CN114547327A - Method, device and equipment for generating space-time big data relation map and storage medium - Google Patents
Method, device and equipment for generating space-time big data relation map and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a device and equipment for generating a space-time big data relation map and a storage medium, and belongs to the technical field of data relation map generation. According to the invention, the target geographic entity data is obtained from the preset space-time big database through the relation map generation instruction, and the geographic entity node relation map is constructed according to the relation between the space-time big data map layer node relation and the space-time big data map layer node, so that the management of various types of space-time big data is realized, and the geographic entity node relation comprises attribute relation, behavior relation, spatial relation, time relation and the like, so that the establishment of the relation of various types of space-time big data can be realized, the technical problems that the relation among various types of space-time big data is difficult to efficiently manage, the analysis and mining of the space-time big data are difficult, and the user experience is not high are solved, the management efficiency of various types of big data is improved, and the efficiency of analyzing and mining the obtained knowledge under the space-time big data environment is improved.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a method, a device, equipment and a storage medium for generating a space-time big data relation map.
Background
The space-time big data is fused with the geographic space information through the time dynamic data, and the space-time big data has the characteristics of large data volume, diversified data formats, strong data real-time performance, large attribute difference and the like. The method has great difficulty in various applications, and can efficiently and feasibly analyze and mine the space-time big data only by establishing the relation map of the space-time big data.
However, in the prior art, the knowledge of the large spatiotemporal data is simply stored, and in the actual application of the large spatiotemporal data, the simple relational expression is performed through the external key association of the traditional relational database, so that the relationship among various types of large spatiotemporal data is difficult to be efficiently managed, and the user experience is not high.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a method, a device, equipment and a storage medium for generating a space-time big data relation map, and aims to solve the technical problems that in the prior art, the relation between various types of space-time big data is difficult to manage efficiently, and the analysis and mining of the space-time big data are difficult, so that the user experience is not high.
In order to achieve the purpose, the invention provides a method for generating a space-time big data relation map, which comprises the following steps:
when a relation map generation instruction is received, acquiring metadata of a target space-time big data map layer from a preset space-time big database according to the relation map generation instruction;
generating space-time big data layer nodes based on the target space-time big data layer metadata, and determining corresponding space-time big data layer node relations according to the relation map generation instruction, wherein the space-time big data layer node relations comprise attribute relations, space relations and time relations;
and constructing a data node relation map according to the space-time big data layer node relation and the space-time big data layer nodes through a preset map construction rule, and performing data management according to the data node relation map.
Optionally, the constructing a data node relation graph according to the spatio-temporal big data graph layer node relation and the spatio-temporal big data graph layer node through a preset graph construction rule includes:
when the space-time big data layer node relation is a spatial relation, a space-time big data node spatial relation map is constructed according to the spatial relation and the space-time big data layer nodes through a preset map construction rule;
when the time-space big data layer node relation is a time relation, a data node time relation graph is constructed according to the time relation and the time-space big data layer node through a preset graph construction rule;
and when the space-time big data layer node relationship is an attribute relationship, constructing a data node attribute relationship map according to the attribute relationship and the space-time big data layer node through a preset map construction rule.
Optionally, the building a space-time big data node space relation graph according to the space relation and the space-time big data graph layer nodes through a preset graph building rule includes:
acquiring space-time big data layer node space information;
traversing and analyzing the nodes of the space-time big data layer according to the space information of the nodes of the space-time big data layer to obtain an initial space relation map;
and labeling the initial spatial relationship map based on the spatial relationship to obtain a spatial relationship map of the space-time big data nodes.
Optionally, the constructing a data node time relation graph according to the time relation and the time-space big data graph layer node through a preset graph construction rule includes:
acquiring node time information of a space-time big data layer;
classifying the space-time big data layer nodes according to the time information of the space-time big data layer nodes to obtain an initial time relation map;
and labeling the initial time relation graph based on the time relation to obtain a data node time relation graph.
Optionally, the constructing a data node attribute relationship graph according to the attribute relationship and the space-time big data graph layer node through a preset graph construction rule includes:
acquiring attribute information of a space-time big data map layer node and identification information corresponding to the space-time big data map layer node;
carrying out attribute classification on the space-time big data layer nodes according to the attribute information of the space-time big data layer nodes and the identification information to obtain an initial attribute relation map;
and marking the initial attribute relation graph based on the attribute relation to obtain a data node attribute relation graph.
Optionally, after the data node relation graph is constructed according to the spatio-temporal big data graph layer node relation and the spatio-temporal big data graph layer node through a preset graph construction rule, the method further includes:
correspondingly storing the data node relation map through a preset map database;
when a query instruction of a user is received, determining node information of a target space-time big data layer according to the query instruction;
and calling a target data node relation map based on the target space-time big data layer node information, and displaying the target data node relation map.
Optionally, the obtaining metadata of the target spatiotemporal big data map layer from a preset big database according to the relation map generation instruction includes:
determining a corresponding data acquisition interface according to the relation map generation instruction;
acquiring initial entity metadata from a preset big database based on the data acquisition interface;
and adjusting the format of the initial entity metadata based on the preset big data standard to obtain the metadata of the target space-time big data layer.
In addition, in order to achieve the above object, the present invention further provides a spatio-temporal big data relation map generating device, including:
the data acquisition module is used for acquiring metadata of a target space-time big data layer from a preset big database according to a relation map generation instruction when the relation map generation instruction is received;
a node confirmation module, configured to generate a spatiotemporal big data layer node based on the target spatiotemporal big data layer metadata, and determine a corresponding spatiotemporal big data layer node relationship according to the relationship map generation instruction, where the spatiotemporal big data layer node relationship includes: attribute relationships, spatial relationships, and temporal relationships;
and the map generation module is used for constructing a data node relation map according to the space-time big data layer node relation and the space-time big data layer nodes through a preset map construction rule and carrying out data management according to the data node relation map.
Further, to achieve the above object, the present invention also provides a data relationship map generation apparatus including: a memory, a processor, and a spatiotemporal big data relational map generation program stored on the memory and executable on the processor, the spatiotemporal big data relational map generation program configured to implement the steps of the spatiotemporal big data relational map generation method as described above.
In addition, to achieve the above object, the present invention further provides a storage medium having a spatiotemporal big data relation map generation program stored thereon, which when executed by a processor implements the steps of the spatiotemporal big data relation map generation method as described above.
According to the method, when a relation map generation instruction is received, target space-time big data layer metadata are obtained from a preset big database according to the relation map generation instruction, space-time big data layer nodes are generated based on the target space-time big data layer metadata, corresponding space-time big data layer node relations are determined according to the relation map generation instruction, the space-time big data layer node relations comprise attribute relations, space relations and time relations, a data node relation map is constructed according to the space-time big data layer node relations and the space-time big data layer nodes through a preset map construction rule, and data management is conducted according to the data node relation map. Compared with the prior art, the target entity data are obtained from the preset big database through the relation graph generation instruction, the data node relation graph is constructed according to the relation between the space-time big data graph layer node relation and the space-time big data graph layer node, so that various kinds of big data can be managed, the space-time big data graph layer node relation comprises the attribute relation, the space relation and the time relation, the relation establishment of various kinds of big data can be realized, the technical problems that the relation among various kinds of space-time big data is difficult to manage efficiently, the analysis and the mining of the space-time big data are difficult, the user experience is not high are solved, and the management efficiency of various kinds of big data is improved.
Drawings
FIG. 1 is a schematic structural diagram of a data relationship map generation apparatus of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of a spatiotemporal big data relation map generation method according to the present invention;
FIG. 3 is a schematic diagram of a data relation map in an embodiment of a spatiotemporal big data relation map generation method of the present invention;
FIG. 4 is a schematic flow chart diagram of a second embodiment of a spatiotemporal big data relation map generation method according to the present invention;
FIG. 5 is a schematic flow chart of a spatiotemporal big data relation map generation method according to a third embodiment of the present invention;
FIG. 6 is a block diagram of a first embodiment of the spatiotemporal big data relation map generating device of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a data relationship map generation device of a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the data relationship map generating apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the architecture shown in FIG. 1 does not constitute a limitation of a data relationship atlas generation apparatus, and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a spatiotemporal big data relationship map generating program.
In the data relationship map generating apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the data relationship map generation apparatus of the present invention may be provided in a data relationship map generation apparatus that calls a spatiotemporal big data relationship map generation program stored in the memory 1005 through the processor 1001 and executes a spatiotemporal big data relationship map generation method provided by an embodiment of the present invention.
The embodiment of the invention provides a space-time big data relation map generation method, and referring to fig. 2, fig. 2 is a flow diagram of a first embodiment of the space-time big data relation map generation method.
In this embodiment, the method for generating the spatio-temporal big data relation map includes the following steps:
step S10: and when a relation map generation instruction is received, acquiring metadata of a target spatiotemporal big data map layer from a preset big database according to the relation map generation instruction.
It should be noted that the execution subject of the present embodiment is a data relationship map generation device, where the data relationship map generation device may be a device having data processing and data transmission functions, and may also be an electronic device such as a control computer, a mobile phone, and a tablet computer.
It should be noted that the relation map generation instruction may be an instruction input by a user based on a control computer, where the relation map generation instruction is used to instruct generation of a big data relation map and perform data management on big data, and in this embodiment and the following embodiments, the construction of the relation map will be described by taking an approval service of a natural resource industry construction land as an example.
The relation map generation instruction further comprises a data acquisition interface for extracting target space-time big data map layer metadata from a preset big database, the control computer performs data cleaning according to the provided data interface, initial entity metadata are screened from a preset big data source, format adjustment is performed on the initial entity metadata based on the preset big data standard, and the target space-time big data map layer metadata are obtained.
It can be understood that the preset big database is used for uniformly storing and managing the relevant spatio-temporal big data corresponding to different engineering projects in the storage unit based on the existing spatio-temporal big data storage technology, where the storage unit may be a read-only memory or a non-volatile memory, and the present embodiment is not particularly limited thereto.
In addition, because the engineering projects processed by users are different, the types of corresponding spatio-temporal big data are also different, for example: in the construction land approval business of the natural resource industry, the corresponding space-time big data may include: the present embodiment is not particularly limited to the current state of land use, the detailed controlled planning, the report on land use, the reserve of land, the condition of land giving a business, the supply of land, the registration of right to use of national construction land, the permission of construction land planning, the permission of construction engineering planning, the permission of construction, the verification of completion planning, the registration of real estate, and the like.
It is easy to understand that the metadata of the target space-time big data layer is used for constructing a corresponding data relation map according to the needs of a user, and since not all data information needs to be used in the construction process of the actual data relation map, the metadata of the target space-time big data layer needed for constructing the data relation map is only needed to be extracted from a preset big database according to a relation map generation instruction input by the user, that is, data entity objects meeting the requirements are extracted from massive, redundant and irregular data sources according to the standards of various data, and corresponding data entity metadata is generated, and the entity metadata may be: data type, data attribute field description, data range, data coordinate system, data precision, data description, data update time, etc., for example: in the construction land approval business, if a land data space map needs to be constructed, data such as the current land utilization situation, the detailed controlled planning and the land parameter information may be used, and the data such as the current land utilization situation, the detailed controlled planning and the land parameter information are marked as target space-time big data map layer metadata for extraction.
Step S20: generating space-time big data layer nodes based on the target space-time big data layer metadata, and determining corresponding space-time big data layer node relations according to the relation map generation instruction, wherein the space-time big data layer node relations comprise attribute relations, space relations and time relations.
It should be noted that the spatio-temporal big data layer node may be a data layer created after unifying the format based on the extracted target metadata according to the user needs, and the spatio-temporal big data layer node is used for creating a data relationship map.
In a specific implementation, taking the natural resource industry construction land approval service as an example, the spatio-temporal big data layer node needs to integrate the relevant data related to the entire construction land approval link according to a uniform format, so as to establish the spatio-temporal big data layer node, where the relevant data may be one or a combination of multiple data information such as a current land utilization status, a controlled detailed planning, a land approval, a land reserve, a land giving planning condition, a land supply, a national construction land use right registration, a construction land planning permission, a construction project permission, a completion planning verification, and a real estate registration, and the embodiment is not particularly limited.
It can be understood that the node relationship of the spatio-temporal big data layer is used for establishing a data relationship graph, since spatio-temporal big data has different characteristics, such as: the relationship between the large spatio-temporal data can be divided into different data connection relationships according to the characteristics of time, space, attributes, behaviors, states, processes and the like, for example: attribute association relationship, spatial relationship, temporal relationship, state relationship, process relationship, etc., which is not specifically limited in this embodiment.
It is worth explaining that the extracted target space-time big data layer metadata has different data characteristics as the space-time big data, different association relations can be artificially established for the target space-time big data layer metadata according to the needs of users in the actual processing process, and corresponding data association relations are generated through data characteristics such as attributes, space, time, states and the like.
In a specific implementation, extracted target spatio-temporal big data layer metadata is constructed into spatio-temporal big data layer nodes, and a data association relation required to be constructed by a user is determined according to a relation map generation instruction input by the user.
Step S30: and constructing a data node relation map according to the space-time big data layer node relation and the space-time big data layer nodes through a preset map construction rule, and performing data management according to the data node relation map.
It should be noted that the data node relationship graph is used to show a connection relationship between a space-time big data graph layer node and a space-time big data graph layer node relationship, and there is directivity between the space-time big data graph layer node and the space-time big data graph layer node relationship, that is, in a specific showing process, the space-time big data graph layer node relationship may point to a certain space-time big data graph layer node, where, referring to fig. 3, an expression form of the data node relationship graph may be node-edge-node, and may be a data graph, a project graph, a control graph, an index graph, and the like in an actual operation, which is not specifically limited in this embodiment.
It will be appreciated that preset graph building rules are used to provide building criteria to the data node relationship graph, such as: constructing a display rule of the data node relationship graph or a storage location after the construction is completed, and the like, which is not particularly limited in this embodiment.
It is worth to be noted that after the space-time big data layer node relation and the space-time big data layer node are obtained, a data node (point) -relation (edge) -data node (edge) data relation model is established according to the space-time big data layer node relation and the space-time big data layer node, the established data relation model is displayed in an image form and recorded as a data relation map, and data management of space-time big data is performed according to the data relation map.
In the concrete implementation, taking the construction land approval service of the natural resource industry as an example, the natural resource relation map is utilized to associate the whole flow service and data such as planning, approval, land supply, surveying and mapping, law enforcement, registration, transaction and the like, so that unified management is realized, and the processing efficiency of the user on the flow service is greatly improved.
In this embodiment, when a relation map generation instruction is received, target spatiotemporal big data layer metadata are acquired from a preset big database according to the relation map generation instruction, spatiotemporal big data layer nodes are generated based on the target spatiotemporal big data layer metadata, and corresponding spatiotemporal big data layer node relations are determined according to the relation map generation instruction, where the spatiotemporal big data layer node relations include attribute relations, spatial relations, and temporal relations, a data node relation map is constructed according to the spatiotemporal big data layer node relations and the spatiotemporal big data layer nodes through a preset map construction rule, and data management is performed according to the data node relation map. According to the embodiment, the target entity data are obtained from the preset big database through the relation map generation instruction, the data node relation map is constructed according to the relation between the space-time big data map layer node relation and the space-time big data map layer node, so that various kinds of big data can be managed, the space-time big data map layer node relation comprises the attribute relation, the spatial relation and the time relation, the establishment of the relation of various kinds of big data can be achieved, the problems that the relation among various kinds of space-time big data is difficult to manage efficiently, the analysis and mining of the space-time big data are difficult, the user experience is not high are solved, and the management efficiency of various kinds of big data is improved.
Referring to fig. 4, fig. 4 is a flowchart illustrating a spatio-temporal big data relation map generating method according to a second embodiment of the present invention.
Based on the first embodiment, in this embodiment, the step S30 includes:
step S301: and when the space-time big data layer node relation is a spatial relation, constructing a space-time big data node spatial relation map according to the spatial relation and the space-time big data layer nodes through a preset map construction rule.
It should be noted that the spatio-temporal big data node spatial relationship graph is used to represent the spatial relationship between the spatio-temporal big data nodes. In actual operation, when the relation of space-time big data layer nodes is determined to be a spatial relation according to a relation map generation instruction of a user, one layer node is determined, the layer node is used as a main body, the spatial intersection relation is associated with other business links, the other business links are used as a set, and the corresponding relation between the adjacent space-time big data layer nodes is associated in the set through the spatial intersection relation.
Further, in order to construct a spatio-temporal big data node spatial relationship graph, the step S301 further includes:
acquiring space-time big data layer node space information;
traversing and analyzing the nodes of the space-time big data layer according to the space information of the nodes of the space-time big data layer to obtain an initial space relation map;
and labeling the initial spatial relationship map based on the spatial relationship to obtain a spatial relationship map of the space-time big data nodes.
It can be understood that the data image node spatial information is used to construct an initial spatial relationship map in combination with spatio-temporal big data layer nodes, for example: the current land utilization situation, the detailed control planning and the like are realized, and the initial spatial relationship map is only a node-edge-node connection model at the moment, and the initial spatial relationship map needs to be labeled to determine the specific direction of the node relationship of the space-time big data map layer, so that the space-time big data node spatial relationship map is obtained.
Further, the step S30 includes: and when the time-space big data layer node relation is a time relation, constructing a data node time relation map according to the time relation and the time-space big data layer node through a preset map construction rule.
It should be noted that the data node time relationship graph is used to represent the time relationship between the large spatio-temporal data nodes. In actual operation, when the relation of the space-time big data layer nodes is determined to be a time relation according to a relation map generation instruction of a user, the time precedence relation is associated with other business links by determining one layer node, and the corresponding relation between the adjacent space-time big data layer nodes is associated in the set through the time precedence relation.
Further, in order to construct a data node time relationship graph, the specific steps include: acquiring node time information of a space-time big data layer;
classifying the space-time big data layer nodes according to the time information of the space-time big data layer nodes to obtain an initial time relation map;
and labeling the initial time relation graph based on the time relation to obtain a data node time relation graph.
It is understood that the data image node time information is used for constructing an initial time relation graph in combination with the spatio-temporal big data graph layer nodes, for example: managing the land utilization status and the controlled detailed plan according to annual division versions and the like.
In the concrete implementation, business logic context needs to be considered in business process handling, so data needs to have a time attribute, and data before and after a business link is filtered through the time attribute, so that logic disorder caused by data quality problems is avoided. The forward tracing time filtering conditions of each business link are as follows: the time is earlier than or equal to the current time, and the time filtering condition of backward tracing of each business link is as follows: the time is later than or equal to the current time.
Further, the step S30 includes: and when the space-time big data layer node relationship is an attribute relationship, constructing a data node attribute relationship map according to the attribute relationship and the space-time big data layer node through a preset map construction rule.
It should be noted that the data node attribute relationship map is used to represent the attribute relationship between the large spatio-temporal data nodes.
Further, in order to obtain the data node attribute relationship graph, the specific steps include:
acquiring attribute information of a space-time big data map layer node and identification information corresponding to the space-time big data map layer node;
carrying out attribute classification on the space-time big data layer nodes according to the attribute information of the space-time big data layer nodes and the identification information to obtain an initial attribute relation map;
and marking the initial attribute relation graph based on the attribute relation to obtain a data node attribute relation graph.
The data image node attribute information is used for constructing an initial attribute relationship map by combining the identification information corresponding to the spatio-temporal big data map layer node and the spatio-temporal big data map layer node, for example: the method comprises the steps of land use approval, land storage, land giving planning conditions, land supply, national construction land use right registration, construction land planning permission, construction project planning permission, construction permission, completion planning verification, real estate registration and the like.
According to the embodiment, the target entity data are obtained from the preset big database through the relation map generation instruction, the data node relation map is constructed according to the relation between the space-time big data map layer node relation and the space-time big data map layer node, so that various kinds of big data can be managed, the space-time big data map layer node relation comprises the attribute relation, the spatial relation and the time relation, the space-time big data node space relation map, the data node time relation map and the data node attribute relation map can be realized, the big data is managed in various map forms, the problems that the relation among various kinds of space-time big data is difficult to manage efficiently, the analysis and mining of the space-time big data are difficult, the technical problem of low user experience is solved, and the management efficiency of various kinds of big data is improved.
Referring to fig. 5, fig. 5 is a flowchart illustrating a spatio-temporal big data relation map generating method according to a third embodiment of the present invention.
Based on the second embodiment described above, in the present embodiment, after the step S30, the method includes:
step S40: and correspondingly storing the data node relation map through a preset map database.
It should be noted that the space-time big data node spatial relationship graph, the data node time relationship graph and the data node attribute relationship graph generated in the above embodiments are created and managed through a graph library, that is, after a data association relationship model of data nodes (points) -relationships (edges) -data nodes (edges) is generated, the data relationship model is stored by using a graph database.
It is easy to understand that the preset graph database is used for storing a data node (point) -relationship (edge) -data node (edge) data relationship model, so that a user can perform operations such as invoking and querying from the preset graph database.
Step S50: and when a query instruction of a user is received, determining node information of the target space-time big data layer according to the query instruction.
The query instruction is used for querying a data relation map required by a user, determining target spatiotemporal big data map layer node information through the query instruction, and further querying the data relation map containing the target spatiotemporal big data map layer node information.
Step S60: and calling a target data node relation map based on the target space-time big data layer node information, and displaying the target data node relation map.
In a specific implementation, the target data node relationship map may be one or more combinations of a space-time big data node spatial relationship map, a data node time relationship map, a data node attribute relationship map, and the like, and this embodiment is not particularly limited.
In addition, taking the examination and approval service of the construction land of the natural resource industry as an example, when the map is displayed, the current land utilization situation and the detail control planning link are independently displayed, and are not associated with other links by using connecting lines.
The embodiment of the invention acquires target entity data from a preset big database through a relation map generation instruction, constructs a data node relation map according to the relation between a space-time big data map layer node relation and a space-time big data map layer node, so as to realize the management of various big data, and the space-time big data map layer node relation comprises an attribute relation, a spatial relation and a time relation, so as to realize a space-time big data node space relation map, a data node time relation map and a data node attribute relation map, performs data management on big data in various map forms, provides query service for a user after the map is established, displays the corresponding data node relation map according to the query instruction of the user, avoids the difficulty in efficiently managing the relation between various kinds of space-time big data and the difficulty in analyzing and mining the space-time big data, the technical problem of low user experience is caused, the management efficiency of various big data is improved, and the use experience of the user is improved.
In addition, an embodiment of the present invention further provides a storage medium, where a spatiotemporal big data relation map generation program is stored, and when executed by a processor, the spatiotemporal big data relation map generation program implements the steps of the spatiotemporal big data relation map generation method described above.
Since the storage medium adopts all technical solutions of all the above embodiments, at least all the beneficial effects brought by the technical solutions of the above embodiments are present, and are not described herein any more.
Referring to fig. 6, fig. 6 is a block diagram illustrating a first embodiment of a spatiotemporal big data relation map generating apparatus according to the present invention.
As shown in fig. 6, the apparatus for generating a spatio-temporal big data relation graph according to an embodiment of the present invention includes:
the data acquisition module 10 is configured to, when a relation map generation instruction is received, acquire metadata of a target spatiotemporal big data map layer from a preset big database according to the relation map generation instruction;
a node confirmation module 20, configured to generate a spatiotemporal big data layer node based on the target spatiotemporal big data layer metadata, and determine a corresponding spatiotemporal big data layer node relationship according to the relationship map generation instruction, where the spatiotemporal big data layer node relationship includes: attribute relationships, spatial relationships, and temporal relationships;
and the map generation module 30 is configured to construct a data node relation map according to the spatio-temporal big data layer node relation and the spatio-temporal big data layer node through a preset map construction rule, and perform data management according to the data node relation map.
In this embodiment, when a relation map generation instruction is received, target spatiotemporal big data layer metadata are acquired from a preset big database according to the relation map generation instruction, spatiotemporal big data layer nodes are generated based on the target spatiotemporal big data layer metadata, and corresponding spatiotemporal big data layer node relations are determined according to the relation map generation instruction, where the spatiotemporal big data layer node relations include attribute relations, spatial relations, and temporal relations, a data node relation map is constructed according to the spatiotemporal big data layer node relations and the spatiotemporal big data layer nodes through a preset map construction rule, and data management is performed according to the data node relation map. According to the embodiment, the target entity data are obtained from the preset big database through the relation map generation instruction, the data node relation map is constructed according to the relation between the space-time big data map layer node relation and the space-time big data map layer node, so that the management of various kinds of big data is realized, the space-time big data map layer node relation comprises the attribute relation, the spatial relation and the time relation, the establishment of the relation of various kinds of big data can be realized, the technical problems that the relation between various kinds of space-time big data is difficult to efficiently manage, the analysis and the mining of the space-time big data are difficult, the user experience is not high are avoided, and the management efficiency of various kinds of big data is improved.
In an embodiment, the map generation module 30 is further configured to, when the spatio-temporal big data layer node relationship is a spatial relationship, construct a spatio-temporal big data node spatial relationship map according to the spatial relationship and the spatio-temporal big data layer node through a preset map construction rule; when the time-space big data layer node relation is a time relation, a data node time relation graph is constructed according to the time relation and the time-space big data layer node through a preset graph construction rule; and when the space-time big data layer node relationship is an attribute relationship, constructing a data node attribute relationship map according to the attribute relationship and the space-time big data layer node through a preset map construction rule.
In an embodiment, the map generation module 30 is further configured to obtain node spatial information of a spatiotemporal big data map layer; traversing and analyzing the nodes of the space-time big data layer according to the space information of the nodes of the space-time big data layer to obtain an initial space relation map; and labeling the initial spatial relationship map based on the spatial relationship to obtain a spatial relationship map of the space-time big data nodes.
In an embodiment, the map generation module 30 is further configured to obtain node time information of a spatio-temporal big data map layer; classifying the space-time big data layer nodes according to the time information of the space-time big data layer nodes to obtain an initial time relation map; and labeling the initial time relation graph based on the time relation to obtain a data node time relation graph.
In an embodiment, the map generation module 30 is further configured to obtain attribute information of a spatio-temporal big data map layer node and identification information corresponding to the spatio-temporal big data map layer node; carrying out attribute classification on the space-time big data layer nodes according to the attribute information of the space-time big data layer nodes and the identification information to obtain an initial attribute relation map; and marking the initial attribute relation graph based on the attribute relation to obtain a data node attribute relation graph.
In an embodiment, the map generation module 30 is further configured to correspondingly store the data node relationship map through a preset map database; when a query instruction of a user is received, determining node information of a target space-time big data layer according to the query instruction; and calling a target data node relation map based on the target space-time big data layer node information, and displaying the target data node relation map.
In an embodiment, the data obtaining module 10 is further configured to determine a corresponding data obtaining interface according to the relation map generating instruction; acquiring initial entity metadata from a preset big database based on the data acquisition interface; and adjusting the format of the initial entity metadata based on the preset big data standard to obtain the metadata of the target space-time big data layer.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited thereto.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not described in detail in this embodiment may refer to the method for generating a spatiotemporal big data relationship map provided in any embodiment of the present invention, and are not described herein again.
Furthermore, it should 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 system 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 system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. Read Only Memory (ROM)/RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (e.g. a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A space-time big data relation map generation method is characterized by comprising the following steps:
when a relation map generation instruction is received, acquiring metadata of a target space-time big data map layer from a preset space-time big database according to the relation map generation instruction;
generating space-time big data layer nodes based on the target space-time big data layer metadata, and determining corresponding space-time big data layer node relation according to the relation map generation instruction;
and constructing a spatio-temporal big data layer node relation map according to the spatio-temporal big data layer node relation and the spatio-temporal big data layer nodes through a preset map construction rule, and performing data management according to the spatio-temporal big data layer relation map.
2. The method for generating a spatiotemporal big data relationship map according to claim 1, wherein the constructing a spatiotemporal big data map layer node relationship map according to the spatiotemporal big data map layer node relationship and the spatiotemporal big data map layer node through a preset map construction rule comprises:
when the space-time big data layer node relation is a spatial relation, a space-time big data node spatial relation map is constructed according to the spatial relation and the data layer nodes through a preset construction rule;
when the time-space big data layer node relation is a time relation, a data node time relation graph is constructed according to the time relation and the data layer nodes through a preset construction rule;
and when the space-time big data layer node relationship is an attribute relationship, constructing a data node attribute relationship map according to the attribute relationship and the data layer nodes through a preset construction rule.
3. The method for generating a spatiotemporal big data relationship graph according to claim 2, wherein the constructing a spatiotemporal big data node spatial relationship graph according to the spatial relationship and the data layer nodes by a preset construction rule comprises:
acquiring space-time big data layer node space information;
traversing and analyzing the nodes of the space-time big data layer according to the space information of the nodes of the space-time big data layer to obtain an initial space relation map;
and labeling the initial spatial relationship map based on the spatial relationship to obtain a spatial relationship map of the space-time big data nodes.
4. The spatiotemporal big data relationship map generation method according to claim 2, wherein the constructing a data node time relationship map according to the time relationship and the spatiotemporal big data map layer nodes by a preset map construction rule comprises:
acquiring time information of nodes of a space-time big data layer;
classifying the space-time big data layer nodes according to the time information of the space-time big data layer nodes to obtain an initial time relation map;
and labeling the initial time relation graph based on the time relation to obtain a time relation graph of the space-time big data node.
5. The method for generating a spatiotemporal big data relationship graph according to claim 2, wherein the constructing a spatiotemporal big data node attribute relationship graph according to the attribute relationship and the spatiotemporal big data layer nodes by a preset graph construction rule comprises:
acquiring attribute information of a space-time big data map layer node and identification information corresponding to the space-time big data map layer node;
carrying out attribute classification on the space-time big data layer nodes according to the attribute information of the space-time big data layer nodes and the identification information to obtain an initial attribute relation map;
and marking the initial attribute relation graph based on the attribute relation to obtain a space-time big data node attribute relation graph.
6. The spatiotemporal big data relationship map generation method according to any one of claims 1 to 5, wherein after the spatiotemporal big data layer node relationship map is constructed according to the spatiotemporal big data layer node relationship and the spatiotemporal big data layer node by a preset map construction rule, the method further comprises:
correspondingly storing the space-time big data node relation map through a preset map database;
when a query instruction of a user is received, determining node information of a target space-time big data layer according to the query instruction;
and calling a target space-time big data node relation graph based on the target space-time big data graph layer node information, and displaying the target space-time big data node relation graph.
7. The spatiotemporal big data relational map generation method according to any one of claims 1 to 5, wherein the obtaining of the metadata of the target spatiotemporal big data map layer from a preset big database according to the relational map generation instruction comprises:
determining a corresponding space-time big data acquisition interface according to the relation map generation instruction;
acquiring initial entity metadata from a preset big database based on the space-time big data acquisition interface;
and adjusting the format of the initial entity metadata based on the preset big data standard to obtain the metadata of the target space-time big data layer.
8. A large space-time big data relation map generating device is characterized by comprising the following components:
the data acquisition module is used for acquiring metadata of a target space-time big data layer from a preset big database according to a relation map generation instruction when the relation map generation instruction is received;
a node confirmation module, configured to generate a spatiotemporal big data layer node based on the target spatiotemporal big data layer metadata, and determine a corresponding spatiotemporal big data layer node relationship according to the relationship map generation instruction, where the spatiotemporal big data layer node relationship includes: attribute relationships, spatial relationships, and temporal relationships;
and the map generation module is used for constructing a space-time big data node relation map according to the space-time big data layer node relation and the space-time big data layer nodes through a preset map construction rule and carrying out data management according to the space-time big data node relation map.
9. A spatiotemporal big data relationship map generating apparatus, characterized in that the spatiotemporal big data relationship map generating apparatus comprises: a memory, a processor, and a spatiotemporal big data relational map generation program stored on the memory and executable on the processor, the spatiotemporal big data relational map generation program configured to implement the spatiotemporal big data relational map generation method according to any one of claims 1 to 7.
10. A storage medium on which a spatiotemporal large data relational map generation program is stored, the spatiotemporal large data relational map generation program implementing the spatiotemporal large data relational map generation method according to any one of claims 1 to 7 when executed by a processor.
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