An integrated data services system and method

Abstract

The present application relates to the technical field of enterprise data integration processing, and especially relates to an integrated data service system and method. The method comprises the following steps: S1, periodically collecting enterprise information of an enterprise; S2, analyzing information completeness 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 access features and analyzing data cycle features; S6, analyzing data merging parameters; S7, merging the enterprise information to obtain merged enterprise information, and numbering the merged enterprise information to obtain merged data numbers; S8, analyzing the collection mapping relationship; S9, constructing a data mapping relationship; and S10, storing the collection mapping relationship and the data mapping relationship to the enterprise database. The method realizes integrated storage processing of enterprise data.

Description

Technical Field

[0001] This invention relates to the field of enterprise data integration and processing technology, and in particular to an integrated data service system and method. Background Technology

[0002] Enterprise data integration can combine, clean, transform, and process data from different systems, applications, and data sources to form a unified data view, enhance the correlation between data, 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 diagnostic module, an evaluation module, and a monitoring module. This invention can evaluate deviations in project data through evaluation reports and present them in the form of reports. For abnormal evaluation reports, the monitoring module can also determine whether there have been changes in internal enterprise information related to the project during its implementation. The feedback from the monitoring module can accurately track the functional departments or personnel within the enterprise that caused the abnormalities. This provides strong data support for enterprise managers' judgments while ensuring the orderly integration and allocation of internal information. However, this invention only analyzes changes and trends in enterprise data. It does not analyze the mapping relationships between data based on data content and stored information within the acquisition period, resulting in low efficiency in enterprise data analysis and inaccurate data integration processing. Summary of the Invention

[0004] The purpose of this 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 objectives, the present invention adopts the following technical solution:

[0006] On one hand, the present invention provides an integrated data service method, comprising:

[0007] Step S1: Periodically collect enterprise information;

[0008] Step S2: 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.

[0009] Step S3: Optimize the enterprise information based on its completeness and store the optimized enterprise information in the enterprise database;

[0010] Step S4: Periodically collect data access information and database storage information from the enterprise database;

[0011] Step S5: Analyze the number of access features based on the data access information, and analyze the data periodic features based on the number of access features.

[0012] Step S6: Analyze the data merging parameters based on data cycle characteristics, database storage information, and information quality parameters;

[0013] Step S7: Merge the enterprise information according to the data merging parameters to obtain merged enterprise information, and number the merged enterprise information to obtain merged data number;

[0014] Step S8: Analyze the collected mapping relationship based on the enterprise information;

[0015] Step S9: Construct a data mapping relationship based on the collection mapping relationship and the merged data number;

[0016] Step S10: Store the acquisition mapping relationship and data mapping relationship in the enterprise database.

[0017] Furthermore, the method for storing and analyzing enterprise information in step S2 includes:

[0018] Step S21: Analyze the completeness of information based on enterprise information;

[0019] Step S22: Store the enterprise information in the enterprise database according to the information completeness.

[0020] Step S23: Analyze the information quality parameters based on the enterprise information.

[0021] Furthermore, in step S21, when analyzing the information completeness, the information completeness is analyzed based on 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).

[0022] In step S22, when storing enterprise information, enterprise information with an information integrity level of 1 is stored.

[0023] In step S23, when analyzing the information quality parameters, the information quality parameters are analyzed based on the enterprise information to obtain the information quality parameter Q(m).

[0024] Further, in step S5, when analyzing the data periodic characteristics, the number of data access information that modifies data is counted as the periodic modification quantity A1(i), the number of data access information that queries is counted as the periodic query quantity A2(i), the number of access operation information is counted as the periodic access quantity A2(i), and the number of different accessing employee names is counted as the accessing employee quantity B(i). The data periodic characteristics are analyzed based on the periodic modification quantity, periodic query quantity, periodic access quantity, and accessing employee quantity to obtain the data periodic characteristics R(i).

[0025] Furthermore, the analysis method for the data merging parameters in step S6 includes:

[0026] Step S61: Analyze the data merging parameters based on the database storage information;

[0027] Step S62: Adjust the analysis process of data merging parameters according to the data periodic characteristics;

[0028] Step S63: Optimize the process of adjusting the data merging parameters based on the information quality parameters.

[0029] Furthermore, in step S61, when analyzing the data merging parameters, the data merging parameters are analyzed based on the database storage information to obtain the data merging parameters F(i).

[0030] In step S62, during the analysis process of adjusting the data merging parameters, the analysis process of the data merging parameters is adjusted according to the data periodicity characteristic R(i). When the periodicity characteristic does not meet the threshold, the analysis process of the data merging parameters is adjusted, and the adjusted data merging parameters are F1(i).

[0031] In step S63, during the process of optimizing the adjustment of data merging parameters, the adjustment process of 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 data merging parameters is optimized, and the optimized data merging parameter is F2(i).

[0032] Furthermore, in step S7, when determining the storage type of enterprise information, the storage type of enterprise information is analyzed according to the data merging parameters to obtain the storage type of enterprise information. The storage type of enterprise information includes Class I and Class II.

[0033] In step S7, when merging enterprise information, the enterprise information is merged according to the storage type of the enterprise information. Enterprise information whose storage type is consecutively of one type or is a single type in each collection period is merged into a group of merged enterprise information. Enterprise information whose storage type is of two types in each collection period is respectively regarded as a group of merged enterprise information. The merged enterprise information is numbered according to the order of the collection period to obtain the merged data number j.

[0034] Furthermore, in step S8, when analyzing the data acquisition mapping relationship, the analysis is performed based on the enterprise information. If the enterprise information is customer information, the data acquisition mapping relationship is set as n1→i, n1 n1(n2), if the enterprise information is employee information, set the data collection mapping relationship as n2→i; if the enterprise information is social media information, set the data collection mapping relationship as n3→i, n3 n3(n2); where n1(n2) represents the name data of the employee who handles customer information, and n3(n2) represents the name of the employee who posts social media information.

[0035] Further, in step S9, when constructing the data mapping relationship, the data mapping relationship is constructed based on the collection mapping relationship and the merged data number. The merged data number is matched with the collection cycle number corresponding to the merged enterprise information to obtain the merged collection cycle number set. The merged collection cycle number set is set as u(j). The data mapping relationship is constructed based on the merged collection cycle number set and the collection mapping relationship. The data mapping relationship is set as i→u(j), u(j)→Z(n2), where Z(n2) represents the name of the accessed employee.

[0036] On the other hand, the present invention also provides an integrated data service system, comprising:

[0037] The data acquisition module is used to periodically collect enterprise information, data access information from the enterprise database, and database storage information.

[0038] The enterprise data collection and analysis module is used to analyze the information completeness and quality parameters based on enterprise information, and to store the enterprise information in the enterprise database based on the information completeness.

[0039] The enterprise data collection and optimization module is used to optimize enterprise information based on its completeness and store the optimized information in the enterprise database.

[0040] The access collection and analysis module is used to analyze the number of access features based on data access information, and to analyze the data periodic characteristics based on the number of access features.

[0041] The data merging and analysis module is used to analyze data merging parameters based on data cycle characteristics, database storage information, and information quality parameters.

[0042] The 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 merged data number;

[0043] The mapping relationship analysis module is used to analyze the collected mapping relationship based on enterprise information, and also to construct the data mapping relationship based on the collected mapping relationship and the merged data number;

[0044] The mapping relationship storage module is used to store the acquisition mapping relationship and data mapping relationship into the enterprise database.

[0045] The beneficial effects of this invention are as follows: By collecting and analyzing enterprise information, data access information and database storage information of enterprises, it achieves comprehensive analysis of enterprise data and access and storage data of enterprise databases, thereby constructing mapping relationships between various data, realizing correlation analysis between data, and storing the collected data and its corresponding mapping relationships, realizing integrated storage of data from different enterprises, facilitating subsequent analysis and retrieval of stored data, thereby improving the efficiency of enterprise data analysis and the accuracy of enterprise data integration processing. Attached Figure Description

[0046] 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.

[0047] Figure 1 This is a flowchart of the integrated data service method in this embodiment.

[0048] Figure 2 This is a flowchart of the enterprise information storage and analysis method in this embodiment.

[0049] Figure 3 This is a flowchart of the data merging parameter analysis method in this embodiment.

[0050] Figure 4 This is a schematic diagram of the integrated data service system in this embodiment. Detailed Implementation

[0051] To more clearly illustrate the present invention, the following description, in conjunction with preferred embodiments and accompanying drawings, further explains the invention. Similar components in the drawings are indicated by the same reference numerals. Those skilled in the art should understand that the specific description below is illustrative rather than restrictive and should not be construed as limiting the scope of protection of the present invention.

[0052] Please see Figure 1 As shown, this is the integrated data service method of this embodiment, including:

[0053] Step S1: Periodically collect enterprise information, including customer information, employee information, and social media information. Customer information includes, but is not limited to, customer name, contact information, and the name of the employee in charge. Employee information includes, but is not limited to, employee name, contact information, and employee address. Social media information includes, but is not limited to, post content, the name of the posting employee, article views, and browsing user information. Browsing user information includes customer name, employee name, and unknown users. Unknown users are a collective term for all users accessing the platform other than customer and employee names. The enterprise information is collected once every preset collection period. In this embodiment, the preset collection period is set to 1 day. This embodiment does not specifically limit the setting of the preset collection period; those skilled in the art can freely set it, such as 2 days, 5 days, or 7 days. The enterprise information is collected through user interaction input.

[0054] Please continue reading. Figure 1 As shown, the integrated data service method further includes:

[0055] Step S2: 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.

[0056] Please see Figure 2 As shown, this is a method for storing and analyzing enterprise information, including:

[0057] Step S21: Analyze the completeness of information based on enterprise information.

[0058] Specifically, in step S21 of this embodiment, when analyzing information completeness, the number of different types of data involved in customer information is counted as the number of customer information contents, the number of data with missing values ​​in customer information is counted as the number of missing customer information, the number of different types of data involved in employee information is counted as the number of employee information contents, the number of data with missing values ​​in employee information is counted as the number of missing employee information, the number of different types of data involved in social media information is counted as the number of social media information contents, the number of data with missing values ​​in social media information is counted as the number of missing social media information, and the information completeness is analyzed based on 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. Let the information completeness be 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 document numbers, n1 represents customer name, n2 represents employee name, n3 represents document 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, customer information includes customer name and customer contact information, so the number of customer information contents in customer information is 2. The document number is defined as the number that distinguishes different document contents, and it is a number arranged in chronological order.

[0059] Specifically, in this embodiment, the analysis of enterprise information in step S21 is used 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 the accuracy of enterprise data integration and processing.

[0060] Specifically, in this embodiment, the enterprise information is a collected CSV file, which contains enterprise information arranged in a fixed content order.

[0061] Please continue reading. Figure 2 As shown, the methods for storing and analyzing enterprise information also include:

[0062] Step S22: Store the enterprise information in the enterprise database according to the information completeness.

[0063] Specifically, in step S22 of 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 current information completeness is stored in the enterprise database; if P(k)≠1, the enterprise information corresponding to the current information completeness is not stored.

[0064] Specifically, in this embodiment, the information integrity analysis in step S22 is used to store complete enterprise information in the enterprise database, thereby achieving the storage of complete enterprise information collected, ensuring the accuracy of subsequent enterprise data analysis, making the analyzed data complete and consistent, and thus improving the efficiency of enterprise data analysis and the accuracy of enterprise data integration processing.

[0065] Please continue reading. Figure 2 As shown, the methods for storing and analyzing enterprise information also include:

[0066] Step S23: Analyze the information quality parameters based on the enterprise information.

[0067] Specifically, in step S23 of this embodiment, when analyzing the information quality parameters, the information quality parameters are analyzed based on enterprise information, and the information quality parameter is set as 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.

[0068] Specifically, in this embodiment, the analysis of enterprise information in step S23 is used to analyze information quality parameters. These parameters represent whether the data from each data source is clear and stable, thereby improving the efficiency of enterprise data analysis and the accuracy of enterprise data integration and processing.

[0069] Please continue reading. Figure 1 As shown, the integrated data service method further includes:

[0070] Step S3: Optimize the enterprise information based on its completeness and store the optimized enterprise information in the enterprise database.

[0071] Specifically, in step S3 of this embodiment, when optimizing enterprise information, enterprise information with information completeness satisfying P(k)≠1 is optimized. If the customer name or employee name in the enterprise information is a missing value, the enterprise information being analyzed 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.

[0072] Specifically, in this embodiment, the information completeness analysis in step S3 is used to optimize the enterprise information, remove missing data from the enterprise information, reduce the impact of missing enterprise information content on data analysis in subsequent analysis, thereby improving the efficiency of enterprise data analysis and the accuracy of enterprise data integration and processing.

[0073] Please continue reading. Figure 1 As shown, the integrated data service method further includes:

[0074] Step S4: Periodically collect data access information and database storage information from the enterprise database. The data access information includes the names of the employees who accessed the database and access operation information. The access operation information includes modifying data, querying data, and other operations. The other operations are a collective term for operations other than modifying and querying data. The database storage information includes the current storage capacity and the maximum storage capacity of the database. The unit of the database storage information is Mb. The data access information and database storage information are collected once every preset collection period. The data access information and database storage information are collected by importing data from the enterprise database management platform.

[0075] Please continue reading. Figure 1 As shown, the integrated data service method further includes:

[0076] Step S5: Analyze the number of access features based on the data access information, and analyze the data periodic features based on the number of access features. The number of access features includes the number of periodic modifications, the number of periodic queries, the number of periodic accesses, and the number of employees accessing the data.

[0077] Specifically, in step S5 of this embodiment, when analyzing the data periodic characteristics, the number of data access information entries that modify data is counted as the periodic modification number, the number of data access information entries that query is counted as the periodic query number, the number of access operation information entries is counted as the periodic access number, and the number of different accessing employee names is counted as the accessing employee number. Based on the periodic modification number, periodic query number, periodic access number, and accessing employee number, the data periodic characteristics are analyzed, and the data periodic characteristic is set as R(i), defined as R(i) = A1(i) × A2(i) / {A3(i) × [A3(i) - B(i) + 1]}, where i represents the collection period number, i ∈ N. + Let A1(i) represent the number of modifications per period, A2(i) represent the number of queries per period, A3(i) represent the number of accesses per period, and B(i) represent the number of employees accessing the data. The collection period number is defined as a identifier to distinguish between data access information and database storage information collected in different collection periods.

[0078] Specifically, in this embodiment, the analysis of data access information in step S5 is used to count the number of periodic modifications, the number of periodic queries, the number of periodic accesses, and the number of employees accessing the database. This allows for the analysis of the number of different operations performed by employees on the enterprise database within the collection period, thereby identifying data periodic characteristics. These characteristics represent the relationship between employee access operations and access volume within the collection period. This enables the analysis of whether frequent modifications to the data during the collection period lead to data errors, thereby improving the efficiency of enterprise data analysis and the accuracy of enterprise data integration and processing.

[0079] Please continue reading. Figure 1 As shown, the integrated data service method further includes:

[0080] Step S6: Analyze the data merging parameters based on data cycle characteristics, database storage information, and information quality parameters.

[0081] Please see Figure 3 As shown, this is the analysis method for data merging parameters, including:

[0082] Step S61: Analyze the data merging parameters based on the information stored in the database.

[0083] Specifically, in step S61 of this embodiment, when analyzing the data merging parameters, the data merging parameters are analyzed based on the database storage information, and the data merging parameters are set as F(i). Where D(i) represents the current storage capacity of the database, d represents the maximum storage capacity of the database, and i maxThis represents the maximum value of the collection period number, and D(i-1) represents the current storage capacity of the database in the previous collection period.

[0084] Specifically, in this embodiment, the database storage information is analyzed in step S61 to determine the data merging parameters. These parameters represent the data storage change characteristics of the enterprise database within the collection period, enabling the analysis of the relationship between the size of the stored data and the preset merging size within each collection period. This improves the efficiency of enterprise data analysis and the accuracy of enterprise data integration processing.

[0085] Please continue reading. Figure 3 As shown, the analysis methods for data merging parameters also include:

[0086] Step S62: Adjust the analysis process of data merging parameters according to the data periodic characteristics.

[0087] Specifically, in step S62 of this embodiment, during the analysis process of adjusting the data merging parameters, the analysis process of the data merging parameters is adjusted according to the data periodicity characteristics. If R(i)≥r, it is determined that the data periodicity characteristics do not meet the threshold, and the analysis process of the data merging parameters is adjusted. The adjusted data merging parameters are F1(i), and F1(i) is set to F(i)×e. R(i)-1 Conversely, if the data periodicity characteristics meet the threshold, no adjustment is made to the analysis process of the data merging parameters; where r represents the feature comparison threshold, 1.1≤r≤1.4. It is understood that this embodiment does not specifically limit the value of the feature comparison threshold; those skilled in the art can freely set it, as long as it satisfies the adjustment of the data merging parameter analysis process. The optimal value for the feature comparison threshold is: r=1.2.

[0088] Specifically, in this embodiment, the analysis of data periodic characteristics in step S62 is used to adjust the analysis process of data merging parameters. The adjusted data merging parameters are related to the operation characteristics of data within the collection period. This increases the data merging parameters for collection periods with higher modification frequencies, reduces the amount of data that may contain errors, thereby improving the efficiency of enterprise data analysis and the accuracy of enterprise data integration processing.

[0089] Please continue reading. Figure 3 As shown, the analysis methods for data merging parameters also include:

[0090] Step S63: Optimize the process of adjusting the data merging parameters based on the information quality parameters.

[0091] Specifically, in step S63 of this embodiment, 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 parameters. If Q(m)≥q, it is determined that the information quality parameters do not meet the threshold, and the adjustment process of the data merging parameters is optimized. The optimized data merging parameters are F2(i), and F2(i) is set to F1(i)×e. Q(m) / 2 Conversely, if the information quality parameters meet the threshold, the adjustment process of the data merging parameters is not optimized; where q represents the quality comparison threshold, 0.05≤q≤0.15. It is understood that this embodiment does not specifically limit the value of the quality comparison threshold; those skilled in the art can freely set it, as long as it satisfies the optimization of the data merging parameter adjustment process. The optimal value of the quality comparison threshold is: q=0.1.

[0092] 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. When there is a lot of missing data in the data, the adjustment process of the data merging parameters is optimized, thereby improving the efficiency of enterprise data analysis and the accuracy of enterprise data integration processing.

[0093] Step S7: Merge the enterprise information according to the data merging parameters to obtain merged enterprise information, and number the merged enterprise information to obtain merged data number.

[0094] Specifically, in step S7 of this embodiment, when determining the storage type of enterprise information, the storage type of enterprise information is analyzed based on the data merging parameters. 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. Here, F(i-1) represents the data merging parameter of the previous collection period, f represents the merging threshold, and 1 ≤ f ≤ 1.2. It is understood that this embodiment does not specifically limit the value of the merging threshold; 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.

[0095] Specifically, in step S7 of this embodiment, when merging enterprise information, the enterprise information is merged according to its storage type. Enterprise information whose storage type is consecutively of one category or is a single category in each collection period is merged into a group of merged enterprise information. Enterprise information whose storage type is of two categories in each collection period is also grouped into separate groups of merged enterprise information. The merged enterprise information is then numbered according to the order of the collection periods to obtain a merged data number. The merged data number is set as j, j∈N.+ .

[0096] Specifically, in step S7 of this embodiment, when merging enterprise information, if the storage types of enterprise information corresponding to five consecutive collection cycles are Class I, Class I, Class II, Class I, and Class II respectively, then they can be divided into four groups of merged enterprise information. The first group consists of the first two consecutive Class I enterprise information, the second group consists of the third Class II enterprise information, the third group consists of the fourth Class I enterprise information, and the fourth group consists of the fifth Class II enterprise information.

[0097] Specifically, in this embodiment, the data merging parameters are analyzed in step S7 to determine the storage type of enterprise information. The storage type of enterprise information is divided into two categories according to the changes in data storage compared to the previous collection period. Category I indicates that the data quality in the current collection period is good and suitable for merging and storage, while Category II indicates that the data quality in the current collection period is poor and unsuitable for merging and storage. This process analyzes and merges enterprise information, enabling the merging analysis of enterprise data, thereby improving the efficiency of enterprise data analysis and the accuracy of enterprise data integration and processing.

[0098] Step S8: Analyze the collected mapping relationship based on the enterprise information.

[0099] Specifically, in step S8 of this embodiment, when analyzing the data collection mapping relationship, the data collection mapping relationship is analyzed based on the enterprise information. If the enterprise information is customer information, the data collection mapping relationship is set as n1→i, n1 n1(n2), if the enterprise information is employee information, set the data collection mapping relationship as n2→i; if the enterprise information is social media information, set the data collection mapping relationship as n3→i, n3 n3(n2); where n1(n2) represents the name data of the employee who handles customer information, and n3(n2) represents the name of the employee who posts social media information.

[0100] Specifically, in this embodiment, the analysis of enterprise information in step S8 is used to analyze the collection mapping relationship. The collection mapping relationship represents 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 the accuracy of enterprise data integration and processing.

[0101] Step S9: Construct a data mapping relationship based on the acquisition mapping relationship and the merged data number.

[0102] Specifically, in step S9 of this embodiment, when constructing the data mapping relationship, the data mapping relationship is constructed based on the collection mapping relationship and the merged data number. The merged data number is matched with the collection cycle number corresponding to the merged enterprise information to obtain the merged collection cycle number set. The merged collection cycle number set is set as u(j), and the data mapping relationship is constructed based on the merged collection cycle number set and the collection mapping relationship. The data mapping relationship is set as i→u(j), u(j)→Z(n2), where Z(n2) represents the name of the accessed employee.

[0103] Specifically, in this embodiment, the analysis of the acquisition mapping relationship and the merged data number in step S9 is used to construct a data mapping relationship, realize the correlation analysis between the acquired data and the merged data of the corresponding acquisition period, associate the acquired data with the data of each acquisition period in the enterprise database, and realize the retrieval of multiple data corresponding to a certain data feature, thereby improving the efficiency of enterprise data analysis and the accuracy of enterprise data integration processing.

[0104] Step S10: Store the collection mapping relationship and data mapping relationship in the enterprise database to associate the data in the enterprise database, realize the integration of various collected data, enhance the correlation between data, and facilitate the storage and retrieval of enterprise database data.

[0105] Please see Figure 4 As shown, this is the integrated data service system of this implementation, including:

[0106] The data acquisition module is used to periodically collect enterprise information, data access information from the enterprise database, and database storage information.

[0107] The enterprise data collection and analysis module is used to analyze the information completeness and quality parameters based on enterprise information, and to store the enterprise information in the enterprise database based on the information completeness. The enterprise data collection and analysis module is connected to the data collection module.

[0108] The enterprise data collection and optimization module is used to optimize enterprise information based on information completeness and store the optimized enterprise information in the enterprise database. The enterprise data collection and optimization module is connected to the enterprise data collection and analysis module.

[0109] The access acquisition and analysis module is used to analyze the number of access features based on the data access information, and to analyze the data periodicity features based on the number of access features. The access acquisition and analysis module is connected to the data acquisition module.

[0110] The data merging and analysis module is used to analyze data merging parameters based on data cycle characteristics, database storage information, and information quality parameters. The data merging and analysis module is connected to the enterprise data acquisition optimization module and the access data acquisition analysis module.

[0111] The 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 merged data number. The enterprise information merging module is connected to the data merging analysis module.

[0112] The mapping relationship analysis module is used to analyze the collected mapping relationship based on enterprise information, and also to construct the data mapping relationship based on the collected mapping relationship and the merged data number. The mapping relationship analysis module is connected to the enterprise information merging module.

[0113] The mapping relationship storage module is used to store the collected mapping relationships and data mapping relationships in the enterprise database. The mapping relationship storage module is connected to the mapping relationship analysis module.

[0114] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. For those skilled in the art, other variations or modifications can be made based on the above description. It is impossible to exhaustively list all the implementation methods here. All obvious variations or modifications derived from the technical solutions of the present invention are still within the protection scope of the present invention.

Claims

1. A method for integrating data services, characterized in that, include: Step S1: Periodically collect enterprise information; Step S2: 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. Step S3: Optimize the enterprise information based on its completeness and store the optimized enterprise information in the enterprise database; Step S4: Periodically collect data access information and database storage information from the enterprise database; Step S5: Analyze the number of access features based on the data access information, and analyze the data periodic features based on the number of access features. Step S6: Analyze the data merging parameters based on data cycle characteristics, database storage information, and information quality parameters; Step S7: Merge the enterprise information according to the data merging parameters to obtain merged enterprise information, and number the merged enterprise information to obtain merged data number; Step S8: Analyze the collected mapping relationship based on the enterprise information; Step S9: Construct a data mapping relationship based on the collection mapping relationship and the merged data number; Step S10: Store the acquisition mapping relationship and data mapping relationship in the enterprise database; The analysis method for the data merging parameters in step S6 includes: Step S61: Analyze the data merging parameters based on the database storage information; Step S62: Adjust the analysis process of data merging parameters according to the data periodic characteristics; Step S63: Optimize the process of adjusting the data merging parameters based on the information quality parameters; When analyzing the data merging parameters, the data merging parameters are analyzed based on the database storage information to obtain the data merging parameters F(i); In step S62, during the analysis process of adjusting the data merging parameters, the analysis process of the data merging parameters is adjusted according to the data periodicity characteristic R(i). When the periodicity characteristic 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 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). When analyzing the data collection mapping relationship, the analysis is based on enterprise information. If the enterprise information is customer information, the data collection mapping relationship is set as n1→i, where n1... n1(n2), if the enterprise information is employee information, set the data collection mapping relationship as n2→i; if the enterprise information is social media information, set the data collection mapping relationship as n3→i, n3 n3(n2); where n1(n2) represents the name data of the employee who handles customer information, and n3(n2) represents the name of the employee who posts social media information; When constructing the data mapping relationship, the data mapping relationship is constructed based on the collection mapping relationship and the merged data number. The merged data number is matched with the collection cycle number corresponding to the merged enterprise information to obtain the merged collection cycle number set. The merged collection cycle number set is set as u(j), and the data mapping relationship is constructed based on the merged collection cycle number set and the collection mapping relationship. The data mapping relationship is set as i→u(j), u(j)→Z(n2), where Z(n2) represents the name of the accessed employee.

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

3. The integrated data service method according to claim 2, characterized in that, In step S21, when analyzing the information completeness, the information completeness is analyzed based on 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 step S22, when storing enterprise information, enterprise information with an information integrity level of 1 is stored. In step S23, when analyzing the information quality parameters, the information quality parameters are analyzed based on 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 step S5, when analyzing the data periodic characteristics, the number of data access information that modifies data is counted as the periodic modification quantity A1(i), the number of data access information that queries is counted as the periodic query quantity A2(i), the number of access operation information is counted as the periodic access quantity A3(i), and the number of different accessing employee names is counted as the accessing employee quantity B(i). The data periodic characteristics are analyzed based on the periodic modification quantity, periodic query quantity, periodic access quantity, and accessing employee quantity to obtain the data periodic characteristics R(i).

5. The integrated data service method according to claim 4, characterized in that, When determining the storage type of enterprise information, the storage type of enterprise information is analyzed based on the data merging parameters to obtain the storage type of enterprise information. The storage type of enterprise information includes Class I and Class II. In step S7, when merging enterprise information, the enterprise information is merged according to the storage type of the enterprise information. Enterprise information whose storage type is consecutively of one type or is a single type in each collection period is merged into a group of merged enterprise information. Enterprise information whose storage type is of two types in each collection period is respectively regarded as a group of merged enterprise information. The merged enterprise information is numbered according to the order of the collection period to obtain the merged data number j.

6. An integrated data service system, applied to the integrated data service method as described in any one of claims 1-5, characterized in that, include: The data acquisition module is used to periodically collect enterprise information, data access information from the enterprise database, and database storage information. The enterprise data collection and analysis module is used to analyze the information completeness and quality parameters based on enterprise information, and to store the enterprise information in the enterprise database based on the information completeness. The enterprise data collection and optimization module is used to optimize enterprise information based on its completeness and store the optimized information in the enterprise database. The access collection and analysis module is used to analyze the number of access features based on data access information, and to analyze the data periodic characteristics based on the number of access features. The data merging and analysis module is used to analyze data merging parameters based on data cycle characteristics, database storage information, and information quality parameters. The 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 merged data number; The mapping relationship analysis module is used to analyze the collected mapping relationship based on enterprise information, and also to construct the data mapping relationship based on the collected mapping relationship and the merged data number; The mapping relationship storage module is used to store the acquisition mapping relationship and data mapping relationship into the enterprise database.

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