Management and control capability analysis and evaluation method based on data assets

Abstract

The invention relates to the technical field of data analysis, in particular to a management and control capability analysis and evaluation method based on data assets, which comprises the following steps: setting traversal logic, traversing data stored in a 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, further recording the captured key information of the stored data in the database; according to the method, accumulation of recording times of key information of stored data in a database is monitored, data assets in the database are traversed, the key information related to the data assets is extracted from the data assets, and the key information is further recorded, so that a visual trend chart is constructed according to the key information which is accumulated and recorded; based on the trend chart, the management and control state of the database storing the data assets on the internal stored data assets is analyzed in a multi-party mode, and finally the ability of the database to manage and control the internal data assets is evaluated.

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 to effectively manage and control enterprise data. It improves data quality and availability by clarifying data standards, standardizing data processes, and ensuring data security. 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; classifying the search word set according to the preset word frequency threshold and the search word frequency set to obtain a search hot word set and a non-search hot word set; extracting search hot words in the search hot word set in turn, calculating the text similarity between the search hot word and each non-search hot word in the non-search hot word set, and obtaining a text similarity set; extracting words greater than a preset word frequency threshold from the text similarity set. The target similarity of the similarity threshold is determined, and the target phrase corresponding to the target similarity is identified, 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; according to 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 according to the number of logins, the product browsing time and the phrase matching degree using a preset product intention formula to obtain a product intention score set.

[0004] The application aims to solve the problem that "the current management and control of e-commerce data assets mainly involves collecting the 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 methods conduct 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 of enterprises, most of them have dedicated databases to store data assets. Such databases are often equipped with a series of management and control functions such as data identification, update, and differentiation, so that enterprises can retrieve data assets stored in the database. However, due to the increase and complexity of data assets in the database, the ability of the database to control data assets gradually becomes weak. If it is used continuously, it will pose a security threat to the data assets stored in the database.

[0006] To this end, we proposed a management and control capability analysis and evaluation method based on data assets. Summary of the invention

[0007] In view of the above-mentioned shortcomings of the prior art, the present invention provides a method for analyzing and evaluating management and control capabilities based on data assets, 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 method for analyzing and evaluating management and control capabilities based on data assets, comprising:

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

[0011] The number of key information records stored in the monitoring database is accumulated. When the number of key information records is not less than three times, a key information dynamic change trend chart is constructed based on the key information, and the regularity of the key information dynamic change trend chart is analyzed;

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

[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 ordinates and the abscissas of the two endpoints of the jth group of line segments; REG is the regularity performance value of the dynamic change trend diagram 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 unity 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: monitor the amount of data stored in the database in real time, identify the increase ratio of the data stored in the database, set the increase ratio triggered by the traversal operation, and traverse the data stored in the database when the increase ratio of the 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] Logic3: monitor the reading frequency of each data stored in the database, calculate the average reading frequency of each data stored in the database, set the average reading frequency triggered by the traversal operation, and traverse the data stored in the database when it is identified that the average reading frequency of each data stored in the database is not less than the average reading frequency triggered by the traversal operation;

[0021] The operation of traversing the database 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 increase ratio triggered by the traversal operation in the Logic1, that is, setting a set of operations of triggering the increase ratio and attenuation factor of the traversal operation, triggering the traversal operation based on the increase ratio and attenuation factor, and its triggering logic is expressed as:

[0023]

[0024] Where: k is the trigger increase ratio; m1, m2, m3, m4, ... are the data growth amounts continuously monitored in the database; M1, M2, M3, M4, ... are the total amounts of data 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 for 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 is not valid, an operation of traversing the data stored in the database is executed.

[0026] Furthermore, 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;

[0027] If the result of the determination of data unity 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 no, the key information is corrected, and an operation of capturing the key information of the data stored in the database is further performed, and then the key information is 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 unity judgment value is obtained for any two sets of data in the database. When the unity judgment value is a non-zero value, it is judged that the data stored in the database does not have unity. Otherwise, it is judged that the data stored in the database has unity.

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

[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 group of key information targets in N′ and N, namely data usage frequency, average data growth rate, and cumulative data access time, are 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 the data usage frequency, the average data growth rate, and the accumulation of 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 the key data is captured, and the vertical axis represents the data usage frequency of the key data, the average data growth rate or the cumulative data access time.

[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 defined by the user, 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 continuously obtained are recorded as REG1 and REG2, REG1>REG2 indicates that the control ability of the database for its internally stored data is on a downward trend, and REG1≤REG2 indicates that the control ability of the database for its internally stored data is on an upward trend;

[0046] Based on the above evaluation logic, a qualified control capability judgment threshold is also set. The qualified control capability judgment threshold is compared with the latest set of REGs, and the control capability of the database on its internally stored data is evaluated based on the comparison result.

[0047] Furthermore, after the evaluation of the control capability of the database on its internally stored data is completed, the change trend of the control capability of the database on its internally stored data is fed back to the computer device held by the user in real time, and the user reads the change trend of the control capability of the database on the computer device. When the control capability of the database on its internally stored data is unqualified, the database is controlled and locked. In the locked state, the database no longer performs data update, reading, transmission, and deletion operations. After the user performs manual maintenance on the database, the user manually unlocks the database.

[0048] The manual maintenance of the database by the user end includes the update of the database running program and the manual setting of 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, the key information related to the data assets is extracted from the data assets by traversing the data assets in the database, and the key information is further recorded, so as to construct a visual trend chart based on the accumulated recorded key information, and then the management and control status of the database storing the data assets for its internally stored data assets is analyzed in multiple aspects based on the trend chart. 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] In order 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 prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 The figure is a flowchart of a method for analyzing and evaluating management and control capabilities based on data assets;

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

[0054] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0055] The present invention will be further described below in conjunction with the embodiments.

[0056] Embodiment 1:

[0057] A data asset management and control capability analysis and evaluation method in this embodiment is as follows: Figure 1 As shown, including:

[0058] Set traversal logic, traverse the data stored in the database in real time based on the traversal logic, and while traversing the data stored in the database, synchronously capture key information of 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: monitor the amount of data stored in the database in real time, identify the increase ratio of the data stored in the database, set the increase ratio triggered by the traversal operation, and traverse the data stored in the database when the increase ratio of the 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] Logic3: monitor the reading frequency of each data stored in the database, calculate the average reading frequency of each data stored in the database, set the average reading frequency triggered by the traversal operation, and traverse the data stored in the database when it is identified that the average reading frequency of each data stored in the database is not less than the average reading frequency triggered by the traversal operation;

[0063] The operation of traversing the database 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] The number of key information records stored in the monitoring database is accumulated. When the number of key information records is not less than three times, a key information dynamic change trend chart is constructed based on the key information, and the regularity of the key information dynamic change trend chart is analyzed;

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

[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 ordinates and the abscissas of the two endpoints of the jth group of line segments; REG is the regularity performance value of the dynamic change trend diagram 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 chart 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 defined by the user, the constant χ∈(0,1), and the initial default setting of the constant is 0.0001;

[0073] After obtaining the regularity performance value REG of the key information dynamic change trend graph, 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 obtained continuously are recorded as REG1 and REG2. REG1>REG2 indicates that the control ability of the database for its internally stored data is on a downward trend, and REG1≤REG2 indicates that the control ability of the database for its internally stored data is on an upward trend.

[0075] Based on the above evaluation logic, a qualified control capability judgment threshold is also set. The qualified control capability judgment threshold is compared with the latest set of REGs, and the control capability of the database on its internally stored data is evaluated based on the comparison result.

[0076] In this embodiment, by executing the method in the above embodiment, the data asset management and control capability analysis and evaluation effect is brought to the database storing data assets, ensuring that the database storing data assets has stable management and control capabilities for data assets, ensuring data asset security, and ensuring the database's stable management and control capabilities for 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 chart 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] Embodiment 2:

[0079] In terms of specific implementation, based on Example 1, this example refers to Figure 1 A method for analyzing and evaluating the management and control capabilities of data assets in Example 1 is further described in detail:

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

[0081]

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

[0083] Among them, the amount of data in the database is based on MB as the unit of measurement, the time unit for 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 is not valid, 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 Logic1 of the traversal logic in Example 1.

[0085] Embodiment 3:

[0086] In terms of specific implementation, based on Example 1, this example refers to Figure 1 A method for analyzing and evaluating the management and control capabilities of data assets 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 is determined;

[0088] If the result of the determination of data unity 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 no, the key information is corrected, and an operation of capturing the key information of the data stored in the database is further performed, and then the key information is recorded;

[0090] Among them, 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 unity determination value is obtained for any two sets of data in the database. When the unity determination value is non-zero, it is determined that the data stored in the database does not have unity. Otherwise, it is determined that the data stored in the database has unity.

[0095] Key information about the data in the database includes: frequency of data use, average rate of data growth, 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 group of key information targets in N′ and N, namely data usage frequency, average data growth rate, and cumulative data access time, are corrected based on the above data usage frequency, average data growth rate, and cumulative data access time.

[0100] In this embodiment, through the setting of the above-mentioned logical formula, the data unity 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 the key data is captured, and the vertical axis represents the data usage frequency of the key data, the average data growth rate or the cumulative data access time.

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

[0104] like Figure 1 As shown, after the evaluation of the control capability of the database on its internally stored data is completed, the change trend of the control capability of the database on its internally stored data is fed back to the computer device held by the user in real time. The user reads the change trend of the control capability of the database on the computer device. When the control capability of the database on its internally stored data is unqualified, the database is controlled and locked. In the locked state, the database will no longer perform data update, reading, transmission, and deletion operations. After the user performs manual maintenance on the database, the user manually unlocks the database.

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

[0106] Through the above settings, the method in Example 1 is further limited to not analyzing and evaluating the database's ability to 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 performed on the control status of the database storing the data assets for its internally stored data assets, and finally, the ability of the database to control its internal data assets is evaluated to ensure that the ability of the database to store and control 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 controlled in the database and the ability of the database to store and control 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 the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such 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 embodiments of the present invention.

Claims

1. A data asset-based management and control capability analysis and evaluation method, characterized in that: include: Set traversal logic, traverse the data stored in the database in real time based on the traversal logic, and while traversing the data stored in the database, synchronously capture key information of the data stored in the database, and further record the captured key information of the data stored in the database; The number of key information records stored in the monitoring database is accumulated. When the number of key information records is not less than three times, a key information dynamic change trend chart is constructed based on the key information, and the regularity of the key information dynamic change trend chart is analyzed; The regularity analysis logic of the key information dynamic change trend diagram is expressed as: 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 ordinates and the abscissas of the two endpoints of the jth group of line segments; REG is the regularity performance value of the dynamic change trend diagram of key information; 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. After obtaining the regularity performance value REG of the key information dynamic change trend diagram, REG is further calibrated based on the data unity determination result in the database, and the calibrated REG is used to evaluate the database's ability to control its internally stored data.

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: monitor the amount of data stored in the database in real time, identify the increase ratio of the data stored in the database, set the increase ratio triggered by the traversal operation, and traverse the data stored in the database when the increase ratio of the 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; Logic3: monitor the reading frequency of each data stored in the database, calculate the average reading frequency of each data stored in the database, set the average reading frequency triggered by the traversal operation, and traverse the data stored in the database when it is identified that the average reading frequency of each data stored in the database is not less than the average reading frequency triggered by the traversal operation; The operation of traversing the database 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. A method for analyzing and evaluating management and control capabilities based on data assets according to claim 2, characterized in that: The operation of setting the increase ratio triggered by the traversal operation in the Logic1, that is, setting a set of operations of triggering the increase ratio and attenuation factor of the traversal operation, triggering the traversal operation based on the increase ratio and attenuation factor, and its triggering logic is expressed as: Where: k is the trigger increase 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, γ 3 , ... are attenuation factors; The data volume in the database is based on MB as the unit of measurement, the time unit of continuous monitoring in Logic1 is 1h, and the attenuation factors γ1, γ2, γ 3 ,...obey,γ1>γ2>γ 3 >..., based on the above logic, when the equation under any set of time units does not hold, an operation of traversing the data stored in the database is performed.

4. The method for analyzing and evaluating the management and control capabilities based on data assets according to claim 1 is characterized in that: 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 unity 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 no, the key information is corrected, and an operation of capturing the key information of the data stored in the database is further performed, and then the key information is 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 method for analyzing and evaluating management and control capabilities based on data assets according to claim 4, characterized in that: The logic for determining the uniqueness of data in the database is: 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 decision 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; 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 unity judgment value is obtained for any two sets of data in the database. When the unity judgment value is a non-zero value, it is judged that the data stored in the database does not have unity. Otherwise, it is judged that the data stored in the database has unity.

6. A method for analyzing and evaluating management and control capabilities based on data assets according to claim 5, characterized in that: The key information of 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′ is the key information after correction; N is the key information before correction; Among them, any group of key information targets in N′ and N, namely data usage frequency, average data growth rate, and cumulative data access time, are corrected based on the above data usage frequency, average data growth rate, and cumulative data access time.

7. The method for analyzing and evaluating the management and control capabilities based on data assets according to claim 1 is 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 the key data is captured, and the vertical axis represents the data usage frequency of the key data, the average data growth rate or the cumulative data access time.

8. The method for analyzing and evaluating the management and control capabilities based on data assets according to claim 1, characterized in that: The calibration logic of the regularity performance value REG of the key information dynamic change trend diagram is expressed as: 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; The value of the constant χ is defined by the user, the constant χ∈(0, 1), and the initial default setting of the constant is 0.0001.

9. The method for analyzing and evaluating the management and control capabilities based on data assets according to claim 1, characterized in that: 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; Based on the above evaluation logic, a qualified control capability judgment threshold is also set. The qualified control capability judgment threshold is compared with the latest set of REGs, and the control capability of the database on its internally stored data is evaluated based on the comparison result.

10. A method for analyzing and evaluating management and control capabilities based on data assets according to claim 1, characterized in that: After the evaluation of the control capability of the database on its internally stored data is completed, the change trend of the control capability of the database on its internally stored data is fed back to the computer device held by the user in real time. The user reads the change trend of the control capability of the database on the computer device. When the control capability of the database on its internally stored data is unqualified, the database is controlled and locked. In the locked state, the database no longer performs data update, reading, transmission, and deletion operations. After the user performs manual maintenance on the database, the user manually unlocks the database. The manual maintenance of the database by the user end includes the update of the database running program and the manual setting of the running configuration.

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