Data asset management method and system

Abstract

The present invention belongs to the technical field of data asset management, and specifically relates to a data asset management method and system. The invention realizes efficient integration and optimized monitoring of data assets, thereby improving the utilization and management efficiency of data assets. Through association analysis and weight calculation, it can accurately identify and integrate duplicate data, avoid data redundancy, and ensure the accuracy and consistency of data assets. At the same time, by collecting call information and assigning monitoring levels, it can monitor the use of data assets in real time, discover potential problems in time and make optimization adjustments, thereby ensuring the security and reliability of data assets.

Description

Technical Field

[0001] The present invention belongs to the technical field of data asset management, and specifically relates to a data asset management method and system. Background Art

[0002] With the explosive growth of data volume, the management of data assets has become increasingly important. Traditional data asset management methods are often unable to effectively cope with large-scale and diversified data assets, resulting in waste of data resources and inefficient management. In order to solve these problems, it is particularly important to optimize and innovate data asset management.

[0003] In the prior art, there are some data asset management methods, but they usually lack in-depth analysis of the correlation between data assets, resulting in the inability to fully tap the potential value of data assets, and the processing of unintegrated data is cumbersome. Correspondingly, its fault tolerance rate will become lower, and due to the large amount of data, it is not conducive to monitoring and management, making it difficult to track and evaluate the use of data assets, which in turn affects the optimal configuration of data assets and decision support. Based on this, this solution provides a data asset management method to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a data asset management method and system that can effectively analyze the correlation between data assets and dynamically adjust the monitoring level according to the actual usage of data assets to improve the utilization efficiency and management efficiency of data resources.

[0005] The technical solution adopted by the present invention is as follows:

[0006] Data asset management methods, including:

[0007] Acquire data asset information, and classify and label the data asset information to obtain multiple data asset directories;

[0008] Performing correlation analysis on all the data asset directories to obtain multiple correlation directories;

[0009] According to the association relationship between each of the associated directories, the data asset information under the associated directories is integrated to obtain multiple integrated data asset packages;

[0010] Collecting the call information of each of the integrated data asset packages, and assigning a monitoring level to the integrated data asset package according to the call information;

[0011] Each of the integrated data asset packages is monitored in a hierarchical manner according to the monitoring level, and a monitoring record is generated. The monitoring level of each of the integrated data asset packages is optimized according to the monitoring record.

[0012] In a preferred solution, the step of obtaining data asset information, classifying and labeling the data asset information, and obtaining multiple data asset directories includes:

[0013] Acquire the data asset information, wherein the data asset information includes data type, data source, and data usage;

[0014] Classify the data asset information according to the data type, and aggregate the data asset information of the same type into independent information subsets;

[0015] Adding feature tags to the independent information subsets, wherein the feature tags corresponding to each independent information subset are unique, so as to distinguish different data asset information subsets;

[0016] The feature tags are associated with corresponding subsets of data asset information to form a data asset catalog.

[0017] In a preferred solution, the step of performing correlation analysis on all the data asset directories to obtain a plurality of correlation directories includes:

[0018] Grouping the plurality of data asset directories in pairs to obtain a plurality of data asset directory groups;

[0019] Vectorizing the feature labels of the independent information subsets under each of the data asset directories to obtain multiple feature vectors;

[0020] Obtaining an association analysis function, inputting feature vectors under a plurality of data asset directory groups into the association analysis function, and recording an output result of the association analysis function as an association score;

[0021] obtaining a screening threshold, and comparing the screening threshold with the association score;

[0022] If the association score is greater than the screening threshold, the data asset directory in the data asset directory group corresponding to the association score is marked as an associated directory;

[0023] If the association score is less than or equal to the screening threshold, the data asset directory in the data asset directory group corresponding to the association score is marked as an independent directory.

[0024] In a preferred solution, the step of integrating the data asset information under the associated directories according to the association relationship between the associated directories to obtain multiple integrated data asset packages includes:

[0025] Obtain all the associated directories and duplicate directory items under the associated directories;

[0026] Calculating the association weight of the repeated catalog item in each of the associated catalogs and marking it as a screening parameter;

[0027] Arrange the screening parameters in descending order, retain the duplicate catalog items in the associated catalog corresponding to the screening parameter with the highest ranking, and delete the duplicate catalog items from the associated catalog corresponding to the screening parameter with a ranking smaller than the highest ranking, to obtain an updated catalog;

[0028] The data asset information subsets under each of the update directories are merged and aggregated to form multiple independent integrated data asset packages.

[0029] In a preferred solution, the step of calculating the association weight of the repeated directory item in each of the associated directories includes:

[0030] Acquire the repeated catalog item and the association score between the repeated catalog item and each of the associated catalogs;

[0031] Obtaining the number of concurrent accesses between the repeated directory item and each of the associated directories;

[0032] A weight calculation function is obtained, and the association score and the number of concurrent access times are input into the weight calculation function, and the output result of the weight calculation function is marked as the association weight.

[0033] In a preferred solution, the step of collecting the call information of each integrated data asset package and assigning a monitoring level to the integrated data asset package according to the call information includes:

[0034] Establishing a monitoring period, and collecting the call information of each integrated data asset package within the monitoring period, wherein the call information includes the call volume and the call duration;

[0035] Normalizing the call volume and call duration of the integrated data asset package to the same dimension to obtain a first condition parameter corresponding to the call volume and a second condition parameter corresponding to the call duration;

[0036] Obtaining an evaluation function, and inputting the first condition parameter and the second condition parameter into the evaluation function together, and marking the output result of the evaluation function as a monitoring evaluation score;

[0037] Obtaining an evaluation interval, wherein a plurality of evaluation intervals are provided, and each evaluation interval corresponds to a monitoring level;

[0038] The monitoring evaluation score is compared with the evaluation interval, the monitoring level corresponding to the monitoring evaluation score is matched, and the monitoring level is synchronously allocated to the integrated data asset package corresponding to the monitoring evaluation score.

[0039] In a preferred solution, when collecting the call information, the node where the call information occurs is recorded as the call node;

[0040] Collecting the time interval between adjacent calling nodes and recording it as the calling interval;

[0041] Obtaining a standard call interval, and comparing the standard call interval with the call interval;

[0042] If the call interval is greater than the standard call interval, the call information under the corresponding adjacent call node is directly recorded as independent call information;

[0043] If the call interval is less than or equal to the standard call interval, determine whether the call information under the corresponding adjacent call nodes is called by the same calling user, and if it is the same calling user, merge the call information under the corresponding adjacent call nodes into independent call information; otherwise, record the call information under the corresponding adjacent call nodes as independent call information;

[0044] Counting the number of calls of all the independent call information and recording it as the call volume;

[0045] The single browsing durations corresponding to all the independent call information are counted and summarized as the call duration.

[0046] In a preferred solution, the step of optimizing the monitoring level of each of the integrated data asset packages according to the monitoring records includes:

[0047] Acquire monitoring records of each integrated data asset package within the monitoring period, wherein the monitoring records include monitoring levels and monitoring evaluation scores;

[0048] Arranging the monitoring evaluation scores according to the chronological order of occurrence;

[0049] Obtaining an optimization condition function, and inputting the arranged monitoring evaluation scores into the optimization condition function one by one, and recording the output result of the optimization condition function as a ranking score;

[0050] The ranking score is converted to be consistent with the monitoring evaluation score dimension, and then the converted ranking score is matched with the evaluation interval, and the monitoring level of each integrated data asset package is reallocated according to the matching result.

[0051] The present invention also provides a data asset management system, using the above data asset management method, including:

[0052] An initialization module, the initialization module is used to obtain data asset information, and classify and label the data asset information to obtain multiple data asset directories;

[0053] A correlation analysis module, the correlation analysis module is used to perform correlation analysis on all the data asset directories to obtain multiple correlation directories;

[0054] A data integration module, the data integration module is used to integrate the data asset information under the associated directories according to the association relationship between the associated directories to obtain multiple integrated data asset packages;

[0055] An allocation module, the allocation module is used to collect the call information of each integrated data asset package and allocate a monitoring level to the integrated data asset package according to the call information;

[0056] An optimization module, wherein the optimization module is used to perform hierarchical monitoring on each of the integrated data asset packages according to the monitoring level, generate monitoring records, and optimize the monitoring level of each of the integrated data asset packages according to the monitoring records.

[0057] And, an electronic device, the electronic device comprising:

[0058] at least one processor;

[0059] and a memory communicatively coupled to the at least one processor;

[0060] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned data asset management method.

[0061] The technical effects achieved by the present invention are:

[0062] The data asset management method provided by the present invention realizes the efficient integration and optimized monitoring of data assets, thereby improving the utilization efficiency and management efficiency of data assets. Through association analysis and weight calculation, it can accurately identify and integrate duplicate data, avoid data redundancy, and ensure the accuracy and consistency of data assets. At the same time, by collecting call information and assigning monitoring levels, it can monitor the use of data assets in real time, discover potential problems in time and make optimization adjustments, thereby ensuring the security and reliability of data assets. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a schematic flow chart of the method of the present invention;

[0064] Figure 2 It is a schematic diagram of the system module of the present invention;

[0065] Figure 3 It is a schematic diagram of the structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0066] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0067] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0068] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The phrase "in a preferred embodiment" that appears in different places in this specification does not refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0069] See also Figure 1 As shown, the present invention provides a data asset management method, including:

[0070] S1. Obtain data asset information, classify and label the data asset information, and obtain multiple data asset directories;

[0071] S2. Perform correlation analysis on all data asset directories to obtain multiple correlation directories;

[0072] S3. Integrate the data asset information under the associated directories according to the association relationship between the associated directories to obtain multiple integrated data asset packages;

[0073] S4. Collect the call information of each integrated data asset package, and assign a monitoring level to the integrated data asset package according to the call information;

[0074] S5. Perform hierarchical monitoring on each integrated data asset package according to the monitoring level, generate monitoring records, and optimize the monitoring level of each integrated data asset package based on the monitoring records.

[0075] As described in the above steps S1-S5, with the continuous growth and diversification of data assets, traditional data management methods can no longer meet the needs of modern enterprises. In order to improve the efficiency and security of data assets, this embodiment provides a data asset management method. Through classification, association analysis, integration processing and monitoring optimization, effective management of data assets is achieved. First, it is necessary to collect and organize data asset information, and classify and label the data asset information accordingly to generate multiple structured data asset directories. After obtaining the data asset directory, it is necessary to perform association analysis to identify the potential connections between the data asset directories. Through association analysis, multiple associated directories can be obtained to facilitate understanding of the interaction between data assets. Then, based on the association relationship between each associated directory, the data asset information under the associated directory is integrated. The goal is to integrate the scattered data asset information into multiple integrated data asset packages to facilitate more efficient management of data resources. The generation of integrated data asset packages is a key step in data asset management, which can improve the utilization efficiency and value of data. In order to ensure the efficient operation and security of the integrated data asset packages, it is also necessary to collect the call information of each integrated data asset package. Based on the call information, the integrated data asset package can be assigned a monitoring level. The setting of the monitoring level is to ensure the reasonable allocation of monitoring resources during the use of the data asset package, thereby ensuring the security and stability of data assets. After the monitoring level of the integrated data asset package is determined, each integrated data asset package will be graded and monitored according to the monitoring level, so as to generate real-time tracking and recording of the operating status and performance of the data asset package. The generation of monitoring records not only helps to discover and solve problems in a timely manner, but also provides a basis for subsequent optimization of monitoring levels. Through the analysis and evaluation of the monitoring records, the monitoring level of each integrated data asset package can be continuously adjusted and optimized to ensure the efficient and safe operation of data assets.

[0076] In a preferred embodiment, the steps of obtaining data asset information, classifying and labeling the data asset information, and obtaining multiple data asset directories include:

[0077] S101. Obtain data asset information, which includes data type, data source, and data usage;

[0078] S102, classify the data asset information according to the data type, and aggregate the data asset information of the same type into independent information subsets;

[0079] S103, adding feature tags to the independent information subsets, wherein the feature tags corresponding to each independent information subset are unique to distinguish different data asset information subsets;

[0080] S104: Associate the feature tags with corresponding data asset information subsets to form a data asset catalog.

[0081] As described in the above steps S101-S104, in order to effectively manage and utilize data assets, it is necessary to summarize multiple data asset catalogs to facilitate the classification and management of data assets. First, it is necessary to comprehensively obtain data asset information. Data asset information includes multiple aspects such as data type, source and purpose. The data type may involve structured data, unstructured data or semi-structured data. The data source may include internal systems, external partners or public data sets, etc. The data purpose may involve business analysis, decision support or customer service, etc. After that, the acquired data asset information will be classified and processed according to the data type, so that the data asset information of the same type can be aggregated together to form an independent data asset information subset. For example, all structured data can be classified into one subset, and all unstructured data can be classified into another subset. , which helps to manage different types of data. On the basis of classification, it is necessary to add feature tags to each independent information subset. The feature tags are unique and can clearly distinguish different data asset information subsets. For example, for a data asset subset containing customer information, you can add feature tags such as "customer information" and "privacy protection". Feature tags not only help to quickly identify the content of the data subset, but also provide corresponding convenience for subsequent data management and use. Finally, the feature tags are associated with the corresponding data asset information subsets to form a data asset directory. The data asset directory is a structured information collection. It not only contains the basic information of the data assets, but also contains the classification and labeling information of the data assets. Through the data asset directory, you can quickly find and use the required data assets, thereby improving the utilization efficiency and value of the data.

[0082] In a preferred embodiment, the step of performing correlation analysis on all data asset directories to obtain multiple correlation directories includes:

[0083] S201, grouping multiple data asset directories in pairs to obtain multiple data asset directory groups;

[0084] S202, performing vectorization processing on the feature labels of the independent information subsets under each data asset directory to obtain multiple feature vectors;

[0085] S203, obtaining an association analysis function, and inputting feature vectors under multiple data asset directory groups into the association analysis function, and recording the output result of the association analysis function as an association score;

[0086] S204, obtaining a screening threshold, and comparing the screening threshold with the correlation score;

[0087] If the correlation score is greater than the screening threshold, the data asset directory in the data asset directory group corresponding to the correlation score is marked as a correlation directory;

[0088] If the correlation score is less than or equal to the screening threshold, the data asset directory in the data asset directory group corresponding to the correlation score is marked as an independent directory.

[0089] As described in the above steps S201-S204, after the data asset directory is output, the association relationship between each data asset directory will be analyzed. First, all existing data asset directories are grouped in pairs to form multiple data asset directory groups. This process involves pairing each data asset directory with other data asset directories to create multiple combinations of two data asset directories. For example, if there are five directories, ten different directory pairings can be obtained. Then, it is necessary to vectorize the feature labels of the independent information subsets under each data asset directory, and convert the feature labels of each independent information subset into feature vectors in numerical form. After the feature vectorization processing is completed, a preset association analysis function is introduced to calculate the association between the data asset directory groups. The expression of the association analysis function is:

[0090] ;

[0091] In the formula, Indicates the association score between the same set of data asset directories. represents the number of feature points in the feature vector, and The characteristic vectors representing the same group of data asset directories respectively can be outputted through the calculation of the association analysis function to reflect the degree of association between the data self-examination directories in each data asset directory group. A preset screening threshold will be introduced later. The screening threshold will be used as a judgment standard to evaluate whether the association score is high enough to indicate whether there is a significant association relationship between the directory groups. If the association score of the data asset directories in the data asset directory group is higher than the screening threshold, then it can be determined that there is an association relationship between the data asset directories in the data asset directory group, and they are synchronously marked as associated directories. On the contrary, it indicates that there is no significant association relationship between the data asset directories in the data asset directory group. At this time, the data asset directories will be marked as independent directories.

[0092] In a preferred embodiment, according to the association relationship between each associated directory, the data asset information under the associated directory is integrated to obtain multiple integrated data asset packages, including:

[0093] S301, obtaining all associated directories and duplicate directory items under the associated directories;

[0094] S302, calculating the association weights of the duplicate directory items in each associated directory and marking them as screening parameters;

[0095] S303, arranging the screening parameters in descending order, retaining duplicate catalog items in the associated catalog corresponding to the screening parameter with the highest ranking, and deleting duplicate catalog items from the associated catalog corresponding to the screening parameter with a ranking smaller than the highest ranking, to obtain an updated catalog;

[0096] S304: merge and aggregate the data asset information subsets under each update directory to form multiple independent integrated data asset packages.

[0097] As described in the above steps S301-S304, after the associated directories and independent directories are output, further data integration processing will be performed to output multiple integrated data asset packages. First, all associated directories need to be obtained and reviewed accordingly to identify duplicate directory items. Then, the associated weights of the duplicate directory items in each associated directory are calculated, and used as screening parameters in the subsequent screening process. The screening parameters are then sorted according to the size of the values ​​to determine which group of associated directories the duplicate directory items should be retained in. At the same time, the duplicate directory items are deleted from other associated directories. In this way, an updated directory can be obtained. Finally, the data asset information subsets under each updated directory are merged and aggregated to form multiple independent integrated data asset packages, which provide corresponding data support for subsequent data analysis and application.

[0098] In a preferred embodiment, the step of calculating the association weights of the duplicate directory items in each associated directory includes:

[0099] Obtaining duplicate directory items and correlation scores between the duplicate directory items and each associated directory;

[0100] Get the number of concurrent accesses between the duplicate directory item and each associated directory;

[0101] Obtain a weight calculation function, input the associated score and the number of concurrent accesses into the weight calculation function, and mark the output result of the weight calculation function as the associated weight.

[0102] In this implementation, in order to calculate the association weight of the duplicate directory item in each associated directory, it is first necessary to obtain the association score of the duplicate directory item in different associated directories, as well as the number of concurrent accesses between the duplicate directory item and each associated directory. The number of concurrent accesses can be used as an indicator to measure the attention paid by the associated directory to the duplicate directory item, and the more accesses, the higher the attention paid by the associated directory to the duplicate directory item. Then, a weight calculation function is introduced. The weight calculation function comprehensively considers the association score and the number of concurrent accesses to calculate the comprehensive association weight of each associated directory to the duplicate directory item, wherein the expression of the weight calculation function is: The expression of the weight calculation function is: , where represents the association weight, and They represent the weight coefficients of the number of concurrent readings and the associated scores, It indicates the number of times the data is accessed during the same period. After calculating the associated weight, the update catalog can be output to facilitate subsequent data asset integration processing.

[0103] In a preferred embodiment, the steps of collecting the call information of each integrated data asset package and assigning a monitoring level to the integrated data asset package according to the call information include:

[0104] S401, construct a monitoring period, and collect call information of each integrated data asset package within the monitoring period, the call information including call volume and call duration;

[0105] S402, normalizing the call volume and call duration of the integrated data asset package to the same dimension, and obtaining a first condition parameter corresponding to the call volume and a second condition parameter corresponding to the call duration;

[0106] S403, obtaining an evaluation function, and inputting the first condition parameter and the second condition parameter into the evaluation function together, and marking the output result of the evaluation function as a monitoring evaluation score;

[0107] S404, obtaining an evaluation interval, wherein a plurality of evaluation intervals are provided, and each evaluation interval corresponds to a monitoring level;

[0108] S405: Compare the monitoring evaluation score with the evaluation interval, match the monitoring level corresponding to the monitoring evaluation score, and synchronously allocate it to the integrated data asset package corresponding to the monitoring evaluation score.

[0109] As mentioned above, in order to ensure effective monitoring of each integrated data asset package, a monitoring period is first constructed. The monitoring period can be fixed or dynamically set according to actual needs. During the monitoring period, the call information of each integrated data asset package needs to be collected. In this embodiment, the call information includes the call volume and the call duration. The call volume refers to the number of times each integrated data asset package is called during the monitoring period, and the call duration refers to the length of time these asset packages are used. Then, the call volume and the call duration need to be normalized. The purpose of normalization is to convert the call volume and the call duration to the same dimension, so as to obtain the first condition parameter corresponding to the call volume and the second condition parameter corresponding to the call duration, so as to compare and analyze the call conditions of different asset packages under the same standard. After the first condition parameter and the second condition parameter are output, a preset evaluation function is simultaneously introduced to comprehensively measure the first condition parameter and the second condition parameter, wherein the expression of the evaluation function is: , where Indicates the monitoring evaluation score, represents the weight factor of the first condition parameter, represents the weight factor of the second condition parameter, represents the first condition parameter, Represents the second condition parameter. The monitoring evaluation score is a comprehensive evaluation of the call of the integrated data asset package, which can reflect the usage and importance of the integrated data asset package during the monitoring period. In order to convert the monitoring evaluation score into a specific monitoring level, this implementation method pre-sets multiple evaluation intervals, and each evaluation interval corresponds to a corresponding monitoring level. For example, a high-risk interval, a medium-risk interval and a low-risk interval can be set to achieve the allocation of different monitoring levels, thereby achieving effective monitoring and management of the integrated data asset package.

[0110] In a preferred embodiment, when collecting the call information, the node where the call information occurs is recorded as the call node;

[0111] Collect the time interval between adjacent call nodes and record it as the call interval;

[0112] Get the standard call interval and compare the standard call interval with the call interval;

[0113] If the call interval is greater than the standard call interval, the call information under the corresponding adjacent call node is directly recorded as independent call information;

[0114] If the call interval is less than or equal to the standard call interval, determine whether the call information under the corresponding adjacent call nodes is called by the same calling user. If it is the same calling user, merge the call information under the corresponding adjacent call nodes into independent call information. Otherwise, record the call information under the corresponding adjacent call nodes as independent call information.

[0115] Count the number of calls of all independent call information and record it as the call volume;

[0116] Count the single browsing duration corresponding to all independent call information and summarize them as call duration.

[0117] In this implementation, during the process of collecting call information, it is necessary to record the specific occurrence node of each call and mark the occurrence node as a call node. At the same time, it is also necessary to pay attention to the time intervals between adjacent call nodes and record these time intervals as call intervals. In order to further analyze the call information, a preset standard call interval is introduced to compare with the call interval. If it is found that a certain call interval is greater than the standard call interval, the call information under the adjacent call node corresponding to the call interval is directly marked as independent call information. However, if the call interval is less than or equal to the standard call interval, it is necessary to further analyze the call information. Specifically, it is necessary to determine whether the call information is initiated by the same calling user. If it is confirmed that the call is made by the same user, then these call information can be merged into an independent call information. If the call is not initiated by the same user, then the call information under the adjacent call nodes needs to be recorded as independent call information. In this way, the number of calls of all independent call information can be counted and recorded as the call volume. In addition, it is also necessary to count the single browsing time corresponding to all independent call information, and add these browsing times to summarize them as the total call time, so as to provide corresponding pre-data support for the allocation of monitoring levels.

[0118] In a preferred embodiment, the step of optimizing the monitoring level of each integrated data asset package according to the monitoring records includes:

[0119] S501. Obtain monitoring records of each integrated data asset package within the monitoring period, where the monitoring records include monitoring levels and monitoring evaluation scores;

[0120] S502, arranging the monitoring evaluation scores according to the chronological order of occurrence;

[0121] S503, obtaining an optimization condition function, and inputting the arranged monitoring evaluation scores into the optimization condition function one by one, and recording the output result of the optimization condition function as a ranking score;

[0122] S504: Convert the ranking score to be consistent with the monitoring evaluation score dimension, then match the converted ranking score with the evaluation interval, and reallocate the monitoring level of each integrated data asset package based on the matching result.

[0123] As described in the above steps S501-S504, in order to optimize the monitoring level of each integrated data asset package, it is first necessary to obtain the monitoring records of each integrated data asset package within a specific monitoring period. The monitoring records not only include the monitoring level of each integrated data asset package, but also the corresponding monitoring evaluation scores. Then, the monitoring evaluation scores are arranged in chronological order to reflect the performance of each integrated data asset package in different time periods. After the monitoring evaluation scores are sorted, a preset optimization condition function is introduced, and the arranged monitoring evaluation scores are input into the optimization condition function one by one, wherein the expression of the optimization condition function is: , where represents the ranking score, Indicates the monitoring evaluation score of the integrated data asset package at the current moment, Indicates the length of the monitoring period, Indicates the time interval between calling nodes. Indicates the number of monitoring evaluation scores within the monitoring period, and It represents the monitoring evaluation scores under adjacent call nodes. After the ranking scores are output, the ranking scores will be matched with the evaluation intervals to adjust the monitoring level of each asset package to ensure that the monitoring level of each asset package matches its actual performance and that monitoring resources are reasonably allocated.

[0124] See also Figure 2 The present invention also provides a data asset management system, using the above data asset management method, including:

[0125] Initialization module: The initialization module is used to obtain data asset information, classify and label the data asset information, and obtain multiple data asset directories;

[0126] The association analysis module is used to perform association analysis on all data asset directories to obtain multiple association directories;

[0127] The data integration module is used to integrate the data asset information under the associated directories according to the association relationship between the associated directories to obtain multiple integrated data asset packages;

[0128] The allocation module is used to collect the call information of each integrated data asset package and allocate the monitoring level to the integrated data asset package according to the call information;

[0129] The optimization module is used to perform hierarchical monitoring on each integrated data asset package according to the monitoring level, generate monitoring records, and optimize the monitoring level of each integrated data asset package according to the monitoring records.

[0130] In the above, the system includes an initialization module, an association analysis module, a data integration module, an allocation module and an optimization module. The initialization module is responsible for obtaining data asset information and classifying and labeling the data asset information accordingly, so as to generate multiple data asset directories. The association analysis module is used to perform corresponding association analysis on all generated data asset directories. Through association analysis, the association relationship between different data assets can be identified, thereby generating multiple association directories, which helps to reveal the internal connection between data assets and provide a basis for data integration. The data integration module integrates the data asset information under the association directory according to the association relationship revealed in each association directory. Through this process, the system can generate multiple integrated data asset packages, and the integrated data asset package will be more complete and systematic, which is convenient for subsequent management and use. The allocation module is responsible for collecting the call information of each integrated data asset package. Based on the call information, the system can assign the corresponding monitoring level to the integrated data asset package. The main function of the optimization module is to perform hierarchical monitoring on each integrated data asset package according to the monitoring level. Through hierarchical monitoring, the system can generate detailed monitoring records, and provide a basis for optimizing the monitoring level based on the monitoring records, so that the monitoring level of each integrated data asset package can be dynamically adjusted to ensure the safety and efficient use of data assets.

[0131] See also Figure 3 , an electronic device, the electronic device comprising:

[0132] at least one processor;

[0133] and a memory communicatively coupled to the at least one processor;

[0134] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the above-mentioned data asset management method.

[0135] The processor of the above-mentioned electronic device may be a central processing unit (CPU), a graphics processing unit (GPU) or other special-purpose processor, the memory may include random access memory (RAM), read-only memory (ROM), flash memory or other forms of storage devices, the computer program may include an operating system, an application or other software modules, and these modules can implement each step in the data asset management method. The electronic device may also include an arithmetic unit and an input / output (I / O) interface. The arithmetic unit is responsible for executing instructions issued by the processor and processing and calculating data, and the input / output (I / O) interface is used to connect external devices such as a keyboard, a mouse, a display, a printer, etc.

[0136] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0137] The above is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be considered as the protection scope of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention shall be implemented according to the conventional means in the art unless otherwise specified and limited.

Claims

1. A data asset management method, characterized in that: include: Acquire data asset information, and classify and label the data asset information to obtain multiple data asset directories; Performing correlation analysis on all the data asset directories to obtain multiple correlation directories; According to the association relationship between each of the associated directories, the data asset information under the associated directories is integrated to obtain multiple integrated data asset packages; Collecting the call information of each of the integrated data asset packages, and assigning a monitoring level to the integrated data asset package according to the call information; Performing hierarchical monitoring on each of the integrated data asset packages according to the monitoring level, generating a monitoring record, and optimizing the monitoring level of each of the integrated data asset packages according to the monitoring record; The step of collecting the call information of each integrated data asset package and assigning a monitoring level to the integrated data asset package according to the call information includes: Establishing a monitoring period, and collecting the call information of each integrated data asset package within the monitoring period, wherein the call information includes the call volume and the call duration; Normalizing the call volume and call duration of the integrated data asset package to the same dimension to obtain a first condition parameter corresponding to the call volume and a second condition parameter corresponding to the call duration; Obtaining an evaluation function, and inputting the first condition parameter and the second condition parameter into the evaluation function together, and marking the output result of the evaluation function as a monitoring evaluation score; Obtaining an evaluation interval, wherein a plurality of evaluation intervals are provided, and each evaluation interval corresponds to a monitoring level; Compare the monitoring evaluation score with the evaluation interval, match the monitoring level corresponding to the monitoring evaluation score, and synchronously allocate it to the integrated data asset package corresponding to the monitoring evaluation score; When collecting the call information, recording the node where the call information occurs as the call node; Collecting the time interval between adjacent calling nodes and recording it as the calling interval; Obtaining a standard call interval, and comparing the standard call interval with the call interval; If the call interval is greater than the standard call interval, the call information under the corresponding adjacent call node is directly recorded as independent call information; If the call interval is less than or equal to the standard call interval, determine whether the call information under the corresponding adjacent call nodes is called by the same calling user, and if it is the same calling user, merge the call information under the corresponding adjacent call nodes into independent call information; otherwise, record the call information under the corresponding adjacent call nodes as independent call information; Counting the number of calls of all the independent call information and recording it as the call volume; Counting the single browsing durations corresponding to all the independent call information and summarizing them as the call duration; The step of optimizing the monitoring level of each integrated data asset package according to the monitoring record includes: Acquire monitoring records of each integrated data asset package within the monitoring period, wherein the monitoring records include monitoring levels and monitoring evaluation scores; Arranging the monitoring evaluation scores according to the chronological order of occurrence; Obtaining an optimization condition function, and inputting the arranged monitoring evaluation scores into the optimization condition function one by one, and recording the output result of the optimization condition function as a ranking score; The ranking score is converted to be consistent with the monitoring evaluation score dimension, and then the converted ranking score is matched with the evaluation interval, and the monitoring level of each integrated data asset package is reallocated according to the matching result.

2. The data asset management method according to claim 1, characterized in that: The step of obtaining data asset information, classifying and labeling the data asset information, and obtaining multiple data asset directories includes: Acquire the data asset information, wherein the data asset information includes data type, data source, and data usage; Classify the data asset information according to the data type, and aggregate the data asset information of the same type into independent information subsets; Adding feature tags to the independent information subsets, wherein the feature tags corresponding to each independent information subset are unique, so as to distinguish different data asset information subsets; The feature tags are associated with corresponding subsets of data asset information to form a data asset catalog.

3. The data asset management method according to claim 1, characterized in that: The step of performing correlation analysis on all the data asset directories to obtain a plurality of correlation directories includes: Grouping the plurality of data asset directories in pairs to obtain a plurality of data asset directory groups; Vectorizing the feature labels of the independent information subsets under each of the data asset directories to obtain multiple feature vectors; Obtaining an association analysis function, inputting feature vectors under a plurality of data asset directory groups into the association analysis function, and recording an output result of the association analysis function as an association score; obtaining a screening threshold, and comparing the screening threshold with the association score; If the association score is greater than the screening threshold, the data asset directory in the data asset directory group corresponding to the association score is marked as an associated directory; If the association score is less than or equal to the screening threshold, the data asset directory in the data asset directory group corresponding to the association score is marked as an independent directory.

4. The data asset management method according to claim 2, characterized in that: The step of integrating the data asset information under the associated directories according to the association relationship between the associated directories to obtain multiple integrated data asset packages includes: Obtain all the associated directories and duplicate directory items under the associated directories; Calculating the association weight of the repeated catalog item in each of the associated catalogs and marking it as a screening parameter; Arrange the screening parameters in descending order, retain the duplicate catalog items in the associated catalog corresponding to the screening parameter with the highest ranking, and delete the duplicate catalog items from the associated catalog corresponding to the screening parameter with a ranking smaller than the highest ranking, to obtain an updated catalog; The data asset information subsets under each of the update directories are merged and aggregated to form multiple independent integrated data asset packages.

5. The data asset management method according to claim 4, characterized in that: The step of calculating the association weight of the repeated directory item in each of the associated directories includes: Acquire the repeated catalog item and the association score between the repeated catalog item and each of the associated catalogs; Obtaining the number of concurrent accesses between the repeated directory item and each of the associated directories; A weight calculation function is obtained, and the association score and the number of concurrent access times are input into the weight calculation function, and the output result of the weight calculation function is marked as the association weight.

6. Data asset management system, characterized by: The data asset management method according to any one of claims 1 to 5 comprises: An initialization module, the initialization module is used to obtain data asset information, and classify and label the data asset information to obtain multiple data asset directories; A correlation analysis module, the correlation analysis module is used to perform correlation analysis on all the data asset directories to obtain multiple correlation directories; A data integration module, the data integration module is used to integrate the data asset information under the associated directories according to the association relationship between the associated directories to obtain multiple integrated data asset packages; An allocation module, the allocation module is used to collect the call information of each integrated data asset package and allocate a monitoring level to the integrated data asset package according to the call information; An optimization module, wherein the optimization module is used to perform hierarchical monitoring on each of the integrated data asset packages according to the monitoring level, generate monitoring records, and optimize the monitoring level of each of the integrated data asset packages according to the monitoring records.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the data asset management method described in any one of claims 1 to 5.

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