Asset data quality collaboration method and device, equipment, storage medium and product

Abstract

The invention discloses an asset data quality collaboration method and device, equipment, a storage medium and a product, and relates to the technical field of data quality management.The asset data quality collaboration method comprises the steps that real-time asset data and historical asset slice data are acquired; determining a first application scene of the real-time asset data and a second application scene of the historical asset slice data; determining an association relationship between the first application scene and the second application scene; acquiring asset real-time service data; and performing data error elimination on the real-time asset data and the historical asset slice data through the asset real-time service data based on the association relationship to obtain quality collaborative asset data. Mutual verification is performed through the real-time asset data and the historical asset slice data, so that the coverage rate of data verification is increased, the influence of real-time scenes such as data caching, net value fluctuation and customer transaction is eliminated through data error elimination, and the accuracy of data verification is improved.

Description

Technical Field

[0001] The present application relates to the field of data quality management technology, and in particular to asset data quality collaborative methods, devices, equipment, storage media and products. Background Art

[0002] The real-time asset platform provides customers with real-time asset inquiries, and the historical asset platform can provide customers with end-of-day data inquiries within the past year. This is a big challenge because the two sets of data from customers at the same time need to ensure strong consistency at the end of the day. This challenge also brings opportunities. We can help each other in the process of quality verification and find each other's problems. Due to multiple complex scenarios such as real-time changes in the net value of wealth management products, fluctuations in foreign exchange rates, and real-time transactions of customers, the collaborative accuracy and mutual verification of asset data output poses severe and complex challenges:

[0003] Currently, there is no platform or system in the industry that can display the data details of each day of asset history. Most of them only display the assets of a fixed day in history, and the verification coverage and accuracy of asset data quality are poor. Summary of the invention

[0004] The main purpose of this application is to provide an asset data quality collaboration method, device, equipment, storage medium and product, aiming to solve the technical problems that the existing asset display usually displays the assets of a fixed historical day, and the verification coverage and accuracy of asset data quality are poor.

[0005] To achieve the above objectives, the present application proposes an asset data quality collaboration method, which includes:

[0006] Obtain real-time asset data and historical asset slice data;

[0007] Determine a first application scenario of the real-time asset data and a second application scenario of the historical asset slice data;

[0008] Determining an association relationship between the first application scenario and the second application scenario;

[0009] Obtain real-time service data of assets;

[0010] Based on the association relationship and through the real-time asset service data, data errors of the real-time asset data and the historical asset slice data are eliminated to obtain quality collaborative asset data.

[0011] In one embodiment, the step of acquiring real-time asset data and historical asset slice data includes:

[0012] Obtain the user account overview data within the target time period at the target time point as the benchmark real-time data;

[0013] Back-calculating is performed based on the benchmark real-time data to obtain the historical asset slice data at the target time point.

[0014] In one embodiment, the step of eliminating data errors of the real-time asset data and the historical asset slice data based on the association relationship and using the real-time asset service data to obtain the quality collaborative asset data includes:

[0015] When the association relationship is a real-time transaction scenario association, real-time transaction data errors are eliminated on the real-time asset data and the historical asset slice data through the real-time asset service data to obtain first quality collaborative asset data;

[0016] When the association relationship is a net value scenario association, the net value historical data error is eliminated for the real-time asset data and the historical asset slice data through the real-time asset service data to obtain the second quality collaborative asset data;

[0017] When the association relationship is a foreign currency scenario association relationship, the foreign currency exchange rate data errors are eliminated for the real-time asset data and the historical asset slice data through the real-time asset service data to obtain third quality collaborative asset data.

[0018] In one embodiment, the step of performing real-time transaction data error elimination on the real-time asset data and the historical asset slice data through the real-time asset service data to obtain first quality collaborative asset data includes:

[0019] forming scenario mode data based on the real-time asset service data;

[0020] Batch integration and cleaning of the scene mode data to obtain consistent data;

[0021] Performing data processing based on the consistent data to obtain processed data;

[0022] The real-time asset data and the historical asset slice data are subjected to transaction adaptation and fund reverse calculation through the processed data to obtain first-quality collaborative asset data.

[0023] In one embodiment, the step of performing net historical data error elimination on the real-time asset data and the historical asset slice data through the real-time asset service data to obtain the second quality collaborative asset data includes:

[0024] Acquire actually used net value data based on the real-time asset service data;

[0025] Constructing a time-to-net-worth pair based on the net-worth data and the time point corresponding to the net-worth data;

[0026] The real-time asset data and the historical asset slice data are subjected to net historical data error elimination through the time net value pair to obtain second quality collaborative asset data.

[0027] In one embodiment, the step of eliminating foreign currency exchange rate data errors on the real-time asset data and the historical asset slice data through the real-time asset service data to obtain third quality collaborative asset data includes:

[0028] Determine the foreign currency corresponding to the real-time asset data and the historical asset slice data;

[0029] Determine the exchange rate deviation corresponding to the foreign currency based on the real-time asset service data;

[0030] Determining whether there is an error between the real-time asset data and the historical asset slice data based on the exchange rate deviation;

[0031] When errors exist, error elimination is performed to obtain third-quality collaborative asset data.

[0032] In addition, to achieve the above purpose, the present application also proposes an asset data quality collaboration device, the asset data quality collaboration device comprising:

[0033] Basic data acquisition module, used to obtain real-time asset data and historical asset slice data;

[0034] An execution module, configured to determine a first application scenario of the real-time asset data and a second application scenario of the historical asset slice data;

[0035] The execution module is further used to determine the association relationship between the first application scenario and the second application scenario;

[0036] Service data module, used to obtain real-time service data of assets;

[0037] The execution module is also used to eliminate data errors of the real-time asset data and the historical asset slice data based on the association relationship and through the real-time asset service data to obtain quality collaborative asset data.

[0038] In addition, to achieve the above-mentioned objectives, the present application also proposes an asset data quality collaborative device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the asset data quality collaborative method described above.

[0039] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the asset data quality collaborative method described above are implemented.

[0040] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the asset data quality collaborative method as described above.

[0041] One or more technical solutions proposed in this application have at least the following technical effects:

[0042] This application obtains real-time asset data and historical asset slice data; determines the first application scenario of real-time asset data and the second application scenario of historical asset slice data; determines the association between the first application scenario and the second application scenario; obtains real-time asset service data; based on the association and through the real-time asset service data, eliminates data errors in real-time asset data and historical asset slice data to obtain quality collaborative asset data. Since real-time asset data and historical asset slice data are used for mutual inspection and verification, the coverage of data verification is improved. By eliminating data errors, the influence of real-time scenarios such as data caching, net value fluctuations, and customer transactions is eliminated, thereby improving the accuracy of data verification. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0045] Figure 1 A flowchart of the first embodiment of the asset data quality collaborative method of the present application is provided;

[0046] Figure 2 A flowchart diagram of the second embodiment of the asset data quality collaborative method of the present application is provided;

[0047] Figure 3 A schematic diagram of a real-time transaction error smoothing process in one implementation of the asset data quality collaborative method of this application;

[0048] Figure 4A flowchart diagram of the third embodiment of the asset data quality collaborative method of the present application;

[0049] Figure 5 A schematic diagram of the hierarchical modules of a real-time and historical data quality collaborative assurance system in one implementation of the asset data quality collaborative method of this application;

[0050] Figure 6 This is a schematic diagram of the module structure of the asset data quality collaboration device according to an embodiment of the present application;

[0051] Figure 7 Schematic diagram of the device structure of the hardware operating environment involved in the asset data quality collaboration method in the embodiment of the present application.

[0052] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0053] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0054] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0055] The main solutions of the embodiments of the present application are: obtaining real-time asset data and historical asset slice data; determining a first application scenario of the real-time asset data and a second application scenario of the historical asset slice data; determining the association between the first application scenario and the second application scenario; obtaining real-time asset service data; based on the association and through the real-time asset service data, eliminating data errors in the real-time asset data and the historical asset slice data to obtain quality collaborative asset data.

[0056] Due to multiple complex scenarios such as real-time changes in the net value of wealth management products, fluctuations in foreign exchange rates, and real-time transactions by customers, the collaborative accuracy and mutual verification of asset data output poses severe and complex challenges:

[0057] 1. How to smooth out the transactions generated by customers in real time at the end of the day? At the end of the day, customers will query the landing devices in the real-time asset middle platform. Customers will generate real-time transactions, involving multiple systems, real-time Kafka data, micro-batch data, and batch data. Identifying transaction settlements between systems and handling the combination of batches and streams have become key and difficult points;

[0058] 2. The real-time fluctuations in the net value of funds and wealth management products and the host cache need to be converted. The bottom layer of the real-time asset middle platform portfolio products is encapsulated. The net value changes of multiple products need to be identified. At the same time, the verification and leveling scenario becomes more complicated due to product cache issues;

[0059] 3. How to calculate the real-time changes in foreign exchange rates? Foreign exchange rates change in milliseconds. Because different products use different calculation methods and rules, there are large differences in accessing assets at different times. How to determine whether there is an abnormality based on the measurement error requires the summary of a general algorithm.

[0060] This application provides a solution that solves the obstacle of self-checking of asset quality within the bank by building a quality mutual inspection system for real-time asset data and historical asset slice data, and expands the coverage and accuracy of data quality verification. By using differentiated customized algorithms to identify and smooth real-time updated data, possible asset quality problems can be discovered to a great extent, reducing risks while improving efficiency, thus safeguarding the development of technology banks.

[0061] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, a virtual device, etc. that can realize the above functions. The following takes the asset data quality collaborative device (referred to as the collaborative device) as an example to illustrate this embodiment and the following embodiments.

[0062] Based on this, the embodiment of the present application provides an asset data quality collaboration method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the asset data quality collaborative method of the present application.

[0063] In this embodiment, the method includes:

[0064] Step S10, obtaining real-time asset data and historical asset slice data;

[0065] It should be noted that in the embodiment of the present application, a real-time and historical data quality collaborative assurance system is constructed, which may include a basic data layer, a service data layer, an execution layer and a data application layer.

[0066] Among them, the basic data layer includes the real-time asset middle platform and the historical asset middle platform; the real-time asset middle platform can be used to obtain real-time asset data, and the historical asset middle platform can be used to obtain historical asset data.

[0067] It should be noted that the above-mentioned real-time asset data is also the data of the target time period near the target time point, and generally speaking, the target time point may be 12:00 a.m. In one implementation, the account overview data of ten minutes near 12:00 a.m. may be extracted as the real-time asset data. The above-mentioned historical asset slice data is also the data formed by inferring the asset data to 12:00 a.m. That is, the steps of obtaining the real-time asset data and the historical asset slice data include:

[0068] Obtain the user account overview data within the target time period at the target time point as the benchmark real-time data;

[0069] Back-calculating is performed based on the benchmark real-time data to obtain the historical asset slice data at the target time point.

[0070] It should be noted that the above-mentioned target time point is 24:00 (12:00 am), and the above-mentioned target time period can be 10 minutes, 30 minutes, etc., which is not limited in this embodiment of the present application.

[0071] It should be explained that the above-mentioned service data layer can connect to multiple upstream systems and introduce real-time service data of assets such as exchange rate, transaction, net value, etc. through three methods: TOPIC, micro-batch and batch.

[0072] Step S20, determining a first application scenario of the real-time asset data and a second application scenario of the historical asset slice data;

[0073] Step S30, determining an association relationship between the first application scenario and the second application scenario;

[0074] It should be noted that when basic data (including real-time asset data and historical asset slice data) and service data are obtained, these basic data and service data can be input into the execution layer. The execution layer sorts out the different scenario associations and personalized adaptation items of each data, and then performs asset backcasting and conversion through the smoothing evaluation algorithm, and performs a secondary evaluation based on the calculation results of the backcasting and conversion, so as to shield the verification interference to the greatest extent and find an accurate solution.

[0075] It is understandable that the above application scenarios may include real-time transaction scenarios, net value processing scenarios, and foreign currency scenarios. The above individual obstacles, that is, personalized obstacles in different application scenarios, can be defined and selected according to the application in actual situations, and the embodiments of the present application are not limited to this.

[0076] It should be noted that when determining the first application scenario of real-time asset data and the second application scenario of historical asset slice data, the real-time asset data and historical asset slice data with an associated relationship can be determined, and then the associated real-time asset data and historical asset slice data can be smoothed (that is, data errors are eliminated) through the real-time service data of assets in the same application scenario to obtain accurate quality collaborative asset data.

[0077] That is, step S40, obtaining real-time asset service data;

[0078] Step S50, based on the association relationship and through the asset real-time service data, data errors are eliminated on the real-time asset data and the historical asset slice data to obtain quality collaborative asset data.

[0079] It should be noted that the above-mentioned data application layer may include applications, through which operations such as monitoring and early warning of quality collaborative asset data, closed-loop management of problems, difference demonstration, and data quality assessment can be realized, and the operation results can be visualized to form a sustainable quality assurance system.

[0080] The embodiment of the present application obtains real-time asset data and historical asset slice data; determines a first application scenario of the real-time asset data and a second application scenario of the historical asset slice data; determines the association between the first application scenario and the second application scenario; obtains real-time asset service data; based on the association and through the real-time asset service data, eliminates data errors in the real-time asset data and the historical asset slice data to obtain quality collaborative asset data. Since real-time asset data and historical asset slice data are used for mutual inspection and verification, the coverage of data verification is improved, and by eliminating data errors, the influence of real-time scenarios such as data caching, net value fluctuations, and customer transactions is eliminated, thereby improving the accuracy of data verification.

[0081] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 The step of eliminating data errors of the real-time asset data and the historical asset slice data based on the association relationship and using the real-time asset service data to obtain quality collaborative asset data includes:

[0082] Step S51, when the association relationship is a real-time transaction scenario association, real-time transaction data errors are eliminated on the real-time asset data and the historical asset slice data through the asset real-time service data to obtain first quality collaborative asset data;

[0083] It should be noted that when the application scenario corresponding to the real-time asset data and the historical asset slice data is a real-time transaction scenario, that is, when the association relationship is a real-time transaction scenario association, the process of eliminating the error of the real-time transaction data can refer to Figure 3 .

[0084] Reference Figure 3 In order to obtain accurate quality collaborative data, collaborative equipment can connect to the data source (Database) of each system to form scenario mode data of three channels: micro-batch, quasi-real-time (Topic), and batch (that is, to obtain real-time asset service data). Through the preset service, these data are batch-stream integrated and cleaned, and then the middleware is used for data mixing and processing to form consistent and standardized processing data, avoiding errors caused by inconsistencies between systems, and realizing the collection of original change data through the intermediate database landing data warehouse.

[0085] It should be noted that in order to more accurately adjust real-time assets, this application goes deep into the underlying logic of each trading system, identifies the actual account movement time, and provides accurate service data by matching the customer's real-time asset landing time at the millisecond level.

[0086] It needs to be explained that due to the complexity of banking products that affect the way assets circulate, an ordinary transaction may even generate five or six transactions at the bottom layer, including transfers within the card, transfers between other banks, product transactions, etc. Personalized algorithms are used for the underlying clearing of funds for each category to avoid the poor coverage accuracy caused by general algorithms.

[0087] It should be noted that based on statistical real-time data and historical data (that is, real-time asset data and historical asset slice data), as well as processed data obtained and processed from the service layer, transaction adaptation can be achieved, and then funds can be reversed, thus achieving quality coordination between real-time asset data and historical asset data.

[0088] Specifically, the step of performing real-time transaction data error elimination on the real-time asset data and the historical asset slice data through the real-time asset service data to obtain first quality collaborative asset data includes:

[0089] forming scenario mode data based on the real-time asset service data;

[0090] Batch integration and cleaning of the scene mode data to obtain consistent data;

[0091] Performing data processing based on the consistent data to obtain processed data;

[0092] The real-time asset data and the historical asset slice data are subjected to transaction adaptation and fund reverse calculation through the processed data to obtain first-quality collaborative asset data.

[0093] Step S52, when the association relationship is a net value scenario association, performing net value historical data error elimination on the real-time asset data and the historical asset slice data through the real-time asset service data to obtain second quality collaborative asset data;

[0094] It should be noted that when the application scenario corresponding to the real-time asset data and the historical asset slice data is a net value scenario, that is, when the association relationship between the first application scenario and the second application scenario is a net value scenario association, the net value historical error can be smoothed based on the time-net value pair solution.

[0095] In the embodiment of the present application, a solution for solving the time-net value pair is proposed based on the reverse operation, instead of obtaining the forward net value. First, due to the uncertainty of the host cache, it is impossible to obtain the accurate forward net value at the time point, but the forward calculation can only perform general net value scene conversion, which cannot cover the user's personalized scene well. By solving the time-net value pair solution, the net value calculation of all scenes can be balanced.

[0096] Step S53, when the association relationship is a foreign currency scenario association relationship, the foreign currency exchange rate data error is eliminated for the real-time asset data and the historical asset slice data through the asset real-time service data to obtain third quality collaborative asset data.

[0097] It should be noted that when the application scenario corresponding to the real-time asset data and the historical asset slice data is a foreign exchange scenario, that is, when the association relationship between the first application scenario and the second application scenario is a foreign exchange scenario association, the foreign exchange rate error can be smoothed by solving the time-net value pair.

[0098] The embodiment of the present application obtains first quality collaborative asset data by eliminating real-time transaction data errors between real-time asset data and historical asset slice data through real-time asset service data when the association relationship is a real-time transaction scenario association; obtains second quality collaborative asset data by eliminating net value historical data errors between real-time asset data and historical asset slice data through real-time asset service data when the association relationship is a net value scenario association; obtains third quality collaborative asset data by eliminating foreign currency exchange rate data errors between real-time asset data and historical asset slice data through real-time asset service data when the association relationship is a foreign currency scenario association. The present application smoothes data differences through different methods based on different scenarios, eliminating the influence of real-time scenarios such as data caching, net value fluctuations, and customer transactions. At the same time, the present application implements relevant difference detailed data, smoothes errors through algorithmic errors, and forms a closed-loop management of data problems, which can effectively identify and control abnormal items. Compared with conventional data verification, it greatly releases operation and maintenance manpower.

[0099] Based on the first embodiment and / or the second embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those of the first embodiment and / or the second embodiment can be referred to the above introduction, and will not be described in detail later. Figure 4 The step of performing net historical data error elimination on the real-time asset data and the historical asset slice data through the real-time asset service data to obtain the second quality collaborative asset data comprises:

[0100] Step S100, obtaining actually used net value data based on the real-time asset service data;

[0101] Step S200, constructing a time-to-net-value pair based on the net-value data and the time point corresponding to the net-value data;

[0102] It is understandable that in order to obtain the time-net value pair, the time at 24:00 in the morning can be set as t0, the net value can be pushed back to the customer query time t2, and the second historical net value v2 corresponding to the time t2 can be obtained. Based on the time t0, it can be pushed back to the previous net value update point time t1, and the first historical net value t1 corresponding to the time t1 can be obtained. Based on the above, two time-net value pairs [(t0, t1), v1] and [(t1, t2), v2] can be obtained. By traversing all time-net value pairs from the early morning to the trading time point in this way, data omissions can be avoided.

[0103] Furthermore, since the real-time asset returns the total value of financial portfolio products, it becomes more complicated to obtain the time-net value pair, product 1 net value group [v1, v2, …, vi], product 2 net value group [v1, v2, …, vj]. Combined with the share data in the real-time asset service data, we get product 1 market value group [a1, a2, …, ai], product 2 market value group [b1, b2, …, bj], and obtain a total of i*j market value combinations through combination traversal. Match the real-time landing assets, obtain the accurate market value combination, and update the time-net value combination at the same time. Specifically, the time-net value combination of product 1 is [(t0, t1), (v1, v2, …, vi)].

[0104] Furthermore, by updating the time-net value combination, the customer's account data can be traversed as a loop body, and by iteratively updating the time-net value pair, the stability of the combination pair can be improved. Under the premise of consistent shares, as long as the solution of the net value-time pair can be found after iteration, the accuracy of smoothing the net value error of the day reaches 100%. That is, step S300, the net value historical data error is eliminated by the time net value of the real-time asset data and the historical asset slice data to obtain the second quality collaborative asset data.

[0105] It should be noted that, for data error elimination in a foreign currency scenario, the step of eliminating foreign currency exchange rate data errors on the real-time asset data and the historical asset slice data through the real-time asset service data to obtain third-quality collaborative asset data includes:

[0106] Determine the foreign currency corresponding to the real-time asset data and the historical asset slice data;

[0107] Determine the exchange rate deviation corresponding to the foreign currency based on the real-time asset service data;

[0108] Determining whether there is an error between the real-time asset data and the historical asset slice data based on the exchange rate deviation;

[0109] When errors exist, error elimination is performed to obtain third-quality collaborative asset data.

[0110] It should be noted that the embodiment of the present application proposes a self-developed LOF algorithm to test the deviation of the exchange rate, calculates the benchmark exchange rate based on the historical asset middle station and the real-time asset middle station, and identifies the original currency of the real-time asset service data obtained by sampling. On the premise of ensuring the consistency of the asset value corresponding to the original currency, and on the basis of the original LOF algorithm, the average deviation is introduced. Based on the average deviation, the identification interval required for error is reduced, thereby improving the accuracy of the assessment.

[0111] Specifically, the formula for calculating the deviation of an exchange rate from other exchange rates can be as follows:

[0112]

[0113] The calculation method of exchange rate deviation LOF can be as follows:

[0114]

[0115] Among them, when the minimum deviation evaluation is adopted, the calculation is performed using formula (1); when avg(k 1 ,...,k N )|≤|k i |≤|k N |, formula (2) is used for calculation; when the convenience is measured within the visible range, formula (3) is used for calculation.

[0116] In the embodiment of the present application, multiple exchange rate neighborhoods are traversed for measurement, and then the exchange rate deviation LOF value is calculated. The smaller the LOF value, the lower the deviation. Furthermore, the average deviation can be introduced as background data. If it is within the identifiable range of net value error smoothing, the minimum distance must be smaller than the average distance, otherwise it is not a deviation caused by the net value error; when the LOF value is less than or equal to 1, it can be considered that the net value error causes asset inconsistency, and the error is eliminated to obtain the third quality collaborative asset data.

[0117] The embodiment of the present application obtains the net value data actually used based on the real-time asset service data; constructs a time-net value pair based on the net value data and the time point corresponding to the net value data; performs net value historical data error elimination on the real-time asset data and the historical asset slice data through the time-net value pair to obtain the second quality collaborative asset data; determines the foreign currency corresponding to the real-time asset data and the historical asset slice data; determines the exchange rate deviation corresponding to the foreign currency based on the real-time asset service data; determines whether there is an error between the real-time asset data and the historical asset slice data based on the exchange rate deviation; when there is an error, eliminates the error to obtain the third quality collaborative asset data. The present application improves the data quality by setting a method for smoothing the data errors caused by caching, real-time update of net value, and real-time update of exchange rate, as well as the corresponding verification algorithm, while greatly improving the coverage and accuracy of data quality problem discovery.

[0118] In one implementation of the embodiment of the present application, the hierarchical module diagram of the real-time and historical data quality collaborative assurance system of the present application can be referred to Figure 5 .

[0119] Specifically, the system may include a basic data layer, a service data layer, an execution layer, and a data application layer. Among them, the basic data layer may include a real-time asset middle platform and a historical asset middle platform. The real-time asset middle platform may select sampled customers near midnight, query the account overview and the land value, and obtain real-time asset data; the historical asset middle platform may obtain the end-of-day slice (that is, historical asset slice data) by inferring the end-of-day slice asset value at 24 o'clock. The service data layer may connect to upstream systems such as real-time trading systems, foreign exchange rate systems, net value systems, and real-time data middle platforms, and introduce real-time asset service data such as exchange rates, transactions, and net values ​​through three methods: TOPIC, micro-batch, and batch.

[0120] Furthermore, the source data (real-time asset data and historical asset slice data) obtained by the basic data layer can be input into the execution layer, and the service data (real-time asset service data) obtained by the service data layer can be input into the execution layer. The execution layer can eliminate errors in real-time asset data and historical asset slice data through operations such as foreign exchange rate smoothing, real-time transaction smoothing, and product net value smoothing, so as to combine the data after error elimination into batches and streams, and make judgments through the client-level execution algorithm to obtain the judgment results.

[0121] It should be noted that the above-mentioned foreign currency exchange rate smoothing can be achieved through steps such as LOF algorithm, average deviation definition, credible interval execution, foreign currency exchange rate labeling, etc.; the above-mentioned real-time transaction smoothing can be achieved through current account backtracking, product purchase backtracking, and combination detail decomposition; the above-mentioned product net value smoothing can be achieved through combination net value decomposition, net value historical backtracking, iterative solution of time-net value pairs and finding the optimal solution.

[0122] It needs to be explained that the above-mentioned current account back-pushing is to push back the asset data based on the current account data, the above-mentioned product purchase back-pushing is to push back the asset data according to the asset products purchased by the account, and the above-mentioned combination detail decomposition is to decompose the product details based on the asset combination purchased by the account, thereby realizing the asset data back-pushing.

[0123] Furthermore, the above-mentioned determination results can be output to the data application layer, through which functions such as monitoring and early warning, closed-loop problem management, difference display, and data quality assessment can be realized, thereby realizing the visualization effect of the data quality assurance system.

[0124] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the asset data quality collaborative method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0125] This application also provides an asset data quality collaboration device, please refer to Figure 6 , the asset data quality collaboration device comprises:

[0126] Basic data acquisition module 10, used to acquire real-time asset data and historical asset slice data;

[0127] An execution module 20 is used to determine a first application scenario of the real-time asset data and a second application scenario of the historical asset slice data;

[0128] The execution module 20 is further configured to determine an association relationship between the first application scenario and the second application scenario;

[0129] A service data module 30, for obtaining real-time service data of assets;

[0130] The execution module 20 is further used to eliminate data errors of the real-time asset data and the historical asset slice data based on the association relationship and through the real-time asset service data to obtain quality collaborative asset data.

[0131] The asset data quality coordination device provided by the present application adopts the asset data quality coordination method in the above-mentioned embodiment, which can solve the technical problem that the existing asset display usually displays assets of a fixed historical day, and the verification coverage rate of asset data quality is poor and the accuracy rate is low. Compared with the prior art, the beneficial effects of the asset data quality coordination device provided by the present application are the same as the beneficial effects of the asset data quality coordination method provided by the above-mentioned embodiment, and the other technical features in the asset data quality coordination device are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0132] The present application provides an asset data quality collaboration device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the asset data quality collaboration method in the above-mentioned embodiment one.

[0133] Reference below Figure 7 , which shows a schematic diagram of the structure of an asset data quality collaboration device suitable for implementing the embodiment of the present application. The asset data quality collaboration device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The asset data quality collaboration device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0134] like Figure 7As shown, the asset data quality collaborative device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the asset data quality collaborative device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the asset data quality collaboration device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows an asset data quality collaboration device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0135] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0136] The asset data quality collaboration device provided by the present application adopts the asset data quality collaboration method in the above-mentioned embodiment, which can solve the technical problem that the existing asset display usually displays assets of a fixed historical day, and the verification coverage rate of asset data quality is poor and the accuracy rate is low. Compared with the prior art, the beneficial effects of the asset data quality collaboration device provided by the present application are the same as the beneficial effects of the asset data quality collaboration method provided by the above-mentioned embodiment, and the other technical features in the asset data quality collaboration device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0137] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0138] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0139] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the asset data quality collaboration method in the above-mentioned embodiment.

[0140] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0141] The computer-readable storage medium may be included in the asset data quality collaborative device; or may exist independently without being assembled into the asset data quality collaborative device.

[0142] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the asset data quality collaboration device, the asset data quality collaboration device:

[0143] Obtain real-time asset data and historical asset slice data;

[0144] Determine a first application scenario of the real-time asset data and a second application scenario of the historical asset slice data;

[0145] Determining an association relationship between the first application scenario and the second application scenario;

[0146] Obtain real-time service data of assets;

[0147] Based on the association relationship and through the real-time asset service data, data errors of the real-time asset data and the historical asset slice data are eliminated to obtain quality collaborative asset data.

[0148] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0149] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0150] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0151] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned asset data quality collaborative method, and can solve the technical problem that the existing asset display usually displays assets of a fixed historical day, and the verification coverage and accuracy of asset data quality are poor. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the asset data quality collaborative method provided by the above-mentioned embodiment, which will not be repeated here.

[0152] The present application also provides a computer program product, including a computer program, which implements the steps of the asset data quality collaborative method as described above when executed by a processor.

[0153] The computer program product provided by this application can solve the technical problem that the existing asset display usually displays assets of a fixed historical day, and the verification coverage and accuracy of asset data quality are poor. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the asset data quality collaboration method provided in the above embodiment, which will not be repeated here.

[0154] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A collaborative approach to asset data quality, It is characterized in that The method comprises: Obtain real-time asset data and historical asset slice data; Determine a first application scenario of the real-time asset data and a second application scenario of the historical asset slice data; Determining an association relationship between the first application scenario and the second application scenario; Obtain real-time service data of assets; Based on the association relationship and through the real-time asset service data, data errors of the real-time asset data and the historical asset slice data are eliminated to obtain quality collaborative asset data.

2. The asset data quality collaborative method according to claim 1, It is characterized in that The step of obtaining real-time asset data and historical asset slice data includes: Obtain the user account overview data within the target time period at the target time point as the benchmark real-time data; Back-calculating is performed based on the benchmark real-time data to obtain the historical asset slice data at the target time point.

3. The asset data quality collaboration method according to claim 2, It is characterized in that The step of eliminating data errors of the real-time asset data and the historical asset slice data based on the association relationship and using the real-time asset service data to obtain quality collaborative asset data includes: When the association relationship is a real-time transaction scenario association, real-time transaction data errors are eliminated on the real-time asset data and the historical asset slice data through the real-time asset service data to obtain first quality collaborative asset data; When the association relationship is a net value scenario association, the net value historical data error is eliminated for the real-time asset data and the historical asset slice data through the real-time asset service data to obtain the second quality collaborative asset data; When the association relationship is a foreign currency scenario association relationship, the foreign currency exchange rate data errors are eliminated for the real-time asset data and the historical asset slice data through the real-time asset service data to obtain third quality collaborative asset data.

4. The asset data quality collaboration method according to claim 3, It is characterized in that The step of performing real-time transaction data error elimination on the real-time asset data and the historical asset slice data through the real-time asset service data to obtain first quality collaborative asset data includes: forming scenario mode data based on the real-time asset service data; Batch integration and cleaning of the scene mode data to obtain consistent data; Performing data processing based on the consistent data to obtain processed data; The real-time asset data and the historical asset slice data are subjected to transaction adaptation and fund reverse calculation through the processed data to obtain first-quality collaborative asset data.

5. The asset data quality collaboration method according to claim 3, It is characterized in that The step of performing net historical data error elimination on the real-time asset data and the historical asset slice data through the real-time asset service data to obtain the second quality collaborative asset data comprises: Acquire actually used net value data based on the real-time asset service data; Constructing a time-to-net-worth pair based on the net-worth data and the time point corresponding to the net-worth data; The real-time asset data and the historical asset slice data are subjected to net historical data error elimination through the time net value pair to obtain second quality collaborative asset data.

6. The asset data quality collaborative method according to claim 3, It is characterized in that The step of eliminating foreign currency exchange rate data errors on the real-time asset data and the historical asset slice data through the real-time asset service data to obtain third quality collaborative asset data includes: Determine the foreign currency corresponding to the real-time asset data and the historical asset slice data; Determine the exchange rate deviation corresponding to the foreign currency based on the real-time asset service data; Determining whether there is an error between the real-time asset data and the historical asset slice data based on the exchange rate deviation; When errors exist, error elimination is performed to obtain third-quality collaborative asset data.

7. An asset data quality collaboration device, It is characterized in that The asset data quality coordination device comprises: Basic data acquisition module, used to obtain real-time asset data and historical asset slice data; An execution module, configured to determine a first application scenario of the real-time asset data and a second application scenario of the historical asset slice data; The execution module is further used to determine the association relationship between the first application scenario and the second application scenario; Service data module, used to obtain real-time service data of assets; The execution module is also used to eliminate data errors of the real-time asset data and the historical asset slice data based on the association relationship and through the real-time asset service data to obtain quality collaborative asset data.

8. An asset data quality collaboration device, It is characterized in that The device includes: a memory, a processor, and an asset data quality collaboration program stored in the memory and executable on the processor, wherein the asset data quality collaboration program is configured to implement the steps of the asset data quality collaboration method according to any one of claims 1 to 7.

9. A storage medium, It is characterized in that An asset data quality collaboration program is stored on the storage medium, and when the asset data quality collaboration program is executed by the processor, the steps of the asset data quality collaboration method according to any one of claims 1 to 7 are implemented.

10. A computer program product, It is characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the asset data quality collaborative method according to any one of claims 1 to 7 are implemented.

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