Integrated data service system and method

Abstract

The invention relates to the technical field of enterprise data integrated processing, in particular to an integrated data service system and method. Comprising the steps of S1, periodically collecting enterprise information of an enterprise; s2, analyzing information integrity and information quality parameters; s3, optimizing and storing the enterprise information; s4, periodically collecting data access information and database storage information of the enterprise database; s5, analyzing the number of the access features, and analyzing the data cycle features; step S6, analyzing the data merging parameters; step S7, merging the enterprise information to obtain merged enterprise information, and numbering the merged enterprise information to obtain a merged data number; step S8, analyzing the acquisition mapping relation; step S9, constructing a data mapping relation; and step S10, storing the acquisition mapping relation and the data mapping relation to an enterprise database. According to the method, integrated storage processing of enterprise data is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise data integration processing, and in particular to an integrated data service system and method. Background Art

[0002] Enterprise data integration can integrate, clean, convert and process data from different systems, applications and data sources to form a unified data view and enhance the correlation between data. Data integration can improve the quality and consistency of enterprise data, promote information sharing and collaborative work, and accelerate data analysis and decision-making.

[0003] Chinese Patent Publication No.: CN115879748A discloses an enterprise information management integration platform based on big data, including a basic data acquisition module, a project coding module, a carry-over module, a diagnosis module, an evaluation module and a supervision module. The invention can evaluate the deviation data in the project data through the evaluation report, and present it in the form of an evaluation report. For the abnormal evaluation report, the supervision module can also be used to determine whether the internal information of the enterprise has changed during the project. The feedback from the supervision module can accurately track the functional department or personnel within the enterprise that caused the abnormality of the project, and then while the internal information of the enterprise is orderly integrated and distributed, it can also provide a strong data support for the judgment of the enterprise's managers. The invention realizes the analysis of enterprise data changes and change trends, but does not realize the analysis of the mapping relationship between data in combination with data content and storage information within the acquisition cycle. There are problems of low efficiency in analyzing enterprise data and inaccurate integration and processing of enterprise data. Summary of the invention

[0004] The object of the present invention is to provide an integrated data service system and method to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions: In one aspect, the present invention provides an integrated data service method, comprising: Step S1, periodically collecting enterprise information of enterprises; Step S2, analyzing information completeness and information quality parameters according to the enterprise information, and storing the enterprise information in the enterprise database according to the information completeness; Step S3, optimizing the enterprise information according to the information completeness, and storing the optimized enterprise information in the enterprise database; Step S4, periodically collecting data access information and database storage information of the enterprise database; Step S5, analyzing the number of access features according to the data access information, and analyzing the data period features according to the number of access features; Step S6, analyzing the data merging parameters according to the data cycle characteristics, database storage information and information quality parameters; Step S7, merging the enterprise information according to the data merging parameter to obtain merged enterprise information, and numbering the merged enterprise information to obtain a merged data number; Step S8, analyzing the acquisition mapping relationship according to the enterprise information; Step S9, constructing a data mapping relationship according to the acquisition mapping relationship and the merged data number; Step S10: storing the acquisition mapping relationship and the data mapping relationship in the enterprise database.

[0006] Furthermore, the storage and analysis method of the enterprise information in step S2 includes: Step S21, analyzing the information completeness according to the enterprise information; Step S22, storing the enterprise information in the enterprise database according to the completeness of the information; Step S23, analyzing information quality parameters according to enterprise information.

[0007] Furthermore, in the step S21, when analyzing the information completeness, the information completeness is analyzed according to the number of customer information contents N(n1), the number of missing customer information NL(n1), the number of employee information contents N(n2), the number of missing employee information NL(n2), the number of social media information contents N(n3), and the number of missing social media information NL(n3) to obtain the information completeness P(k); In the step S22, when storing the enterprise information, the enterprise information with the information completeness of 1 is stored; In the step S23, when analyzing the information quality parameter, the information quality parameter is analyzed according to the enterprise information to obtain the information quality parameter Q(m).

[0008] Furthermore, in step S5, when analyzing the data cycle characteristics, the number of data access information whose access operation information is to modify data is counted as the cycle modification number A1(i), the number of data access information whose access operation information is to query operation is counted as the cycle query number A2(i), the number of access operation information is counted as the cycle access number A2(i), the number of different visiting employee names is counted as the visiting employee number B(i), and the data cycle characteristics are analyzed according to the cycle modification number, cycle query number, cycle access number and visiting employee number to obtain the data cycle characteristics R(i).

[0009] Furthermore, the analysis method of the data merging parameters in step S6 includes: Step S61, analyzing the data merging parameters according to the database storage information; Step S62, adjusting the analysis process of data merging parameters according to the data cycle characteristics; Step S63, optimizing the adjustment process of the data merging parameters according to the information quality parameters.

[0010] Furthermore, in the step S61, when analyzing the data merging parameters, the data merging parameters are analyzed according to the database storage information to obtain the data merging parameters F(i); In the step S62, when adjusting the analysis process of the data merging parameters, the analysis process of the data merging parameters is adjusted according to the data period feature R(i). When the period feature does not meet the threshold, the analysis process of the data merging parameters is adjusted, and the adjusted data merging parameters are F1(i); In the step S63, when optimizing the adjustment process of the data merging parameters, the adjustment process of the data merging parameters is optimized according to the information quality parameter Q(m). When the information quality parameter does not meet the threshold, the adjustment process of the data merging parameters is optimized, and the optimized data merging parameters are F2(i).

[0011] Furthermore, in the step S7, when determining the storage type of the enterprise information, the storage type of the enterprise information is analyzed according to the data merging parameter to obtain the storage type of the enterprise information, and the storage type of the enterprise information includes one type and two types; In step S7, when merging enterprise information, the enterprise information is merged according to the storage type of the enterprise information, and the enterprise information whose storage type of enterprise information is continuously one category or one category in each collection cycle is merged into a group of merged enterprise information, and the enterprise information whose storage type of enterprise information is two categories in each collection cycle is respectively taken as a group of merged enterprise information, and the merged enterprise information is numbered in the order of the collection cycle to obtain the merged data number j.

[0012] Furthermore, in step S8, when analyzing the acquisition mapping relationship, the acquisition mapping relationship is analyzed according to the enterprise information. If the enterprise information is customer information, the acquisition mapping relationship is set to n1→i, n1 n1(n2), if the enterprise information is employee information, set the collection mapping relationship to n2→i; if the enterprise information is social media information, set the collection mapping relationship to n3→i, n3 n3(n2); where n1(n2) represents the name data of the employee who connects with the customer information, and n3(n2) represents the name of the employee who posts the social media information.

[0013] Furthermore, in step S9, when constructing the data mapping relationship, the data mapping relationship is constructed according to the collection mapping relationship and the merged data number, the merged data number is corresponded to the collection cycle number corresponding to the merged enterprise information to obtain a merged collection cycle number set, the merged collection cycle number set is set to u(j), and the data mapping relationship is constructed according to the merged collection cycle number set and the collection mapping relationship, and the data mapping relationship is set to i→u(j), u(j)→Z(n2), where Z(n2) represents the name of the visited employee.

[0014] On the other hand, the present invention also provides an integrated data service system, comprising: A data collection module is used to periodically collect enterprise information, data access information of the enterprise database, and database storage information; The enterprise collection and analysis module is used to analyze the information completeness and information quality parameters based on the enterprise information, and store the enterprise information in the enterprise database according to the information completeness; Enterprise collection optimization module, which is used to optimize enterprise information according to the completeness of information and store the optimized enterprise information in the enterprise database; An access collection and analysis module is used to analyze the number of access features according to data access information, and to analyze the data cycle features according to the number of access features; A data merging and analysis module is used to analyze data merging parameters according to data cycle characteristics, database storage information and information quality parameters; An enterprise information merging module is used to merge enterprise information according to data merging parameters to obtain merged enterprise information, and to number the merged enterprise information to obtain a merged data number; A mapping relationship analysis module is used to analyze the acquisition mapping relationship based on the enterprise information, and also to construct a data mapping relationship based on the acquisition mapping relationship and the merged data number; The mapping relationship storage module is used to store the acquisition mapping relationship and the data mapping relationship in the enterprise database.

[0015] The beneficial effects of the present invention are as follows: by collecting and analyzing the enterprise information of the enterprise, the data access information of the enterprise database and the database storage information, the storage analysis of the collected data can be realized by integrating the enterprise data and the access and storage data of the enterprise database, so as to construct a mapping relationship between the various data, realize the correlation analysis between the data, and then store the collected data and its corresponding mapping relationship, realize the integrated storage of different enterprise data, facilitate the subsequent analysis and retrieval of the stored data, thereby improving the analysis efficiency of the enterprise data and improving the accuracy of the integrated processing of the enterprise data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a flow chart of the integrated data service method of this embodiment.

[0018] Figure 2 This is a flow chart of the method for storing and analyzing enterprise information in this embodiment.

[0019] Figure 3 Flow chart of the method for analyzing data merging parameters in this embodiment.

[0020] Figure 4 This is a schematic diagram of the structure of the integrated data service system of this embodiment. DETAILED DESCRIPTION

[0021] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0022] See also Figure 1 As shown, the integrated data service method of this embodiment includes: Step S1, periodically collect enterprise information of the enterprise, the enterprise information includes customer information, employee information and social media information, the customer information includes but is not limited to customer name, customer contact information and contact employee name; the employee information includes but is not limited to employee name, employee contact information and employee address and other data; the social media information includes but is not limited to post content, posting employee name, article views and browsing user information, the browsing user information includes customer name, employee name and unknown user, the unknown user is a general term for other visiting users except customer name and employee name, the collection frequency of the enterprise information is once per preset collection period, in this embodiment, the preset collection period is set to 1 day, in this embodiment, the setting of the preset collection period is not specifically limited, and technical personnel in this field can freely set it, such as it can also be set to 2 days, 5 days and 7 days, etc., the collection method of the enterprise information is user interactive input.

[0023] Please continue reading Figure 1 As shown, the integrated data service method also includes: Step S2, analyzing information completeness and information quality parameters according to the enterprise information, and storing the enterprise information in the enterprise database according to the information completeness.

[0024] See also Figure 2 As shown, it is a storage and analysis method for enterprise information, including: Step S21, analyzing the information completeness based on the enterprise information.

[0025] Specifically, in step S21 described in this embodiment, when analyzing the information completeness, the number of different types of data involved in the customer information is counted as the number of customer information contents, the number of data with missing values ​​in the customer information is counted as the number of missing customer information, the number of different types of data involved in the employee information is counted as the number of employee information contents, the number of data with missing values ​​in the employee information is counted as the number of missing employee information, the number of different types of data involved in the social media information is counted as the number of social media information contents, the number of data with missing values ​​in the social media information is counted as the number of missing social media information, and the information completeness is analyzed according to the number of customer information contents, the number of missing customer information, the number of employee information contents, the number of missing employee information, the number of social media information contents, and the number of missing social media information. Suppose Assume that the information completeness is P(k), and set P(k)=1-NL(k) / N(k); where k represents the data content number, k∈{U(m)}, U(m) represents the set of enterprise information numbers, m represents the enterprise information content type number, m={1,2,3}, U(1) represents the set of customer names, U(2) represents the set of employee names, U(3) represents the set of publication numbers, n1 represents the customer name, n2 represents the employee name, n3 represents the publication number, when k∈U(1), NL(n1) represents the number of missing customer information, N(n1) represents the number of customer information contents, when k∈U(2), NL(n2) represents the number of missing employee information, N(n2) represents the number of employee information contents, when k∈U(3), NL(n3) represents the number of missing social media information, N(n3) represents the number of social media information contents. The number of different types of data in enterprise information is defined as the number of different data contents corresponding to each data. For example, if customer information includes customer name and customer contact information, the number of customer information contents of customer information is 2. The document number is defined as the number to distinguish different document contents, which is a number arranged in chronological order.

[0026] Specifically, in this embodiment, the enterprise information is analyzed through the step S21 to analyze the completeness of the information, to analyze whether the data in the collected enterprise information is complete and clear, and to determine whether the collected data is in a preset format, thereby improving the efficiency of enterprise data analysis and improving the accuracy of enterprise data integration processing.

[0027] Specifically, the enterprise information described in this embodiment is a collected CSV file, which is enterprise information arranged in a fixed content order.

[0028] Please continue reading Figure 2 As shown, the storage and analysis method for enterprise information also includes: Step S22: storing the enterprise information in the enterprise database according to the completeness of the information.

[0029] Specifically, in step S22 described in this embodiment, when storing enterprise information, the enterprise information is stored according to the information completeness. If P(k)=1, the enterprise information corresponding to the information completeness currently analyzed is stored in the enterprise database; if P(k)≠1, the enterprise information corresponding to the information completeness currently analyzed is not stored.

[0030] Specifically, in this embodiment, the information completeness is analyzed through step S22 to store the complete enterprise information in the enterprise database, thereby realizing the storage of the collected complete enterprise information, ensuring the accuracy of subsequent analysis of the enterprise data, making the analysis data complete and unified, thereby improving the efficiency of enterprise data analysis and improving the accuracy of enterprise data integration processing.

[0031] Please continue reading Figure 2 As shown, the storage and analysis method for enterprise information also includes: Step S23, analyzing information quality parameters according to enterprise information.

[0032] Specifically, in step S23 of this embodiment, when analyzing the information quality parameter, the information quality parameter is analyzed according to the enterprise information, and the information quality parameter is set to Q(m). , where NU(1) represents the number of customer names in the set of customer names, NU(2) represents the number of employee names in the set of employee names, and NU(3) represents the number of document numbers in the set of document numbers.

[0033] Specifically, in this embodiment, the enterprise information is analyzed through step S23 to analyze the information quality parameters, and the information quality parameters are used to indicate whether the data corresponding to each data source collected are clear and stable, thereby realizing the analysis of the data content quality of each data, thereby improving the analysis efficiency of the enterprise data and improving the accuracy of the enterprise data integration processing.

[0034] Please continue reading Figure 1 As shown, the integrated data service method also includes: Step S3, optimizing the enterprise information according to the information completeness, and storing the optimized enterprise information in the enterprise database.

[0035] Specifically, in step S3 described in this embodiment, when optimizing enterprise information, the enterprise information whose information completeness satisfies P(k)≠1 is optimized. If the customer name or employee name in the enterprise information is a missing value, the currently analyzed enterprise information is not optimized; otherwise, the data with missing values ​​in the enterprise information is deleted, and the enterprise information after deleting the missing values ​​is stored in the enterprise database.

[0036] Specifically, in this embodiment, the information completeness is analyzed through step S3 to optimize the enterprise information, remove missing data in the enterprise information, and reduce the impact of missing enterprise information content in subsequent analysis on data analysis, thereby improving the analysis efficiency of enterprise data and improving the accuracy of enterprise data integration processing.

[0037] Please continue reading Figure 1 As shown, the integrated data service method also includes: Step S4, periodically collect data access information and database storage information of the enterprise database, the data access information includes the name of the accessing employee and the access operation information, the access operation information includes modifying data, querying data and other operations, the other operations are a general term for other operations except modifying operations and querying data, the database storage information includes the current storage capacity of the database and the maximum storage capacity of the database, the unit of the database storage information is Mb, the collection frequency of the data access information and the database storage information is once per preset collection period, and the collection method of the data access information and the database storage information is obtained by importing data from the enterprise database management platform.

[0038] Please continue reading Figure 1 As shown, the integrated data service method also includes: Step S5, analyzing the access feature quantity according to the data access information, and analyzing the data period feature according to the access feature quantity, wherein the access feature quantity includes the period modification quantity, the period query quantity, the period access quantity and the access employee quantity.

[0039] Specifically, in step S5 described in this embodiment, when analyzing the data cycle characteristics, the number of data access information whose access operation information is to modify data is counted as the cycle modification number, the number of data access information whose access operation information is to query operation is counted as the cycle query number, the number of access operation information is counted as the cycle access number, the number of different visiting employee names is counted as the visiting employee number, and the data cycle characteristics are analyzed according to the cycle modification number, the cycle query number, the cycle access number and the visiting employee number, and the data cycle characteristics are set to R(i), and R(i)=A1(i)×A2(i) / {A3(i)×[A3(i)-B(i)+1]} is set, where i represents the collection cycle number, i∈N + , A1(i) represents the number of periodic modifications, A2(i) represents the number of periodic queries, A3(i) represents the number of periodic accesses, and B(i) represents the number of accessing employees. The collection period number is defined as the number used to distinguish the data access information and database storage information collected in different collection periods.

[0040] Specifically, in this embodiment, the data access information is analyzed through the step S5 to count the number of periodic modifications, the number of periodic queries, the number of periodic visits and the number of visiting employees, so as to analyze the number of times employees perform different operations to access the enterprise database during the collection period, thereby analyzing the data cycle characteristics, and using the data cycle characteristics to represent the characteristic relationship between the employee access operation status and the access volume during the collection period, so as to analyze whether the data is frequently modified during the collection period, resulting in data errors, thereby improving the analysis efficiency of enterprise data and improving the accuracy of enterprise data integration processing.

[0041] Please continue reading Figure 1 As shown, the integrated data service method also includes: Step S6, analyzing the data merging parameters according to the data cycle characteristics, database storage information and information quality parameters.

[0042] See also Figure 3 As shown, it is an analysis method of data merging parameters, including: Step S61, analyzing the data merging parameters according to the database storage information.

[0043] Specifically, in step S61 of this embodiment, when analyzing the data merging parameter, the data merging parameter is analyzed according to the database storage information, and the data merging parameter is set to F(i). , where D(i) represents the current storage capacity of the database, d represents the maximum storage capacity of the database, and i max Indicates the maximum value of the collection cycle number, and D(i-1) indicates the current storage capacity of the database in the previous collection cycle.

[0044] Specifically, in this embodiment, the database storage information is analyzed through the step S61 to analyze the data merging parameters, and the data merging parameters are used to represent the data storage change characteristics of the enterprise database during the collection period, so as to analyze the relationship between the size of the stored data in each collection period and the preset merging size, thereby improving the analysis efficiency of the enterprise data and improving the accuracy of the enterprise data integration processing.

[0045] Please continue reading Figure 3 As shown, the analysis method for data merging parameters also includes: Step S62, adjusting the analysis process of the data merging parameters according to the data cycle characteristics.

[0046] Specifically, in step S62 of this embodiment, when adjusting the analysis process of the data merging parameter, the analysis process of the data merging parameter is adjusted according to the data period characteristics. If R(i)≥r, it is determined that the data period characteristics do not meet the threshold value, and the analysis process of the data merging parameter is adjusted. The adjusted data merging parameter is F1(i), and F1(i)=F(i)×e R(i)-1 ; On the contrary, it is determined that the data period feature meets the threshold value, and the analysis process of the data merging parameters is not adjusted; wherein r represents the feature comparison threshold value, 1.1≤r≤1.4. It can be understood that the value of the feature comparison threshold value is not specifically limited in this embodiment, and those skilled in the art can freely set it, as long as the adjustment of the analysis process of the data merging parameters is satisfied. The optimal value of the feature comparison threshold value is: r=1.2.

[0047] Specifically, in this embodiment, the step S62 analyzes the data cycle characteristics to adjust the analysis process of the data merging parameters, so that the adjusted data merging parameters are related to the operation characteristics of the data in the collection cycle, increase the data merging parameters of the collection cycle with a higher modification frequency, and reduce the amount of merging of data that may contain errors, thereby improving the analysis efficiency of enterprise data and improving the accuracy of enterprise data integration processing.

[0048] Please continue reading Figure 3 As shown, the analysis method for data merging parameters also includes: Step S63, optimizing the adjustment process of the data merging parameters according to the information quality parameters.

[0049] Specifically, in step S63 of this embodiment, when optimizing the adjustment process of the data merging parameter, the adjustment process of the data merging parameter is optimized according to the information quality parameter. If Q(m)≥q, it is determined that the information quality parameter does not meet the threshold value, and the adjustment process of the data merging parameter is optimized. The optimized data merging parameter is F2(i), and F2(i)=F1(i)×eQ(m) / 2 ; On the contrary, it is determined that the information quality parameter meets the threshold value, and the adjustment process of the data merging parameter is not optimized; wherein q represents the quality comparison threshold value, 0.05≤q≤0.15. It can be understood that the value of the quality comparison threshold value is not specifically limited in this embodiment, and those skilled in the art can freely set it, as long as the optimization of the adjustment process of the data merging parameter is satisfied. The optimal value of the quality comparison threshold value is: q=0.1.

[0050] Specifically, in this embodiment, the information quality parameters are analyzed in step S63 to optimize the adjustment process of the data merging parameters, so that the optimized data merging parameters are related to the data quality of the collected enterprise data, and the adjustment process of the data merging parameters is optimized when there is a lot of missing data in the data, thereby improving the analysis efficiency of the enterprise data and improving the accuracy of the enterprise data integration processing.

[0051] Step S7, merging the enterprise information according to the data merging parameters to obtain merged enterprise information, and numbering the merged enterprise information to obtain a merged data number.

[0052] Specifically, in step S7 described in this embodiment, when determining the storage type of enterprise information, the storage type of enterprise information is analyzed according to the data merging parameter. If F(i)+F(i-1)≤f, the storage type of enterprise information in the current collection period is determined to be Class I; otherwise, the storage type of enterprise information in the current collection period is determined to be Class II; wherein, F(i-1) represents the data merging parameter of the previous collection period, and f represents the merging threshold, 1≤f≤1.2. It can be understood that the value of the merging threshold is not specifically limited in this embodiment, and those skilled in the art can freely set it as long as it satisfies the determination of the storage type of enterprise information. The optimal value of the merging threshold is: f=1.1.

[0053] Specifically, in step S7 of this embodiment, when enterprise information is merged, the enterprise information is merged according to the storage type of the enterprise information, and the enterprise information whose storage type of enterprise information is continuously one type or one type in each collection cycle is merged into a group of merged enterprise information, and the enterprise information whose storage type of enterprise information is two types in each collection cycle is respectively taken as a group of merged enterprise information, and the merged enterprise information is numbered in the order of the collection cycle to obtain a merged data number, and the merged data number is set to j, j∈N + .

[0054] Specifically, in step S7 of this embodiment, when merging enterprise information, there are five consecutive collection cycles corresponding to the enterprise information whose storage types are one category, one category, two categories, one category, and two categories, respectively. Then, it can be divided into four groups of merged enterprise information. The first group is the first two consecutive enterprise information of one category, the second group is the third enterprise information of two categories, the third group is the fourth enterprise information of one category, and the fourth group is the fifth enterprise information of two categories.

[0055] Specifically, in this embodiment, the data merging parameters are analyzed in step S7 to analyze the storage type of the enterprise information, and the storage type of the enterprise information is divided into two categories according to the data storage changes compared with the previous collection cycle. The first category indicates that the data quality of the current collection cycle is good and suitable for merged storage, and the second category indicates that the data quality of the current collection cycle is poor and not suitable for merged storage. In this way, the merged enterprise information is analyzed and the merged analysis of the enterprise data is realized, thereby improving the analysis efficiency of the enterprise data and improving the accuracy of the integrated processing of the enterprise data.

[0056] Step S8, analyzing the acquisition mapping relationship according to the enterprise information.

[0057] Specifically, in step S8 of this embodiment, when analyzing the acquisition mapping relationship, the acquisition mapping relationship is analyzed according to the enterprise information. If the enterprise information is customer information, the acquisition mapping relationship is set to n1→i, n1 n1(n2), if the enterprise information is employee information, set the collection mapping relationship to n2→i; if the enterprise information is social media information, set the collection mapping relationship to n3→i, n3 n3(n2); where n1(n2) represents the name data of the employee who connects with the customer information, and n3(n2) represents the name of the employee who posts the social media information.

[0058] Specifically, in this embodiment, the enterprise information is analyzed through the step S8 to analyze the collection mapping relationship, and the collection mapping relationship is used to represent the correspondence between the currently collected enterprise information and the time of its collection cycle, as well as the correspondence between the collected enterprise data and the users stored in the enterprise database, thereby improving the efficiency of enterprise data analysis and improving the accuracy of enterprise data integration processing.

[0059] Step S9: construct a data mapping relationship according to the collected mapping relationship and the merged data number.

[0060] Specifically, in step S9 described in this embodiment, when constructing the data mapping relationship, the data mapping relationship is constructed according to the collection mapping relationship and the merged data number, the merged data number is corresponded to the collection cycle number corresponding to the merged enterprise information to obtain a merged collection cycle number set, the merged collection cycle number set is set to u(j), and the data mapping relationship is constructed according to the merged collection cycle number set and the collection mapping relationship, and the data mapping relationship is set to i→u(j), u(j)→Z(n2), where Z(n2) represents the name of the visited employee.

[0061] Specifically, in this embodiment, the collection mapping relationship and the merged data number are analyzed through step S9 to construct a data mapping relationship, realize the correlation analysis between the collected data and the merged data of the corresponding collection period, and associate the collected data with the data of each collection period in the enterprise database, so that multiple data corresponding to a certain data feature can be retrieved, thereby improving the analysis efficiency of the enterprise data and improving the accuracy of the enterprise data integration processing.

[0062] Step S10, storing the collection mapping relationship and the data mapping relationship in the enterprise database to associate the data in the enterprise database, realize the integration of the collected multiple data, enhance the correlation between the data, and facilitate the data storage and retrieval of the enterprise database.

[0063] See also Figure 4 As shown, it is the integrated data service system implemented in this embodiment, including: A data collection module is used to periodically collect enterprise information, data access information of the enterprise database, and database storage information; An enterprise collection and analysis module is used to analyze information integrity and information quality parameters based on enterprise information, and store enterprise information in an enterprise database based on information integrity. The enterprise collection and analysis module is connected to the data collection module; An enterprise collection optimization module is used to optimize enterprise information according to information completeness and store the optimized enterprise information in an enterprise database. The enterprise collection optimization module is connected to the enterprise collection analysis module; An access collection and analysis module, used to analyze the number of access features according to the data access information, and to analyze the data cycle features according to the number of access features, the access collection and analysis module being connected to the data collection module; A data merging and analyzing module, used to analyze data merging parameters according to data cycle characteristics, database storage information and information quality parameters, and the data merging and analyzing module is connected with the enterprise acquisition optimization module and the access acquisition analysis module; An enterprise information merging module, used to merge the enterprise information according to the data merging parameters to obtain the merged enterprise information, and to number the merged enterprise information to obtain the merged data number, the enterprise information merging module is connected to the data merging analysis module; A mapping relationship analysis module, used to analyze the acquisition mapping relationship according to the enterprise information, and also used to construct a data mapping relationship according to the acquisition mapping relationship and the merged data number, the mapping relationship analysis module is connected to the enterprise information merging module; The mapping relationship storage module is used to store the acquisition mapping relationship and the data mapping relationship in the enterprise database, and the mapping relationship storage module is connected to the mapping relationship analysis module.

[0064] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the embodiments here. All obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. An integrated data service method, characterized in that: include: Step S1, periodically collecting enterprise information of enterprises; Step S2, analyzing information completeness and information quality parameters according to the enterprise information, and storing the enterprise information in the enterprise database according to the information completeness; Step S3, optimizing the enterprise information according to the information completeness, and storing the optimized enterprise information in the enterprise database; Step S4, periodically collecting data access information and database storage information of the enterprise database; Step S5, analyzing the number of access features according to the data access information, and analyzing the data period features according to the number of access features; Step S6, analyzing the data merging parameters according to the data cycle characteristics, database storage information and information quality parameters; Step S7, merging the enterprise information according to the data merging parameter to obtain merged enterprise information, and numbering the merged enterprise information to obtain a merged data number; Step S8, analyzing the acquisition mapping relationship according to the enterprise information; Step S9, constructing a data mapping relationship according to the acquisition mapping relationship and the merged data number; Step S10: storing the acquisition mapping relationship and the data mapping relationship in the enterprise database.

2. The integrated data service method according to claim 1, characterized in that: The storage and analysis method of enterprise information in step S2 includes: Step S21, analyzing the information completeness according to the enterprise information; Step S22, storing the enterprise information in the enterprise database according to the completeness of the information; Step S23, analyzing information quality parameters according to enterprise information.

3. The integrated data service method according to claim 2, characterized in that: In the step S21, when analyzing the information completeness, the information completeness is analyzed according to the number of customer information contents N(n1), the number of missing customer information NL(n1), the number of employee information contents N(n2), the number of missing employee information NL(n2), the number of social media information contents N(n3), and the number of missing social media information NL(n3) to obtain the information completeness P(k); In the step S22, when storing the enterprise information, the enterprise information with the information completeness of 1 is stored; In the step S23, when analyzing the information quality parameter, the information quality parameter is analyzed according to the enterprise information to obtain the information quality parameter Q(m).

4. The integrated data service method according to claim 3, characterized in that: In the step S5, when analyzing the data cycle characteristics, the number of data access information whose access operation information is to modify data is counted as the cycle modification number A1(i), the number of data access information whose access operation information is to query operation is counted as the cycle query number A2(i), the number of access operation information is counted as the cycle access number A2(i), the number of different visiting employee names is counted as the visiting employee number B(i), and the data cycle characteristics are analyzed according to the cycle modification number, the cycle query number, the cycle access number and the visiting employee number to obtain the data cycle characteristics R(i).

5. The integrated data service method according to claim 4, characterized in that: The analysis method of the data merging parameters in step S6 includes: Step S61, analyzing the data merging parameters according to the database storage information; Step S62, adjusting the analysis process of data merging parameters according to the data cycle characteristics; Step S63, optimizing the adjustment process of the data merging parameters according to the information quality parameters.

6. The integrated data service method according to claim 5, characterized in that: In the step S61, when analyzing the data merging parameters, the data merging parameters are analyzed according to the database storage information to obtain the data merging parameters F(i); In the step S62, when adjusting the analysis process of the data merging parameters, the analysis process of the data merging parameters is adjusted according to the data period feature R(i). When the period feature does not meet the threshold, the analysis process of the data merging parameters is adjusted, and the adjusted data merging parameters are F1(i); In the step S63, when optimizing the adjustment process of the data merging parameters, the adjustment process of the data merging parameters is optimized according to the information quality parameter Q(m). When the information quality parameter does not meet the threshold, the adjustment process of the data merging parameters is optimized, and the optimized data merging parameters are F2(i).

7. The integrated data service method according to claim 6, characterized in that: In the step S7, when determining the storage type of the enterprise information, the storage type of the enterprise information is analyzed according to the data merging parameter to obtain the storage type of the enterprise information, and the storage type of the enterprise information includes one type and two types; In step S7, when merging enterprise information, the enterprise information is merged according to the storage type of the enterprise information, and the enterprise information whose storage type of enterprise information is continuously one category or one category in each collection cycle is merged into a group of merged enterprise information, and the enterprise information whose storage type of enterprise information is two categories in each collection cycle is respectively taken as a group of merged enterprise information, and the merged enterprise information is numbered in the order of the collection cycle to obtain the merged data number j.

8. The integrated data service method according to claim 7, characterized in that: In step S8, when analyzing the acquisition mapping relationship, the acquisition mapping relationship is analyzed according to the enterprise information. If the enterprise information is customer information, the acquisition mapping relationship is set to n1→i, n1 n1(n2), if the enterprise information is employee information, set the collection mapping relationship to n2→i; if the enterprise information is social media information, set the collection mapping relationship to n3→i, n3 n3(n2); where n1(n2) represents the name data of the employee who connects with the customer information, and n3(n2) represents the name of the employee who posts the social media information.

9. The integrated data service method according to claim 8, characterized in that: In step S9, when constructing the data mapping relationship, the data mapping relationship is constructed according to the collection mapping relationship and the merged data number, the merged data number is corresponded to the collection cycle number corresponding to the merged enterprise information to obtain a merged collection cycle number set, the merged collection cycle number set is set to u(j), and the data mapping relationship is constructed according to the merged collection cycle number set and the collection mapping relationship, and the data mapping relationship is set to i→u(j), u(j)→Z(n2), where Z(n2) represents the name of the visited employee.

10. An integrated data service system, applied to the integrated data service method according to any one of claims 1 to 9, characterized in that: include: A data collection module is used to periodically collect enterprise information, data access information of the enterprise database, and database storage information; The enterprise collection and analysis module is used to analyze the information completeness and information quality parameters based on the enterprise information, and store the enterprise information in the enterprise database according to the information completeness; Enterprise collection optimization module, which is used to optimize enterprise information according to the completeness of information and store the optimized enterprise information in the enterprise database; An access collection and analysis module is used to analyze the number of access features according to data access information, and to analyze the data cycle features according to the number of access features; A data merging and analysis module is used to analyze data merging parameters according to data cycle characteristics, database storage information and information quality parameters; An enterprise information merging module is used to merge enterprise information according to data merging parameters to obtain merged enterprise information, and to number the merged enterprise information to obtain a merged data number; A mapping relationship analysis module is used to analyze the acquisition mapping relationship based on the enterprise information, and also to construct a data mapping relationship based on the acquisition mapping relationship and the merged data number; The mapping relationship storage module is used to store the acquisition mapping relationship and the data mapping relationship in the enterprise database.

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