Business data quality evaluation method and device, equipment and storage medium

Abstract

The embodiment of the invention provides a business data quality evaluation method and device, equipment and a storage medium, and belongs to the technical field of financial science and technology. The method comprises the following steps: acquiring current business data and historical evaluation index data of a financial platform; performing rule verification on the current service data to obtain current rule verification data; extracting current evaluation index data from the current rule verification data; wherein the current evaluation index data comprises a current abnormal rule quantity, a current abnormal data quantity and a current data total quantity, the current abnormal rule quantity represents a rule quantity that the current service data does not pass through a verification rule, and the current abnormal data quantity is a data quantity that the current service data does not pass through the verification rule; and performing quality evaluation on the current service data according to the current abnormal rule quantity, the current abnormal data quantity, the current total data quantity and the historical evaluation index data to obtain data quality evaluation data. According to the embodiment of the invention, manpower for evaluating the business data can be reduced.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and in particular to a method and apparatus, device and storage medium for evaluating the quality of business data. Background Art

[0002] In order to strengthen the management of financial institutions and reduce financial risks, financial institutions need to regularly submit business data to relevant management departments. In order to reduce the difficulty of relevant management departments in reviewing business data, financial institutions need to conduct quality assessments on business data in advance (such as the pass rate of business data in relevant verification rules) before submitting business data, and ensure that the quality of business data meets the requirements before submitting business data. Among them, when doing quality assessment of business data, it is necessary to conduct rule verification on business data to obtain verification data, and determine the quality of business data based on the verification data.

[0003] In related technologies, the quality assessment of business data mainly relies on manual work, and the amount of verification data is huge, which not only consumes a lot of manpower, but also affects the efficiency of business data reporting. Therefore, how to reduce the manpower in business data quality assessment has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to provide a method and apparatus, device and storage medium for evaluating the quality of business data, aiming to reduce the manpower required for evaluating the quality of business data.

[0005] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a method for evaluating the quality of service data, the method comprising:

[0006] Acquire current business data and historical evaluation index data of the financial platform; wherein the historical evaluation index data is an index for evaluating the quality of historical business data, and the current business data is at least one of the following data: text data, image data, and audio and video data;

[0007] Performing rule verification on the current business data to obtain current rule verification data;

[0008] Extracting current evaluation index data from the current rule verification data; wherein the current evaluation index data includes: the current abnormal rule quantity, the current abnormal data quantity and the current total data quantity, the current abnormal rule quantity represents the number of rules that the current business data fails to pass the verification rules, and the current abnormal data quantity is the data quantity of the current business data that fails to pass the verification rules;

[0009] The quality of the current business data is evaluated according to the current abnormal rule quantity, the current abnormal data quantity, the current total data quantity and the historical evaluation index data to obtain data quality evaluation data.

[0010] In some embodiments, the historical evaluation index data includes: a historical abnormal rule quantity, a historical abnormal data quantity, and a historical data total quantity; the quality evaluation of the current business data is performed according to the current abnormal rule quantity, the current abnormal data quantity, the current data total quantity, and the historical evaluation index data to obtain data quality evaluation data, including:

[0011] Determine the current abnormal data proportion according to the current abnormal data volume and the current total data volume;

[0012] Determine the proportion of historical abnormal data according to the amount of historical abnormal data and the total amount of historical data;

[0013] According to the historical abnormal rule quantity and the current abnormal rule quantity, the current business data is evaluated for rule passing variation quantity, and rule passing variation quantity evaluation data is obtained; wherein the rule passing variation quantity evaluation data represents the rule variation quantity of the current business data passing the verification rule;

[0014] According to the amount of historical abnormal data and the amount of current abnormal data, the pass rate of the current business data is evaluated to obtain first pass rate evaluation data; wherein the first pass rate evaluation data represents the change in the amount of data of the current business that passes the verification rule;

[0015] According to the current abnormal data proportion and the preset total abnormal data proportion, the pass rate of the current business data is evaluated to obtain second pass rate evaluation data; wherein the second pass rate evaluation data represents the change in the proportion of data that passes the verification rule of the current business data;

[0016] The rule pass variation evaluation data, the first pass rate evaluation data and the second pass rate evaluation data are spliced ​​to obtain the data quality evaluation data.

[0017] In some embodiments, after extracting the current evaluation index data from the current rule verification data, the method further includes:

[0018] Filtering out the current abnormal rule from the verification rules according to the current abnormal rule quantity;

[0019] Obtaining historical exception rules; wherein the historical exception rules are verification rules that the historical business data fails to pass;

[0020] Filtering out new exception rules and matching rules from the verification rules according to the historical exception rules and the current exception rules, wherein the matching rules are verification rules that the current business data complies with;

[0021] Filtering out a selected abnormal rule from the verification rules according to the historical abnormal data proportion and the current abnormal data proportion;

[0022] Filtering selected abnormal rules from the verification rules according to the preset rule quantity and the current abnormal data quantity;

[0023] Filtering target abnormal rules from the verification rules according to the preset rule quantity and the current abnormal data proportion;

[0024] The newly added exception rule, the matching rule, the selected exception rule, the chosen exception rule and the target exception rule are visualized to obtain an exception rule view.

[0025] In some embodiments, the selecting the abnormal rule from the verification rules according to the historical abnormal data proportion and the current abnormal data proportion includes:

[0026] Calculate the change rate of the historical abnormal data proportion and the current abnormal data proportion to obtain the abnormal data proportion growth rate;

[0027] The selected abnormal rule is screened out from the verification rules according to a preset proportion change threshold and the abnormal data proportion growth rate.

[0028] In some embodiments, after extracting the current evaluation index data from the current rule verification data, the method further includes:

[0029] Obtaining the amount of the current business data reported by the financial platform to obtain the current amount of reported data;

[0030] Obtaining the number of historical business data reported by the financial platform to obtain the amount of historical reported data;

[0031] Performing a reporting change assessment based on the current reported data volume and the historical reported data volume to obtain reporting change assessment data;

[0032] Filtering selected business data from the current business data according to the reported change assessment data;

[0033] The reported change assessment data and the selected business data are visualized to obtain a reported exception view.

[0034] In some embodiments, after extracting the current evaluation index data from the current rule verification data, the method further includes:

[0035] Comparing the historical evaluation index data with the current evaluation index data to obtain data comparison information;

[0036] The data comparison information is visualized to obtain an indicator data comparison view.

[0037] In some embodiments, after determining the historical abnormal data proportion according to the historical abnormal data volume and the total historical data volume, the method further includes:

[0038] Acquire the institution information of the financial platform according to the preset quantity and the current abnormal rule quantity to obtain a first institution information set;

[0039] Acquire the institution information of the financial platform according to the preset number and the current abnormal data volume to obtain a second institution information set;

[0040] Acquire the institution information of the financial platform according to the preset number and the current abnormal data proportion to obtain a third institution information set;

[0041] The first institution information set, the second institution information set and the third institution information set are visualized to obtain an abnormal institution view.

[0042] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides a device for evaluating the quality of service data, the device comprising:

[0043] A data acquisition module, used to acquire current business data and historical evaluation index data of the financial platform; wherein the historical evaluation index data is an index for evaluating the quality of historical business data, and the current business data is at least one of the following data: text data, image data, and audio and video data;

[0044] A rule verification module, used to perform rule verification on the current business data to obtain current rule verification data;

[0045] An indicator extraction module is used to extract current evaluation indicator data from the current rule verification data; wherein the current evaluation indicator data includes: the current abnormal rule quantity, the current abnormal data quantity and the current total data quantity, the current abnormal rule quantity represents the number of rules that the current business data fails to pass the verification rules, and the current abnormal data quantity is the data quantity of the current business data that fails to pass the verification rules;

[0046] The quality assessment module is used to perform quality assessment on the current business data according to the current abnormal rule quantity, the current abnormal data quantity, the current total data quantity and the historical assessment indicator data to obtain data quality assessment data.

[0047] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes a computer device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.

[0048] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0049] The quality assessment method, device, equipment and storage medium of business data proposed in this application obtain the current business data and historical assessment index data of the financial platform, first perform rule verification on the current business data to obtain the current rule verification data, and then extract the current assessment index data of the current business data from the current rule verification data, and the current assessment index data includes the current abnormal rule quantity, the current abnormal data quantity and the current total data quantity, so as to perform quality assessment on the current business data according to the current abnormal rule quantity, the current abnormal data quantity, the current total data quantity and the historical assessment index data. Therefore, by automatically performing quality assessment on the current business data, and the quality assessment process is to perform quality assessment based on the rule quantity and data quantity of the current business data that fail to pass the verification rules, it not only saves manpower in the quality assessment of business data, improves the efficiency of business data reporting, but also can improve the accuracy of business data quality assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a flow chart of a method for evaluating the quality of business data provided by an embodiment of the present application;

[0051] Figure 2 It is a schematic diagram of the verification status of current business data in the business data quality assessment method provided in an embodiment of the present application;

[0052] Figure 3 is a flow chart of a method for evaluating the quality of business data provided by another embodiment of the present application;

[0053] FIG. 4( a ) is a diagram showing rule changes of a method for evaluating the quality of business data provided in an embodiment of the present application;

[0054] FIG4( b ) is a data change diagram of the method for evaluating the quality of business data provided by an embodiment of the present application;

[0055] Figure 5 is a flow chart of a method for evaluating the quality of business data provided by another embodiment of the present application;

[0056] Figure 6 is a flow chart of a method for evaluating the quality of business data provided by another embodiment of the present application;

[0057] Figure 7 Schematic diagram of an abnormal mechanism of the method for evaluating the quality of business data provided in an embodiment of the present application;

[0058] Figure 8 is a flow chart of a method for evaluating the quality of business data provided by another embodiment of the present application;

[0059] Fig. 9 It is an abnormal rule view of the quality assessment method of business data provided by the embodiment of the present application;

[0060] Fig.10 yes Figure 8 Flow chart of step S804 in FIG.

[0061] Fig.11 yes Figure 1 Flow chart of step S104 in FIG.

[0062] Fig.12 is a schematic diagram of data quality assessment data in the method for quality assessment of business data provided in an embodiment of the present application;

[0063] Fig.13 is a schematic diagram of the structure of a device for evaluating the quality of business data provided by an embodiment of the present application;

[0064] Fig.14 It is a schematic diagram of the hardware structure of the computer device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0066] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0067] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0068] First, some nouns involved in this application are analyzed:

[0069] Artificial intelligence (AI) is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. AI is a branch of computer science that attempts to understand the essence of intelligence and produce a new type of intelligent machine that can respond in a similar way to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing and expert systems. AI can simulate the information process of human consciousness and thinking. AI is also a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0070] Examination and Analysis System Technology (EAST): It is an inspection and analysis system with independent intellectual property rights, which aims to meet the development trend of big data and help regulatory authorities improve inspection efficiency. The system includes functional modules such as bank standardized data extraction, on-site inspection project management, data model generation tools, data model publishing and management. Its core is: ① Building a relatively open data analysis platform to achieve flexible organization, screening, extraction, modeling, mining and analysis of bank business data; ② Establishing a set of universal and relatively closed data collection standards to include risk data points of concern to regulators.

[0071] Report: A tool that displays and analyzes data in a tabular form. Reports can present data from various angles and dimensions to help managers understand business conditions, make decisions, and evaluate corporate performance.

[0072] Kanban: It is a system framework suitable for implementing agile and DevOps software development. Its core lies in the full visualization of work and real-time communication based on work. Through the intuitive display of each work item in the Kanban, team members can clearly understand the status and progress of each work.

[0073] In order to facilitate financial management departments to pay attention to financial risk data points, financial institutions need to regularly report business data to relevant management departments to achieve financial supervision. Since relevant management departments have high requirements for business data reported by financial institutions, and the verification rules are complicated, in order to meet the verification requirements of relevant management departments, the quality requirements for business data are extremely high. Therefore, before submitting business data, financial institutions need to verify the business data in advance, and conduct quality assessment of business data based on the verification results, so as to adjust the business data according to the quality assessment results. In related technologies, the quality assessment of business data mainly relies on manual completion. Because the amount of business data is huge, the use of manual assessment not only consumes a lot of manpower, but also affects the reporting efficiency of business data. Therefore, how to reduce the manpower of business data evaluation has become a technical problem that needs to be solved urgently.

[0074] Based on this, the embodiments of the present application provide a method and apparatus, equipment and storage medium for evaluating the quality of business data, which is intended to obtain current rule verification data by performing rule verification on current business data, and extract current evaluation index data from the current rule verification data, so as to perform quality evaluation on current business data according to current evaluation index data and historical evaluation index data, and realize automatic evaluation of current business data, without the need for manual evaluation of current business data, and reduce manpower in the evaluation of current business data, thereby improving the reporting efficiency of business data. At the same time, when doing quality evaluation, the current evaluation index data includes the current abnormal rule quantity, the current abnormal data quantity and the current total data quantity, so as to evaluate the current business data in terms of the number of rules that fail to pass the verification rules and the amount of data that fail to pass the verification rules, so as to realize a more comprehensive evaluation of the current business data and make the quality evaluation of the current business data more accurate.

[0075] The quality assessment method, apparatus, device and storage medium for business data provided in the embodiments of the present application are specifically described through the following embodiments. First, the quality assessment method for business data in the embodiments of the present application is described.

[0076] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0077] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0078] The quality assessment method of business data provided in the embodiment of the present application relates to the field of financial technology. The quality assessment method of business data provided in the embodiment of the present application can be applied to the terminal, can also be applied to the server side, and can also be software running in the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or it can be configured as a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the quality assessment method of business data, etc., but is not limited to the above forms.

[0079] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0080] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0081] Figure 1 is an optional flowchart of the method for evaluating the quality of business data provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S104.

[0082] Step S101, obtaining current business data and historical evaluation index data of the financial platform; wherein the historical evaluation index data is an index used to evaluate the quality of historical business data, and the current business data is at least one of the following data: text data, image data, and audio and video data;

[0083] Step S102, performing rule verification on current business data to obtain current rule verification data;

[0084] Step S103, extracting current evaluation index data from the current rule verification data; wherein the current evaluation index data includes: the current abnormal rule quantity, the current abnormal data quantity and the current total data quantity, the current abnormal rule quantity represents the number of rules that the current business data fails to pass the verification rules, and the current abnormal data quantity is the data quantity of the current business data that fails to pass the verification rules;

[0085] Step S104, performing quality assessment on the current business data according to the current abnormal rule quantity, the current abnormal data quantity, the current total data quantity and the historical assessment index data to obtain data quality assessment data.

[0086] Steps S101 to S104 shown in the embodiment of the present application, by obtaining the current business data and historical evaluation index data of the financial platform, first perform rule verification on the current business data to obtain current rule verification data, and extract current evaluation index data from the current rule verification data. Among them, the current evaluation index data includes: the current abnormal rule quantity, the current abnormal data quantity and the current total data quantity, so as to perform quality evaluation on the current business data according to the current abnormal rule quantity, the current abnormal data quantity and the current total data quantity to obtain data quality evaluation data. Therefore, by automatically performing quality evaluation on the current business data, and realizing quality evaluation from two aspects, the quantity of rules that have not passed and the quantity of data that have not passed the verification rules, it is possible to save manpower for business data quality evaluation, improve the efficiency of business data reporting, and also improve the evaluation accuracy of business data.

[0087] In step S101 of some embodiments, the current business data is the business data of the current period, the historical business data is the business data of the historical period, and the historical period can be a preset period before the current period, or any period in the historical period before the current period. For example, the current period can be the current month, the historical period can be the previous month, and the historical period can also be the previous two months. This embodiment does not make specific restrictions on the historical period, but the time interval between the current period and the historical period is the same.

[0088] It should be noted that the historical evaluation index data is the index data used to evaluate the quality of historical business data. Specifically, historical rule verification data is obtained by performing rule verification on historical business data, and historical evaluation index data is extracted from the historical rule verification data.

[0089] In some embodiments, current business data and historical business data are stored in a data report format, and historical rule verification data is stored in a list format, which is defined as a historical field verification list data storage table.

[0090] In step S102 of some embodiments, the current rule verification data corresponding to the current business data is defined as the current field verification list data storage table, and the current field verification list data storage is updated according to a preset period, and the preset period can be defined as 1 day. This embodiment does not limit the preset period. It should be noted that the rule verification of the current business data is to verify the current business data according to a preset number of verification rules, and the preset number of verification rules are different. Among them, the verification rules include basic rules and customized rules. The basic rules are used to verify the basic problems of the current business data, such as data consistency, data duplication, data missingness, etc. The customized rules represent the requirements of the relevant management departments for business data, such as non-empty verification, association verification and numerical comparison verification, etc. The non-empty verification requires that some information in the current business data is not allowed to be empty, the association verification requires that there must be an association between some information in the current business data, and the numerical comparison verification involves some contract start and end times, contract amounts, etc. in the current business data.

[0091] As disclosed above, the current rule verification data is also called the current field verification list data storage table. The current business data includes multiple business reports. Each business report records the financial business information of the financial platform in the current period. Therefore, to verify the current business data is to verify each business report. Therefore, the current field verification list data storage table not only records the rule information of all business report data that fail to pass the verification rules, but also records the data information that fails to pass the verification rules. Among them, the current field verification list data storage table includes two types of fields, namely data type fields and non-data type fields. The data type field includes the number of verification rules, the number of rules that fail to pass the verification rules, and the failure rate of data that fails to pass the verification rules. The non-data type field includes: the rule number of the verification rule, the verification category, the name of the data table involved in the verification, the data table code involved in the verification, the name of the data item, the data item code, the description of the reporting requirements and the analysis reasons for failure to pass the verification, etc. Therefore, the current evaluation index data can be extracted through the content recorded in the current field verification list data storage table.

[0092] In step S103 of some embodiments, as disclosed above, the current rule verification data records the rule information of the current business data that has not passed the verification rules and the data information that has not passed the verification rules. The field is directly extracted from the current verification rule data to represent the number of rules that have not passed the verification rules to obtain the current abnormal rule quantity, and then the field is represented as the amount of data that has not passed the verification rules as the current abnormal data quantity, and the amount of data involved in the verification rules is taken as the current total data quantity. It should be noted that the current abnormal rule quantity, the current abnormal data quantity and the current total data quantity are mainly used for the quality assessment of the current business data. In addition, this embodiment also obtains the total number of verification rules as the current total rule quantity, and determines the current abnormal rule proportion based on the ratio between the current abnormal rule quantity and the current total rule quantity, and determines the current abnormal data proportion based on the ratio between the current abnormal data quantity and the current total data quantity.

[0093] In some embodiments, the quality assessment method of business data is applied to the inspection and analysis system, and a dashboard is set on the inspection and analysis system to display the current total number of rules, the current number of abnormal rules, the current abnormal rule ratio, the current abnormal data volume, the current total data volume, and the current abnormal data ratio. Figure 2 As shown, if the current total number of rules is 2706, the current number of abnormal rules is 63, the current abnormal rules account for 2.3282%, the current abnormal data volume is 844289, the current total data volume is 81635232 and the current abnormal data accounts for 0.017%, so that relevant personnel of the financial institution can clearly understand the verification status of the current business data according to the data displayed on the dashboard, and determine whether the current business data needs to be adjusted according to the data displayed on the dashboard.

[0094] It should be noted that after the current evaluation index data is displayed, in order to facilitate the management of business data throughout the entire cycle, it is necessary to compare the evaluation index data of the current period and the historical period, so as to find out the business data with abnormal changes and realize the early discovery of major anomalies in business data management.

[0095] See also Figure 3 In some embodiments, after step S103, the method for evaluating the quality of business data may also include but is not limited to steps S301 to S302:

[0096] Step S301, comparing historical evaluation index data with current evaluation index data to obtain data comparison information;

[0097] Step S302: Visualize the data comparison information to obtain an indicator data comparison view.

[0098] In step S301 of some embodiments, there may be multiple historical evaluation index data. Data comparison information is obtained by comparing multiple historical evaluation index data with current evaluation index data. The data comparison information can analyze changes in the business data verification process within multiple time periods.

[0099] Specifically, the historical evaluation index data includes: the total amount of historical rules, the amount of historical abnormal rules, the proportion of historical abnormal rules, the amount of historical abnormal data, the total amount of historical data, and the proportion of historical abnormal data; the current evaluation index data includes: the total amount of current rules, the amount of current abnormal rules, the proportion of current abnormal rules, the amount of current abnormal data, the total amount of current data, and the proportion of current abnormal data; the data comparison information includes: abnormal rule comparison information, total amount of rules comparison information, abnormal rule proportion comparison information, total amount of data comparison information, abnormal data comparison information, and abnormal data proportion comparison information. The total amount of rules comparison information is obtained by comparing multiple historical total amounts of rules with the current total amount of rules, the abnormal rule comparison information is obtained by comparing multiple historical abnormal rule amounts with the current abnormal rule amount, and the abnormal rule comparison information is obtained by comparing multiple historical abnormal rule proportions with the current abnormal rule proportions; the total amount of historical data is compared with the current total amount of data to obtain the total amount of data comparison information, the multiple historical abnormal data amounts are compared with the current abnormal data amounts to obtain the abnormal data comparison information, and the multiple historical abnormal data proportions are compared with the current abnormal data proportions to obtain the abnormal data proportion comparison information. Therefore, by comparing the total number of rules, the situations of rules that fail the rules, and the data situations, we can clearly understand the changes in data verification during the data management period, so as to find abnormal business data.

[0100] In step S302 of some embodiments, the data comparison information is displayed in a visual form to obtain an indicator data comparison view, and the indicator data comparison view intuitively checks the changes in the current business data. Among them, the indicator data comparison view includes: a rule change view and a data change view. The rule change view is generated by visualizing the abnormal rule comparison information, the total rule comparison information, and the abnormal rule ratio comparison information, and the data change view is obtained by visualizing the total data comparison information, the abnormal data comparison information, and the abnormal data ratio comparison information.

[0101] For example, the rule change view is shown in Figure 4(a), and the data change view is shown in Figure 4(b). Figure 4(a) shows the changes in the number of abnormal rules, the total number of rules, and the proportion of abnormal rules in multiple time periods. Figure 4(b) shows the changes in the total amount of data, the amount of abnormal data, and the proportion of abnormal data in multiple time periods, so as to find the business data in the abnormal time period.

[0102] In steps S301 to S302 shown in this embodiment, data comparison information is obtained by comparing historical evaluation index data with current evaluation index data, and the data comparison information is then displayed in the form of an index data comparison view. This allows the changes in business data during the verification process to be clearly understood, so as to facilitate finding abnormal business data.

[0103] It should be noted that, as disclosed above, the business data does not pass the verification rules, and it is easy to overlook the abnormal amount of business data reported during the business data reporting process. Therefore, it is necessary to collect and display the data volume changes in the business data reporting process, which is conducive to correcting the business data with abnormal reporting volume before the business data is submitted to the relevant management department.

[0104] Please refer to Figure 5 In some embodiments, after step S103, the method for evaluating the quality of business data may further include but is not limited to steps S501 to S505:

[0105] Step S501, obtaining the amount of current business data reported by the financial platform to obtain the current amount of reported data;

[0106] Step S502, obtaining the number of historical business data submitted by the financial platform to obtain the amount of historical submitted data;

[0107] Step S503, performing a reporting change assessment based on the current reported data volume and the historical reported data volume to obtain reporting change assessment data;

[0108] Step S504, selecting selected business data from current business data according to the submitted change assessment data;

[0109] Step S505, visualize the submitted change assessment data and the selected business data to obtain a submitted exception view.

[0110] In step S501 and step S502 of some embodiments, the current data volume reported is the data volume of business data reported by the financial platform in the current period, and the historical data volume reported is the data volume of historical business data reported by the financial platform in the historical period. It should be noted that each current business data comes from a financial platform, and there are multiple financial platforms, so there are multiple current data volumes reported, so that the reporting volume management of each financial platform is realized.

[0111] In step S503 of some embodiments, the reporting change assessment data characterizes the deviation of the current reporting data volume relative to the historical reporting data volume, and can analyze whether the reporting volume of the current business data of the financial platform is abnormal. It should be noted that the absolute value of the deviation rate between the current reporting data volume and the historical reporting data volume is calculated as the reporting change assessment data. The calculation formula for the reporting change assessment data is: (current reporting data volume - historical reporting data volume) / historical reporting data volume * 100%. For example, if the historical reporting data volume is 358 and the current reporting data volume is 269, then the reporting change assessment data is 24.86%.

[0112] In step S504 and step S505 of some embodiments, selected business data is screened out from the current business data according to the reported change assessment data, specifically, the current business data whose reported change assessment data is greater than the preset assessment threshold is taken as the selected business data, that is, the current business data with abnormal reported volume is determined. For example, if the preset assessment threshold is 20%, and the reported change assessment data of the current business data is 24.86%, then the current business data of the corresponding financial platform is determined to be the selected business data, and the selected business data and the reported change assessment data are displayed in a visual chart to obtain a reported abnormal view. Therefore, the abnormal situation of the reported volume of the current business data can be intuitively viewed through the reported abnormal view.

[0113] In steps S501 to S505 shown in the present embodiment, the reporting change assessment data is determined based on the current reported data volume and the historical reported data volume, selected business data is screened out from the current business data based on the reporting change assessment data, and the reporting change assessment data and the selected business data are displayed in a visual form, so that the financial platforms with abnormal reporting volumes and the abnormal current business data of the financial platforms can be intuitively viewed.

[0114] It should be noted that the business data of each financial platform is managed independently, and only the verification status of the business data collected by each financial platform can be mastered. It is impossible to compare the business data verification status of other financial platforms, and it is difficult to find the verification anomaly gap between other financial institutions. Based on this, the embodiment of the present application adds verification anomaly comparisons of multiple financial platforms, and displays abnormal financial institutions in multiple verification dimensions, so that financial institutions that fail to pass the verification can adjust their respective business data in a timely manner to submit better quality business data to relevant management departments.

[0115] See also Figure 6 In some embodiments, after step S103, the method for evaluating the quality of business data may also include but is not limited to steps S601 to S604:

[0116] Step S601, obtaining the institution information of the financial platform according to the preset quantity and the current abnormal rule quantity, and obtaining a first institution information set;

[0117] Step S602, obtaining the institution information of the financial platform according to the preset number and the current abnormal data volume to obtain a second institution information set;

[0118] Step S603, obtaining the institution information of the financial platform according to the preset number and the current abnormal data ratio, and obtaining a third institution information set;

[0119] Step S604, visualize the first institution information set, the second institution information set and the third institution information set to obtain an abnormal institution view.

[0120] In step S601 of some embodiments, the current abnormal rule quantity is sorted from large to small, and the institution information of the financial platforms ranked before the preset number is obtained and combined into the first institution information set. For example, if the preset number is 10, the institution information of the current abnormal rule quantity ranked before 10 is combined into the first institution information set.

[0121] In step S602 of some embodiments, the abnormal data is sorted from large to small according to the current amount, and the institutional information of the financial platforms before a preset number before sorting is obtained and combined into a second institutional information set.

[0122] In step S603 of some embodiments, the abnormal data is sorted from large to small according to the current amount, and the institutional information of the financial platforms before a preset number before sorting is obtained and combined into a third institutional information set.

[0123] In step S604 of some embodiments, by visualizing the first institution information set, the second institution information set and the third institution information set into an abnormal institution diagram, the abnormal institution diagram represents the financial institution before the number of rules that fail verification, the amount of data and the proportion of data, which can motivate the financial institution to improve the quality of its business data.

[0124] The abnormal institution diagram can display the top 10 institution information in three dimensions: the current abnormal rule quantity, the current abnormal data quantity, and the current abnormal data proportion, and display the quantity of each financial institution in the corresponding dimension. For example, the abnormal institution diagram corresponding to the third institution information set in the current abnormal data proportion dimension is as follows: Figure 7 As shown, through Figure 7 The top 10 financial institutions with the highest proportion of abnormal data can be visually identified, which can motivate financial institutions to improve the data quality of their current business data.

[0125] In steps S601 to S604 illustrated in this embodiment, by displaying the current amount of abnormal rules, the current amount of abnormal data and the current abnormal data percentage of a previously preset amount of institutional information in the form of visual charts, respectively, and displaying the previously preset amount of institutional information from three dimensions of the amount of abnormal rules, the amount of abnormal data and the percentage of abnormal data, not only can financial institutions be aware of the differences in the business data verification results, but also financial institutions with poor verification results can be encouraged to improve the quality of their business data.

[0126] It should be noted that in the embodiment of the present application, a rule failure prompt page is added to the dashboard, and the rule failure prompt page displays an abnormal rule view, showing the verification rules that the financial platform has failed in the current period and the verification rules with a higher probability of failure, so that the managers of the financial platform can adjust the current business data in a targeted manner according to the abnormal rule view to improve the verification pass rate of the current business data.

[0127] See also Figure 8 In some embodiments, after step S103, the method for evaluating the quality of business data may also include but is not limited to steps S801 to S807:

[0128] Step S801, filtering out the current abnormal rule from the verification rules according to the current abnormal rule quantity;

[0129] Step S802, obtaining historical anomaly rules; wherein the historical anomaly rules are verification rules that historical business data fails to pass;

[0130] Step S803, filtering out new exception rules and matching rules from the verification rules according to the historical exception rules and the current exception rules, where the matching rules are the verification rules that the current business data passes;

[0131] Step S804, selecting an abnormal rule from the verification rules according to the historical abnormal data ratio and the current abnormal data ratio;

[0132] Step S805, selecting an abnormal rule from the verification rules according to the preset rule quantity and the current abnormal data quantity;

[0133] Step S806, filtering out target abnormal rules from the verification rules according to the preset rule quantity and the current abnormal data ratio;

[0134] Step S807 , visualizing the newly added exception rule, the matching rule, the selected exception rule, the chosen exception rule and the target exception rule to obtain an exception rule view.

[0135] In step S801 of some embodiments, the current exception rule is a verification rule that the current business data does not pass, and the current field verification list data storage table stores the current rule serial number that fails the verification rule, the current exception rule is extracted from the verification rule according to the current rule serial number, and the number of current exception rules is the current exception rule quantity.

[0136] In step S802 of some embodiments, the historical exception rule is a verification rule that the historical business data fails to pass, and the historical field verification list data storage table stores the historical rule serial numbers that fail the verification rules, and the historical exception rule is extracted from the verification rules according to the historical rule serial numbers.

[0137] In step S803 of some embodiments, the newly added exception rule is a newly added verification rule that does not pass the current business data, and the matching rule is a verification rule that the current business data passes relative to the historical exception rule. Specifically, the current exception rule that is different from the historical exception rule in the current exception rule is used as the newly added exception rule, and the historical exception rule that is different from the current exception rule in the historical exception rule is used as the matching rule. For example, if there are 5 current exception rules, the 5 current exception rules are defined as JG1, JG3, JG8, JG12 and JG15, and there are 6 historical exception rules, and the 6 historical exception rules are defined as JG2, JG3, JG9, JG12, JG15 and JG19; then it is determined that there are 2 newly added exception rules, the 2 newly added exception rules are JG1 and JG8, and there are 3 matching rules, and the 3 matching rules are JG2, JG8 and JG19.

[0138] In step S804 of some embodiments, the selected abnormal rule is a verification rule that the growth rate of the current abnormal data ratio is too high compared to the historical abnormal data ratio.

[0139] In step S805 of some embodiments, a verification rule with a larger amount of abnormal data represented by an illegal rule is selected, that is, a verification rule with a higher probability of failure of a data item in the current business data. Specifically, the verification rules are first sorted from large to small according to the current amount of abnormal data, and then the verification rules before the preset rule amount before sorting are selected as the selected abnormal rules, and the number of selected abnormal rules is equal to the preset rule amount. For example, if the preset rule amount is 10, the top 10 verification rules are selected from large to small according to the current amount of abnormal data as the selected abnormal rules.

[0140] In step S806 of some embodiments, the target abnormal rule is a verification rule with a high abnormal data ratio. Specifically, the verification rules are sorted from large to small according to the current abnormal data ratio, and the verification rules with a preset rule quantity before sorting are used as the target abnormal rule to determine the verification rule with a large data verification abnormality. For example, the preset rule quantity is 10, and the verification rules with the top 10 current abnormal data ratios are used as the target abnormal rule.

[0141] In step S807 of some embodiments, an exception rule view is obtained by displaying the newly added exception rules, matching rules, selected exception rules, chosen exception rules and target exception rules in a visual form. It should be noted that the rules displayed on the dashboard in the exception rule view are not displayed through the prompt page, and the exception rule view can be switched to display the newly added exception rules, matching rules, selected exception rules, chosen exception rules and target exception rules, and each page displays the display parameters corresponding to the rules, and the display parameters include: rule number, rule sequence number, verification category, verification table name, verification table name code, data item name, data item code, definition description of data reporting requirements, etc. For example, Fig. 9 As shown, Fig. 9 The exception rule view corresponding to the matching rule is shown. Fig. 9 You can clearly see the verification rules and verification information that the current business data no longer violates.

[0142] In steps S801 to S807 shown in this embodiment, the verification rules that are no longer violated in the current time period and the newly added verification rules that fail are displayed in a visual manner compared with the historical time period. At the same time, the verification rules in which the proportion of abnormal data in the current time period has increased significantly and the amount of abnormal data and the proportion of abnormal data are higher than those in the historical time period are displayed. This allows the financial platform to adjust the current business data according to these displayed verification rules to improve the verification pass rate of the current business data.

[0143] See also Fig.10 In some embodiments, step S804 may include but is not limited to steps S1001 to S1002:

[0144] Step S1001, calculating the change rate of the abnormal data proportion based on the historical abnormal data proportion and the current abnormal data proportion, and obtaining the abnormal data proportion growth rate;

[0145] Step S1002, selecting an abnormal rule from the verification rules according to a preset percentage change threshold and an abnormal data percentage growth rate.

[0146] In step S1001 of some embodiments, the abnormal data percentage growth rate represents the growth of the data percentage of the current business data that does not pass the verification rule. Specifically, the difference between the current abnormal data percentage and the historical abnormal data percentage is obtained as the abnormal data percentage growth rate.

[0147] In step S1002 of some embodiments, the verification rule in which the growth rate of the proportion of abnormal data is greater than a preset proportion change threshold is used as the selected abnormal rule to determine the verification rule with a larger rate of change of the amount of abnormal data. This can assist the managers of the financial platform to adjust the corresponding data items in the current business data according to the selected abnormal rule, thereby reducing the growth rate of the proportion of abnormal data in the current business data and improving the verification pass rate of the current business data.

[0148] In steps S1001 to S1002 shown in this embodiment, the growth rate of the abnormal data proportion is determined according to the historical abnormal data proportion and the current abnormal data proportion, and the verification rule whose abnormal data proportion growth rate is greater than the proportion change threshold is used as the selected abnormal rule, and the verification rule that fails the verification and has a high data proportion growth is determined. The selected abnormal rule can assist the management personnel to adjust the current business data and improve the verification pass rate of the current business data.

[0149] See also Fig.11 In some embodiments, step S104 includes but is not limited to steps S1101 to S1106:

[0150] Step S1101, determining the current abnormal data proportion according to the current abnormal data volume and the current total data volume;

[0151] Step S1102, determining the proportion of historical abnormal data according to the amount of historical abnormal data and the total amount of historical data;

[0152] Step S1103, evaluating the rule passing variation of the current business data according to the historical abnormal rule quantity and the current abnormal rule quantity, and obtaining the rule passing variation evaluation data; wherein the rule passing variation evaluation data represents the variation of the current business data passing the verification rule;

[0153] Step S1104, evaluating the pass rate of the current business data according to the historical abnormal data volume and the current abnormal data volume, and obtaining first pass rate evaluation data; wherein the first pass rate evaluation data represents the change in the amount of data of the current business data that passes the verification rule;

[0154] Step S1105, evaluating the pass rate of the current business data according to the current abnormal data ratio and the preset total abnormal data ratio, to obtain second pass rate evaluation data; wherein the second pass rate evaluation data represents the change in the data ratio of the current business data that passes the verification rule;

[0155] Step S1106, concatenating the rule passing variation evaluation data, the first pass rate evaluation data and the second pass rate evaluation data to obtain data quality evaluation data.

[0156] In step S1103 of some embodiments, the rule passing change evaluation data characterizes the change in the current business data passing the verification rules, specifically, the change in the number of rules that pass the rules for the current business data compared to the historical business data. It should be noted that the larger the rule passing change evaluation data, the more rules that characterize the current business data passing the verification rules compared to the historical business data, and conversely, the smaller the rule passing change evaluation data, the more rules that characterize the current business data failing to pass the verification rules compared to the historical business data. Therefore, by taking the change in the number of rules that pass the verification rules for the current business data as a consideration of the quality of the current business data, the pass rate of the current business data reported to the relevant management department can be improved.

[0157] Specifically, the rule change evaluation data is represented in the form of a score. The benchmark score for determining the rule change evaluation is 20 points. If the current abnormal rule quantity is reduced by 1 verification rule relative to the historical abnormal rule quantity, 0.5 points are added to the benchmark score, otherwise 0.5 points are deducted. The upper limit score of the rule change evaluation data is 25 points, and the lower limit score is 0 points. Therefore, the calculation formula for the rule change evaluation data is: rule change evaluation data = MAX(MIN(20+(historical abnormal rule quantity-current abnormal rule quantity)*0.5,25),0).

[0158] In step S1104 of some embodiments, the first pass rate evaluation data represents the change in the amount of data of the current business data that passes the verification rules, and the higher the first pass rate evaluation data is, the lower the amount of data representing that the current business data does not pass the verification rules is, and conversely, the lower the first pass rate evaluation data is, the higher the amount of data representing that the current business data does not pass the verification rules is, so that the change in the amount of data of the current business data that passes the verification rules can be reflected through the first pass rate evaluation data.

[0159] Specifically, the first pass rate assessment data is represented in the form of a score, and the benchmark score for the pass rate assessment of the current abnormal data volume is determined to be 20. Compared with the historical abnormal data volume, the pressure drop rate of the current abnormal data volume is multiplied by 12.5 to add points, and vice versa, points are deducted according to the same standard, and the upper limit score of the first pass rate assessment data is 25 points, and the lower limit score is 0 points. Therefore, the calculation formula of the first pass rate assessment data is: first pass rate assessment data = MAX(MIN(20+(historical abnormal data volume-current abnormal data volume) / historical abnormal data volume*12.5,25),0).

[0160] In step S1105 of some embodiments, the second pass rate evaluation data represents the change in the proportion of data that passes the verification rules of the current business data, and the higher the second pass rate evaluation data, the lower the current abnormal data proportion compared to the total abnormal data proportion, and vice versa, the lower the second pass rate evaluation data, the higher the current abnormal data proportion compared to the total abnormal data proportion. It should be noted that the total abnormal data proportion is the sum of the current abnormal data proportions of all financial platforms.

[0161] Specifically, the second pass rate assessment data is represented in the form of a score, and the benchmark score for the pass rate assessment of the current abnormal data ratio is determined to be 40, and the score addition and subtraction method of the pass rate assessment process is based on the deviation rate between the current abnormal data ratio and the total abnormal data ratio multiplied by 10. If the current abnormal data ratio is lower than the total abnormal data ratio, the more points will be added, and vice versa, the more points will be deducted. Therefore, the calculation formula for the second pass rate assessment data is: second pass rate assessment data = MAX (40-(current abnormal data ratio-total abnormal data ratio) / total abnormal data ratio*10,0).

[0162] In step S1106 of some embodiments, the way of splicing the rule through the change evaluation data, the first pass rate evaluation data and the second pass rate evaluation data can be a variety of splicing methods such as direct summation, weighted summation and average value. This embodiment does not make specific restrictions on the splicing method of the rule through the change evaluation data, the first pass rate evaluation data and the second pass rate evaluation data. For example, if the direct rule is summed through the change evaluation data, the first pass rate evaluation data and the second pass rate evaluation data to obtain data quality evaluation data, then the data quality evaluation data analyzes the quality of the current business data from three dimensions and can be used as an indicator for improving the verification pass rate of the current business data.

[0163] For example, Fig.12 As shown, the current business data is the business data of Bank A. The rules of Bank A's business data are displayed through change evaluation data, first pass rate evaluation data, second pass rate evaluation data and data quality evaluation data, and the ranking order corresponding to Bank A's data quality evaluation data is displayed, so that the management personnel of Bank A can adjust the business data according to the displayed data and improve the verification pass rate of the current business data.

[0164] In steps S1101 to S1106 shown in this embodiment, by performing quality assessment on the current business data from three dimensions, namely, the amount of abnormal rules, the amount of abnormal data, and the proportion of abnormal data, data quality assessment data that can improve the verification pass rate of the current business data is obtained.

[0165] In the embodiment of the present application, if the quality assessment method of business data is applied to the banking scenario, the current business data and historical assessment index data of the bank are collected, and the current business data is the business data of this month, and the historical assessment index data is the assessment index data for evaluating the historical business data of the previous month. Among them, the historical assessment index number includes: the total amount of historical rules, the amount of historical abnormal rules, the amount of historical abnormal data, and the total amount of historical data. The amount of historical abnormal rules is the number of rules that the historical business data does not pass the verification rules, and the amount of historical abnormal data is the amount of data that the historical business data does not pass the verification rules. Before the quality assessment of the current business data, in order to facilitate the bank's management personnel to understand the verification of the business data generated this month, the current business data is firstly checked by rules to obtain the current rule verification data, and then the current assessment index data is extracted from the current rule verification data. Among them, the current assessment index data includes: the total amount of current rules, the amount of current abnormal rules, the amount of current abnormal data, and the total amount of current data. The ratio between the current abnormal rule amount and the current rule total amount is obtained to obtain the current abnormal rule proportion, and then the ratio between the current abnormal data amount and the current data total amount is obtained to obtain the current abnormal data proportion. By setting up a dashboard and setting up a rule verification overview view on the dashboard, and the rule verification overview view displays the current total number of rules, the current number of abnormal rules, the current abnormal rule ratio, the current abnormal data volume, the current total data volume and the current abnormal data ratio, the bank's managers can adjust the current business data according to the displayed rule verification overview view to improve the verification pass rate of the current business data. In addition, in order to determine in advance whether there are reporting anomalies in the current business data, it is necessary to compare and display the current evaluation index data and the historical evaluation index data. Specifically, first obtain the ratio between the historical abnormal rule volume and the historical total rule volume to obtain the historical abnormal rule ratio, and then obtain the ratio between the historical abnormal data volume and the historical total data volume to obtain the historical abnormal data ratio. Then, by setting up an indicator data comparison view, and the indicator data comparison view displays the comparison between the current total number of rules and the historical total number of rules, the current number of abnormal rules and the historical number of abnormal rules, the current proportion of abnormal rules and the historical proportion of abnormal rules, the current total data and the historical total data, the current amount of abnormal data and the historical amount of abnormal data, and the current proportion of abnormal data and the historical proportion of data, bank managers can clearly understand whether the verification of current business data has caused large fluctuations and can adjust the current business data in time.

[0166] Furthermore, in order to more clearly understand the abnormal situation of the reporting of the current business data, the current reporting data volume of the current business data is obtained, the historical reporting data volume of the historical business data is obtained, and then the deviation rate between the current reporting data volume and the historical reporting data volume is obtained as the reporting change assessment data. If the reporting change assessment data is greater than the preset assessment threshold, the corresponding current business data is used as the selected business data, and the selected business data and the reporting change assessment data are displayed on the dashboard, so that the management personnel can clearly understand whether there is an abnormality in the reporting volume of the current business data. At the same time, the bank branches of the financial platform are displayed on the dashboard according to the current abnormal rule volume, the current abnormal data volume and the current abnormal data ratio from large to small, so that the bank branches can understand each other's rule verification situation and realize the supervision of each bank institution in reporting business data.

[0167] Furthermore, in order to facilitate the bank to adjust the current business data, first obtain the verification rules that the current business data fails to pass as the current exception rules, and obtain the verification rules that the historical business data fails to pass as the historical exception rules. According to the current exception rules and historical exception rules, determine the new exception rules and the matching rules, and the new exception rules are the verification rules that the current business data fails to pass, and the matching rules are the verification rules that the current business data passes relative to the historical exception rules. At the same time, the difference between the historical exception data ratio and the current exception data ratio is obtained to obtain the abnormal data ratio growth rate, and the verification rule corresponding to the abnormal data ratio growth rate greater than the preset ratio change threshold is used as the selected abnormal rule. Then, sort the verification rules from large to small according to the current abnormal data volume, and then use the verification rules before the preset rule volume before sorting as the selected abnormal rule, and sort the verification rules from large to small according to the current abnormal data ratio, and use the verification rules of the preset rule volume before sorting as the target abnormal rule. Finally, the newly added exception rules, matching rules, selected exception rules, chosen exception rules and target exception rules are displayed on the dashboard in the form of visual charts, so that bank managers can understand the verification rules that the current business data has not passed and the verification rules with a large amount of abnormal data, so that they can adjust the current business data in time to improve the verification pass rate of the current business data.

[0168] When evaluating the current business data, in order to more accurately characterize the quality of the current business data. First, determine that the benchmark score for the rule change evaluation is 20 points. If the current abnormal rule quantity is reduced by 1 verification rule relative to the historical abnormal rule quantity, add 0.5 points on the basis of the benchmark score, otherwise deduct 0.5 points to determine the rule change evaluation data. Then determine that the benchmark score for the pass rate evaluation of the current abnormal data volume is 20. Compared with the historical abnormal data volume, the pressure drop rate of the current abnormal data volume is multiplied by 12.5 to add points, otherwise deduct points according to the same standard to determine the first pass rate evaluation data. Determine that the benchmark score for the pass rate evaluation of the current abnormal data ratio is 40, and the score addition and subtraction method of the pass rate evaluation process is to multiply the deviation rate of the current abnormal data ratio and the total abnormal data ratio by 10 to obtain the second pass rate evaluation data. Finally, the rule pass change evaluation data, the first pass rate evaluation data, and the second pass rate evaluation data are spliced ​​to obtain the data quality evaluation data of the current business data. Therefore, the quality assessment of the current business data is completed from three aspects: the number of failed rules, the amount of failed data, and the proportion of failed data relative to the historical business data of last month. This not only realizes the automated assessment of the current business data and reduces the manpower during the assessment, but also obtains data quality assessment data that accurately represents the quality of the current business data.

[0169] See also Fig.13 The embodiment of the present application also provides a device for evaluating the quality of business data, which can implement the above-mentioned method for evaluating the quality of business data, and the device includes:

[0170] The data acquisition module 1301 is used to acquire the current business data and historical evaluation index data of the financial platform; wherein the historical evaluation index data is an index for evaluating the quality of historical business data, and the current business data is at least one of the following data: text data, image data, and audio and video data;

[0171] The rule verification module 1302 is used to perform rule verification on the current business data to obtain current rule verification data;

[0172] The indicator extraction module 1303 is used to extract the current evaluation indicator data from the current rule verification data; wherein the current evaluation indicator data includes: the current abnormal rule quantity, the current abnormal data quantity and the current total data quantity, the current abnormal rule quantity represents the number of rules that the current business data fails to pass the verification rules, and the current abnormal data quantity is the data quantity of the current business data that fails to pass the verification rules;

[0173] The quality assessment module 1304 is used to perform quality assessment on the current business data according to the current abnormal rule quantity, the current abnormal data quantity, the current total data quantity and the historical assessment index data to obtain data quality assessment data.

[0174] The specific implementation of the service data quality assessment device is substantially the same as the specific implementation of the service data quality assessment method described above, and will not be described in detail herein.

[0175] The embodiment of the present application also provides a computer device, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned service data quality assessment method when executing the computer program. The computer device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0176] See also Fig.14 , Fig.14 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0177] The processor 1401 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0178] The memory 1402 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1402 can store an operating system and other applications. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1402, and the processor 1401 calls and executes the quality assessment method of the service data in the embodiment of this application;

[0179] Input / output interface 1403, used to implement information input and output;

[0180] The communication interface 1404 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0181] A bus 1405 that transmits information between the various components of the device (e.g., the processor 1401, the memory 1402, the input / output interface 1403, and the communication interface 1404);

[0182] The processor 1401 , the memory 1402 , the input / output interface 1403 and the communication interface 1404 are connected to each other in communication within the device via a bus 1405 .

[0183] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the quality assessment method of the business data described above is implemented.

[0184] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0185] The quality assessment method, device, equipment and storage medium of business data provided in the embodiments of the present application obtain current assessment index data by extracting assessment indexes from the verified current business data, and the current assessment index data includes the current abnormal rule quantity, the current abnormal data quantity and the current data total quantity, and performs quality assessment on the current business data according to the current abnormal rule quantity, the current abnormal data quantity, the current data total quantity and the historical assessment index data, so as to realize the automated quality assessment of the current business data and reduce the manpower in the quality assessment of the current business data. At the same time, the quality assessment process is realized by the situation that the current business data fails the rules in the rule verification process, and the assessment of the current business data can be completed more accurately to obtain data quality assessment data that can accurately reflect the quality of the current business data.

[0186] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0187] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0188] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0189] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0190] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0191] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0192] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0193] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0194] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0195] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0196] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for evaluating the quality of business data, characterized in that: The method comprises: Acquire current business data and historical evaluation index data of the financial platform; wherein the historical evaluation index data is an index for evaluating the quality of historical business data, and the current business data is at least one of the following data: text data, image data, and audio and video data; Performing rule verification on the current business data to obtain current rule verification data; Extracting current evaluation index data from the current rule verification data; wherein the current evaluation index data includes: the current abnormal rule quantity, the current abnormal data quantity and the current total data quantity, the current abnormal rule quantity represents the number of rules that the current business data fails to pass the verification rules, and the current abnormal data quantity is the amount of data that the current business data fails to pass the verification rules; The quality of the current business data is evaluated according to the current abnormal rule quantity, the current abnormal data quantity, the current total data quantity and the historical evaluation index data to obtain data quality evaluation data.

2. The method according to claim 1, characterized in that The historical evaluation index data includes: the historical abnormal rule quantity, the historical abnormal data quantity and the historical data total amount; the quality evaluation of the current business data is performed according to the current abnormal rule quantity, the current abnormal data quantity, the current data total amount and the historical evaluation index data to obtain data quality evaluation data, including: Determine the current abnormal data proportion according to the current abnormal data volume and the current total data volume; Determine the proportion of historical abnormal data according to the amount of historical abnormal data and the total amount of historical data; According to the historical abnormal rule quantity and the current abnormal rule quantity, the current business data is evaluated for rule passing variation quantity, and rule passing variation quantity evaluation data is obtained; wherein the rule passing variation quantity evaluation data represents the rule variation quantity of the current business data passing the verification rule; According to the amount of historical abnormal data and the amount of current abnormal data, the pass rate of the current business data is evaluated to obtain first pass rate evaluation data; wherein the first pass rate evaluation data represents the change in the amount of data of the current business that passes the verification rule; According to the current abnormal data proportion and the preset total abnormal data proportion, the pass rate of the current business data is evaluated to obtain second pass rate evaluation data; wherein the second pass rate evaluation data represents the change in the proportion of data that passes the verification rule of the current business data; The rule pass variation evaluation data, the first pass rate evaluation data and the second pass rate evaluation data are spliced ​​to obtain the data quality evaluation data.

3. The method according to claim 2, characterized in that After extracting the current evaluation index data from the current rule verification data, the method further includes: Filtering out the current abnormal rule from the verification rules according to the current abnormal rule quantity; Obtaining historical exception rules; wherein the historical exception rules are verification rules that the historical business data fails to pass; Filtering out new exception rules and matching rules from the verification rules according to the historical exception rules and the current exception rules, wherein the matching rules are verification rules passed by the current business data; Filtering out a selected abnormal rule from the verification rules according to the historical abnormal data proportion and the current abnormal data proportion; Filtering selected abnormal rules from the verification rules according to the preset rule quantity and the current abnormal data quantity; Filtering target abnormal rules from the verification rules according to the preset rule quantity and the current abnormal data proportion; The newly added exception rule, the matching rule, the selected exception rule, the chosen exception rule and the target exception rule are visualized to obtain an exception rule view.

4. The method according to claim 3, characterized in that The selecting an abnormal rule from the verification rules according to the historical abnormal data proportion and the current abnormal data proportion includes: Calculate the change rate of the historical abnormal data proportion and the current abnormal data proportion to obtain the abnormal data proportion growth rate; The selected abnormal rule is screened out from the verification rules according to a preset proportion change threshold and the abnormal data proportion growth rate.

5. The method according to any one of claims 1 to 4, characterized in that: After extracting the current evaluation index data from the current rule verification data, the method further includes: Obtaining the amount of the current business data reported by the financial platform to obtain the current amount of reported data; Obtaining the number of historical business data reported by the financial platform to obtain the amount of historical reported data; Performing a reporting change assessment based on the current reported data volume and the historical reported data volume to obtain reporting change assessment data; Filtering selected business data from the current business data according to the reported change assessment data; The reported change assessment data and the selected business data are visualized to obtain a reported exception view.

6. The method according to any one of claims 1 to 4, characterized in that: After extracting the current evaluation index data from the current rule verification data, the method further includes: Comparing the historical evaluation index data with the current evaluation index data to obtain data comparison information; The data comparison information is visualized to obtain an indicator data comparison view.

7. The method according to any one of claims 2 to 4, characterized in that: After determining the proportion of historical abnormal data according to the amount of historical abnormal data and the total amount of historical data, the method further includes: Acquire the institution information of the financial platform according to the preset quantity and the current abnormal rule quantity to obtain a first institution information set; Acquire the institution information of the financial platform according to the preset number and the current abnormal data volume to obtain a second institution information set; Acquire the institution information of the financial platform according to the preset number and the current abnormal data proportion to obtain a third institution information set; The first institution information set, the second institution information set and the third institution information set are visualized to obtain an abnormal institution view.

8. A device for evaluating the quality of business data, characterized in that: The device comprises: A data acquisition module, used to acquire current business data and historical evaluation index data of the financial platform; wherein the historical evaluation index data is an index for evaluating the quality of historical business data, and the current business data is at least one of the following data: text data, image data, and audio and video data; A rule verification module, used to perform rule verification on the current business data to obtain current rule verification data; An indicator extraction module is used to extract current evaluation indicator data from the current rule verification data; wherein the current evaluation indicator data includes: the current abnormal rule quantity, the current abnormal data quantity and the current total data quantity, the current abnormal rule quantity represents the number of rules that the current business data fails to pass the verification rules, and the current abnormal data quantity is the data quantity of the current business data that fails to pass the verification rules; The quality assessment module is used to perform quality assessment on the current business data according to the current abnormal rule quantity, the current abnormal data quantity, the current total data quantity and the historical assessment indicator data to obtain data quality assessment data.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the method for evaluating the quality of business data according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for evaluating the quality of business data according to any one of claims 1 to 7 is implemented.

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