Intelligent analysis method and device for enterprise data

Abstract

The application discloses an intelligent analysis method and device for enterprise data, and the method comprises the following steps: determining an enterprise database to be analyzed, gathering all fields included in all enterprise databases to obtain a candidate field set, performing a preprocessing operation on all candidate fields included in the candidate field set to obtain a candidate entity field set; based on a preset target model, for each candidate entity field included in the candidate entity field set, inputting the candidate entity field into the target model to obtain a field analysis result of the candidate entity field; and generating a target analysis result according to the field analysis result of all candidate entity fields. It can be seen that the application can realize intelligent analysis of enterprise data, which is beneficial to improving the efficiency and accuracy of data analysis.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to an intelligent analysis method and apparatus for enterprise data. Background Technology

[0002] In real life, enterprise management is inseparable from data processing. Currently, enterprise data analysis and processing requires first aggregating and tracing the data, then relying on manual analysis and scoring, and multiple rounds of communication and confirmation with other business departments within the enterprise. However, the sheer volume of data within enterprises makes the existing reliance on manual data analysis inefficient and prone to inaccuracies. Therefore, providing a new method for enterprise data analysis to improve its efficiency is of paramount importance. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide an intelligent analysis method and device for enterprise data, which can realize intelligent analysis of enterprise middle platform master data, which is conducive to improving the efficiency and accuracy of data analysis.

[0004] To address the aforementioned technical problems, the first aspect of this invention discloses an intelligent analysis method for enterprise data, the method comprising:

[0005] Identify the enterprise database to be analyzed, and gather all fields included in all the enterprise databases to obtain a candidate field set;

[0006] Perform preprocessing operations on all candidate fields included in the candidate field set to obtain a candidate entity field set;

[0007] Based on the preset target model, for each candidate entity field included in the candidate entity field set, the candidate entity field is input into the target model to obtain the field analysis result of the candidate entity field;

[0008] Based on the field analysis results of all the candidate entity fields, a target analysis result is generated; wherein, the target analysis result is used to represent the data analysis results of all the enterprise databases.

[0009] As an optional implementation, in the first aspect of the present invention, the step of performing preprocessing operations on all candidate fields included in the candidate field set to obtain a candidate entity field set includes:

[0010] For each candidate field included in the candidate field set, the candidate field is analyzed based on a preset field analysis algorithm to obtain the candidate analysis result of the candidate field. The candidate analysis result is used to represent the field information of the candidate field.

[0011] For each candidate field included in the candidate field set, a candidate entity field set is determined based on the candidate analysis results of that candidate field.

[0012] As an optional implementation, in the first aspect of the present invention, determining the candidate entity field set based on the candidate analysis result of each candidate field included in the candidate field set includes:

[0013] For each candidate field included in the candidate field set, based on the candidate analysis results of all candidate fields, the association information between the candidate field and each remaining candidate field is analyzed to obtain the association information set corresponding to the candidate field; the association information is used to represent the field similarity relationship between the candidate field and each remaining candidate field.

[0014] For each candidate field included in the candidate field set, the target remaining candidate field corresponding to the candidate field is determined according to the associated information set corresponding to the candidate field. A merging operation is performed on the candidate field and the target remaining candidate field corresponding to the candidate field to obtain the merging result of the candidate field.

[0015] Based on all the merge results, determine the set of candidate entity fields.

[0016] As an optional implementation, in the first aspect of the present invention, determining the target remaining candidate field corresponding to each candidate field included in the candidate field set, based on the associated information set corresponding to the candidate field, includes:

[0017] For each candidate field included in the candidate field set, it is determined whether there is target related information in the related information set corresponding to the candidate field that satisfies the preset related conditions. When it is determined that there is target related information, all remaining candidate fields corresponding to the target related information are determined as target remaining candidate fields.

[0018] As an optional implementation, in the first aspect of the present invention, before inputting each candidate entity field included in the candidate entity field set into the target model based on a preset target model to obtain the field analysis result of the candidate entity field, the method further includes:

[0019] Determine all target factors and the factor weights for each target factor, and determine the target data template; wherein, the target factors are those that have an impact on enterprise database analysis;

[0020] A target model is constructed based on all the target factors, the factor weights of each target factor, and the target data template.

[0021] As an optional implementation, in the first aspect of the present invention, after generating the target analysis result based on the field analysis results of all the candidate entity fields, the method further includes:

[0022] Based on the target analysis results, determine whether the target analysis results meet the pre-set analysis result conditions;

[0023] When it is determined that the target analysis result does not meet the preset analysis result conditions, the reasons why the target analysis result does not meet the preset analysis result conditions are analyzed.

[0024] Based on the stated reasons for the objective, the factors to be adjusted in the target model and the target weight corresponding to each of the factors to be adjusted are determined.

[0025] Based on the target weight corresponding to each of the factors to be adjusted, an update operation is performed on the target model to obtain the updated target model.

[0026] As an optional implementation, in the first aspect of the present invention, after inputting each candidate entity field included in the candidate entity field set into the target model based on a preset target model to obtain the field analysis result of the candidate entity field, and before generating the target analysis result based on the field analysis results of all the candidate entity fields, the method further includes:

[0027] Based on the field score corresponding to the field analysis result of each candidate entity field included in the target analysis result, the field analysis results of all candidate entity fields are sorted according to the pre-set sorting conditions to obtain the target sequence;

[0028] The step of generating the target analysis result based on the field analysis results of all the candidate entity fields includes:

[0029] Based on the field analysis results of all the candidate entity fields and the target sequence, a target analysis result is generated.

[0030] A second aspect of the present invention discloses an intelligent analysis device for enterprise data, the device comprising:

[0031] The determination module is used to determine the enterprise database to be analyzed.

[0032] The aggregation module is used to aggregate all fields included in all the enterprise databases to obtain a candidate field set;

[0033] The processing module is used to perform preprocessing operations on all candidate fields included in the candidate field set to obtain a candidate entity field set;

[0034] The input module is used to input each candidate entity field included in the candidate entity field set into the target model based on a preset target model, and obtain the field analysis result of the candidate entity field.

[0035] The generation module is used to generate target analysis results based on the field analysis results of all the candidate entity fields; wherein the target analysis results are used to represent the data analysis results of all the enterprise databases.

[0036] As an optional implementation, in the second aspect of the present invention, the processing module performs preprocessing operations on all candidate fields included in the candidate field set to obtain the candidate entity field set, specifically including:

[0037] For each candidate field included in the candidate field set, the candidate field is analyzed based on a preset field analysis algorithm to obtain the candidate analysis result of the candidate field. The candidate analysis result is used to represent the field information of the candidate field.

[0038] For each candidate field included in the candidate field set, a candidate entity field set is determined based on the candidate analysis results of that candidate field.

[0039] As an optional implementation, in a second aspect of the present invention, the processing module determines the candidate entity field set for each candidate field included in the candidate field set based on the candidate analysis result of that candidate field in the specific manner including:

[0040] For each candidate field included in the candidate field set, based on the candidate analysis results of all candidate fields, the association information between the candidate field and each remaining candidate field is analyzed to obtain the association information set corresponding to the candidate field; the association information is used to represent the field similarity relationship between the candidate field and each remaining candidate field.

[0041] For each candidate field included in the candidate field set, the target remaining candidate field corresponding to the candidate field is determined according to the associated information set corresponding to the candidate field. A merging operation is performed on the candidate field and the target remaining candidate field corresponding to the candidate field to obtain the merging result of the candidate field.

[0042] Based on all the merge results, determine the set of candidate entity fields.

[0043] As an optional implementation, in a second aspect of the present invention, the processing module determines the target remaining candidate field corresponding to each candidate field included in the candidate field set based on the associated information set corresponding to that candidate field, specifically including:

[0044] For each candidate field included in the candidate field set, it is determined whether there is target related information in the related information set corresponding to the candidate field that satisfies the preset related conditions. When it is determined that there is target related information, all remaining candidate fields corresponding to the target related information are determined as target remaining candidate fields.

[0045] As an optional implementation, in a second aspect of the present invention, the determining module is further configured to determine all target factors and the factor weights of each target factor, and to determine a target data template, before the input module, based on a preset target model, inputs each candidate entity field included in the candidate entity field set into the target model to obtain the field analysis result of the candidate entity field; wherein, the target factors are factors that have an impact on enterprise database analysis;

[0046] The device further includes:

[0047] A construction module is used to construct a target model based on all the target factors, the factor weights of each target factor, and the target data template.

[0048] As an optional implementation, in a second aspect of the invention, the apparatus further includes:

[0049] The judgment module is used to determine whether the target analysis result meets the preset analysis result conditions based on the target analysis result after the generation module generates the target analysis result based on the field analysis results of all the candidate entity fields.

[0050] An analysis module is used to analyze the reasons why the target analysis result does not meet the preset analysis result conditions when the judgment module determines that the target analysis result does not meet the preset analysis result conditions.

[0051] The determining module is further configured to determine the factors to be adjusted in the target model and the target weight corresponding to each of the factors to be adjusted based on the target reasons;

[0052] The update module is used to perform an update operation on the target model according to the target weight corresponding to each of the factors to be adjusted, so as to obtain the updated target model.

[0053] As an optional implementation, in a second aspect of the invention, the apparatus further includes:

[0054] The sorting module is used to sort the field analysis results of all candidate entity fields according to a preset sorting condition after the input module inputs the candidate entity field into the target model based on a preset target model and obtains the field analysis result of the candidate entity field, and before the generation module generates the target analysis result based on the field analysis results of all the candidate entity fields, the sorting module obtains the target sequence by sorting the field analysis results of all the candidate entity fields according to the field score corresponding to the field analysis result of each candidate entity field included in the target analysis result.

[0055] The generation module generates the target analysis result based on the field analysis results of all the candidate entity fields in the following specific ways:

[0056] Based on the field analysis results of all the candidate entity fields and the target sequence, a target analysis result is generated.

[0057] A third aspect of the present invention discloses another intelligent analysis device for enterprise data, the device comprising:

[0058] Memory containing executable program code;

[0059] A processor coupled to the memory;

[0060] The processor calls the executable program code stored in the memory to execute the intelligent analysis method for enterprise data disclosed in the first aspect of the present invention.

[0061] The fourth aspect of the present invention discloses a computer-storable medium storing computer instructions, which, when invoked, are used to execute the intelligent analysis method for enterprise data disclosed in the first aspect of the present invention.

[0062] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0063] In this embodiment of the invention, a database of enterprises to be analyzed is determined, and all fields included in all enterprise databases are aggregated to obtain a candidate field set. Preprocessing operations are performed on all candidate fields included in the candidate field set to obtain a candidate entity field set. Based on a preset target model, for each candidate entity field included in the candidate entity field set, the candidate entity field is input into the target model to obtain the field analysis result for that candidate entity field. Based on the field analysis results of all candidate entity fields, the target analysis result is generated. Therefore, implementing this invention enables intelligent analysis of enterprise middleware master data, which is beneficial for improving the efficiency and accuracy of data analysis. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0065] Figure 1 This is a flowchart illustrating an intelligent analysis method for enterprise data disclosed in an embodiment of the present invention;

[0066] Figure 2 This is a flowchart illustrating another intelligent analysis method for enterprise data disclosed in an embodiment of the present invention;

[0067] Figure 3 This is a schematic diagram of the structure of an intelligent analysis device for enterprise data disclosed in an embodiment of the present invention;

[0068] Figure 4 This is a schematic diagram of the structure of another intelligent analysis device for enterprise data disclosed in an embodiment of the present invention;

[0069] Figure 5 This is a schematic diagram of the structure of another intelligent analysis device for enterprise data disclosed in an embodiment of the present invention. Detailed Implementation

[0070] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0071] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0072] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0073] This invention discloses an intelligent analysis method and apparatus for enterprise data, which can intelligently analyze enterprise master data, thereby improving the efficiency and accuracy of data analysis. These will be described in detail below.

[0074] Example 1

[0075] Please see Figure 1 , Figure 1 This is a flowchart illustrating an intelligent analysis method for enterprise data disclosed in an embodiment of the present invention. Figure 1 The described intelligent analysis method for enterprise data can be applied to intelligent analysis devices for enterprise data, or to cloud servers or local servers for intelligent analysis of enterprise data; this embodiment of the invention does not impose limitations. Figure 1 As shown, the intelligent analysis method for enterprise data can include the following operations:

[0076] 101. Determine the enterprise database to be analyzed, and gather all fields included in all enterprise databases to obtain a candidate field set.

[0077] In this embodiment of the invention, the number of enterprise databases can be one or more, and this embodiment of the invention does not limit the number of databases.

[0078] In this embodiment of the invention, the enterprise database includes the enterprise's master data. Master data refers to one or more attributes describing core business entities (such as customers, suppliers, locations, products, and inventory). Therefore, master data refers to the core business objects discovered during enterprise business architecture analysis; it is the foundational data of the various existing IT systems within the enterprise that involve core business processes across the value chain.

[0079] In this embodiment of the invention, optionally, the enterprise database can be one or more of the following: a financial system database, a human resources system database, a business system database, a collaborative office system database, and a logistics management system database. Further optionally, one independent system can correspond to one enterprise database.

[0080] 102. Perform preprocessing operations on all candidate fields included in the candidate field set to obtain the candidate entity field set.

[0081] In this embodiment of the invention, optionally, the candidate entity field set includes several candidate entity fields, and all candidate entity fields included in the candidate entity field set are unique entity fields. For example, the candidate entity field set may contain candidate entity fields such as "employee", "goods", and "transaction record" at the same time, but the candidate entity field set may not contain two fields with different names, such as "product" and "goods", but with the same meaning.

[0082] 103. Based on the preset target model, for each candidate entity field included in the candidate entity field set, input the candidate entity field into the target model to obtain the field analysis result of the candidate entity field.

[0083] In this embodiment of the invention, optionally, the field analysis result of each candidate entity field is used to represent the field score of that candidate entity field. The field score of each candidate entity field may include the score corresponding to one or more dimensions among similarity, sharing, stability, standard form, and independence between the candidate entity field and the standard target field, or it may be a comprehensive score of multiple dimensions.

[0084] 104. Generate the target analysis results based on the field analysis results of all candidate entity fields.

[0085] In this embodiment of the invention, the target analysis result is used to represent the data analysis results of all enterprise databases.

[0086] In a further optional embodiment of the present invention, after generating the target analysis result based on the field analysis results of all candidate entity fields, the method further includes:

[0087] Based on the target analysis results, generate visual data reports;

[0088] Send the visualized data report to the terminal corresponding to the enterprise data governance personnel so that the enterprise data governance personnel can view the visualized data report;

[0089] Among them, the visual data reports are used to display the data analysis results of all enterprise databases.

[0090] It should be noted that visualized data reports can be a combination of one or more of the following: images, tables, text, and videos. By generating visualized data reports based on the results of target analysis, enterprise data governance personnel can more intuitively understand the enterprise's data and the analysis results, which helps improve the efficiency and accuracy of their data review and verification.

[0091] It is evident that implementation Figure 1 The described intelligent analysis method for enterprise data can identify the enterprise database to be analyzed, aggregate all fields included in all enterprise databases to obtain a candidate field set, perform preprocessing operations on all candidate fields included in the candidate field set to obtain a candidate entity field set, and, based on a preset target model, input each candidate entity field included in the candidate entity field set into the target model to obtain the field analysis result of that candidate entity field. Based on the analysis results of all candidate entity fields, the target analysis result is generated. This method can achieve intelligent analysis of enterprise middle platform master data, which is conducive to improving the efficiency and accuracy of data analysis.

[0092] Example 2

[0093] Please see Figure 2 , Figure 2 This is a flowchart illustrating an intelligent analysis method for enterprise data disclosed in an embodiment of the present invention. Figure 2 The described intelligent analysis method for enterprise data can be applied to intelligent analysis devices for enterprise data, or to cloud servers or local servers for intelligent analysis of enterprise data; this embodiment of the invention does not impose limitations. Figure 2 As shown, the intelligent analysis method for enterprise data can include the following operations:

[0094] 201. Determine the enterprise database to be analyzed, and gather all fields included in all enterprise databases to obtain a candidate field set.

[0095] 202. For each candidate field included in the candidate field set, analyze the candidate field based on the preset field analysis algorithm to obtain the candidate analysis result of the candidate field. The candidate analysis result is used to represent the field information of the candidate field.

[0096] In this embodiment of the invention, optionally, the preset field analysis algorithm is an NLP algorithm. It should be noted that NLP algorithm is a Natural Language Processing algorithm. NLP algorithm uses computers to analyze and generate natural language (text, speech), with the aim of enabling humans to interact with computer systems in the form of natural language, thereby making information management more convenient and effective.

[0097] In this embodiment of the invention, optionally, the field information of the candidate field may include the meaning of the candidate field. For example, when the candidate field is "employee", the field information of the candidate field may be "staff member"; when the candidate field is "goods", the field information of the candidate field may be "item".

[0098] 203. For each candidate field included in the candidate field set, determine the candidate entity field set based on the candidate analysis results of that candidate field.

[0099] In this embodiment of the invention, the field information of each candidate entity field included in the candidate entity field set is different.

[0100] 204. Based on the preset target model, for each candidate entity field included in the candidate entity field set, input the candidate entity field into the target model to obtain the field analysis result of the candidate entity field.

[0101] 205. Generate the target analysis results based on the field analysis results of all candidate entity fields.

[0102] In this embodiment of the invention, for other descriptions of steps 201 and steps 204-205, please refer to the detailed description of steps 101-104 in Embodiment 1. These descriptions will not be repeated in this embodiment of the invention.

[0103] It is evident that implementation Figure 2The described intelligent analysis method for enterprise data can identify the enterprise database to be analyzed, aggregate all fields included in all enterprise databases to obtain a candidate field set, analyze each candidate field in the candidate field set based on a preset field analysis algorithm to obtain the candidate analysis result for that candidate field, determine a candidate entity field set for each candidate field in the candidate field set based on the candidate analysis result, input each candidate entity field in the candidate entity field set into the target model based on a preset target model to obtain the field analysis result for that candidate entity field, and generate the target analysis result based on the field analysis results of all candidate fields. This method can achieve intelligent analysis of enterprise middle platform master data, which is conducive to improving the efficiency and accuracy of data analysis.

[0104] In an optional embodiment, for each candidate field included in the candidate field set, a candidate entity field set is determined based on the candidate analysis results of that candidate field, including:

[0105] For each candidate field included in the candidate field set, based on the candidate analysis results of all candidate fields, analyze the association information between the candidate field and each remaining candidate field other than the candidate field, and obtain the association information set corresponding to the candidate field; the association information is used to represent the field similarity relationship between the candidate field and each remaining candidate field;

[0106] For each candidate field included in the candidate field set, the target remaining candidate field corresponding to the candidate field is determined based on the set of associated information corresponding to the candidate field. A merging operation is then performed on the candidate field and the target remaining candidate field corresponding to the candidate field to obtain the merging result of the candidate fields.

[0107] Based on all the merge results, determine the set of candidate entity fields.

[0108] In this optional embodiment, each candidate field has its corresponding set of associated information.

[0109] In this optional embodiment, the associated information set may optionally include at least one associated information. The number of associated information pieces included in the associated information set is equal to the number of all remaining candidate fields.

[0110] In this optional embodiment, the candidate entity field set may include at least one candidate entity field, and all remaining candidate fields are candidate fields in the candidate field set.

[0111] In this optional embodiment, optionally, a merging operation is performed on the candidate field and the target remaining candidate fields corresponding to the candidate field to obtain the merging result of the candidate field, including:

[0112] Based on the candidate analysis results of the candidate field, a merging operation is performed on the candidate field and the target remaining candidate fields corresponding to the candidate field to obtain the merging result of the candidate field. The merging result of the candidate field is used to represent the field information of the candidate field and all target remaining candidate fields corresponding to the candidate field.

[0113] In this optional embodiment, the method may further include:

[0114] For each candidate field included in the candidate field set, the merging process of the candidate field is recorded during the process of performing a merging operation on the candidate field and the target remaining candidate fields corresponding to the candidate field to obtain the merging result of the candidate field.

[0115] In this way, by recording the merging process of the candidate field, the intuitiveness and convenience of the data analysis process can be improved for subsequent enterprise data governance personnel, as well as the intuitiveness and convenience of review, tracing and retrospection for enterprise data governance personnel. It also helps to improve the accuracy, intelligence and efficiency of handling the results of erroneous merging operations.

[0116] For example, when the candidate field is "product", and the target remaining candidate fields corresponding to the candidate field include "goods", "items", and "goods", a merge operation is performed on the candidate field and all target remaining candidate fields corresponding to the candidate field, and the merged result can be "items"; when the candidate field is "employee name", and the target remaining candidate fields corresponding to the candidate field include "employee name" and "user name", a merge operation is performed on the candidate field and all target remaining candidate fields corresponding to the candidate field, and the merged result can be "employee name".

[0117] As can be seen, implementing this optional embodiment can, for each candidate field included in the candidate field set, analyze the association information between the candidate field and each remaining candidate field other than the candidate field based on the candidate analysis results of all candidate fields, obtain the association information set corresponding to the candidate field, determine the target remaining candidate field corresponding to each candidate field based on the association information set corresponding to all candidate fields, perform a merging operation on each candidate field and the target remaining candidate field corresponding to each candidate field, obtain the merging result of each candidate field, and determine the candidate entity field set based on all merging results. This can improve the accuracy and intelligence of determining the candidate entity field set, thereby improving the accuracy and intelligence of obtaining the field analysis results of each candidate entity field, which in turn helps to improve the accuracy and intelligence of generating the target analysis results. Furthermore, by performing a merging operation on the candidate fields and the target remaining candidate fields, it can solve the problem of increased master data due to non-standard and inconsistent naming, which helps to improve the efficiency of obtaining the candidate entity field set and generating the target analysis results.

[0118] In another optional embodiment, for each candidate field included in the candidate field set, the target remaining candidate field corresponding to the candidate field is determined based on the associated information set corresponding to the candidate field, including:

[0119] For each candidate field included in the candidate field set, determine whether there is target related information in the related information set corresponding to the candidate field that satisfies the preset related conditions;

[0120] When it is determined that target-related information exists, the remaining candidate fields corresponding to all target-related information are determined as target remaining candidate fields.

[0121] In this optional embodiment, optionally, for each candidate field included in the candidate field set, determining whether there is target related information in the related information set corresponding to the candidate field that satisfies the preset related conditions may include:

[0122] For each candidate field included in the candidate field set, calculate the degree of association of each piece of information in the associated information set corresponding to the candidate field, and determine whether there is any key associated information in the associated information set corresponding to the candidate field with a degree of association greater than the preset degree of association;

[0123] When it is determined that there is key related information in the related information set corresponding to the candidate field with a degree of correlation greater than the preset degree of correlation, it is determined that there is target related information in the related information set corresponding to the candidate field that satisfies the preset correlation conditions.

[0124] When it is determined that there is no key related information with a degree of correlation greater than the preset degree of correlation in the related information set corresponding to the candidate field, it is determined that there is no target related information in the related information set corresponding to the candidate field that satisfies the preset correlation conditions.

[0125] In this optional embodiment, when it is determined that there is no target associated information, it can be determined that there is no corresponding target remaining candidate field for the candidate field, and the process can be terminated.

[0126] As can be seen, implementing this optional embodiment can determine whether there is target related information in the related information set corresponding to each candidate field included in the candidate field set that satisfies the preset related conditions for the related information. If so, the remaining candidate fields corresponding to all target related information are determined as target remaining candidate fields. This can improve the accuracy and reliability of determining target remaining candidate fields, thereby improving the accuracy and reliability of determining the candidate entity field set, and further improving the accuracy and reliability of obtaining the field analysis results and target analysis results for each candidate entity field.

[0127] In another optional embodiment, based on a preset target model, before inputting each candidate entity field included in the candidate entity field set into the target model to obtain the field analysis result of the candidate entity field, the method further includes:

[0128] Determine all target factors and their respective weights, as well as the target data template; where target factors are those that have an impact on the enterprise database analysis.

[0129] A target model is constructed based on all target factors, the factor weights of each target factor, and the target data template.

[0130] In this optional embodiment, the target factors may optionally include one or more of the following: business relevance factors, data sharing factors, data stability factors, data standardization factors, and entity independence factors; the target data template is an industry master data template. The industry master data template can be a pre-defined industry standard data template.

[0131] In this optional embodiment, the score range of the target factors can be 0-10 points; wherein, the factor weight of business relevance factors can be 20%-30%; the factor weight of data sharing factors can be 20%-30%; the factor weight of data stability factors can be 20%-30%; the factor weight of data standardization factors can be 10%-20%; and the factor weight of entity independence factors can be 10%-20%. Furthermore, the factor weights of all target factors can be preset, manually adjusted, or automatically adjusted according to the actual analysis situation. This embodiment of the invention does not impose any limitations.

[0132] In this optional embodiment, further optionally, for the enterprise data to be analyzed, the enterprise data has a corresponding analysis score for each target factor. Specifically, the enterprise data to be analyzed is compared with an industry master data template to obtain the analysis score for business relevance factors; statistical analysis is performed on the data table references and business interface call logs of the enterprise data to obtain the analysis score for data sharing factors; statistical analysis is performed on the database table editing operations through the database operation logs of the enterprise data to obtain the analysis score for data stability factors; NLP algorithms are used to analyze the table fields of the enterprise data to form an entity object model, and a duplication comparison is performed to obtain the analysis score for data standardization factors; NLP algorithms are used to analyze the table fields of the enterprise data to form a relationship graph, and the entity independence factors are analyzed based on the relationship graph.

[0133] As can be seen, implementing this optional embodiment can determine all target factors and the factor weight of each target factor, as well as the target data template. Based on all target factors, the factor weight of each target factor, and the target data target, a target model can be constructed, which can improve the accuracy and reliability of constructing the target model. This is beneficial to improving the accuracy and reliability of the subsequent field analysis results of each candidate entity field obtained based on the target model, and thus to improving the accuracy and reliability of the target analysis results obtained from the enterprise database.

[0134] In yet another optional embodiment, after generating the target analysis result based on the field analysis results of all candidate entity fields, the method further includes:

[0135] Based on the target analysis results, determine whether the target analysis results meet the pre-set analysis result conditions;

[0136] When it is determined that the target analysis result does not meet the preset analysis result conditions, analyze the target reasons why the target analysis result does not meet the preset analysis result conditions;

[0137] Based on the objective reasons, determine the factors to be adjusted in the objective model and the target weight corresponding to each factor to be adjusted;

[0138] Based on the target weight corresponding to each factor to be adjusted, an update operation is performed on the target model to obtain the updated target model.

[0139] In this optional embodiment, determining whether the target analysis results meet pre-set analysis result conditions based on the target analysis results may include:

[0140] Based on the target analysis results, determine the degree of matching between the target analysis results and the target data template;

[0141] Determine whether the degree of matching between the target analysis results and the target data template is greater than or equal to a pre-set matching degree threshold;

[0142] When it is determined that the degree of matching between the target analysis result and the target data template is greater than or equal to the preset matching degree threshold, the target analysis result is determined to meet the preset analysis result conditions.

[0143] When it is determined that the degree of matching between the target analysis result and the target data template is less than the preset matching degree threshold, it is determined that the target analysis result does not meet the preset analysis result conditions.

[0144] In this optional embodiment, the number of factors to be adjusted can be one or more, and this embodiment of the invention is not limited thereto. Optionally, the target weight corresponding to each factor to be adjusted is the target weight that the factor to be adjusted to needs to be adjusted to.

[0145] In this optional embodiment, optionally, the factor weight of each target factor after the updated target model is the adjusted factor weight.

[0146] In this optional embodiment, for example, when the target cause is used to indicate that the data stability factor has a significant impact on the target analysis result, the data stability factor is identified as a factor to be adjusted, and the factor weight of the data stability factor is increased.

[0147] As can be seen, implementing this optional embodiment can determine whether the target analysis result meets the preset analysis result conditions. If not, it analyzes the reasons why the target analysis result does not meet the preset analysis result conditions. Based on the reasons, it determines the adjustment factors of the target model and the target weights corresponding to each adjustment factor. According to the target weights corresponding to each adjustment factor, it performs an update operation on the target model to obtain the updated target model. This can improve the intelligence of determining the target model and improve the accuracy and reliability of performing update operations on the target model, thereby improving the accuracy and reliability of obtaining target analysis results corresponding to other enterprise databases.

[0148] In another optional embodiment, based on a preset target model, for each candidate entity field included in the candidate entity field set, after inputting the candidate entity field into the target model to obtain the field analysis result of the candidate entity field, and before generating the target analysis result based on the field analysis results of all candidate entity fields, the method further includes:

[0149] Based on the field score corresponding to the field analysis result of each candidate entity field included in the target analysis result, the field analysis results of all candidate entity fields are sorted according to the pre-set sorting conditions to obtain the target sequence;

[0150] Based on the field analysis results of all candidate entity fields, the target analysis results are generated, including:

[0151] Based on the field analysis results of all candidate entity fields and the target sequence, the target analysis results are generated.

[0152] In this optional embodiment, the field score corresponding to the field analysis result of each candidate entity field may include: the score corresponding to each target factor in the candidate entity field, or the comprehensive score corresponding to all target factors in the candidate entity field.

[0153] In this optional embodiment, optionally, the field analysis results of all candidate entity fields are sorted according to a pre-set sorting condition based on the field score corresponding to the field analysis result of each candidate entity field included in the target analysis result, to obtain the target sequence, which may include:

[0154] Based on the field score corresponding to the field analysis result of each candidate entity field included in the target analysis result, the field analysis results of all candidate entity fields are sorted according to a preset sorting method from high to low to obtain the target sequence.

[0155] As can be seen, implementing this optional embodiment can sort the field analysis results of all candidate entity fields according to the pre-set sorting conditions to obtain the target sequence. Based on the field analysis results of all candidate entity fields and the target sequence, the target analysis results are generated. This can improve the convenience and intuitiveness for enterprise data governance personnel to view the target analysis results, and also help to improve the intelligence of generating the target analysis results.

[0156] Example 3

[0157] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of an intelligent analysis device for enterprise data disclosed in an embodiment of the present invention. Figure 3 As shown, the intelligent analysis device for enterprise data may include:

[0158] Module 301 is used to determine the enterprise database to be analyzed.

[0159] The aggregation module 302 is used to aggregate all fields included in all enterprise databases to obtain a set of candidate fields;

[0160] Processing module 303 is used to perform preprocessing operations on all candidate fields included in the candidate field set to obtain a candidate entity field set;

[0161] The input module 304 is used to input each candidate entity field included in the candidate entity field set into the target model based on a preset target model, and obtain the field analysis result of the candidate entity field.

[0162] The generation module 305 is used to generate target analysis results based on the field analysis results of all candidate entity fields; wherein, the target analysis results are used to represent the data analysis results of all enterprise databases.

[0163] It is evident that implementation Figure 3 The described apparatus can identify the enterprise database to be analyzed, aggregate all fields included in all enterprise databases to obtain a candidate field set, perform preprocessing operations on all candidate fields included in the candidate field set to obtain a candidate entity field set, and, based on a preset target model, input each candidate entity field included in the candidate entity field set into the target model to obtain the field analysis result of the candidate entity field. Based on the analysis results of all candidate entity fields, the target analysis result is generated. This enables intelligent analysis of enterprise middleware master data, which is beneficial to improving the efficiency and accuracy of data analysis.

[0164] In an optional embodiment, the processing module 303 performs preprocessing operations on all candidate fields included in the candidate field set to obtain the candidate entity field set. Specifically, this includes:

[0165] For each candidate field included in the candidate field set, the candidate field is analyzed based on a preset field analysis algorithm to obtain the candidate analysis result of the candidate field. The candidate analysis result is used to represent the field information of the candidate field.

[0166] For each candidate field included in the candidate field set, a candidate entity field set is determined based on the candidate analysis results of that candidate field.

[0167] It is evident that implementation Figure 3 The described apparatus can identify the enterprise database to be analyzed, aggregate all fields included in all enterprise databases to obtain a candidate field set, analyze each candidate field in the candidate field set based on a preset field analysis algorithm to obtain a candidate analysis result for that candidate field, determine a candidate entity field set for each candidate field in the candidate field set based on the candidate analysis result, input each candidate entity field in the candidate entity field set into the target model based on a preset target model to obtain a field analysis result for that candidate entity field, and generate a target analysis result based on the field analysis results of all candidate fields. This enables intelligent analysis of enterprise middleware master data, which is beneficial to improving the efficiency and accuracy of data analysis.

[0168] In another optional embodiment, the processing module 303 determines the candidate entity field set for each candidate field included in the candidate field set based on the candidate analysis result of that candidate field in the following specific ways:

[0169] For each candidate field included in the candidate field set, based on the candidate analysis results of all candidate fields, analyze the association information between the candidate field and each remaining candidate field other than the candidate field, and obtain the association information set corresponding to the candidate field; the association information is used to represent the field similarity relationship between the candidate field and each remaining candidate field;

[0170] For each candidate field included in the candidate field set, the target remaining candidate field corresponding to the candidate field is determined based on the set of associated information corresponding to the candidate field. A merging operation is then performed on the candidate field and the target remaining candidate field corresponding to the candidate field to obtain the merging result of the candidate field.

[0171] Based on all the merge results, determine the set of candidate entity fields.

[0172] It is evident that implementation Figure 3 The described apparatus can, for each candidate field included in the candidate field set, analyze the association information between the candidate field and each remaining candidate field other than the candidate field, based on the candidate analysis results of all candidate fields, to obtain the association information set corresponding to the candidate field. Based on the association information set corresponding to all candidate fields, it determines the target remaining candidate field corresponding to each candidate field, performs a merging operation on each candidate field and the target remaining candidate field corresponding to each candidate field, obtains the merging result of each candidate field, and determines the candidate entity field set based on all merging results. This can improve the accuracy and intelligence of determining the candidate entity field set, thereby improving the accuracy and intelligence of obtaining the field analysis results of each candidate entity field, which in turn helps to improve the accuracy and intelligence of generating the target analysis results. Furthermore, by performing a merging operation on the candidate fields and the target remaining candidate fields, it can solve the problem of increased master data due to non-standard and inconsistent naming, which helps to improve the efficiency of obtaining the candidate entity field set and generating the target analysis results.

[0173] In another optional embodiment, the processing module 303 determines the target remaining candidate field corresponding to each candidate field included in the candidate field set based on the associated information set corresponding to that candidate field, specifically including the following methods:

[0174] For each candidate field included in the candidate field set, determine whether there is target related information in the related information set corresponding to the candidate field that satisfies the preset related conditions. When it is determined that there is target related information, determine the remaining candidate fields corresponding to all target related information as target remaining candidate fields.

[0175] It is evident that implementation Figure 3 The described apparatus can determine whether there is target related information in the related information set corresponding to each candidate field included in the candidate field set that satisfies the preset related conditions for the related information. If so, it determines the remaining candidate fields corresponding to all target related information as target remaining candidate fields. This can improve the accuracy and reliability of determining target remaining candidate fields, thereby improving the accuracy and reliability of determining the candidate entity field set, and further improving the accuracy and reliability of obtaining the field analysis results and target analysis results for each candidate entity field.

[0176] In yet another alternative embodiment, such as Figure 4As shown, the determining module 301 is also used to determine all target factors and the factor weight of each target factor, and to determine the target data template, before the input module 304 inputs each candidate entity field included in the candidate entity field set into the target model based on the preset target model and obtains the field analysis result of the candidate entity field; wherein, the target factors are factors that have an impact on the enterprise database analysis;

[0177] The device also includes:

[0178] Module 306 is used to build the target model based on all target factors, the factor weights of each target factor, and the target data template.

[0179] It is evident that implementation Figure 4 The described apparatus is capable of determining all target factors and the factor weight of each target factor, as well as determining the target data template. Based on all target factors, the factor weight of each target factor, and the target data target, it constructs a target model, which can improve the accuracy and reliability of constructing the target model. This is beneficial to improving the accuracy and reliability of subsequent field analysis results obtained based on the target model for each candidate entity field, and in turn, to improving the accuracy and reliability of the target analysis results obtained from the enterprise database.

[0180] In yet another alternative embodiment, such as Figure 4 As shown, the device also includes:

[0181] The judgment module 307 is used to determine whether the target analysis result meets the preset analysis result conditions after the generation module 305 generates the target analysis result based on the field analysis results of all candidate entity fields.

[0182] The analysis module 308 is used to analyze the reasons why the target analysis result does not meet the preset analysis result conditions when the judgment module 307 determines that the target analysis result does not meet the preset analysis result conditions.

[0183] The determination module 301 is also used to determine the factors to be adjusted in the target model and the target weight corresponding to each factor to be adjusted based on the target reasons;

[0184] The update module 309 is used to perform an update operation on the target model according to the target weight corresponding to each factor to be adjusted, so as to obtain the updated target model.

[0185] It is evident that implementation Figure 4The described device can determine whether the target analysis results meet the preset analysis result conditions. If not, it analyzes the reasons why the target analysis results do not meet the preset analysis results. Based on the reasons, it determines the adjustment factors of the target model and the target weights corresponding to each adjustment factor. According to the target weights corresponding to each adjustment factor, it performs an update operation on the target model to obtain an updated target model. This can improve the intelligence of determining the target model and the accuracy and reliability of performing update operations on the target model, thereby improving the accuracy and reliability of subsequent target analysis results obtained from other enterprise databases.

[0186] In yet another alternative embodiment, such as Figure 4 As shown, the device also includes:

[0187] The sorting module 310 is used to sort the field analysis results of all candidate entity fields according to the field score corresponding to the field analysis results of each candidate entity field included in the candidate entity field set in the target model after the input module 304 inputs the candidate entity field into the target model based on the preset target model and obtains the field analysis results of the candidate entity field. Before the generation module 305 generates the target analysis results based on the field analysis results of all candidate entity fields, the sorting module 310 sorts the field analysis results of all candidate entity fields according to the preset sorting conditions to obtain the target sequence.

[0188] The generation module 305 generates the target analysis result based on the field analysis results of all candidate entity fields in the following ways:

[0189] Based on the field analysis results of all candidate entity fields and the target sequence, the target analysis results are generated.

[0190] It is evident that implementation Figure 4 The described device can sort the field analysis results of all candidate entity fields according to pre-set sorting conditions to obtain a target sequence. Based on the field analysis results of all candidate entity fields and the target sequence, it generates target analysis results, which can improve the convenience and intuitiveness of enterprise data governance personnel in viewing target analysis results, and also help improve the intelligence of generating target analysis results.

[0191] Example 4

[0192] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of another intelligent analysis device for enterprise data disclosed in an embodiment of the present invention. For example... Figure 5 As shown, the intelligent analysis device for enterprise data may include:

[0193] Memory 401 storing executable program code;

[0194] Processor 402 coupled to memory 401;

[0195] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the intelligent analysis method for enterprise data described in Embodiment 1 or Embodiment 2 of the present invention.

[0196] Example 5

[0197] This invention discloses a computer-storable medium storing computer instructions. When these computer instructions are invoked, they are used to execute the steps in the intelligent analysis method for enterprise data described in Embodiment 1 or Embodiment 2 of this invention.

[0198] Example 6

[0199] This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the intelligent analysis method for enterprise data described in Embodiment 1 or Embodiment 2.

[0200] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0201] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0202] Finally, it should be noted that the intelligent analysis method and apparatus for enterprise data disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent analysis method for enterprise data, characterized in that, The method includes: Identify the enterprise database to be analyzed, and gather all fields included in all the enterprise databases to obtain a candidate field set; Perform preprocessing operations on all candidate fields included in the candidate field set to obtain a candidate entity field set; Based on the preset target model, for each candidate entity field included in the candidate entity field set, the candidate entity field is input into the target model to obtain the field analysis result of the candidate entity field; Based on the field analysis results of all the candidate entity fields, a target analysis result is generated; wherein, the target analysis result is used to represent the data analysis results of all the enterprise databases; Before obtaining the field analysis result of each candidate entity field in the candidate entity field set, based on a preset target model, the method further includes: Determine all target factors and the factor weights for each target factor, and determine the target data template; wherein, the target factors are those that have an impact on enterprise database analysis; Based on all the target factors, the factor weights of each target factor, and the target data template, a target model is constructed; After generating the target analysis result based on the field analysis results of all the candidate entity fields, the method further includes: Based on the target analysis results, determine whether the target analysis results meet the pre-set analysis result conditions; When it is determined that the target analysis result does not meet the preset analysis result conditions, the reasons why the target analysis result does not meet the preset analysis result conditions are analyzed. Based on the stated reasons for the objective, the factors to be adjusted in the target model and the target weight corresponding to each of the factors to be adjusted are determined. Based on the target weight corresponding to each of the factors to be adjusted, an update operation is performed on the target model to obtain the updated target model; The step of determining whether the target analysis result meets the preset analysis result conditions based on the target analysis result includes: Based on the target analysis results, determine the degree of matching between the target analysis results and the target data template; When the matching degree is greater than or equal to a preset matching degree threshold, the target analysis result is determined to meet the preset analysis result conditions; when the matching degree is less than the preset matching degree threshold, the target analysis result is determined not to meet the preset analysis result conditions.

2. The intelligent analysis method for enterprise data according to claim 1, characterized in that, The step of performing preprocessing operations on all candidate fields included in the candidate field set to obtain a candidate entity field set includes: For each candidate field included in the candidate field set, the candidate field is analyzed based on a preset field analysis algorithm to obtain the candidate analysis result of the candidate field. The candidate analysis result is used to represent the field information of the candidate field. For each candidate field included in the candidate field set, a candidate entity field set is determined based on the candidate analysis results of that candidate field.

3. The intelligent analysis method for enterprise data according to claim 2, characterized in that, The step of determining a candidate entity field set based on the candidate analysis results of each candidate field included in the candidate field set includes: For each candidate field included in the candidate field set, based on the candidate analysis results of all candidate fields, the association information between the candidate field and each remaining candidate field is analyzed to obtain the association information set corresponding to the candidate field; the association information is used to represent the field relationship between the candidate field and each remaining candidate field. For each candidate field included in the candidate field set, the target remaining candidate field corresponding to the candidate field is determined according to the associated information set corresponding to the candidate field. A merging operation is performed on the candidate field and the target remaining candidate field corresponding to the candidate field to obtain the merging result of the candidate field. Based on all the merge results, determine the set of candidate entity fields.

4. The intelligent analysis method for enterprise data according to claim 3, characterized in that, For each candidate field included in the candidate field set, determining the target remaining candidate field corresponding to that candidate field based on the associated information set corresponding to that candidate field includes: For each candidate field included in the candidate field set, it is determined whether there is target related information in the related information set corresponding to the candidate field that satisfies the preset related conditions. When it is determined that there is target related information, all remaining candidate fields corresponding to the target related information are determined as target remaining candidate fields.

5. The intelligent analysis method for enterprise data according to claim 4, characterized in that, The method, after inputting each candidate entity field included in the candidate entity field set into the target model to obtain the field analysis result of the candidate entity field based on the preset target model, and before generating the target analysis result based on the field analysis results of all the candidate entity fields, further includes: Based on the field score corresponding to the field analysis result of each candidate entity field included in the target analysis result, the field analysis results of all candidate entity fields are sorted according to the pre-set sorting conditions to obtain the target sequence; The step of generating the target analysis result based on the field analysis results of all the candidate entity fields includes: Based on the field analysis results of all the candidate entity fields and the target sequence, a target analysis result is generated.

6. An intelligent analysis device for enterprise data, characterized in that, The device includes: The determination module is used to determine the enterprise database to be analyzed. The aggregation module is used to aggregate all fields included in all the enterprise databases to obtain a candidate field set; The processing module is used to perform preprocessing operations on all candidate fields included in the candidate field set to obtain a candidate entity field set; The input module is used to input each candidate entity field included in the candidate entity field set into the target model based on a preset target model, and obtain the field analysis result of the candidate entity field. A generation module is used to generate target analysis results based on the field analysis results of all the candidate entity fields; wherein, the target analysis results are used to represent the data analysis results of all the enterprise databases; The determining module is further configured to determine all target factors and the factor weights of each target factor, and to determine the target data template, before the input module inputs each candidate entity field included in the candidate entity field set into the target model based on a preset target model to obtain the field analysis result of the candidate entity field; wherein, the target factors are factors that have an impact on enterprise database analysis; The device further includes: A construction module is used to construct a target model based on all the target factors, the factor weights of each target factor, and the target data template; The device further includes: The judgment module is used to determine whether the target analysis result meets the preset analysis result conditions based on the target analysis result after the generation module generates the target analysis result based on the field analysis results of all the candidate entity fields. An analysis module is used to analyze the reasons why the target analysis result does not meet the preset analysis result conditions when the judgment module determines that the target analysis result does not meet the preset analysis result conditions. The determining module is further configured to determine the factors to be adjusted in the target model and the target weight corresponding to each of the factors to be adjusted based on the target reasons; The update module is used to perform an update operation on the target model according to the target weight corresponding to each of the factors to be adjusted, so as to obtain the updated target model; The specific methods by which the judgment module determines whether the target analysis result meets the preset analysis result conditions based on the target analysis result include: Based on the target analysis results, determine the degree of matching between the target analysis results and the target data template; When the matching degree is greater than or equal to a preset matching degree threshold, the target analysis result is determined to meet the preset analysis result conditions; when the matching degree is less than the preset matching degree threshold, the target analysis result is determined not to meet the preset analysis result conditions.

7. An intelligent analysis device for enterprise data, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the intelligent analysis method for enterprise data as described in any one of claims 1-5.

8. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the intelligent analysis method for enterprise data as described in any one of claims 1-5.

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