Data service graph building method based on logic analysis

Abstract

The invention provides a data service graph building method based on logic analysis, which belongs to the technical field of data analysis, and specifically comprises the following steps of: determining an optimization object in power data by utilizing a deviation condition with data formats of other power data in different data association tables; determining associated power data of the optimization object according to the data processing conditions of the optimization object in different data association tables, and determining an association coefficient of the associated power data and the optimization object according to the data processing conditions of the associated power data and the optimization object in different data association tables, and an optimization processing strategy of the data format of the optimization object is determined according to different data formats of the associated power data and the association coefficient of the optimization object, so that the reliability of data processing is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of data analysis, and in particular to a method for building a data business graph based on logic analysis. Background Art

[0002] The amount of existing data within power grid companies is very large. Under the current circumstances, there are generally problems such as unclear data standards, fragmented knowledge, inconsistent business data, and high application barriers. Therefore, clarifying the relationship between different data and improving the usability of data have become technical issues that need to be solved urgently.

[0003] In order to solve the above technical solutions, in the invention patent application CN202110006716.2 "A method for displaying the impact of external environment on the power grid by superimposing the logic diagram of a power system", the logic diagram and the external environment information are superimposed, the impact of the external environment on the line is more intuitively calculated, and the position of the affected line is mapped to the logic diagram in proportion, so that the location of the cause of the power grid fault is more intelligent. However, it is not difficult to find the following technical problems through analysis: Different power data often have a certain degree of correlation when processing different business types. For example, there is a certain correlation coefficient between power load data, operating data between different power equipment and electricity costs. Therefore, if the data format of power data cannot be stored and converted according to the correlation between different power data, it may have a certain degree of impact on the availability of power data.

[0004] In response to the above technical problems, the present invention provides a method for building a data business graph based on logical analysis. Summary of the invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: In order to solve the above technical problems, the present invention provides a method for building a data business graph based on logic analysis, which specifically includes: S1 determines table-related data of different power tables using the parsing results of the power data of the power table, determines related power tables in different power tables according to the table-related data and data processing conditions of the table-related data, and constructs a data business graph between tables based on the related data table; S2: using the data business graph to determine the data association table of the power data in different associated power tables, and using the deviation of the data format of other power data in the different data association tables to determine the optimization object in the power data; S3 determines the associated power data of the optimization object according to the data processing conditions of the optimization object in different data association tables, and determines the correlation coefficient between the associated power data and the optimization object according to the data processing conditions of the associated power data and the optimization object in different data association tables; S4 determines an optimization processing strategy for the data format of the optimization object according to different data formats of the associated power data and the correlation coefficient with the optimization object.

[0006] The beneficial effects of the present invention are: 1. In the present invention, related power tables in different power tables are determined according to table-related data and data processing conditions of the table-related data, taking into account both the number of identical table-related data between different power tables and the similarity of data processing conditions of table-related data between different power tables, thereby realizing the screening of related power tables from multiple dimensions and laying the foundation for building a data business graph.

[0007] 2. In the present invention, the optimization processing strategy of the data format of the optimization object is determined according to the data formats of different associated power data and the correlation coefficient with the optimization object, thereby realizing the optimization processing of the data format of the optimization object, fully considering the number and correlation coefficient of associated power data in different data formats, improving the efficiency and reliability of data processing, and also laying the foundation for further improving data quality.

[0008] A further technical solution is that the table-associated data of the power table is the power data in the power table.

[0009] A further technical solution is that the method for determining the associated power table is: Determine similarities of table-related data between power tables, and determine identical table-related data between the power tables based on the similarities, taking them as identical related data, and determine basic data similarity coefficients between the power tables using the number of identical related data; Determine the data proportion similarity coefficient between power tables according to the quantity proportion of the same related data in different power tables; Determine the associated power data during data processing in different power tables based on data processing conditions of the same associated data in different power tables, and determine the data processing similarity coefficient between power tables in combination with the data processing methods in different power tables; The table similarity coefficients between the power tables are determined by the basic data similarity coefficients, the data proportion similarity coefficients and the data processing similarity coefficients, and the associated power tables are determined based on the table similarity coefficients.

[0010] A further technical solution is that when the table similarity coefficient between the power tables is greater than a preset similarity coefficient, the power tables are determined to be associated power tables.

[0011] A further technical solution is that the associated power data is power data that needs to be combined when optimizing objects in a data association table for data processing.

[0012] A further technical solution is that the method for determining the correlation coefficient between the associated power data and the optimization object is: The data association table that needs to be associated with power data during data processing is determined by using the data processing status of the optimization processing object in different data association tables, and the data association table is used as the processing association table; The correlation coefficient between the associated power data and the optimization object is determined by the proportion of the number of the processing association table in the data association table.

[0013] A further technical solution is that the method for determining the optimization processing strategy of the data format of the optimization object is: Based on different data formats of the associated power data, the associated power data having the same data format are divided into the same data group; Determine the group correlation coefficients between different data groups and the optimization object according to the number of correlated power data in different data groups and the correlation coefficients between different correlated power data and the optimization object; The group correlation coefficient is used to determine an optimization processing strategy for the data format of the optimization object.

[0014] Other features and advantages will be described in the following description, and partly become apparent from the description, or understood by practicing the invention. The purpose and other advantages of the invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0015] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0017] Figure 1 It is a flow chart of a method for building a data business graph based on logical analysis; Figure 2 is a flow chart of a method for determining an associated power table; Figure 3 The present invention is a flowchart of a method for determining an optimization target in power data. DETAILED DESCRIPTION

[0018] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that the present invention will be comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar structures, and thus their detailed description will be omitted.

[0019] The terms "a", "an", "the", and "said" are used to indicate the presence of one or more elements / components / etc.; the terms "comprising" and "having" are used to express an open-ended inclusive meaning and mean that additional elements / components / etc. may be present in addition to the listed elements / components / etc.

[0020] To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, according to one aspect of the present invention, a method for building a data business graph based on logic parsing is provided, which specifically includes: S1 determines table-related data of different power tables using the parsing results of the power data of the power table, determines related power tables in different power tables according to the table-related data and data processing conditions of the table-related data, and constructs a data business graph between tables based on the related data table; Further, the table-associated data of the power table is the power data in the power table.

[0021] It is understandable that if Figure 2 As shown, the method for determining the associated power table is: Determine similarities of table-related data between power tables, and determine identical table-related data between the power tables based on the similarities, taking them as identical related data, and determine basic data similarity coefficients between the power tables using the number of identical related data; Determine the data proportion similarity coefficient between power tables according to the quantity proportion of the same related data in different power tables; Determine the associated power data during data processing in different power tables based on data processing conditions of the same associated data in different power tables, and determine the data processing similarity coefficient between power tables in combination with the data processing methods in different power tables; The table similarity coefficients between the power tables are determined by the basic data similarity coefficients, the data proportion similarity coefficients and the data processing similarity coefficients, and the associated power tables are determined based on the table similarity coefficients.

[0022] It should also be noted that when the table similarity coefficient between the power tables is greater than a preset similarity coefficient, the power tables are determined to be associated power tables.

[0023] In another embodiment, the method for determining the associated power table includes steps S11-S13, specifically: S11 determines the similarity of table association data between power tables; Optionally, before entering step S12, determine the similarity of table association data between power tables, and determine whether the power tables have the same table association data based on the similarity. If so, enter step S12; if not, determine that the power tables are not associated with each other. S12 regards the same table-related data as the same related data, determines the basic data similarity coefficient between the power tables by using the number of the same related data, determines the data ratio similarity coefficient between the power tables according to the number ratio of the same related data in different power tables, determines the associated power data when processing data in different power tables based on the data processing conditions of the same related data in different power tables, and determines the data processing similarity coefficient between the power tables in combination with the data processing methods in different power tables; S13 determines the table similarity coefficient between the power tables according to the basic data similarity coefficient, the data proportion similarity coefficient and the data processing similarity coefficient, and determines the associated power tables based on the table similarity coefficient.

[0024] Furthermore, the data business graph between the tables is constructed based on the associated data table, specifically including: The associated data table is used to determine the associated relationship between tables, and the associated relationship is used to construct a data business graph between tables.

[0025] Optionally, the above step S12 includes steps S121-S123, specifically: S121: taking the same table-related data as the same related data, determining the basic data similarity coefficient between the power tables by using the number of the same related data, and judging whether the basic data similarity coefficient between the power tables is less than the similarity coefficient threshold, if so, proceeding to step S122, if not, proceeding to step S13; S122 determines the data proportion similarity coefficient between the power tables according to the proportion of the same associated data in different power tables, and judges whether the data proportion similarity coefficient meets the requirement. If so, proceeds to step S123. If not, it is determined that the power tables are not associated with each other. S123 determines the associated power data during data processing in different power tables based on the data processing conditions of the same associated data in different power tables, and determines the data processing similarity coefficient between the power tables in combination with the data processing methods in different power tables, and judges whether the data processing similarity coefficient between the power tables meets the requirements. If so, enters step S13, if not, determines that the power tables are not associated with each other.

[0026] S2: using the data business graph to determine the data association table of the power data in different associated power tables, and using the deviation of the data format of other power data in the different data association tables to determine the optimization object in the power data; It can be understood that the data format of the power data is determined according to the storage data format of the power data in the information system.

[0027] Specifically, Figure 3 As shown, the method for determining the optimization object in the power data is: Determine the power data with a deviation in data format and the power data with a consistent data format in different data association tables according to the deviation in data format from other power data, and use them as format deviation data and format consistent data; Determine the format optimization requirements of different data association tables according to the number of format deviation data and the number of format consistent data in different data association tables; The optimization requirement value of the power data is determined by format optimization requirements of different data association tables, and whether the power data is an optimization object is determined based on the optimization requirement value.

[0028] Further, when the optimization demand value of the power data is greater than a preset demand threshold, the power data is determined to be an optimization object.

[0029] It is understandable that when the power data is not an optimization object, the data format of the power data is not optimized temporarily.

[0030] In another possible embodiment, the method for determining the optimization object in the power data includes steps S21-S23, specifically: S21 determines the power data with a data format deviation and the power data with a consistent data format in different data association tables according to the deviation of the data format with other power data, and uses them as format deviation data and format consistent data; Optionally, the above step S21 includes: The power data with deviations in data formats and the power data with consistent data formats in different data association tables are determined based on the deviations in data formats from other power data, and they are used as format deviation data and format consistent data to determine whether there is a data association table in which the number of format deviation data does not meet the requirements. If so, it is determined that the power data belongs to the optimization object. If not, it goes to step S22.

[0031] S22: taking the data association table with format deviation data as the data deviation table, and determining the format optimization requirements of different data association tables according to the number of format deviation data and the number of format consistent data in different data deviation tables; S23 determines the optimization requirement value of the power data according to the format optimization requirements of different data association tables, and determines whether the power data is an optimization object based on the optimization requirement value.

[0032] S3 determines the associated power data of the optimization object according to the data processing conditions of the optimization object in different data association tables, and determines the correlation coefficient between the associated power data and the optimization object according to the data processing conditions of the associated power data and the optimization object in different data association tables; Furthermore, the associated power data is power data that needs to be combined when optimizing objects in the data association table for data processing.

[0033] Specifically, the method for determining the correlation coefficient between the associated power data and the optimization object is: The data association table that needs to be associated with power data during data processing is determined by using the data processing status of the optimization processing object in different data association tables, and the data association table is used as the processing association table; The correlation coefficient between the associated power data and the optimization object is determined by the proportion of the number of the processing association table in the data association table.

[0034] It should also be noted that S221-S223 in the above step S22 are specifically: S221: The data association table with format deviation data is used as a data deviation table, and whether the number of the data deviation tables is less than the preset number of tables is determined. If so, the process proceeds to step S222; if not, the process proceeds to step S23; S222 determines whether the number of the format deviation data is less than a preset number, if so, proceeds to the next step, if not, proceeds to step S23; S24 determines the format optimization requirements of different data association tables according to the number of format deviation data and the number of format consistent data in different data deviation tables, and judges whether there is a data association table whose format optimization requirement is greater than the preset value of the requirement. If so, enters step S23; if not, determines that the power data does not belong to the optimization object.

[0035] S4 determines an optimization processing strategy for the data format of the optimization object according to different data formats of the associated power data and the correlation coefficient with the optimization object.

[0036] Furthermore, the method for determining the optimization processing strategy of the data format of the optimization object is: Based on different data formats of the associated power data, the associated power data having the same data format are divided into the same data group; Determine the group correlation coefficients between different data groups and the optimization object according to the number of correlated power data in different data groups and the correlation coefficients between different correlated power data and the optimization object; The group correlation coefficient is used to determine an optimization processing strategy for the data format of the optimization object.

[0037] It should be further explained that the determination of the optimization processing strategy for the data format of the optimization object using the group correlation coefficient specifically includes: Determine whether there is a data group whose group correlation coefficient is greater than a preset coefficient threshold, if so, proceed to the next step, if not, do not perform the optimization process of the data format of the optimization object; The data format corresponding to the data group with the largest group correlation coefficient is used as the optimization target of the optimization object, and the data format of the optimization object is optimized using the optimization target.

[0038] It can also be understood that the method for determining the optimization processing strategy of the data format of the optimization object includes steps S41-S43, specifically: S41 divides the related power data with the same data format into the same data group based on the data formats of the different related power data; Optionally, the above step S41 includes the following contents: Based on the data formats of different associated power data, the associated power data with the same data format are divided into the same data group, and it is determined whether there is a data group in which the number of associated power data is greater than a preset number threshold. If so, proceed to the next step; if not, the data format optimization processing of the optimization object is not performed for the time being.

[0039] S42: taking a data group whose number of associated power data is greater than a preset number threshold as a candidate data group, taking a candidate data group with the largest number of associated power data as a reference data group, and determining group correlation coefficients between different candidate data groups and the optimization object according to the number of associated power data in different candidate data groups and the correlation coefficients between different associated power data and the optimization object; S43: taking the data format corresponding to the data group with the largest group correlation coefficient as the optimization target of the optimization object, and using the optimization target to perform optimization processing on the data format of the optimization object.

[0040] In another embodiment: Specifically, the above step S42 includes steps S421-S423, which are specifically: S421: take the data group with the number of associated power data greater than the preset number threshold as the candidate data group, take the candidate data group with the largest number of associated power data as the reference data group, and determine whether the deviations of the number of associated power data between different candidate data groups and the reference data group are all greater than the preset number deviation. If so, proceed to step S422; if not, proceed to step S43; S422 determines whether the average value of the correlation coefficients between different associated power data of the reference data group and the optimization object is the largest. If so, the data format of the optimization object is optimized using the data format corresponding to the reference data group. If not, proceed to step S423. S423 determines the group correlation coefficients between different candidate data groups and the optimization object through the number of associated power data in different candidate data groups and the correlation coefficients between different associated power data and the optimization object, and judges whether there is an alternative data group whose group correlation coefficient meets the requirements. If so, enters step S43; if not, the optimization processing of the data format of the optimization object is temporarily not performed.

[0041] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0042] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0043] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A method for building a data business graph based on logical analysis, characterized in that: Specifically include: Determine the table-related data of different power tables using the analysis results of the power data of the power table, and determine the related power tables in the different power tables according to the table-related data and the data processing conditions of the table-related data, and construct the data business graph between the tables based on the related data table; Determine the data association table of the power data in different associated power tables by using the data business graph, and determine the optimization object in the power data by using the deviation of the data format of other power data in the different data association tables; Determine the associated power data of the optimization object according to the data processing conditions of the optimization object in different data association tables, and determine the correlation coefficient between the associated power data and the optimization object according to the data processing conditions of the associated power data and the optimization object in different data association tables; An optimization processing strategy for the data format of the optimization object is determined according to different data formats of associated power data and correlation coefficients with the optimization object.

2. The method for constructing a data business graph based on logic analysis according to claim 1, characterized in that: The table-related data of the power table is the power data in the power table.

3. The method for constructing a data business graph based on logic analysis according to claim 1, characterized in that: The method for determining the associated power table is: Determine similarities of table-related data between power tables, and determine identical table-related data between the power tables based on the similarities, taking them as identical related data, and determine basic data similarity coefficients between the power tables using the number of identical related data; Determine the data proportion similarity coefficient between power tables according to the quantity proportion of the same related data in different power tables; Determine the associated power data during data processing in different power tables based on data processing conditions of the same associated data in different power tables, and determine the data processing similarity coefficient between power tables in combination with the data processing methods in different power tables; The table similarity coefficients between the power tables are determined by the basic data similarity coefficients, the data proportion similarity coefficients and the data processing similarity coefficients, and the associated power tables are determined based on the table similarity coefficients.

4. The method for constructing a data business graph based on logic analysis as claimed in claim 3, characterized in that: When the table similarity coefficient between the power tables is greater than a preset similarity coefficient, the power tables are determined to be associated power tables.

5. The method for constructing a data business graph based on logic analysis according to claim 1, characterized in that: The data business graph between the tables is constructed based on the associated data table, specifically including: The associated data table is used to determine the associated relationship between tables, and the associated relationship is used to construct a data business graph between tables.

6. The method for constructing a data business graph based on logic analysis according to claim 1, characterized in that: The data format of the power data is determined according to the storage data format of the power data in the information system.

7. The method for constructing a data business graph based on logic analysis according to claim 1, characterized in that: The method for determining the optimization object in the power data is: Determine the power data with a deviation in data format and the power data with a consistent data format in different data association tables according to the deviation in data format from other power data, and use them as format deviation data and format consistent data; Determine the format optimization requirements of different data association tables according to the number of format deviation data and the number of format consistent data in different data association tables; The optimization requirement value of the power data is determined by format optimization requirements of different data association tables, and whether the power data is an optimization object is determined based on the optimization requirement value.

8. The method for constructing a data business graph based on logic analysis according to claim 7, characterized in that: When the optimization demand value of the power data is greater than a preset demand threshold, the power data is determined to be an optimization object.

9. The method for constructing a data business graph based on logic analysis according to claim 1, characterized in that: When the power data is not an optimization object, the data format of the power data is not optimized temporarily.

10. The method for constructing a data business graph based on logic analysis according to claim 1, characterized in that: The method for determining the correlation coefficient between the associated power data and the optimization object is: The data association table that needs to be associated with power data during data processing is determined by using the data processing status of the optimization processing object in different data association tables, and the data association table is used as the processing association table; The correlation coefficient between the associated power data and the optimization object is determined by the proportion of the number of the processing association table in the data association table.

Images