A method for analyzing and evaluating management and control capabilities based on data assets

Abstract

The present invention relates to the field of data analysis technology, and specifically to a method for analyzing and evaluating the management and control capabilities of data assets, comprising: setting traversal logic, traversing the data stored in the database in real time based on the traversal logic, synchronously capturing key information of the data stored in the database when traversing the data stored in the database, and further recording the captured key information of the data stored in the database; monitoring the cumulative number of records of the key information of the data stored in the database, and extracting key information related to the data assets from the data assets by traversing the data assets in the database, and further recording the key information, thereby constructing a visual trend chart based on the accumulated recorded key information, and then conducting multi-faceted analysis based on the trend chart on the management and control status of the database storing the data assets for its internally stored data assets, and finally evaluating the database's ability to manage and control its internal data assets.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a method for analyzing and evaluating management and control capabilities based on data assets. Background Art

[0002] Data asset management and control is an important measure for effectively managing and controlling enterprise data. By clarifying data standards, standardizing data processes, ensuring data security, and other means, it improves data quality and availability. It can help enterprises fully tap the value of data, provide accurate basis for decision-making, and enhance the core competitiveness of enterprises.

[0003] The invention patent with application number 202411013650.X discloses an e-commerce data asset management method based on a business middle platform, which is characterized in that the method includes: using a pre-built business middle platform to obtain e-commerce data of e-commerce users, wherein the e-commerce data includes: user ID, number of logins, search word set, search word frequency set, product click ID set and product browsing time set; according to the preset word frequency threshold and the search word frequency set, the search word set is classified into search heat, and a search hot word set and a non-search hot word set are obtained; the search hot words are extracted in turn from the search hot word set, and the text similarity between the search hot word and each non-search hot word in the non-search hot word set is calculated to obtain a text similarity set; the words with a value greater than a preset value are extracted from the text similarity set. The target similarity of the similarity threshold is used to identify the target phrases corresponding to the target similarity, the target phrases are summarized to obtain a target phrase set, and the target word vector of the target phrase set is calculated: the product click ID is extracted in sequence from the product click ID set, the product description text of the product click ID is obtained, the product phrase is extracted from the product description text, and the product word vector of the product phrase is calculated; based on the product word vector and the target word vector, the phrase matching degree between the target phrase set and the product phrase is calculated; the product browsing time is extracted in sequence from the product browsing time set, and the product intention score of the e-commerce user for the product click ID is calculated using a preset product intention formula based on the number of logins, product browsing time and phrase matching degree to obtain a product intention score set.

[0004] This application aims to solve the problem that "the current management and control of e-commerce data assets mainly involves collecting e-commerce data of e-commerce users of a single e-commerce platform, and achieving the purpose of managing and controlling the e-commerce data assets of the e-commerce users through research and processing of the e-commerce data. However, the existing method conducts data asset management on the e-commerce data of each e-commerce platform, which not only consumes a lot of manpower but also leads to low management efficiency."

[0005] However, for the management of business data assets, most enterprises have dedicated databases to store data assets. These databases are often equipped with a series of management and control functions such as data identification, updating, and differentiation to facilitate enterprises to retrieve data assets stored in the database. However, due to the increase and complexity of data assets in the database, the database's ability to control data assets gradually becomes weak. If used continuously, it will pose a security threat to the data assets stored in the database.

[0006] To this end, we propose a data asset-based management and control capability analysis and evaluation method. Summary of the Invention

[0007] In response to the above-mentioned shortcomings of the existing technology, the present invention provides a data asset-based management and control capability analysis and evaluation method, which solves the technical problems raised in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] A data asset-based management and control capability analysis and assessment method, including:

[0010] Set traversal logic, traverse the data stored in the database in real time based on the traversal logic, synchronously capture key information of the data stored in the database while traversing the data stored in the database, and further record the captured key information of the data stored in the database;

[0011] Monitoring the cumulative number of key information records stored in the database. When the number of key information records is no less than three times, constructing a key information dynamic change trend chart based on the key information, and analyzing the regularity of the key information dynamic change trend chart;

[0012] The regularity analysis logic of the key information dynamic change trend diagram is expressed as follows:

[0013]

[0014] Where: REG(a) is the regularity performance value of the dynamic change trend graph constructed based on the data usage frequency a in the key information; REG(b) is the regularity performance value of the dynamic change trend graph constructed based on the average data growth rate b in the key information; REG(c) is the regularity performance value of the dynamic change trend graph constructed based on the cumulative data access time c in the key information; m is the set of line segments separated by the horizontal axis in the dynamic change trend graph; l j is the length of the jth group of line segments; l j+1 is the length of the j+1th group of line segments; is the ratio of the difference between the ordinate and the abscissa of the two endpoints of the jth group of line segments; REG is the regularity performance value of the dynamic change trend graph of key information;

[0015] The calculation logic of REG(b) and REG(c) is the same as that of REG(a). The larger the REG value, the better the regularity of the key information dynamic change trend graph. Conversely, the smaller the REG value, the worse the regularity of the key information dynamic change trend graph.

[0016] After obtaining the regularity performance value REG of the key information dynamic change trend diagram, REG is further calibrated based on the data uniqueness determination result in the database, and the calibrated REG is used to evaluate the database's ability to control its internally stored data.

[0017] Furthermore, the traversal logic includes:

[0018] Logic 1: Monitors the amount of data stored in the database in real time, identifies the increase ratio of the data stored in the database, sets the increase ratio triggered by the traversal operation, and traverses the data stored in the database when the increase ratio of the identified data stored in the database is not less than the increase ratio triggered by the traversal operation;

[0019] Logic2: Set the traversal cycle and perform periodic traversal on the data stored in the database based on the traversal cycle;

[0020] Logic 3: Monitors the read frequency of each data stored in the database, calculates the average read frequency of each data stored in the database, sets the average read frequency triggered by the traversal operation, and traverses the data stored in the database when it is determined that the average read frequency of each data stored in the database is not less than the average read frequency triggered by the traversal operation;

[0021] The database traversal operation is performed in real time based on the above logic. The average reading frequency calculation logic of the data in logic3 is expressed as: the ratio of the cumulative number of times each data in the database is read within the specified time threshold to the specified time threshold.

[0022] Furthermore, the operation of setting the amplification ratio triggered by the traversal operation in the Logic 1, that is, setting a set of operations of triggering the amplification ratio and attenuation factor of the traversal operation, triggering the traversal operation based on the amplification ratio and attenuation factor, and its triggering logic is expressed as follows:

[0023]

[0024] Where: k is the trigger amplification ratio; m1, m2, m3, m4, ... are the data growth rates continuously monitored in the database; M1, M2, M3, M4, ... are the total data volumes continuously monitored in the database; γ1, γ2, γ3, ... are the attenuation factors;

[0025] Among them, the amount of data in the database is based on MB as the unit of measurement, the time unit of continuous monitoring in Logic1 is 1h, the attenuation factors γ1, γ2, γ3,... obey, γ1>γ2>γ3>..., based on the above logic, when the formula under any set of time unit monitoring does not hold, an operation of traversing the data stored in the database is performed once.

[0026] Furthermore, the data stored in the database is manually uploaded by a database management user, and the database management user is not unique. Before capturing the key information of the data stored in the database, the uniqueness of the data in the database is determined;

[0027] If the result of the determination of data uniqueness in the database is yes, an operation of capturing key information of the data stored in the database is further performed and the key information is recorded;

[0028] If the result of the determination of data unity in the database is negative, the key information is corrected, and an operation of capturing the key information of the data stored in the database is further performed, and the key information is then recorded;

[0029] When the key information is recorded, the uniqueness determination result of the data in the database corresponding to the key information and the key information capture timestamp are simultaneously marked on the key information.

[0030] Furthermore, the logic for determining the uniqueness of data in the database is:

[0031]

[0032] Where: P is the data uniqueness judgment value in the database; n is the total number of data copies in the database; f(sim(i,i+1)) is the judgment function; sim(i,i+1) is the similarity between data i and data i+1 in the database; sim(T i ,T i+1 ) is the text similarity between data i and data i+1; sim(C i ,C i+1 ) is the image similarity between data i and data i+1; sim(Q i ,Q i+1 ) is the audio similarity between data i and data i+1;

[0033] Among them, the judgment function f(sim(i,i+1)) takes a value of 0 or 1. When sim(i,i+1)=1, f(sim(i,i+1)) takes a value of 1. When sim(i,i+1)≠1, f(sim(i,i+1)) takes a value of 0. Based on the above, the uniqueness judgment value of any two groups of data in the database is obtained. When the uniqueness judgment value is non-zero, it is judged that the data stored in the database does not have uniqueness. Otherwise, it is judged that the data stored in the database has uniqueness.

[0034] Furthermore, the key information of the data in the database includes: data usage frequency, average data growth rate, and cumulative data access time;

[0035] The correction logic of key information is:

[0036]

[0037] Where: N' is the key information after correction; N is the key information before correction;

[0038] Among them, any one of the key information targets in N′ and N, namely, data usage frequency, average data growth rate, and cumulative data access time, is corrected based on the above data usage frequency, average data growth rate, and cumulative data access time.

[0039] Furthermore, when constructing the key information dynamic change trend graph, three groups of trend graphs are constructed based on data usage frequency, average data growth rate, and cumulative data access time;

[0040] The key information dynamic change trend chart is a line chart. The horizontal axis of the key information dynamic change trend chart represents the number of times key data is captured, and the vertical axis represents the data usage frequency, average data growth rate or cumulative data access time of the key data.

[0041] Furthermore, the calibration logic of the regularity performance value REG of the key information dynamic change trend diagram is expressed as:

[0042]

[0043] Where: REG corr is the regularity performance value of the dynamic change trend diagram of key information after calibration; g0 is the number of key information marked with a singleness judgment result of no; χ is a constant;

[0044] The value of the constant χ is user-defined, the constant χ∈(0, 1), and the initial default setting of the constant is 0.0001.

[0045] Furthermore, the regularity performance value REG of the key information dynamic change trend graph is continuously obtained based on the update of the recorded key information, and the two latest sets of regularity performance values ​​REG of the key information dynamic change trend graphs obtained continuously are recorded as REG1 and REG2. REG1>REG2 indicates that the database's ability to control its internally stored data is on a downward trend, and REG1≤REG2 indicates that the database's ability to control its internally stored data is on an upward trend.

[0046] Based on the above evaluation logic, a threshold for determining the qualified management and control capability is also set. The threshold for determining the qualified management and control capability is compared with the latest set of REGs, and the database's ability to control the data stored internally is evaluated based on the comparison results.

[0047] Furthermore, after the evaluation of the database's ability to manage and control the data stored internally is completed, the database will provide real-time feedback on the changing trend of its ability to manage and control the data stored internally to the computer device held by the user. The user can read the changing trend of the database's ability to manage and control the data stored internally on the computer device. If the database's ability to manage and control the data stored internally fails to meet the requirements, the database will be locked. In the locked state, the database will no longer perform data update, reading, transmission, or deletion operations. After the user manually maintains the database, the user can manually unlock the database.

[0048] The manual maintenance of the database by the user includes updating the database running program and manually setting the running configuration.

[0049] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0050] The present invention provides a method for analyzing and evaluating the management and control capabilities based on data assets. During the execution of the method, by traversing the data assets in the database, key information related to the data assets is extracted from the data assets, and the key information is further recorded, thereby constructing a visual trend chart based on the accumulated recorded key information. Then, based on the trend chart, a multi-faceted analysis is performed on the management and control status of the database storing the data assets for its internally stored data assets. Finally, the ability of the database to manage and control its internal data assets is evaluated, ensuring that the ability of the database to store and manage data assets can be monitored in real time, thereby providing an adaptive optimization basis for the database and ensuring the security of the data assets stored and managed in the database and the ability of the database to store and manage data assets. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0052] Figure 1 This is a flowchart of a data asset-based management and control capability analysis and evaluation method;

[0053] Figure 2 This is an example diagram of a dynamic change trend diagram of key information in the present invention. DETAILED DESCRIPTION

[0054] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0055] The present invention will be further described below with reference to the embodiments.

[0056] Example 1:

[0057] This embodiment is a data asset management and control capability analysis and evaluation method, such as Figure 1 Shown, including:

[0058] Set traversal logic, traverse the data stored in the database in real time based on the traversal logic, synchronously capture key information of the data stored in the database while traversing the data stored in the database, and further record the captured key information of the data stored in the database;

[0059] The traversal logic includes:

[0060] Logic 1: Monitors the amount of data stored in the database in real time, identifies the increase ratio of the data stored in the database, sets the increase ratio triggered by the traversal operation, and traverses the data stored in the database when the increase ratio of the identified data stored in the database is not less than the increase ratio triggered by the traversal operation;

[0061] Logic2: Set the traversal cycle and perform periodic traversal on the data stored in the database based on the traversal cycle;

[0062] Logic 3: Monitors the read frequency of each data stored in the database, calculates the average read frequency of each data stored in the database, sets the average read frequency triggered by the traversal operation, and traverses the data stored in the database when it is determined that the average read frequency of each data stored in the database is not less than the average read frequency triggered by the traversal operation;

[0063] The database traversal operation is performed in real time based on the above logic. The average reading frequency calculation logic of the data in logic3 is expressed as: the ratio of the cumulative number of times each data in the database is read within the specified time threshold to the specified time threshold;

[0064] Monitoring the cumulative number of key information records stored in the database. When the number of key information records is no less than three times, constructing a key information dynamic change trend chart based on the key information, and analyzing the regularity of the key information dynamic change trend chart;

[0065] The regularity analysis logic of the key information dynamic change trend diagram is expressed as follows:

[0066]

[0067] Where: REG(a) is the regularity performance value of the dynamic change trend graph constructed based on the data usage frequency a in the key information; REG(b) is the regularity performance value of the dynamic change trend graph constructed based on the average data growth rate b in the key information; REG(c) is the regularity performance value of the dynamic change trend graph constructed based on the cumulative data access time c in the key information; m is the set of line segments separated by the horizontal axis in the dynamic change trend graph; l j is the length of the jth group of line segments; l j+1 is the length of the j+1th group of line segments; is the ratio of the difference between the ordinate and the abscissa of the two endpoints of the jth group of line segments; REG is the regularity performance value of the dynamic change trend graph of key information;

[0068] The calculation logic of REG(b) and REG(c) is the same as that of REG(a). The larger the REG value, the better the regularity of the key information dynamic change trend graph. Conversely, the smaller the REG value, the worse the regularity of the key information dynamic change trend graph.

[0069] The calibration logic of the regularity performance value REG of the key information dynamic change trend graph is expressed as:

[0070]

[0071] Where: REG corr is the regularity performance value of the dynamic change trend diagram of key information after calibration; g0 is the number of key information marked with a singleness judgment result of no; χ is a constant;

[0072] The value of the constant χ is user-defined, and the constant χ∈(0, 1) is initially set to 0.0001 by default.

[0073] After obtaining the regularity performance value REG of the key information dynamic change trend graph, the REG is further calibrated based on the data uniqueness determination result in the database, and the calibrated REG is used to evaluate the database's ability to control its internally stored data;

[0074] The regularity performance value REG of the key information dynamic change trend graph is continuously obtained based on the update of the recorded key information. The two latest sets of regularity performance values ​​REG of the key information dynamic change trend graph are recorded as REG1 and REG2. REG1>REG2 indicates that the database's ability to control its internally stored data is on a downward trend, and REG1≤REG2 indicates that the database's ability to control its internally stored data is on an upward trend.

[0075] Based on the above evaluation logic, a threshold for determining the qualified management and control capability is also set. The threshold for determining the qualified management and control capability is compared with the latest set of REGs, and the database's ability to control the data stored internally is evaluated based on the comparison results.

[0076] In this embodiment, by executing the method in the above embodiment, the database storing data assets is provided with data asset management and control capability analysis and evaluation effects, ensuring that the database storing data assets has stable data asset management and control capabilities, ensuring data asset security, and guaranteeing the database's stable management and control capabilities over its internally stored data assets;

[0077] It should be noted that the method in this embodiment, in the process of analyzing and evaluating the database's ability to control data assets, is based on the regularity of the dynamic change trend diagram of key information of data assets, so that the output results of the solution can better represent the database's overall control capabilities for data assets.

[0078] Example 2:

[0079] In terms of specific implementation, based on Example 1, this example refers to Figure 1 The data asset management and control capability analysis and evaluation method in Example 1 is further described in detail:

[0080] In Logic1, the operation of setting the amplification ratio triggered by the traversal operation is to set a set of operations of triggering the amplification ratio and attenuation factor of the traversal operation. The traversal operation is triggered based on the amplification ratio and attenuation factor. The triggering logic is expressed as follows:

[0081]

[0082] Where: k is the trigger amplification ratio; m1, m2, m3, m4, ... are the data growth rates continuously monitored in the database; M1, M2, M3, M4, ... are the total data volumes continuously monitored in the database; γ1, γ2, γ3, ... are the attenuation factors;

[0083] The amount of data in the database is measured in MB, the time unit for continuous monitoring in Logic1 is 1 hour, and the attenuation factors γ1, γ2, γ3, ... obey the rule of γ1>γ2>γ3>... Based on the above logic, when the formula does not hold under any set of time unit monitoring, an operation of traversing the data stored in the database is executed.

[0084] In this embodiment, through the above settings, further step execution data support is provided for the step execution of the method in Example 1, and further application logic limitation is provided for the traversal logic Logic1 in Example 1.

[0085] Example 3:

[0086] In terms of specific implementation, based on Example 1, this example refers to Figure 1 The data asset management and control capability analysis and evaluation method in Example 1 is further described in detail:

[0087] The data stored in the database is manually uploaded by the database management end user, and the database management end user is not unique. Before capturing the key information of the data stored in the database, the uniqueness of the data in the database must be determined;

[0088] If the result of the determination of data uniqueness in the database is yes, an operation of capturing key information of the data stored in the database is further performed and the key information is recorded;

[0089] If the result of the determination of data unity in the database is negative, the key information is corrected, and an operation of capturing the key information of the data stored in the database is further performed, and the key information is then recorded;

[0090] When the key information is recorded, the uniqueness determination result of the data in the database corresponding to the key information and the key information capture timestamp are simultaneously marked on the key information;

[0091] The logic for determining the uniqueness of data in the database is:

[0092]

[0093] Where: P is the data uniqueness judgment value in the database; n is the total number of data copies in the database; f(sim(i,i+1)) is the judgment function; sim(i,i+1) is the similarity between data i and data i+1 in the database; sim(Ti ,T i+1 ) is the text similarity between data i and data i+1; sim(C i ,C i+1 ) is the image similarity between data i and data i+1; sim(Q i ,Q i+1 ) is the audio similarity between data i and data i+1;

[0094] Wherein, the determination function f(sim(i,i+1)) takes a value of 0 or 1. When sim(i,i+1)=1, f(sim(i,i+1)) takes a value of 1. When sim(i,i+1)≠1, f(sim(i,i+1)) takes a value of 0. Based on the above, the singleness determination value is obtained for any two sets of data in the database. When the singleness determination value is non-zero, it is determined that the data stored in the database does not have singleness. Otherwise, it is determined that the data stored in the database has singleness.

[0095] Key information about the data in the database includes: data usage frequency, average data growth rate, and cumulative data access time;

[0096] The correction logic of key information is:

[0097]

[0098] Where: N' is the key information after correction; N is the key information before correction;

[0099] Among them, any one of the key information targets in N′ and N, namely, data usage frequency, average data growth rate, and cumulative data access time, is corrected based on the above data usage frequency, average data growth rate, and cumulative data access time.

[0100] In this embodiment, by setting the above-mentioned logical formula, the data uniqueness determination logic and the correction logic of key information in the database are further limited, providing necessary parameter support for the execution process of the method in Example 1.

[0101] like Figure 1 As shown, when constructing the key information dynamic change trend chart, three groups of trend charts are constructed based on data usage frequency, average data growth rate, and cumulative data access time.

[0102] The key information dynamic change trend chart is a line chart. The horizontal axis of the key information dynamic change trend chart represents the number of times key data is captured, and the vertical axis represents the data usage frequency, average data growth rate or cumulative data access time of the key data.

[0103] Through the above settings, the construction logic and specific expression of the key information dynamic change trend chart are further limited. For further information, please refer to Figure 2 The example diagram given in .

[0104] like Figure 1 As shown, after the evaluation of the database's ability to control its internally stored data is completed, the database's ability to control its internally stored data will be fed back to the computer device held by the user in real time on the trend of changes in the ability to control the database. The user reads the trend of changes in the database's ability to control its internally stored data on the computer device. If the database's ability to control its internally stored data fails to meet the requirements, the database will be locked. In the locked state, the database will no longer perform data update, reading, transmission, or deletion operations. After the user manually maintains the database, the user manually unlocks the database.

[0105] The manual maintenance of the database by the user includes updating the database running program and manually setting the running configuration.

[0106] The above configuration further limits the method in Example 1 to the analysis and evaluation logic of the database's ability to manage and control its internally stored data assets.

[0107] In summary, during the execution of the method in the above embodiment, by traversing the data assets in the database, key information related to the data assets is extracted from the data assets, and the key information is further recorded, so as to construct a visual trend chart based on the accumulated recorded key information, and then based on the trend chart, a multi-faceted analysis is conducted on the management and control status of the database storing the data assets for its internally stored data assets, and finally, the ability of the database to manage its internal data assets is evaluated to ensure that the ability of the database to store and manage data assets can be monitored in real time, thereby providing an adaptive optimization basis for the database and ensuring the security of the data assets stored and managed in the database and the ability of the database to store and manage data assets.

[0108] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A data asset-based management and control capability analysis and evaluation method, characterized in that: include: Setting traversal logic, traversing the data stored in the database in real time based on the traversal logic, synchronously capturing key information of the data stored in the database while traversing the data stored in the database, and further recording the captured key information of the data stored in the database; Monitoring the cumulative number of key information recorded in the data stored in the database. When the key information is recorded at least three times, constructing a key information dynamic change trend chart based on the key information, and analyzing the regularity of the key information dynamic change trend chart; The regularity analysis logic of the key information dynamic change trend diagram is expressed as follows: ； Where: REG(a) is the regularity performance value of the dynamic change trend graph constructed based on the data usage frequency a in the key information; REG(b) is the regularity performance value of the dynamic change trend graph constructed based on the average data growth rate b in the key information; REG(c) is the regularity performance value of the dynamic change trend graph constructed based on the cumulative data access time c in the key information; m It is a collection of line segments separated by the horizontal axis in a dynamic trend chart; is the length of the j-th group of line segments; is the length of the j+1th group of line segments; is the ratio of the difference between the ordinates and the abscissas of the two endpoints of the jth group of line segments; REG It is the regularity performance value of the key information dynamic change trend chart; The calculation logic of REG (b) and REG (c) is the same as that of REG (a). REG The larger the value, the better the regularity of the key information dynamic change trend graph; conversely, the smaller the value, the worse the regularity of the key information dynamic change trend graph; Regularity performance value of the dynamic change trend graph of the key information REG After obtaining, further determine the result of data uniqueness in the database. REG Perform calibration and apply the calibrated REG Evaluate the database's ability to manage and control the data stored within it.

2. A data asset management and control capability analysis and evaluation method according to claim 1, characterized in that: The traversal logic includes: Logic 1: Monitors the amount of data stored in the database in real time, identifies the increase ratio of the data stored in the database, sets the increase ratio triggered by the traversal operation, and traverses the data stored in the database when the increase ratio of the identified data stored in the database is not less than the increase ratio triggered by the traversal operation; Logic2: Set the traversal cycle and perform periodic traversal on the data stored in the database based on the traversal cycle; Logic 3: Monitors the read frequency of each data stored in the database, calculates the average read frequency of each data stored in the database, sets the average read frequency triggered by the traversal operation, and traverses the data stored in the database when it is determined that the average read frequency of each data stored in the database is not less than the average read frequency triggered by the traversal operation; The database traversal operation is performed in real time based on the above logic. The average reading frequency calculation logic of the data in logic3 is expressed as: the ratio of the cumulative number of times each data in the database is read within the specified time threshold to the specified time threshold.

3. The method for analyzing and evaluating data asset management and control capabilities according to claim 2, wherein: The operation of setting the amplification ratio triggered by the traversal operation in Logic1, that is, setting a set of operations of triggering the amplification ratio and attenuation factor of the traversal operation, triggering the traversal operation based on the amplification ratio and attenuation factor, and its triggering logic is expressed as: ； Where: k To trigger the amplification ratio; The amount of data growth continuously monitored in the database; The total amount of data continuously monitored in the database; is the attenuation factor; The data volume in the database is based on MB as the unit of measurement, the time unit of continuous monitoring in Logic1 is 1 hour, and the attenuation factor is obey, Based on the above logic, when the equation does not hold under any set of time unit monitoring, an operation of traversing the data stored in the database is executed.

4. The method for analyzing and evaluating data asset management and control capabilities according to claim 1, wherein: The data stored in the database is manually uploaded by the database management end user, and the database management end user is not unique. Before capturing the key information of the data stored in the database, the uniqueness of the data in the database is determined; If the result of the determination of data uniqueness in the database is yes, an operation of capturing key information of the data stored in the database is further performed and the key information is recorded; If the result of the determination of data unity in the database is negative, the key information is corrected, and an operation of capturing the key information of the data stored in the database is further performed, and the key information is then recorded; When the key information is recorded, the uniqueness determination result of the data in the database corresponding to the key information and the key information capture timestamp are simultaneously marked on the key information.

5. A data asset management and control capability analysis and evaluation method according to claim 4, characterized in that: The logic for determining the uniqueness of data in the database is: ； Where: P It is the value for determining the uniqueness of the data in the database; n The total number of data copies in the database; is the decision function; is the similarity between data i and data i+1 in the database; is the text similarity between data i and data i+1; is the image similarity between data i and data i+1; is the audio similarity between data i and data i+1; Among them, the decision function The value is 0 or 1. hour, The value is 1. hour, The value is 0. Based on the above, the uniqueness determination value is obtained for any two sets of data in the database. When the uniqueness determination value is non-zero, it is determined that the data stored in the database does not have uniqueness. Otherwise, it is determined that the data stored in the database has uniqueness.

6. A data asset management and control capability analysis and evaluation method according to claim 5, characterized in that: Key information about the data in the database includes: data usage frequency, average data growth rate, and cumulative data access time; The correction logic of key information is: ； Where: N 9 is the revised key information; N This is the key information before correction; in, N 9 and N The key information target is any one of the following: data usage frequency, average data growth rate, and cumulative data access time. Based on the above, the data usage frequency, average data growth rate, and cumulative data access time are corrected.

7. The method for analyzing and evaluating the management and control capabilities of data assets according to claim 1, characterized in that: When constructing the key information dynamic change trend graph, three groups of trend graphs are constructed based on data usage frequency, average data growth rate, and cumulative data access time. The key information dynamic change trend chart is a line chart. The horizontal axis of the key information dynamic change trend chart represents the number of times key data is captured, and the vertical axis represents the data usage frequency, average data growth rate or cumulative data access time of the key data.

8. The method for analyzing and evaluating the management and control capabilities of data assets according to claim 1, characterized in that: Regularity performance value of the dynamic change trend graph of the key information REG The calibration logic is expressed as: ； Where: It is the regularity performance value of the dynamic change trend diagram of key information after calibration; The number of key information marked with a singleness determination result of no; x is a constant; Among them, the constant x The value is defined by the user, constant , the constant is initially set to 0.0001 by default.

9. The method for analyzing and evaluating the management and control capabilities of data assets according to claim 1, characterized in that: Regularity performance value of the dynamic change trend graph of the key information REG Based on the continuous update of the recorded key information, the regularity performance value of the dynamic change trend chart of the latest two sets of key information is continuously obtained REG Record REG 1. REG 2, REG 1> REG 2 means that the database's ability to control its internally stored data is declining. REG 1≤ REG 2 indicates that the database's ability to control its internally stored data is on the rise; Based on the above evaluation logic, a threshold for determining the qualified management and control capability is also set. REG Compare and evaluate the database's ability to control the data stored within it based on the comparison results.

10. The method for analyzing and evaluating the management and control capabilities of data assets according to claim 1, characterized in that: After the database completes the evaluation of its ability to manage and control its internally stored data, the database will provide real-time feedback on the changing trend of its ability to manage and control its internally stored data to the computer device held by the user. The user can read the changing trend of the database's ability to manage and control its internally stored data on the computer device. If the database's ability to manage and control its internally stored data fails to meet the requirements, the database will be locked. In the locked state, the database will no longer perform data update, reading, transmission, or deletion operations. After the user manually maintains the database, the user can manually unlock the database. The manual maintenance of the database by the user includes updating the database running program and manually setting the running configuration.

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