counterparty, the regressive internal transactions of each transaction initiator, and the regressive internal transactions of each transaction counterparty, matching processing is performed to obtain the internal transactions that finally passed the cross-validation, which may include:

[0140] Based on the internal transactions that failed the initial cross-validation and the internal transactions that failed the initial cross-validation belonging to the transaction initiator, the first matching is performed with the regressed internal transactions of each transaction counterparty. When the first matching is successful, the internal transactions that finally passed the cross-validation are obtained.

[0141] In one embodiment, according to the internal transactions that failed the initial cross-validation and the internal transactions that failed the initial cross-validation of the transaction initiator, the internal transactions that failed the initial cross-validation and the internal transactions that failed the initial cross-validation of the transaction counterparty, the regressed internal transactions of each transaction initiator, and the regressed internal transactions of each transaction counterparty, matching processing is performed to obtain the internal transactions that finally passed the cross-validation, which may also include:

[0142] When the first matching fails, the internal transactions that failed the initial cross-validation and the internal transactions that failed the initial cross-validation belonging to the counterparty are used as the basis for a second matching with the regressed internal transactions of each transaction initiator. When the second matching passes, the internal transactions that finally passed the cross-validation are obtained.

[0143] In one embodiment, according to the internal transactions that failed the initial cross-validation and the internal transactions that failed the initial cross-validation of the transaction initiator, the internal transactions that failed the initial cross-validation and the internal transactions that failed the initial cross-validation of the transaction counterparty, the regressed internal transactions of each transaction initiator, and the regressed internal transactions of each transaction counterparty, matching processing is performed to obtain the internal transactions that finally passed the cross-validation, which may also include:

[0144] When the second matching fails, the regressed internal transaction of each transaction initiator is used as a benchmark, and a third matching is performed with the regressed internal transaction of each transaction counterparty. When the third matching passes, the final cross-validated internal transaction is obtained.

[0145] In one embodiment, the cross-verification processing device for internal transactions may further include a retention processing unit, which is used to:

[0146] When the third match fails, the internal transaction is temporarily stored in the retained internal transaction database;

[0147] The database of stranded internal transactions is tested once a day. When it is detected that the data loading date of the database is approaching the reporting and supervision deadline, risk warning information is pushed to both transaction entities of the stranded internal transaction.

[0148] In one embodiment, the above-mentioned retention processing unit can also be used to: when it is detected that the data loading date of the retained internal transaction database is not close to the reporting and supervision deadline date, perform the cloud graph restoration and matching operation of the internal transaction on the next day for the retained internal transaction.

[0149] In one embodiment, the cross-validation processing method for internal transactions may further include a dynamic adjustment unit for:

[0150] According to the maximum transaction date difference between two random transactions retained in the internal transaction database, the extreme value of the transaction date difference vector in the judgment condition matrix aggregation is dynamically adjusted.

[0151] In one embodiment, the dynamic adjustment unit is further used to:

[0152] According to the transaction date differences of the internal transactions that have completed cross-validation, a normal distribution graph is dynamically prepared, and according to the distribution probability of the date difference in the normal distribution graph, it is fed back in sequence from large to small as the priority choice of the transaction date difference vector in the judgment condition matrix aggregation.

[0153] In one embodiment, the cross-validation processing method for internal transactions may further include a push unit for: forming consolidated data of the marked internal transactions included in the marked internal transaction database and then pushing the consolidated data to the regulatory agency.

[0154] In one embodiment, the cross-verification processing method of the internal transaction may further include a sending unit, which is used to send the matched internal transaction to the terminal of the business personnel to verify the matching result if the matching is successful.

[0155] An embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the computer program.

[0156] An embodiment of the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned cross-verification processing method for internal transactions when executing the computer program.

[0157] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the cross-verification processing method for internal transactions is implemented.

[0158] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the cross-verification processing method for internal transactions is implemented.

[0159] Compared with the technical solution in the prior art that combines automatic data acquisition and manual identification to perform internal transaction cross-validation, which has low efficiency and accuracy, the cross-validation processing solution for internal transactions provided by the embodiment of the present invention has the following beneficial technical effects: an internal transaction database is pre-established from internal transaction information obtained from multiple source system databases, and internal transaction information that has not completed cross-validation is obtained from the internal transaction database; multiple types of transaction feature vectors are extracted from the internal transaction information that has not completed cross-validation to form a judgment condition matrix aggregation; based on the judgment condition matrix aggregation and the key elements extracted from the data source image, a cloud image metering transaction element item set is formed to perform cross-validation of internal transactions, which can realize efficient and accurate cross-validation of internal transactions, and improve the accuracy and efficiency of cross-validation processing of internal transactions.

[0160] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0161] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0162] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0163] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0164] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A cross-validation processing method for internal transactions, characterized in that: include: Acquire internal transaction information that has not completed cross-validation from an internal transaction database; the internal transaction database is an internal transaction database pre-established based on internal transaction information acquired from multiple source system databases; Extracting multiple types of transaction feature vectors from the internal transaction information that has not completed cross-validation to form a judgment condition matrix aggregation; When it is determined that the cross-validation between the internal transactions recorded by at least two transaction parties has passed according to the judgment condition matrix aggregation, the internal transactions that have passed the preliminary cross-validation are obtained; According to the identifier of the internal transaction that has passed the preliminary cross-verification, the data source image of the internal transaction that has passed the preliminary cross-verification is retrieved from the source system database; After extracting key elements from the data source image, a cloud image measurement transaction element item set is formed, and the cloud image measurement transaction element item sets corresponding to the internal transactions recorded by at least two transaction parties are matched. If the match is successful, the internal transaction that has passed the preliminary cross-validation of the cloud image verification is obtained; The internal transactions that have passed the preliminary cross-validation are marked as internal transactions that have completed cross-validation and included in the marked internal transaction database.

2. The method according to claim 1, characterized in that Extract various types of transaction feature vectors from the internal transaction information that has not completed cross-validation to form a judgment condition matrix aggregation, including: Extracting various types of transaction feature vectors from internal transaction information that has not completed cross-validation; Multiple types of transaction feature vectors are combined into matrices in different ways; The matrices formed by different combinations are processed by disambiguation to form the first-layer matrix group; The first layer matrix group is used as the initial layer matrix group, and the following steps of forming the judgment condition matrix aggregation are cyclically executed. The following operations are performed in each cycle: Get the current periodic layer matrix group; Each two matrices in the current periodic layer matrix group are used to form a new matrix according to matrix operations, and multiple new matrices are processed by disambiguation to form the next periodic layer matrix group; When the next period layer matrix group does not meet the preset termination condition, the next period layer matrix group is used as the current period layer matrix group to perform the next period operation until the next period layer matrix group meets the preset termination condition, and the judgment condition matrix aggregation is obtained.

3. The method according to claim 1, characterized in that It also includes the following cloud graph restoration and matching operations for internal transactions that fail the initial cross-validation and internal transactions that fail the initial cross-validation: When it is determined that the cross-validation between the internal transactions recorded by at least two transaction parties fails according to the judgment condition matrix aggregation, the internal transaction that fails the preliminary cross-validation is obtained; When the cloud image measurement transaction element item set corresponding to the internal transactions recorded by at least two transaction parties fails to match, the internal transaction that fails the preliminary cross-verification is obtained; For the internal transactions that failed the initial cross-verification and the internal transactions that failed the initial cross-verification belonging to the transaction initiator, retrieve the data source image of each transaction initiator of the internal transactions that failed the initial cross-verification and the internal transactions that failed the initial cross-verification from the source system database; For the internal transactions belonging to the transaction counterparties that have not passed the preliminary cross-validation and the internal transactions that have not passed the preliminary cross-validation, retrieve the data source image of each transaction counterparty of the internal transactions belonging to the transaction counterparties that have not passed the preliminary cross-validation and the internal transactions that have not passed the preliminary cross-validation from the source system database; After extracting key elements from the data source image of each transaction initiator, a knowledge graph of cloud image measurement transaction elements of each transaction initiator is constructed; After extracting key elements from the data source image of each transaction counterparty, a knowledge graph of cloud image measurement transaction elements of each transaction counterparty is constructed; Restore the edge features of the cloud image measurement transaction element knowledge graph of each transaction initiator to the regressed internal transaction of each transaction initiator; Restore the edge features of the knowledge graph of cloud image measurement transaction elements of each transaction counterparty to the regressed internal transaction of each transaction counterparty; Based on the internal transactions that failed the initial cross-validation and the internal transactions that failed the initial cross-validation belonging to the transaction initiator, the internal transactions that failed the initial cross-validation and the internal transactions that failed the initial cross-validation belonging to the transaction counterparty, the regressed internal transactions of each transaction initiator, and the regressed internal transactions of each transaction counterparty, matching processing is performed to obtain the internal transactions that finally passed the cross-validation.

4. The method according to claim 3, characterized in that According to the internal transactions that failed the initial cross-validation and the internal transactions that failed the initial cross-validation of the transaction initiator, the internal transactions that failed the initial cross-validation and the internal transactions that failed the initial cross-validation of the transaction counterparty, the regressive internal transactions of each transaction initiator, and the regressive internal transactions of each transaction counterparty, the internal transactions that finally passed the cross-validation are matched and processed, including: Based on the internal transactions that failed the initial cross-validation and the internal transactions that failed the initial cross-validation belonging to the transaction initiator, the first matching is performed with the regressed internal transactions of each transaction counterparty. When the first matching is successful, the internal transactions that finally passed the cross-validation are obtained.

5. The method according to claim 4, characterized in that Also includes: When the first matching fails, the internal transactions that failed the initial cross-validation and the internal transactions that failed the initial cross-validation belonging to the counterparty are used as the basis for a second matching with the regressed internal transactions of each transaction initiator. When the second matching passes, the internal transactions that finally passed the cross-validation are obtained.

6. The method according to claim 5, characterized in that Also includes: When the second matching fails, the regressed internal transaction of each transaction initiator is used as a benchmark, and a third matching is performed with the regressed internal transaction of each transaction counterparty. When the third matching passes, the final cross-validated internal transaction is obtained.

7. The method according to claim 6, characterized in that Also includes: When the third match fails, the internal transaction is temporarily stored in the stranded internal transaction database; The database of stranded internal transactions is tested once a day. When it is detected that the data loading date of the database is approaching the reporting and supervision deadline, risk warning information is pushed to both transaction entities of the stranded internal transaction.

8. The method according to claim 7, characterized in that Also includes: When it is detected that the data loading date of the retained internal transaction database is not close to the reporting and supervision deadline, the cloud graph restoration and matching operation of the retained internal transaction is performed on the next day.

9. The method according to claim 7, characterized in that Also includes: According to the maximum transaction date difference between two random transactions retained in the internal transaction database, the extreme value of the transaction date difference vector in the judgment condition matrix aggregation is dynamically adjusted.

10. The method according to claim 1, characterized in that Also includes: According to the transaction date differences of the internal transactions that have completed cross-validation, a normal distribution graph is dynamically prepared, and according to the distribution probability of the date difference in the normal distribution graph, it is fed back in sequence from large to small as the priority choice of the transaction date difference vector in the judgment condition matrix aggregation.

11. The method according to claim 1, characterized in that Also includes: The marked internal transactions included in the marked internal transaction database will be formed into consolidated data and pushed to the regulatory agency.

12. The method according to any one of claims 1 to 11, characterized in that: Also includes: If the match is successful, the internal transaction that has passed the match will be sent to the business personnel's terminal to verify the matching result.

13. A cross-verification processing device for internal transactions, characterized in that: include: An acquisition unit is used to acquire the internal transaction information that has not completed cross-validation from an internal transaction database; the internal transaction database is an internal transaction database pre-established based on the internal transaction information acquired from multiple source system databases; A matrix aggregation construction unit is used to extract multiple types of transaction feature vectors from the internal transaction information that has not completed cross-validation to form a judgment condition matrix aggregation; A preliminary cross-validation unit, configured to obtain an internal transaction that has passed the preliminary cross-validation when it is determined that the cross-validation between the internal transactions recorded by at least two transaction parties has passed according to the judgment condition matrix aggregation; The backtracking image unit is used to retrieve the data source image of the internal transaction that has passed the preliminary cross-verification from the source system database according to the identifier of the internal transaction that has passed the preliminary cross-verification; A cloud image measurement processing unit is used to extract key elements from the data source image to form a cloud image measurement transaction element item set, match the cloud image measurement transaction element item sets corresponding to the internal transactions recorded by at least two transaction parties, and if the match is successful, obtain the internal transaction that has passed the preliminary cross-validation of the cloud image verification; The cross-validation unit is optimized to mark the internal transactions that have passed the preliminary cross-validation as internal transactions that have completed the cross-validation and include them in the marked internal transaction database.

14. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 12 is implemented.

15. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 12 is implemented.

16. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 12 is implemented.

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