CN115827921A - Data fusion system, data fusion method and device, and computer storage medium - Google Patents
Data fusion system, data fusion method and device, and computer storage medium Download PDFInfo
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Abstract
The embodiment of the disclosure provides a data fusion method, and the specific implementation scheme is as follows: the mapping module is used for receiving the multi-source heterogeneous data, processing the multi-source heterogeneous data to generate a data object and an object relation, and importing the data object and the object relation into a graph database; the plug-in module analyzes the multi-source heterogeneous data by adopting an artificial intelligence algorithm to obtain an analysis result; and the fusion module is used for fusing the data object and the object relation in the graph database with the analysis result respectively to obtain a fusion result. Through the embodiment, the efficiency of multi-source heterogeneous data fusion is improved.
Description
Technical Field
Embodiments of the present disclosure relate to the field of computer technologies, and in particular, to a data fusion system, a data fusion method and apparatus, and a computer-readable storage medium.
Background
Under the age of big data information, data fusion has new meanings, and particularly, the data fusion becomes a technical means of data and information due to the generation of digital technologies such as the internet, the internet of things and cloud computing.
In the traditional data fusion technology, the fusion effect on multi-source heterogeneous data is poor, the joint analysis on various information cannot be realized, and better support can not be provided for the data analysis work of multiple industries.
Disclosure of Invention
Embodiments described herein provide a data fusion system, a data fusion method and apparatus, and a computer-readable storage medium storing a computer program.
According to a first aspect of the present disclosure, a data fusion system is provided. The system comprises: the mapping module is used for receiving the multi-source heterogeneous data, processing the multi-source heterogeneous data to generate a data object and an object relation, and importing the data object and the object relation into a graph database; the plug-in module analyzes the multi-source heterogeneous data by adopting an artificial intelligence algorithm to obtain an analysis result; and the fusion module is used for fusing the data object and the object relation in the graph database with the analysis result respectively to obtain a fusion result.
In some embodiments of the present disclosure, the system further comprises: the system further comprises: the search module is used for searching the data object imported into the graph database to obtain a search result and sending the search result to the fusion module; the fusion module is also used for counting and displaying the retrieval result.
In some embodiments of the disclosure, when the multi-source heterogeneous data includes unstructured data, the mapping module is further configured to extract a data body of the unstructured data, associate the data body, and store the data body in a body database.
In some embodiments of the present disclosure, the system further comprises: the object operation interface is used for receiving operation information which is input by a user and is used for operating the multi-source heterogeneous data; the mapping module is also used for receiving the operation information and converting the multi-source heterogeneous data into data objects and object relations based on the operation information.
In some embodiments of the present disclosure, the system further comprises: and in response to the data object having the location attribute, the fusion module marks the data object on the geographic sand table interface to display the location of the data object on the geographic information system.
In some embodiments of the disclosure, the geographic sand table interface is further configured to receive input information, and the fusion module determines and displays a data object corresponding to the input information in the multi-source heterogeneous data on the geographic sand table interface based on the input information and the analysis result.
In some embodiments of the present disclosure, the system further comprises: and the fusion module receives the statistical conditions through the statistical sand table interface and determines the relationship between different data objects corresponding to the statistical conditions and the statistical result.
According to a second aspect of the present disclosure, a data fusion method is provided. The method comprises the following steps: receiving multi-source heterogeneous data; processing multi-source heterogeneous data to generate a data object and an object relation; analyzing the multi-source heterogeneous data by adopting an artificial intelligence algorithm to obtain an analysis result; and fusing the data object and the object relation with the analysis result respectively to obtain a fusion result.
In some embodiments of the disclosure, the fusing the data object and the object relationship with the analysis result respectively to obtain the fusion result includes: based on the parsing result, data objects and object relationships in the graph database are expanded.
In some embodiments of the disclosure, the fusing the data object and the object relationship with the analysis result respectively to obtain the fusion result includes: and displaying the analysis result through a geographic sand table interface with a geographic information system, receiving input information of a user through the geographic sand table interface, and determining and displaying a data object corresponding to the input information in the multi-source heterogeneous data on the geographic sand table interface based on the input information and the analysis result.
In some embodiments of the disclosure, the fusing the data object and the object relationship with the analysis result respectively to obtain the fusion result includes: and receiving the statistical conditions through the statistical sand table interface, and determining the relationship statistical result between different data objects corresponding to the statistical conditions.
According to a third aspect of the present disclosure, a data fusion apparatus is provided. The device comprises: a receiving unit configured to receive multi-source heterogeneous data; the processing unit is configured to process multi-source heterogeneous data to generate a data object and an object relation; the analysis unit is configured to analyze the multi-source heterogeneous data by adopting an artificial intelligence algorithm to obtain an analysis result; and the fusion unit is configured to fuse the data object and the object relation with the analysis result respectively to obtain a fusion result.
In some embodiments of the disclosure, the above fusion unit is further configured to: based on the parsing result, data objects and object relationships in the graph database are expanded.
In some embodiments of the disclosure, the fusion unit is further configured to: and displaying the analysis result through a geographic sand table interface with a geographic information system, receiving input information of a user through the geographic sand table interface, and determining and displaying a data object corresponding to the input information in the multi-source heterogeneous data on the geographic sand table interface based on the input information and the analysis result.
In some embodiments of the present disclosure, the fusion unit is further configured to: and receiving the statistical conditions through the statistical sand table interface, and determining the relationship statistical result between different data objects corresponding to the statistical conditions.
According to a fourth aspect of the present disclosure, there is provided a computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method according to the first aspect of the present disclosure.
The data fusion system comprises a mapping module, a database and a mapping module, wherein the mapping module receives multi-source heterogeneous data, processes the multi-source heterogeneous data to generate a data object and an object relation, and imports the data object and the object relation into a database; secondly, the plug-in module analyzes the multi-source heterogeneous data by adopting an artificial intelligence algorithm to obtain an analysis result; and finally, fusing the data object and the object relation in the graph database with the analysis result by the fusion module to obtain a fusion result. Therefore, the analysis result obtained through the analysis of the plug-in module is fused with the data object and the object relation, so that the information of the data object in the graph database is richer, and the analysis efficiency of the joint analysis of multi-industry multi-source heterogeneous data is improved.
Drawings
To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments will be briefly described below, it being understood that the drawings described below relate only to some embodiments of the present disclosure, and not to limit the present disclosure, wherein:
FIG. 1 is a block diagram of one embodiment of a data fusion system according to the present disclosure;
FIG. 2 is a schematic illustration of a display of an object manipulation interface according to the present disclosure;
FIG. 3 is a schematic illustration of one display of a geographic sand table interface of the present disclosure;
FIG. 4 is a schematic illustration of a display of a statistical sand table interface according to the present disclosure;
FIG. 5 is a flow diagram of one embodiment of a data fusion method according to the present disclosure;
FIG. 6 is a schematic block diagram of an embodiment of a data fusion apparatus according to the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described below in detail and completely with reference to the accompanying drawings. It is to be understood that the described embodiments are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the disclosure without any inventive step, are also within the scope of protection of the disclosure.
In order to solve the problem of poor fusion effect of multi-source heterogeneous data in the conventional technology, the present disclosure provides a simple and efficient data fusion system, referring to fig. 1, which shows a data fusion system 100 according to the present disclosure, the system comprising: a mapping module 101, a plug-in module 102 and a fusion module 103. The mapping module 101 is configured to receive multi-source heterogeneous data, process the multi-source heterogeneous data to generate a data object and an object relationship, and import the data object and the object relationship into a graph database.
In this embodiment, the multi-source heterogeneous data is a data source in multiple forms, and the multi-source heterogeneous data includes: structured data, semi-structured data, and unstructured data; structured data can be represented and stored using a relational database, such as MySQL, oracle, SQL Server, etc., representing data in two dimensions. Corresponding information can be obtained through the inherent key value; the semi-structured data can obtain corresponding information through flexible key value adjustment, and the format of the data is not fixed, for example json, and the information stored under the same key value may be numerical, text or dictionary or list. Semi-structured data, belonging to the same class of entities, may have different attributes, even if they are grouped together, the order of these attributes is not important. The unstructured data is data without a fixed structure, and comprises office documents, texts, pictures, various reports, images, audio/video information and the like in all formats. The storage is generally performed directly in its entirety, and is generally stored in a binary data format.
Optionally, the multi-source heterogeneous data may further include third-party data input by a third party, where the third-party data is data other than data structures distinguishable by the data fusion system, and the structure of the third-party data may be defined by the third party, and after obtaining the third-party data, the data fusion system determines the structure of the third-party data by communicating with the third party, and converts the third-party data into structural data.
In this embodiment, processing the multi-source heterogeneous data includes: the method comprises the steps of extracting entities and entity relations of multi-source heterogeneous data to obtain data objects and object relations in the multi-source heterogeneous data, and when the entities of the structured data and the semi-structured data are extracted, extracting the data objects and the object relations directly based on expression forms of the structured data and the semi-structured data, for example, extracting fields of tables stored in a relational database corresponding to the structured data and relations among the fields to obtain the data objects and the object relations.
In this embodiment, for unstructured data, the unstructured data includes: metadata and a data body, the processing of the multi-source heterogeneous data comprising: extracting metadata of unstructured data, taking the metadata as a data object, analyzing the relation among the metadata, and taking the relation among the metadata as an object relation.
In this embodiment, the generated data object has at least one object attribute, the object attribute is used to characterize the characteristics of the data object, each data object establishes an object relationship with other data objects through the respective object attribute, the data object is used to represent an entity in the knowledge graph, and the object relationship is used to represent the relationship between the entities.
The plug-in module 102 analyzes the multi-source heterogeneous data by adopting an artificial intelligence algorithm to obtain an analysis result.
In this embodiment, the artificial intelligence algorithm in the plug-in module may be a preset algorithm, or may be an algorithm loaded into the plug-in module in real time in a loading manner, and the artificial intelligence algorithm used is different for the multi-source heterogeneous data with different data structures, for example, for the video data, the artificial intelligence algorithm includes: face recognition, image enhancement, pose estimation, etc. For text data, the artificial intelligence algorithm includes: material identification, document content identification, and the like.
Optionally, the plug-in module may further analyze the multi-source heterogeneous data by using an artificial intelligence algorithm after receiving an activation signal of the artificial intelligence algorithm for different multi-source heterogeneous data, specifically, the activation signal may be a signal sent by a user selecting a document object (multi-source heterogeneous data) in the intelligent sand table interface and a personal artificial intelligence algorithm corresponding to the document, and the system sends the activation signal to the plug-in module and displays an analysis result for the user in the intelligent sand table interface.
The fusion module 103 is configured to fuse the data object and the object relationship in the graph database with the analysis result, respectively, to obtain a fusion result.
In this embodiment, the parsing result includes: the fusion of the data object and the object relation with the analysis result respectively to obtain a fusion result comprises the following steps: the method includes detecting a relationship between a data object and an analysis object of a plurality of analysis results, associating the data object with the analysis object in response to the data object having a relationship with the analysis object, and importing the analysis object and the relationship between the data object and the analysis object into a graph database.
Optionally, the parsing result includes: the above fusing the data object and the object relationship with the analysis result to obtain a fused result further includes: detecting whether the relationship between the data objects matches the first relationship, and in response to the data objects not matching the first relationship, importing the first relationship and an analytic object associated with the first relationship into the graph database.
The data fusion system comprises a mapping module, a database and a mapping module, wherein the mapping module receives multi-source heterogeneous data, processes the multi-source heterogeneous data to generate a data object and an object relation, and imports the data object and the object relation into a database; secondly, the plug-in module analyzes the multi-source heterogeneous data by adopting an artificial intelligence algorithm to obtain an analysis result; and finally, fusing the data object and the object relation in the graph database with the analysis result by the fusion module to obtain a fusion result. Therefore, the analysis result obtained by the analysis of the plug-in module is fused with the data object and the object relation, so that the information of the data object in the graph database is richer, and the analysis efficiency of the joint analysis of multi-industry multi-source heterogeneous data is improved.
Optionally, in this embodiment, the fusion module includes: the system comprises a graph exploration analysis submodule, a histogram analysis submodule and a visualization analysis submodule. The graph exploration and analysis sub-module supports the functions of expanding the relationship based on a certain data object, providing user-defined graph query for users and the like by depending on the capacity of a graph database. The histogram analysis submodule carries out multi-dimensional statistics and display on data objects displayed on the intelligent sand table interface, provides a convenient range reverse selection function according to a statistical result, and provides a user with the ability of rapidly mastering the overall situation when the analysis objects are too large. The visualized analysis submodule can provide a visualized analysis environment for a user and provide production data for multi-user cooperation and assistant decision-making based on GIS (Geographic Information System) visualization, remote sensing image analysis, report visualization and intelligent report generation.
In some embodiments of the present disclosure, the data fusion system further includes: a search module (not shown in the figures). The search module is used for searching the data objects in the imported graph database to obtain a search result, and sending the search result to the fusion module, and the fusion module is also used for counting and displaying the search result.
In this embodiment, the search module may provide flexible configurable search conditions and strong fuzzy search support, and the search module may search for related information in the graph database based on preset candidate search conditions (e.g., data object names, object relationship names), and perform data statistics on search results to provide a situation overview.
Optionally, the system further includes: and in the intelligent sand table interface, the search module can also extract the attributes of the imported data objects as candidate search conditions, and the user can complete the configuration of the search conditions only by simply checking the intelligent sand table interface. The intelligent retrieval module user can select all or part of the search results to enter the fusion module to carry out further data fusion.
The data fusion system provided by the embodiment comprises the search module, the search module is used for searching the data objects in the graph database, and the fusion module is used for counting and displaying the search results of the search module, so that the search effect of multi-source heterogeneous data is improved, and the display effect of the search results is also improved.
Optionally, the fusion module is further configured to display the analysis result on an intelligent sand table interface, receive an operation of a user on an analysis object in the analysis result, determine the objects to be imported, the relationships between the objects to be imported, and the relationships between the objects to be imported and each data object based on the operation of the user on the analysis object in the analysis result, and import the relationships between the objects to be imported, and the relationships between the objects to be imported and each data object into the graph database.
In this embodiment, the object in the analysis result may be a data object already identified by the data fusion system, or may also be a data object not identified by the data fusion system, and the object to be imported is a data object in a relationship to be analyzed selected by a user, and the data object to be analyzed by the user may be determined based on an operation of the user on the analysis object in the analysis result.
Optionally, the fusion module further displays the data object and the object relationship through an intelligent sandbox interface, receives a modification operation (for example, merging the data object, splitting the data object, deleting the object relationship, adding the object relationship, and the like) of the user on the data object and the object relationship, and modifies the data object and the object relationship in the graph data based on the modification operation of the user on the data object and the object relationship to obtain a new data object and an object relationship.
In some optional implementations of the embodiment, when the multi-source heterogeneous data includes unstructured data, the mapping module is further configured to extract a data body of the unstructured data, associate the data body, and store the data body in the body database.
In this optional implementation, after the data main body is extracted, the data main body may be analyzed by the plug-in module to obtain the data object corresponding to the data main body, and the data object corresponding to the data main body is associated with the data main body, and the data main body is associated with the data object, so that the data main body corresponding to the data object may be conveniently and quickly obtained when the fusion module analyzes the data object, and the analysis result corresponding to the data main body may be obtained.
In this optional implementation manner, when the multi-source heterogeneous data includes unstructured data, the mapping module extracts metadata of the unstructured data, and generates a data object and an object relationship based on the metadata. The plug-in module analyzes the data main body of the unstructured data (namely the data content of the unstructured data) by adopting an artificial intelligence algorithm, and the fusion module fuses the data object, the object relation and the analysis result corresponding to the data main body together to obtain the data content of multi-level and detailed multi-source heterogeneous data.
In another embodiment of the disclosure, the fusion module may analyze the associated timing data of the entity, running a timing analysis algorithm relying on the capabilities of the timing database. For example: for a certain sensor entity in a graph database, the historical data of the sensor is stored in a time sequence database, and a user can call the time sequence data through a fusion module to carry out analysis.
In some embodiments of the present disclosure, the system further comprises: the object operation interface is used for receiving operation information which is input by a user and is used for operating the multi-source heterogeneous data; the mapping module is also used for receiving the operation information and converting the multi-source heterogeneous data into a data object and an object relation based on the operation information.
In this embodiment, the object operation interface is an interface supporting a user to drag and connect a data object, as shown in fig. 2, in the object operation interface, the user places a data object 1, a data object 2, and a data object 3 in multi-source heterogeneous data in the object operation interface through a drag operation, and establishes an object relationship, namely, a relationship 1, between the data object 1 and the data object 2 by connecting an attribute name a of the data object 1 and an attribute name b1 of the data object 2; an object relationship, relationship 2, is established between the data object 2 and the data object 3 by connecting the attribute name b2 of the data object 2 and the attribute name c of the data object 3.
In this embodiment, the data object is names of different types of entities such as people, plants, animals, events, scenes, and the like in the multi-source heterogeneous data, the data object has at least one object attribute, and the attribute name in the object operation interface is a representative of the object attribute.
The data fusion system provided by this embodiment further includes: and the object operation interface is used for allowing a user to simply and conveniently operate the data in the multi-source heterogeneous data through the object operation interface, so that the data fusion efficiency of the multi-source heterogeneous data is improved.
In some embodiments of the present disclosure, the system further comprises: and in response to the data object having the location attribute, the fusion module marks the data object on the geographic sand table interface to display the location of the data object on the geographic information system.
In this embodiment, the geographic information system may be a GIS system, and the GIS system may collect, store, manage, compute, analyze, display, and describe geographic distribution data related to the entire or a portion of the earth's surface space. After the multi-source heterogeneous data is processed to generate the data objects and the object relationships, the object attributes of some data objects may have location attributes, and the location attributes are used for representing location information of the data objects, for example, the data objects are people, and the location attributes of the people are geographic locations (for example, longitude and latitude) where the people are located.
In this embodiment, the fusion module determines the position of the data object in the geographic information system based on the position attribute of the data object, and marks the data object at the position, thereby displaying the position of the data object in the geographic information system. Specifically, as shown in fig. 3, three data objects S1, S2, and S3 are respectively marked at different positions of the geographic sand table interface, and the positions of the data objects in the geographic information system are reflected by the different positions of the geographic sand table interface.
The data fusion system provided by this embodiment further includes: the user can simply and conveniently mark the data with the position attribute in the multi-source heterogeneous data through the geographic sand table interface, and the information comprehension degree of the user on the data object is improved.
In some optional implementation manners of this embodiment, the geographic sand table interface is further configured to receive input information, and the fusion module determines and displays, on the geographic sand table interface, a data object corresponding to the input information in the multi-source heterogeneous data based on the input information and the analysis result.
In this embodiment, the input information may include: position points in different geographical area ranges or geographical area ranges to be identified corresponding to different data objects; and when the input information is position points in different geographic area ranges, determining data objects related to the position points in the different geographic area ranges in the analysis result, and displaying the data objects related to the position points in the different geographic area ranges in the multi-source heterogeneous data on a geographic sand table interface.
And when the input information is the geographical area range to be identified corresponding to different data objects, determining the geographical area range to be identified of the current data object in the analysis result, and displaying all data objects related to the geographical area range to be identified of the current data object in the multi-source heterogeneous data on a geographical sand table interface.
In the optional implementation mode, the data object corresponding to the input information in the multi-source heterogeneous data is determined based on the input information and the analysis result, targeted information analysis can be performed based on the requirements of the user, and the information fusion effect is improved.
In some embodiments of the present disclosure, the system further comprises: and the fusion module receives the statistical conditions through the statistical sand table interface and determines the relationship between different data objects corresponding to the statistical conditions and the statistical result.
In this embodiment, the statistical sand table interface is provided with an information input control, a statistical condition for performing statistics on all data objects and object relationships of the multi-source heterogeneous data can be obtained through the information input control fusion module, the data objects are counted based on the statistical condition to obtain a statistical result, and the statistical result is displayed on the statistical sand table interface.
In this embodiment, the statistical conditions include: for conditions such as clustering and summing of different data objects, as shown in fig. 4, two object types are preset on a statistical sand table interface for current multi-source heterogeneous data, two types of objects are displayed on the statistical sand table interface, such as people and ships on the statistical sand table interface, and after the fusion module performs statistics on the multi-source heterogeneous data, it is determined that all things are 103, wherein the number of people is 3, and the 3 people are R1, R2 and R3 shown in fig. 4.
Optionally, the fusion module is further configured to mark the data object corresponding to the statistical result after selecting the statistical result, as in fig. 4, after selecting the bar graph related to the person in fig. 4, mark the data object corresponding to the statistical result at R1, R2, and R3 accordingly.
The data fusion system provided by the embodiment further includes: and the statistical sand table interface is used for analyzing the data in the multi-source heterogeneous data simply and conveniently by a user, so that the information awareness of the user on the data object is improved.
Optionally, the fusion module may further perform statistical calculation on the data object and the object relationship by using a preset statistical algorithm (such as summation, clustering, and the like) to obtain a statistical result, and the statistical sand table interface is further configured to display statistical data of the data object and the object relationship.
Referring to FIG. 5, a flow 500 is shown of one embodiment of a data fusion method according to the present disclosure, the data fusion method comprising the steps of:
In this embodiment, the multi-source heterogeneous data is a data source in multiple forms, and the multi-source heterogeneous data includes: structured data, semi-structured data, and unstructured data; the structured data can be represented and stored by using a relational database, the data in a two-dimensional form is represented, and corresponding information can be acquired through the inherent key values. The semi-structured data can obtain corresponding information through flexible key value adjustment, and the format of the data is not fixed. The unstructured data is data without a fixed structure, and comprises office documents, texts, pictures, various reports, images, audio/video information and the like in all formats.
Optionally, the multi-source heterogeneous data may also include third party data entered by a third party.
In this embodiment, processing the multi-source heterogeneous data includes: and when the entity extraction is carried out on the structured data and the semi-structured data, the data object and object relation extraction can be directly carried out on the basis of the expression forms of the structured data and the semi-structured data.
In this embodiment, for unstructured data, the unstructured data includes: metadata and a data body, the processing of the multi-source heterogeneous data comprising: extracting metadata of unstructured data, taking the metadata as a data object, analyzing the relation among the metadata, and taking the relation among the metadata as an object relation.
In this embodiment, the artificial intelligence algorithm may be a preset algorithm, or an algorithm that is loaded into the plug-in module in real time by loading or the like, and the artificial intelligence algorithm is different for the multisource heterogeneous data with different data structures, for example, for the video data, the artificial intelligence algorithm includes: face recognition, image enhancement, pose estimation, and the like. For text data, artificial intelligence algorithms include: material identification, document content identification, and the like.
And step 504, fusing the data object and the object relation with the analysis result respectively to obtain a fusion result.
In this embodiment, the parsing result includes: the fusion of the data object and the object relation with the analysis result respectively to obtain a fusion result comprises the following steps: the method includes detecting a relationship between a data object and an analysis object of a plurality of analysis results, associating the data object with the analysis object in response to the data object having a relationship with the analysis object, and importing the analysis object and the relationship between the data object and the analysis object into a graph database.
Optionally, the parsing result includes: the above fusing the data object and the object relationship with the analysis result to obtain a fused result further includes: detecting whether the relationship between the data objects matches the first relationship, and in response to the data objects not matching the first relationship, importing the first relationship and an analytic object associated with the first relationship into the graph database.
The data fusion method provided by the embodiment comprises the steps of firstly, receiving multi-source heterogeneous data, secondly, processing the multi-source heterogeneous data to generate a data object and an object relation, and importing the data object and the object relation into a graph database; thirdly, analyzing the multi-source heterogeneous data by adopting an artificial intelligence algorithm to obtain an analysis result; and finally, fusing the data objects and the object relations in the graph database with the analysis result respectively to obtain a fusion result. Therefore, the analysis result is fused with the data object and the object relation, so that the information of the data object in the graph database is richer, and the analysis efficiency of the joint analysis of multi-industry multi-source heterogeneous data is improved.
In some optional implementation manners of this embodiment, the fusing the data object and the object relationship with the analysis result respectively to obtain a fusion result includes: based on the parsing result, data objects and object relationships in the graph database are expanded.
In this embodiment, after the execution subject on which the data fusion method operates obtains the analysis result, the analysis object in the analysis result is determined, and in response to that the analysis object and the data object have an association relationship, the analysis object and the association relationship are imported into the graph database, so that the relationship between the data object and the object in the graph database is expanded.
In this embodiment, the association relationship includes: relationships between the analytical objects and the data objects.
According to the method for obtaining the fusion result, the data object and object relation is expanded through the analysis result, the richness of the data object and object relation in the graph database is improved, and the diversity of data is guaranteed.
Optionally, after obtaining the analysis result, the execution entity on which the data fusion method operates determines an attribute value of an object attribute of the analysis object in the analysis result, and in response to that the attribute value of the analysis object has an association relationship with an object attribute of the data object, introduces the attribute of the analysis object and the association relationship into the graph database, thereby expanding the data object and object relationship in the graph database.
In some optional implementation manners of this embodiment, the fusing the data object and the object relationship with the analysis result, and obtaining the fusion result includes: and displaying the analysis result through a geographic sand table interface with a geographic information system, receiving input information of a user through the geographic sand table interface, and determining and displaying a data object corresponding to the input information in the multi-source heterogeneous data on the geographic sand table interface based on the input information and the analysis result.
Optionally, the fusing the data object and the object relationship with the analysis result respectively to obtain a fused result further includes: in response to the data object having the location attribute, the data object is marked on the geographic sandbox interface.
In this embodiment, the input information may include: position points in different geographical area ranges or geographical area ranges to be identified corresponding to different data objects; and when the input information is position points in different geographic area ranges, determining data objects related to the position points in the different geographic area ranges in the analysis result, and displaying the data objects related to the position points in the different geographic area ranges in the multi-source heterogeneous data on a geographic sand table interface.
And when the input information is the geographical area range to be identified corresponding to different data objects, determining the geographical area range to be identified of the current data object in the analysis result, and displaying all data objects related to the geographical area range to be identified of the current data object in the multi-source heterogeneous data on a geographical sand table interface.
According to the method for obtaining the fusion result, the data object corresponding to the input information in the multi-source heterogeneous data is determined based on the input information and the analysis result, targeted information analysis can be performed based on the requirements of the user, and the information fusion effect is improved.
In some embodiments of the disclosure, the fusing the data object and the object relationship with the analysis result respectively to obtain the fusion result includes: and receiving the statistical conditions through the statistical sand table interface, and determining the relationship statistical result between different data objects corresponding to the statistical conditions.
In this embodiment, the statistical sand table interface is provided with an information input control, a statistical condition for performing statistics on all data objects and object relationships of the multi-source heterogeneous data can be obtained through the information input control fusion module, the data objects are counted based on the statistical condition to obtain a statistical result, and the statistical result is displayed on the statistical sand table interface.
In this embodiment, the statistical conditions include: clustering, summing, etc. conditions for different data objects.
Optionally, the fusing the data object and the object relationship with the analysis result respectively to obtain a fused result further includes: and marking the data object corresponding to the statistical result after the statistical result is selected.
According to the method for obtaining the fusion result, the data in the multi-source heterogeneous data can be simply and conveniently analyzed through the statistical sand table interface, and the information awareness of the user on the data object is improved.
With continued reference to fig. 6, as an implementation of the method shown in fig. 5, the present application provides a data fusion apparatus, which corresponds to the method embodiment shown in fig. 5 and can be applied to various electronic devices.
As shown in fig. 6, the data fusion apparatus 600 of the present embodiment may include: receiving section 601, processing section 602, analyzing section 603, and fusing section 604. The receiving unit 601 may be configured to receive multi-source heterogeneous data. The processing unit 602 may be configured to process multi-source heterogeneous data, and generate a data object and an object relationship. The analyzing unit 603 may be configured to analyze the multi-source heterogeneous data by using an artificial intelligence algorithm, so as to obtain an analysis result. The fusion unit 604 may be configured to fuse the data object and the object relationship with the analysis result to obtain a fusion result.
In some embodiments of the present disclosure, the fusion unit is further configured to: based on the parsing result, data objects and object relationships in the graph database are expanded.
In some embodiments of the disclosure, the fusion unit is further configured to: and displaying the analysis result through a geographic sand table interface with a geographic information system, receiving input information of a user through the geographic sand table interface, and determining and displaying a data object corresponding to the input information in the multi-source heterogeneous data on the geographic sand table interface based on the input information and the analysis result.
In some embodiments of the disclosure, the above fusion unit is further configured to: and receiving the statistical conditions through the statistical sand table interface, and determining the relationship statistical result between different data objects corresponding to the statistical conditions.
In the data fusion device provided by this embodiment, firstly, the receiving unit 601 receives multi-source heterogeneous data, secondly, the processing unit 602 processes the multi-source heterogeneous data to generate a data object and an object relationship, and imports the data object and the object relationship into a graph database; thirdly, the analysis unit 603 analyzes the multi-source heterogeneous data by adopting an artificial intelligence algorithm to obtain an analysis result; finally, the fusion unit 604 fuses the data object and the object relationship in the graph database with the analysis result, respectively, to obtain a fusion result. Therefore, the analysis result is fused with the data object and the object relation, so that the information of the data object in the graph database is richer, and the analysis efficiency of the joint analysis of multi-industry multi-source heterogeneous data is improved.
The data fusion method provided by the embodiment of the disclosure can be applied to any electronic device with a display function, for example, electronic paper, a mobile phone, a tablet computer, a television, a notebook computer, a digital photo frame, a wearable device, or a navigator.
In other embodiments of the present disclosure, a computer-readable storage medium is also provided, in which a computer program is stored, wherein the computer program, when executed by a processor, is capable of implementing the steps of the method as shown in fig. 5.
The data fusion method provided by the embodiment comprises the steps of firstly, receiving multi-source heterogeneous data, secondly, processing the multi-source heterogeneous data to generate a data object and an object relation, and importing the data object and the object relation into a graph database; thirdly, analyzing the multi-source heterogeneous data by adopting an artificial intelligence algorithm to obtain an analysis result; and finally, fusing the data objects and the object relations in the graph database with the analysis result respectively to obtain a fusion result. Therefore, the analysis result is fused with the data object and the object relation, so that the information of the data object in the graph database is richer, and the analysis efficiency of the joint analysis of multi-industry multi-source heterogeneous data is improved.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus and methods according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As used herein and in the appended claims, the singular forms of words include the plural and vice versa, unless the context clearly dictates otherwise. Thus, when referring to the singular, the plural of the corresponding terms is generally included. Similarly, the terms "comprising" and "including" are to be construed as being inclusive rather than exclusive. Likewise, the terms "include" and "or" should be construed as inclusive unless such an interpretation is explicitly prohibited herein. Where the term "example" is used herein, particularly when it comes after a set of terms, it is merely exemplary and illustrative, and should not be considered exclusive or extensive.
Further aspects and ranges of adaptability will become apparent from the description provided herein. It should be understood that various aspects of the present disclosure may be implemented alone or in combination with one or more other aspects. It should also be understood that the description and specific examples herein are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure.
Several embodiments of the present disclosure have been described in detail above, but it is apparent that various modifications and variations can be made to the embodiments of the present disclosure by those skilled in the art without departing from the spirit and scope of the present disclosure. The scope of the present disclosure is defined by the appended claims.
Claims (10)
1. A data fusion system, the system comprising:
the mapping module is used for receiving multi-source heterogeneous data, processing the multi-source heterogeneous data to generate a data object and an object relation, and importing the data object and the object relation into a graph database;
the plug-in module analyzes the multi-source heterogeneous data by adopting an artificial intelligence algorithm to obtain an analysis result;
and the fusion module is used for fusing the data object and the object relation in the graph database with the analysis result respectively to obtain a fusion result.
2. The system of claim 1, further comprising:
the search module is used for searching the data object imported into the graph database to obtain a search result and sending the search result to the fusion module;
the fusion module is also used for counting and displaying the retrieval result.
3. The system of claim 1, wherein,
when the multi-source heterogeneous data comprises unstructured data, the mapping module is further used for extracting a data main body of the unstructured data, associating the data main body and storing the data main body in a main body database.
4. The system of any of claims 1-3, further comprising:
the object operation interface is used for receiving operation information which is input by a user and is used for operating the multi-source heterogeneous data;
the mapping module is further used for receiving the operation information and converting the multi-source heterogeneous data into data objects and object relations based on the operation information.
5. The system of any of claims 1-3, further comprising:
a geographic sandbox interface with a geographic information system, the fusion module marking the data object on the geographic sandbox interface to display the location of the data object on the geographic information system in response to the data object having a location attribute.
6. The system of claim 5, wherein the geographic sand table interface is further configured to receive input information, and the fusion module determines and displays a data object corresponding to the input information in the multi-source heterogeneous data on the geographic sand table interface based on the input information and the parsing result.
7. The system of any of claims 1-3, further comprising:
and the fusion module receives the statistical conditions through the statistical sand table interface and determines the relationship between different data objects corresponding to the statistical conditions and the statistical result.
8. A method of data fusion, the method comprising:
receiving multi-source heterogeneous data;
processing the multi-source heterogeneous data to generate a data object and an object relation;
analyzing the multi-source heterogeneous data by adopting an artificial intelligence algorithm to obtain an analysis result;
and fusing the data object and the object relation with the analysis result respectively to obtain a fusion result.
9. A data fusion apparatus, the apparatus comprising:
a receiving unit configured to receive multi-source heterogeneous data;
the processing unit is configured to process the multi-source heterogeneous data to generate a data object and an object relation;
the analysis unit is configured to adopt an artificial intelligence algorithm to analyze the multi-source heterogeneous data to obtain an analysis result;
and the fusion unit is configured to fuse the data object and the object relation with the analysis result respectively to obtain a fusion result.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method as claimed in claim 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117591025A (en) * | 2023-11-27 | 2024-02-23 | 海南榕树家信息科技有限公司 | Multi-source heterogeneous data processing system |
| CN117591025B (en) * | 2023-11-27 | 2024-05-10 | 海南榕树家信息科技有限公司 | Multi-source heterogeneous data processing system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117591025A (en) * | 2023-11-27 | 2024-02-23 | 海南榕树家信息科技有限公司 | Multi-source heterogeneous data processing system |
| CN117591025B (en) * | 2023-11-27 | 2024-05-10 | 海南榕树家信息科技有限公司 | Multi-source heterogeneous data processing system |
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