Investment product data association method and system and electronic equipment

Abstract

The embodiment of the invention provides an investment product data association method and system and electronic equipment, and relates to the technical field of computer application. The investment product data association method comprises the following steps: acquiring a plurality of investment product data records in a plurality of data sources; for each investment product data record, extracting a key field of the investment product data record, the key field being used for identifying an investment product corresponding to the investment product data record; calculating the similarity of key fields of different investment product data records; under the condition that the similarity of the key fields of the different investment product data records reaches a preset similarity condition, establishing an association relationship of the different investment product data records; and merging the investment product data records with the association relationship to obtain a merged data record. Through the investment product data association method and system and the electronic equipment provided by the embodiment of the invention, the accuracy of data association of the same investment product can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer application, in particular to an investment product data correlation method, system and electronic equipment. BACKGROUND

[0002] The data related to investment products plays a very important role in investment decision-making and the like. In order to better support investment decision-making and the like, the data related to investment products needs to be managed. In the related art, the data related to investment products is generally scattered, and in order to better integrate the data related to investment products, an investment product data correlation method is urgently needed. SUMMARY

[0003] The purpose of the embodiments of the present application is to provide an investment product data correlation method, system and electronic equipment to improve the accuracy of correlation of the same investment product data. The specific technical solutions are as follows:

[0004] In a first aspect, an investment product data correlation method is provided, comprising:

[0005] Obtaining a plurality of investment product data records in a plurality of data sources;

[0006] For each investment product data record, extracting a key field of the investment product data record, the key field being used to identify an investment product corresponding to the investment product data record;

[0007] Calculating the similarity of the key fields of different investment product data records;

[0008] In the case where the similarity of the key fields of different investment product data records reaches a preset similarity condition, establishing a correlation relationship of the different investment product data records;

[0009] Merging the investment product data records related to the established correlation relationship to obtain a merged data record.

[0010] Optionally, the key field includes a key field corresponding to each key data type; the similarity of the key fields of different investment product data records reaching a preset similarity condition includes: the similarity corresponding to each key data type of the key fields of each key data type of different investment product data records respectively reaching a similarity condition corresponding to each key data type.

[0011] The calculation of the similarity of the key fields of different investment product data records includes:

[0012] Calculating the similarity corresponding to each key data type of the key fields of each key data type of different investment product data records.

[0013] Optionally, after the association relationship of the different investment product data records is established, the method further comprises:

[0014] The data source and data change information corresponding to the different investment product data records are recorded in the association relationship.

[0015] Optionally, after the data source and data change information corresponding to the different investment product data records are recorded in the association relationship, the method further comprises:

[0016] According to the different data sources of the different investment product data records for which the association relationship is established, a hierarchical structure of the different investment product data records for which the association relationship is established is constructed, the hierarchical structure is used to distinguish the data quality levels of the investment product data records, and the hierarchical structure comprises a main data source and an auxiliary data source.

[0017] Optionally, after the investment product data records for which the association relationship is established are merged to obtain the merged data records, the method further comprises:

[0018] A user interaction interface is displayed, and the user interaction interface comprises at least one of the following contents: a query option, an analysis option, and an export option.

[0019] When it is detected that the query option in the user interaction interface is triggered, the merged data records are displayed; and / or,

[0020] When it is detected that the analysis option in the user interaction interface is triggered, an analysis submenu is displayed; an analysis instruction input through the analysis submenu is received; based on the analysis instruction, the merged data records are analyzed to obtain and display analysis processing results; and / or,

[0021] When it is detected that the export option in the user interaction interface is triggered, an export submenu is displayed; an export file identifier and a data time range input through the export submenu are received; target data matching the export file identifier and the data time range is retrieved from the merged data records; and a data report containing the target data is generated.

[0022] Optionally, after the investment product data records for which the association relationship is established are merged to obtain the merged data records, the method further comprises:

[0023] The merged data records are compared with historical stored data.

[0024] If the merged data record and the historical stored data both store data of the same investment product on the same day, the merged data record and the historical stored data are compared for data of the same investment product on the same day, and if the data are inconsistent, an exception is prompted.

[0025] Alternatively, for data of a target investment product on a target date, if the historical stored data do not store data of the target investment product on the target date, whether to prompt an exception is determined according to a preset strategy.

[0026] Optionally, after the investment product data records to be associated are merged to obtain the merged data record, the method further includes:

[0027] comparing the merged data record with a preset exception range;

[0028] If a data record in the merged data record falls within the preset exception range, an exception is prompted.

[0029] Optionally, after the plurality of investment product data records in the plurality of data sources are obtained, the method further includes:

[0030] preprocessing the obtained plurality of investment product data records, wherein the preprocessing at least includes at least one of the following operations: data cleaning, deduplication and formatting;

[0031] The key field extraction module is configured to extract, for each investment product data record, a key field of the investment product data record, the key field being used to identify an investment product corresponding to the investment product data record.

[0032] The key field extraction module is configured to extract, for each investment product data record, a key field of the investment product data record, the key field being used to identify an investment product corresponding to the investment product data record.

[0033] In a second aspect, an investment product data association system is provided, including:

[0034] An acquisition module is configured to acquire a plurality of investment product data records in a plurality of data sources.

[0035] A key field extraction module is configured to extract, for each investment product data record, a key field of the investment product data record, the key field being used to identify an investment product corresponding to the investment product data record.

[0036] A calculation module is configured to calculate a similarity of the key fields of different investment product data records.

[0037] The association module is configured to establish an association relationship between different investment product data records when the similarity of key fields of the different investment product data records reaches a preset similarity condition, and combine investment product data records associated with the association relationship to obtain a combined data record.

[0038] In a third aspect, an electronic device is provided, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus.

[0039] The memory is configured to store a computer program.

[0040] The processor is configured to execute the program stored in the memory to implement the method steps of any one of the first aspect.

[0041] The embodiment of the present application further provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method steps of any one of the first aspect.

[0042] The embodiment of the present application further provides a computer program product comprising instructions which, when executed on a computer, cause the computer to perform the investment product data association method of any one of the above aspects.

[0043] The embodiment of the present application has the following beneficial effects:

[0044] In the embodiment of the present application, the key field used to identify the investment product corresponding to the investment product data record is extracted, and the similarity of the key fields of different investment product data records is calculated, and the accuracy of the same investment product corresponding to different investment product data records can be improved by the similarity of the key fields of different investment product data records reaching the preset similarity condition. In the case where the similarity of the key fields of different investment product data records reaches the preset similarity condition, the association relationship between different investment product data records is established, and the investment product data records associated with the association relationship are combined to obtain a combined data record, which can improve the accuracy of identifying and associating the investment product data records of the same investment product, and can also be understood as improving the accuracy of associating the same investment product data.

[0045] Of course, implementing any product or method of the present application does not necessarily require all the advantages described above. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only represent some of the embodiments of the present application, and other embodiments can also be obtained by those skilled in the art based on these drawings.

[0047] Figure 1 The first flowchart of the investment product data association method provided by the embodiments of the present application is shown in the figure.

[0048] Figure 2 The second flowchart of the investment product data association method provided by the embodiments of the present application is shown in the figure.

[0049] Figure 3 The third flowchart of the investment product data association method provided by the embodiments of the present application is shown in the figure.

[0050] Figure 4 The fourth flowchart of the investment product data association method provided by the embodiments of the present application is shown in the figure.

[0051] Figure 5 The fifth flowchart of the investment product data association method provided by the embodiments of the present application is shown in the figure.

[0052] Figure 6 The sixth flowchart of the investment product data association method provided by the embodiments of the present application is shown in the figure.

[0053] Figure 7 The structure diagram of the investment product data association system provided by the embodiments of the present application is shown in the figure.

[0054] Figure 8 The structure diagram of the electronic device provided by the embodiments of the present application is shown in the figure. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only represent some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art based on the present application also belong to the scope of protection of the present application.

[0056] With reference to Figure 1 The embodiments of the present application provide an investment product data association method, which comprises:

[0057] S11, obtaining a plurality of investment product data records in a plurality of data sources;

[0058] S12, for each investment product data record, extracting a key field of the investment product data record, the key field being used to identify an investment product corresponding to the investment product data record;

[0059] S13, calculating a similarity of the key fields of different investment product data records;

[0060] S14, in a case where the similarity of the key fields of different investment product data records reaches a preset similarity condition, establishing an association relationship of the different investment product data records;

[0061] S15, merging investment product data records associated with the association relationship to obtain a merged data record.

[0062] In the embodiment of the present application, the key field used to identify the investment product corresponding to the investment product data record is extracted, and the similarity of the key fields of different investment product data records is calculated. The similarity of the key fields of different investment product data records reaching the preset similarity condition can improve the accuracy of different investment product data records corresponding to the same investment product. In the case where the similarity of the key fields of different investment product data records reaches the preset similarity condition, the association relationship of the different investment product data records is established, and the investment product data records associated with the association relationship are merged to obtain a merged data record, which can improve the accuracy of identifying and associating the investment product data records of the same investment product, and can also be understood as improving the accuracy of associating the same investment product data.

[0063] The embodiment of the present application solves the problem in the prior art that it is difficult to accurately identify and associate different data records belonging to the same investment product by relying on manual experience and manual matching, resulting in low accuracy of data association.

[0064] Moreover, the established data association relationship helps to form a complete investment product data file, providing a solid foundation for in-depth analysis, and the associated data is convenient for further fusion and analysis, improving the overall value of the data and enhancing the data integration.

[0065] Meanwhile, compared with relying on manual operation, the investment product data association method provided by the embodiment of the present application improves the efficiency of investment product data association.

[0066] The investment product data association method provided by the embodiment of the present application can be applied to an electronic device. Specifically, the electronic device can be a server, a controller, etc.

[0067] In S11, the plurality of data sources can be determined according to actual needs or experience, etc. For example, the plurality of data sources can include a third-party institution database, an enterprise internal database, an enterprise mailbox, and an enterprise local file, etc.

[0068] For example, when the investment product is a fund such as a private fund, the third-party institution database can be a third-party institution net worth database, the enterprise internal database can be an enterprise internal net worth database, the enterprise mailbox can include net worth file data, and the enterprise local file can be a local file including the net worth file data.

[0069] The investment product data records in the data sources can be acquired in the same manner or in different manners for different data sources.

[0070] In one manner, the investment product data records are acquired from the multiple dispersed data sources in real time. For example, each data source is monitored, and for a data source, if an investment product data record is generated in the data source, the newly generated investment product data record is acquired from the data source.

[0071] In another implementation manner, the investment product data records are acquired from each data source in the multiple dispersed data sources once every preset time period. For example, each data source stores the investment product data records generated by the data source, and the execution subject of the embodiment of the application acquires the investment product data records generated by each data source in the preset time period from each data source every preset time period. The preset time period can be determined according to actual requirements or experience, for example, 1 hour, 1 day, etc.

[0072] In another implementation manner, the investment product data records in the data sources are acquired in different manners for different data sources.

[0073] For example, for the database type data sources such as the third-party institution database and the enterprise internal database, the investment product data records in the databases can be acquired in a message system pushing manner. Specifically, when an investment product data record is generated in a database, the generated investment product data record is stored in a local message system, and the execution subject of the embodiment of the application acquires the investment product data record from the local message system of the database. The message system can be selected according to actual requirements, for example, it can be a distributed publish-subscribe message system (kafka).

[0074] For the data source being an enterprise mailbox, the investment product data records in the enterprise mailbox can be pulled through a mailbox protocol. For example, a preset file format can be set in the mailbox protocol, and the preset file format can be a file format in which the investment product data records can be generated in the enterprise mailbox, such as an excel file or a pdf file. In this way, when there is a file in the preset file format in the enterprise mailbox, the file is sent to the execution subject of the embodiment of the application.

[0075] For the data source being the enterprise local file, the investment product data record of the investment product is obtained by using the preset rule or the large language model. For example, in the embodiment of the present application, for each investment product, the investment product data record of the investment product is obtained by using the preset rule or the large language model. The investment product data record of an investment product can be obtained by using the large prediction model intelligent semantic similarity, that is, the investment product data record similar in semantics to the investment product is intelligently obtained by using the large prediction model. The large prediction model can be pre-trained, and the output is the semantic similarity between the investment product data record and an investment product. The investment product data record with a similarity greater than a threshold is obtained from the enterprise local file.

[0076] The investment product data record is a record of the related data of the investment product. The related data can be different types of data of different dimensions of the investment product.

[0077] For example, the investment product is a private equity fund, and the investment product data record can be a record of the net value data of the private equity fund.

[0078] The embodiment of the present application can also include a data preprocessing process. Specifically, after S11, the following can also be included:

[0079] The obtained plurality of investment product data records are preprocessed, wherein the preprocessing at least includes at least one of the following operations: data cleaning, deduplication and formatting.

[0080] Data cleaning includes identifying extreme data values, intelligently filling in missing data and labeling missing data. For example, a data range can be pre-set, and the data in the investment product data record is compared with the data range. If it is outside the data range, it is considered as an extreme value. At the same time, a data template can also be pre-set. The data template can include multiple fields required by the data. The data in the investment product data record is compared with the multiple fields in the data template. If one or more fields are missing, the data in the investment product data record is considered as missing data and is filled in and labeled as missing.

[0081] Data deduplication is used to delete duplicate data from the same source, and consistent data from different sources is deduplicated, such as keeping only one piece of data from different sources.

[0082] Formatting is used to unify data of different formats. For example, different dates, net values, etc. are unified, such as storing the date in the multiple investment product data records in the format of YYYY-mm-dd, YYYYmmdd, YYYY year mm month dd, etc.

[0083] Compared with the prior art, the data preprocessing process provided by the embodiment of the present application not only improves the efficiency of data preprocessing, but also improves the data quality, provides a basis for subsequent data association, and solves the problem of difficult data fusion caused by inconsistent data formats and structures from different sources and relying on manual operation in the prior art.

[0084] In addition, the automatic data preprocessing process realizes efficient data cleaning and the like.

[0085] In S12, the key field is used to identify the investment product data record corresponding to the investment product. In one way, the investment product data records with the same key field represent the data records of the same investment product.

[0086] The key field can include key fields corresponding to a plurality of key data types respectively.

[0087] For example, when the investment product is a private fund, the plurality of key data types can include fund name, management team, establishment date, etc., and the key fields corresponding to the plurality of key data types respectively represent the specific values of the fund name, the management team, and the establishment date.

[0088] In one implementable manner, when the plurality of investment product data records obtained are preprocessed after S11, S12 specifically includes:

[0089] For each investment product data record after preprocessing, the key field of each investment product data record after preprocessing is extracted.

[0090] For each investment product data record, the process of extracting the key field of each investment product data record after preprocessing can also be understood as the process of field analysis and field matching.

[0091] In S13, any way of calculating similarity can be used to calculate the similarity of the key fields of different investment product data records, and the embodiment of the present application does not limit the way of calculating similarity.

[0092] In one implementable manner, the similarity of the key fields of two investment product data records in the plurality of investment product data records can be calculated respectively.

[0093] For example, for each investment product data record, the similarity between the key field of the investment product data record and the key field of each investment product data record other than the investment product data record is calculated respectively. Alternatively, two investment product data records are randomly selected in sequence, and the similarity of the key fields of the selected two investment product data records is calculated respectively, until the similarity between the key field of each investment product data record and the key field of each investment product data record other than the investment product data record is calculated for each investment product data record in the plurality of investment product data records.

[0094] The similarity between the key fields of any two investment product data records can be calculated in any manner. For example, whether the key fields of the two investment product data records are the same is compared. Alternatively, the key fields of the two investment product data records can be first vectorized, and the similarity between the vectorized key fields of the two investment product data records is calculated.

[0095] When the key fields include key fields corresponding to different types of key data respectively, S13 can include: calculating the similarity of the key fields corresponding to each type of key data respectively for different investment product data records.

[0096] In an implementation manner, for each key field in each investment product data record, the similarity between each key field of the investment product data record and each key field of each investment product data record other than the investment product data record is calculated respectively. Alternatively, two investment product data records are randomly selected in sequence, and the similarity of each key field of the selected two investment product data records is calculated respectively, until the similarity between each key field of each investment product data record and each key field of each investment product data record other than the investment product data record is calculated for each investment product data record in the plurality of investment product data records.

[0097] For the two investment product data records whose each key field is to be calculated, for each type of key data, whether the key fields of the two investment product data records under the type of key data are the same is compared respectively. Alternatively, for each type of key data, the key fields of the two investment product data records under the type of key data are first vectorized, and the similarity between the vectorized key fields of the two investment product data records under the type of key data is calculated.

[0098] The process of calculating the similarity of the key fields of each key data type of different investment product data records respectively can also be understood as calculating the similarity between different data records by using a text mining technique and a similarity determination algorithm to identify records belonging to the same investment product, such as identifying records of the same fund.

[0099] In S14, the similarity of the key fields of different investment product data records reaches a preset similarity condition, that is, the different investment product data records can be understood as belonging to the same investment product. In the case where the similarity of the key fields of different investment product data records reaches the preset similarity condition, the association relationship between different investment product data records is established, which can also be understood as associating investment product data records of the same investment product.

[0100] The similarity reaching the preset similarity condition can be determined according to the specific way of calculating the similarity.

[0101] For example, when the process in S13 is to compare whether the key fields of different investment product data records are the same, the similarity reaching the preset similarity condition in S14 can be that the key fields of different investment product data records are the same.

[0102] When the process in S13 is to calculate the similarity between vectors, the similarity reaching the preset similarity condition can be understood as the calculated similarity being greater than or equal to a preset similarity threshold. The preset similarity threshold can be determined according to actual requirements or experience, etc. For example, 100%, 90%, etc.

[0103] When the key fields include key fields corresponding to multiple key data types respectively, the similarity of the key fields of different investment product data records reaching the preset similarity condition includes: the similarity of the key fields of each key data type of different investment product data records respectively reaching a similarity condition corresponding to each key data type.

[0104] In this case, when the process in S13 is to compare whether the key fields of different investment product data records are the same, the similarity of the key fields of each key data type of different investment product data records respectively reaching a similarity condition corresponding to each key data type can be understood as: for each key data type, the key fields of different investment product data records under each key data type are the same.

[0105] When the process in S13 is to calculate the similarity of the key fields of the investment product data records to be calculated, the similarity reaches the preset similarity condition can be understood as the similarity corresponding to the key fields of each key data type of different investment product data records respectively being greater than or equal to the similarity threshold corresponding to each key data type. The similarity threshold corresponding to each key data type can be the same or different.

[0106] In S14, when the similarity of the key fields of different investment product data records reaches the preset similarity condition, the association relationship of the different investment product data records is established, that is, the different investment product data records whose similarity of the key fields reaches the preset similarity condition are associated. For example, the different investment product data records whose similarity of the key fields reaches the preset similarity condition are stored correspondingly.

[0107] S14 is the process of association establishment, that is, the association relationship between the data records is established according to the result of the similarity analysis.

[0108] In S15, the investment product data records associated are merged, which can also be understood as the investment product data records associated are fused, and specifically, the investment product data records associated can be formed into a complete data view.

[0109] The data from different sources is integrated into a unified data view, which is convenient for a user to comprehensively understand the situation of the investment product.

[0110] The investment product data records associated can be understood as the investment product data records of the same investment product, and in the process of forming a complete data view from the investment product data records associated, the investment product data records of one investment product can be formed into one view, or the investment product data records of each investment product can be respectively displayed on one view.

[0111] Specifically, forming a complete data view can also be understood as displaying the investment product data records associated in a visual manner. For example, the merged data records are displayed through a table or a chart.

[0112] S15 can also be called the process of data integration.

[0113] In the embodiment of the application, the similarity determination and data association are used to ensure the accuracy of the investment product data, and the data fusion is used to form a unified data view for the investment product.

[0114] In an optional embodiment, as Figure 2As shown, after the association relationship between the different investment product data records is established in S14, the method can further include:

[0115] S21, recording the data source and the data change information corresponding to the different investment product data records in the association relationship.

[0116] The data table can include the change time and the change content.

[0117] The data source and the data change information can also be referred to as the version information of the data record. S21 can be understood as recording the version information of each data update in the data fusion process, that is, the version record.

[0118] In an optional embodiment, as shown, Figure 3 After S21, the method further includes:

[0119] S31, constructing a hierarchical structure of the different investment product data records with which the association relationship is established, according to the different data sources of the different investment product data records with which the association relationship is established, the hierarchical structure being used to distinguish the data quality levels of the investment product data records, and the hierarchical structure including a main data source and an auxiliary data source.

[0120] For example, the enterprise internal database, the enterprise mailbox, and the enterprise local file are marked as the main data source of the high-quality level, and the third-party agency database is marked as the auxiliary data source.

[0121] This process is the process of constructing the data hierarchy. Through the construction of the data hierarchy, the availability and applicability of the investment product data are improved.

[0122] The constructed data hierarchy structure enables the user to quickly locate the key data, such as quickly finding the main data source of the higher-quality level, according to the needs, and improves the readability and ease of use of the investment product data.

[0123] Further, on the basis of distinguishing the core data source and the auxiliary data source, the user is supported to select the appropriate data according to the scene, and this process can also be understood as the process of data marking.

[0124] For example, in the daily research scene of the private equity fund, the core data source and the auxiliary data source are used at the same time to expand the data range.

[0125] In the embodiment of the present application, the detailed history of data update is provided through version control and data marking, so that the changes and sources of data for investment products are clear and visible, and the transparency of investment product data is enhanced. Moreover, the version information of data facilitates the user to trace the change record of data, enhances the traceability of investment product data, and improves the credibility of data for investment products. Meanwhile, the user can select suitable data for analysis according to the version information and data marking, meets the needs in different scenarios, and improves the flexibility of application of investment product data. The sources and update states of data are recorded at the same time of data fusion, so that the user can quickly select and use suitable data according to specific scenarios and needs, improves the availability and applicability of data, and realizes flexible data version control and marking.

[0126] In an optional embodiment, as shown in FIG. 15, after S15, the following can also be included: Figure 4

[0127] S41, displaying a user interaction interface, the user interaction interface including at least one of the following contents: a query option, an analysis option, and an export option;

[0128] And S42, S43, and / or S44 can be performed.

[0129] S42, when detecting triggering of the query option in the user interaction interface, displaying the merged data record;

[0130] S43, when detecting triggering of the analysis option in the user interaction interface, displaying an analysis submenu; receiving an analysis instruction input through the analysis submenu; based on the analysis instruction, performing analysis processing on the merged data record, obtaining and displaying an analysis processing result;

[0131] S44, when detecting triggering of the export option in the user interaction interface, displaying an export submenu; receiving an export file identifier and a data time range input through the export submenu; retrieving target data matching the export file identifier and the data time range from the merged data record; generating a data report containing the target data.

[0132] Wherein, the triggering of the query option, the analysis option, and / or the export option can include clicking and the like.

[0133] In this way, the user can set the fields of the export file, the data time range of the export file, and the like. The self-defined data report generation and data visualization are supported, the personalized data display needs of the user are met, and customization for investment product data is realized.

[0134] ​The embodiment of the present application provides an intuitive user interface for users to query, analyze and export data, so that the users can easily query, analyze and export data, and the complexity of operation is reduced.

[0135] In addition, the fused data supports deeper data analysis and mining, and provides more abundant information for investment product research and decision-making.

[0136] In an optional embodiment, as shown in FIG. 15, after S15, the process can further include: Figure 5

[0137] S51, comparing the merged data record with the historical stored data;

[0138] And S52 or S53 can be performed.

[0139] S52, if the merged data record and the historical stored data both store data of the same investment product on the same day, comparing the data of the same investment product on the same day stored in the merged data record and the historical stored data, and in the case that the data is inconsistent, prompting an exception;

[0140] S53, for the data of the target investment product on the target date, if there is no data of the target investment product on the target date in the historical stored data, determining whether to prompt an exception according to a preset strategy.

[0141] The target date can be any date, which can be determined according to actual needs.

[0142] The preset strategy can indicate whether to start the 'history empty alarm', for example, starting the 'history empty alarm' by the preset strategy, and not starting the 'history empty alarm' by the preset strategy. For example, when the preset strategy indicates to start, the system automatically marks 'data missing' and alarms; and if the preset strategy indicates not to start, when there is no data of the target investment product on the target date in the historical stored data, no exception is prompted.

[0143] Figure 5 As shown in FIG. 15, this process can also be called a consistency checking process.

[0144] ​For example, the combined data record is a data record of net value data of a private equity fund. If the net value data of a product on a certain date already exists, data comparison will be performed during secondary update. If the newly accessed net value data is inconsistent with the historical stored data, an exception will be prompted. If the historical stored data does not exist, whether the user pre-set historical empty exception is prompted for corresponding processing. Whether the user pre-set historical empty exception is prompted for corresponding processing specifically includes determining whether to prompt an exception according to the pre-set strategy if the historical stored data does not contain data of the target investment product on the target date.

[0145] In an optional embodiment, as shown in FIG. 6, Figure 6 after S15, the process can further include:

[0146] S61, comparing the combined data record with a pre-set exception range;

[0147] S62, if the data record in the combined data record falls within the pre-set exception range, prompting an exception.

[0148] Figure 6 As shown in FIG. 6, the process can also be understood as an exception detection process.

[0149] Figure 5 As shown in FIG. 6, the consistency check process and Figure 6 the exception detection process can be collectively referred to as an automatic verification process, which realizes intelligent verification.

[0150] On the basis of automatic verification, the process can further include a manual review process in the embodiment of the present application. For the problem data found in automatic verification, a manual review interface is provided to allow the business team to intervene and correct. Specifically, on the basis of Figure 5 and Figure 6 , the review interface can be displayed while prompting an exception. The review interface can include exception-related data, so that the person in charge of the business team can review according to the exception-related data displayed in the review interface. Further, the review interface can include a correction input box, and the person in charge can input the corrected data through the review interface. In this way, manual intervention is supported on the basis of automatic verification.

[0151] The mechanism of combining automatic verification and manual intervention in the embodiments of the present application ensures the accuracy and reliability of the investment product data, the timely discovery and correction of abnormal data reduces the research and investment risks caused by data errors, and risk control is realized. At the same time, the manual intervention link enables the business team to participate in the data quality management of the investment product, improves the transparency of the data management of the investment product and the satisfaction of the user. The intelligent data verification is introduced, which can automatically identify abnormal data during data fusion and prompt. When the inconsistent data points are prompted as described above, manual intervention is supported to ensure the accuracy and reliability of the data. This mechanism not only improves the efficiency of data processing, but also provides double protection for data quality.

[0152] Through the above, the embodiments of the present application solve the key problems of data management and application in the investment product industry and provide a brand-new data fusion solution. Not only the comprehensiveness and accuracy of the investment product data are improved, but also the efficiency of data management and the quality of research and investment decision are significantly improved through intelligent data processing and verification technology. In addition, the data version control and data marking process of the embodiments of the present application provide a more flexible and convenient data use experience for users, so that the research and investment of investment products and investment decisions are more scientific, reasonable and efficient.

[0153] In summary, for the investment product industry, it is a revolutionary technological progress, which not only solves the long-standing problems of the industry, but also provides strong data support and decision-making tools for the future development of investment products. Through the embodiments of the present application, the investment product management team will be able to more accurately and efficiently conduct research and analysis, creating greater value for investors.

[0154] The embodiments of the present application realize comprehensive integration, efficient management, accurate analysis and convenient use of investment product data, significantly improve the quality and efficiency of research and investment decisions of investment products, and provide strong technical support for the development of investment products. Through a series of data management and processing technologies, comprehensive integration, efficient cleaning, intelligent verification and flexible fusion of investment product data are realized, thereby providing an accurate, comprehensive and hierarchical investment product data source.

[0155] In one example, the investment product in the embodiments of the present application can be a private equity fund, which realizes intelligent fusion and verification management of comprehensive private equity fund data.

[0156] Private equity funds, as an important part of the financial market, provide investors with diverse investment options and relatively high potential returns. However, the data management and application of private equity funds face many challenges. Due to the lack of unified standards and norms in the disclosure of private equity fund data, there is a lack of a comprehensive and unified private equity data source in the market. This leads to the incompleteness and inaccuracy of data in the investment research and decision-making process of private equity funds. Although existing data providers provide some private equity fund data, these data often have incomplete coverage, are not updated in a timely manner, and are difficult to combine with local private data in the hands of business teams, limiting the depth and breadth of private equity fund research.

[0157] In the existing practice of private equity fund data management, data collection and processing mainly rely on manual operation and simple data processing tools, or designated third-party data providers. Manual operation and simple data processing tools but due to the lack of automated tools, these steps are often inefficient and prone to errors; third-party data providers this means that the quality of the data is not guaranteed, and the data from different providers is isolated. Data correlation and integration mainly rely on the experience and manual matching of business teams, and there is a lack of systematic methods to identify and correlate different data records belonging to the same fund. Data verification usually relies on manual review, but due to the large amount of data and diverse sources, this method is difficult to ensure the accuracy and consistency of the data.

[0158] The prior art relies on manual experience and manual matching, and it is difficult to accurately identify and associate different data records belonging to the same fund, resulting in low accuracy of data association. The embodiment of the present application obtains a plurality of private equity fund data records in a plurality of data sources; for each private equity fund data record, extracts the key field of the private equity fund data record, the key field being used to identify the private equity fund corresponding to the private equity fund data record; calculates the similarity of the key fields of different private equity fund data records; establishes an association relationship between different private equity fund data records when the similarity of the key fields of different private equity fund data records reaches a preset similarity condition; and merges the private equity fund data records with the established association relationship to obtain a merged data record. The key field used to identify the private equity fund corresponding to the private equity fund data record is extracted, and the similarity of the key fields of different private equity fund data records is calculated. The accuracy of different private equity fund data records corresponding to the same private equity fund can be improved by the similarity of the key fields of different private equity fund data records reaching the preset similarity condition. In the case where the similarity of the key fields of different private equity fund data records reaches the preset similarity condition, the association relationship between different private equity fund data records is established, and the private equity fund data records with the established association relationship are merged to obtain a merged data record, which can improve the accuracy of identifying and associating private equity fund data records of the same private equity fund, and can also be understood as improving the accuracy of the same private equity fund data association. It can be understood that the similarity determination and data association are used to ensure the accuracy of data association, and the problem of poor data association accuracy is solved. It can also be understood that the problem of relying on manual operation in the prior art, which is prone to human error and affects data quality, is solved, and the data quality of associated data is improved.

[0159] Moreover, the problem of tedious data preprocessing steps and low efficiency of relying on manual operation in the prior art is solved, the data processing efficiency is improved, and the efficiency of data association is improved.

[0160] In addition, through data preprocessing and further forming a complete data view by the private equity fund data records with the established association relationship, the problem of difficult data fusion due to inconsistent data format and structure from different sources in the prior art is solved.

[0161] At the same time, through Figure 5 and Figure 6 the automatic verification process, the problem of relying mainly on manual review for data verification in the prior art, which is difficult to ensure the accuracy and consistency of data when facing a large amount of data, and is prone to missing errors and exceptions, is solved, and the problem of insufficient data is avoided.

[0162] The embodiment of the present application realizes the comprehensiveness of data by integrating private equity fund data from multiple dispersed data sources, forming a unified and comprehensive database, introducing data preprocessing and similarity determination algorithms, fusing data, and forming a more comprehensive data source. The data comprehensiveness in the prior art is solved, which is due to the non-standard data disclosure, lack of unified planning in private equity fund data sources, and data comprehensiveness. Through mutual verification, abnormal data is judged, and the opportunity for manual intervention is provided, further improving the accuracy of data. Through a user-friendly interface, the business team can easily manage and use data, while providing flexible data fusion and version control functions, realizing the ease of use of data. The accurate association and comprehensive integration of multi-source investment product data such as private equity fund data, and the efficient data verification and quality control mechanism are realized, ensuring the high accuracy and reliability of the data. In addition, it also has flexible version control and user-friendly data access interface, greatly improving the usability and user experience of investment product data such as private equity fund data. In general, a comprehensive and hierarchical data fusion solution is provided, realizing efficient management, accurate analysis and convenient use of private equity fund data, thereby significantly improving the quality and efficiency of private equity fund investment research and investment decision-making. Not only the comprehensiveness and accuracy of private equity fund data are improved, but also the efficiency of data management and the quality of research and decision-making are significantly improved through intelligent data processing and verification technology. In addition, the data version control and marking system in the embodiment of the present application provides a more flexible and convenient data use experience for users, making the investment research and investment decision-making of private equity funds more scientific, reasonable and efficient.

[0163] Corresponding to the investment product data association method provided by the above embodiment, the embodiment of the present application also provides an investment product data association system, as shown in Figure 7 The investment product data association system comprises:

[0164] An acquisition module 701 is configured to acquire a plurality of investment product data records in a plurality of data sources.

[0165] A key field extraction module 702 is configured to extract a key field of each investment product data record, the key field being used to identify an investment product corresponding to the investment product data record.

[0166] A calculation module 703 is configured to calculate a similarity of the key fields of different investment product data records.

[0167] An association module 704 is configured to establish an association relationship between different investment product data records when the similarity of the key fields of the different investment product data records reaches a preset similarity condition, and to combine the investment product data records associated with the association relationship to obtain a combined data record.

[0168] Optionally, the key fields include key fields corresponding to a plurality of key data types respectively; the similarity of the key fields of the different investment product data records reaching a preset similarity condition includes: the similarity of the key fields of each key data type of the different investment product data records respectively reaching a similarity condition corresponding to each key data type.

[0169] The computing module 703 is specifically configured to calculate the similarity of the key fields of each key data type of the different investment product data records respectively.

[0170] Optionally, the system further includes:

[0171] The version recording module is configured to record the data sources and the data change information corresponding to the different investment product data records in the association relationship after the association relationship between the different investment product data records is established.

[0172] Optionally, the system further includes:

[0173] The hierarchical structure constructing module is configured to, after the data sources and the data change information corresponding to the different investment product data records are recorded in the association relationship, construct a hierarchical structure of the different investment product data records for which the association relationship is established according to the differences in the data sources of the different investment product data records for which the association relationship is established, the hierarchical structure being used to distinguish the data quality levels of the investment product data records, and the hierarchical structure including a main data source and an auxiliary data source.

[0174] Optionally, the system further includes:

[0175] The user interaction interface display module is configured to, after the investment product data records for which the association relationship is established are merged to obtain the merged data records, display a user interaction interface, the user interaction interface including at least one of the following contents: a query option, an analysis option and an export option.

[0176] The interaction module is configured to, when detecting a trigger of the query option in the user interaction interface, display the merged data records; and / or, when detecting a trigger of the analysis option in the user interaction interface, display an analysis submenu; receive an analysis instruction input through the analysis submenu; based on the analysis instruction, perform analysis processing on the merged data records to obtain and display an analysis processing result; and / or, when detecting a trigger of the export option in the user interaction interface, display an export submenu; receive an export file identifier and a data time range input through the export submenu; search target data matching the export file identifier and the data time range from the merged data records; and generate a data report containing the target data.

[0177] Optionally, the system further includes:

[0178] The first data verification module is used to merge the data records of investment products with established associations, and after obtaining the merged data records, compare the merged data records with the historically stored data; if the merged data records and the historically stored data both store data of the same investment product on the same day, then compare the data of the same investment product on the same day stored in the merged data records and the historically stored data, and if the two are inconsistent, prompt an abnormality; or, for the data of the target investment product on the target date, if there is no data of the target investment product on the target date in the historically stored data, determine whether to prompt an abnormality according to a preset strategy.

[0179] Optionally, the system further comprises:

[0180] The second data verification module is used to merge the investment product data records with established associations, and after obtaining the merged data records, compare the merged data records with a preset abnormal range; if the data records in the merged data records fall into the preset abnormal range, an abnormality is prompted.

[0181] Optionally, the system further comprises:

[0182] a data preprocessing module, configured to, after acquiring a plurality of investment product data records from a plurality of data sources, preprocess the acquired plurality of investment product data records, wherein the preprocessing comprises at least one of the following operations: data cleaning, deduplication, and formatting;

[0183] The key field extraction module 702 is specifically configured to extract the key fields of each pre-processed investment product data record.

[0184] The embodiment of the present invention further provides an electronic device, such as Figure 8 As shown, it includes a processor 801 , a communication interface 802 , a memory 803 and a communication bus 804 , wherein the processor 801 , the communication interface 802 and the memory 803 communicate with each other via the communication bus 804 .

[0185] Memory 803, used for storing computer programs;

[0186] The processor 801 is configured to implement the steps of the above-mentioned investment product data association method when executing the program stored in the memory 803 .

[0187] The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0188] The communication interface is used for communication between the above electronic device and other devices.

[0189] The memory can include a Random Access Memory (RAM) and can also include a Non-Volatile Memory (NVM), for example, at least one disk memory. Optionally, the memory can also be at least one storage device located away from the aforementioned processor.

[0190] The processor mentioned above can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0191] In another embodiment provided by the application, a computer readable storage medium is also provided, and the computer readable storage medium stores a computer program. When the computer program is executed by a processor, the steps of any investment product data association method described above are implemented.

[0192] In another embodiment provided by the application, a computer program product containing instructions is also provided, and when the computer program product is run on a computer, the computer is caused to execute any investment product data association method in the above embodiments.

[0193] In the embodiments described above, all or some of the steps can be implemented by software, hardware or firmware, or any combination thereof. When implemented by software, all or some of the steps can be implemented in the form of one or more computer programs. The computer program can be stored in any computer readable medium, and loaded into the computer system for execution. The computer readable medium includes: a computer storage medium and a computer communication medium. The computer storage medium includes: volatile media (such as random access memory (RAM) and others) and non-volatile media (such as read-only memory (ROM), floppy disks, CD-ROMs, optical disks, hard disks, etc.). The computer communication medium includes: computer networks and other media.

[0194] It should be noted that, in this document, the terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. In addition, the terms "comprise", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or other elements inherent to such a process, method, article or device. Without more limitations, the element defined by the statement "comprises a" does not exclude the presence of additional identical elements in the process, method, article or device that includes the element.

[0195] Each of the embodiments in the specification is described in a related manner, and the same or similar parts between each of the embodiments can be referred to each other. Each of the embodiments focuses on the difference from other embodiments. In particular, for system, electronic device, computer readable storage medium and computer program product embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0196] The above merely describes the preferred embodiments of the present application, but is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for associating investment product data, characterized in that: include: Obtain multiple investment product data records from multiple data sources; For each investment product data record, extract a key field of the investment product data record, where the key field is used to identify the investment product corresponding to the investment product data record; Calculate the similarity of key fields of data records of different investment products; When the similarity of the key fields of different investment product data records reaches a preset similarity condition, establishing an association relationship between the different investment product data records; Merge the associated investment product data records to obtain merged data records.

2. The method according to claim 1, characterized in that The key fields include key fields corresponding to a plurality of key data types; the similarity of the key fields of different investment product data records meets a preset similarity condition, including: the similarity corresponding to the key fields of each key data type of different investment product data records respectively meets the similarity condition corresponding to each key data type; The calculation of the similarity of key fields of different investment product data records includes: Calculate the similarity of key fields of each key data type for different investment product data records.

3. The method according to claim 1, characterized in that After establishing the association relationship between the different investment product data records, the method further includes: The data sources and data change information corresponding to the different investment product data records are recorded in the association relationship.

4. The method according to claim 3, characterized in that After recording the data sources and data change information corresponding to the different investment product data records in the association relationship, the method further includes: In view of the differences in data sources of different investment product data records with established associations, a hierarchical structure of different investment product data records with established associations is constructed. The hierarchical structure is used to distinguish the data quality levels of the investment product data records. The hierarchical structure includes a primary data source and an auxiliary data source.

5. The method according to claim 1, wherein After merging the associated investment product data records to obtain the merged data records, the method further includes: Displaying a user interaction interface, wherein the user interaction interface includes at least one of the following: a query option, an analysis option, and an export option; When a triggering of a query option in the user interaction interface is detected, displaying the merged data record; and / or, When a triggering of the analysis option in the user interaction interface is detected, an analysis submenu is displayed; an analysis instruction input through the analysis submenu is received; based on the analysis instruction, the merged data records are analyzed and processed to obtain and display the analysis results; and / or, When the triggering of the export option in the user interaction interface is detected, an export submenu is displayed; an export file identifier and a data time range input through the export submenu are received; target data matching the export file identifier and the data time range are retrieved from the merged data records; and a data report containing the target data is generated.

6. The method according to claim 1, characterized in that After merging the associated investment product data records to obtain the merged data records, the method further includes: comparing the merged data record with historical stored data; If both the merged data record and the historical stored data store data for the same investment product on the same day, then compare the data for the same investment product on the same day stored in the merged data record and the historical stored data, and if the two are inconsistent, prompt an exception; Alternatively, for the data of the target investment product on the target date, if there is no data of the target investment product on the target date in the historical storage data, it is determined according to a preset strategy whether to prompt an abnormality.

7. The method according to claim 1, characterized in that After merging the associated investment product data records to obtain the merged data records, the method further includes: comparing the merged data record with a preset abnormal range; If a data record in the merged data record falls within the preset abnormal range, an abnormality prompt is given.

8. The method according to any one of claims 1 to 7, characterized in that After obtaining a plurality of investment product data records from a plurality of data sources, the method further includes: Preprocessing the acquired multiple investment product data records, wherein the preprocessing includes at least one of the following operations: data cleaning, deduplication, and formatting; The step of extracting key fields of each investment product data record includes: For each pre-processed investment product data record, a key field of each pre-processed investment product data record is extracted.

9. An investment product data association system, characterized in that: include: An acquisition module, used to acquire multiple investment product data records from multiple data sources; A key field extraction module, configured to extract, for each investment product data record, a key field of the investment product data record, wherein the key field is used to identify the investment product corresponding to the investment product data record; A calculation module, used to calculate the similarity of key fields of data records of different investment products; The association module is used to establish an association relationship between different investment product data records when the similarity of key fields of different investment product data records reaches a preset similarity condition; and merge the investment product data records with established association relationships to obtain merged data records.

10. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory for storing computer programs; A processor, configured to implement the method steps described in any one of claims 1 to 8 when executing a program stored in a memory.

Images