CN115757573B - Processing method and device of map data and storage medium - Google Patents
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Abstract
The application discloses a processing method of map data, which comprises the following steps: acquiring multi-source heterogeneous data, and judging the type of the multi-source heterogeneous data; directly associating fields in the structured data according to preset association rules to form association relations; judging whether unstructured data in the multi-source heterogeneous data has metadata or not; if the metadata exists, extracting the metadata, and associating the metadata with other data sets to form an association relation; if the metadata does not exist, configuring the metadata for the unstructured data, and associating the configured metadata with other data sets to form an association relationship; defining the attribute of the association relationship, and structuring the multi-source heterogeneous data according to the defined association relationship; and generating new relation data by taking the defined association relation as a connection, and importing the relation data into a graph database.
Description
Technical Field
The present application relates to the field of data processing, and in particular, to a method and apparatus for processing map data, and a storage medium.
Background
With the development of information technology, big data technology has become an important research direction and application hotspot of society. As is well known, big data is a technology for searching meaningful association and excavating a change rule of things from massive multidimensional ordinary data and accurately predicting the development trend of the things. Clearly, mass is one important feature of large data, while another important feature is the heterogeneity of data. The data may be divided into structured data, semi-structured data, and unstructured data according to the difference of structures. Heterogeneous data refers to data in a variety of data formats, with different data models and semantic environments.
When the knowledge graph is constructed by multi-source heterogeneous data, the data are generally required to be classified, screened and even metadata are reconfigured for use, however, the existing mode is too complicated in process and has a certain threshold, and the efficiency is not high.
Disclosure of Invention
In order to solve the technical problems, the application provides a processing method and device of map data and a storage medium.
The first aspect of the present application provides a method for processing map data, including:
acquiring multi-source heterogeneous data, and judging the type of the multi-source heterogeneous data;
directly associating fields in the structured data according to a preset association rule to form an association relation;
judging whether the unstructured data in the multi-source heterogeneous data have metadata or not;
if the metadata exists, extracting the metadata, and associating the metadata with other data sets to form an association relation;
if the metadata does not exist, configuring the metadata for the unstructured data, and associating the configured metadata with other data sets to form an association relationship;
defining the attribute of the association relationship, and structuring the multi-source heterogeneous data according to the defined association relationship;
and generating new relation data by taking the defined association relation as connection, and importing the relation data into a graph database.
Optionally, if the metadata exists, extracting the metadata includes:
if the number of the unstructured data of the same type reaches the preset number, extracting metadata in the unstructured data of the same type, and constructing a structured data table of the unstructured data of the same type by using the metadata.
Optionally, the acquiring multi-source heterogeneous data includes:
and responding to the drag operation of the user on the target table in the visual interface, and reading the target table.
Optionally, the attribute of the association relationship includes that the values of the same fields in the structured data are equal, and the self-defined rule is contained or satisfied;
the association may also include partial fields of metadata in unstructured data having equal values, containing, or meeting self-defined rules.
The second aspect of the present application provides a processing apparatus for map data, comprising:
the acquisition unit is used for acquiring multi-source heterogeneous data and judging the type of the multi-source heterogeneous data;
the first association unit is used for directly associating the fields in the structured data according to a preset association rule to form an association relation;
the judging unit is used for judging whether the unstructured data in the multi-source heterogeneous data have metadata or not;
a second association unit configured to:
if the metadata exists, extracting the metadata, and associating the metadata with other data sets to form an association relation;
a third association unit configured to:
if the metadata does not exist, configuring the metadata for the unstructured data, and associating the configured metadata with other data sets to form an association relationship;
the definition unit is used for defining the attribute of the association relationship and structuring the multi-source heterogeneous data according to the defined association relationship;
a generating unit for: and generating new relation data by taking the defined association relation as connection, and importing the relation data into a graph database.
The second association unit is specifically configured to:
if the number of the unstructured data of the same type reaches the preset number, extracting metadata in the structured data of the same type, and constructing a structured data table of the unstructured data of the same type by using the metadata.
The acquisition unit is specifically configured to:
and responding to the drag operation of the user on the target table in the visual interface, and reading the target table.
A third aspect of the present application provides an apparatus for processing map data, the apparatus comprising:
a processor, a memory, an input-output unit, and a bus;
the processor is connected with the memory, the input/output unit and the bus;
the memory holds a program that the processor invokes to perform the method of any of the first aspect and optionally the method of the first aspect.
A fourth aspect of the application provides a computer readable storage medium having stored thereon a program which when executed on a computer performs the method of any of the first aspect and optionally the first aspect.
From the above technical scheme, the application has the following advantages:
the application provides a map data processing method, which can carry out high-efficiency import processing on multi-source heterogeneous data, judge the type of the multi-source heterogeneous data, directly extract fields in metadata to be associated if the multi-source heterogeneous data is structured data to form an association relation, further judge whether metadata exists for unstructured data, configure new metadata for the unstructured data and associate the metadata if the metadata does not exist, associate the metadata with the metadata, and attribute definition is carried out on the association relation after the association relation is formed, so that a certain mapping relation is formed between the associated data, new relation data is generated based on the mapping relation, the generated relation data is structured, so that the relation data can be directly imported into a map database, and in the process, a user only needs to select the data needing to be associated, so that the multi-source heterogeneous data can be easily imported into the map database, and the efficiency of data processing and analysis is effectively improved.
Drawings
In order to more clearly illustrate the technical solutions of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an embodiment of a method for processing profile data provided in the present application;
FIGS. 2, 3 and 4 are schematic diagrams of data association;
FIG. 5 is a schematic structural diagram of an embodiment of a processing device for map data provided in the present application;
fig. 6 is a schematic structural diagram of another embodiment of a processing device for map data provided in the present application.
Detailed Description
Based on the above, the application provides a map data processing method which is used for importing data, improving the data analysis capability and improving the analysis efficiency.
It should be noted that the processing method of the map data provided by the application can be applied to a terminal, a system and a server, for example, the terminal can be a smart phone or a computer, a tablet computer, a smart television, a smart watch, a portable computer terminal and a fixed terminal such as a desktop computer. For convenience of explanation, the present application is exemplified by using the terminal as the execution subject.
In practice, if the data to be analyzed is multi-source heterogeneous, it is difficult to perform association fusion analysis, and when sharing data clues across departments and across units, it is difficult to efficiently share because there is no unified data model and mechanism, based on this, the map data processing method provided by the present application is used to process multi-source heterogeneous data, and solve the problem of pain points when processing multi-source heterogeneous data. The method is described in detail below as an example.
Referring to fig. 1, fig. 1 is a flowchart of an embodiment of a method for processing map data according to the present application, where the method for processing map data includes:
101. acquiring multi-source heterogeneous data, and judging the type of the multi-source heterogeneous data;
firstly, multi-source heterogeneous data are acquired, for example, a user pre-lists tables of different sources required to be processed, the tables are dragged into a visual interface through a dragging operation, the system reads and imports the tables to process, firstly, the types of the multi-source heterogeneous data are judged, the types refer to the classification of the data into structured data and unstructured data, and the structured data can be directly used for association processing by virtue of the structured fields, and the unstructured data may not have metadata or may not be uniform.
102. Directly associating fields in the structured data according to a preset association rule to form an association relation;
after judging the type of the multi-source heterogeneous data in step 101, the fields of the structured data can be directly associated to form an association relationship, for example, two tables are associated through a certain same field, an association rule can be preset, the association relationship in the application can represent connection or mapping between one type of data, the association relationship can be defined as an attribute, for example, the relationship can be defined as an equal relationship, an inclusion relationship or other self-defined rule, and the like, and the association relationship can be set according to the needs of actual application scenes.
103. Judging whether the unstructured data in the multi-source heterogeneous data have metadata or not;
104. if the metadata exists, extracting the metadata, and associating the metadata with other data sets to form an association relation;
105. if the metadata does not exist, configuring the metadata for the unstructured data, and associating the configured metadata with other data sets to form an association relationship;
for unstructured data, because the unstructured data may not have metadata or the metadata may not be uniform, firstly, whether the unstructured data has metadata needs to be judged, if the metadata has metadata, the metadata is extracted, such as a document file, an extraction ID, a title, an author and the like, the metadata which can be extracted does not need to be filled manually, the metadata is used for associating with other data sets, association rules are similar to those of the metadata through field association in the step 102, if the metadata does not exist, an analyst is reminded to configure the metadata for the unstructured data and then associate the metadata with each other, further, for some batches of similar unstructured data, the metadata of the same type can be extracted and used for forming a structured data table, and then the structured data table is used for carrying out batch association processing, so that the processing efficiency can be effectively improved, and of course, a corresponding piece of metadata can be created for some single or possibly small quantities of similar unstructured data.
106. Defining the attribute of the association relationship, and structuring the multi-source heterogeneous data according to the defined association relationship;
the association relationship can be obtained through steps 102, 104 and 105, the association relationship represents the connection between two data sets, after the association relationship is obtained, the user defines the association relationship, referring to fig. 2, the association relationship exists between the entity 1 and the entity 2, wherein the connection can be defined as owned by the field identity card number to represent the ownership relationship between the entity 1 and the entity 2, in this way, an analyst can define each association relationship according to the actually applied scene, thus the association between the two data sets is generated, the association is suitable for the actual analysis requirement, and the basis is provided for the subsequent import diagram database.
107. And generating new relation data by taking the defined association relation as connection, and importing the relation data into a graph database.
In step 106, the analyst defines the association relationship, and generates new relationship data through the defined association relationship, where the relationship data includes the original data set associated with each other and the data set generated according to the association relationship, for example, in fig. 2, the entity 1 and the entity 2 are connected through an owned relationship, and a table is generated according to the owned relationship, where the field identification card number used for association and the owned vehicle identification number are recorded. In this way, the generated relational data can be imported directly into the graph database.
According to the method provided by the embodiment, the multi-source heterogeneous data can be efficiently imported, the type of the acquired multi-source heterogeneous data is judged, if the multi-source heterogeneous data is structured data, the fields in the metadata are directly extracted to be associated to form an association relation, and if the unstructured data is unstructured, whether the metadata is further judged, if the metadata is not, new metadata is configured for the unstructured data, the association is performed, if the metadata is used for association, after the association relation is formed, attribute definition is performed on the association relation, so that a certain mapping relation is formed between the associated data, new relation data is generated based on the mapping relation, the generated relation data is structured, and therefore the relation data can be directly imported into a graph database.
For a clearer description of the method provided by the application, the method is exemplified below:
example one, processing of structured data tables
1. The obtained structured data includes two entity tables of personnel and vehicles, as follows:
entity 1
Entity 2
2. The user executes a drag operation to drag the two tables into a visual panel of the platform, at the moment, only the metadata of the tables, namely field information, is extracted, and the user adds an owned relationship between the two fields of the personnel ID and the owner ID, see FIG. 3;
3. in step 2, the relationship of [ possession ] can be configured to be field congruent, namely, when the personnel id= owner ID, a corresponding relationship is established, and other matching modes can be also configured here, such as field inclusion, regular matching and the like;
after the user finishes the task of the last step, clicking the map key, the platform can automatically realize the following data conversion in the background:
forming a data table of the [ possession ] relationship, completing the relationship mapping from the personnel entity to the vehicle entity, and obtaining the following table through a field join:
and automatically generating a graph database insertion configuration, and inserting the entity table and the relation table into the graph database.
Example two, assuming a set of electronic documents related to the technical solution, the user can drag the entire folder into the visualization panel, the platform automatically extracts its metadata, or the user configures the metadata for it to form a structured table, as follows:
entity 1
The user can drag in the personnel entity again, and associate the personnel entity 1 with the document entity 2:
entity 2
As shown in fig. 4, the association relationship is defined as writing, a table with the writing as relationship is finally generated, and the data is imported into the graph database, and it is noted that after the data is imported into the graph database, the original electronic document is not imported into the graph database, and the finally generated relationship data is imported, wherein the relationship data comprises the entities with the association relationship and the relationship table with the association relationship as relationship.
The embodiments of the method of the present application are described above, and the apparatus and the storage medium according to the present application will be described below.
Referring to fig. 5, the present application provides a map data processing apparatus, the apparatus comprising:
an obtaining unit 501, configured to obtain multi-source heterogeneous data, and determine a type of the multi-source heterogeneous data;
the first association unit 502 is configured to directly associate fields in the structured data according to a preset association rule, so as to form an association relationship;
a judging unit 503, configured to judge whether there is metadata in unstructured data in the multi-source heterogeneous data;
a second association unit 504, configured to:
if the metadata 505 exists, extracting the metadata, and associating the metadata with other data sets to form an association relation;
a third association unit 506, configured to:
if the metadata does not exist, configuring the metadata for the unstructured data, and associating the configured metadata with other data sets to form an association relationship;
a defining unit 507, configured to define an attribute of the association relationship, and structure the multi-source heterogeneous data according to the defined association relationship;
a generating unit 508, configured to: and generating new relation data by taking the defined association relation as connection, and importing the relation data into a graph database.
Optionally, the second association unit is specifically configured to:
if the number of the unstructured data of the same type reaches the preset number, extracting metadata in the structured data of the same type, and constructing a structured data table of the unstructured data of the same type by using the metadata.
Optionally, the acquiring unit is specifically configured to:
and responding to the drag operation of the user on the target table in the visual interface, and reading the target table.
The attribute of the association relationship comprises the value of the same field in the structured data is equal, and the value of the same field in the structured data contains or meets the self-defined rule;
the association may also include partial fields of metadata in unstructured data having equal values, containing, or meeting self-defined rules.
Referring to fig. 6, the present application further provides a device for processing map data, including:
a processor 601, a memory 602, an input/output unit 603, and a bus 604;
the processor 601 is connected to the memory 602, the input-output unit 603, and the bus 604;
the memory 602 holds a program, and the processor 601 calls the program to execute a processing method of any of the map data as described above.
The present application also relates to a computer-readable storage medium having a program stored thereon, which when run on a computer causes the computer to perform the method of processing map data as any one of the above.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (RAM, random access memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Claims (4)
1. A method of processing profile data, the method comprising:
responding to drag operation of a user on a plurality of target forms in a visual interface, reading the plurality of target forms, and judging the types of the plurality of target forms;
directly associating the fields in the structured data according to a preset association rule to form an association relationship;
judging whether the unstructured data in the target tables have metadata or not;
if the metadata exists, extracting the metadata, and associating the metadata with other structured data or other unstructured data with the metadata by using the metadata to form an association relationship;
if the metadata does not exist, configuring metadata for the unstructured data, and associating the configured metadata with other structured data or other unstructured data with metadata to form an association relationship;
defining the attribute of the association relationship, and structuring the plurality of target tables according to the defined association relationship so that the plurality of target tables are associated with each other through the defined association relationship;
generating new relation data by taking the defined association relation as connection, and importing the relation data into a graph database;
the association relation comprises the fact that values of the same fields in the structured data are equal, and the self-defined rules are contained or met;
the association relation also comprises that the values of partial fields of the metadata in the unstructured data are equal, and the self-defined rules are contained or met;
the extracting the metadata if the metadata exists comprises:
if the number of the unstructured data of the same type reaches the preset number, extracting metadata in the unstructured data of the same type, and constructing a structured data table of the unstructured data of the same type by using the extracted metadata.
2. A device for processing map data, the device comprising:
the visual interface comprises an acquisition unit, a display unit and a display unit, wherein the acquisition unit is used for responding to the drag operation of a user on a plurality of target forms in the visual interface, reading the plurality of target forms and judging the types of the plurality of target forms;
the first association unit is used for directly associating the fields in the structured data in the plurality of target tables according to a preset association rule to form an association relation;
a judging unit, configured to judge whether unstructured data in the plurality of target tables has metadata;
a second association unit configured to:
if the metadata exists, extracting the metadata, and associating the metadata with other structured data or other unstructured data with the metadata by using the metadata to form an association relationship;
a third association unit configured to:
if the metadata does not exist, configuring metadata for the unstructured data, and associating the configured metadata with other structured data or other unstructured data with metadata to form an association relationship;
the definition unit is used for defining the attribute of the association relation, structuring the plurality of target tables according to the defined association relation, and enabling the plurality of target tables to be associated with each other through the defined association relation;
a generating unit for: generating new relation data by taking the defined association relation as connection, and importing the relation data into a graph database;
the association relation comprises the fact that values of the same fields in the structured data are equal, and the self-defined rules are contained or met;
the association relation also comprises that the values of partial fields of the metadata in the unstructured data are equal, and the self-defined rules are contained or met;
the extracting the metadata if the metadata exists comprises:
if the number of the unstructured data of the same type reaches the preset number, extracting metadata in the unstructured data of the same type, and constructing a structured data table of the unstructured data of the same type by using the extracted metadata.
3. A device for processing map data, the device comprising:
a processor, a memory, an input-output unit, and a bus;
the processor is connected with the memory, the input/output unit and the bus;
the memory holds a program that the processor invokes to perform the method as claimed in claim 1.
4. A computer readable storage medium having stored thereon a program which, when executed on a computer, performs the method as claimed in claim 1.
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| CN202211384184.7A CN115757573B (en) | 2022-11-07 | 2022-11-07 | Processing method and device of map data and storage medium |
| PCT/CN2022/140734 WO2024098517A1 (en) | 2022-11-07 | 2022-12-21 | Graph data processing method and apparatus, and storage medium |
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| CN202211384184.7A CN115757573B (en) | 2022-11-07 | 2022-11-07 | Processing method and device of map data and storage medium |
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| CN111708773A (en) * | 2020-08-13 | 2020-09-25 | 江苏宝和数据股份有限公司 | Multi-source scientific and creative resource data fusion method |
| CN112182236A (en) * | 2020-09-18 | 2021-01-05 | 成都数联铭品科技有限公司 | Knowledge graph construction method and system and electronic equipment |
| CN112860908A (en) * | 2021-01-27 | 2021-05-28 | 云南电网有限责任公司电力科学研究院 | Knowledge graph automatic construction method based on multi-source heterogeneous power equipment data |
| CN114462603A (en) * | 2022-02-09 | 2022-05-10 | 中国银行股份有限公司 | Knowledge graph generation method and device for data lake |
| CN114417018A (en) * | 2022-03-28 | 2022-04-29 | 金现代信息产业股份有限公司 | Full-process visual configuration system and method of knowledge graph |
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| CN115757573A (en) | 2023-03-07 |
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