Audit data value evaluation method and device

Abstract

The invention relates to an audit data value evaluation method and equipment. The method comprises the following steps: constructing an audit data rule base; marking a source attribute to which the data type information belongs; constructing a data value to-be-evaluated set; and based on the audit data value evaluation system, matching and comparing the data value to-be-evaluated set with the rules of the corresponding types in the audit data rule base to obtain the score of each influence factor, calculating the weight of each influence factor, and calculating to obtain an audit data value evaluation result. When the auditing data rule base is constructed, the data dictionary, the national standard, the local standard, the industrial standard and the expert knowledge base are comprehensively considered, subjective evaluation and objective evaluation are fused, and the auditing data value is evaluated based on the four influence factors of the data quality, the data endogenous value, the data application value and the market value. And an evaluation result with relatively high accuracy and comprehensiveness is obtained.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an audit data value evaluation method and device. Background Art

[0002] Audit data refers to data that is spontaneously generated, collected, maintained, managed, and owned during the long-term operation of the audit object. The data sources involved in the audit mainly include the internal management systems, financial systems, business systems, and external data sources (such as public databases, third-party data providers, etc.) of the audit objects such as enterprises, institutions, and individuals. Audit data types include structured data (such as tables, databases) and unstructured data (such as text, pictures, audio, video, etc.). The content carried by audit data covers the financial records, transaction information, business activities, internal controls and other related information of the audit object. As an important part of data resources, audit data plays an indispensable enabling role in improving government governance capabilities, improving and safeguarding people's livelihood services, and cultivating new momentum for economic development.

[0003] Current data value assessment generally adopts economic value method, decision value method, cost value method, strategic value method, expert evaluation method, etc. As for the value of audit data in the audit field, there is no mature audit data value evaluation method in the industry.

[0004] Therefore, how to improve the accurate and comprehensive evaluation of the value of audit data is a problem that technical personnel in this field need to solve. Summary of the invention

[0005] Based on this, it is necessary to provide an audit data value evaluation method and equipment to address the above technical issues.

[0006] In order to achieve the above object, the present invention provides an audit data value evaluation method, comprising:

[0007] Step 1: Obtain the data dictionary of the target database, and build an audit data rule base based on the data dictionary of the target database, with reference to national standards, local standards, industry standards and expert knowledge base;

[0008] Step 2: Obtain the audit data to be evaluated in the target database, use a hash table to perform a pre-check on the database table of the audit data to be evaluated, identify the data type information and file structure information contained in the audit data to be evaluated, and mark the source attribute to which the data type information belongs;

[0009] Step 3: Load the audit data to be evaluated according to the marked source attributes, use the bidirectional long short-term memory network to perform named entity recognition on the audit data to be evaluated, obtain the named entity information of the audit data to be evaluated, perform format screening, duplicate checking and key information extraction on the audit data to be evaluated in turn, obtain key quality characteristic information, and construct the data value to be evaluated set based on the source attributes, file structure information, named entity information and key quality characteristic information;

[0010] Step 4: Based on the four influencing factors of data quality, data intrinsic value, data application value and market value, an audit data value evaluation system is constructed. Based on the audit data value evaluation system, the data value set to be evaluated is matched and compared with the corresponding types of rules in the audit data rule library to obtain the score of each influencing factor. The hierarchical analysis method is used to calculate the weight of each influencing factor. The score of each influencing factor and the weight of each influencing factor are input into the audit data value evaluation model to calculate the audit data value evaluation result.

[0011] Optionally, the data dictionary includes: definition and description information of target database objects, attribute information of target database objects, association relationships of target database objects, target database users and authority information, and parameters and configuration information of the target database.

[0012] Optionally, in step 2, a hash table is used to perform a pre-check of the library table on the audit data to be evaluated, and the data type information and file structure information contained in the audit data to be evaluated are identified, and the source attribute to which the data type information belongs is marked accordingly, including:

[0013] NLP technology is used to identify the data type and length of the table header items and the first five non-blank rows of the audit data to be evaluated, and the data type information contained in the audit data to be evaluated is obtained. OCR technology is used to perform structured recognition on the audit data to be evaluated, and the file structure information contained in the audit data to be evaluated is obtained. The identified content is then concatenated with the file name of the audit data to be evaluated to obtain concatenated data. The concatenated data is then converted into a character format and converted into a character vector or a string scalar. The MD5 algorithm is used to calculate the hash value, which is matched and compared with the hash value of the stored source attribute to mark the source attribute to which the data type information belongs.

[0014] Optionally, in step 3, the audit data to be evaluated is loaded according to the marked source attributes, and a bidirectional long short-term memory network is used to perform named entity recognition on the audit data to be evaluated to obtain named entity information of the audit data to be evaluated, including:

[0015] The audit data to be evaluated is loaded according to the marked source attributes, and data cleaning is performed on the audit data to obtain the cleaned named data information. A bidirectional long short-term memory network is used to perform named entity recognition on the cleaned named data information to obtain the named entity information of the audit data to be evaluated.

[0016] Optionally, in step 4, factors affecting data quality include six secondary factors, namely accuracy, completeness, consistency, accessibility, standardization and timeliness; factors affecting data intrinsic value include four secondary factors, namely data scale, data frequency, data structure and data granularity; factors affecting data application value include five secondary factors, namely user visits, user browsing or downloads, proportion of business data, proportion of field or subject data and proportion of industry data; and factors affecting market value include five secondary factors, namely data scarcity, external data cooperation, proportion of data with high download rate, expected social value benefits and expected economic value benefits.

[0017] Optionally, in step 4, based on the audit data value evaluation system, the data value set to be evaluated is matched and compared with the rules under the corresponding attributes in the audit data rule library to obtain a score for each influencing factor, including:

[0018] According to the marked source attributes, the data value set to be evaluated is matched and compared with the corresponding types of rules in the audit data rule library. Based on the four influencing factors of data quality, data intrinsic value, data application value and market value, the error rate and score of the data information contained in the data value set to be evaluated under each influencing factor are calculated to obtain the score of each influencing factor.

[0019] Optionally, the calculation formula of the audit data value evaluation model is as follows:

[0020]

[0021] The constraints are: , V is the audit data value score, Q is the data quality score, N is the data intrinsic value score, A is the data application value score, and M is the market value score. is the weight of data quality, is the weight of the intrinsic value of data, is the weight of data application value, The weight of the data application value.

[0022] The present invention also provides an audit data value evaluation device, comprising:

[0023] A data dictionary acquisition module is used to acquire the data dictionary of the target database;

[0024] Audit data rule base construction module, used to construct the audit data rule base according to the data dictionary of the target database, with reference to national standards, local standards, industry standards and expert knowledge base;

[0025] The audit data acquisition and pre-detection module is used to obtain the audit data to be evaluated from the target database, use a hash table to perform pre-detection on the database table of the audit data to be evaluated, identify the data type information and file structure information contained in the audit data to be evaluated, and mark the source attribute to which the data type information belongs accordingly;

[0026] A named entity recognition module is used to load the audit data to be evaluated according to the marked source attributes, and use a bidirectional long short-term memory network to perform named entity recognition on the audit data to be evaluated to obtain the named entity information of the audit data to be evaluated;

[0027] The data processing module is used to perform format screening, duplicate checking and key information extraction on the audit data to be evaluated, and obtain key quality characteristic information;

[0028] The data set construction module is used to construct a data value evaluation set based on source attributes, file structure information, named entity information and key quality characteristic information;

[0029] Audit data value evaluation system construction module, which is used to build an audit data value evaluation system based on four influencing factors: data quality, data intrinsic value, data application value and market value;

[0030] The matching and comparison module is used to match and compare the data value set to be evaluated with the corresponding types of rules in the audit data rule library based on the audit data value evaluation system to obtain the score of each influencing factor;

[0031] The weight calculation module is used to calculate the weight of each influencing factor using the hierarchical analysis method;

[0032] The data value evaluation module is used to input the score of each influencing factor and the weight of each influencing factor into the audit data value evaluation model to calculate the audit data value evaluation result.

[0033] The present invention also provides a computer device, the computer device comprising: a processor, the processor is used to implement the steps of any of the above methods when executing a computer program stored in a memory.

[0034] The present invention also provides a computer-readable storage medium on which computer instructions are stored. When the computer instructions are executed by a processor, the steps described in any of the above methods are implemented.

[0035] The advantages and beneficial effects of the present invention are: the present invention provides an audit data value evaluation method and device, which is based on the audit data value evaluation model, combined with NLP technology, OCR technology, MD5 algorithm and bidirectional long short-term memory network, and specifically constructs a data value to be evaluated set with source attributes. According to the source attributes of the audit data to be evaluated, the rules of the corresponding types are matched and compared in the audit data rule base, and the four influencing factors of data quality, data intrinsic value, data application value and market value are comprehensively considered to obtain a score for each influencing factor, and the weight of each influencing factor is calculated by using the hierarchical analysis method, thereby obtaining the audit data value evaluation result. When constructing the audit data rule base, the method comprehensively considers the data dictionary, national standards, local standards, industry standards and expert knowledge base, integrates subjective evaluation with objective evaluation, and evaluates the audit data value based on the four influencing factors of data quality, data intrinsic value, data application value and market value, thereby obtaining an evaluation result with high accuracy and comprehensiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flowchart of an audit data value evaluation method in an embodiment of the present invention;

[0037] Figure 2 It is a structural schematic diagram of an audit data value evaluation device in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below through specific implementation methods in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0039] To facilitate understanding of this embodiment, the embodiment of the present invention is described in detail below.

[0040] Embodiment 1

[0041] Figure 1 A flowchart of an audit data value evaluation method provided in Example 1 of the present invention.

[0042] refer to Figure 1 , the evaluation method comprises the following steps:

[0043] S1. Obtain the data dictionary of the target database, and build an audit data rule library based on the data dictionary of the target database, with reference to national standards, local standards, industry standards and expert knowledge base.

[0044] In this embodiment, in order to build an audit data rule base, it is first necessary to obtain the data dictionary of the target database, which mainly contains the following information: definition and description information of target database objects, attribute information of target database objects, association relationships of target database objects, target database users and permission information, parameters and configuration information of the target database, etc.

[0045] It should be understood that the data dictionary is an important component of the target database. It is mainly used to store a collection of various objects in the target database, including the definition and description information of all objects in the target database. The objects here are also audit data. The data dictionary can help users understand the various objects and data structures in the target database, as well as the relationships and associations between them.

[0046] The definition and description information of the target database objects include but are not limited to tables, views, indexes, stored procedures, etc.; the attribute information of the target database objects include but are not limited to the name, type, size, creation time, modification time, etc. of the objects; the association relationships of the target database objects include but are not limited to the relationship between tables, the relationship between views and tables, the relationship between indexes and tables, etc.; the target database user and permission information includes but is not limited to the user's name, role, permission, etc.; the target database parameter and configuration information includes but is not limited to the database name, character set, time zone, etc.

[0047] In this embodiment, the audit data rule base is constructed with reference to national standards, local standards, industry standards and expert knowledge base. Specifically, the data dictionary of audit data is obtained, and the evaluation rules of audit data with different source attributes are determined with reference to national standards, industry standards and the standards of the province to which the city belongs or the local standards of the city, and expert knowledge resources are gathered to form an expert knowledge base. The evaluation rules involved in the expert knowledge base are mainly formulated for various secondary influencing factors in the audit data value evaluation system, and then the audit data rule base is constructed by comprehensively considering national standards, industry standards, the standards of the province to which the city belongs or the local standards of the city and the expert knowledge base.

[0048] In this embodiment, the audit data rule library includes pre-rules, basic rules, business rules and intelligent rules; the pre-rules include database naming rules and database structure rules, which are used to detect whether the naming of the database and the structure of the database comply with the pre-specifications of the audit data quality evaluation; the basic rules include audit data format rules, which are used to detect format quality problems of audit data; the business rules include general rules and scope of use rules, which are used to detect whether the general identification data and scope of use data of the audit data comply with the general rules and scope of use rules established by national standards, local standards and industry standards; the intelligent rules include data feature rules, usage scenario rules, business model rules, supply and demand relationship rules, application risk rules and association verification rules, which are used to detect the specific data feature information, application scenarios, business models, market supply and demand relationships, risk factors of the audit data and the degree of logical association matching between the audit data and users and / or other data and / or business applications.

[0049] Among them, the pre-rules involved in the present invention are mainly used to detect whether the naming and structure of the database meet the pre-specifications of the audit data quality evaluation; the basic rules are mainly used to detect the general format quality problems of the audit data (such as missing data and duplicate data, etc.) without the help of national standards, industry standards and local standards; business rules are data quality rules mainly established according to national, industry and local standards, and are composed of general rules (such as unified credit codes, organizational codes, certificate numbers representing personal identity, mobile phone numbers and email addresses, etc.) and usage scope rules. Among them, the usage scope indicates the breadth of data to which the data can be applied, including industries, fields and regions. Domain, industry refers to the industry to which data can be applied, field refers to the field and application scenario to which data can be applied, and region refers to the area to which data can be applied, such as administrative divisions. It should be understood that: if the scope of data use is larger, the data utilization rate will be higher, thereby creating more economic benefits and the value of data assets will be higher. However, there are exceptions. If the scope of data use is small but the degree of professionalism is high, the data will also have higher value. Therefore, the data use scope rules need to be combined with the data user to analyze the data asset value; intelligent rules mainly include: data feature rules, usage scenario rules, business model rules, supply and demand relationship rules, application risk rules and association verification rules, among which data features Rules refer to the objective description of data asset objects, including information such as data attributes and data scale. Data attribute information includes data source, data type and data activity, and data scale information includes data volume, growth rate and update rate. Usage scenarios refer to the usage described from multiple dimensions such as data usage, data openness and data usage frequency in specific audit application scenarios. Business models refer to the profit model of secondary development with audit data assets as the core, including data product and service models, empowerment models and financial models. It should be understood that the same data assets will have different values ​​due to their different usage scopes, usage scenarios and business models. ; Supply and demand relationship is to analyze the asset value of data from the perspective of market supply and demand, among which data scarcity is measured by the supply quantity of a certain type of data in the market and the number of suppliers; in the data source market, if the supply quantity of a certain type of data or the number of suppliers is scarce, then the value of its data assets will be higher; value density further measures the effectiveness of data supply; market size is measured by the number of market demands; data relevance includes the degree of logical association between data and users, such as the association between information retrieval results and the search subject expected by users, the association between data and data with logical relationships, and the association between data and business application implementations;It should be understood that: in data applications, the higher the data relevance, the higher the value created by it with other data assets. When data is used for decision support, it is often necessary to integrate data from different information systems. The higher the data relevance, the higher the efficiency of its integration and utilization, and the higher the value of the data assets it embodies. Risk factors affect the risk-return ratio and discount rate, including management risk, circulation risk, data security risk and supervision risk, etc., which can be determined by referring to the results of the data security risk assessment report. ;

[0050] S2. Obtain the audit data to be evaluated from the target database, use a hash table to perform a pre-check on the database table of the audit data to be evaluated, identify the data type information and file structure information contained in the audit data to be evaluated, and mark the source attribute to which the data type information belongs.

[0051] In this embodiment, the source attribute involved in the present invention is that the audit data is classified according to the line classification method given in 6.2 of "Basic Principles and Methods of Information Classification and Coding GB / T 7027-2002", that is, according to the information source characteristics of the audit data, it is divided into several levels one by one, and each level is divided into several categories. The same-level categories of the same branch form a parallel relationship, and the categories of different levels form a subordinate relationship.

[0052] It should be understood that there are a large amount of audit data in the target database involved in the present invention, and the audit data to be evaluated only belongs to one type.

[0053] In this embodiment, a hash table is used to perform a pre-check of the library table on the audit data to be evaluated, identify the data type information and file structure information contained in the audit data to be evaluated, and mark the source attribute of the data type information accordingly, including:

[0054] NLP technology is used to identify the data type and length of the table header items and the first five non-blank rows of the audit data to be evaluated, and the data type information contained in the audit data to be evaluated is obtained. OCR technology is used to perform structured recognition on the audit data to be evaluated, and the file structure information contained in the audit data to be evaluated is obtained. The identified content is then concatenated with the file name of the audit data to be evaluated to obtain concatenated data. The concatenated data is then converted into a character format and converted into a character vector or a string scalar. The MD5 algorithm is used to calculate the hash value, which is matched and compared with the hash value of the stored source attribute to mark the source attribute to which the data type information belongs.

[0055] In this embodiment, NLP technology is used to process table header data to identify data type information of different types of audit data, and then OCR technology is used to perform structured recognition to obtain file structure information of different types of audit data. The data type information, file structure information and file name are then spliced ​​to obtain spliced ​​data, and the spliced ​​data is then converted to a character format that is convenient for MD5 algorithm operations, and then a hash value is calculated. The calculated hash value is matched and compared with the hash value of the stored source attribute to determine and mark the source attribute to which the data type information belongs.

[0056] S3. Load the audit data to be evaluated according to the marked source attributes, use a bidirectional long short-term memory network to perform named entity recognition on the audit data to be evaluated, obtain the named entity information of the audit data to be evaluated, perform format screening, duplicate checking and key information extraction on the audit data to be evaluated in turn, obtain key quality characteristic information, and construct a data value to be evaluated set based on source attributes, file structure information, named entity information and key quality characteristic information.

[0057] In this embodiment, the audit data to be evaluated is loaded according to the marked source attributes, and a bidirectional long short-term memory network is used to perform named entity recognition on the audit data to be evaluated to obtain the named entity information of the audit data to be evaluated, including:

[0058] The audit data to be evaluated is loaded according to the marked source attributes, and data cleaning is performed on the audit data to obtain the cleaned named data information. A bidirectional long short-term memory network is used to perform named entity recognition on the cleaned named data information to obtain the named entity information of the audit data to be evaluated.

[0059] In this embodiment, the present invention adopts a bidirectional long short-term memory network (BiLSTM) to perform named entity recognition on the named data information of the audit data, so as to obtain the named entity information of the audit data to be evaluated, which is used for subsequent matching and comparison with the pre-rules in the audit data rule base.

[0060] In this embodiment, the audit data to be evaluated is subjected to format screening, duplication checking and key information extraction to obtain key quality characteristic information, wherein format screening may adopt Excel format screening technology, the purpose of which is to detect incomplete data, erroneous data and duplicate data, duplication checking may adopt hash table duplication checking method, the purpose of which is to detect duplicate data, key information extraction may adopt NLP technology to extract relevant key information, and the results obtained by the three processing methods are summarized to form key quality characteristic information (key quality characteristic information includes but is not limited to unified credit code, organization code, certificate number representing personal identity, mobile phone number, email address, scope of use information, related information, feature information, application scenarios, business model, market supply and demand relationship, risk factors, etc.).

[0061] In this embodiment, source attributes, file structure information, named entity information and key quality characteristic information are integrated, and a set of data values ​​to be evaluated with source attributes is specifically constructed.

[0062] S4. Based on the four influencing factors of data quality, data intrinsic value, data application value and market value, an audit data value evaluation system is constructed. Based on the audit data value evaluation system, the data value set to be evaluated is matched and compared with the corresponding types of rules in the audit data rule library to obtain the score of each influencing factor. The hierarchical analysis method is used to calculate the weight of each influencing factor. The score of each influencing factor and the weight of each influencing factor are input into the audit data value evaluation model to calculate the audit data value evaluation result.

[0063] In this embodiment, based on the four influencing factors of data quality, data intrinsic value, data application value and market value, an audit data value evaluation system is constructed (as shown in Table 1 below), where data quality influencing factors include six secondary influencing factors of accuracy, completeness, consistency, accessibility, standardization and timeliness; data intrinsic value influencing factors include four secondary influencing factors of data scale, data frequency, data structure and data granularity; data application value influencing factors include five secondary influencing factors of user visits, user browsing or downloading volume, business data proportion, field or subject data proportion and industry data proportion; market value influencing factors include five secondary influencing factors of data scarcity, external data cooperation, high download rate data proportion, expected social value benefits and expected economic value benefits.

[0064] Table 1 Audit data value evaluation system

[0065] Among them, refer to Table 1 for the description of the secondary influencing factors in data quality (Q):

[0066] Normativity: The degree to which data conforms to data standards, data models, business rules, metadata, or authoritative reference data.

[0067] Completeness: The degree to which data elements are assigned values ​​as required by data rules.

[0068] Accuracy: The degree to which data accurately represents the true value of the real entity (actual object) it describes.

[0069] Consistency: The degree to which data do not contradict other data used in a specific context.

[0070] Timeliness: The degree to which data is correct over time.

[0071] Accessibility: The degree to which data can be accessed.

[0072] Description of each secondary influencing factor in the intrinsic value of data (N):

[0073] Data size: the number of data items stored in the database.

[0074] Data frequency: Monthly update rate of fields and / or daily update rate of fields.

[0075] Data structure: the proportion of structured data.

[0076] Data granularity: the proportion of district and county data.

[0077] Description of each secondary influencing factor in data application value (A):

[0078] User visits: Number of website visits and / or monthly visits.

[0079] Business data ratio: the ratio of data with business significance.

[0080] Proportion of domain or subject data: The proportion of data with clear domain or subject attributes.

[0081] Industry data proportion: the proportion of data with clear industry attributes.

[0082] Explanation of each secondary influencing factor in market value (M):

[0083] Data scarcity: scarcity of data fields and content.

[0084] External data cooperation: external data cooperation unit or cooperation data volume.

[0085] Proportion of data with high download rate: the proportion of data sets whose total download rate and download times exceed the preset threshold.

[0086] Expected social value benefits: the degree of improvement in convenience and / or the degree of improvement in satisfaction.

[0087] Expected economic value benefits: diversity of application scenarios and / or economic benefits achieved.

[0088] In this embodiment, based on the audit data value evaluation system, the data value set to be evaluated is matched and compared with the rules under the corresponding attributes in the audit data rule library to obtain the score of each influencing factor, including:

[0089] According to the marked source attributes, the data value set to be evaluated is matched and compared with the corresponding types of rules in the audit data rule library. Based on the four influencing factors of data quality, data intrinsic value, data application value and market value, the error rate and score of the data information contained in the data value set to be evaluated under each influencing factor are calculated to obtain the score of each influencing factor.

[0090] In this embodiment, according to the source attributes of the audit data to be evaluated, the corresponding types of rules are matched and compared in the audit data rule library to obtain the data detection results, and based on the four influencing factors of data quality, data intrinsic value, data application value and market value, the number of qualified data, the number of abnormal data, the error rate and the score of the data information contained in the data value to be evaluated set under the four influencing factors are calculated to form a score for each influencing factor.

[0091] In this embodiment, the present invention adopts the hierarchical analysis method to determine the weight of each influencing factor. The basic idea of ​​the hierarchical analysis method is to transform the overall judgment of the weights of multiple dimensions that constitute a complex problem into a "two-by-two comparison" of these dimensions, and then transform it into a ranking judgment of the overall weights of these dimensions, and finally establish the weight of each dimension.

[0092] In this embodiment, the specific steps of using the hierarchical analysis method to calculate the weight of each dimension indicator are as follows:

[0093] (1) Based on each level of influencing factors in the data value evaluation system, a hierarchical structure of influencing factors is constructed.

[0094] (2) Based on the hierarchical structure of influencing factors, the relative importance of each level of influencing factors in the data value evaluation system is assigned, a judgment matrix is ​​constructed, and the judgment matrix is ​​calculated. The maximum eigenvalue and eigenvector of are normalized to obtain the ranking weights of the relative importance of the influencing factors at the same level to the influencing factors at the previous level.

[0095] (3) Hierarchical single sorting and its consistency test,

[0096] Due to the influence of various subjective and objective factors, it is difficult for the judgment matrix to be strictly consistent. Therefore, after obtaining the characteristic roots, the consistency of the judgment matrix needs to be tested.

[0097] In order to test the consistency of the judgment matrix, it is necessary to calculate its consistency index CI, which is defined as: , when CI=0, the judgment matrix has complete consistency. Among them, The larger the CI is, the worse the consistency of the judgment matrix is. In order to check whether the judgment matrix has satisfactory consistency, it is also necessary to compare the CI with the average random consistency index RI, where the value of the average random consistency index RI is as shown in Table 2 below.

[0098] Table 2 The value of average random consistency index RI

[0099]

[0100] Considering that the deviation from consistency may be caused by random reasons, when checking whether the judgment matrix has satisfactory consistency, it is also necessary to compare CI and random consistency index RI to obtain the test coefficient CR, the formula is: ; Generally, if , then the judgment matrix is ​​considered to have passed the consistency test, otherwise it does not have satisfactory consistency. In addition, the pairwise comparisons between specific influencing factors are generally obtained by survey interviews and expert consultation methods, and then the judgment matrix is ​​constructed according to the importance of each influencing factor for calculation.

[0101] In this embodiment, the calculation formula of the audit data value evaluation model is as follows:

[0102]

[0103] The constraints are: , V is the audit data value score, Q is the data quality score, N is the data intrinsic value score, A is the data application value score, and M is the market value score. is the weight of data quality, is the weight of the intrinsic value of data, is the weight of data application value, The weight of the data application value.

[0104] The present embodiment provides an audit data value evaluation method, comprising: obtaining a data dictionary of a target database, and constructing an audit data rule library based on the data dictionary of the target database and taking national standards, local standards, industry standards and expert knowledge base as references; obtaining audit data to be evaluated of the target database, using a hash table to perform a pre-detection of the database table on the audit data to be evaluated, identifying the data type information and file structure information contained in the audit data to be evaluated, and correspondingly marking the source attribute to which the data type information belongs; loading the audit data to be evaluated according to the marked source attributes, using a bidirectional long short-term memory network to perform named entity recognition on the audit data to be evaluated, obtaining the named entity information of the audit data to be evaluated, performing format screening, duplicate checking and key information extraction on the audit data to be evaluated in turn, obtaining key quality characteristic information, and constructing a data value to-be-evaluated set based on the source attribute, file structure information, named entity information and key quality characteristic information; and based on the four aspects of data quality, data intrinsic value, data application value and market value. Influencing factors are taken into account to construct an audit data value evaluation system. Based on the audit data value evaluation system, the data value set to be evaluated is matched and compared with the corresponding types of rules in the audit data rule library to obtain the score of each influencing factor. The hierarchical analysis method is used to calculate the weight of each influencing factor. The score of each influencing factor and the weight of each influencing factor are input into the audit data value evaluation model to calculate the audit data value evaluation result. This implementation method is based on the audit data value evaluation model, combines NLP technology, OCR technology, MD5 algorithm and bidirectional long short-term memory network, and specifically constructs a data value set to be evaluated with source attributes. According to the source attributes of the audit data to be evaluated, the corresponding types of rules are matched and compared in the audit data rule library. The four influencing factors of data quality, data intrinsic value, data application value and market value are comprehensively considered to obtain the score of each influencing factor, and the hierarchical analysis method is used to calculate the weight of each influencing factor to obtain the audit data value evaluation result. When constructing the audit data rule base, this method comprehensively considered the data dictionary, national standards, local standards, industry standards and expert knowledge base, integrated subjective evaluation with objective evaluation, and evaluated the value of audit data based on four influencing factors: data quality, data intrinsic value, data application value and market value, thereby obtaining an evaluation result with high accuracy and comprehensiveness.

[0105] Embodiment 2

[0106] This embodiment provides an audit data value evaluation device 200 based on the above embodiment 1. Figure 2, used to implement the steps of an audit data value evaluation method described in the above embodiment 1, the device 200 mainly includes: a data dictionary acquisition module 201, an audit data rule base construction module 202, an audit data acquisition and pre-detection module 203, a named entity recognition module 204, a data processing module 205, a data set construction module 206, an audit data value evaluation system construction module 207, a matching comparison module 208, a weight calculation module 209 and a data value evaluation module 210, wherein,

[0107] A data dictionary acquisition module 201 is used to acquire a data dictionary of a target database;

[0108] The audit data rule base construction module 202 is used to construct the audit data rule base according to the data dictionary of the target database, with reference to national standards, local standards, industry standards and expert knowledge base;

[0109] The audit data acquisition and pre-detection module 203 is used to acquire the audit data to be evaluated of the target database, use a hash table to perform pre-detection of the database table on the audit data to be evaluated, identify the data type information and file structure information contained in the audit data to be evaluated, and mark the source attribute to which the data type information belongs;

[0110] The named entity recognition module 204 is used to load the audit data to be evaluated according to the marked source attributes, and use a bidirectional long short-term memory network to perform named entity recognition on the audit data to be evaluated to obtain the named entity information of the audit data to be evaluated;

[0111] The data processing module 205 is used to sequentially perform format screening, duplicate checking and key information extraction on the audit data to be evaluated to obtain key quality characteristic information;

[0112] A data set construction module 206 is used to construct a data value to be evaluated set based on source attributes, file structure information, named entity information and key quality characteristic information;

[0113] Audit data value evaluation system construction module 207, used to construct an audit data value evaluation system based on four influencing factors: data quality, data intrinsic value, data application value and market value;

[0114] The matching and comparison module 208 is used to match and compare the data value to be evaluated set with the corresponding type of rules in the audit data rule library based on the audit data value evaluation system to obtain the score of each influencing factor;

[0115] The weight calculation module 209 is used to calculate the weight of each influencing factor using the hierarchical analysis method;

[0116] The data value evaluation module 210 is used to input the score of each influencing factor and the weight of each influencing factor into the audit data value evaluation model to calculate the audit data value evaluation result.

[0117] Embodiment 3

[0118] Based on the above embodiment 1, this embodiment further provides a computer device, which includes: a processor, and the processor is used to implement the steps of any of the above methods when executing a computer program stored in a memory.

[0119] Embodiment 4

[0120] The present invention also provides a computer-readable storage medium on which computer instructions are stored. When the computer instructions are executed by a processor, the steps of any of the above methods are implemented. Exemplarily, the computer program can be divided into one or more modules / units, and the one or more modules / units are stored in a memory and executed by a processor to complete the present invention. One or more modules / units can be a series of computer program instruction segments that can perform specific functions, and the instruction segments are used to describe the execution process of the computer program in a computer device.

[0121] The computer device may be a desktop computer, a notebook, a PDA, a cloud server, or other computer device. The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the computer device may include more or fewer components, or a combination of certain components, or different components. For example, the computer device may also include an input / output device, a network access device, a bus, and the like.

[0122] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0123] The memory may be an internal storage unit of a computer device, such as a hard disk or memory of a computer device. The memory may also be an external storage device of a computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the computer device. Furthermore, the memory may include both an internal storage unit of a computer device and an external storage device. The memory is used to store computer programs and other programs and data required by the computer device. The memory may also be used to temporarily store data that has been output or is to be output.

[0124] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0125] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0126] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0127] In the embodiments provided by the present invention, it should be understood that the disclosed devices / computer equipment and methods can be implemented in other ways. For example, the device / computer equipment embodiments described above are only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. 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.

[0128] The units described 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.

[0129] In addition, each functional unit in each embodiment of the present invention 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.

[0130] If the integrated module / 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 present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. The computer program may include computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier signals and telecommunication signals.

[0131] The above are only preferred implementations of the present invention. It should be pointed out that a number of modifications and improved technical solutions made by those skilled in the art without departing from the technical solution should also be deemed to fall within the scope of protection required by the claims.

Claims

1. A method for evaluating the value of audit data, characterized in that: include: Step 1: Obtain the data dictionary of the target database, and build an audit data rule base based on the data dictionary of the target database, with reference to national standards, local standards, industry standards and expert knowledge base; Step 2: Obtain the audit data to be evaluated in the target database, use a hash table to perform a pre-check on the database table of the audit data to be evaluated, identify the data type information and file structure information contained in the audit data to be evaluated, and mark the source attribute to which the data type information belongs; Step 3: Load the audit data to be evaluated according to the marked source attributes, use the bidirectional long short-term memory network to perform named entity recognition on the audit data to be evaluated, obtain the named entity information of the audit data to be evaluated, perform format screening, duplicate checking and key information extraction on the audit data to be evaluated in turn, obtain key quality characteristic information, and construct the data value to be evaluated set based on the source attributes, file structure information, named entity information and key quality characteristic information; Step 4: Based on the four influencing factors of data quality, data intrinsic value, data application value and market value, an audit data value evaluation system is constructed. Based on the audit data value evaluation system, the data value set to be evaluated is matched and compared with the corresponding types of rules in the audit data rule library to obtain the score of each influencing factor. The hierarchical analysis method is used to calculate the weight of each influencing factor. The score of each influencing factor and the weight of each influencing factor are input into the audit data value evaluation model to calculate the audit data value evaluation result.

2. According to claim 1, a method for evaluating the value of audit data is characterized in that: The data dictionary includes: definition and description information of target database objects, attribute information of target database objects, association relationships of target database objects, target database users and authority information, and parameters and configuration information of the target database.

3. The audit data value evaluation method according to claim 1 is characterized in that: In step 2, a hash table is used to perform a pre-check of the library table on the audit data to be evaluated, and the data type information and file structure information contained in the audit data to be evaluated are identified, and the source attribute to which the data type information belongs is marked accordingly, including: NLP technology is used to identify the data type and length of the table header items and the first five non-blank rows of the audit data to be evaluated, and the data type information contained in the audit data to be evaluated is obtained. OCR technology is used to perform structured recognition on the audit data to be evaluated, and the file structure information contained in the audit data to be evaluated is obtained. The identified content is then concatenated with the file name of the audit data to be evaluated to obtain concatenated data. The concatenated data is then converted into a character format and converted into a character vector or a string scalar. The MD5 algorithm is used to calculate the hash value, which is matched and compared with the hash value of the stored source attribute to mark the source attribute to which the data type information belongs.

4. The audit data value evaluation method according to claim 1 is characterized in that: In step 3, the audit data to be evaluated is loaded according to the marked source attributes, and a bidirectional long short-term memory network is used to perform named entity recognition on the audit data to be evaluated to obtain named entity information of the audit data to be evaluated, including: The audit data to be evaluated is loaded according to the marked source attributes, and data cleaning is performed on the audit data to obtain the cleaned named data information. A bidirectional long short-term memory network is used to perform named entity recognition on the cleaned named data information to obtain the named entity information of the audit data to be evaluated.

5. The audit data value evaluation method according to claim 1 is characterized in that: In step 4, the factors affecting data quality include six secondary factors, namely, accuracy, completeness, consistency, accessibility, standardization and timeliness; the factors affecting data intrinsic value include four secondary factors, namely, data scale, data frequency, data structure and data granularity; the factors affecting data application value include five secondary factors, namely, user visits, user browsing or downloading, proportion of business data, proportion of field or subject data and proportion of industry data; the factors affecting market value include five secondary factors, namely, data scarcity, external data cooperation, proportion of data with high download rate, expected social value benefits and expected economic value benefits.

6. The audit data value evaluation method according to claim 1 is characterized in that: In step 4, based on the audit data value evaluation system, the data value set to be evaluated is matched and compared with the rules under the corresponding attributes in the audit data rule library to obtain the score of each influencing factor, including: According to the marked source attributes, the data value set to be evaluated is matched and compared with the corresponding types of rules in the audit data rule library. Based on the four influencing factors of data quality, data intrinsic value, data application value and market value, the error rate and score of the data information contained in the data value set to be evaluated under each influencing factor are calculated to obtain the score of each influencing factor.

7. The audit data value evaluation method according to claim 1 is characterized in that: The calculation formula of the audit data value evaluation model is as follows: The constraints are: , V is the audit data value score, Q is the data quality score, N is the data intrinsic value score, A is the data application value score, and M is the market value score. is the weight of data quality, is the weight of the intrinsic value of data, is the weight of data application value, The weight of the data application value.

8. An audit data value evaluation device, characterized in that: include: A data dictionary acquisition module is used to acquire the data dictionary of the target database; Audit data rule base construction module, used to construct the audit data rule base according to the data dictionary of the target database, with reference to national standards, local standards, industry standards and expert knowledge base; The audit data acquisition and pre-detection module is used to obtain the audit data to be evaluated from the target database, use a hash table to perform pre-detection on the database table of the audit data to be evaluated, identify the data type information and file structure information contained in the audit data to be evaluated, and mark the source attribute to which the data type information belongs accordingly; A named entity recognition module is used to load the audit data to be evaluated according to the marked source attributes, and use a bidirectional long short-term memory network to perform named entity recognition on the audit data to be evaluated to obtain the named entity information of the audit data to be evaluated; The data processing module is used to perform format screening, duplicate checking and key information extraction on the audit data to be evaluated, and obtain key quality characteristic information; The data set construction module is used to construct a data value evaluation set based on source attributes, file structure information, named entity information and key quality characteristic information; Audit data value evaluation system construction module, which is used to build an audit data value evaluation system based on four influencing factors: data quality, data intrinsic value, data application value and market value; The matching and comparison module is used to match and compare the data value set to be evaluated with the corresponding types of rules in the audit data rule library based on the audit data value evaluation system to obtain the score of each influencing factor; The weight calculation module is used to calculate the weight of each influencing factor using the hierarchical analysis method; The data value evaluation module is used to input the score of each influencing factor and the weight of each influencing factor into the audit data value evaluation model to calculate the audit data value evaluation result.

9. A computer device, characterized in that: The computer device comprises: a processor, and the processor is used to implement the steps of any method according to claim 1-7 when executing a computer program stored in a memory.

10. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed by a processor, the steps of any method as claimed in claim 1-7 are implemented.

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