Data processing method, device, electronic device and medium

Abstract

The embodiments of the present application provide a data processing method, device, electronic device and medium, which are intended to improve the efficiency and accuracy of valuation data storage. The method includes: obtaining original valuation data from a node belonging to a first role, wherein the original valuation data is valuation data belonging to a target annuity plan; the original valuation data includes valuation detail data of multiple assets; reconstructing the valuation detail data of the multiple assets multiple times according to different classification granularities in turn to obtain target valuation data that meets the target classification standard, wherein the nth reconstruction is performed on the valuation data obtained by the n-1th reconstruction; n is a positive integer greater than or equal to 1; uploading the target valuation data to a node belonging to a second role; wherein the first role is the custodian role in the target annuity plan, and the second role is a role in the target annuity business that is different from the custodian role.

Description

Technical Field

[0001] The present application relates to the field of computer processing technology, and in particular to a data processing method, device, electronic equipment and medium. Background Art

[0002] At present, in the field of pension, some enterprises will choose to establish their own enterprise annuities. Among them, enterprise annuities are a supplementary pension system, which refers to the supplementary pension insurance system independently established by enterprises and their employees on the basis of participating in basic pension insurance in accordance with the law. When operating enterprise annuities, it is generally initiated by the principal and managed by the trustee under the supervision of the China Banking and Insurance Regulatory Commission. In the actual operation process, in addition to the principal and the trustee, it also involves the custodian, investment manager, account manager, etc. Among them, the custodian can be responsible for the accounting and valuation of the enterprise annuity fund, and review and examine the net value of the fund property calculated by the investment manager. Generally speaking, the custodian is a qualified bank.

[0003] Among them, the enterprise entrusts the enterprise annuity to the trustee, and the trustee will establish a targeted annuity plan for the enterprise. Once the annuity plan starts operating, data corresponding to the annuity plan will be generated. For example, the custodian will generate valuation data of various investment assets. In related technologies, the custodian will regularly send valuation data of the annuity plan's accounting and valuation to the principal and the trustee. However, due to the different accounting systems of different custodians and the different accounting methods of different branches of the same custodial bank, the trustee and the principal need a large number of people to adjust the data when receiving the valuation data before they can store it in their own system. As a result, the timeliness and accuracy of the valuation data are low. Summary of the invention

[0004] In order to solve the above problems, the present application provides a data processing method, device, electronic device and medium, aiming to improve the timeliness and accuracy of valuation data.

[0005] In a first aspect of an embodiment of the present application, a data processing method is provided, the method comprising:

[0006] Obtaining original valuation data from a node belonging to the first role, wherein the original valuation data includes valuation detail data of a plurality of assets; the original valuation data is valuation data belonging to a target annuity plan;

[0007] The valuation detail data of the multiple assets are reconstructed multiple times in sequence according to different classification granularities to obtain target valuation data that meets the target classification standard, wherein the nth reconstruction is performed on the valuation data obtained by the n-1th reconstruction; n is a positive integer greater than or equal to 1;

[0008] Uploading the target valuation data to a node belonging to the second role in the blockchain;

[0009] The first role is the custodian role in the target annuity plan, and the second role is a role in the target annuity plan that is different from the custodian role.

[0010] Optionally, the valuation detail data of the multiple assets are reconstructed multiple times according to different classification granularities, including:

[0011] Obtaining multiple mapping tables from the node belonging to the second role, wherein the same mapping table includes at least one project identifier under the same classification granularity, and different mapping tables have different classification granularities;

[0012] According to the order of the size of the classification granularity, the valuation detail data of the multiple assets are iteratively matched with the at least one project identifier in the multiple mapping tables in turn to achieve multiple reconstructions of the valuation detail data of the multiple assets.

[0013] Optionally, iteratively matching the valuation detail data of the multiple assets with the at least one item identifier in the multiple mapping tables in order of the size of the classification granularity to achieve multiple reconstructions of the valuation detail data of the multiple assets includes:

[0014] According to the order of the classification granularity from large to small, the nth mapping table for matching is obtained;

[0015] Reclassifying the valuation details data belonging to each project identifier in the n-1th mapping table in the currently obtained valuation data according to at least one project identifier in the nth mapping table to obtain valuation data for the next iterative matching; wherein n is a positive integer greater than or equal to 1;

[0016] Repeat the above process until all mapping tables are traversed.

[0017] Optionally, the method further comprises:

[0018] Based on the original valuation data, verifying the target valuation data;

[0019] When it is verified that the target valuation data and the original valuation data are different, determining difference data where the difference occurs, the difference data including item identifiers belonging to different classification granularities;

[0020] The multiple mapping tables are updated based on item identifiers belonging to different classification granularities in the difference data.

[0021] Optionally, the method further comprises:

[0022] Inputting the target valuation data and the original valuation data into a decision network model;

[0023] Obtaining difference data between the original valuation data and the target valuation data output by the decision network model, wherein the difference data includes item identifiers belonging to different classification granularities;

[0024] The multiple mapping tables are updated based on item identifiers belonging to different classification granularities in the difference data.

[0025] Optionally, the method further comprises:

[0026] Obtaining valuation data samples, the valuation data samples comprising: a target valuation data sample, an original valuation data sample, a difference data sample after verifying the target valuation data sample, and a difference mapping table sample corresponding to the difference data sample;

[0027] The estimated data samples are used as training samples to train a preset decision tree network until the difference between the difference data predicted by the decision tree network and the difference data samples meets a set condition, thereby obtaining the decision network model.

[0028] Optionally, updating the plurality of mapping tables based on identifications of items belonging to different classification granularities of the difference data includes:

[0029] Determine, from the plurality of mapping tables, at least one difference mapping table corresponding to the classification granularity to which each item identifier in the difference data belongs;

[0030] Based on the difference data, the item identifier in the difference mapping table is updated.

[0031] Optionally, the method is applied to a blockchain, wherein the blockchain includes the node belonging to the first role and the node belonging to the second role;

[0032] Obtain raw valuation data from the nodes belonging to the first role, including:

[0033] Acquire the original valuation data from the blockchain; the original valuation data is uploaded to the blockchain by the node belonging to the first role;

[0034] Uploading the target valuation data to a node belonging to the second role;

[0035] According to a second aspect of an embodiment of the present application, a data processing device is provided, the device comprising:

[0036] A data acquisition module, used to obtain original valuation data from a node belonging to the first role, wherein the original valuation data includes valuation detail data of a plurality of assets; the original valuation data is valuation data belonging to a target annuity plan;

[0037] A data arrangement module is used to reconstruct the valuation detail data of the multiple assets multiple times according to different classification granularities in turn to obtain target valuation data that meets the target classification standard, wherein the nth reconstruction is performed on the valuation data obtained by the n-1th reconstruction; n is a positive integer greater than or equal to 1;

[0038] A data uploading module, used to upload the target valuation data to a node belonging to the second role in the blockchain;

[0039] The first role is the custodian role in the target annuity plan, and the second role is a role in the target annuity plan that is different from the custodian role.

[0040] According to a third aspect of an embodiment of the present application, an electronic device is provided, including a memory and a processor, wherein the processor is configured with the data processing method as described in the first aspect.

[0041] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, wherein a computer program stored in the medium enables a processor to execute the data processing method as described in the first aspect.

[0042] The technical solution of the embodiment of the present application has at least the following advantages:

[0043] The technical solution of the embodiment of the present application can obtain original valuation data from the node belonging to the first role, wherein the original valuation data includes detailed valuation data of multiple assets; the detailed valuation data of the multiple assets are reconstructed multiple times according to different classification granularities to obtain target valuation data that meets the target classification standards, wherein the nth reconstruction is performed on the valuation data obtained by the n-1th reconstruction, and the target valuation data is uploaded to the node belonging to the second role.

[0044] Since the valuation details of multiple assets in the original valuation data can be reconstructed multiple times according to different classification granularities, the valuation data can be classified according to the size of the categories in turn. For example, after being classified according to the major categories, the results of the major category classification are classified according to the minor categories, so that the valuation details of multiple assets can be sorted into target valuation data that meet the target classification standards. In this way, the original valuation data with different data structures uploaded by the banks of multiple custodians can be sorted into target valuation data that meet the target classification standards, so that the trustee or principal can store the valuation data of each custodian's bank, thereby avoiding manual adjustment of data, thereby improving the efficiency and accuracy of storing valuation data. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0046] Figure 1 It is a schematic diagram of an implementation environment in which a data processing method shown in an embodiment of the present application is applied;

[0047] Figure 2 It is a schematic diagram of a step flow of a data processing method shown in an embodiment of the present application;

[0048] Figure 3 It is a schematic diagram of a table of valuation details data in the valuation data issued by the custodian bank according to an embodiment of the present application;

[0049] Figure 4 It is a schematic diagram of an implementation environment in which another data processing method according to an embodiment of the present application is applied;

[0050] Figure 5 It is a schematic diagram of the steps of reconstructing the valuation detail data of multiple assets multiple times according to an embodiment of the present application;

[0051] Figure 6 It is a schematic diagram of a process of continuously learning and optimizing a decision tree network according to an embodiment of the present application;

[0052] Figure 7 A schematic diagram of a process for reconstructing detailed asset valuation data according to multiple mapping tables shown in an embodiment of the present application;

[0053] Figure 8 is a schematic diagram of three mapping tables shown in an embodiment of the present application;

[0054] Fig. 9 It is a schematic diagram of the framework of a data processing device shown in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application. It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are only examples of devices and methods consistent with some aspects of the present application as detailed in the attached claims.

[0056] In view of the fact that in the relevant technology, trustees and principals need to manually adjust the valuation data before storing it in their own systems, which leads to low timeliness and accuracy of the valuation data, the applicant proposes the following technical concept: in order of the size of the classification granularity, the valuation details data of multiple assets in the valuation data are iteratively reconstructed multiple times, so as to obtain the target valuation data that meets the target classification standards, avoid manual adjustment of data, and thus improve the timeliness and accuracy of the valuation data.

[0057] Reference Figure 1 As shown, a schematic diagram of an implementation environment of a data processing method according to an embodiment of the present application is shown, such as Figure 1 As shown in Figure 1, in enterprise annuities, there are various roles involved in enterprise annuities, such as the principal, trustee, custodian, and investment manager. The principal, trustee, custodian, and investment manager are nodes in the entire annuity business. The custodian can refer to the custodian bank, such as Figure 1 As shown, there can be multiple custodians and investment managers.

[0058] To facilitate understanding of this application, some technical terms used in this application are introduced below:

[0059] Valuation data: data generated by the valuation of invested assets in the market, generally including: portfolio information, date, holding products, account code and account name, number of holdings, holding cost, holding market value, valuation appreciation, market value of the portfolio layer, unit net asset value yield and other data.

[0060] The valuation data is the data obtained after valuing each asset. Valuation of each asset constitutes the valuation detail data of each asset, and the valuation detail data of multiple assets constitute the original valuation data. In this way, the valuation data can be understood as data composed of multiple valuation detail data. Each valuation detail data may include an asset identifier, a subject identifier, a product identifier, etc. The asset identifier is used to uniquely identify the asset, while the subject identifier is used to identify the major investment category to which the asset belongs, and the product identifier is used to identify the investment subcategory described by the resource. The asset identifier can be the asset code, the subject identifier can also be the subject code, and the product identifier can also be the product code.

[0061] Assets: can be understood as the assets invested, generally the smallest unit in the valuation data. For example, if the corporate pension is invested in bonds, the bonds are divided into government bonds and corporate bonds. Among them, there are multiple investment assets under corporate bonds. For example, it includes the corporate bonds of Company A and the corporate bonds of Company B. Among them, the corporate bonds of Company A and Company B can be called the invested assets.

[0062] Reference Figure 2 As shown, a schematic diagram of the steps of a data processing method according to an embodiment of the present application is shown, wherein the nodes belonging to the first role and the nodes belonging to the second role may refer to the clients on each smart device in the annuity plan, such as Figure 1 As shown, the method of the present application can be applied to the nodes of the trustee or the principal, such as Figure 2 As shown, the following steps may be specifically included:

[0063] Step S201: obtaining original valuation data from a node belonging to a first role, wherein the original valuation data includes valuation detail data of a plurality of assets.

[0064] The original valuation data is the valuation data belonging to the target annuity plan.

[0065] In this embodiment, the first role may refer to the custodian role, and specifically, the original valuation data may be obtained from the node of the first role. The target annuity plan may refer to the annuity plan of the target enterprise. For example, if enterprise A entrusts party B with an enterprise annuity and party B manages the enterprise annuity, party B is the trustee node. At this time, the valuation details of multiple assets in the annuity plan of enterprise A are obtained from the node of the first role, that is, from the custodian's node (the custodian entrusted by party B).

[0066] In some embodiments, since the first role is the custodian role, in practice, multiple custodian banks may be involved, and multiple custodian banks may upload the original valuation data to the chain. The original valuation data uploaded by multiple custodian banks may be obtained, and then the subsequent steps S202 to S203 may be performed for the original valuation data of each custodian bank.

[0067] Among them, although different investment managers may use assets as the smallest unit for valuation, due to differences in valuation calculation methods, accounting systems used, etc., the original valuation data sent by different custodians will be different.

[0068] As mentioned above, each valuation detail data may include an asset identifier, a subject identifier, a product identifier, etc. The asset identifier is used to uniquely identify the asset, while the subject identifier is used to identify the major investment category to which the asset belongs, and the product identifier is used to identify the minor investment category described by the resource. In practice, the positions of the asset identifier, subject identifier, and product identifier in the valuation detail data sent by different custodian banks are also different.

[0069] For example, Figure 3 As shown, a schematic diagram of the valuation details in the valuation data issued by the three custodian banks is shown, Figure 3 As shown in the figure, in the valuation data sent by custodian bank A, the subject identifier is arranged first, and the product identifier is arranged last; in the valuation data sent by custodian bank B, the product identifier is arranged before the asset identifier, and the valuation details of the assets are classified and sorted according to the products corresponding to the product identifier. In the valuation data sent by custodian bank C, the valuation details are arranged in alphabetical order according to the names of the assets, which means that the valuation details sent by custodian bank C are not classified according to any sub-category or major category.

[0070] In summary, due to the different standards and accounting methods of various custodian banks for valuation data, there are differences in the data structure of valuation data. Therefore, it is necessary to standardize the valuation data sent by various custodian banks into valuation data that conforms to the target data structure.

[0071] Step S202: reconstructing the valuation detail data of the multiple assets multiple times in accordance with different classification granularities to obtain target valuation data that meets the target classification standard.

[0072] The nth reconstruction is performed on the estimated data obtained by the n-1th reconstruction; n is a positive integer greater than or equal to 1.

[0073] In this embodiment, the valuation detail data of multiple assets can be reconstructed multiple times according to different classification granularities, and each reconstruction is for the same classification granularity. Reconstruction can refer to adjusting the position of the valuation detail data of multiple assets in the valuation data and the position of the value of each attribute in the valuation detail data, so as to achieve the purpose of classifying and adjusting the valuation detail data of multiple assets according to categories such as subjects and products.

[0074] For example, Figure 3 As shown, when reconstructing, the detailed data of assets belonging to a single product can be arranged together, and the detailed data of valuations of multiple products under a single subject can be arranged together, thereby adjusting the data structure of the valuation data, so that the adjusted target valuation data meets the target classification standard.

[0075] Among them, when adjusting, reconstruction can be performed in order from large to small classification granularity. During reconstruction, the nth reconstruction is performed on the valuation data obtained by the n-1th reconstruction, that is, the next data reconstruction is performed on the result obtained by the previous data reconstruction, so that the multiple reconstructions of this application are a linear iterative reconstruction.

[0076] In a feasible implementation scheme, the valuation detail data of multiple assets may be reconstructed first according to categories with larger granularity, for example, the valuation detail data of multiple assets may be reconstructed first according to subjects, thereby realizing the classification of the valuation detail data according to subjects. Then, on this basis, the valuation detail data of multiple assets may be reconstructed according to categories with smaller granularity, for example, the valuation detail data under each subject may be classified according to products, thereby realizing the classification of the valuation detail data according to subjects and products.

[0077] After multiple reconstructions, target valuation data can be obtained, and the target valuation data can meet the target classification standard, wherein the target classification standard corresponds to the order of each classification granularity followed during the reconstruction.

[0078] For example, after multiple reconstructions, the valuation details of multiple assets in the original valuation data will be classified and arranged according to the categories corresponding to each classification granularity. In this way, the data structure of the target valuation data can be in the form of {Subject A: [Product A1<Asset 1, Asset 2, Asset 3>》], [Product A2<Asset 4, Asset 5, Asset 6>》]}, {Subject B: [Product B1<Asset 7, Asset 8, Asset 9>》]}, where {} represents the major category, [] represents the minor category, and <> represents the asset. In this way, the target classification standard is based on the classification standard with subjects as the major category and products as the minor category. When reconstructing, it will first be reconstructed according to subjects and then reconstructed according to products.

[0079] Of course, the subjects and products mentioned in the above examples are only exemplary and do not represent specific limitations of the present application. In some embodiments, the classification granularity can have multiple granularities, such as 4 or 5 granularities, so that the valuation detail data of multiple assets can be reconstructed multiple times according to the size of the classification granularity.

[0080] Step S203: Upload the target valuation data to the node belonging to the second role.

[0081] In this embodiment, after obtaining the target valuation data, the target valuation data can be uploaded to the node belonging to the second role, or the target valuation data can be stored locally, wherein the second role can be a role in the target annuity plan that is different from the custodian role, such as the principal, or the account manager surrounding the trustee, wherein the node of the second role can also be the subject that reconstructs the original valuation data. For example, if the trustee reconstructs the original valuation data, the target valuation data obtained after reconstruction will be stored in the node of the second role, or uploaded to the cloud storage device corresponding to the node of the second role.

[0082] By adopting the technical solution of the embodiment of the present application, the valuation details data of multiple assets in the original valuation data can be reconstructed multiple times according to different classification granularities, so that the valuation details data can be classified in sequence according to the size of the categories. In this way, the original valuation data with different data structures uploaded by multiple custodian banks can be sorted into target valuation data that meets the target classification standard, so as to facilitate the trustee or principal to store the valuation data of each custodian bank, thereby avoiding manual adjustment of data, thereby improving the efficiency and accuracy of storing valuation data.

[0083] In yet another embodiment, referring to Figure 4 As shown, another communication environment schematic diagram of an embodiment of the present application is shown. Figure 4 As shown, the data processing method can be applied to a blockchain, wherein the blockchain includes nodes belonging to the roles of the principal and the trustee, and the node can be understood as a client running on a smart device. In this way, the blockchain includes the node belonging to the first role and the node belonging to the second role.

[0084] Among them, Figure 4 As shown, when applied to the blockchain, all parties involved in the annuity plan can be added to the blockchain as a node. For example, in an enterprise's corporate annuity, there is generally one principal, one or more trustees, and several investment managers and custodians. In this way, the blockchain can include multiple investment managers and multiple custodians.

[0085] In practice, in an annuity plan, the client can organize all parties in the annuity plan to go on-chain, so that all parties in the annuity plan can serve as nodes in the blockchain, and the business and data involved in the annuity plan can be carried out in the blockchain.

[0086] In this embodiment, all parties involved in the enterprise annuity can interact and transmit data on the chain. The data transmitted on the chain can be the enterprise annuity's position data, transaction details data, enterprise annuity contracts, etc. For example, the custodian can put the valuation data of the annuity plan on the chain. Among them, all parties abide by the smart contract in the process of data interaction, that is, all business related to enterprise annuities conducted in the blockchain abide by the smart contract. A smart contract is a computer protocol designed to disseminate, verify or execute a contract in an information-based manner. Smart contracts can allow trusted transactions without a third party, and these transactions are traceable and irreversible. In this way, the security of enterprise annuity business in the blockchain can be guaranteed through smart contracts.

[0087] In an embodiment of the present application, the custodian may upload the original valuation data to the blockchain, wherein, due to the relative confidentiality of the valuation data, the original valuation data may be encrypted and uploaded to the blockchain. Afterwards, after the principal or trustee obtains the original valuation data on the blockchain, the principal or trustee may decrypt the original valuation data using the decryption key held by the trustee, thereby obtaining the decrypted original valuation data.

[0088] Specifically, in the above step S201, the original valuation data can be obtained from the blockchain; the original valuation data is uploaded to the blockchain by the node belonging to the first role.

[0089] Next, the original valuation data may be reconstructed into target valuation data by the trustee's node or the principal's node according to the process of step S202, and then, in step S203, the target valuation data is uploaded to the blockchain.

[0090] Since the original valuation data is obtained on the blockchain, and after the target valuation data is obtained, the target valuation data is uploaded to the blockchain, the valuation data can be transmitted in the blockchain, thereby improving the efficiency of obtaining the valuation data, and because the valuation data is located in the blockchain, the security of the valuation data can be guaranteed.

[0091] In a feasible implementation scheme, the process of reconstructing the valuation detail data of the multiple assets multiple times according to different classification granularities is introduced in detail.

[0092] Reference Figure 5 As shown, a schematic diagram of the steps of reconstructing the valuation detail data of multiple assets in an embodiment of the present application is shown, such as Figure 5As shown, the following steps may be specifically included:

[0093] Step S501: multiple mapping tables are obtained from the node belonging to the second role, wherein the same mapping table includes at least one item identifier under the same classification granularity, and different mapping tables have different classification granularities.

[0094] In this embodiment, Figure 4 As shown, multiple mapping tables on the node belonging to the second role can be obtained from the blockchain, wherein, as described in the above embodiment, the second role can be other subjects related to the enterprise annuity on the blockchain, such as the principal, or the second role can be the subject that reconstructs the original valuation data. In this way, multiple mapping tables can be obtained from the node of the second role.

[0095] Each mapping table can be used to reconstruct the valuation details of multiple assets. Specifically, each mapping table can include at least one project identifier under the same classification granularity, and different mapping tables can have different classification granularities. In this way, when the valuation details of multiple assets are reconstructed multiple times according to different classification granularities, each reconstruction can be performed based on a mapping table of the corresponding classification granularity.

[0096] In this embodiment, at least one project identifier under the same classification granularity may refer to identifiers of multiple projects belonging to the same category. For example, if the classification granularity is subject, and the representation is classified by subject, then the classification granularity may include stocks and debts, and the codes of stocks and bonds may be used as project identifiers.

[0097] For example, it is assumed that there are three mapping tables 1, 2 and 3, wherein mapping table 1 includes at least one project identifier at the subject classification granularity, mapping table 2 includes at least one project identifier at the product granularity, and mapping table 3 includes at least one project identifier at the asset classification granularity.

[0098] Step S502: iteratively matching the valuation detail data of the multiple assets with the at least one project identifier in the multiple mapping tables in order of the size of the classification granularity, so as to achieve multiple reconstructions of the valuation detail data of the multiple assets.

[0099] In this embodiment, the valuation detail data of multiple assets can be reconstructed in order according to the size order of classification granularity, for example, according to the order of classification granularity from large to small. Specifically, in each reconstruction, multiple valuation detail data can be matched with at least one project identifier in the current mapping table, so that the valuation data matched with at least one project identifier in the current mapping table can be reconstructed. For example, for each project identifier in the current mapping table, the corresponding identifier of the valuation detail data of each asset is matched with the project identifier, and the valuation detail data with successful matching can be retained. The retained valuation detail data can be aggregated according to the order of at least one project identifier in the current mapping table, and the valuation detail data belonging to the same project identifier can be aggregated, so as to obtain multiple valuation detail data corresponding to each project identifier that are aggregated together.

[0100] For example, taking mapping table 1 as an example, it includes at least one project identifier under the subject classification granularity. Assuming that the project identifiers are stocks and bonds, the valuation detail data of assets belonging to stocks can be aggregated together, and the valuation detail data of assets belonging to bonds can be aggregated together.

[0101] By analogy, each time reconstruction is performed, it can be performed according to the corresponding mapping table.

[0102] When adopting this implementation scheme, since multiple mapping tables are used to match the valuation detail data of multiple assets, the valuation detail data can be reconstructed through multiple mapping expressions to organize it into target valuation data that meets the target classification standards.

[0103] Since multiple mapping tables are used, the reconstruction of the original valuation data can be adjusted accordingly by adding or deleting the item identifiers managed in the mapping tables, so that the target valuation data obtained after reconstruction meets the required classification standards.

[0104] Accordingly, in a feasible implementation scheme, when iteratively matching the valuation detail data of the multiple assets with the at least one project identifier in the multiple mapping tables in order of the size of the classification granularity to achieve multiple reconstructions of the valuation detail data of the multiple assets, the nth mapping table for the current matching can be obtained in order from large to small of the classification granularity; and the valuation detail data of the assets belonging to each project identifier in the n-1th mapping table in the currently obtained valuation data are reclassified according to at least one project identifier in the nth mapping table to obtain the valuation data for the next iterative matching.

[0105] Wherein, n is a positive integer greater than or equal to 1, and n is less than or equal to the total number N of the plurality of mapping tables.

[0106] In this embodiment, the valuation details data of multiple assets can be reconstructed in order from large to small classification granularity. Since the nth reconstruction is performed on the valuation data obtained by the n-1th reconstruction, the nth reconstruction is based on the nth mapping table. Among them, during the nth reconstruction, it is necessary to perform the reconstruction on the valuation data obtained by the n-1th reconstruction, so that the valuation data obtained by the n-1th reconstruction can be obtained, and then the valuation details data of the assets belonging to each project identifier in the n-1th mapping table in the valuation data are determined. From the above embodiment, it can be seen that the valuation details data retained by the n-1th reconstruction can be aggregated according to the order of at least one project identifier in the n-1th mapping table. Therefore, for each project identifier in the n-1th mapping table, the valuation data obtained by the n-1th reconstruction has aggregated valuation details data for the project identifier.

[0107] In practice, during the nth reconstruction, the valuation details of the assets belonging to each project identifier in the n-1th mapping table can be reclassified according to at least one project identifier in the nth mapping table to obtain the valuation data after the nth reconstruction.

[0108] For example, the order of the mapping tables based on the reconstruction is mapping table 1, mapping table 2, and mapping table 3, and the original valuation data is reconstructed in the order of classification granularity from large to small. Specifically, in the first reconstruction, according to at least one item identifier (including stocks and bonds) under the subject classification granularity included in mapping table 1, the valuation detail data of assets belonging to stocks can be aggregated together, and the valuation detail data of assets belonging to bonds can be aggregated together to obtain valuation data 1.

[0109] Afterwards, the valuation data 1 is reconstructed for the second time according to the mapping table 2. The mapping table 2 includes at least one project identifier (government bonds, corporate bonds) under the product classification granularity. The valuation detail data of the assets belonging to bonds in the valuation data 1 are classified according to government bonds and corporate bonds to obtain the classified and aggregated valuation detail data belonging to government bonds and the valuation detail data belonging to corporate bonds, thereby obtaining the valuation data 2.

[0110] Finally, the valuation data 2 is reconstructed for the second time according to the mapping table 3. The mapping table 3 includes at least one project identifier under the asset classification granularity. The valuation details data belonging to the corporate bonds in the valuation data 2 are classified according to the assets to obtain the valuation data 3. In this way, the valuation data 3 is the target valuation data.

[0111] Of course, in some feasible implementation schemes, each mapping table may include, in addition to at least one item identifier under the same classification granularity, a variety of data attribute parameters, wherein the various data attribute parameters may include data content used to describe the original valuation data, and may also include data content used to be added to the original valuation data. In this way, when reconstructing the valuation data, the integrity of the valuation detail data can be ensured, and according to user needs, the required data content can be added to the original valuation data during data reconstruction.

[0112] For example, for mapping table 1, in addition to multiple account identifiers belonging to the accounts, it can also include data attributes such as entry time, entry date, asset type, custodian code, status, operator, etc., among which the custodian code and asset type are used to describe the content of the original valuation data, while the entry time, entry date, and operator are used to add content to the original valuation data. Therefore, during reconstruction, more detailed information in the original valuation data can be retained. At the same time, since the entry time, entry date, and operator are added, the required content can be added to the valuation data during data reconstruction, thereby improving the completeness of the data.

[0113] Of course, different mapping tables may include different data attribute parameters, which can be determined according to actual needs and will not be described in detail here.

[0114] In practice, since the valuation details data of multiple assets are reconstructed according to multiple mapping tables, the multiple mapping tables can be updated and maintained to improve the accuracy of the reconstructed target valuation data and reduce the difference between the target valuation data and the original valuation data. In some feasible implementation schemes, the updating and maintenance of the mapping tables can be implemented as follows:

[0115] Implementation method one: the target valuation data can be verified based on the original valuation data; when it is verified that there is a difference between the target valuation data and the original valuation data, the difference data where the difference occurs is determined, and the difference data includes project identifiers belonging to different classification granularities; and based on the project identifiers belonging to different classification granularities in the difference data, the multiple mapping tables are updated.

[0116] In this embodiment, when verifying the target valuation data based on the original valuation data, the number of valuation detail data items contained in the target valuation data can be compared with the number of valuation detail data items contained in the original valuation data. Since one valuation detail data item corresponds to one asset, it can be determined whether there is a difference between the two items based on the number of valuation detail data items contained in the two items.

[0117] When the number of valuation details data included in the two is inconsistent, it can be determined that there is a difference between the target valuation data and the original valuation data. At this time, the difference data where the difference occurs can be located. Specifically, when locating the difference data where the difference occurs, it can be done in the following manner:

[0118] First determine whether the number of valuation detail data items in the major categories is consistent. Specifically, determine whether the number of valuation detail data items belonging to the same subject identifier is consistent. Since the subjects are major categories, the subjects with differences can be quickly located based on whether the number of valuation detail data items with the same subject identifier is consistent.

[0119] Afterwards, for the valuation detail data under the subject identification with differences, determine whether the number of valuation detail data of each subcategory is consistent. Specifically, for multiple valuation detail data under the subject with differences, determine whether the number of valuation detail data belonging to the same product identification in the original valuation data and the target valuation data is consistent. In this way, the product identification with differences can be located.

[0120] Afterwards, the asset identifications with differences can be determined for the valuation details under the product identifications with differences. Specifically, the valuation details under the product identifications with differences can be compared one by one to screen out the asset identifications with differences.

[0121] It can be seen from the above comparison process that when a difference occurs, the difference data can be located, and the difference data can include the subject identification, product identification and asset identification of the difference. In practice, the difference data can include any one of the subject identification, product identification and asset identification of the difference, or all three, or any two of them.

[0122] For example, problems with the account identification may make it impossible to organize some valuation detail data in the original valuation data into the target valuation data. For example, if the stock identification is not included in the account identification, it is impossible to organize the valuation detail data belonging to the stock in the original valuation data into the target valuation data. In this way, the difference data will include the account identification.

[0123] Specifically, the project identifier of the difference can be determined based on the difference data. Since the difference data may include at least one project identifier of different classification granularities, that is to say, the difference data may include the project identifier of the large category and the project identifier of the small category where the difference occurs. In this way, based on the project identifiers of different classification granularities in the difference data, multiple mapping tables can be updated to update the project identifiers in the mapping tables.

[0124] Implementation method two: input the target valuation data and the original valuation data into a decision network model; obtain difference data between the original valuation data and the target valuation data output by the decision network model, and the difference data includes project identifiers belonging to different classification granularities; and based on the project identifiers belonging to different classification granularities in the difference data, update the multiple mapping tables.

[0125] In this embodiment, the decision network model can be used to verify the target valuation data based on the original valuation data to determine whether there is a difference between the original valuation data and the target valuation data, and then locate the difference data, such as the process of locating the difference data mentioned above. The difference data includes project identifiers belonging to different classification granularities, and thus the project identifiers in multiple mapping tables are updated based on the project identifiers belonging to different classification granularities in the difference data.

[0126] In this implementation manner, the decision network model may be obtained by pre-training a decision tree network using training samples.

[0127] Among them, the process of obtaining the decision network model can be described as follows:

[0128] First, a valuation data sample is obtained, wherein the valuation data sample includes: a target valuation data sample, an original valuation data sample, a difference data sample after verifying the target valuation data sample, and a difference mapping table sample corresponding to the difference data sample.

[0129] Next, the preset decision tree network is trained using the estimated data samples as training samples until the difference between the difference data predicted by the decision tree network and the difference data samples meets the set conditions, thereby obtaining the decision network model.

[0130] In this embodiment, the target valuation data sample in the valuation data sample is a sample after data reconstruction of the original valuation data sample, and the difference data sample is a difference data sample obtained after verifying the target valuation data sample based on the original valuation data sample, wherein the process of obtaining the difference data sample can be performed manually and is a standard difference data sample.

[0131] The difference mapping table sample may be a mapping table sample that needs to be maintained and is manually determined based on the difference data sample.

[0132] When the valuation data samples are used as training samples to train the decision tree network, the decision tree network can use the existing decision tree algorithm to verify the target valuation data samples based on the original valuation data samples, and then output the difference data samples and the mapping table corresponding to the difference data samples.

[0133] Reference Figure 6 As shown, a schematic diagram of the process of continuously learning and optimizing the decision tree network in an embodiment of the present application is shown, such as Figure 6 As shown, in actual operation, the original valuation data and target valuation data can be manually checked and compared multiple times to obtain valuation data samples, so as to train the decision tree network multiple times. Through continuous manual comparison and continuous machine learning, the accuracy of the decision tree network in locating the difference data and the mapping table corresponding to the difference data is improved, and finally full mechanization is achieved to automatically maintain and update the mapping table.

[0134] In this embodiment, when training the decision tree network, the parameters of the decision tree algorithm used by the decision tree network can be updated according to the difference data and the corresponding mapping table output by the decision tree network, as well as the difference data samples and the corresponding mapping table samples. After multiple training and updates, a decision tree network model can be obtained.

[0135] Through the decision tree network model, the target valuation data can be automatically verified each time the target valuation data is obtained. If there is a difference in the verification, the mapping table can be updated.

[0136] In this embodiment, when the multiple mapping tables are updated based on the item identifiers belonging to different classification granularities in the difference data, at least one difference mapping table corresponding to the classification granularity to which each item identifier in the difference data belongs can be determined from the multiple mapping tables; and the item identifiers in the difference mapping table can be updated based on the difference data.

[0137] In specific implementation, since the difference data includes project identifiers belonging to different classification granularities, the classification granularity to which each project identifier belongs can be determined, and then a mapping table corresponding to the classification granularity can be determined, and the mapping table is the difference mapping table.

[0138] The classification granularities of the project identifiers included in the difference data may be different or the same. In the same case, it indicates that a problem has occurred at the same classification granularity, while in different cases, it indicates that a problem has occurred at multiple classification granularities.

[0139] For example, if the difference data includes corporate bond identifier A, which corresponds to the product classification granularity, the product mapping table can be determined as the difference mapping table. If the difference data includes corporate bond identifier A and asset identifier B, the product mapping table and the asset mapping table can be determined as the difference mapping table.

[0140] The difference mapping table may not include the project identifiers of the same classification granularity in the difference data. In this case, the lack of project identifiers in the difference mapping table will result in the failure to retain the valuation details corresponding to the project identifiers in the original valuation data. For example, the original valuation data contains the valuation details of corporate bonds, but there is no corporate bond identifier in the mapping table, so the valuation details of corporate bonds are missing in the target valuation data.

[0141] The difference mapping table may also include project identifiers belonging to the same classification granularity. In this case, the difference mapping table includes redundant project identifiers, which results in the target valuation data being added with valuation detail data that is not in the original valuation data. For example, in some actual situations, there is no corporate bond data in the original valuation data, but there is still a corporate bond identifier in the mapping table. In this case, when the data is reconstructed, an empty valuation detail data of the corporate bond will still be added.

[0142] In this embodiment, the project identifier in the difference mapping table can be updated according to the difference data. Specifically, when the number of valuation details of the asset in the target valuation data is greater than the original valuation data, the project identifier in the difference data can be deleted from the difference mapping table. When the number of valuation details of the asset in the target valuation data is less than the original valuation data, the project identifier in the difference data can be added to the difference mapping table.

[0143] For example, if the difference data includes corporate bond identifier A, corporate bond identifier A corresponds to the product classification granularity, then the product mapping table can be determined as a difference mapping table. If the number of valuation detail data under corporate bond identifier A in the target valuation data is greater than the original valuation data, it indicates that the target valuation data includes data that is not in the original valuation data, and therefore, it is necessary to delete corporate bond identifier A in the product mapping table. If the number of valuation detail data under corporate bond identifier A in the target valuation data is less than the original valuation data, it indicates that some data in the original valuation data is not included in the target valuation data, and therefore, it is necessary to add corporate bond identifier A in the product mapping table.

[0144] Of course, if the difference data also includes asset identifier B, the asset mapping table can be updated accordingly. Specifically, when the original valuation data contains valuation details of asset identifier B, asset identifier B can be added to the asset mapping table. When the original valuation data does not contain valuation details of asset identifier B, asset identifier B can be deleted from the asset mapping table.

[0145] In a feasible implementation scheme, after updating and maintaining multiple mapping tables, the original valuation data can be reconstructed multiple times according to the updated and maintained mapping tables. The reconstruction process can be referred to as described in the above embodiment. In this way, the difference between the reconstructed target valuation data and the original valuation data can be reduced, thereby improving the accuracy of data sorting.

[0146] Reference Figure 7 and Figure 8 As shown, Figure 7 A schematic diagram of a process for reconstructing valuation data according to multiple mapping tables is shown. Figure 8 A schematic diagram showing three types of mapping tables, combined with Figure 6 and Figure 7 As shown in the figure, how to sort out the valuation data according to multiple mapping tables is specifically introduced:

[0147] like Figure 7 As shown, Figure 7 The subject mapping library, product element library (product mapping library) and asset distribution mapping library are the mapping tables described in this application. Figure 7 They are the details of the identifiers contained in the account mapping library, product mapping library, and asset distribution mapping library in this example. Figure 7 As shown, in the account mapping library, the valuation account code is the project identifier at the classification granularity, generally including multiple valuation account codes, and different valuation account codes represent different accounts. In the product mapping library, the product code is the project identifier at the classification granularity, generally including multiple product codes, and different product codes represent different products (such as government bonds and corporate bonds). In the asset distribution mapping library, the asset code value is the project identifier at the classification granularity, generally including multiple asset code values, and different asset code values ​​represent different assets.

[0148] like Figure 8 As shown, each mapping library can also include a variety of data attribute parameters, which can be obtained from Figure 8 It can be seen that different mapping libraries include data attribute parameters such as entry time, entry date, operator, last operator, last modification date, and last modification time. These data attribute parameters can be understood as data content that needs to be added to the valuation data when reconstructing the valuation data.

[0149] Reference Figure 7 As shown:

[0150] First, the original valuation data is matched with the account mapping table by major asset categories. In this way, the original valuation data can be classified by account. For example, the original valuation data can be classified by stocks and bonds, thereby obtaining valuation data that completes the confirmation of major asset categories.

[0151] Next, the product element confirmation is performed on the valuation data that has completed the asset category confirmation. Specifically, the valuation data that has completed the asset category confirmation is matched with the product mapping library to further reclassify the valuation data that has completed the asset category confirmation according to the sub-category. For example, bond assets are classified and sorted according to corporate bonds and government bonds to complete the asset type and market type confirmation, thereby obtaining the valuation data that has completed the asset type confirmation and market type confirmation.

[0152] Afterwards, the valuation data that has completed the confirmation of asset type and market type is sorted according to the asset distribution mapping library to obtain the result data.

[0153] like Figure 7 As shown, after obtaining the result data, data verification and validation may also be included to update and maintain each mapping library. After obtaining the result data, it may be verified whether the data has a difference. If there is no difference, the result data may be stored. If there is a difference, the cause of the difference is located, that is, the difference data is located as described in the above embodiment.

[0154] Afterwards, according to the located reasons for the differences, it is determined which mapping library is missing among the three mapping libraries. This process can be carried out by a decision network model.

[0155] If it is found that all three mapping libraries are missing, the three mapping libraries can be maintained and the project identifiers can be added to the mapping libraries.

[0156] The technical solution of the embodiment of the present application has the following advantages:

[0157] First, it can improve the accuracy and timeliness of valuation data.

[0158] Since the valuation details of multiple assets in the original valuation data can be reconstructed multiple times according to different classification granularities, the valuation data can be classified according to the size of the categories in turn. For example, after being classified according to the major categories, the results of the major category classification are classified according to the minor categories, so that the valuation details of multiple assets can be sorted into target valuation data that meet the target classification standards. In this way, the original valuation data with different data structures uploaded by multiple custodian banks can be sorted into target valuation data that meet the target classification standards, so that the trustee or principal can store the valuation data of each custodian bank, thereby avoiding manual adjustment of data, thereby improving the efficiency and accuracy of storing valuation data.

[0159] Second, it can improve the transmission security and efficiency of valuation data.

[0160] Since the original valuation data is obtained on the blockchain, and after the target valuation data is obtained, the target valuation data is uploaded to the blockchain, the valuation data can be transmitted in the blockchain, thereby improving the efficiency of obtaining the valuation data, and because the valuation data is located in the blockchain, the security of the valuation data can be guaranteed.

[0161] Third, dynamic maintenance and updating of multiple mapping tables can be achieved, thereby improving the accuracy of valuation data.

[0162] Since multiple mapping tables are used, the reconstruction of the original valuation data can be adjusted accordingly by adding or deleting the item identifiers managed in the mapping tables, so that the target valuation data obtained after reconstruction meets the required classification standards.

[0163] Furthermore, since multiple mapping tables can be updated and maintained according to the difference between the target valuation data and the original valuation data, the accuracy of the reconstructed target valuation data can be improved and the difference between the target valuation data and the original valuation data can be reduced.

[0164] Fourth, the valuation data collation can be fully automated.

[0165] Since the decision tree network model can automatically verify the target valuation data each time it is obtained, if there is a difference in the verification, the mapping table can be updated. In addition, the decision network model can improve the accuracy of the decision network model in locating the difference data and the mapping table corresponding to the difference data through continuous manual comparison and continuous machine learning, and finally achieve full mechanization to automatically maintain and update the mapping table, thereby improving the efficiency of updating and maintaining the mapping table and reducing labor costs.

[0166] Based on the same inventive concept as the above embodiment, the present application also provides a data processing device, referring to Fig. 9 As shown, a schematic diagram of the framework of the data processing device of an embodiment of the present application is shown. Figure 8 As shown, the following modules may be included:

[0167] The data acquisition module 901 is used to obtain original valuation data from the node belonging to the first role, wherein the original valuation data includes valuation details of multiple assets; the original valuation data is the valuation data belonging to the target annuity plan;

[0168] The data sorting module 902 is used to reconstruct the valuation detail data of the multiple assets multiple times according to different classification granularities in turn to obtain target valuation data that meets the target classification standard, wherein the nth reconstruction is performed on the valuation data obtained by the n-1th reconstruction; n is a positive integer greater than or equal to 1;

[0169] A data uploading module 903, configured to upload the target valuation data to a node belonging to the second role;

[0170] The first role is the custodian role in the target annuity plan, and the second role is a role in the target annuity business that is different from the custodian role.

[0171] Optionally, the data sorting module 902 includes:

[0172] A mapping table obtaining unit, configured to obtain a plurality of mapping tables from a node belonging to a second role in the blockchain, wherein the same mapping table includes at least one project identifier under the same classification granularity, and different mapping tables have different classification granularities;

[0173] A classification reconstruction unit is used to iteratively match the valuation detail data of the multiple assets with the at least one project identifier in the multiple mapping tables in order of the size of the classification granularity, so as to achieve multiple reconstructions of the valuation detail data of the multiple assets.

[0174] Optionally, the classification reconstruction unit includes:

[0175] A mapping table determination subunit, used to obtain the nth mapping table for matching at the time in the order of the classification granularity from large to small;

[0176] a classification subunit, for reclassifying the valuation detail data belonging to each project identifier in the n-1th mapping table in the currently obtained valuation data according to at least one project identifier in the nth mapping table, to obtain valuation data for the next iterative matching; wherein n is a positive integer greater than or equal to 1;

[0177] The repeating subunit is used to repeat the above process until the entire mapping table is traversed.

[0178] Optionally, the device further comprises the following modules:

[0179] A verification module, configured to verify the target valuation data based on the original valuation data;

[0180] A difference data determination module, used for determining difference data of the difference when it is verified that the target valuation data and the original valuation data are different, wherein the difference data includes item identifiers belonging to different classification granularities;

[0181] An updating module is used to update the multiple mapping tables based on the item identifiers belonging to different classification granularities in the difference data.

[0182] Optionally, the device further comprises the following modules:

[0183] An input module, used for inputting the target valuation data and the original valuation data into a decision network model;

[0184] An acquisition module, used to obtain difference data between the original valuation data and the target valuation data output by the decision network model, wherein the difference data includes item identifiers belonging to different classification granularities;

[0185] An updating module is used to update the multiple mapping tables based on the item identifiers belonging to different classification granularities in the difference data.

[0186] Optionally, the device further comprises the following modules:

[0187] A sample data acquisition module is used to obtain a valuation data sample, wherein the valuation data sample includes: a target valuation data sample, an original valuation data sample, a difference data sample after verifying the target valuation data sample, and a difference mapping table sample corresponding to the difference data sample;

[0188] The training module is used to train the preset decision tree network using the estimated data samples as training samples until the difference between the difference data predicted by the decision tree network and the difference data samples meets the set conditions, thereby obtaining the decision network model.

[0189] Optionally, the update module includes:

[0190] a difference mapping table determining unit, configured to determine, from the plurality of mapping tables, at least one difference mapping table corresponding to the classification granularity to which each item identifier in the difference data belongs;

[0191] An updating unit is used to update the item identifier in the difference mapping table based on the difference data.

[0192] Optionally, the device is applied to a blockchain, and the blockchain includes the node belonging to the first role and the node belonging to the second role;

[0193] The data acquisition module 901 may be specifically used to obtain the original valuation data from the blockchain; the original valuation data is uploaded to the blockchain by the node belonging to the first role;

[0194] The data uploading module 903 may be specifically used to upload the target valuation data to the blockchain.

[0195] An embodiment of the present invention further provides an electronic device, which may include a memory and a processor; wherein the data processing method is configured on the processor.

[0196] The embodiment of the present application also provides a computer storage medium, which stores a computer program that enables a processor to execute the data processing method.

[0197] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0198] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of 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 embodiments of 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 codes.

[0199] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the 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 the 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 terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal 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.

[0200] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including 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.

[0201] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement 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.

[0202] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0203] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0204] The data processing method, device, electronic device and medium provided by the present invention are introduced in detail above. The principles and implementation methods of the present invention are explained in this article using specific examples. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A data processing method, characterized in that: The method comprises: Obtaining original valuation data from a node belonging to the first role, wherein the original valuation data includes valuation detail data of a plurality of assets; the original valuation data is valuation data belonging to a target annuity plan; The valuation detail data of the multiple assets are reconstructed multiple times in sequence according to different classification granularities to obtain target valuation data that meets the target classification standard, wherein the nth reconstruction is performed on the valuation data obtained by the n-1th reconstruction; n is a positive integer greater than or equal to 1; Uploading the target valuation data to a node belonging to the second role; The first role is the custodian role in the target annuity plan, and the second role is a role in the target annuity plan that is different from the custodian role; The valuation detail data of the multiple assets are reconstructed multiple times according to different classification granularities, including: Obtaining multiple mapping tables from the node belonging to the second role, wherein the same mapping table includes at least one project identifier under the same classification granularity, and different mapping tables have different classification granularities; Iteratively matching the valuation detail data of the multiple assets with the at least one item identifier in the multiple mapping tables in order of the size of the classification granularity, so as to achieve multiple reconstructions of the valuation detail data of the multiple assets; The method further comprises: Obtaining valuation data samples, the valuation data samples comprising: a target valuation data sample, an original valuation data sample, a difference data sample after verifying the target valuation data sample, and a difference mapping table sample corresponding to the difference data sample; Using the estimated data samples as training samples, training a preset decision tree network until the difference between the difference data predicted by the decision tree network and the difference data samples meets a set condition, thereby obtaining the decision network model; Inputting the target valuation data and the original valuation data into a decision network model; Obtaining difference data between the original valuation data and the target valuation data output by the decision network model, wherein the difference data includes item identifiers belonging to different classification granularities; The multiple mapping tables are updated based on item identifiers belonging to different classification granularities in the difference data.

2. The method according to claim 1, characterized in that Iteratively matching the valuation detail data of the multiple assets with the at least one item identifier in the multiple mapping tables in order of the size of the classification granularity to achieve multiple reconstructions of the valuation detail data of the multiple assets, including: According to the order of the classification granularity from large to small, the nth mapping table for matching is obtained; Reclassifying the valuation details data belonging to each project identifier in the n-1th mapping table in the currently obtained valuation data according to at least one project identifier in the nth mapping table to obtain valuation data for the next iterative matching; wherein n is a positive integer greater than or equal to 1; Repeat the above process until all mapping tables are traversed.

3. The method according to claim 2, characterized in that The method further comprises: Based on the original valuation data, verifying the target valuation data; When it is verified that the target valuation data and the original valuation data are different, determining difference data where the difference occurs, the difference data including item identifiers belonging to different classification granularities; The multiple mapping tables are updated based on item identifiers belonging to different classification granularities in the difference data.

4. The method according to any one of claim 3, characterized in that: Based on the identification of items belonging to different classification granularities of the difference data, the multiple mapping tables are updated, including: Determine, from the plurality of mapping tables, at least one difference mapping table corresponding to the classification granularity to which each item identifier in the difference data belongs; Based on the difference data, the item identifier in the difference mapping table is updated.

5. The method according to any one of claims 1 to 4, characterized in that: The method is applied to a blockchain, wherein the blockchain includes the node belonging to the first role and the node belonging to the second role; Obtain raw valuation data from the node belonging to the first role, including: Acquire the original valuation data from the blockchain; the original valuation data is uploaded to the blockchain by the node belonging to the first role; Uploading the target valuation data to a node belonging to the second role; The target valuation data is uploaded to the blockchain.

6. A data processing device, characterized in that: The device comprises: A data acquisition module, used to obtain original valuation data from a node belonging to the first role, wherein the original valuation data includes valuation detail data of a plurality of assets; the original valuation data is valuation data belonging to a target annuity plan; A data arrangement module is used to reconstruct the valuation detail data of the multiple assets multiple times according to different classification granularities in turn to obtain target valuation data that meets the target classification standard, wherein the nth reconstruction is performed on the valuation data obtained by the n-1th reconstruction; n is a positive integer greater than or equal to 1; A data uploading module, used for uploading the target valuation data to a node belonging to the second role; The first role is the custodian role in the target annuity plan, and the second role is a role in the target annuity business that is different from the custodian role; The data sorting module comprises: A mapping table obtaining unit, used to obtain a plurality of mapping tables from the node belonging to the second role, wherein the same mapping table includes at least one item identifier under the same classification granularity, and different mapping tables have different classification granularities; A classification reconstruction unit, configured to iteratively match the valuation detail data of the multiple assets with the at least one item identifier in the multiple mapping tables in order of the size of the classification granularity, so as to realize multiple reconstructions of the valuation detail data of the multiple assets; The device also includes the following modules: A sample data acquisition module is used to obtain a valuation data sample, wherein the valuation data sample includes: a target valuation data sample, an original valuation data sample, a difference data sample after verifying the target valuation data sample, and a difference mapping table sample corresponding to the difference data sample; A training module, used to train a preset decision tree network using the estimated data samples as training samples, until the difference between the difference data predicted by the decision tree network and the difference data samples meets a set condition, thereby obtaining the decision network model; An input module, used for inputting the target valuation data and the original valuation data into a decision network model; An acquisition module, used to obtain difference data between the original valuation data and the target valuation data output by the decision network model, wherein the difference data includes item identifiers belonging to different classification granularities; An updating module is used to update the multiple mapping tables based on the item identifiers belonging to different classification granularities in the difference data.

7. An electronic device, characterized in that: It comprises a memory and a processor; wherein the processor is configured with the data processing method according to any one of claims 1 to 5.

8. An annuity trading platform, characterized in that: The trading platform is configured with a data processing method as described in any one of claims 1 to 5.

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