Enterprise data management method and system based on big data

Abstract

The invention provides an enterprise data management method based on big data, which belongs to the technical field of data management, and is characterized in that when data is corrected, the relationship among a plurality of data in the data needing to be corrected is combined, and the relationship is matched with a data value in a cloud server; and the local data is corrected by using the data obtained from the cloud data, so that the accuracy of data correction is improved, and the environment consistency in the target data and the cloud data is further ensured. The invention provides an enterprise data management system based on big data and electronic equipment.

Description

Technical Field

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

[0002] With the rapid development of information technology, the amount of enterprise data has shown an explosive growth. Enterprises need to effectively manage and utilize this data to improve decision-making efficiency and market competitiveness.

[0003] To address this challenge, enterprises must adopt advanced data management technologies to ensure the integrity and accuracy of data. The introduction of big data technologies enables enterprises to extract valuable information from massive data, thus providing strong support for decision-making. In the 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 the rapid processing and analysis of large-scale data. In addition, by using machine learning algorithms, enterprises can discover potential patterns and trends from data, further enhancing the utilization value of data.

[0004] In the prior art, when enterprises use big data to correct the management data within their own enterprises, they may directly call the relevant data in the cloud data database to correct their own data, and the environmental consistency between the data to be corrected and the cloud data is relatively poor. Summary of the Invention

[0005] Embodiments of this application provide an enterprise data management method and system based on big data to improve the above problems.

[0006] To achieve the above object, this application adopts the following technical solutions:

[0007] In a first aspect, embodiments of this application propose an enterprise data management method based on big data, and the method includes:

[0008] Identify target data from a target database, where the target data is the data that needs to be corrected;

[0009] 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, where the first correlation function is used to characterize the relationship between the first feature value and the second feature value;

[0010] Determine multiple first matching eigenvalues that match the first eigenvalue based on big data, determine multiple second target matching eigenvalues that belong to the same data source as the multiple first matching eigenvalues based on the multiple first matching eigenvalues, and obtain multiple second correlation functions between the multiple first matching eigenvalues and the multiple second target matching eigenvalues. The second correlation function is used to characterize the relationship between a first matching eigenvalue and a second target matching eigenvalue;

[0011] Compare the first correlation function with the multiple second correlation functions, and determine that the second target matching eigenvalue corresponding to the second correlation function with the highest fitting degree is the target second target matching eigenvalue;

[0012] Determine multiple second matching eigenvalues that match the second eigenvalue based on big data, determine multiple first target matching eigenvalues that belong to the same data source as the multiple second matching eigenvalues based on the multiple second matching eigenvalues, and obtain multiple third correlation functions between the multiple second matching eigenvalues and the multiple first target matching eigenvalues. The third correlation function is used to characterize the relationship between a second matching eigenvalue and a first target matching eigenvalue;

[0013] Compare the first correlation function with the multiple third correlation functions, and determine that the first target matching eigenvalue corresponding to the third correlation function with the highest fitting degree is the target first target matching eigenvalue;

[0014] Correct the target data based on the target first target matching eigenvalue and the target second target matching eigenvalue.

[0015] Combined with the first aspect, in some embodiments, correcting the target data based on the target first target matching eigenvalue and the target second target matching eigenvalue includes:

[0016] Determine a target correlation function based on the target first target matching eigenvalue and the target second target matching eigenvalue;

[0017] Fit the target correlation function with the first correlation function, and obtain a corrected correlation function according to the fitting result;

[0018] Correct the target data based on the corrected correlation function.

[0019] Combined with the first aspect, in some embodiments, correcting the target data based on the corrected correlation function includes:

[0020] Determine a second corrected eigenvalue based on the corrected correlation function and the first eigenvalue;

[0021] Determine a first corrected eigenvalue based on the corrected correlation function and the second eigenvalue.

[0022] In combination with the first aspect, in some embodiments, at least two different types of basic feature values corresponding to the target data are determined, which are the first feature value and the second feature value respectively, 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 the target data to obtain multiple sub-data;

[0024] Upload the multiple sub-data to the cloud server for matching. According to the result returned by the cloud server, determine at least two sub-data with the highest occurrence frequency in the cloud database corresponding to the cloud server from the multiple sub-data, and use them as the basic feature values.

[0025] In combination with the first aspect, in some embodiments, upload the multiple sub-data to the cloud server for matching. According to the result returned by the cloud server, determine at least two sub-data with the highest occurrence frequency in the database corresponding to the cloud server from the multiple sub-data, and use them as the basic feature values, including:

[0026] Obtain the corresponding information of the sub-data. The corresponding information includes the information granularity, information characteristics, and information parameters of the sub-data;

[0027] Retrieve in the cloud database based on the corresponding information, and determine the occurrence frequency of the sub-data in the cloud database according to the retrieval result.

[0028] In combination with the first aspect, in some embodiments, determine multiple first matching feature values that match the first feature value based on big data, determine multiple second target matching feature values that belong to the same data source as the multiple first matching features based on 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 function is used to characterize the relationship between a first matching feature value and a second target matching feature value, including:

[0029] Retrieve in the cloud database based on the corresponding information of the first feature value, and determine all the data in the cloud database that match the corresponding information of the first feature value as the first matching feature values.

[0030] In combination with the first aspect, in some embodiments, at least two different types of basic feature values corresponding to the target data are determined, which are the first feature value and the second feature value respectively, 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] Input the first eigenvalue and the second eigenvalue into a neural network model and perform iterative calculations. Stop the calculations and output the first correlation function when a preset condition is met. The preset condition is that the first correlation function in the iterative calculation process converges to a preset range.

[0032] In a second aspect, an embodiment of the present application proposes an enterprise data management system based on big data, and the system is configured to:

[0033] Identify target data from the target database, where the target data is the data that needs to be corrected;

[0034] Determine at least two different types of basic eigenvalues corresponding to the target data, namely the first eigenvalue and the second eigenvalue, and obtain the first correlation function between the first eigenvalue and the second eigenvalue. The first correlation function is used to characterize the relationship between the first eigenvalue and the second eigenvalue;

[0035] Based on big data, determine multiple first matching eigenvalues that match the first eigenvalue. Based on the multiple first matching eigenvalues, determine multiple second target matching eigenvalues that belong to the same data source as the multiple first matching eigenvalues, and obtain multiple second correlation functions between the multiple first matching eigenvalues and the multiple second target matching eigenvalues. The second correlation function is used to characterize the relationship between one first matching eigenvalue and one second target matching eigenvalue;

[0036] Compare the first correlation function with the multiple second correlation functions, and determine that the second target matching eigenvalue corresponding to the second correlation function with the highest fitting degree is the target second target matching eigenvalue;

[0037] Based on big data, determine multiple second matching eigenvalues that match the second eigenvalue. Based on the multiple second matching eigenvalues, determine multiple first target matching eigenvalues that belong to the same data source as the multiple second matching eigenvalues, and obtain multiple third correlation functions between the multiple second matching eigenvalues and the multiple first target matching eigenvalues. The third correlation function is used to characterize the relationship between one second matching eigenvalue and one first target matching eigenvalue;

[0038] Compare the first correlation function with the multiple third correlation functions, and determine that the first target matching eigenvalue corresponding to the third correlation function with the highest fitting degree is the target first target matching eigenvalue;

[0039] Correct the target data based on the target first target matching eigenvalue and the target second target matching eigenvalue.

[0040] Combined with the second aspect, in some embodiments, the system is configured to:

[0041] Revising target data based on a target first target matching eigenvalue and a target second target matching eigenvalue, including:

[0042] Determining a target correlation function based on the target first target matching eigenvalue and the target second target matching eigenvalue;

[0043] Fitting the target correlation function with a first correlation function and obtaining a revised correlation function according to the fitting result;

[0044] Revising the target data based on the revised correlation function.

[0045] Combined with the second aspect, in some embodiments, the system is configured to:

[0046] Revising the target data based on the revised correlation function, including:

[0047] Determining a second revised eigenvalue based on the revised correlation function and a first eigenvalue;

[0048] Determining a first revised eigenvalue based on the revised correlation function and a second eigenvalue.

[0049] Combined with the second aspect, in some embodiments, the system is configured to:

[0050] Determining at least two different types of basic eigenvalues corresponding to the target data, namely a first eigenvalue and a second eigenvalue, and obtaining a first correlation function between the first eigenvalue and the second eigenvalue, where the first correlation function is used to characterize the relationship between the first eigenvalue and the second eigenvalue, including:

[0051] Obtaining the target data and splitting the target data to obtain multiple sub-data;

[0052] Uploading the multiple sub-data to a cloud server for matching, and according to the result returned by the cloud server, determining at least two sub-data with the highest occurrence frequency in the cloud database corresponding to the cloud server from the multiple sub-data, and using them as the basic eigenvalues.

[0053] Combined with the second aspect, in some embodiments, the system is configured to:

[0054] Uploading the multiple sub-data to a cloud server for matching, and according to the result returned by the cloud server, determining at least two sub-data with the highest occurrence frequency in the database corresponding to the cloud server from the multiple sub-data, and using them as the basic eigenvalues, including:

[0055] Obtaining the corresponding information of the sub-data, where the corresponding information includes the information granularity, information features, and information parameters of the sub-data;

[0056] Retrieve in the cloud database based on the corresponding information, and determine the frequency of occurrence of the sub-data in the cloud database according to the retrieval result.

[0057] Combined with the second aspect, in some embodiments, the system is configured to:

[0058] Determine a plurality of first matching feature values that match the first eigenvalue based on big data, determine a plurality of second target matching feature values that belong to the same data source as the plurality of first matching features based on the plurality of first matching feature values, and obtain 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 is used to characterize the relationship between a first matching feature value and a second target matching feature value, including:

[0059] Retrieve in the cloud database based on the corresponding information of the first eigenvalue, and determine that all the data in the cloud database that match the corresponding information of the first eigenvalue are the first matching feature values.

[0060] Combined with the second aspect, in some embodiments, the system is configured to:

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

[0062] Input the first eigenvalue and the second eigenvalue into the neural network model and perform iterative calculations. Stop the calculations and output the first correlation function when the preset condition is satisfied. The preset condition is that the first correlation function in the iterative calculation process converges to the preset range.

[0063] A third aspect of the embodiments of the present invention proposes 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, and the instructions are executed by the at least one processor so that the at least one processor can execute the method proposed in the first aspect of the embodiments of the present invention.

[0065] A fourth aspect of the embodiments of the present invention proposes a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the method proposed in the first aspect of the embodiments of the present invention.

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

[0067] A method and system for enterprise data management based on big data proposed by this application. After determining the data that needs to be corrected in the enterprise data, at least two different types of basic characteristic values corresponding to the target data are determined, namely the first characteristic value and the second characteristic value, and the first correlation function between the first characteristic value and the second characteristic value is obtained. The first correlation function is used to characterize the relationship between the first characteristic value and the second characteristic value, and the first correlation function between the first characteristic value and the second characteristic value is obtained. Then, based on big data, multiple first matching characteristic values that match the first characteristic value are determined, and multiple second target matching characteristic values belonging to the same data source as the multiple first matching characteristics are determined based on the multiple first matching characteristic values, and multiple second correlation functions between the multiple first matching characteristic values and the multiple second target matching characteristic values are obtained. Then, the first correlation function is compared with the multiple second correlation functions, and the second target matching characteristic value corresponding to the second correlation function with the highest fitting degree is determined as the target second target matching characteristic value. Then, based on big data, multiple second matching characteristic values that match the second characteristic value are determined, and multiple first target matching characteristic values belonging to the same data source as the multiple second matching characteristics are determined based on the multiple second matching characteristic values, and multiple third correlation functions between the multiple second matching characteristic values and the multiple first target matching characteristic values are obtained. Then, the first correlation function is compared with the multiple third correlation functions, and the first target matching characteristic value corresponding to the third correlation function with the highest fitting degree is determined as the target first target matching characteristic value. Finally, the target data is corrected based on the target first target matching characteristic value and the target second target matching characteristic value. A method for enterprise data management based on big data proposed by this application, when correcting data, combines the relationships between multiple data in the data that needs to be corrected, and uses this relationship to match with the data values in the cloud server, and uses the data obtained from the cloud data 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a flowchart of a method for enterprise data management based on big data proposed by an embodiment of this application. DETAILED DESCRIPTION

[0069] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0070] An embodiment of this application proposes a method for enterprise data management based on big data. Please refer toFigure 1 , including the following steps:

[0071] S101: Identify target data from the target database, where the target data is the data that needs to be corrected.

[0072] Specifically, in this embodiment, for enterprise data, it can be factory control data or personnel management data as an example. The cloud database in this embodiment is a shared database of multiple same types. In this embodiment, for the sake of easy understanding, taking personnel salary as an example, in some other embodiments, enterprise data can also be some other data, which is not limited here.

[0073] It can be understood that in this step, the target database is the database of the enterprise itself. When the database is upgraded, the data needs to be corrected, that is, the target data in the database is corrected.

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

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

[0076] Exemplarily, as an implementation manner, the first characteristic value and the second characteristic value can be input into a neural network model and iteratively calculated. When a preset condition is met, the calculation stops and the first correlation function is output, where the preset condition is that the first correlation function in the iterative calculation process converges to a preset range.

[0077] It can be understood that the first characteristic value and the second characteristic value can be input into a carefully designed neural network model and iteratively calculated multiple times. In each iteration process, the neural network will perform complex calculations based on the input characteristic values to update its internal parameters. This process will continue until the preset condition is met. The preset condition means that during the iterative calculation process, the value of the first correlation function gradually converges and finally stabilizes within a certain preset numerical range. Once this condition is met, the calculation will stop and the value of the current first correlation function will be output. This output value represents a certain correlation or relationship between the input characteristic values.

[0078] As a way to determine whether a data value can be used as a characteristic value, the following method can be adopted:

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

[0080] It can be understood that the multiple sub-data can be various information data, which are not limited in this embodiment. Exemplarily, it can be employee salary, employee working years, employee attendance completion, etc., and each value corresponds to a sub-data.

[0081] S1022: Upload the multiple sub-data to the cloud server for matching. According to the result returned by the cloud server, determine at least two sub-data with the highest frequency of occurrence in the cloud database corresponding to the cloud server from the multiple sub-data, and use them as the basic characteristic values.

[0082] It can be understood that since the data in the cloud data is diverse, when the multiple sub-data are matched with the data in the cloud server, the data with a higher frequency of occurrence after matching has a larger base, so it is more accurate in comprehensive consideration. Therefore, in this embodiment, at least two sub-data with the highest frequency of occurrence in the cloud database corresponding to the cloud server can be determined from the multiple sub-data and used as the basic characteristic values. It can be understood that the corresponding information of the sub-data can be obtained, and the corresponding information includes the information granularity, information characteristics, and information parameters of the sub-data; based on the corresponding information, retrieve in the cloud database, and determine the frequency of occurrence of the sub-data in the cloud database according to the retrieval result. Of course, some other methods can also be used, which are not limited here.

[0083] S103: Determine multiple first matching characteristic values that match the first characteristic value based on big data, determine multiple second target matching characteristic values that belong to the same data source as the multiple first matching characteristics based on the multiple first matching characteristic values, and obtain multiple second correlation functions between the multiple first matching characteristic values and the multiple second target matching characteristic values. The second correlation function is used to characterize the relationship between a first matching characteristic value and a second target matching characteristic value.

[0084] It can be understood that in this embodiment, by uploading the enterprise's data to the cloud server, the data can be matched and corrected with the data in the cloud server. Exemplarily, in this embodiment, after uploading the age of the employee to the cloud server, multiple different salaries corresponding to multiple employees of the same age in multiple enterprises can be obtained. Then, in the same way as in step S103, multiple second correlation functions between the multiple first matching characteristic values and the multiple second target matching characteristic values can be obtained.

[0085] As an implementation manner, retrieval can be performed in the cloud database based on the corresponding information of the first eigenvalue, and all the data in the cloud database that match the corresponding information of the first eigenvalue are determined as the first matching eigenvalues.

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

[0087] The comparison and analysis of the first correlation function with multiple second correlation functions is carried out by comparing their similarities and differences one by one, so as to evaluate the fitting degree between each second correlation function and the first correlation function. It can be understood that in this embodiment, by comparing the first correlation function with multiple second correlation functions, other enterprise employees with the most consistent salary construction at the same age in the enterprise can be obtained, and the environmental consistency between the target data and the cloud data is ensured.

[0088] S105: Based on big data, determine multiple second matching eigenvalues that match the second eigenvalue, determine multiple first target matching eigenvalues that belong to the same data source as the multiple second matching eigenvalues based on the multiple second matching eigenvalues, and obtain multiple third correlation functions between the multiple second matching eigenvalues and the multiple first target matching eigenvalues. The third correlation function is used to represent the relationship between a second matching eigenvalue and a first target matching eigenvalue.

[0089] It can be understood that, similar to step S105, in this way, other enterprise employees of other ages with the same salary in the enterprise can be obtained.

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

[0091] It can be understood that a detailed comparison and analysis of the first correlation function with multiple third correlation functions is carried out to evaluate their similarity and fitting degree. Through this comparison, one or more third correlation functions that are closest to the first correlation function and have the highest fitting degree can be found. Among these third correlation functions, a specific function needs to be determined, whose corresponding fitting degree is the highest, so as to ensure that it can accurately reflect the characteristics of the first correlation function. Finally, the third correlation function with the highest fitting degree will be selected, and the first target matching eigenvalue corresponding to it will be determined as the final target first target matching eigenvalue. This process ensures that the eigenvalue that best matches the first correlation function can be found.

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

[0093] It can be understood that after obtaining the target first target matching eigenvalue and the target second target matching eigenvalue, since the target first target matching eigenvalue and the target second target matching eigenvalue are data obtained through function fusion, therefore, in this embodiment, combining the target first target matching eigenvalue and the target second target matching eigenvalue to correct the data of the enterprise can comprehensively consider the comprehensive situation of related enterprises.

[0094] Specifically, as an implementation manner, the second correction eigenvalue can be determined based on the correction correlation function and the first eigenvalue, and the first correction eigenvalue can be determined based on the correction correlation function and the second eigenvalue. The first correction eigenvalue and the second correction eigenvalue obtained in the above manner can be used to replace the first eigenvalue and the second eigenvalue in the original data.

[0095] A method for enterprise data management based on big data proposed in this application, after determining the data that needs to be corrected in enterprise data, determines at least two different types of basic characteristic values corresponding to the target data, namely the first characteristic value and the second characteristic value, and obtains the first correlation function between the first characteristic value and the second characteristic value. The first correlation function is used to characterize the relationship between the first characteristic value and the second characteristic value, and obtains the first correlation function between the first characteristic value and the second characteristic value. Then, based on big data, determines multiple first matching characteristic values that match the first characteristic value, determines multiple second target matching characteristic values that belong to the same data source as the multiple first matching characteristics based on the multiple first matching characteristic values, and obtains multiple second correlation functions between the multiple first matching characteristic values and the multiple second target matching characteristic values. Then, compares the first correlation function with the multiple second correlation functions, and determines the second target matching characteristic value corresponding to the second correlation function with the highest fitting degree as the target second target matching characteristic value. Then, based on big data, determines multiple second matching characteristic values that match the second characteristic value, determines multiple first target matching characteristic values that belong to the same data source as the multiple second matching characteristics based on the multiple second matching characteristic values, and obtains multiple third correlation functions between the multiple second matching characteristic values and the multiple first target matching characteristic values. Then, compares the first correlation function with the multiple third correlation functions, and determines the first target matching characteristic value corresponding to the third correlation function with the highest fitting degree as the target first target matching characteristic value. Finally, corrects the target data based on the target first target matching characteristic value and the target second target matching characteristic value. A method for enterprise data management based on big data proposed in this application, when correcting data, combines the relationships between multiple data in the data that needs to be corrected, and uses this relationship to match with the data values in the cloud server, and uses the data obtained from the cloud data 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.

[0096] Based on the same inventive concept, an embodiment of this application also proposes a system for enterprise data management based on big data, and this system is configured to:

[0097] Confirm the target data from the target database, and the target data is the data that needs to be corrected;

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

[0099] Determine multiple first matching eigenvalues that match the first eigenvalue based on big data, determine multiple second target matching eigenvalues that belong to the same data source as the multiple first matching eigenvalues based on the multiple first matching eigenvalues, and obtain multiple second correlation functions between the multiple first matching eigenvalues and the multiple second target matching eigenvalues. The second correlation function is used to characterize the relationship between a first matching eigenvalue and a second target matching eigenvalue;

[0100] Compare the first correlation function with the multiple second correlation functions, and determine that the second target matching eigenvalue corresponding to the second correlation function with the highest fitting degree is the target second target matching eigenvalue;

[0101] Determine multiple second matching eigenvalues that match the second eigenvalue based on big data, determine multiple first target matching eigenvalues that belong to the same data source as the multiple second matching eigenvalues based on the multiple second matching eigenvalues, and obtain multiple third correlation functions between the multiple second matching eigenvalues and the multiple first target matching eigenvalues. The third correlation function is used to characterize the relationship between a second matching eigenvalue and a first target matching eigenvalue;

[0102] Compare the first correlation function with the multiple third correlation functions, and determine that the first target matching eigenvalue corresponding to the third correlation function with the highest fitting degree is the target first target matching eigenvalue;

[0103] Correct the target data based on the target first target matching eigenvalue and the target second target matching eigenvalue.

[0104] In some embodiments, the system is configured to:

[0105] Correct the target data based on the target first target matching eigenvalue and the target second target matching eigenvalue, including:

[0106] Determine a target correlation function based on the target first target matching eigenvalue and the target second target matching eigenvalue;

[0107] Fit the target correlation function with the first correlation function, and obtain a corrected correlation function according to the fitting result;

[0108] Correct the target data based on the corrected correlation function.

[0109] In some embodiments, the system is configured to:

[0110] Correct the target data based on the corrected correlation function, including:

[0111] Determine a second corrected eigenvalue based on the corrected correlation function and the first eigenvalue;

[0112] Determine the first corrected eigenvalue based on the corrected correlation function and the second eigenvalue.

[0113] In some embodiments, the system is configured to:

[0114] Determine at least two different types of basic eigenvalues corresponding to the target data, namely the first eigenvalue and the second eigenvalue, and obtain the first correlation function between the first eigenvalue and the second eigenvalue. The first correlation function is used to characterize the relationship between the first eigenvalue and the second eigenvalue, including:

[0115] Obtain the target data, and split the target data to obtain multiple sub-data;

[0116] Upload the multiple sub-data to the cloud server for matching. According to the result returned by the cloud server, determine at least two sub-data with the highest frequency of occurrence in the cloud database corresponding to the cloud server from the multiple sub-data, and use them as the basic eigenvalues.

[0117] In some embodiments, the system is configured to:

[0118] Upload the multiple sub-data to the cloud server for matching. According to the result returned by the cloud server, determine at least two sub-data with the highest frequency of occurrence in the database corresponding to the cloud server from the multiple sub-data, and use them as the basic eigenvalues, including:

[0119] Obtain the corresponding information of the sub-data. The corresponding information includes the information granularity, information features, and information parameters of the sub-data;

[0120] Retrieve in the cloud database based on the corresponding information, and determine the frequency of occurrence of the sub-data in the cloud database according to the retrieval result.

[0121] In some embodiments, the system is configured to:

[0122] Based on big data, determine multiple first matching eigenvalues that match the first eigenvalue. Based on the multiple first matching eigenvalues, determine multiple second target matching eigenvalues belonging to the same data source as the multiple first matching eigenvalues, and obtain multiple second correlation functions between the multiple first matching eigenvalues and the multiple second target matching eigenvalues. The second correlation function is used to characterize the relationship between a first matching eigenvalue and a second target matching eigenvalue, including:

[0123] Retrieve in the cloud database based on the corresponding information of the first eigenvalue, and determine all the data in the cloud database that match the corresponding information of the first eigenvalue as the first matching eigenvalues.

[0124] A method for managing enterprise data based on big data proposed in this application, after determining the data that needs to be corrected in the enterprise data, determines at least two different types of basic feature values corresponding to the target data, namely the first feature value and the second feature value, and obtains the 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, and obtains the first correlation function between the first feature value and the second feature value. Then, based on big data, determines multiple first matching feature values that match the first feature value, determines multiple second target matching feature values that belong to the same data source as the multiple first matching features based on the multiple first matching feature values, and obtains multiple second correlation functions between the multiple first matching feature values and the multiple second target matching feature values. Then, compares the first correlation function with the multiple second correlation functions, and determines 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. Then, based on big data, determines multiple second matching feature values that match the second feature value, determines multiple first target matching feature values that belong to the same data source as the multiple second matching features based on the multiple second matching feature values, and obtains multiple third correlation functions between the multiple second matching feature values and the multiple first target matching feature values. Then, compares the first correlation function with the multiple third correlation functions, and determines 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. Finally, corrects the target data based on the target first target matching feature value and the target second target matching feature value. A system for managing enterprise data based on big data proposed in this application, when correcting data, combines the relationships between multiple data in the data that needs to be corrected, and uses this relationship to match with the data values in the cloud server, and uses the data obtained from the cloud data 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.

[0125] Based on the same inventive concept, an embodiment of the present application also proposes an electronic device, the electronic device 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, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for managing enterprise data based on big data according to the embodiment of the present application.

[0127] In addition, to achieve the above object, an embodiment of the present application also proposes a computer-readable storage medium, storing a computer program, and when the computer program is executed by a processor, it implements the method for managing enterprise data based on big data according to the embodiment of the present application.

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

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

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

[0131] Among them, the memory is used to store the software program for implementing the solution of the present invention and is controlled by the processor for execution. The specific implementation method can refer to the above method embodiments and will not be elaborated here.

[0132] Optionally, the memory can be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or can also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory can be integrated with the processor or exist independently and be coupled to the processor through the interface circuit of the electronic device. The embodiments of the present invention do not make specific limitations on this.

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

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

[0135] Optionally, the transceiver may be integrated with the processor or exist independently and be coupled to the processor through the interface circuit of the router. The embodiments of the present invention do not make specific limitations on this.

[0136] In addition, for the technical effects of the electronic device, reference may be made to the technical effects of the data transmission method in the foregoing method embodiments, which will not be elaborated herein.

[0137] It should be understood that the processor in the embodiments of the present invention may be a central processing unit (CPU), and the processor may also 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 may be a microprocessor or the processor may also be any conventional processor, etc.

[0138] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory. The volatile memory may be a 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 SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink 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 combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The 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, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, or a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0140] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood with reference to the context.

[0141] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0142] It should be understood that in various embodiments of the present invention, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0143] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

Claims

1. An enterprise data management method based on big data, characterized in that, The method includes: Identifying target data from a target database, where the target data is the data that needs to be corrected; Determining at least two different types of basic feature values corresponding to the target data, namely a first feature value and a second feature value respectively, and obtaining a first correlation function between the first feature value and the second feature value, where the first correlation function is used to characterize the relationship between the first feature value and the second feature value; Based on big data, determining a plurality of first matching feature values that match the first feature value, determining a plurality of second target matching feature values that belong to the same data source as the plurality of first matching features 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, where the second correlation function is used to characterize the relationship between one first matching feature value and one second target matching feature value; Comparing the first correlation function with the plurality of second correlation functions, and determining 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; Based on big data, determining a plurality of second matching feature values that match the second feature value, determining a plurality of first target matching feature values that belong to the same data source as the plurality of second matching features based on the plurality of second matching feature values, and obtaining a plurality of third correlation functions between the plurality of second matching feature values and the plurality of first target matching feature values, where the third correlation function is used to characterize the relationship between one second matching feature value and one first target matching feature value; Comparing the first correlation function with the plurality of third correlation functions, and determining 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; Correcting the target data based on the target first target matching feature value and the target second target matching feature value.

2. The enterprise data management method based on big data according to claim 1, characterized in that Correcting the target data based on the target first target matching feature value and the target second target matching feature value includes: 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 enterprise data management method based on big data according to claim 2, characterized in that, Correcting the target data based on the corrected correlation function includes: 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. A method for enterprise data management based on big data according to claim 1, characterized in that, Determining at least two different types of basic feature values corresponding to the target data, namely a first feature value and a second feature value respectively, and obtaining a first correlation function between the first feature value and the second feature value, where the first correlation function is used to characterize the relationship between the first feature value and the second feature value, includes: Obtaining the target data, and splitting the target data to obtain a plurality of sub-data; Upload multiple pieces of the sub-data to a cloud server for matching. According to the result returned by the cloud server, determine at least two pieces of the sub-data with the highest occurrence frequency in the cloud database corresponding to the cloud server from the multiple pieces of the sub-data, and use them as the basic feature values.

5. The enterprise data management method based on big data according to claim 4, characterized in that, Upload multiple pieces of the sub-data to a cloud server for matching. According to the result returned by the cloud server, determine at least two pieces of the sub-data with the highest occurrence frequency in the database corresponding to the cloud server from the multiple pieces of the sub-data, and use them as the basic feature values, including: Obtain the corresponding information of the sub-data, where the corresponding information includes the information granularity, information features, and information parameters of the sub-data; Based on the corresponding information, perform a retrieval in the cloud database, and determine the occurrence frequency of the sub-data in the cloud database according to the retrieval result.

6. The enterprise data management method based on big data according to claim 5, characterized in that 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 features, and obtain multiple second correlation functions between the multiple first matching feature values and the multiple second target matching feature values. The second correlation function is used to characterize the relationship between one first matching feature value and one second target matching feature value, including: Based on the corresponding information of the first feature value, perform a retrieval in the cloud database, and determine that all the data in the cloud database that matches the corresponding information of the first feature value is the first matching feature value.

7. A method for enterprise data management based on big data according to any one of claims 1-6, characterized in that, Determine at least two different types of basic feature values corresponding to the target data, which are the first feature value and the second feature value respectively, and obtain the 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: Input the first feature value and the second feature value into a neural network model for iterative calculation. When a preset condition is met, stop the calculation and output the first correlation function, where the preset condition is that the first correlation function in the iterative calculation process converges to a preset range.

8. An enterprise data management system based on big data, characterized in that, The system is configured to: Identify target data from a target database, where the target data is the data that needs to be corrected; Determine at least two different types of basic feature values corresponding to the target data, which are the first feature value and the second feature value respectively, and obtain the 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; Determine a plurality of first matching feature values that match the first eigenvalue based on big data, determine a plurality of second target matching feature values that belong to the same data source as the plurality of first matching features based on the plurality of first matching feature values, and obtain a plurality of second correlation functions between the plurality of first matching feature values and the plurality of second target matching feature values, where the second correlation function is used to characterize the relationship between one first matching feature value and one second target matching feature value; Compare the first correlation function with the plurality of second correlation functions, and determine that the second target matching feature value corresponding to the second correlation function with the highest fitting degree is the target second target matching feature value; Determine a plurality of second matching feature values that match the second eigenvalue based on big data, determine a plurality of first target matching feature values that belong to the same data source as the plurality of second matching features based on the plurality of second matching feature values, and obtain a plurality of third correlation functions between the plurality of second matching feature values and the plurality of first target matching feature values, where the third correlation function is used to characterize the relationship between one second matching feature value and one first target matching feature value; Compare the first correlation function with the plurality of third correlation functions, and determine that the first target matching feature value corresponding to the third correlation function with the highest fitting degree is the target first target matching feature value; Correct the target data based on the target first target matching feature value and the target second target matching feature value.

9. An enterprise data management system based on big data according to claim 8, characterized in that, The system is configured to: Correct the target data based on the target first target matching feature value and the target second target matching feature value, including: Determine a target correlation function based on the target first target matching feature value and the target second target matching feature value; Fit the target correlation function with the first correlation function, and obtain a corrected correlation function according to the fitting result; Correct the target data based on the corrected correlation function.

10. An electronic device, characterized in that, Comprising: At least one processor; And a memory communicatively connected to at least one of the processors; Wherein, the memory stores instructions executable by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can execute the method according to any one of claims 1-7.

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