CN116450613A - Resource-oriented geographic sample data service method, equipment and storage medium - Google Patents
Resource-oriented geographic sample data service method, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to a resource-oriented geographic sample data service method, equipment and a storage medium, which are used for establishing a conceptual model and a logic model of geographic artificial intelligent sample data service metadata description; establishing a geographic artificial intelligence sample data service resource system; constructing a resource-oriented geographic artificial intelligence sample data service interface and a mapping relation between the interface and the geographic artificial intelligence sample data service resource; defining a uniform resource description identifier of a geographic artificial intelligence sample data service resource of a representational state transition style, and binding the uniform resource description identifier with a sample data service interface; a network access interface for publishing a geographic artificial intelligence sample data service. The invention enhances the intelligent service capability of the space data infrastructure, thereby supporting the construction of the space data infrastructure with the ready artificial intelligence, meeting the sharing service requirement of the multi-source heterogeneous geographic artificial intelligence sample data and providing powerful support for the sharing and application of the geographic artificial intelligence sample data.
Description
Technical Field
The present invention relates to the field of geographic artificial intelligence technologies, and in particular, to a method, an apparatus, and a storage medium for serving geographic sample data for resources.
Background
The implementation of projects such as national remote sensing earth observation and geographical national condition census and the like already forms massive earth observation data with strong timeliness, wide coverage and rich information quantity. The evolution of geographic artificial intelligence technology has injected new vitality into these applications of earth observation data. Remote sensing image interpretation and monitoring technology based on deep learning has shown a certain advantage over the traditional method, and is popularized and applied by related departments. The academia also uses these technologies increasingly to better utilize ever-increasing earth observation data, and is standing in the data science sector to study based on geographic artificial intelligence analysis models, using semi-automated or automated means to help reduce the human costs in these sectors, such as smart cities, resource environment management, disaster emergency response, etc.
Although mass earth observation data is accumulated in the field of geographic space at present, a large amount of open source and available sample data is lacking, and the earth observation artificial intelligence model and application are restricted to be further developed as main bottlenecks, and a resource-oriented, interoperable and standardized sample service method needs to be researched to solve the sharing service requirement of multi-source heterogeneous geographic artificial intelligence sample data.
Disclosure of Invention
In order to solve the technical problems in the prior art, the invention aims to provide a resource-oriented geographic sample data service method, equipment and a storage medium, which support the release of geographic artificial intelligence sample data meeting the model analysis requirement as sample data service and provide powerful support for sharing and application of the geographic artificial intelligence sample data.
In order to achieve the above object, the present invention provides a resource-oriented geographic sample data service method, which includes the following steps:
step S1, establishing a conceptual model and a logic model of geographic artificial intelligence sample data service metadata description;
s2, establishing a geographic artificial intelligence sample data service resource system;
s3, constructing a resource-oriented geographic artificial intelligence sample data service interface and a mapping relation between the interface and the geographic artificial intelligence sample data service resource;
s4, defining a uniform resource description identifier of the geographic artificial intelligence sample data service resource of the expressive state transition style, and binding with a sample data service interface;
and S5, publishing a network access interface of the geographic artificial intelligence sample data service.
According to an aspect of the present invention, in the step S1, specifically includes:
step S11, constructing a geographic artificial intelligence sample data service metadata description conceptual model, wherein the metadata description conceptual model at least comprises the following concepts:
a geographic artificial intelligence sample data service provider TDServiceProvider representing metadata information of a provider of the geographic artificial intelligence sample data service, a service instance trainigdatasetservice representing metadata information of a geographic artificial intelligence sample data service instance, a geographic artificial intelligence sample data set trainigdataset representing metadata information of a sample data set issued as a sample data service, a geographic artificial intelligence sample data service operation TDServiceOperation representing metadata information of related operations provided by the geographic artificial intelligence sample data service instance, a geographic artificial intelligence sample data service parameter tdservicename representing settable parameter information of a geographic artificial intelligence sample data service operation;
step S12, constructing a geographic artificial intelligence sample data service metadata description logic model based on the metadata description conceptual model,
the necessary attributes of the geographic artificial intelligence sample data service provider class TDServiceProvider at least comprise service provider names and service provider contact information; the necessary attributes of the geographic artificial intelligence sample data service instance class TrainigDatasetService at least comprise an identification, a name, a description, an address, a release time, a task type, a sample level type and a time-space range; the necessary attributes of the geographic artificial intelligent sample data set class TrainigDataset at least comprise sample data set identification, name, description, version number and sample data volume; the necessary attributes of the geographic artificial intelligence sample data service operation class TDServiceoperation at least comprise an operation name, a distributed computing platform list and an operation description; the necessary attributes of the geographic artificial intelligence sample data service parameter class TDServiceParameter at least comprise parameter names, parameter directions and descriptions.
According to an aspect of the present invention, in the step S2, specifically includes:
step S21, defining various resources related to the geographic artificial intelligence sample data service and resource categories to which the resources belong, wherein the resource categories comprise aggregate resources, individual resources and view resources;
and S22, defining resource numbers and hierarchical relations of various resources related to the geographic artificial intelligence sample data service.
According to one aspect of the present invention, in the step S21, the various resources involved in the geographic artificial intelligence sample data service include:
a service instance, a sample data set description, and a sample data unit belonging to an individual resource;
a sample data set, belonging to a set resource;
a sample dataset task view, a sample dataset traceability view, a sample dataset quality view, a sample dataset change set view belonging to a view resource.
According to one aspect of the invention, the resource numbers and the hierarchical relationships of various resources related to the geographic artificial intelligence sample data service are specifically as follows:
the resource number of the service instance is 1, and no upper layer resource exists;
the resource number of the sample data set is 2, and the upper layer resource is 1;
the resource number described by the sample data set is 3, and the upper layer resource is 2;
the resource number of the sample data set is 4, and the upper layer resource is 3;
the resource number of the task view of the sample data set is 5, and the upper layer resource is 3;
the resource number of the traceable view of the sample data set is 6, and the upper layer resource is 3;
the resource number of the sample data set quality view is 7, and the upper layer resource is 3;
the resource number of the sample data set change set view is 8, and the upper layer resource is 3;
the resource number of the sample data unit is 9, and the upper layer resource is 4.
According to an aspect of the present invention, in the step S3, specifically includes:
step S31, defining various interfaces related to the geographic artificial intelligence sample data service;
and S32, mapping various interfaces related to the geographic artificial intelligence sample data service with various resources related to the geographic artificial intelligence sample data service one by one.
According to an aspect of the present invention, there is provided an electronic apparatus including: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, the one or more computer programs are stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory, so that the electronic device performs a resource-oriented geographic sample data service method according to any of the above technical solutions.
According to an aspect of the present invention, there is provided a computer readable storage medium storing computer instructions which, when executed by a processor, implement a resource-oriented geographic sample data service method according to any of the above-mentioned technical solutions.
The invention provides a resource-oriented geographic sample data service method, equipment and a storage medium, which are characterized in that firstly, a conceptual model and a logic model for supporting the expression of sample data service metadata are established, a geographic artificial intelligence sample data service resource system is constructed by analyzing the property of geographic artificial intelligence sample data resources, finally, a resource-oriented geographic artificial intelligence sample data service interface is designed to realize a sample data service architecture, and a network access interface of the geographic artificial intelligence sample data service is issued, so that the required geographic artificial intelligence sample data can be obtained through the network access interface, the traditional file-based geographic artificial intelligence sample data sharing mode is broken through, the resource-oriented, interoperable and standardized sample service is realized, the intelligent service capability of a space data infrastructure is effectively enhanced, the space data infrastructure which is ready for construction is supported, the sharing service requirement of multi-source heterogeneous geographic artificial intelligence sample data is met, and powerful support is provided for the sharing and application of the geographic artificial intelligence sample data.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 schematically illustrates a flow diagram for constructing a resource-oriented geographic sample data service method provided in one embodiment of the invention;
FIG. 2 schematically illustrates an overall process diagram for building a resource-oriented geographic artificial intelligence sample data service in accordance with one embodiment of the invention;
FIG. 3 schematically illustrates a geographic artificial intelligence sample data service metadata description conceptual model diagram in accordance with one embodiment of the invention;
FIG. 4 schematically illustrates a geographic artificial intelligence sample data service metadata description logic model diagram in accordance with one embodiment of the invention;
FIG. 5 schematically illustrates a geographic artificial intelligence sample data services resource hierarchy in accordance with one embodiment of the invention;
FIG. 6 schematically illustrates a geographic artificial intelligence sample data service interface diagram in accordance with one embodiment of the invention;
FIG. 7 schematically illustrates a geographic artificial intelligence sample data service access flow in accordance with one embodiment of the invention.
Detailed Description
The description of the embodiments of this specification should be taken in conjunction with the accompanying drawings, which are a complete description of the embodiments. In the drawings, the shape or thickness of the embodiments may be enlarged and indicated simply or conveniently. Furthermore, portions of the structures in the drawings will be described in terms of separate descriptions, and it should be noted that elements not shown or described in the drawings are in a form known to those of ordinary skill in the art.
Any references to directions and orientations in the description of the embodiments herein are for convenience only and should not be construed as limiting the scope of the invention in any way. The following description of the preferred embodiments will refer to combinations of features, which may be present alone or in combination, and the invention is not particularly limited to the preferred embodiments. The scope of the invention is defined by the claims.
As shown in fig. 1 and 2, the method for serving geographic sample data for resource according to the present invention includes the following steps:
step S1, establishing a conceptual model and a logic model of geographic artificial intelligence sample data service metadata description;
s2, establishing a geographic artificial intelligence sample data service resource system;
s3, constructing a resource-oriented geographic artificial intelligence sample data service interface and a mapping relation between the interface and the geographic artificial intelligence sample data service resource;
s4, defining a uniform resource description identifier of the geographic artificial intelligence sample data service resource of the expressive state transition style, and binding with a sample data service interface;
and S5, publishing a network access interface of the geographic artificial intelligence sample data service.
In the embodiment, a conceptual model and a logic model for supporting the expression of sample data service metadata are firstly established, a geographic artificial intelligence sample data service resource system is constructed by analyzing the property of geographic artificial intelligence sample data resources, finally a resource-oriented geographic artificial intelligence sample data service interface is designed to realize a sample data service architecture, a network access interface of the geographic artificial intelligence sample data service is distributed, required geographic artificial intelligence sample data can be obtained through the network access interface, a traditional file-based geographic artificial intelligence sample data sharing mode is broken through, resource-oriented interoperable standardized sample service is realized, and the intelligent service capability of a spatial data infrastructure is effectively enhanced, so that the construction of the artificial intelligence ready spatial data infrastructure is supported, the sharing service requirement of multi-source heterogeneous geographic artificial intelligence sample data is met, and powerful support is provided for sharing and application of the geographic artificial intelligence sample data.
As shown in fig. 3 and 4, in one embodiment of the present invention, preferably, in step S1, the method specifically includes:
step S11, constructing a geographic artificial intelligence sample data service metadata description conceptual model, wherein the metadata description conceptual model at least comprises the following concepts:
a geographic artificial intelligence sample data service provider TDServiceProvider representing metadata information of a provider of the geographic artificial intelligence sample data service, a service instance trainigdatasetservice representing metadata information of a geographic artificial intelligence sample data service instance, a geographic artificial intelligence sample data set trainigdataset representing metadata information of a sample data set issued as a sample data service, a geographic artificial intelligence sample data service operation TDServiceOperation representing metadata information of related operations provided by the geographic artificial intelligence sample data service instance, a geographic artificial intelligence sample data service parameter tdservicename representing settable parameter information of a geographic artificial intelligence sample data service operation;
step S12, constructing a geographic artificial intelligence sample data service metadata description logic model based on the metadata description conceptual model,
wherein the necessary attributes of the geographic artificial intelligence sample data service provider class TDServiceProvider at least comprise a service provider name and a service provider contact information contact; the necessary attributes of the geographic artificial intelligence sample data service instance class, the TrainigDatasetService, at least comprise an identification id, a name, a description, an address, a release time publishTime, a task type aiTasks, a sample level type aiLevels and a time space range extension; the necessary attributes of the geographic artificial intelligent sample data set class TrainingDataset at least comprise sample data set identification id, name, description, version number version and sample data quantity amountOfTrainingData; the necessary attributes of the geographic artificial intelligence sample data service operation class TDServiceoperation at least comprise an operation name, a distributed computing platform list dcp and an operation description; the necessary attributes of the geographic artificial intelligence sample data service parameter class TDServiceParameter at least comprise a parameter name, a parameter direction and a description.
In one embodiment of the present invention, preferably, in step S2, specifically includes:
step S21, defining various resources related to the geographic artificial intelligence sample data service and resource categories to which the resources belong, wherein the resource categories comprise aggregate resources, individual resources and view resources;
and S22, defining resource numbers and hierarchical relations of various resources related to the geographic artificial intelligence sample data service.
In one embodiment of the present invention, preferably, in step S21, the various resources involved in the geographic artificial intelligence sample data service include:
a service instance (trainigdatasetservice), a sample data set description (trainigdataset), a sample data unit (TrainingData) belonging to an individual resource;
a sample data set (trackdataset list), a sample data set (trackdatalist) belonging to a set resource;
a sample dataset Task view (Task list), a sample dataset traceability view (Labeling list), a sample dataset quality view (TDQuality list), a sample dataset change set view (TDChangeset list) belonging to a view resource.
As shown in fig. 5, in one embodiment of the present invention, the resource numbers and the hierarchical relationships of the various resources involved in the geographic artificial intelligence sample data service are preferably as follows:
the resource number of the service instance is 1, and no upper layer resource exists;
the resource number of the sample data set is 2, and the upper layer resource is 1;
the resource number described by the sample data set is 3, and the upper layer resource is 2;
the resource number of the sample data set is 4, and the upper layer resource is 3;
the resource number of the task view of the sample data set is 5, and the upper layer resource is 3;
the resource number of the traceable view of the sample data set is 6, and the upper layer resource is 3;
the resource number of the sample data set quality view is 7, and the upper layer resource is 3;
the resource number of the sample data set change set view is 8, and the upper layer resource is 3;
the resource number of the sample data unit is 9, and the upper layer resource is 4.
In one embodiment of the present invention, preferably, in step S3, the method specifically includes:
step S31, defining various interfaces related to the geographic artificial intelligence sample data service;
and S32, mapping various interfaces related to the geographic artificial intelligence sample data service with various resources related to the geographic artificial intelligence sample data service one by one.
As shown in fig. 5, various interfaces involved in the geographic artificial intelligence sample data service include:
1) The GetTrainingDatasetService interface: acquiring metadata information of a geographic artificial intelligence sample data service;
(2) The GetTrainingDatasetCollection interface: acquiring metadata information of all sample data sets meeting query conditions, which are provided in a geographic artificial intelligence sample data service;
(3) The GetTrainingDataset interface: acquiring metadata information of a certain sample data set provided in the geographic artificial intelligence sample data service based on the data set identification;
(4) The GetTrainingDataCollection interface: acquiring all or part of sample data units contained in the data set provided in the geographic artificial intelligence sample data service based on the data set identification and other constraint parameters;
(5) The GetTrainingDatasetTask interface: acquiring task description information of a data set provided in a geographic artificial intelligence sample data service based on the data set identification;
(6) GetTrainingDatasetProv interface: acquiring labeling process traceability information of a data set provided in a geographic artificial intelligence sample data service based on the data set identification;
(7) The GetTrainingDatasetQuality interface: acquiring quality evaluation result description information of a data set provided in a geographic artificial intelligence sample data service based on the data set identification;
(8) The GetTrainingDatasetChangeset interface: acquiring version update description information of a data set provided in a geographic artificial intelligence sample data service based on the data set identification;
(9) GetTrainingData interface: sample data units provided in the geographic artificial intelligence sample data service are obtained based on the data set identification and the data unit identification.
As shown in fig. 5, the mapping relationship between the interface and various resources related to the geographic artificial intelligence sample data service is as follows:
(1) The GetTrainingDatasetService interface: corresponding to the resource number 1;
(2) The GetTrainingDatasetCollection interface: corresponding to resource number 2;
(3) The GetTrainingDataset interface: corresponding to resource number 3;
(4) The GetTrainingDataCollection interface: corresponding to resource number 4;
(5) The GetTrainingDatasetTask interface: corresponding to resource number 5;
(6) GetTrainingDatasetProv interface: corresponding to the resource number 6;
(7) The GetTrainingDatasetQuality interface: corresponding to resource number 7;
(8) The GetTrainingDatasetChangeset interface: corresponding to resource number 8;
(9) GetTrainingData interface: corresponding to resource number 9.
As shown in fig. 1 and 6, in one embodiment of the present invention, preferably, in step S4, a uniform resource description identifier of a service resource of a representational state transfer style bound to a geographic artificial intelligence sample data service interface is defined, including:
(1) "/": binding with a GetTrainingDatasetService interface;
(2) "/selections": binding with a GetTrainingDatasetCollection interface;
(3) "/collections/{ collections Id }": binding with a GetTrainingDataset interface, wherein { collectionId } represents a set sample dataset identifier parameter;
(4) "/collections/{ collections Id }/tracking-data": binding with a GetTrainingDataCollection interface, wherein { collectionId } represents a set sample dataset identifier parameter; the method comprises the steps of carrying out a first treatment on the surface of the
(5) "/collections/{ collections Id }/tracking-data/task": binding with a GetTrainingDatasetTask interface, wherein { collectionId } represents a set sample dataset identifier parameter; the method comprises the steps of carrying out a first treatment on the surface of the
(6) "/collections/{ collections Id }/tracking-data/protocol": binding with a gettrainingdatasetpro interface, wherein { collectionId } represents a set sample dataset identifier parameter; the method comprises the steps of carrying out a first treatment on the surface of the
(7) "/collections/{ collections Id }/tracking-data/quality": binding with a GetTrainingDatasetQuality interface, wherein { collectionId } represents a set sample dataset identifier parameter; the method comprises the steps of carrying out a first treatment on the surface of the
(8) "/collections/{ collections Id }/tracking-data/change": binding with a GetTrainingDatasetChangeset interface, wherein { collectionId } represents a set sample dataset identifier parameter; the method comprises the steps of carrying out a first treatment on the surface of the
(9) "/collections/{ collections }/tracking-data/{ tracking dataId": binding with the GetTrainingData interface, wherein { collectionId } represents a set sample data set identifier parameter and { trainingDataId } represents a set sample data unit identifier parameter.
On the basis, as shown in fig. 1 and fig. 7, a network server is utilized to issue a geographic artificial intelligence sample data service, and a user can realize the geographic artificial intelligence sample data service through the uniform resource descriptor, and the specific access flow is as follows:
(1) Accessing/obtaining metadata information of a geographic artificial intelligence sample data service, wherein the metadata information comprises content and access modes of the sample data service;
(2) Accessing "/collections" to obtain metadata of all sample data sets provided in the geographic artificial intelligence sample data service, supporting filtering returned results by setting constraint conditions;
(3) Accessing "/collections/{ collections Id }" to acquire metadata information of a specific geographic artificial intelligence sample data set, and helping to read and use the sample data set after acquiring the sample data set;
(4) Accessing URI "/collections/{ collection Id }/tracking-data" to obtain all sample data units contained in a specific sample data set, and supporting filtering a returned result by setting constraint conditions;
(5) Acquiring task information, tracing information, quality information and change set information of a sample data set by using a corresponding view resource interface;
(6) The specific sample data unit is acquired by setting sample data unit identification access "/collection/{ collection id }/tracking-data/{ tracking dataid }".
According to an aspect of the present invention, there is provided an electronic apparatus including: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, the one or more computer programs are stored in the memory, and when the electronic device is running, the processor executes the one or more computer programs stored in the memory, so that the electronic device performs a resource-oriented geographic sample data service method according to any of the above technical solutions.
According to an aspect of the present invention, there is provided a computer readable storage medium storing computer instructions which, when executed by a processor, implement a resource-oriented geographic sample data service method according to any of the above technical solutions.
The invention discloses a resource-oriented geographic sample data service method, equipment and a storage medium, wherein the resource-oriented geographic sample data service method comprises the following steps: step S1, establishing a conceptual model and a logic model of geographic artificial intelligence sample data service metadata description; s2, establishing a geographic artificial intelligence sample data service resource system; s3, constructing a resource-oriented geographic artificial intelligence sample data service interface and a mapping relation between the interface and the geographic artificial intelligence sample data service resource; s4, defining a uniform resource description identifier of the geographic artificial intelligence sample data service resource of the expressive state transition style, and binding with a sample data service interface; s5, a network access interface for issuing a geographic artificial intelligence sample data service; firstly, a conceptual model and a logic model for supporting the expression of sample data service metadata are established, a geographic artificial intelligence sample data service resource system is constructed by analyzing the property of geographic artificial intelligence sample data resources, finally, a resource-oriented geographic artificial intelligence sample data service interface is designed to realize a sample data service architecture, a network access interface of the geographic artificial intelligence sample data service is distributed, the required geographic artificial intelligence sample data can be obtained through the network access interface, the traditional file-based geographic artificial intelligence sample data sharing mode is broken through, the resource-oriented, interoperable and standardized sample service is realized, the intelligent service capability of a spatial data infrastructure is effectively enhanced, the construction artificial intelligence ready spatial data infrastructure is supported, the sharing service requirement of multi-source heterogeneous geographic artificial intelligence sample data is met, and powerful support is provided for sharing and application of the geographic artificial intelligence sample data.
Furthermore, it should be noted that the present invention can be provided as a method, an apparatus, or a computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the invention may take the form of a computer program product on one or more computer-usable storage media having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.
It is finally pointed out that the above description of the preferred embodiments of the invention, it being understood that although preferred embodiments of the invention have been described, it will be obvious to those skilled in the art that, once the basic inventive concepts of the invention are known, several modifications and adaptations can be made without departing from the principles of the invention, and these modifications and adaptations are intended to be within the scope of the invention. It is therefore intended that the following claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Claims (8)
1. A resource-oriented geographic sample data service method, comprising the steps of:
step S1, establishing a conceptual model and a logic model of geographic artificial intelligence sample data service metadata description;
s2, establishing a geographic artificial intelligence sample data service resource system;
s3, constructing a resource-oriented geographic artificial intelligence sample data service interface and a mapping relation between the interface and the geographic artificial intelligence sample data service resource;
s4, defining a uniform resource description identifier of the geographic artificial intelligence sample data service resource of the expressive state transition style, and binding with a sample data service interface;
and S5, publishing a network access interface of the geographic artificial intelligence sample data service.
2. The resource-oriented geographic sample data service method according to claim 1, wherein in step S1, specifically comprising:
step S11, constructing a geographic artificial intelligence sample data service metadata description conceptual model, wherein the metadata description conceptual model at least comprises the following concepts:
a geographic artificial intelligence sample data service provider representing metadata information of a provider of the geographic artificial intelligence sample data service, a service instance representing metadata information of a geographic artificial intelligence sample data service instance, a geographic artificial intelligence sample data set representing metadata information of a sample data set published as a sample data service, a geographic artificial intelligence sample data service operation representing metadata information of related operations provided by the geographic artificial intelligence sample data service instance, a geographic artificial intelligence sample data service parameter representing settable parameter information of the geographic artificial intelligence sample data service operation;
step S12, constructing a geographic artificial intelligence sample data service metadata description logic model based on the metadata description conceptual model,
wherein the necessary attributes of the geographic artificial intelligence sample data service provider class at least comprise service provider names and service provider contact information; the necessary attributes of the geographic artificial intelligence sample data service instance class at least comprise identification, name, description, address, release time, task type, sample level type and time space range; the necessary attributes of the geographic artificial intelligence sample data set class at least comprise sample data set identification, name, description, version number and sample data volume; the necessary attributes of the geographic artificial intelligence sample data service operation class at least comprise an operation name, a distributed computing platform list and an operation description; the necessary attributes of the geographic artificial intelligence sample data service parameter class at least comprise parameter names, parameter directions and descriptions.
3. The resource-oriented geographic sample data service method according to claim 2, wherein in step S2, specifically comprising:
step S21, defining various resources related to the geographic artificial intelligence sample data service and resource categories to which the resources belong, wherein the resource categories comprise aggregate resources, individual resources and view resources;
and S22, defining resource numbers and hierarchical relations of various resources related to the geographic artificial intelligence sample data service.
4. A method of resource-oriented geographic sample data service as claimed in claim 3, characterised in that in step S21 the various types of resources involved in the geographic artificial intelligence sample data service comprise:
a service instance, a sample data set description, and a sample data unit belonging to an individual resource;
a sample data set, belonging to a set resource;
a sample dataset task view, a sample dataset traceability view, a sample dataset quality view, a sample dataset change set view belonging to a view resource.
5. The resource-oriented geographic sample data service method as claimed in claim 4, wherein the resource numbers and the hierarchical relationships of the various resources involved in the geographic artificial intelligence sample data service are as follows:
the resource number of the service instance is 1, and no upper layer resource exists;
the resource number of the sample data set is 2, and the upper layer resource is 1;
the resource number described by the sample data set is 3, and the upper layer resource is 2;
the resource number of the sample data set is 4, and the upper layer resource is 3;
the resource number of the task view of the sample data set is 5, and the upper layer resource is 3;
the resource number of the traceable view of the sample data set is 6, and the upper layer resource is 3;
the resource number of the sample data set quality view is 7, and the upper layer resource is 3;
the resource number of the sample data set change set view is 8, and the upper layer resource is 3;
the resource number of the sample data unit is 9, and the upper layer resource is 4.
6. The resource-oriented geographic sample data service method according to claim 5, wherein in step S3, specifically comprising:
step S31, defining various interfaces related to the geographic artificial intelligence sample data service;
and S32, mapping various interfaces related to the geographic artificial intelligence sample data service with various resources related to the geographic artificial intelligence sample data service one by one.
7. An electronic device, comprising: one or more processors, one or more memories, and one or more computer programs; wherein the processor is connected to the memory, the one or more computer programs being stored in the memory, which when executed by the electronic device, causes the electronic device to perform the resource-oriented geographic sample data service method of any of claims 1 to 6.
8. A computer readable storage medium storing computer instructions which, when executed by a processor, implement the resource-oriented geographical sample data service method of any one of claims 1 to 6.
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