A Big Data-Based Enterprise Data Management Method and System

Abstract

This application proposes a big data-based enterprise data management method, belonging to the field of data management technology. When correcting data, it combines the relationships between multiple data points in the data to be corrected and uses these relationships to match data values ​​in a cloud server. Data obtained from the cloud data is then used to correct the local data, thereby improving the accuracy of data correction and further ensuring the consistency between the target data and the cloud data environment. This application also proposes a big data-based enterprise data management system and an electronic device.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to an enterprise data management method and system based on big data. Background Technology

[0002] With the rapid development of information technology, the amount of enterprise data is growing explosively. Enterprises need to effectively manage and utilize this data to improve decision-making efficiency and market competitiveness.

[0003] To meet this challenge, enterprises must adopt advanced data management technologies to ensure data integrity and accuracy. The introduction of big data technologies enables enterprises to extract valuable information from massive amounts of data, thereby providing strong support for decision-making. In a big data environment, enterprise data management methods need to possess efficient data processing capabilities to cope with the diversity and complexity of data. By adopting distributed computing frameworks such as Hadoop or Spark, enterprises can achieve rapid processing and analysis of large-scale data. Furthermore, by utilizing machine learning algorithms, enterprises can discover potential patterns and trends in data, further enhancing the value of data utilization.

[0004] In existing technologies, when enterprises use big data to correct their internal management data, they may directly call relevant data in the cloud database to correct their own data. However, the data to be corrected has poor consistency with the environment in the cloud database. Summary of the Invention

[0005] This application provides a big data-based enterprise data management method and system to improve the above-mentioned problems.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] In a first aspect, embodiments of this application propose a big data-based enterprise data management method, the method comprising:

[0008] The target data was identified from the target database; the target data is the data that needs to be corrected.

[0009] Identify at least two different types of basic feature values ​​corresponding to the target data, namely a first feature value and a second feature value, and obtain a first correlation function between the first feature value and the second feature value. The first correlation function is used to characterize the relationship between the first feature value and the second feature value.

[0010] Based on big data, multiple first matching feature values ​​that match the first feature value are determined. Based on the multiple first matching feature values, multiple second target matching feature values ​​that belong to the same data source as the multiple first matching feature values ​​are determined. Multiple second association functions between the multiple first matching feature values ​​and the multiple second target matching feature values ​​are obtained. The second association functions are used to characterize the relationship between a first matching feature value and a second target matching feature value.

[0011] The first correlation function is compared with multiple second correlation functions, and the second target matching feature value corresponding to the second correlation function with the highest fitting degree is determined as the target second target matching feature value;

[0012] Based on big data, multiple second matching feature values ​​that match the second feature value are determined. Based on the multiple second matching feature values, multiple first target matching feature values ​​that belong to the same data source as the multiple second matching feature values ​​are determined. Multiple third association functions between the multiple second matching feature values ​​and the multiple first target matching feature values ​​are obtained. The third association functions are used to characterize the relationship between a second matching feature value and a first target matching feature value.

[0013] The first correlation function is compared with multiple third correlation functions, and the first target matching feature value corresponding to the third correlation function with the highest fitting degree is determined as the target first target matching feature value;

[0014] The target data is corrected based on the first target matching feature value and the second target matching feature value.

[0015] In conjunction with the first aspect, in some implementations, the target data is modified based on the first target matching feature value and the second target matching feature value, including:

[0016] The target association function is determined based on the first target matching feature value and the second target matching feature value.

[0017] The target correlation function is fitted to the first correlation function, and the corrected correlation function is obtained based on the fitting result.

[0018] The target data is corrected based on the modified correlation function.

[0019] In conjunction with the first aspect, in some implementations, the target data is modified based on a modified correlation function, including:

[0020] The second modified eigenvalue is determined based on the modified correlation function and the first eigenvalue;

[0021] The first modified eigenvalue is determined based on the modified correlation function and the second eigenvalue.

[0022] In conjunction with the first aspect, in some implementations, at least two different types of basic feature values ​​corresponding to the target data are determined, namely a first feature value and a second feature value, and a first correlation function between the first feature value and the second feature value is obtained. The first correlation function is used to characterize the relationship between the first feature value and the second feature value, including:

[0023] Obtain the target data and split it into multiple sub-data.

[0024] Multiple sub-data sets are uploaded to the cloud server and matched. Based on the results returned by the cloud server, at least two sub-data sets that appear most frequently in the cloud database corresponding to the cloud server are determined from the multiple sub-data sets and used as basic feature values.

[0025] In conjunction with the first aspect, in some implementations, multiple sub-data items are uploaded to a cloud server and matched. Based on the results returned by the cloud server, at least two sub-data items that appear most frequently in the database corresponding to the cloud server are determined from the multiple sub-data items and used as basic feature values, including:

[0026] Obtain the corresponding information for the sub-data, including the information granularity, information features, and information parameters of the sub-data;

[0027] Based on the corresponding information, a search is performed in the cloud database, and the frequency of the sub-data in the cloud database is determined according to the search results.

[0028] In conjunction with the first aspect, in some implementations, multiple first matching feature values ​​that match a first feature value are determined based on big data; multiple second target matching feature values ​​belonging to the same data source as the multiple first matching feature values ​​are determined based on the multiple first matching feature values; and multiple second association functions are obtained between the multiple first matching feature values ​​and the multiple second target matching feature values. The second association functions are used to characterize the relationship between a first matching feature value and a second target matching feature value, including:

[0029] Based on the corresponding information of the first feature value, a search is performed in the cloud database, and all data in the cloud database that match the corresponding information of the first feature value are identified as the first matching feature value.

[0030] In conjunction with the first aspect, in some implementations, at least two different types of basic feature values ​​corresponding to the target data are determined, namely a first feature value and a second feature value, and a first correlation function between the first feature value and the second feature value is obtained. The first correlation function is used to characterize the relationship between the first feature value and the second feature value, including:

[0031] The first feature value and the second feature value are input into the neural network model and iteratively calculated. When a preset condition is met, the calculation stops and the first correlation function is output. The preset condition is that the first correlation function converges to a preset range during the iterative calculation process.

[0032] Secondly, embodiments of this application propose a big data-based enterprise data management system, which is configured as follows:

[0033] The target data was identified from the target database; the target data is the data that needs to be corrected.

[0034] Identify at least two different types of basic feature values ​​corresponding to the target data, namely a first feature value and a second feature value, and obtain a first correlation function between the first feature value and the second feature value. The first correlation function is used to characterize the relationship between the first feature value and the second feature value.

[0035] Based on big data, multiple first matching feature values ​​that match the first feature value are determined. Based on the multiple first matching feature values, multiple second target matching feature values ​​that belong to the same data source as the multiple first matching feature values ​​are determined. Multiple second association functions between the multiple first matching feature values ​​and the multiple second target matching feature values ​​are obtained. The second association functions are used to characterize the relationship between a first matching feature value and a second target matching feature value.

[0036] The first correlation function is compared with multiple second correlation functions, and the second target matching feature value corresponding to the second correlation function with the highest fitting degree is determined as the target second target matching feature value;

[0037] Based on big data, multiple second matching feature values ​​that match the second feature value are determined. Based on the multiple second matching feature values, multiple first target matching feature values ​​that belong to the same data source as the multiple second matching feature values ​​are determined. Multiple third association functions between the multiple second matching feature values ​​and the multiple first target matching feature values ​​are obtained. The third association functions are used to characterize the relationship between a second matching feature value and a first target matching feature value.

[0038] The first correlation function is compared with multiple third correlation functions, and the first target matching feature value corresponding to the third correlation function with the highest fitting degree is determined as the target first target matching feature value;

[0039] The target data is corrected based on the first target matching feature value and the second target matching feature value.

[0040] In conjunction with the second aspect, in some implementations, the system is configured as follows:

[0041] The target data is corrected based on the first target matching feature value and the second target matching feature value, including:

[0042] The target association function is determined based on the first target matching feature value and the second target matching feature value.

[0043] The target correlation function is fitted to the first correlation function, and the corrected correlation function is obtained based on the fitting result.

[0044] The target data is corrected based on the modified correlation function.

[0045] In conjunction with the second aspect, in some implementations, the system is configured as follows:

[0046] The target data is corrected based on the modified correlation function, including:

[0047] The second modified eigenvalue is determined based on the modified correlation function and the first eigenvalue;

[0048] The first modified eigenvalue is determined based on the modified correlation function and the second eigenvalue.

[0049] In conjunction with the second aspect, in some implementations, the system is configured as follows:

[0050] Identify at least two different types of basic feature values ​​corresponding to the target data, namely a first feature value and a second feature value, and obtain a first correlation function between the first feature value and the second feature value. The first correlation function is used to characterize the relationship between the first feature value and the second feature value, including:

[0051] Obtain the target data and split it into multiple sub-data.

[0052] Multiple sub-data sets are uploaded to the cloud server and matched. Based on the results returned by the cloud server, at least two sub-data sets that appear most frequently in the cloud database corresponding to the cloud server are determined from the multiple sub-data sets and used as basic feature values.

[0053] In conjunction with the second aspect, in some implementations, the system is configured as follows:

[0054] Multiple data sets are uploaded to a cloud server and matched. Based on the results returned by the cloud server, at least two data sets that appear most frequently in the database corresponding to the cloud server are identified from the multiple data sets and used as basic feature values, including:

[0055] Obtain the corresponding information for the sub-data, including the information granularity, information features, and information parameters of the sub-data;

[0056] Based on the corresponding information, a search is performed in the cloud database, and the frequency of the sub-data in the cloud database is determined according to the search results.

[0057] In conjunction with the second aspect, in some implementations, the system is configured as follows:

[0058] Based on big data, multiple first matching feature values ​​are identified that match the first feature value. Based on these multiple first matching feature values, multiple second target matching feature values ​​belonging to the same data source are identified. Multiple second association functions are obtained between the multiple first matching feature values ​​and the multiple second target matching feature values. These second association functions characterize the relationship between a first matching feature value and a second target matching feature value, including:

[0059] Based on the corresponding information of the first feature value, a search is performed in the cloud database, and all data in the cloud database that match the corresponding information of the first feature value are identified as the first matching feature value.

[0060] In conjunction with the second aspect, in some implementations, the system is configured as follows:

[0061] Identify at least two different types of basic feature values ​​corresponding to the target data, namely a first feature value and a second feature value, and obtain a first correlation function between the first feature value and the second feature value. The first correlation function is used to characterize the relationship between the first feature value and the second feature value, including:

[0062] The first feature value and the second feature value are input into the neural network model and iteratively calculated. When a preset condition is met, the calculation stops and the first correlation function is output. The preset condition is that the first correlation function converges to a preset range during the iterative calculation process.

[0063] A third aspect of this invention provides an electronic device, which includes:

[0064] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method proposed in the first aspect of the present invention.

[0065] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in the first aspect of the present invention.

[0066] In summary, the above method and apparatus have the following technical effects:

[0067] This application proposes a big data-based enterprise data management method and system. After identifying the data in the enterprise data that needs correction, it determines at least two different types of basic feature values ​​corresponding to the target data, namely a first feature value and a second feature value. A first correlation function is obtained between the first and second feature values, which characterizes the relationship between them. Then, based on big data, multiple first matching feature values ​​matching the first feature value are determined. Based on these multiple first matching feature values, multiple second target matching feature values ​​belonging to the same data source are determined. Multiple second correlation functions are obtained between these multiple first matching feature values ​​and the multiple second target matching feature values. Finally, the system... The first correlation function is compared with multiple second correlation functions, and the second target matching feature value corresponding to the second correlation function with the highest fitting degree is determined as the target second target matching feature value. Then, based on big data, multiple second matching feature values ​​that match the second feature values ​​are determined. Based on the multiple second matching feature values, multiple first target matching feature values ​​belonging to the same data source as the multiple second matching feature values ​​are determined. Multiple third correlation functions between the multiple second matching feature values ​​and the multiple first target matching feature values ​​are obtained. Then, the first correlation function is compared with the multiple third correlation functions, and the first target matching feature value corresponding to the third correlation function with the highest fitting degree is determined as the target first target matching feature value. Finally, the target data is corrected based on the target first target matching feature value and the target second target matching feature value. This application proposes a big data-based enterprise data management method. When correcting data, it combines the relationship between multiple data in the data to be corrected and uses this relationship to match the data values ​​in the cloud server. The data obtained from the cloud data is used to correct the local data, thereby improving the accuracy of data correction and further ensuring the environmental consistency between the target data and the cloud data. Attached Figure Description

[0068] Figure 1 This is a flowchart illustrating an enterprise data management method based on big data, as proposed in an embodiment of this application. Detailed Implementation

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] This application proposes a big data-based enterprise data management method. Please refer to [link / reference]. Figure 1 This includes the following steps:

[0071] S101: The target data is identified from the target database. The target data is the data that needs to be corrected.

[0072] Specifically, in this embodiment, enterprise data can be factory control data or personnel management data. The cloud database in this embodiment is a combination of various shared databases of the same type. For ease of understanding, personnel payroll is used as an example. In other embodiments, enterprise data can be other types of data, which are not limited here.

[0073] Understandably, in this step, the target database is the company's own database. When upgrading the database, the data needs to be corrected, that is, the target data in the database needs to be corrected.

[0074] S102: Determine at least two different types of basic feature values ​​corresponding to the target data, namely a first feature value and a second feature value, and obtain a first correlation function between the first feature value and the second feature value. The first correlation function is used to characterize the relationship between the first feature value and the second feature value.

[0075] Understandably, in this embodiment, the target data is taken as an example of an employee's salary. In this embodiment, the first feature value can be the employee's age, and the second feature value can be the employee's salary. In some embodiments, there may also be a third feature value, a fourth feature value, etc., which are not limited here. In this embodiment, the relationship between employee age and salary can be obtained based on artificial intelligence or other methods.

[0076] For example, as one implementation, the first feature value and the second feature value can be input into the neural network model and iteratively calculated. When a preset condition is met, the calculation stops and the first correlation function is output. The preset condition is that the first correlation function converges to a preset range during the iterative calculation process.

[0077] Understandably, the first and second feature values ​​can be input into a carefully designed neural network model and subjected to multiple iterative calculations. During each iteration, the neural network performs complex calculations based on the input feature values ​​to update its internal parameters. This process continues until a preset condition is met. The preset condition refers to the fact that, during the iterative calculations, the value of the first correlation function gradually converges and eventually stabilizes within a certain preset numerical range. Once this condition is met, the calculation stops, and the current value of the first correlation function is output. This output value represents a certain correlation or relationship between the input feature values.

[0078] To determine whether a data value can be used as a feature value, one approach is as follows:

[0079] S1021: Obtain the target data and split the target data into multiple sub-data.

[0080] It is understood that the multiple sub-data can be various kinds of information data, and this embodiment is not limited to any particular type. For example, they could be employee salaries, employee length of service, employee attendance completion rate, etc., with each value corresponding to one sub-data.

[0081] S1022: Upload multiple sub-data to the cloud server and match them. Based on the results returned by the cloud server, determine at least two sub-data that appear most frequently in the cloud database corresponding to the cloud server from the multiple sub-data and use them as basic feature values.

[0082] Understandably, given the diverse nature of cloud data, matching multiple sub-data sets with data in the cloud server involves matching them. Data sets with higher frequency of occurrence after matching have a larger base number, leading to more accurate comprehensive evaluation. Therefore, in this embodiment, at least two sub-data sets with the highest frequency of occurrence in the cloud database corresponding to the cloud server can be identified from multiple sub-data sets and used as basic feature values. Understandably, corresponding information for the sub-data sets can be obtained, including the information granularity, information features, and information parameters. Based on this information, a search is performed in the cloud database, and the frequency of occurrence of the sub-data sets in the cloud database is determined according to the search results. Of course, other methods can also be used, which are not limited here.

[0083] S103: Based on big data, determine multiple first matching feature values ​​that match the first feature value; based on the multiple first matching feature values, determine multiple second target matching feature values ​​that belong to the same data source as the multiple first matching feature values; and obtain multiple second correlation functions between the multiple first matching feature values ​​and the multiple second target matching feature values. The second correlation functions are used to characterize the relationship between a first matching feature value and a second target matching feature value.

[0084] Understandably, in this embodiment, uploading enterprise data to a cloud server allows for matching and correction of the data with the data in the cloud server. For example, in this embodiment, after uploading employee ages to the cloud server, multiple employees of the same age from multiple enterprises can be identified with multiple different salaries. Then, using the same method as in step S103, multiple second correlation functions between multiple first matching feature values ​​and multiple second target matching feature values ​​can be obtained.

[0085] As one implementation method, a search can be performed in the cloud database based on the corresponding information of the first feature value, and all data in the cloud database that match the corresponding information of the first feature value can be identified as the first matching feature value.

[0086] S104: Compare the first correlation function with multiple second correlation functions, and determine the second target matching feature value corresponding to the second correlation function with the highest fitting degree as the target second target matching feature value.

[0087] The comparative analysis of the first correlation function with multiple second correlation functions evaluates the degree of fit between each second correlation function and the first correlation function by comparing their similarities and differences one by one. Understandably, in this embodiment, comparing the first correlation function with multiple second correlation functions can identify other employees in the company whose salaries are most consistent with those of the same age, while ensuring environmental consistency between the target data and the cloud data.

[0088] S105: Based on big data, determine multiple second matching feature values ​​that match the second feature value; based on the multiple second matching feature values, determine multiple first target matching feature values ​​that belong to the same data source as the multiple second matching feature values; and obtain multiple third association functions between the multiple second matching feature values ​​and the multiple first target matching feature values. The third association functions are used to characterize the relationship between a second matching feature value and a first target matching feature value.

[0089] Understandably, similar to step S105, this method can be used to obtain other employees of the same age and salary within the company.

[0090] S106: Compare the first correlation function with multiple third correlation functions, and determine the first target matching feature value corresponding to the third correlation function with the highest fitting degree as the target first target matching feature value.

[0091] Understandably, a detailed comparative analysis is performed between the first correlation function and multiple third correlation functions to assess their similarity and fit. This comparison identifies one or more third correlation functions that are closest to and have the highest fit with the first correlation function. Among these third correlation functions, a specific function needs to be determined that has the highest fit, ensuring it accurately reflects the characteristics of the first correlation function. Finally, this third correlation function with the highest fit is selected, and its corresponding first target matching feature value is determined as the final target first target matching feature value. This process ensures that the feature value that best matches the first correlation function is found.

[0092] S107: Correct the target data based on the first target matching feature value and the second target matching feature value.

[0093] Understandably, after obtaining the first target matching feature value and the second target matching feature value, since the first target matching feature value and the second target matching feature value are data obtained by function fusion, in this embodiment, the data of this enterprise is corrected by combining the first target matching feature value and the second target matching feature value, so as to comprehensively consider the overall situation of the relevant enterprise.

[0094] Specifically, as one implementation method, a second modified eigenvalue can be determined based on the modified correlation function and the first eigenvalue, and a first modified eigenvalue can be determined based on the modified correlation function and the second eigenvalue. The first and second modified eigenvalues ​​obtained in the above manner can be used to replace the first and second eigenvalues ​​in the original data.

[0095] This application proposes a big data-based enterprise data management method. After identifying the data in the enterprise data that needs correction, it determines at least two different types of basic feature values ​​corresponding to the target data, namely a first feature value and a second feature value. A first correlation function is obtained between the first and second feature values, which characterizes the relationship between them. Then, based on big data, multiple first matching feature values ​​matching the first feature value are determined. Based on these multiple first matching feature values, multiple second target matching feature values ​​belonging to the same data source are determined, and multiple second correlation functions are obtained between these first and second target matching feature values. Finally, the first... The method involves comparing a correlation function with multiple second correlation functions to determine the second target matching feature value corresponding to the second correlation function with the highest fit. Then, based on big data, multiple second matching feature values ​​matching the second feature value are determined. Based on these second matching feature values, multiple first target matching feature values ​​belonging to the same data source are determined. Multiple third correlation functions between the multiple second matching feature values ​​and the multiple first target matching feature values ​​are obtained. These first correlation functions are then compared with the multiple third correlation functions, and the first target matching feature value corresponding to the third correlation function with the highest fit is determined. Finally, the target data is corrected based on the target first target matching feature value and the target second target matching feature value. This application proposes a big data-based enterprise data management method that, when correcting data, combines the relationships between multiple data points in the data to be corrected and uses these relationships to match data values ​​in a cloud server. Data obtained from the cloud data is used to correct local data, thereby improving the accuracy of data correction and further ensuring the consistency between the target data and the environment in the cloud data.

[0096] Based on the same inventive concept, embodiments of this application also propose a big data-based enterprise data management system, which is configured as follows:

[0097] The target data was identified from the target database; the target data is the data that needs to be corrected.

[0098] Identify at least two different types of basic feature values ​​corresponding to the target data, namely a first feature value and a second feature value, and obtain a first correlation function between the first feature value and the second feature value. The first correlation function is used to characterize the relationship between the first feature value and the second feature value.

[0099] Based on big data, multiple first matching feature values ​​that match the first feature value are determined. Based on the multiple first matching feature values, multiple second target matching feature values ​​that belong to the same data source as the multiple first matching feature values ​​are determined. Multiple second association functions between the multiple first matching feature values ​​and the multiple second target matching feature values ​​are obtained. The second association functions are used to characterize the relationship between a first matching feature value and a second target matching feature value.

[0100] The first correlation function is compared with multiple second correlation functions, and the second target matching feature value corresponding to the second correlation function with the highest fitting degree is determined as the target second target matching feature value;

[0101] Based on big data, multiple second matching feature values ​​that match the second feature value are determined. Based on the multiple second matching feature values, multiple first target matching feature values ​​that belong to the same data source as the multiple second matching feature values ​​are determined. Multiple third association functions between the multiple second matching feature values ​​and the multiple first target matching feature values ​​are obtained. The third association functions are used to characterize the relationship between a second matching feature value and a first target matching feature value.

[0102] The first correlation function is compared with multiple third correlation functions, and the first target matching feature value corresponding to the third correlation function with the highest fitting degree is determined as the target first target matching feature value;

[0103] The target data is corrected based on the first target matching feature value and the second target matching feature value.

[0104] In some implementations, the system is configured as follows:

[0105] The target data is corrected based on the first target matching feature value and the second target matching feature value, including:

[0106] The target association function is determined based on the first target matching feature value and the second target matching feature value.

[0107] The target correlation function is fitted to the first correlation function, and the corrected correlation function is obtained based on the fitting result.

[0108] The target data is corrected based on the modified correlation function.

[0109] In some implementations, the system is configured as follows:

[0110] The target data is corrected based on the modified correlation function, including:

[0111] The second modified eigenvalue is determined based on the modified correlation function and the first eigenvalue;

[0112] The first modified eigenvalue is determined based on the modified correlation function and the second eigenvalue.

[0113] In some implementations, the system is configured as follows:

[0114] Identify at least two different types of basic feature values ​​corresponding to the target data, namely a first feature value and a second feature value, and obtain a first correlation function between the first feature value and the second feature value. The first correlation function is used to characterize the relationship between the first feature value and the second feature value, including:

[0115] Obtain the target data and split it into multiple sub-data.

[0116] Multiple sub-data sets are uploaded to the cloud server and matched. Based on the results returned by the cloud server, at least two sub-data sets that appear most frequently in the cloud database corresponding to the cloud server are determined from the multiple sub-data sets and used as basic feature values.

[0117] In some implementations, the system is configured as follows:

[0118] Multiple data sets are uploaded to a cloud server and matched. Based on the results returned by the cloud server, at least two data sets that appear most frequently in the database corresponding to the cloud server are identified from the multiple data sets and used as basic feature values, including:

[0119] Obtain the corresponding information for the sub-data, including the information granularity, information features, and information parameters of the sub-data;

[0120] Based on the corresponding information, a search is performed in the cloud database, and the frequency of the sub-data in the cloud database is determined according to the search results.

[0121] In some implementations, the system is configured as follows:

[0122] Based on big data, multiple first matching feature values ​​are identified that match the first feature value. Based on these multiple first matching feature values, multiple second target matching feature values ​​belonging to the same data source are identified. Multiple second association functions are obtained between the multiple first matching feature values ​​and the multiple second target matching feature values. These second association functions characterize the relationship between a first matching feature value and a second target matching feature value, including:

[0123] Based on the corresponding information of the first feature value, a search is performed in the cloud database, and all data in the cloud database that match the corresponding information of the first feature value are identified as the first matching feature value.

[0124] This application proposes a big data-based enterprise data management method. After identifying the data in the enterprise data that needs correction, it determines at least two different types of basic feature values ​​corresponding to the target data, namely a first feature value and a second feature value. A first correlation function is obtained between the first and second feature values, which characterizes the relationship between them. Then, based on big data, multiple first matching feature values ​​matching the first feature value are determined. Based on these multiple first matching feature values, multiple second target matching feature values ​​belonging to the same data source are determined, and multiple second correlation functions are obtained between these first and second target matching feature values. Finally, the first... The process involves comparing a correlation function with multiple second correlation functions to determine the second target matching feature value corresponding to the second correlation function with the highest fit. Then, based on big data, multiple second matching feature values ​​matching the second feature value are determined. Based on these second matching feature values, multiple first target matching feature values ​​belonging to the same data source are determined. Multiple third correlation functions between the multiple second matching feature values ​​and the multiple first target matching feature values ​​are obtained. These first correlation functions are then compared with the multiple third correlation functions, and the first target matching feature value corresponding to the third correlation function with the highest fit is determined. Finally, the target data is corrected based on the target first target matching feature value and the target second target matching feature value. This application proposes a big data-based enterprise data management system that, when correcting data, combines the relationships between multiple data points in the data to be corrected and uses these relationships to match data values ​​in a cloud server. Data obtained from the cloud data is used to correct local data, thereby improving the accuracy of data correction and further ensuring the consistency between the target data and the environment in the cloud data.

[0125] Based on the same inventive concept, embodiments of this application also propose an electronic device, which includes:

[0126] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the big data-based enterprise data management method of the present application embodiments.

[0127] Furthermore, to achieve the above objectives, embodiments of this application also propose a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the enterprise data management method based on big data as described in the embodiments of this application.

[0128] The following is a detailed introduction to the various components of the electronic device:

[0129] In this context, the processor is the control center of the electronic device. It can be a single processor or a collective term for multiple processing elements. For example, a processor can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0130] Alternatively, the processor can perform various functions of the electronic device by running or executing software programs stored in memory and by calling data stored in memory.

[0131] The memory is used to store the software program that executes the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can be referred to the above method embodiment, which will not be repeated here.

[0132] Optionally, the memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory can be integrated with the processor or exist independently and coupled to the processor through an interface circuit of an electronic device; this embodiment of the invention does not specifically limit this.

[0133] A transceiver is used to communicate with network devices or with terminal devices.

[0134] Optionally, the transceiver may include a receiver and a transmitter. The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.

[0135] Optionally, the transceiver can be integrated with the processor or exist independently and coupled to the processor through the router's interface circuit. This embodiment of the invention does not specifically limit this.

[0136] Furthermore, the technical effects of the electronic device can be referred to the technical effects of the data transmission method in the above method embodiments, and will not be repeated here.

[0137] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0138] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0139] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0140] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0141] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0142] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0143] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

Claims

1. A big data-based enterprise data management method, characterized by, The method comprises: Confirming target data from a target database, the target data being data that needs to be corrected; Determining at least two different types of basic characteristic values corresponding to the target data, respectively a first characteristic value and a second characteristic value, and obtaining a first correlation function between the first characteristic value and the second characteristic value, the first correlation function being used to represent the relationship between the first characteristic value and the second characteristic value; Determining a plurality of first matching characteristic values matched with the first characteristic value based on big data, determining a plurality of second target matching characteristic values belonging to the same data source as the plurality of first matching characteristic values based on the plurality of first matching characteristic values, and obtaining a plurality of second correlation functions between the plurality of first matching characteristic values and the plurality of second target matching characteristic values, the second correlation function being used to represent the relationship between the first matching characteristic value and the second target matching characteristic value; Comparing the first correlation function with the plurality of second correlation functions, and determining that the second target matching characteristic value corresponding to the second correlation function with the highest fitting degree as a target second target matching characteristic value; Determining a plurality of second matching characteristic values matched with the second characteristic value based on big data, determining a plurality of first target matching characteristic values belonging to the same data source as the plurality of second matching characteristic values based on the plurality of second matching characteristic values, and obtaining a plurality of third correlation functions between the plurality of second matching characteristic values and the plurality of first target matching characteristic values, the third correlation function being used to represent the relationship between the second matching characteristic value and the first target matching characteristic value; Comparing the first correlation function with the plurality of third correlation functions, and determining that the first target matching characteristic value corresponding to the third correlation function with the highest fitting degree as a target first target matching characteristic value; Correcting the target data based on the target first target matching characteristic value and the target second target matching characteristic value; Determining at least two different types of basic characteristic values corresponding to the target data, respectively a first characteristic value and a second characteristic value, and obtaining a first correlation function between the first characteristic value and the second characteristic value, the first correlation function being used to represent the relationship between the first characteristic value and the second characteristic value, comprising: Obtaining the target data, and splitting the target data to obtain a plurality of sub-data; Uploading the plurality of sub-data to a cloud server and performing matching, and determining at least two sub-data with the highest frequency of occurrence in a cloud database corresponding to the cloud server from the plurality of sub-data according to the result returned by the cloud server, and taking the at least two sub-data as the basic characteristic values; Uploading the plurality of sub-data to a cloud server and performing matching, and determining at least two sub-data with the highest frequency of occurrence in a database corresponding to the cloud server from the plurality of sub-data according to the result returned by the cloud server, and taking the at least two sub-data as the basic characteristic values, comprising: obtaining corresponding information of the sub-data, the corresponding information including information granularity, information characteristics and information parameters of the sub-data; performing retrieval in the cloud database based on the corresponding information, and determining the frequency of occurrence of the sub-data in the cloud database according to the retrieval result.

2. The big data based enterprise data management method of claim 1, wherein, correcting the target data based on the target first target matching feature value and the target second target matching feature value, including: determining a target correlation function based on the target first target matching feature value and the target second target matching feature value; fitting the target correlation function with the first correlation function, and obtaining a corrected correlation function according to the fitting result; correcting the target data based on the corrected correlation function.

3. The big data based enterprise data management method of claim 2, wherein, correcting the target data based on the corrected correlation function, including: determining a second corrected feature value based on the corrected correlation function and the first feature value; determining a first corrected feature value based on the corrected correlation function and the second feature value.

4. The big data based enterprise data management method of claim 1, wherein, determining a plurality of first matching feature values matched with the first feature value based on big data, determining a plurality of second target matching feature values belonging to the same data source as the plurality of first matching feature values based on the plurality of first matching feature values, and obtaining a plurality of second correlation functions between the plurality of first matching feature values and the plurality of second target matching feature values, the second correlation function being used to represent the relationship between one first matching feature value and one second target matching feature value, including: performing retrieval in the cloud database based on the corresponding information of the first feature value, and determining all data in the cloud database matched with the corresponding information of the first feature value as the first matching feature value.

5. The big data based enterprise data management method according to any one of claims 1-4, characterized in that, determining at least two different types of basic feature values corresponding to the target data, respectively a first feature value and a second feature value, and obtaining a first correlation function between the first feature value and the second feature value, the first correlation function being used to represent the relationship between the first feature value and the second feature value, including: inputting the first feature value and the second feature value into a neural network model and performing iterative calculation, and stopping calculation and outputting the first correlation function when a preset condition is met, wherein the preset condition is that the first correlation function in the iterative calculation process converges to a preset range.

6. A big data based enterprise data management system, characterized by, The system is configured to: confirm target data from a target database, the target data being data that needs to be corrected; determine at least two different types of basic feature values corresponding to the target data, respectively a first feature value and a second feature value, and obtain a first correlation function between the first feature value and the second feature value, the first correlation function being used to represent the relationship between the first feature value and the second feature value; determining a plurality of first matching characteristic values matched with the first characteristic value based on big data, determining a plurality of second target matching characteristic values belonging to the same data source as the plurality of first matching characteristic values based on the plurality of first matching characteristic values, and obtaining a plurality of second correlation functions between the plurality of first matching characteristic values and the plurality of second target matching characteristic values, the second correlation function being used to represent the relationship between one first matching characteristic value and one second target matching characteristic value; comparing the first correlation function with the plurality of second correlation functions, and determining that the second target matching characteristic value corresponding to the second correlation function with the highest fitting degree as a target second target matching characteristic value; determining a plurality of second matching characteristic values matched with the second characteristic value based on big data, determining a plurality of first target matching characteristic values belonging to the same data source as the plurality of second matching characteristic values based on the plurality of second matching characteristic values, and obtaining a plurality of third correlation functions between the plurality of second matching characteristic values and the plurality of first target matching characteristic values, the third correlation function being used to represent the relationship between one second matching characteristic value and one first target matching characteristic value; comparing the first correlation function with the plurality of third correlation functions, and determining that the first target matching characteristic value corresponding to the third correlation function with the highest fitting degree as a target first target matching characteristic value; correcting the target data based on the target first target matching characteristic value and the target second target matching characteristic value; determining at least two different types of basic characteristic values corresponding to the target data, respectively first characteristic value and second characteristic value, and obtaining a first correlation function between the first characteristic value and the second characteristic value, the first correlation function being used to represent the relationship between the first characteristic value and the second characteristic value, comprising: obtaining the target data, and splitting the target data to obtain a plurality of sub-data; uploading the plurality of sub-data to a cloud server and matching, determining at least two sub-data with the highest frequency of occurrence in the cloud database corresponding to the cloud server from the plurality of sub-data according to the result returned by the cloud server, and taking them as the basic characteristic values; uploading the plurality of sub-data to a cloud server and matching, determining at least two sub-data with the highest frequency of occurrence in the database corresponding to the cloud server from the plurality of sub-data according to the result returned by the cloud server, and taking them as the basic characteristic values, comprising: obtaining corresponding information of the sub-data, the corresponding information including information granularity, information characteristics and information parameters of the sub-data; based on the corresponding information, searching in the cloud database, and determining the frequency of occurrence of the sub-data in the cloud database according to the search result.

7. The big data based enterprise data management system of claim 6, wherein, The system is configured to: correcting the target data based on the target first target matching characteristic value and the target second target matching characteristic value, comprising: determining a target correlation function based on the target first target matching characteristic value and the target second target matching characteristic value; fitting the target correlation function to the first correlation function, and obtaining a modified correlation function according to a result of the fitting; correcting the target data based on the modified correlation function.

8. An electronic device, comprising: comprising: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method as claimed in any one of claims 1-5.

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