CN109063114B - Heterogeneous data integration method and device for energy cloud platform, terminal and storage medium - Google Patents
Heterogeneous data integration method and device for energy cloud platform, terminal and storage medium Download PDFInfo
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Abstract
The invention discloses a heterogeneous data integration method, a heterogeneous data integration device, a heterogeneous data integration terminal and a storage medium of an energy cloud platform, wherein the method comprises the following steps: constructing a global ontology of the energy cloud platform based on the SSN; extracting a local ontology by using a mapping rule according to table and field description information provided by an energy management system; semanticizing the data of the energy management system to obtain RDF triple data, and calculating semantic similarity of corresponding Chinese description information according to the table and the description information of the field; according to the semantic similarity, constructing a semantic relation between the global ontology and the local ontology by using ontology mapping; packaging a TDB of Jena to store the RDF triple data, a mapping rule set, the global ontology, the local ontology and extension data in the TDB. The invention can solve the problems of various syntaxes, semantics and system heterogeneity in system data, improve the working efficiency of data processing, and also improve the universality, reusability and expansibility of a system platform.
Description
Technical Field
The invention relates to the technical field of data integration, in particular to a heterogeneous data integration method, a heterogeneous data integration device, a heterogeneous data integration terminal and a storage medium of an energy cloud platform.
Background
The modern energy management system is established on the basis of the technology of the Internet of things and has the characteristics of high efficiency and strong real-time performance. In order to promote the further development of the modern energy management system, people solve the common problems of equipment access, data storage, service management and the like in the energy management system by constructing an energy cloud platform, and reduce the development and maintenance cost of the energy management system. At present, data access schemes of energy cloud platforms mostly focus on solving real-time transmission and storage of mass data, and a large amount of energy management system data can be accessed to the energy cloud platforms. However, the data of the energy management system has great heterogeneity, and the energy cloud platform lacks effective processing on the data heterogeneity, so that the data is difficult to share, and greater value cannot be generated.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present invention is to provide a heterogeneous data integration method, apparatus, terminal and storage medium for an energy cloud platform, which can solve various syntax, semantics and system heterogeneous problems in system data, improve data processing efficiency, and also improve the universality, reusability and expansibility of the system platform.
To solve the above problem, an embodiment of the present invention provides a heterogeneous data integration method for an energy cloud platform, which is suitable for being executed in a computing device, and includes:
constructing a global ontology of the energy cloud platform based on the SSN;
extracting a corresponding local ontology by using a mapping rule according to the description information of the table and the description information of the field provided by the energy management system;
semanticizing the data of the energy management system to obtain RDF ternary group data;
calculating the semantic similarity of corresponding Chinese description information according to the description information of the table and the description information of the fields;
according to the semantic similarity, constructing a semantic relation between the global ontology and the local ontology by using ontology mapping;
packaging a TDB of Jena to store the RDF triple data, a mapping rule set, the global ontology, the local ontology and extension data in the TDB.
Further, the global ontology of the energy cloud platform is constructed based on the multiplexing SSN ontology, and specifically includes:
and constructing a global ontology of the energy cloud platform in a manual editing mode by adopting a seven-step method proposed by Stanford university and referring to an energy management related standard and a construction model on the basis of the reuse ontology SSN.
Further, the extracting, according to the description information of the table and the description information of the field provided by the energy management system, a corresponding local ontology by using a mapping rule specifically includes:
when the energy management system is accessed to the energy platform, a system ID is allocated to serve as a naming space of a local body;
mapping a table into a class in an ontology, and mapping description information of the table into rdfs: comment of the corresponding class;
mapping fields as attributes in the body, and mapping the description information of the fields as rdfs: comment of the corresponding fields; wherein the content of the first and second substances,
when the field is a main key, mapping processing is not carried out;
when the field is a foreign key, mapping the field into an object type attribute;
when the field is a non-foreign key, the field is mapped to an attribute of the data type.
Further, semantization is performed on the data of the energy management system to obtain RDF triple data, which specifically includes:
loading corresponding local ontologies according to the system ID, and combining the local ontologies into corresponding namespaces;
identifying a class in a local body to which each piece of table data in the data of the energy management system belongs, and generating a corresponding class instance according to the table data to obtain RDF triple data of the class;
and identifying the attribute in the local ontology to which the field in the data of the energy management system belongs, and analyzing the value of the field to obtain the RDF triple data of the attribute.
Further, the calculating the semantic similarity of the corresponding chinese description information according to the description information of the table and the description information of the field specifically includes:
dividing the Chinese description information into words, removing stop words to obtain two Chinese phrases P1(w1,w2,…,wn)，P2(w1,w2,…,wm) Wherein n is<＝m；
Traverse P1Is provided with P1The middle term is wi(i-1, 2, … n), with P2Each word in the dictionary is subjected to similarity calculation according to the synonym forest semantic dictionary, and the value sim with the highest similarity is recordedj(j＝1,2,…n)；
The semantic similarity of the phrase is taken as SIM, namely the semantic similarity of the Chinese description information, wherein, the semantic similarity formula of the Chinese description information is,
further, the building the semantic relationship between the global ontology and the local ontology by using ontology mapping according to the semantic similarity specifically includes:
performing simple ontology mapping, identifying classes corresponding to the global ontology and the local ontology one by adopting a top-down algorithm, calculating a semantic relation between attributes in classes with similar semantics, and acquiring a part of rule sets and matched classes and attributes;
filtering out matched body elements;
and (4) carrying out complex ontology mapping, calculating attributes with similar semantics by adopting a bottom-up algorithm, and establishing a mapping relation of the classes to which the attributes belong.
The embodiment of the invention also provides a heterogeneous data integration device of the energy cloud platform, which comprises the following steps:
the global ontology construction module is used for constructing a global ontology of the energy cloud platform based on the SSN;
the local ontology construction module is used for extracting a corresponding local ontology by using a mapping rule according to the description information of the table and the description information of the field provided by the energy management system;
the semantization module is used for semantizing the data of the energy management system to obtain RDF ternary group data;
the semantic similarity calculation module is used for calculating the semantic similarity of corresponding Chinese description information according to the description information of the table and the description information of the fields;
the semantic relation construction module is used for constructing the semantic relation between the global ontology and the local ontology by using ontology mapping according to the semantic similarity;
and the packaging module is used for packaging the TDB of Jena so as to store the RDF triple data, the mapping rule set, the global body, the local body and the extension data in the TDB.
The embodiment of the invention also provides a heterogeneous data integration terminal of an energy cloud platform, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the heterogeneous data integration method of the energy cloud platform is realized.
The embodiment of the invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program runs, a device where the computer-readable storage medium is located is controlled to execute the above heterogeneous data integration method for the energy cloud platform.
The embodiment of the invention has the following beneficial effects:
the embodiment of the invention provides a heterogeneous data integration method, a heterogeneous data integration device, a terminal and a storage medium for an energy cloud platform, wherein the method comprises the following steps: constructing a global ontology of the energy cloud platform based on the SSN; extracting a corresponding local ontology by using a mapping rule according to table and field description information provided by an energy management system; semanticizing the data of the energy management system to obtain RDF triple data, and calculating semantic similarity of corresponding Chinese description information according to the table and the description information of the field; according to the semantic similarity, constructing a semantic relation between the global ontology and the local ontology by using ontology mapping; packaging a TDB of Jena to store the RDF triple data, a mapping rule set, the global ontology, the local ontology and extension data in the TDB. The invention can solve the problems of various syntaxes, semantics and system heterogeneity in system data, improve the working efficiency of data processing, and also improve the universality, reusability and expansibility of a system platform.
Drawings
Fig. 1 is a schematic flowchart of a heterogeneous data integration method for an energy cloud platform according to a first embodiment of the present invention;
fig. 2 is a schematic diagram of semanticizing data of the energy management system to obtain RDF triple data according to the first embodiment of the present invention;
FIG. 3 is a diagram illustrating the semantics of data provided by the first embodiment of the present invention;
FIG. 4 is a diagram illustrating ontology mapping according to a first embodiment of the present invention;
FIG. 5 is a diagram illustrating simple ontology mapping according to a first embodiment of the present invention;
FIG. 6 is a diagram illustrating a complex ontology mapping according to a first embodiment of the present invention;
FIG. 7 is a diagram illustrating a data storage according to a first embodiment of the present invention;
FIG. 8 is a diagram illustrating a data query interface design according to a first embodiment of the present invention;
fig. 9 is a schematic structural diagram of a heterogeneous data integration apparatus of an energy cloud platform according to a second embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Please refer to fig. 1-8 for a first embodiment of the present invention.
As shown in fig. 1, fig. 1 is a schematic flowchart of a heterogeneous data integration method of an energy cloud platform according to a first embodiment of the present invention. The heterogeneous data integration method of the energy cloud platform is suitable for being executed in a computing device and comprises the following steps:
s101, constructing a global ontology of the energy cloud platform based on the SSN.
Specifically, a seven-step method proposed by Stanford university is adopted, and on the basis of the reuse ontology SSN, a global ontology of the energy cloud platform is constructed in a manual editing mode by referring to energy management related standards and a construction model.
And S102, extracting a corresponding local ontology by using a mapping rule according to the description information of the table and the description information of the field provided by the energy management system.
Specifically, when the energy management system accesses the energy platform, a system ID is assigned as a namespace of the local ontology. For example, if the ID of a system is "sys-01", the corresponding local ontology namespace is http:// edu.scut.ensave/sys-01 #.
Mapping the table to the Class (i.e. owl: Class) in the ontology, and forming the IRI of the corresponding Class in the form of (namespace + table name)cBuilding triplets (IRI)cRdf type, own Class), and mapping the description information of the table to rdfs comment of the corresponding Class, constructing a triple (IRI)cAnd rdfs: comment, table description information). For example, the table name is EneryType, and according to the mapping rule, there are:
<http://edu.scut.ensave/sys-01#EneryType>rdf:type owl:Class。
< http:// edu.scut.ensave/sys-01# EneryType > rdfs: comment "energy type".
Mapping fields to attributes in ontologies, combining IRIs of attributes in the form of (namespace + has + class name + field name)pAnd mapping the description information of the field to rdfs: comment, namely triple-with-triple (IRI) of the corresponding fieldpRdfs: comment) by which relationships between attributes and classes are established, i.e., there are triplets (IRI)p，rdfs:domain,IRIc). The processing of the fields is divided into three cases:
when the field is a main key, mapping processing is not carried out;
when the field is a foreign key, then the field is mapped to an object type attribute, i.e., a triple (IRI) is constructedpRdf: type, own: ObjectProperty), and the field names referenced by the foreign keys are grouped into IRI according to the same rulerAs an attribute rdfs seaAlso, there is a triple (IRI)p,rdfs:seeAlso,IRIr)；
When the field is not a foreign key, then the field is mapped to an attribute of the data type, i.e., a triple (IRI) is constructedp,rdf:type,owl:DatatypeProperty)；
It should be noted that IRI composed of field names referenced by foreign keys according to the same rule is used as the value of rdfs: seeAlso for the attribute.
S103, semantization is carried out on the data of the energy management system to obtain RDF ternary group data.
As shown in fig. 2-3, fig. 2 is a schematic diagram of semantization of data of the energy management system to obtain RDF triple data according to the first embodiment of the present invention, and fig. 3 is a schematic diagram of semantization of data according to the first embodiment of the present invention.
Specifically, according to the system ID, loading corresponding local ontologies through a semantic storage module, and combining the local ontologies into a corresponding namespace Sn. If the system ID is "sys-01", the corresponding namespace is http:// edu.scut.
The XML data is parsed and the contents in the tag < data > are retrieved.
And identifying a class in a local body to which each piece of table data in the data of the energy management system belongs, and generating a corresponding class instance according to the table data to obtain RDF triple data of the class.
It should be noted that, first, a table name (tag name of each piece of XML data) S is recognizedtObtaining the IRI of the corresponding classcIs (S)n+St) By IRIcAnd acquiring the corresponding class from the local ontology. Such asCross label<EneryType>Obtaining the EneryType of the table name, and then the IRI corresponding to the typecIs (S)n+ EneryType), i.e.<http://edu.scut.ensave/sys-01#EneryType>And acquiring the corresponding class from the local ontology according to the IRI.
The primary key value in each XML data is SkeyThe class to which is IRIcTo (S)n+Skey) IRI combined into an exampleiGenerating triplets (IRI)i,rdf:type,IRIc)，(IRIiAnd rdf type, own NamedIndInaviral). For example, the primary key value of the first data in the figure is 001, i.e.,
IRI of the exampleiIn order to realize the purpose,<http://edu.scut.ensave/sys-01#001>，
IRI of the genuscIn order to realize the purpose,<http://edu.scut.ensave/sys-01#EneryType>，
namely, there are three groups:
<http://edu.scut.ensave/sys-01#001>
rdf:type<http://edu.scut.ensave/sys-01#EneryType>；
rdf:type owl:NamedIndividual.
and identifying the attribute in the local ontology to which the field in the data of the energy management system belongs, and analyzing the value of the field to obtain the RDF triple data of the attribute.
Note that the field name S is acquired by the tagfThe value of the analyzed field is V, and the corresponding example is IRIi. Corresponding resource IRI available from field namespIs (S)n+has+St+Sf) The corresponding attributes are loaded from the local ontology. The processing of attributes is divided into two categories: one is data type attribute, directly constructs triple (IRI)i,IRIiV). One is object type attribute, and obtains corresponding class IRI according to rdfs-range relationr(which is actually the class in which the table referenced by the foreign key is mapped in the local ontology), and obtaining the corresponding attribute IRI according to the rdfs: seeAlso relationshiprpFirst, create IRIrThe anonymous example of (A) has a triple (: anon, rdf: type, IRI)r) The anonymous instance has a value V, i.e., a triplet (: a)non,IRIrpV), finally (IRI)i,IRIp,:anon)。
And S104, calculating the semantic similarity of the corresponding Chinese description information according to the description information of the table and the description information of the fields.
Specifically, the calculating the semantic similarity of the corresponding chinese description information according to the description information of the table and the description information of the field specifically includes:
dividing the Chinese description information into words, removing stop words to obtain two Chinese phrases P1(w1,w2,…,wn)，P2(w1,w2,…,wm) Wherein n is<＝m；
Traverse P1Is provided with P1The middle term is wi(i-1, 2, … n), with P2Each word in the dictionary is subjected to similarity calculation according to the synonym forest semantic dictionary, and the value sim with the highest similarity is recordedj(j＝1,2,…n)；
The semantic similarity of the phrase is taken as SIM, namely the semantic similarity of the Chinese description information, wherein, the semantic similarity formula of the Chinese description information is,
and S105, constructing the semantic relation between the global ontology and the local ontology by using ontology mapping according to the semantic similarity.
As shown in fig. 4, fig. 4 is a schematic diagram of ontology mapping provided by the first embodiment of the present invention.
Specifically, simple ontology mapping is carried out, a top-down algorithm is adopted to identify classes corresponding to the global ontology and the local ontology one by one, the semantic relation between attributes in classes with similar semantics is calculated, and a part of rule sets and matched classes and attributes are obtained;
filtering out matched body elements;
and (4) carrying out complex ontology mapping, calculating attributes with similar semantics by adopting a bottom-up algorithm, and establishing a mapping relation of the classes to which the attributes belong.
It can be understood that, on the basis of calculating the semantic similarity of the Chinese description information, the ontology mapping is divided into a simple ontology mapping and a complex ontology mapping, after the simple ontology mapping is performed, a part of rule sets and matched class lists are obtained, the matched classes are filtered, on the basis of the simple ontology mapping, the complex ontology mapping is performed, a part of rule sets are obtained, and then all the rule sets are collected and output.
In this embodiment, the simple ontology mapping is a top-down algorithm, and handles the case of 1:1 matching, that is, the case where there are one-to-one corresponding classes in the global ontology and the local ontology. The idea of simple ontology mapping is that if two classes are similar, then the two classes have similar properties. Therefore, when the ontology mapping is carried out, the similar classes in the ontology are matched first, and the similar attributes between the two classes are matched immediately, so that the 1:1 simple ontology mapping is completed.
As shown in fig. 5, fig. 5 is a schematic diagram of a simple ontology mapping provided by the first embodiment of the present invention. The specific steps of the simple ontology mapping are as follows:
loading global ontology OntoglobalAnd local ontology Ontolocal；
Analyzing the ontology to respectively obtain the class lists L of the global ontology and the local ontologyglobal，Llocal；
Traverse LlocalTaking it as class ClocalChinese description information DesclocalAnd L isglobalClass C inglobalChinese description information DescglobalSemantic similarity calculation is carried out through a natural language processing module, and the semantic similarity of the two is obtained to be simdescWill (C)local,Cglobal,simdesc) Saving to the semantic similarity calculation result list LsimPerforming the following steps;
setting similarity threshold SIMminInitialize the matching list LmatchWill list LsimSorting according to similarity from big to small, then traversing LsimObtained byTaking each calculation result (C)local,Cglobal,simdesc)；
If simdescSmaller than SIMminIf yes, ending the matching;
if ClocalOr CglobalOne of them being present in LmatchIf so, not matching the result;
c to be matchedlocal，CglobalIs added to LmatchPerforming the following steps;
generating a triplet (C)local owl:equivalentClass Cglobal) Added to result R;
match Clocal，CglobalThe matching process is similar to that of the matching body class list, and is not repeated;
and returning a result R.
In this embodiment, the complex ontology mapping is a bottom-up algorithm, and handles the case of 1: n matching, that is, there is a case where one class corresponds to multiple classes in the global ontology and the local ontology. The idea of the complex ontology mapping is that if the attributes of one class are similar to the attributes of a plurality of classes, a one-to-many mapping relationship exists between the class and the plurality of classes.
As shown in fig. 6, fig. 6 is a schematic diagram of a complex ontology mapping provided by the first embodiment of the present invention. The specific steps of the complex ontology mapping are as follows:
removing global ontology Onto according to the matching list of the simple ontology mappingglobalAnd local ontology OntolocalOn the basis of the classes and the corresponding attributes, performing complex ontology mapping;
analyzing the ontology, and respectively obtaining the attribute lists L of the global ontology and the local ontologyglobal，Llocal；
Traverse LlocalGet its attribute PlocalChinese description information DesclocalAnd L isglobalProperty P inglobalChinese description information DescglobalCalculating the semantic similarity through the natural language processing module to obtain the semantic similarity sim of the twodescWill (P)local,Pglobal,simdesc) Saving to the semantic similarity calculation result list LsimPerforming the following steps;
to LsimSorting according to the similarity from big to small, traversing LsimRemoval of simdescMatching results less than the threshold while de-duplicating and retaining only the result with the greatest similarity between attributes, e.g. with the result in the list (P)local,Pglobal1,0.99)，(Plocal,,Pglobal20.88), then the second calculation is removed. After sorting and de-duplication, L' is obtainedsim；
Go through LsimObtaining P in the calculation resultglobalClass C in corresponding global ontologyglobalGo through LsimThe remaining elements, if the result (P')global,Plocal,simdesc) P' of ChineseglobalClass of the corresponding global ontology is also CglobalThen obtain PlocalCorresponding class ClocalAccording to the calculation result, the triple rule can be obtained,
(Clocal,rdfs:subClassOf,Cglobal)，
(P＇global,owl:equivalentProperty,Plocal)；
and obtaining the rule set of the complex ontology mapping after the traversal is finished.
It can be understood that, in the embodiment, the chinese description information of the field and the table is used as the basis of the semantic calculation, and compared with the semantic calculation based on the table and the field name, the semantic calculation is more consistent with the actual development situation and more accurate.
S106, packaging the TDB of Jena to store the RDF triple data, the mapping rule set, the global ontology, the local ontology and the extension data in the TDB.
As shown in fig. 7-8, fig. 7 is a schematic diagram of data storage according to a first embodiment of the present invention, and fig. 8 is a schematic diagram of a data query interface design according to the first embodiment of the present invention.
In this embodiment, the TDB encapsulating Jena is implemented by inputting only the ID and storage data allocated by the system during storage, generating the storage address of the corresponding system by the internal address register according to the system ID, then automatically completing the database creation and writing operation, and finally storing the storage data in the database. When data is inquired, the system ID is required to be input, the corresponding storage address is acquired through the address inquirer, then the inside of the module can be automatically connected with the database according to the address, and corresponding inquiry is executed after the data is read. In TDB, each system stores four types of data, respectively:
SYS _ NAMED _ LOCAL: a local body;
SYS _ NAMED _ DATA: RDF data;
SYS _ NAMED _ RULES: a rule set;
SYS _ NAMED _ INF: and expanding the data.
The expansion data is the shared data (RDF triple data) which is expanded through a Reasoner (Reasoner) combined with a mapping rule set, a global ontology of an energy cloud platform and a local ontology, and the expanded shared data set is obtained.
It should be noted that, in the present embodiment, the ontology inference technology is used to implement data expansion, and a SPARQL query language processing engine ARQ is encapsulated to implement unified query of RDF data;
in this embodiment, the query encapsulates the ARQ module in the semantic development framework Jena, and a query callback is added in the query process of an ARQ standard, which can be customized to implement data processing.
It can be understood that, in the embodiment, the database TDB is adopted, and the RDF data storage and query method has the advantages of high performance and convenience in query compared with a storage scheme adopting a relational database.
The heterogeneous data integration method for the energy cloud platform provided by the embodiment is used for realizing heterogeneous data integration of the energy cloud platform based on a semantic technology, has high universality, and meets the requirements that the energy cloud platform is continuously connected to different energy management systems and integrates heterogeneous data among the different energy management systems, and the semantic energy management data lays a solid foundation for intelligent processing of subsequent data. Meanwhile, the hybrid ontology method is adopted in the embodiment, so that the expansibility is good, and with the continuous expansion of the scale of the energy cloud platform, the knowledge in the aspect of energy management internet of things represented by the global ontology can be continuously expanded, which is used as data accumulation for the research in the field of semantic internet of things.
Please refer to fig. 9 for a second embodiment of the present invention.
As shown in fig. 9, fig. 9 is a schematic structural diagram of a heterogeneous data integration apparatus of an energy cloud platform according to a second embodiment of the present invention.
The embodiment of the invention also provides a heterogeneous data integration device of the energy cloud platform, which comprises the following steps:
and the global ontology construction module 201 is used for constructing a global ontology of the energy cloud platform based on the multiplexing ontology SSN.
Specifically, a seven-step method proposed by Stanford university is adopted, and on the basis of the reuse ontology SSN, a global ontology of the energy cloud platform is constructed in a manual editing mode by referring to energy management related standards and a construction model.
And the local ontology building module 202 is configured to extract a corresponding local ontology according to the table description information and the field description information provided by the energy management system by using the mapping rule.
Specifically, when the energy management system accesses the energy platform, a system ID is assigned as a namespace of the local ontology. For example, if the ID of a system is "sys-01", the corresponding local ontology namespace is http:// edu.scut.ensave/sys-01 #.
Mapping the table to the Class (i.e. owl: Class) in the ontology, and forming the IRI of the corresponding Class in the form of (namespace + table name)cBuilding triplets (IRI)cRdf type, own Class), and mapping the description information of the table to rdfs comment of the corresponding Class, constructing a triple (IRI)cAnd rdfs: comment, table description information). For example, the table name is EneryType, and according to the mapping rule, there are:
<http://edu.scut.ensave/sys-01#EneryType>rdf:type owl:Class。
< http:// edu.scut.ensave/sys-01# EneryType > rdfs: comment "energy type".
Mapping fields to attributes in ontologies, combining IRIs of attributes in the form of (namespace + has + class name + field name)pAnd mapping the description information of the field to rdfs: comment, namely triple-with-triple (IRI) of the corresponding fieldpRdfs: comment) by which relationships between attributes and classes are established, i.e., there are triplets (IRI)p，rdfs:domain,IRIc). The processing of the fields is divided into three cases:
when the field is a main key, mapping processing is not carried out;
when the field is a foreign key, then the field is mapped to an object type attribute, i.e., a triple (IRI) is constructedpRdf: type, own: ObjectProperty), and the field names referenced by the foreign keys are grouped into IRI according to the same rulerAs an attribute rdfs seaAlso, there is a triple (IRI)p,rdfs:seeAlso,IRIr)；
When the field is not a foreign key, then the field is mapped to an attribute of the data type, i.e., a triple (IRI) is constructedp,rdf:type,owl:DatatypeProperty)；
It should be noted that IRI composed of field names referenced by foreign keys according to the same rule is used as the value of rdfs: seeAlso for the attribute.
And the semantization module 203 is used for semanticizing the data of the energy management system to obtain RDF ternary group data.
As shown in fig. 2-3, fig. 2 is a schematic diagram of semantization of data of the energy management system to obtain RDF triple data according to the first embodiment of the present invention, and fig. 3 is a schematic diagram of semantization of data according to the first embodiment of the present invention.
Specifically, according to the system ID, loading corresponding local ontologies through a semantic storage module, and combining the local ontologies into a corresponding namespace Sn. If the system ID is "sys-01", the corresponding namespace is http:// edu.scut.
The XML data is parsed and the contents in the tag < data > are retrieved.
And identifying a class in a local body to which each piece of table data in the data of the energy management system belongs, and generating a corresponding class instance according to the table data to obtain RDF triple data of the class.
It should be noted that, first, a table name (tag name of each piece of XML data) S is recognizedtObtaining the IRI of the corresponding classcIs (S)n+St) By IRIcAnd acquiring the corresponding class from the local ontology. E.g. by means of a label<EneryType>Obtaining the EneryType of the table name, and then the IRI corresponding to the typecIs (S)n+ EneryType), i.e.<http://edu.scut.ensave/sys-01#EneryType>And acquiring the corresponding class from the local ontology according to the IRI.
The primary key value in each XML data is SkeyThe class to which is IRIcTo (S)n+Skey) IRI combined into an exampleiGenerating triplets (IRI)i,rdf:type,IRIc)，(IRIiAnd rdf type, own NamedIndInaviral). For example, the primary key value of the first data in the figure is 001, i.e.,
IRI of the exampleiIn order to realize the purpose,<http://edu.scut.ensave/sys-01#001>，
IRI of the genuscIn order to realize the purpose,<http://edu.scut.ensave/sys-01#EneryType>，
namely, there are three groups:
<http://edu.scut.ensave/sys-01#001>
rdf:type<http://edu.scut.ensave/sys-01#EneryType>；
rdf:type owl:NamedIndividual.
and identifying the attribute in the local ontology to which the field in the data of the energy management system belongs, and analyzing the value of the field to obtain the RDF triple data of the attribute.
Note that the field name S is acquired by the tagfThe value of the analyzed field is V, and the corresponding example is IRIi. Corresponding resource IRI available from field namespIs (S)n+has+St+Sf) Loading from local bodiesThe corresponding attribute. The processing of attributes is divided into two categories: one is data type attribute, directly constructs triple (IRI)i,IRIiV). One is object type attribute, and obtains corresponding class IRI according to rdfs-range relationr(which is actually the class in which the table referenced by the foreign key is mapped in the local ontology), and obtaining the corresponding attribute IRI according to the rdfs: seeAlso relationshiprpFirst, create IRIrThe anonymous example of (A) has a triple (: anon, rdf: type, IRI)r) The anonymous instance has a value V, i.e., there are triples (: anon, IRI)rpV), finally (IRI)i,IRIp,:anon)。
And the semantic similarity calculation module 204 is configured to calculate semantic similarity of corresponding chinese description information according to the description information of the table and the description information of the field.
Specifically, the calculating the semantic similarity of the corresponding chinese description information according to the description information of the table and the description information of the field specifically includes:
dividing the Chinese description information into words, removing stop words to obtain two Chinese phrases P1(w1,w2,…,wn)，P2(w1,w2,…,wm) Wherein n is<＝m；
Traverse P1Is provided with P1The middle term is wi(i-1, 2, … n), with P2Each word in the dictionary is subjected to similarity calculation according to the synonym forest semantic dictionary, and the value sim with the highest similarity is recordedj(j＝1,2,…n)；
The semantic similarity of the phrase is taken as SIM, namely the semantic similarity of the Chinese description information, wherein, the semantic similarity formula of the Chinese description information is,
a semantic relation constructing module 205, configured to construct a semantic relation between the global ontology and the local ontology according to the semantic similarity by using ontology mapping.
As shown in fig. 4, fig. 4 is a schematic diagram of ontology mapping provided by the first embodiment of the present invention.
Specifically, simple ontology mapping is carried out, a top-down algorithm is adopted to identify classes corresponding to the global ontology and the local ontology one by one, the semantic relation between attributes in classes with similar semantics is calculated, and a part of rule sets and matched classes and attributes are obtained;
filtering out matched body elements;
and (4) carrying out complex ontology mapping, calculating attributes with similar semantics by adopting a bottom-up algorithm, and establishing a mapping relation of the classes to which the attributes belong.
It can be understood that, on the basis of calculating the semantic similarity of the Chinese description information, the ontology mapping is divided into a simple ontology mapping and a complex ontology mapping, after the simple ontology mapping is performed, a part of rule sets and matched class lists are obtained, the matched classes are filtered, on the basis of the simple ontology mapping, the complex ontology mapping is performed, a part of rule sets are obtained, and then all the rule sets are collected and output.
In this embodiment, the simple ontology mapping is a top-down algorithm, and handles the case of 1:1 matching, that is, the case where there are one-to-one corresponding classes in the global ontology and the local ontology. The idea of simple ontology mapping is that if two classes are similar, then the two classes have similar properties. Therefore, when the ontology mapping is carried out, the similar classes in the ontology are matched first, and the similar attributes between the two classes are matched immediately, so that the 1:1 simple ontology mapping is completed.
As shown in fig. 5, fig. 5 is a schematic diagram of a simple ontology mapping provided by the first embodiment of the present invention. The specific steps of the simple ontology mapping are as follows:
loading global ontology OntoglobalAnd local ontology Ontolocal；
Analyzing the ontology to respectively obtain the class lists L of the global ontology and the local ontologyglobal，Llocal；
Traverse LlocalTaking it as class ClocalChinese description information DesclocalAnd L isglobalClass C inglobalChinese description information DescglobalSemantic similarity calculation is carried out through a natural language processing module, and the semantic similarity of the two is obtained to be simdescWill (C)local,Cglobal,simdesc) Saving to the semantic similarity calculation result list LsimPerforming the following steps;
setting similarity threshold SIMminInitialize the matching list LmatchWill list LsimSorting according to similarity from big to small, then traversing LsimObtaining each calculation result (C)local,Cglobal,simdesc)；
If simdescSmaller than SIMminIf yes, ending the matching;
if ClocalOr CglobalOne of them being present in LmatchIf so, not matching the result;
c to be matchedlocal，CglobalIs added to LmatchPerforming the following steps;
generating a triplet (C)local owl:equivalentClass Cglobal) Added to result R;
match Clocal，CglobalThe matching process is similar to that of the matching body class list, and is not repeated;
and returning a result R.
In this embodiment, the complex ontology mapping is a bottom-up algorithm, and handles the case of 1: n matching, that is, there is a case where one class corresponds to multiple classes in the global ontology and the local ontology. The idea of the complex ontology mapping is that if the attributes of one class are similar to the attributes of a plurality of classes, a one-to-many mapping relationship exists between the class and the plurality of classes.
As shown in fig. 6, fig. 6 is a schematic diagram of a complex ontology mapping provided by the first embodiment of the present invention. The specific steps of the complex ontology mapping are as follows:
removing global ontology Onto according to the matching list of the simple ontology mappingglobalAnd local ontology OntolocalAnd corresponding toOn the basis of the attributes, performing complex ontology mapping;
analyzing the ontology, and respectively obtaining the attribute lists L of the global ontology and the local ontologyglobal，Llocal；
Traverse LlocalGet its attribute PlocalChinese description information DesclocalAnd L isglobalProperty P inglobalChinese description information DescglobalCalculating the semantic similarity through the natural language processing module to obtain the semantic similarity sim of the twodescWill (P)local,Pglobal,simdesc) Saving to the semantic similarity calculation result list LsimPerforming the following steps;
to LsimSorting according to the similarity from big to small, traversing LsimRemoval of simdescMatching results less than the threshold while de-duplicating and retaining only the result with the greatest similarity between attributes, e.g. with the result in the list (P)local,Pglobal1,0.99)，(Plocal,,Pglobal20.88), then the second calculation is removed. After sorting and de-duplication, L' is obtainedsim；
Go through LsimObtaining P in the calculation resultglobalClass C in corresponding global ontologyglobalGo through LsimThe remaining elements, if the result (P')global,Plocal,simdesc) P' of ChineseglobalClass of the corresponding global ontology is also CglobalThen obtain PlocalCorresponding class ClocalAccording to the calculation result, the triple rule can be obtained,
(Clocal,rdfs:subClassOf,Cglobal)，
(P＇global,owl:equivalentProperty,Plocal)；
and obtaining the rule set of the complex ontology mapping after the traversal is finished.
It can be understood that, in the embodiment, the chinese description information of the field and the table is used as the basis of the semantic calculation, and compared with the semantic calculation based on the table and the field name, the semantic calculation is more consistent with the actual development situation and more accurate.
An encapsulating module 206, configured to encapsulate the TDB of Jena, so as to store the RDF triple data, the mapping rule set, the global ontology, the local ontology, and the extension data in the TDB.
As shown in fig. 7-8, fig. 7 is a schematic diagram of data storage according to a first embodiment of the present invention, and fig. 8 is a schematic diagram of a data query interface design according to the first embodiment of the present invention.
In this embodiment, the TDB encapsulating Jena is implemented by inputting only the ID and storage data allocated by the system during storage, generating the storage address of the corresponding system by the internal address register according to the system ID, then automatically completing the database creation and writing operation, and finally storing the storage data in the database. When data is inquired, the system ID is required to be input, the corresponding storage address is acquired through the address inquirer, then the inside of the module can be automatically connected with the database according to the address, and corresponding inquiry is executed after the data is read. In TDB, each system stores four types of data, respectively:
SYS _ NAMED _ LOCAL: a local body;
SYS _ NAMED _ DATA: RDF data;
SYS _ NAMED _ RULES: a rule set;
SYS _ NAMED _ INF: and expanding the data.
The expansion data is the shared data (RDF triple data) which is expanded through a Reasoner (Reasoner) combined with a mapping rule set, a global ontology of an energy cloud platform and a local ontology, and the expanded shared data set is obtained.
It should be noted that, in the present embodiment, the ontology inference technology is used to implement data expansion, and a SPARQL query language processing engine ARQ is encapsulated to implement unified query of RDF data;
in this embodiment, the query encapsulates the ARQ module in the semantic development framework Jena, and a query callback is added in the query process of an ARQ standard, which can be customized to implement data processing.
It can be understood that, in the embodiment, the database TDB is adopted, and the RDF data storage and query method has the advantages of high performance and convenience in query compared with a storage scheme adopting a relational database.
The heterogeneous data integration device of the energy cloud platform provided by the embodiment realizes heterogeneous data integration of the energy cloud platform based on the semantic technology, has higher universality, and meets the requirements that the energy cloud platform is continuously connected to different energy management systems and integrates heterogeneous data among the different energy management systems, and the semantic energy management data lays a solid foundation for intelligent processing of subsequent data. Meanwhile, the hybrid ontology method is adopted in the embodiment, so that the expansibility is good, and with the continuous expansion of the scale of the energy cloud platform, the knowledge in the aspect of energy management internet of things represented by the global ontology can be continuously expanded, which is used as data accumulation for the research in the field of semantic internet of things.
The embodiment of the invention also provides a heterogeneous data integration terminal of an energy cloud platform, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the heterogeneous data integration method of the energy cloud platform is realized.
The embodiment of the invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program runs, a device where the computer-readable storage medium is located is controlled to execute the above heterogeneous data integration method for the energy cloud platform.
The foregoing is directed to the preferred embodiment of the present invention, and it is understood that various changes and modifications may be made by one skilled in the art without departing from the spirit of the invention, and it is intended that such changes and modifications be considered as within the scope of the invention.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Claims (9)
1. A heterogeneous data integration method of an energy cloud platform, which is suitable for being executed in a computing device, is characterized by comprising the following steps:
constructing a global ontology of the energy cloud platform based on the SSN;
extracting a corresponding local ontology by using a mapping rule according to the description information of the table and the description information of the field provided by the energy management system;
semanticizing the data of the energy management system to obtain RDF ternary group data;
calculating the semantic similarity of corresponding Chinese description information according to the description information of the table and the description information of the fields;
according to the semantic similarity, constructing a semantic relation between the global ontology and the local ontology by using ontology mapping;
packaging a TDB of Jena to store the RDF triple data, a mapping rule set, the global ontology, the local ontology and extension data in the TDB.
2. The heterogeneous data integration method of the energy cloud platform according to claim 1, wherein the global ontology of the energy cloud platform is constructed based on the multiplexing SSN ontology, and specifically comprises:
and constructing a global ontology of the energy cloud platform in a manual editing mode by adopting a seven-step method proposed by Stanford university and referring to an energy management related standard and a construction model on the basis of the reuse ontology SSN.
3. The method for integrating the heterogeneous data of the energy cloud platform according to claim 1, wherein the corresponding local ontology is extracted by using a mapping rule according to description information of a table and description information of a field provided by an energy management system, and specifically comprises:
when the energy management system is accessed to the energy cloud platform, a system ID is allocated to serve as a naming space of a local body;
mapping a table into a class in an ontology, and mapping description information of the table into rdfs: comment of the corresponding class;
mapping fields as attributes in the body, and mapping the description information of the fields as rdfs: comment of the corresponding fields; wherein the content of the first and second substances,
when the field is a main key, mapping processing is not carried out;
when the field is a foreign key, mapping the field into an object type attribute;
when the field is a non-foreign key, the field is mapped to an attribute of the data type.
4. The heterogeneous data integration method of the energy cloud platform according to claim 3, wherein the data of the energy management system is semantically converted to obtain RDF triple data, specifically:
loading corresponding local ontologies according to the system ID, and combining the local ontologies into corresponding namespaces;
identifying a class in a local body to which each piece of table data in the data of the energy management system belongs, and generating a corresponding class instance according to the table data to obtain RDF triple data of the class;
and identifying the attribute in the local ontology to which the field in the data of the energy management system belongs, and analyzing the value of the field to obtain the RDF triple data of the attribute.
5. The method for integrating the heterogeneous data of the energy cloud platform according to claim 1, wherein the semantic similarity of the corresponding chinese description information is calculated according to the description information of the table and the description information of the field, and specifically comprises:
after the Chinese description information is subjected to word segmentation and word removal stopping processingTo obtain two Chinese phrases P1(w1,w2,…,wn)，P2(w1,w2,…,wm) Wherein n is<＝m；
Traverse P1Is provided with P1The middle term is wi(i-1, 2, … n), with P2Each word in the dictionary is subjected to similarity calculation according to the synonym forest semantic dictionary, and the value sim with the highest similarity is recordedj(j＝1,2,…n)；
The semantic similarity of the phrase is taken as SIM, namely the semantic similarity of the Chinese description information, wherein, the semantic similarity formula of the Chinese description information is,
6. the method for integrating the heterogeneous data of the energy cloud platform according to claim 1, wherein the semantic relationship between the global ontology and the local ontology is constructed by using ontology mapping according to the semantic similarity, and specifically comprises:
performing simple ontology mapping, identifying classes corresponding to the global ontology and the local ontology one by adopting a top-down algorithm, calculating a semantic relation between attributes in classes with similar semantics, and acquiring a part of rule sets and matched classes and attributes;
filtering out matched body elements;
and (4) carrying out complex ontology mapping, calculating attributes with similar semantics by adopting a bottom-up algorithm, and establishing a mapping relation of the classes to which the attributes belong.
7. A heterogeneous data integration device of an energy cloud platform, comprising:
the global ontology construction module is used for constructing a global ontology of the energy cloud platform based on the SSN;
the local ontology construction module is used for extracting a corresponding local ontology by using a mapping rule according to the description information of the table and the description information of the field provided by the energy management system;
the semantization module is used for semantizing the data of the energy management system to obtain RDF ternary group data;
the semantic similarity calculation module is used for calculating the semantic similarity of corresponding Chinese description information according to the description information of the table and the description information of the fields;
the semantic relation construction module is used for constructing the semantic relation between the global ontology and the local ontology by using ontology mapping according to the semantic similarity;
and the packaging module is used for packaging the TDB of Jena so as to store the RDF triple data, the mapping rule set, the global body, the local body and the extension data in the TDB.
8. A heterogeneous data integration terminal of an energy cloud platform, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the heterogeneous data integration method of the energy cloud platform according to any one of claims 1 to 6 when executing the computer program.
9. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus on which the computer-readable storage medium is located to perform the method for heterogeneous data integration of an energy cloud platform according to any one of claims 1 to 6.
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