Data asset management-based data directory intelligent arrangement system

Abstract

The invention discloses a data directory intelligent arrangement system based on data asset management, and relates to the technical field of data management, and the system comprises the steps: building an initialized enterprise data asset pool and an initialized enterprise data asset information pool, carrying out the association binding of the initialized enterprise data asset pool and the initialized enterprise data asset information pool, obtaining an initialized enterprise data asset time sequence pool, and according to an enterprise data link path under a time sequence, carrying out the data directory arrangement; analyzing the relevance between each group of data assets, and generating an enterprise data asset time sequence relevance path map; determining project progress requirements of each department of the enterprise, performing data division for the enterprise data asset time sequence association path map, obtaining a project data asset classification set of each department, analyzing the calling frequency of project data of each department, analyzing the project data asset state of each department, and generating a data asset calling frequency index; and based on the data asset calling frequency index in the project data asset classification set of each department, sorting and establishing a data asset directory. The method has the advantages that data redundancy is reduced, and the utilization efficiency of data assets is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to an intelligent data catalog arrangement system based on data asset management. Background Art

[0002] Intelligent orchestration of data catalogs in data asset management refers to helping users quickly locate, understand and utilize data through automated metadata collection, intelligent classification and grading, and association mapping, thereby maximizing the value of data assets, and intelligently arranging index catalogs to efficiently support data governance, data analysis, and data applications, thereby promoting enterprise digital transformation and intelligent upgrades.

[0003] Existing data asset management relies too much on manual data labeling, and the amount of enterprise data is usually large, which makes the efficiency and accuracy of manual data standards poor. Secondly, after the manually labeled data labels are classified, the correlation between enterprise data assets cannot provide practical reference for subsequent projects, resulting in the low value of enterprise data asset catalog compilation. Summary of the invention

[0004] In order to solve the above technical problems, an intelligent data catalog orchestration system based on data asset management is provided. This technical solution solves the problem that the existing data asset management is too dependent on manual data labeling, and the amount of enterprise data is usually large, which makes manual data labeling inefficient and low in accuracy. Secondly, after the manually labeled data labels are classified, the correlation between enterprise data assets cannot provide actual reference for subsequent projects, resulting in the low value of enterprise data asset catalog orchestration.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A data catalog intelligent arrangement system based on data asset management, comprising:

[0007] Data collection module: The data collection module is used to obtain all data asset information attributes within the enterprise based on enterprise database query, and perform data preprocessing to establish and initialize the enterprise data asset pool;

[0008] The data pool module is electrically connected to the data acquisition module. The data pool module is used to associate and bind with the initialized enterprise data asset information pool according to the generation timestamp of the enterprise data based on the initialized enterprise data asset pool, so as to obtain the initialized enterprise data asset timing pool;

[0009] A relationship path module, the relationship path module is electrically connected to the data pool module, and the relationship path module is used to analyze the correlation between each group of data assets according to the enterprise data link path under the time sequence based on the initialization of the enterprise data asset time sequence pool, and generate an enterprise data asset time sequence correlation path map;

[0010] Project log module: The project log module is used to obtain the project progress logs of each department in the enterprise and determine the project progress requirements of each department in the enterprise;

[0011] The data partitioning module is electrically connected to the project log module and the relationship path module. The data partitioning module is used to partition data according to the project progress requirements of each department of the enterprise and the time-series association path map of the enterprise data assets to obtain a classification set of project data assets of each department;

[0012] A data status analysis module, which is electrically connected to the data partitioning module. The data status analysis module is used to analyze the data asset status of each department's project based on the classification set of each department's project data assets and the call frequency of each department's project data, and generate a data asset call frequency index;

[0013] The directory module is electrically connected to the data status analysis module, and is used to sort and organize a data asset directory based on the data asset call frequency index in the data asset classification set of each department project.

[0014] Preferably, the relationship path module internally includes:

[0015] The scatter visualization unit, based on the initialization of the enterprise data asset time series pool, marks the enterprise data generation link path under unit time, and establishes a scatter diagram of the enterprise data generation link path;

[0016] A correlation analysis unit generates a link path scatter plot based on the enterprise data, calculates the adjacent scatter points of each enterprise data link path in the scatter plot, performs correlation analysis, and obtains a correlation index scatter plot of the adjacent link path scatter points of the enterprise data;

[0017] A significance verification unit verifies the significance index of each scatter point adjacent to the link path of the enterprise data in the correlation index scatter point diagram based on the correlation index scatter point diagram of the scatter points of the adjacent link paths of the enterprise data, and obtains the significance index scatter point diagram of the scatter points of the adjacent link paths of the enterprise data;

[0018] The data partitioning unit determines the significance index of the enterprise data adjacent link path scatter points, and divides the data according to the significance range threshold of each adjacent link path scatter point in the scatter plot, so as to obtain the enterprise data asset time series correlation path map;

[0019] The calculation of the correlation analysis of the adjacent scatter points of each enterprise data link path in the scatter plot is specifically as follows:

[0020]

[0021] In the formula, r is the correlation index between adjacent scattered points in the enterprise data link path, x i and i is the ith adjacent scattered point in the data link path, and n is the total number of scattered points.

[0022] Preferably, the data partitioning module includes:

[0023] The decision tree unit, based on the Random Forest, establishes the decision tree corresponding to the project requirements of each department of the enterprise, and forms a random forest model for the project requirements of each department of the enterprise;

[0024] The preference feature unit determines the preference feature data of the project schedule requirements of each department based on the project schedule requirements of each department of the enterprise;

[0025] The preference label unit uses the preference characteristic data of the project progress requirements of each department as the segmentation condition of the leaf nodes in the random forest model of the project requirements of each department of the enterprise according to the decision tree corresponding to the project requirements of each department of the enterprise. The enterprise data asset time series association path map is substituted as the root node, and the leaf nodes are segmented according to the maximum information gain of the enterprise data under each path to obtain the demand preference label data of each department project.

[0026] The data packaging unit packages the demand preference label data of each department's projects and generates a classification set of data assets for each department's projects;

[0027] The specific method of splitting leaf nodes based on the maximum information gain of enterprise data under each path is as follows:

[0028]

[0029] In the formula, g(D,A) is the information gain of the project schedule demand preference characteristic data A of each department to the enterprise data asset time series association path map D, H(D|A) is the conditional entropy of the enterprise data asset time series association path map D under the condition of the project schedule demand preference characteristic data A of each department, D j is the enterprise data under the jth path in the enterprise data asset temporal association path graph, p jk is the proportion value of the kth sample data in the enterprise data under the jth link path, l is the total number of link paths in the enterprise data asset time series association path map, and m is the total number of enterprise data samples under the link path.

[0030] Preferably, the data status analysis module internally includes:

[0031] The call information collection unit is used to obtain the historical enterprise data call information log of the project progress of each department; the call information log includes: call time, call department, and call category data;

[0032] The call event collection unit, based on the historical enterprise data call information log of the project progress of each department, counts the call events corresponding to all call information logs in a unit time; the call events include adding, deleting, checking and modifying data;

[0033] The frequency benchmark calculation unit calculates the call event frequency benchmark value of the enterprise data in the project data asset classification set of each department based on the historical enterprise data call information log of the project progress of each department and the call events corresponding to all the call information logs in a unit time;

[0034] The model building unit establishes a probability prediction model for enterprise data call events of each department according to the call event frequency benchmark value of enterprise data call in the project data asset classification set of each department;

[0035] The event prediction unit substitutes the classification set of project data assets of each department into the enterprise data call event probability prediction model of each department to generate the enterprise data call event prediction probability of each department per unit time;

[0036] The normalization unit uses the normalization formula to normalize the predicted probability of enterprise data call events of each department under unit time, and obtains the relative value of the predicted probability of enterprise data call events of each department;

[0037] The index generation unit uses the relative value of the predicted probability of enterprise data call events of each department per unit time as the call attribute of the data asset classification set to generate a data asset call frequency index;

[0038] Among them, the enterprise data call event probability prediction model of each department is as follows:

[0039]

[0040] Where V i'j'k' The relative value of the predicted probability of the call event for the i'th department to call the k'th enterprise data in the j'th time period, The predicted probability value of the call event of the i'th department calling the k'th enterprise data in the j'th time period, F i'j'k' is the call event baseline value for the i'th department calling the k'th enterprise data in the j'th time period, C i'j'k' is the number of call events of the i'th department calling the k'th enterprise data in the j'th time period, T j' is the length of the j'th time period, N' i' is the total number of data assets of the i'th department, exp is an exponential function, and β is a linear regression coefficient. Compared with the prior art, the beneficial effects of the present invention are:

[0041] The present invention proposes an intelligent data catalog arrangement scheme based on data asset management, which generates a time-series correlation path map of enterprise data assets by analyzing the production cycle and reference link path of enterprise data, divides data assets in combination with the project progress requirements of each department of the enterprise, determines the data classification set of each department, and creates a data asset call index and establishes a data asset catalog based on the data asset call frequency of each department. The advantages of the present invention are: reducing data redundancy and improving the utilization efficiency of data assets. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a framework diagram of a data catalog intelligent orchestration system based on data asset management;

[0043] Figure 2 This is the internal framework diagram of the relationship path module;

[0044] Figure 3 This is the internal framework diagram of the data partitioning module;

[0045] Figure 4 This is the internal framework diagram of the data status analysis module. DETAILED DESCRIPTION

[0046] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0047] Reference Figure 1 As shown, a data catalog intelligent arrangement system based on data asset management includes:

[0048] Data collection module: The data collection module is used to obtain all data asset information attributes within the enterprise based on enterprise database query, and perform data preprocessing to establish and initialize the enterprise data asset pool;

[0049] The data pool module is electrically connected to the data acquisition module. The data pool module is used to associate and bind with the initialized enterprise data asset information pool according to the generation timestamp of the enterprise data based on the initialized enterprise data asset pool, so as to obtain the initialized enterprise data asset timing pool;

[0050] A relationship path module, the relationship path module is electrically connected to the data pool module, and the relationship path module is used to analyze the correlation between each group of data assets according to the enterprise data link path under the time sequence based on the initialization of the enterprise data asset time sequence pool, and generate an enterprise data asset time sequence correlation path map;

[0051] Project log module: The project log module is used to obtain the project progress logs of each department in the enterprise and determine the project progress requirements of each department in the enterprise;

[0052] The data partitioning module is electrically connected to the project log module and the relationship path module. The data partitioning module is used to partition data according to the project progress requirements of each department of the enterprise and the time-series association path map of the enterprise data assets to obtain a classification set of project data assets of each department;

[0053] A data status analysis module, which is electrically connected to the data partitioning module. The data status analysis module is used to analyze the data asset status of each department's project based on the classification set of each department's project data assets and the call frequency of each department's project data, and generate a data asset call frequency index;

[0054] The directory module is electrically connected to the data status analysis module, and is used to sort and organize a data asset directory based on the data asset call frequency index in the data asset classification set of each department project.

[0055] This solution generates a time-series correlation path map of enterprise data assets by analyzing the production cycle and reference link path of enterprise data, divides data assets according to the project progress requirements of each department of the enterprise, determines the data classification set of each department, and creates a data asset call index and establishes a data asset directory based on the data asset call frequency of each department. The advantages of the present invention are: reducing data redundancy and improving the utilization efficiency of data assets.

[0056] Reference Figure 2 As shown, the relationship path module includes:

[0057] The scatter visualization unit, based on the initialization of the enterprise data asset time series pool, marks the enterprise data generation link path under unit time, and establishes a scatter diagram of the enterprise data generation link path;

[0058] A correlation analysis unit generates a link path scatter plot based on the enterprise data, calculates the adjacent scatter points of each enterprise data link path in the scatter plot, performs correlation analysis, and obtains a correlation index scatter plot of the adjacent link path scatter points of the enterprise data;

[0059] A significance verification unit verifies the significance index of each scatter point adjacent to the link path of the enterprise data in the correlation index scatter point diagram based on the correlation index scatter point diagram of the scatter points of the adjacent link paths of the enterprise data, and obtains the significance index scatter point diagram of the scatter points of the adjacent link paths of the enterprise data;

[0060] The data partitioning unit determines the significance index of the enterprise data adjacent link path scatter points, and divides the data according to the significance range threshold of each adjacent link path scatter point in the scatter plot, so as to obtain the enterprise data asset time series correlation path map;

[0061] The calculation of the correlation analysis of the adjacent scatter points of each enterprise data link path in the scatter plot is specifically as follows:

[0062]

[0063] In the formula, r is the correlation index between adjacent scattered points in the enterprise data link path, x i and i is the ith adjacent scattered point in the data link path, and n is the total number of scattered points.

[0064] It should be noted that the significance range threshold is to verify the strength of the correlation between the scatter points of adjacent link paths, and the significance range threshold is set as follows: |r|>0.95: significant correlation, |r|≥0.8: high correlation, 0.5≤|r|<0.8: moderate correlation, 0.3≤|r|<0.5: low correlation, |r|<0.3: weak correlation; through the significance verification, the enterprise data corresponding to the scatter points of adjacent link paths are segmented to improve the accuracy of data segmentation;

[0065] It should be noted that since the link paths of adjacent scattered points in the link path scatter points generated by enterprise data usually include but are not limited to one or more, in this solution, when performing significance verification segmentation, the running cross-adjacent paths are segmented into one or more link paths to ensure the integrity of the data.

[0066] Reference Figure 3 As shown, the data partitioning module includes:

[0067] The decision tree unit, based on the Random Forest, establishes the decision tree corresponding to the project requirements of each department of the enterprise, and forms a random forest model for the project requirements of each department of the enterprise;

[0068] The preference feature unit determines the preference feature data of the project schedule requirements of each department based on the project schedule requirements of each department of the enterprise;

[0069] The preference label unit uses the preference characteristic data of the project progress requirements of each department as the segmentation condition of the leaf nodes in the random forest model of the project requirements of each department of the enterprise according to the decision tree corresponding to the project requirements of each department of the enterprise. The enterprise data asset time series association path map is substituted as the root node, and the leaf nodes are segmented according to the maximum information gain of the enterprise data under each path to obtain the demand preference label data of each department project.

[0070] The data packaging unit packages the demand preference label data of each department's projects and generates a classification set of data assets for each department's projects;

[0071] The specific method of splitting leaf nodes based on the maximum information gain of enterprise data under each path is as follows:

[0072]

[0073] In the formula, g(D,A) is the information gain of the project schedule demand preference characteristic data A of each department to the enterprise data asset time series association path map D, H(D|A) is the conditional entropy of the enterprise data asset time series association path map D under the condition of the project schedule demand preference characteristic data A of each department, D j is the enterprise data under the jth path in the enterprise data asset temporal association path graph, p jk is the proportion value of the kth sample data in the enterprise data under the jth link path, l is the total number of link paths in the enterprise data asset time series association path map, and m is the total number of enterprise data samples under the link path.

[0074] This solution uses the random forest algorithm to build a decision tree model for enterprise department project requirements, and uses the preference feature data of project schedule requirements to segment leaf nodes to maximize information gain to optimize the model, thereby accurately classifying and predicting project requirements. Its beneficial effects are to improve decision-making accuracy, optimize resource allocation, enhance project management capabilities, and promote data-driven enterprise decision-making models.

[0075] Reference Figure 4 As shown, the data status analysis module includes:

[0076] The information collection unit is called to obtain the historical enterprise data call information log of the project progress of each department; the call information log includes: call time, call department, call category data;

[0077] The call event collection unit, based on the historical enterprise data call information log of the project progress of each department, counts the call events corresponding to all call information logs in a unit time; the call events include adding, deleting, checking and modifying data;

[0078] The frequency benchmark calculation unit calculates the call event frequency benchmark value of the enterprise data in the project data asset classification set of each department based on the historical enterprise data call information log of the project progress of each department and the call events corresponding to all the call information logs in a unit time;

[0079] The model building unit establishes a probability prediction model for enterprise data call events of each department according to the call event frequency benchmark value of enterprise data call in the project data asset classification set of each department;

[0080] The event prediction unit substitutes the classification set of project data assets of each department into the enterprise data call event probability prediction model of each department to generate the enterprise data call event prediction probability of each department per unit time;

[0081] The normalization unit uses the normalization formula to normalize the predicted probability of enterprise data call events of each department under unit time, and obtains the relative value of the predicted probability of enterprise data call events of each department;

[0082] The index generation unit uses the relative value of the predicted probability of enterprise data call events of each department per unit time as the call attribute of the data asset classification set to generate a data asset call frequency index;

[0083] Among them, the enterprise data call event probability prediction model of each department is as follows:

[0084]

[0085] Where V i'j'k' The relative value of the predicted probability of the call event for the i'th department to call the k'th enterprise data in the j'th time period, The predicted probability value of the call event of the i'th department calling the k'th enterprise data in the j'th time period, F i'j'k' is the call event baseline value for the i'th department calling the k'th enterprise data in the j'th time period, C i'j'k' is the number of call events of the i'th department calling the k'th enterprise data in the j'th time period, T j' is the length of the j'th time period, N' i' is the total number of data assets of the i'th department, exp is the exponential function, and β is the linear regression coefficient.

[0086] The implementation principle of this solution is to collect and analyze the historical enterprise data call information logs of the project progress of each department, count and calculate the call event frequency benchmark value of each department's project data assets, and establish an enterprise data call event probability prediction model based on this. This model can predict the probability of enterprise data call events in each department within a unit of time in the future, and obtain relative values ​​through normalization processing, and finally generate a data asset call frequency index. It provides support for enterprises to optimize data asset management and data utilization efficiency.

[0087] The use process of the present invention is:

[0088] Step 1: Based on the enterprise database query, obtain all data asset information attributes within the enterprise, perform data preprocessing, and establish an initial enterprise data asset pool;

[0089] Step 2: Based on the initialized enterprise data asset pool, the generated timestamp of the enterprise data is associated and bound with the initialized enterprise data asset information pool to obtain the initialized enterprise data asset time series pool;

[0090] Step 3: Based on the initialization of the enterprise data asset time series pool, mark the enterprise data generation link path under unit time, and establish a scatter plot of the enterprise data generation link path;

[0091] Step 4: Generate a link path scatter plot based on the enterprise data, calculate the adjacent scatter points of each enterprise data link path in the scatter plot, perform correlation analysis, and obtain a correlation index scatter plot of the adjacent link path scatter points of the enterprise data;

[0092] Step 5: Based on the correlation index scatter plot of the scatter points of the adjacent link paths of the enterprise data, verify the significance index of each scatter point adjacent to the link path of the enterprise data in the correlation index scatter plot, and obtain the significance index scatter plot of the scatter points of the adjacent link paths of the enterprise data;

[0093] Step 6: Determine the significance index of the scatter points of the adjacent link paths of the enterprise data. In the scatter plot, the significance range threshold of each scatter point of the adjacent link path is used to divide the data, and obtain the time series correlation path map of the enterprise data assets.

[0094] Step 7: Obtain the project progress logs of each department within the enterprise and determine the project progress requirements of each department;

[0095] Step 8: Based on the Random Forest, establish a decision tree corresponding to the project requirements of each department of the enterprise to form a random forest model for the project requirements of each department of the enterprise;

[0096] Step 9: Based on the project progress requirements of each department of the enterprise, determine the project progress requirement preference characteristic data of each department;

[0097] Step 10: According to the decision tree corresponding to the project requirements of the enterprise departments, the characteristic data of the project progress requirements and preferences of each department are used as the segmentation conditions of the leaf nodes in the random forest model of the project requirements of each department of the enterprise. The enterprise data asset time series association path map is substituted as the root node, and the leaf nodes are segmented according to the maximum information gain of the enterprise data under each path to obtain the demand preference label data of each department project;

[0098] Step 11: Pack the demand preference label data of each department project to generate a classification set of data assets for each department project;

[0099] Step 12: Obtain the historical enterprise data call information log of each department's project progress;

[0100] Step 13: Based on the historical enterprise data call information logs of the project progress of each department, count the call events corresponding to all call information logs in a unit time;

[0101] Step 14: Based on the historical enterprise data call information logs of the project progress of each department and the call events corresponding to all call information logs in a unit of time, the call event frequency benchmark value of the enterprise data in the project data asset classification set of each department is calculated;

[0102] Step 15: Establish a probability prediction model for enterprise data call events of each department based on the call event frequency benchmark value of enterprise data call in the project data asset classification set of each department;

[0103] Step 16: Substitute the classification set of project data assets of each department into the enterprise data call event probability prediction model of each department to generate the enterprise data call event prediction probability of each department per unit time;

[0104] Step 17: Using the normalization formula, normalize the predicted probability of enterprise data call events of each department per unit time to obtain the relative value of the predicted probability of enterprise data call events of each department;

[0105] Step 18: The relative value of the predicted probability of enterprise data call events of each department per unit time is used as the call attribute of the data asset classification set to generate a data asset call frequency index;

[0106] Step 19: Sort the data asset call frequency index in the data asset classification set of each department project to form a data asset catalog.

[0107] In summary, the advantages of the present invention are: by analyzing the production cycle and reference link path of enterprise data to generate a time-series correlation path map of enterprise data assets, combining the project progress requirements of each department of the enterprise, dividing data assets, determining the data classification set of each department, and creating a data asset call index and establishing a data asset directory based on the data asset call frequency of each department. The advantages of the present invention are: reducing data redundancy and improving the utilization efficiency of data assets.

[0108] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A data catalog intelligent arrangement system based on data asset management, characterized in that: include: Data collection module: The data collection module is used to obtain all data asset information attributes within the enterprise based on enterprise database query, and perform data preprocessing to establish and initialize the enterprise data asset pool; The data pool module is electrically connected to the data acquisition module. The data pool module is used to associate and bind with the initialized enterprise data asset information pool according to the generation timestamp of the enterprise data based on the initialized enterprise data asset pool, so as to obtain the initialized enterprise data asset timing pool; A relationship path module, the relationship path module is electrically connected to the data pool module, and the relationship path module is used to analyze the correlation between each group of data assets according to the enterprise data link path under the time sequence based on the initialization of the enterprise data asset time sequence pool, and generate an enterprise data asset time sequence correlation path map; Project log module: The project log module is used to obtain the project progress logs of each department in the enterprise and determine the project progress requirements of each department in the enterprise; The data partitioning module is electrically connected to the project log module and the relationship path module. The data partitioning module is used to partition data according to the project progress requirements of each department of the enterprise and the time-series association path map of the enterprise data assets to obtain a classification set of project data assets of each department; A data status analysis module, which is electrically connected to the data partitioning module. The data status analysis module is used to analyze the data asset status of each department's project based on the classification set of each department's project data assets and the call frequency of each department's project data, and generate a data asset call frequency index; The directory module is electrically connected to the data status analysis module, and is used to sort and organize a data asset directory based on the data asset call frequency index in the data asset classification set of each department project.

2. According to claim 1, a data catalog intelligent arrangement system based on data asset management is characterized in that: The relationship path module includes: The scatter visualization unit, based on the initialization of the enterprise data asset time series pool, marks the enterprise data generation link path under unit time, and establishes a scatter diagram of the enterprise data generation link path; A correlation analysis unit generates a link path scatter plot based on the enterprise data, calculates the adjacent scatter points of each enterprise data link path in the scatter plot, performs correlation analysis, and obtains a correlation index scatter plot of the adjacent link path scatter points of the enterprise data; A significance verification unit verifies the significance index of each scatter point adjacent to the link path of the enterprise data in the correlation index scatter point diagram based on the correlation index scatter point diagram of the scatter points of the adjacent link paths of the enterprise data, and obtains the significance index scatter point diagram of the scatter points of the adjacent link paths of the enterprise data; The data partitioning unit determines the significance index of the adjacent link path scatter points of the enterprise data and divides the data according to the significance range threshold of each adjacent link path scatter point in the scatter diagram to obtain the time series correlation path map of the enterprise data assets.

3. The data catalog intelligent arrangement system based on data asset management according to claim 2 is characterized in that: The correlation analysis of the adjacent scattered points of each enterprise data link path in the calculation scatter diagram is specifically as follows: In the formula, r is the correlation index between adjacent scattered points in the enterprise data link path, x i and i is the ith adjacent scattered point in the data link path, and n is the total number of scattered points.

4. The data catalog intelligent arrangement system based on data asset management according to claim 3 is characterized in that: The data partitioning module includes: The decision tree unit, based on the Random Forest, establishes the decision tree corresponding to the project requirements of each department of the enterprise, and forms a random forest model for the project requirements of each department of the enterprise; The preference feature unit determines the preference feature data of the project schedule requirements of each department based on the project schedule requirements of each department of the enterprise; The preference label unit uses the preference characteristic data of the project progress requirements of each department as the segmentation condition of the leaf nodes in the random forest model of the project requirements of each department of the enterprise according to the decision tree corresponding to the project requirements of each department of the enterprise. The enterprise data asset time series association path map is substituted as the root node, and the leaf nodes are segmented according to the maximum information gain of the enterprise data under each path to obtain the demand preference label data of each department project. The data packaging unit packages the demand preference label data of each department's projects and generates a classification set of data assets for each department's projects.

5. The data catalog intelligent arrangement system based on data asset management according to claim 4 is characterized in that: The leaf node segmentation is performed based on the maximum information gain of the enterprise data under each path: In the formula, g(D,A) is the information gain of the project schedule demand preference characteristic data A of each department to the enterprise data asset time series association path map D, H(D|A) is the conditional entropy of the enterprise data asset time series association path map D under the condition of the project schedule demand preference characteristic data A of each department, D j is the enterprise data under the jth link path in the enterprise data asset temporal association path graph, p jk is the proportion value of the kth sample data in the enterprise data under the jth link path, l is the total number of link paths in the enterprise data asset time series association path map, and m is the total number of enterprise data samples under the link path.

6. The data catalog intelligent arrangement system based on data asset management according to claim 5 is characterized in that: The data status analysis module includes: The information collection unit is called to obtain the historical enterprise data call information log of the project progress of each department; the call information log includes: call time, call department, call category data; The call event collection unit, based on the historical enterprise data call information log of the project progress of each department, counts the call events corresponding to all call information logs in a unit time; the call events include adding, deleting, checking and modifying data; The frequency benchmark calculation unit calculates the call event frequency benchmark value of the enterprise data in the project data asset classification set of each department based on the historical enterprise data call information log of the project progress of each department and the call events corresponding to all the call information logs in a unit time; The model building unit establishes a probability prediction model for enterprise data call events of each department according to the call event frequency benchmark value of enterprise data call in the project data asset classification set of each department; The event prediction unit substitutes the classification set of project data assets of each department into the enterprise data call event probability prediction model of each department to generate the enterprise data call event prediction probability of each department per unit time; The normalization unit uses the normalization formula to normalize the predicted probability of enterprise data call events of each department under unit time, and obtains the relative value of the predicted probability of enterprise data call events of each department; The index generation unit uses the relative value of the predicted probability of enterprise data call events of each department per unit time as the call attribute of the data asset classification set to generate a data asset call frequency index.

7. The data catalog intelligent arrangement system based on data asset management according to claim 6 is characterized in that: The specific prediction model of enterprise data call event probability of each department is as follows: Where V i'j'k' The relative value of the predicted probability of the call event for the i'th department to call the k'th enterprise data in the j'th time period, The predicted probability value of the call event of the i'th department calling the k'th enterprise data in the j'th time period, F i'j'k' is the call event baseline value for the i'th department calling the k'th enterprise data in the j'th time period, C i'j'k' is the number of call events of the i'th department calling the k'th enterprise data in the j'th time period, T j' is the length of the j'th time period, N' i' is the total number of data assets of the i'th department, exp is the exponential function, and β is the linear regression coefficient.

Images