Data processing method, device and electronic equipment

Abstract

The embodiments of the present application provide a data processing method, device and electronic device. The method includes: obtaining at least two types of meta-information of each target object from the system, the meta-information is used to represent a feature of the target object; performing feature conversion on each type of meta-information to obtain an intermediate eigenvalue of the meta-information, the intermediate eigenvalue is used to quantify the feature of the meta-information; mapping the intermediate eigenvalue of the meta-information to a value between a set interval to obtain a meta-eigenvalue of the meta-information; performing mathematical statistics on the meta-eigenvalues ​​corresponding to each type of meta-information of the target object to obtain a comprehensive eigenvalue of the target object, the comprehensive eigenvalue is used to characterize the importance of the target object in the system. The technical solution of the embodiments of the present application can enhance the accuracy of data processing and standardize data management.

Description

Technical Field

[0001] The present application relates to the field of computers and data processing technology, and in particular to a data processing method, device, and electronic equipment. Background Art

[0002] In data management scenarios, such as those involving data asset management, issues such as data quality and value assessment need to be addressed during data development, requiring a series of data processing steps. However, existing technologies lack a specific solution for processing this type of data, making it difficult to guarantee accurate data processing. Therefore, enhancing the accuracy of data processing and standardizing data management are pressing technical challenges. Summary of the Invention

[0003] The embodiments of the present application provide a data processing method, apparatus, computer program product or computer program, computer-readable medium, and electronic device, which can enhance the accuracy of data processing and standardize data management, at least to a certain extent.

[0004] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by practice of the present application.

[0005] According to one aspect of an embodiment of the present application, a data processing method is provided, the method comprising: obtaining at least two types of meta-information of each target object from a system, the meta-information being used to represent a feature of the target object; performing feature conversion on each type of meta-information to obtain an intermediate eigenvalue of the meta-information, the intermediate eigenvalue being used to quantify the feature of the meta-information; mapping the intermediate eigenvalue of the meta-information to a value within a set interval to obtain a meta-eigenvalue of the meta-information; performing mathematical statistics on the meta-eigenvalues ​​corresponding to each type of meta-information of the target object to obtain a comprehensive eigenvalue of the target object, the comprehensive eigenvalue being used to characterize the importance of the target object in the system.

[0006] According to one aspect of an embodiment of the present application, a data processing device is provided, comprising: an acquisition unit, configured to acquire at least two types of meta-information of each target object from a system, the meta-information being used to represent a feature of the target object; a conversion unit, configured to perform feature conversion on each type of meta-information to obtain an intermediate feature value of the meta-information, the intermediate feature value being used to quantify the feature of the meta-information; a mapping unit, configured to map the intermediate feature value of the meta-information to a value within a set interval to obtain a meta-feature value of the meta-information; and a statistical unit, configured to perform mathematical statistics on the meta-feature values ​​corresponding to each type of meta-information of the target object to obtain a comprehensive feature value of the target object, the comprehensive feature value being used to characterize the importance of the target object in the system.

[0007] In some embodiments of the present application, based on the aforementioned scheme, the conversion unit is configured as: when the meta-information is a numerical value, normalizing the meta-information to obtain the intermediate eigenvalue of the meta-information; when the meta-information is a non-numerical value, evaluating the meta-information through a preset evaluation model corresponding to the meta-information to obtain the intermediate eigenvalue of the meta-information.

[0008] In some embodiments of the present application, based on the aforementioned solution, when the meta information is a call dependency relationship between target objects, the meta information is evaluated using the following preset evaluation model to obtain an intermediate feature value of the meta information:

[0009]

[0010] Among them, V(x) represents the intermediate eigenvalue of the x-th target object when its meta-information is the call dependency relationship between target objects; P(x) represents the number of times the x-th target object is called; C(i) represents the set of target objects that call the x-th target object.

[0011] In some embodiments of the present application, based on the aforementioned solution, when the meta information is an application dependency of a target object, the meta information is evaluated using the following preset evaluation model to obtain an intermediate feature value of the meta information:

[0012]

[0013] Where W(x) represents the intermediate eigenvalue of the s-th target object when its meta-information is the application dependency of the target object; A i represents the i-th type of service of the s-th target object; n represents the number of service types under the s-th target object; g(A i ) represents the number of functional modules under the i-th type of service.

[0014] In some embodiments of the present application, based on the aforementioned scheme, the mapping unit is configured to: map the intermediate eigenvalue of the meta information to a value between a set interval to obtain the meta-eigenvalue of the meta information, including any one of the following: mapping the intermediate eigenvalue of the meta information to a value between a set interval by linear normalization to obtain the meta-eigenvalue of the meta information; mapping the intermediate eigenvalue of the meta information to a value between a set interval by standard deviation normalization to obtain the meta-eigenvalue of the meta information; mapping the intermediate eigenvalue of the meta information to a value between a set interval by ranking percentage to obtain the meta-eigenvalue of the meta information.

[0015] In some embodiments of the present application, based on the aforementioned scheme, the statistical unit is configured to: perform weighted summation of the meta-feature values ​​corresponding to the various types of meta-information based on the preset weights of the meta-feature values ​​corresponding to the various types of meta-information to obtain the comprehensive feature value of the target object.

[0016] In some embodiments of the present application, based on the aforementioned scheme, the device also includes: a sorting unit, which is used to perform mathematical statistics on the meta-feature values ​​corresponding to various types of meta-information of the target object to obtain the comprehensive feature value of the target object, and then sort each target object based on the size relationship between the comprehensive feature values ​​of each target object; and a determination unit, which is used to determine the importance level of the target object in the system based on the sorting position of the target object in the sorting.

[0017] In some embodiments of the present application, based on the aforementioned solution, the target object includes a data asset object, and the data asset object includes at least two types of meta information, and the meta information is used to represent a feature of the data asset object.

[0018] According to one aspect of an embodiment of the present application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the data processing method described in the above embodiments.

[0019] According to one aspect of an embodiment of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the data processing method as described in the above embodiment is implemented.

[0020] According to one aspect of an embodiment of the present application, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the data processing method described in the above embodiments.

[0021] In the technical solutions provided in some embodiments of the present application, by performing feature conversion on each of at least two categories of metadata of each target object, an intermediate feature value for quantifying the feature of the metadata can be obtained, and the intermediate feature value of the metadata is mapped to a value between a set interval to obtain the meta-feature value of the metadata. Further, by performing mathematical statistics on the meta-feature values ​​corresponding to each category of metadata of the target object, a comprehensive feature value can be obtained. Since the comprehensive feature value is a quantitative value that characterizes the importance of the target object in the system, it enables the accuracy of processing the target object metadata data to be improved, solves problems such as data quality and value assessment in the data development process, and standardizes data management.

[0022] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings are incorporated into and constitute a part of the specification, illustrating embodiments consistent with the present application and, together with the specification, explaining the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can derive other drawings based on these drawings without inventive effort. In the drawings:

[0024] Figure 1 A schematic diagram showing an exemplary system architecture to which the technical solutions of the embodiments of the present application can be applied;

[0025] Figure 2 A flow chart of a data processing method according to an embodiment of the present application is shown;

[0026] Figure 3 A detailed flow chart of feature conversion for each type of meta-information according to an embodiment of the present application is shown;

[0027] Figure 4 shows a meta-information model diagram according to an embodiment of the present application;

[0028] Figure 5 shows a meta-information model diagram according to an embodiment of the present application;

[0029] Figure 6A flow chart of a method according to an embodiment of the present application is shown after performing mathematical statistics on meta-feature values ​​corresponding to various types of meta-information of the target object to obtain a comprehensive feature value of the target object;

[0030] Figure 7 A block diagram of a data processing device according to an embodiment of the present application is shown;

[0031] Figure 8 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0032] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0033] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner.In the following description, many specific details are provided so as to provide a full understanding of the embodiments of the present application. However, it will be appreciated by those skilled in the art that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps etc. can be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0034] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0035] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0036] Figure 1 A schematic diagram shows an exemplary system architecture to which the technical solutions of the embodiments of the present application can be applied.

[0037] like Figure 1 As shown, the system architecture may include terminal devices (such as Figure 1101, tablet computer 102, and portable computer 103, which may also be a desktop computer, etc.), network 104, and server 105. Network 104 is a medium for providing a communication link between the terminal device and server 105. Network 104 can include various connection types, such as wired communication links, wireless communication links, etc.

[0038] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as needed. For example, the server 105 may be a server cluster consisting of multiple servers.

[0039] In one embodiment of the present application, the server 105 can obtain at least two types of meta-information of each target object from the system of the terminal device, and the meta-information is used to represent a feature of the target object; then the server 105 performs feature conversion on each type of meta-information to obtain an intermediate feature value of the meta-information, and maps the intermediate feature value of the meta-information to a value between a set interval to obtain a meta-feature value of the meta-information, wherein the intermediate feature value is used to quantify the feature of the meta-information; finally, the server 105 performs mathematical statistics on the meta-feature values ​​corresponding to each type of meta-information of the target object to obtain a comprehensive feature value of the target object, and the comprehensive feature value is used to characterize the importance of the target object in the system.

[0040] It should be noted that the data processing method provided in the embodiments of the present application is generally executed by the server 105, and accordingly, the data processing device is generally provided in the server 105. However, in other embodiments of the present application, the terminal device may also have similar functions to the server, thereby executing the data processing solution provided in the embodiments of the present application.

[0041] The following is a detailed description of the implementation details of the technical solution of the embodiment of the present application:

[0042] Figure 2 A flowchart of a data processing method according to an embodiment of the present application is shown. The data processing method can be executed by a device having a computing and processing function, such as Figure 1 Refer to the server 105 shown in Figure 2 As shown, the data processing method includes at least steps 210 to 270, which are described in detail as follows:

[0043] In step 210, at least two types of meta-information of each target object are obtained from the system, where the meta-information is used to represent a feature of the target object.

[0044] In one embodiment of the present application, the system may refer to a data asset management system, and the target object may refer to a data asset object in the data asset management system, that is, a data unit, such as a data table, a data packet, or a text document data.

[0045] In other embodiments of the present application, the system may also refer to a database or a data platform.

[0046] In the present application, the system includes at least two target objects.

[0047] In this application, each target object in the system includes at least two types of metadata, one of which represents a characteristic of the target object. For example, if the target object is a data table, its metadata can refer to the concept of the data table, the "bloodline relationship" between the data table and other data tables, or the access popularity of the data table, that is, the number of times the data table has been accessed or referenced.

[0048] It should be noted that the concept of a data table can refer to a summary or description of a data table. A data table can also refer to a "bloodline relationship" with other data tables. For example, if data table A is derived from data table B, then data table B and data table A have a "parent-child" bloodline relationship. The popularity of a data table can refer to the number of times the data table has been accessed or referenced.

[0049] In the present application, when the target object includes a data asset object, the data asset object includes at least two types of meta information, and the meta information is used to represent a feature of the data asset object.

[0050] In one embodiment of the present application, before step 210 , meta information of the target object in the system may be defined.

[0051] Specifically, technical meta information and business meta information may be defined.

[0052] Technical metadata refers to technical statistical indicators generated during the data development process. It is used by data warehouse designers and managers for development and ongoing management of the data warehouse. This includes: data source information; descriptions of data transformations; definitions of objects and data structures within the data warehouse; rules for data cleansing and updating; and mappings from source data to destination data. Common examples of technical metadata include data lineage, fan-in and fan-out numbers, field names, field lengths, and database table structures.

[0053] Business meta-information refers to the use of business names, definitions, descriptions and other information to represent various attributes and concepts in the data environment. To a certain extent, the business context behind all data can be regarded as business meta-information, such as business names, business definitions, and business descriptions.

[0054] Continue to refer to Figure 2 In step 230, feature conversion is performed on each type of meta-information to obtain an intermediate feature value of the meta-information, and the intermediate feature value is used to quantify the feature of the meta-information.

[0055] In one embodiment of the present application, the feature conversion is performed on each type of meta information to obtain the intermediate feature value of the meta information, which can be calculated as follows: Figure 3 Follow the steps shown.

[0056] See also Figure 3 , shows a detailed flow chart of feature conversion for each type of metadata according to an embodiment of the present application. Specifically, it includes steps 231 to 232:

[0057] Step 231 : When the meta information is a numerical value, normalize the meta information to obtain an intermediate eigenvalue of the meta information.

[0058] In step 231, the meta information is normalized, and the meta information may be normalized in a normal distribution manner.

[0059] Specifically, for example, the system includes 5 data tables. When the metadata is the file size of the data table, that is, the file sizes of the 5 data tables are 100KB, 200KB, 300KB, 400KB, and 500KB respectively. By normalizing according to the normal distribution, the file sizes (meta-information) corresponding to the 5 data tables are normalized, and the intermediate eigenvalues ​​of the metadata are: -1.42, -0.71, 0, 0.71, and 1.42 respectively.

[0060] Step 232 : When the meta-information is a non-numeric value, the meta-information is evaluated using a preset evaluation model corresponding to the meta-information to obtain an intermediate characteristic value of the meta-information.

[0061] In an example of step 232, when the meta information is a call dependency relationship between target objects, the meta information is evaluated using the following preset evaluation model to obtain an intermediate feature value of the meta information:

[0062]

[0063] Among them, V(x) represents the intermediate eigenvalue of the x-th target object when its meta-information is the call dependency relationship between target objects; P(x) represents the number of times the x-th target object is called; C(i) represents the set of target objects that call the x-th target object.

[0064] In order to make those skilled in the art better understand the model, the following Figure 4 This model will be described.

[0065] See also Figure 4 , shows a meta-information model diagram provided according to an exemplary embodiment of the present application.

[0066] exist Figure 4 In the equation ( ), A, B, C, D, E, F, and G represent seven target objects, respectively. Target object A is called by target object B and target object C, meaning that target object A is called two times, P(A). The target object set C(i) that calls target object A includes target object B and target object C (where target object A and target object B have a "father-son" "bloodline relationship," and target object A and target object C have a "father-son" "bloodline relationship"). Target object B is called by target object D, target object E, and target object F, and target object C is called by target object E, target object F, and target object G. Therefore, V(A) = P(A) + V(B) + V(C) = 2 + 3 + 3 = 8.

[0067] In this application, when the meta-information is the calling dependency relationship between target objects, the importance of the target object in being called can be determined through the preset evaluation model (Formula 1), thereby providing a reference for evaluating the importance of the target object.

[0068] In another example of step 232, when the meta information is an application dependency of a target object, the meta information is evaluated using the following preset evaluation model to obtain an intermediate feature value of the meta information:

[0069]

[0070] Where W(x) represents the intermediate eigenvalue of the s-th target object when its meta-information is the application dependency of the target object; A i represents the i-th type of service of the s-th target object; n represents the number of service types under the s-th target object; g(A i ) represents the number of functional modules under the i-th type of service.

[0071] In order to make those skilled in the art better understand the model, the following Figure 5 This model will be described.

[0072] See also Figure 5 , shows a meta-information model diagram provided according to another exemplary embodiment of the present application.

[0073] exist Figure 5 In the example, S represents a target object, A1, A2, A3, ..., An represent the n service types under target object S. Furthermore, F1 and F2 represent the functional modules under service type A1, F3 represents the functional modules under service type A2, F4 and F5 represent the functional modules under service type A3, and F6 and F7 represent the functional modules under service type A4. Based on this, we can see that when n = 4, W(S) = 4 × 7 = 28.

[0074] In this application, when the meta-information is the application dependency between target objects, the importance of the target object in terms of application can be determined by the preset evaluation model (Formula 2), thereby providing a reference for evaluating the importance of the target object. Figure 2 In step 250, the intermediate characteristic value of the meta information is mapped to a value within a set interval to obtain the meta characteristic value of the meta information.

[0075] In the present application, the set interval may be the interval from 0 to 1, or the interval from 0 to 2, or the interval from 0 to 10, and the present application does not make any specific limitation on this.

[0076] In one embodiment of the present application, the intermediate eigenvalue of the meta-information is mapped to a value between a set interval to obtain the meta-eigenvalue of the meta-information. The intermediate eigenvalue of the meta-information can be mapped to a value between a set interval by linear normalization to obtain the meta-eigenvalue of the meta-information.

[0077] Linear normalization, that is, X after processing = (X before processing - minimum value) / (maximum value - minimum value), where the maximum and minimum values ​​refer to the maximum values ​​in the set of values ​​of different target objects under the same dimension (i.e. the same type of meta-information). Such processing can map all the values ​​in this set into the interval [0,1] according to the size difference. In one embodiment of the present application, the intermediate eigenvalue of the meta-information is mapped to a value between the set interval to obtain the meta-eigenvalue of the meta-information, and the meta-eigenvalue of the meta-information can be obtained by mapping the intermediate eigenvalue of the meta-information to a value between the set interval by standard deviation normalization.

[0078] Standard deviation standardization is to standardize the data by the mean and standard deviation of the original data (i.e., the characteristic value of a certain class of different target objects).

[0079] Specifically, for example, the system includes 5 data tables A, B, C, D, and E, and the intermediate eigenvalues ​​of the 5 data tables A, B, C, D, and E under a certain meta-information are x1, x2, x3, x4, and x5, respectively. Among them, the average value of x1, x2, x3, x4, and x5 is k, and the standard deviation is b. Then, the meta-eigenvalues ​​of the data tables A, B, C, D, and E are (x1-k) / b, (x2-k) / b, (x3-k) / b, (x4-k) / b, and (x5-k) / b. In one embodiment of the present application, the intermediate eigenvalue of the meta-information is mapped to a value within a set interval to obtain the meta-eigenvalue of the meta-information. This can be achieved by mapping the intermediate eigenvalue of the meta-information to a value within a set interval by ranking percentage to obtain the meta-eigenvalue of the meta-information. That is, the values ​​of different target objects under the same dimension (i.e., the same type of meta-information) are sorted from low to high. After obtaining the ranking of each target object, the meta-feature value corresponding to each target object is obtained based on the proportion of the ranking of each target object to the number of all target objects.

[0080] Specifically, for example, the system includes 5 data tables A, B, C, D, and E. The intermediate eigenvalues ​​of the 5 data tables A, B, C, D, and E under a certain meta-information are -1.42, -0.71, 0, 0.71, and 1.42, respectively. They are sorted from high to low according to the intermediate eigenvalues: E, D, C, B, A. Therefore, the (weighted) meta-eigenvalues ​​of the 5 data tables under the meta-information are: E=5 / 5=100%, D=4 / 5=80%, C=3 / 5=60%, B=2 / 5=40%, and A=1 / 5=20%.

[0081] In other embodiments of the present application, the intermediate characteristic value of the meta-information is mapped to a value between a set interval to obtain the meta-characteristic value of the meta-information. Alternatively, the intermediate characteristic value of the meta-information is mapped to a value between a set interval by other means, for example, a base value is added to the meta-characteristic value of the meta-information at the same time.

[0082] Specifically, the system includes 5 data tables A, B, C, D, and E. The intermediate eigenvalues ​​of the 5 data tables A, B, C, D, and E under a certain meta-information are -1.42, -0.71, 0, 0.71, and 1.42, respectively. In order to map these five intermediate eigenvalues ​​to the range of 0 to 4, the value 2 can be added to the five intermediate eigenvalues, and the meta-eigenvalues ​​obtained are 0.42, 1.71, 2, 2.71, and 3.42, respectively.

[0083] Continue to refer to Figure 2In step 270, mathematical statistics are performed on the meta-feature values ​​corresponding to various types of meta-information of the target object to obtain a comprehensive feature value of the target object. The comprehensive feature value is used to represent the importance of the target object in the system.

[0084] In one embodiment of the present application, mathematical statistics are performed on the meta-feature values ​​corresponding to various types of meta-information of the target object. The meta-feature values ​​corresponding to various types of meta-information can be weighted summed up based on preset weights of the meta-feature values ​​corresponding to various types of meta-information to obtain a comprehensive feature value of the target object.

[0085] In a specific implementation, the preset weight may be set according to the influence of various types of meta-information on the evaluation of the importance of data assets.

[0086] Specifically, for example, when the target object is a data table, the data table includes 6 types of meta information, and the meta-feature values ​​corresponding to the 6 types of meta information are 0.4, 0.5, 0.3, 0.8, 0.6, and 0.8 respectively, and the preset weights corresponding to the 6 types of meta information are 0.2, 0.1, 0.2, 0.3, 0.1, and 0.1 respectively.

[0087] Based on this, the comprehensive characteristic value of the data table is: 0.2×0.4+0.1×0.5+0.2×0.3+0.3×0.8+0.1×0.6+0.1×0.8=0.57.

[0088] It can be understood that, in the present application, the comprehensive characteristic value of the target object can be a number between 0 and 1, wherein the closer the comprehensive characteristic value of a target object is to 1, the more important the target object is among all target objects.

[0089] In other embodiments of the present application, mathematical statistics are performed on the meta-feature values ​​corresponding to various types of meta-information of the target object, or the meta-feature values ​​corresponding to various types of meta-information are averaged, and the obtained average value is used as the comprehensive feature value of the target object.

[0090] In this application, after performing mathematical statistics on the meta-feature values ​​corresponding to various meta-information of the target object to obtain the comprehensive feature value of the target object, the following steps can also be performed: Figure 6 Steps shown.

[0091] Reference Figure 6 , shows a flow chart of a method according to an embodiment of the present application after performing mathematical statistics on the meta-feature values ​​corresponding to various types of meta-information of the target object to obtain the comprehensive feature value of the target object, specifically including steps 280 to 290:

[0092] Step 280 : sorting the target objects based on the magnitude relationship between the comprehensive feature values ​​of the target objects.

[0093] Step 290: Determine the importance level of the target object in the system based on the ranking position of the target object in the ranking.

[0094] In steps 280 to 290, by evaluating and grading the importance of target objects, effective operation and accumulation of data assets are achieved, which plays a key role in digital operation and data asset management.

[0095] Specifically, in some embodiments of the present application, each target object may be sorted in descending order according to its comprehensive feature value, wherein the higher the sorting position, the higher the importance level of the target object in the system. For example, if the target objects M1, M2, M3, M4, M5, and M6 are sorted in descending order according to their comprehensive feature values, then the importance level of M6 and M5 can be determined to be A. + , M4, M3 importance level is A - , M2, M1 importance level is B + .

[0096] In other embodiments of the present application, each target object may be sorted in ascending order according to its comprehensive characteristic value, wherein the later the sorting position is, the higher the importance level of the target object in the system.

[0097] In the present application, by determining the importance level of the target object in the system, the importance of the target object in the system can be more intuitively understood.

[0098] The technical solution of the above-mentioned embodiment of the present application can obtain an intermediate characteristic value for quantifying the characteristics of the meta-information by performing feature conversion on each of at least two types of meta-information of each target object, and map the intermediate characteristic value of the meta-information to a value between a set interval to obtain the meta-characteristic value of the meta-information. Furthermore, a comprehensive characteristic value can be obtained by performing mathematical statistics on the meta-characteristic values ​​corresponding to various types of meta-information of the target object. Since the comprehensive characteristic value is a quantitative value that characterizes the importance of the target object in the system, it makes it possible to improve the accuracy of processing the target object meta-information data, solve problems such as data quality and value assessment in the data development process, and standardize data management.

[0099] The following describes an embodiment of the device of the present application, which can be used to execute the data processing method in the above embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the data processing method in the above embodiment of the present application.

[0100] Figure 7 A block diagram of a data processing device according to an embodiment of the present application is shown.

[0101] Reference Figure 7 As shown, a data processing device 700 according to an embodiment of the present application includes: an acquisition unit 701 , a conversion unit 702 , a mapping unit 703 and a statistics unit 704 .

[0102] Among them, the acquisition unit 701 is used to obtain at least two types of meta-information of each target object from the system, and the meta-information is used to represent a feature of the target object; the conversion unit 702 is used to perform feature conversion on each type of meta-information to obtain an intermediate feature value of the meta-information, and the intermediate feature value is used to quantify the feature of the meta-information; the mapping unit 703 is used to map the intermediate feature value of the meta-information to a value between a set interval to obtain a meta-feature value of the meta-information; the statistical unit 704 is used to perform mathematical statistics on the meta-feature values ​​corresponding to each type of meta-information of the target object to obtain a comprehensive feature value of the target object, and the comprehensive feature value is used to characterize the importance of the target object in the system.

[0103] In some embodiments of the present application, based on the aforementioned scheme, the conversion unit 702 is configured as: when the meta-information is a numerical value, normalizing the meta-information to obtain the intermediate eigenvalue of the meta-information; when the meta-information is a non-numerical value, evaluating the meta-information through a preset evaluation model corresponding to the meta-information to obtain the intermediate eigenvalue of the meta-information.

[0104] In some embodiments of the present application, based on the aforementioned solution, when the meta information is a call dependency relationship between target objects, the meta information is evaluated using the following preset evaluation model to obtain an intermediate feature value of the meta information:

[0105]

[0106] Among them, V(x) represents the intermediate eigenvalue of the x-th target object when its meta-information is the call dependency relationship between target objects; P(x) represents the number of times the x-th target object is called; C(i) represents the set of target objects that call the x-th target object.

[0107] In some embodiments of the present application, based on the aforementioned solution, when the meta information is an application dependency of a target object, the meta information is evaluated using the following preset evaluation model to obtain an intermediate feature value of the meta information:

[0108]

[0109] Where W(x) represents the intermediate eigenvalue of the s-th target object when its meta-information is the application dependency of the target object; A i represents the i-th type of service of the s-th target object; n represents the number of service types under the s-th target object; g(A i ) represents the number of functional modules under the i-th type of service.

[0110] In some embodiments of the present application, based on the aforementioned scheme, the mapping unit 703 is configured to: map the intermediate eigenvalue of the meta information to a value between a set interval to obtain the meta-eigenvalue of the meta information, including any one of the following: mapping the intermediate eigenvalue of the meta information to a value between a set interval by linear normalization to obtain the meta-eigenvalue of the meta information; mapping the intermediate eigenvalue of the meta information to a value between a set interval by standard deviation normalization to obtain the meta-eigenvalue of the meta information; mapping the intermediate eigenvalue of the meta information to a value between a set interval by ranking percentage to obtain the meta-eigenvalue of the meta information.

[0111] In some embodiments of the present application, based on the aforementioned scheme, the statistical unit 704 is configured to: perform weighted summation of the meta-feature values ​​corresponding to the various types of meta-information based on the preset weights of the meta-feature values ​​corresponding to the various types of meta-information to obtain the comprehensive feature value of the target object.

[0112] In some embodiments of the present application, based on the aforementioned scheme, the device also includes: a sorting unit, which is used to perform mathematical statistics on the meta-feature values ​​corresponding to various types of meta-information of the target object to obtain the comprehensive feature value of the target object, and then sort each target object based on the size relationship between the comprehensive feature values ​​of each target object; and a determination unit, which is used to determine the importance level of the target object in the system based on the sorting position of the target object in the sorting.

[0113] In some embodiments of the present application, based on the aforementioned solution, the target object includes a data asset object, and the data asset object includes at least two types of meta information, and the meta information is used to represent a feature of the data asset object.

[0114] Figure 8 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown.

[0115] It should be noted that Figure 8 The computer system 800 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0116] like Figure 8 As shown, the computer system 800 includes a central processing unit (CPU) 801, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 802 or the program loaded from the storage part 808 into the random access memory (RAM) 803, such as executing the method described in the above embodiment. Various programs and data required for system operation are also stored in the RAM 803. The CPU 801, ROM 802 and RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0117] The following components are connected to the I / O interface 805: an input section 806 including a keyboard, a mouse, and the like; an output section 807 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 808 including a hard disk and the like; and a communication section 809 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. Removable media 811, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 810 as needed, so that computer programs read therefrom can be installed into the storage section 808 as needed.

[0118] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 809, and / or installed from a removable medium 811. When the computer program is executed by the central processing unit (CPU) 801, the various functions defined in the system of the present application are executed.

[0119] It should be noted that the computer-readable medium shown in the embodiments of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0120] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0121] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. In some cases, the names of these units do not constitute limitations on the units themselves.

[0122] As another aspect, the present application further provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method provided in the first aspect or various optional implementations of the first aspect.

[0123] As another aspect, the present application further provides a computer-readable medium, which may be included in the electronic device described in the above embodiments, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device implements the method described in the above embodiments.

[0124] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the application, the features and functions of two or more modules or units described above can be concretized in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0125] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0126] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed herein.

[0127] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A data processing method, characterized in that: The method comprises: Obtaining at least two types of meta-information of each target object from a data asset management system, the meta-information being used to represent a characteristic of the target object; the target object comprising a data table in the data asset management system, the meta-information comprising call dependency relationships between the data tables and application dependency relationships between the data tables; Performing feature conversion on each type of meta-information to obtain intermediate feature values ​​of the meta-information, wherein the intermediate feature values ​​are used to quantify features of the meta-information; Mapping the intermediate characteristic value of the meta information to a value within a set interval to obtain a meta characteristic value of the meta information; Performing mathematical statistics on meta-feature values ​​corresponding to various meta-information of the target object to obtain a comprehensive feature value of the target object, wherein the comprehensive feature value is used to characterize the importance of the target object in the data asset management system; When the meta information is an application dependency of a target object, the meta information is evaluated using the following preset evaluation model to obtain an intermediate feature value of the meta information: in, W (s) indicates the an intermediate characteristic value of a target object when the meta information is an application dependency of the target object; Indicates the The first target object Types of services; Indicates the The number of service types under each target object; Indicates the The number of functional modules under each type of service.

2. The method according to claim 1, characterized in that The step of performing feature conversion on each type of meta-information to obtain an intermediate feature value of the meta-information includes: When the meta information is a numerical value, normalizing the meta information to obtain an intermediate eigenvalue of the meta information; When the meta-information is a non-numeric value, the meta-information is evaluated using a preset evaluation model corresponding to the meta-information to obtain an intermediate characteristic value of the meta-information.

3. The method according to claim 2, characterized in that When the meta information is a call dependency relationship between target objects, the meta information is evaluated using the following preset evaluation model to obtain an intermediate feature value of the meta information: in, Indicates the an intermediate feature value of a target object when the meta information is a call dependency relationship between target objects; Indicates the The number of times the target object is called; Indicates calling A target object collection of target objects.

4. The method according to claim 1, wherein Mapping the intermediate characteristic value of the meta information to a value within a set interval to obtain the meta characteristic value of the meta information includes any one of the following: Mapping the intermediate eigenvalue of the meta-information to a value within a set interval by linear normalization to obtain a meta-eigenvalue of the meta-information; Mapping the intermediate eigenvalue of the meta-information to a value within a set interval by standard deviation normalization to obtain a meta-eigenvalue of the meta-information; The intermediate characteristic value of the meta information is mapped to a value within a set interval in a ranking percentage manner to obtain the meta characteristic value of the meta information.

5. The method according to claim 1, wherein The performing mathematical statistics on the meta-feature values ​​corresponding to various meta-information of the target object includes: Based on the preset weights of the meta-feature values ​​corresponding to the various types of meta-information, the meta-feature values ​​corresponding to the various types of meta-information are weighted and summed to obtain a comprehensive feature value of the target object.

6. The method according to claim 1, characterized in that After performing mathematical statistics on the meta-feature values ​​corresponding to various types of meta-information of the target object to obtain a comprehensive feature value of the target object, the method further includes: Sort each target object based on the size relationship between the comprehensive feature values ​​of each target object; An importance level of the target object in the data asset management system is determined based on the ranking position of the target object in the ranking.

7. The method according to any one of claims 1 to 6, characterized in that The target object includes a data asset object, and the data asset object includes at least two types of meta information, and the meta information is used to represent a feature of the data asset object.

8. A data processing device, characterized in that: include: an acquisition unit configured to acquire at least two types of meta-information of each target object from a data asset management system, wherein the meta-information is used to represent a characteristic of the target object; the target object includes a data table in the data asset management system, and the meta-information includes call dependency relationships between the data tables and application dependency relationships between the data tables; a conversion unit, configured to perform feature conversion on each type of meta-information to obtain an intermediate feature value of the meta-information, wherein the intermediate feature value is used to quantify the feature of the meta-information; a mapping unit, configured to map the intermediate characteristic value of the meta-information to a value within a set interval to obtain a meta-characteristic value of the meta-information; a statistical unit, configured to perform mathematical statistics on meta-feature values ​​corresponding to various meta-information of the target object to obtain a comprehensive feature value of the target object, wherein the comprehensive feature value is used to represent the importance of the target object in the data asset management system; When the meta information is an application dependency of a target object, the meta information is evaluated using the following preset evaluation model to obtain an intermediate feature value of the meta information: in, W (s) indicates the an intermediate characteristic value of a target object when the meta information is an application dependency of the target object; Indicates the The first target object Types of services; Indicates the The number of service types under each target object; Indicates the The number of functional modules under each type of service.

9. An electronic device, characterized in that: include: processor; as well as a memory for storing executable instructions of the processor; The processor is configured to execute the executable instructions to implement the method according to any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

11. A computer-readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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