Method, device, equipment and storage medium for generating spatiotemporal big data relationship graph

Abstract

The present invention discloses a method, device, equipment and storage medium for generating a spatiotemporal big data relationship map, and belongs to the technical field of data relationship map generation. The present invention obtains target geographic entity data from a preset spatiotemporal big data database through a relationship map generation instruction, and constructs a geographic entity node relationship map according to the spatiotemporal big data layer node relationship and the connection between the spatiotemporal big data layer nodes, so as to realize the management of various types of spatiotemporal big data, and the geographic entity node relationship includes attribute relationship, behavior relationship, spatial relationship and time relationship, etc., which can realize the establishment of relationships among multiple types of spatiotemporal big data, avoid the difficulty in efficiently managing the relationship between various types of spatiotemporal big data, and the difficulty in analyzing and mining spatiotemporal big data, resulting in a low user experience. The technical problem improves the management efficiency of various types of big data, and the efficiency of analyzing and mining knowledge acquisition in a spatiotemporal big data environment.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a method, device, equipment and storage medium for generating a spatiotemporal big data relationship graph. Background Art

[0002] Spatiotemporal big data is a fusion of temporal dynamic data and geographic spatial information, and has the characteristics of large data volume, diversified data formats, strong real-time data, and large attribute differences. It is very difficult to use it directly in various applications. Only by establishing a relationship map of spatiotemporal big data can the analysis and mining of spatiotemporal big data be carried out efficiently and feasibly.

[0003] However, the existing technology simply stores spatiotemporal big data knowledge. In actual spatiotemporal big data applications, it is difficult to efficiently manage the relationships between various types of spatiotemporal big data through simple relationship expressions through traditional relational database foreign key associations, and the user experience is not good.

[0004] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of the present invention is to provide a method, device, equipment and storage medium for generating a spatiotemporal big data relationship graph, aiming to solve the technical problems that the existing technology is difficult to efficiently manage the relationships between various types of spatiotemporal big data, and the analysis and mining of spatiotemporal big data is difficult, resulting in a low user experience.

[0006] To achieve the above object, the present invention provides a method for generating a spatiotemporal big data relationship graph, the method comprising the following steps:

[0007] When receiving the relationship map generation instruction, obtaining the target spatiotemporal big data layer metadata from the preset spatiotemporal big data database according to the relationship map generation instruction;

[0008] Generate a spatiotemporal big data layer node based on the target spatiotemporal big data layer metadata, and determine the corresponding spatiotemporal big data layer node relationship according to the relationship map generation instruction, wherein the spatiotemporal big data layer node relationship includes an attribute relationship, a spatial relationship, and a time relationship;

[0009] A data node relationship map is constructed according to the node relationship of the spatiotemporal big data layer and the nodes of the spatiotemporal big data layer using preset map construction rules, and data management is performed according to the data node relationship map.

[0010] Optionally, constructing a data node relationship graph according to the spatiotemporal big data layer node relationship and the spatiotemporal big data layer node through a preset graph construction rule includes:

[0011] When the relationship between the spatiotemporal big data layer nodes is a spatial relationship, a spatiotemporal big data node spatial relationship graph is constructed according to the spatial relationship and the spatiotemporal big data layer nodes by using a preset graph construction rule;

[0012] When the relationship between the nodes of the spatiotemporal big data layer is a time relationship, a data node time relationship graph is constructed according to the time relationship and the spatiotemporal big data layer nodes by using a preset graph construction rule;

[0013] When the relationship between the spatiotemporal big data layer nodes is an attribute relationship, a data node attribute relationship graph is constructed according to the attribute relationship and the spatiotemporal big data layer nodes through preset graph construction rules.

[0014] Optionally, the step of constructing a spatiotemporal big data node spatial relationship graph according to the spatial relationship and the spatiotemporal big data layer nodes by using a preset graph construction rule includes:

[0015] Get the spatial information of spatiotemporal big data layer nodes;

[0016] Traversing and analyzing the spatiotemporal big data layer nodes according to the spatiotemporal big data layer node spatial information to obtain an initial spatial relationship map;

[0017] The initial spatial relationship map is annotated based on the spatial relationship to obtain a spatiotemporal big data node spatial relationship map.

[0018] Optionally, constructing a data node time relationship graph according to the time relationship and the spatiotemporal big data layer node by using a preset graph construction rule includes:

[0019] Get the time information of the spatiotemporal big data layer node;

[0020] Classify the spatiotemporal big data layer nodes according to the spatiotemporal big data layer node time information to obtain an initial time relationship graph;

[0021] The initial time relationship graph is annotated based on the time relationship to obtain a data node time relationship graph.

[0022] Optionally, constructing a data node attribute relationship graph according to the attribute relationship and the spatiotemporal big data layer node through a preset graph construction rule includes:

[0023] Obtaining spatiotemporal big data layer node attribute information and identification information corresponding to the spatiotemporal big data layer node;

[0024] Attribute classification is performed on the spatiotemporal big data layer nodes according to the spatiotemporal big data layer node attribute information and the identification information to obtain an initial attribute relationship graph;

[0025] The initial attribute relationship graph is annotated based on the attribute relationship to obtain a data node attribute relationship graph.

[0026] Optionally, after constructing a data node relationship graph according to the spatiotemporal big data layer node relationship and the spatiotemporal big data layer node through a preset graph construction rule, the method further includes:

[0027] The data node relationship graph is stored accordingly through a preset graph database;

[0028] Upon receiving a query instruction from a user, determining target spatiotemporal big data layer node information according to the query instruction;

[0029] The target data node relationship map is called based on the target spatiotemporal big data layer node information, and the target data node relationship map is displayed.

[0030] Optionally, the step of obtaining target spatiotemporal big data layer metadata from a preset big data database according to the relationship graph generation instruction includes:

[0031] Determine the corresponding data acquisition interface according to the relationship graph generation instruction;

[0032] Acquire initial entity metadata from a preset big database based on the data acquisition interface;

[0033] The format of the initial entity metadata is adjusted based on the preset big data standard to obtain the target spatiotemporal big data layer metadata.

[0034] In addition, to achieve the above-mentioned purpose, the present invention also proposes a device for generating a spatiotemporal big data relationship graph, the device comprising:

[0035] A data acquisition module, used for acquiring target spatiotemporal big data layer metadata from a preset big data database according to the relationship map generation instruction when receiving the relationship map generation instruction;

[0036] A node confirmation module is used to generate a spatiotemporal big data layer node based on the target spatiotemporal big data layer metadata, and determine the corresponding spatiotemporal big data layer node relationship according to the relationship map generation instruction, wherein the spatiotemporal big data layer node relationship includes: attribute relationship, spatial relationship and time relationship;

[0037] A graph generation module is used to construct a data node relationship graph according to the node relationship of the spatiotemporal big data layer and the nodes of the spatiotemporal big data layer through preset graph construction rules, and perform data management according to the data node relationship graph.

[0038] In addition, to achieve the above-mentioned purpose, the present invention also proposes a data relationship graph generation device, which includes: a memory, a processor, and a spatiotemporal big data relationship graph generation program stored in the memory and executable on the processor, wherein the spatiotemporal big data relationship graph generation program is configured to implement the steps of the spatiotemporal big data relationship graph generation method as described above.

[0039] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which a spatiotemporal big data relationship graph generation program is stored, and when the spatiotemporal big data relationship graph generation program is executed by a processor, the steps of the spatiotemporal big data relationship graph generation method as described above are implemented.

[0040] The present invention obtains target spatiotemporal big data layer metadata from a preset big database according to the relationship map generation instruction when receiving a relationship map generation instruction, generates a spatiotemporal big data layer node based on the target spatiotemporal big data layer metadata, and determines the corresponding spatiotemporal big data layer node relationship according to the relationship map generation instruction, wherein the spatiotemporal big data layer node relationship includes attribute relationship, spatial relationship and time relationship, and constructs a data node relationship map according to the spatiotemporal big data layer node relationship and the spatiotemporal big data layer node through a preset map construction rule, and performs data management according to the data node relationship map. Compared with the prior art, the present invention obtains target entity data from a preset big database through a relationship map generation instruction, and constructs a data node relationship map according to the connection between the spatiotemporal big data layer node relationship and the spatiotemporal big data layer node, so as to realize the management of various types of big data, and the spatiotemporal big data layer node relationship includes attribute relationship, spatial relationship and time relationship, which can realize the establishment of relationships among multiple types of big data, avoids the difficulty in efficiently managing the relationships between various types of spatiotemporal big data, and the difficulty in analyzing and mining spatiotemporal big data, resulting in a low user experience technical problem, and improves the management efficiency of various types of big data. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the structure of a data relationship graph generating device for a hardware operating environment involved in an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of the process of the first embodiment of the method for generating a spatiotemporal big data relationship graph of the present invention;

[0043] Figure 3 A schematic diagram of a data relationship graph of an embodiment of a method for generating a spatiotemporal big data relationship graph of the present invention;

[0044] Figure 4 This is a flow chart of the second embodiment of the method for generating a spatiotemporal big data relationship graph according to the present invention;

[0045] Figure 5 It is a schematic diagram of the process of the third embodiment of the method for generating a spatiotemporal big data relationship graph of the present invention;

[0046] Figure 6 This is a structural block diagram of the first embodiment of the device for generating a spatiotemporal big data relationship graph according to the present invention.

[0047] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0048] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0049] Reference Figure 1 , Figure 1 A schematic diagram of the structure of a device for generating a data relationship graph of a hardware operating environment involved in an embodiment of the present invention.

[0050] like Figure 1 As shown, the data relationship graph generation device 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. Among them, the communication bus 1002 is used to realize the connection and 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 and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (Wireless-Fidelity, Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM), or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk storage. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0051] Those skilled in the art will understand that Figure 1 The structure shown in does not constitute a limitation on the data relationship graph generating device, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0052] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a spatiotemporal big data relationship graph generation program.

[0053] exist Figure 1 In the data relationship graph generating device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the data relationship graph generating device of the present invention can be set in the data relationship graph generating device, and the data relationship graph generating device calls the spatiotemporal big data relationship graph generation program stored in the memory 1005 through the processor 1001, and executes the spatiotemporal big data relationship graph generation method provided by the embodiment of the present invention.

[0054] The embodiment of the present invention provides a method for generating a spatiotemporal big data relationship graph, referring to Figure 2 , Figure 2 It is a flow chart of the first embodiment of a method for generating a spatiotemporal big data relationship graph according to the present invention.

[0055] In this embodiment, the method for generating a spatiotemporal big data relationship graph includes the following steps:

[0056] Step S10: upon receiving a relationship graph generation instruction, obtaining target spatiotemporal big data layer metadata from a preset big data database according to the relationship graph generation instruction.

[0057] It should be noted that the executor of this embodiment is a data relationship map generating device, wherein the data relationship map generating device can be a device with data processing and data transmission functions, and can also be an electronic device such as a control computer, a mobile phone, and a tablet computer. This embodiment does not impose specific restrictions on this. In this embodiment and the following embodiments, the control computer will be used as an example for explanation.

[0058] It is worth noting that the relationship map generation instruction can be an instruction input by the user based on the control computer. The relationship map generation instruction is used to instruct the generation of a big data relationship map and perform data management on the big data. In this embodiment and the following embodiments, the construction land approval business in the natural resources industry will be taken as an example to illustrate the construction of the relationship map.

[0059] Among them, the relationship map generation instruction also includes a data acquisition interface for extracting the target spatiotemporal big data layer metadata from a preset big data database, controlling the computer to perform data cleaning according to the provided data interface, screening out the initial entity metadata from the preset big data source, and adjusting the format of the initial entity metadata based on the preset big data standard to obtain the target spatiotemporal big data layer metadata.

[0060] It can be understood that the preset big database is based on the existing spatiotemporal big data storage technology to uniformly store and manage the relevant spatiotemporal big data corresponding to different engineering projects in a storage unit, wherein the storage unit can be a read-only memory or a non-volatile memory, etc., and this embodiment does not impose specific restrictions on this.

[0061] In addition, since the engineering projects processed by users are different, the corresponding types of spatiotemporal big data are also different. For example, in the construction land approval business of the natural resources industry, the corresponding spatiotemporal big data may include: land use status, control detailed planning, land approval, land reserves, land transfer planning conditions, land supply, state-owned construction land use right registration, construction land planning permit, construction project planning permit, construction permit, completion plan verification, real estate registration, etc. This embodiment does not make specific restrictions on this.

[0062] It is easy to understand that the target spatiotemporal big data layer metadata is used to construct the corresponding data relationship map according to the needs of the user. Since not all data information needs to be used in the actual data relationship map construction process, it is only necessary to extract the target spatiotemporal big data layer metadata required for constructing the data relationship map from the preset big database according to the relationship map generation instruction input by the user, that is, according to the standards of various types of data, extract the data entity objects that meet the requirements from the massive, redundant, and non-standard data sources, and generate the corresponding data entity metadata. The entity metadata can be: data type, data attribute field description, data range, data coordinate system, data accuracy, data description, data update time, etc. For example: in the construction land approval business, if it is necessary to construct a land data spatial map, land use status, control detailed planning and land parameter information and other data may be used. The land use status, control detailed planning and land parameter information and other data are marked as target spatiotemporal big data layer metadata for extraction.

[0063] Step S20: Generate spatiotemporal big data layer nodes based on the target spatiotemporal big data layer metadata, and determine the corresponding spatiotemporal big data layer node relationships according to the relationship graph generation instruction, wherein the spatiotemporal big data layer node relationships include attribute relationships, spatial relationships, and time relationships.

[0064] It should be noted that the spatiotemporal big data layer node can be a data layer created after formatting is unified based on the extracted target metadata according to user needs, and the spatiotemporal big data layer node is used to establish a data relationship graph.

[0065] In the specific implementation, taking the construction land approval business of the natural resources industry as an example, the spatiotemporal big data layer node needs to integrate the relevant data involved in the entire construction land approval link in a unified format, thereby establishing a spatiotemporal big data layer node, wherein the relevant data can be one or more combinations of land use status, control detailed planning, land approval, land reserves, land transfer planning conditions, land supply, state-owned construction land use right registration, construction land planning permit, construction project planning permit, construction permit, completion planning verification, real estate registration and other data information, and this embodiment does not make specific restrictions.

[0066] It can be understood that the node relationship of the spatiotemporal big data layer is used to establish a data relationship map. Since spatiotemporal big data has different characteristics, such as time, space, attributes, behavior, state, process, etc., the relationship between spatiotemporal big data can be divided into different data connection relationships according to these characteristics, such as attribute association relationship, spatial relationship, time relationship, state relationship, process relationship, etc. This embodiment does not impose specific restrictions on this.

[0067] It is worth noting that the extracted target spatiotemporal big data layer metadata has different data characteristics like the spatiotemporal big data. In the actual processing process, different association relationships can be artificially formulated for these target spatiotemporal big data layer metadata according to user needs, and corresponding data association relationships can be generated through data characteristics such as attributes, space, time, and status.

[0068] In a specific implementation, the extracted target spatiotemporal big data layer metadata is constructed as a spatiotemporal big data layer node, and instructions are generated based on the relationship graph input by the user to determine the data association relationship that the user needs to construct. In this embodiment, the attribute relationship, spatial relationship, and time relationship in the spatiotemporal big data layer node relationship are used as examples for explanation.

[0069] Step S30: construct a data node relationship graph according to the spatiotemporal big data layer node relationship and the spatiotemporal big data layer nodes through preset graph construction rules, and perform data management according to the data node relationship graph.

[0070] It should be noted that the data node relationship map is used to display the connection relationship between the spatiotemporal big data layer nodes and the spatiotemporal big data layer node relationship, and there is a directionality between the spatiotemporal big data layer nodes and the spatiotemporal big data layer node relationship, that is, in the specific display process, the spatiotemporal big data layer node relationship can point to a certain spatiotemporal big data layer node, where the reference Figure 3 The data node relationship graph may be expressed in the form of node-edge-node, and in actual operation may be a data graph, a project graph, a management and control graph, an indicator graph, etc., which is not specifically limited in this embodiment.

[0071] It is understandable that the preset graph construction rules are used to provide construction standards for the data node relationship graph, such as: display rules for constructing the data node relationship graph or the storage location after the construction is completed, etc. This embodiment does not impose specific restrictions on this.

[0072] It is worth noting that after obtaining the spatiotemporal big data layer node relationship and the spatiotemporal big data layer nodes, a data relationship model of data node (point)-relationship (edge)-data node (edge) will be established based on the spatiotemporal big data layer node relationship and the spatiotemporal big data layer nodes. The constructed data relationship model will be displayed in the form of an image, recorded as a data relationship map, and data management of the spatiotemporal big data will be performed based on the data relationship map.

[0073] In the specific implementation, taking the construction land approval business in the natural resources industry as an example, the natural resources relationship map is used to link the full process business and data such as planning, approval, land supply, surveying and mapping, law enforcement, registration and transaction to achieve unified management, greatly improving the user's processing efficiency for process business.

[0074] This embodiment, when receiving a relationship map generation instruction, obtains target spatiotemporal big data layer metadata from a preset big database according to the relationship map generation instruction, generates spatiotemporal big data layer nodes based on the target spatiotemporal big data layer metadata, and determines the corresponding spatiotemporal big data layer node relationship according to the relationship map generation instruction, the spatiotemporal big data layer node relationship includes attribute relationship, spatial relationship and time relationship, and constructs a data node relationship map according to the spatiotemporal big data layer node relationship and the spatiotemporal big data layer node through a preset map construction rule, and performs data management according to the data node relationship map. This embodiment obtains target entity data from a preset big database through a relationship map generation instruction, and constructs a data node relationship map according to the relationship between the spatiotemporal big data layer node relationship and the spatiotemporal big data layer node, so as to realize the management of various types of big data, and the spatiotemporal big data layer node relationship includes attribute relationship, spatial relationship and time relationship, which can realize the establishment of relationships among multiple types of big data, avoids the difficulty in efficiently managing the relationships between various types of spatiotemporal big data, and the difficulty in analyzing and mining spatiotemporal big data, resulting in a low user experience technical problem, and improves the management efficiency of various types of big data.

[0075] refer to Figure 4 , Figure 4 It is a flow chart of the second embodiment of a method for generating a spatiotemporal big data relationship graph according to the present invention.

[0076] Based on the above first embodiment, in this embodiment, step S30 includes:

[0077] Step S301: When the relationship between the nodes of the spatiotemporal big data layer is a spatial relationship, a spatiotemporal big data node spatial relationship graph is constructed according to the spatial relationship and the spatiotemporal big data layer nodes using preset graph construction rules.

[0078] It should be noted that the spatial relationship map of spatiotemporal big data nodes is used to represent the spatial relationship between spatiotemporal big data nodes. In actual operation, according to the user's relationship map generation instruction, when the spatiotemporal big data layer node relationship is determined to be a spatial relationship, a layer node is determined, and the layer node is used as the main body. The spatial intersection relationship is associated with other business links, and this is used as a set. The corresponding relationship between adjacent spatiotemporal big data layer nodes is associated in this set through the spatial intersection relationship.

[0079] Furthermore, in order to construct a spatial relationship graph of spatiotemporal big data nodes, the step S301 further includes:

[0080] Get the spatial information of spatiotemporal big data layer nodes;

[0081] Traversing and analyzing the spatiotemporal big data layer nodes according to the spatiotemporal big data layer node spatial information to obtain an initial spatial relationship map;

[0082] The initial spatial relationship map is annotated based on the spatial relationship to obtain a spatiotemporal big data node spatial relationship map.

[0083] It can be understood that the spatial information of data image nodes is used to construct an initial spatial relationship map in combination with the spatiotemporal big data layer nodes, such as: land use status, control detailed planning, etc. Since the initial spatial relationship map at this time is only a node-edge-node connection model, the initial spatial relationship map also needs to be labeled to determine the specific direction of the spatiotemporal big data layer node relationship and obtain the spatiotemporal big data node spatial relationship map.

[0084] In addition, the step S30 also includes: when the relationship between the nodes of the spatiotemporal big data layer is a time relationship, constructing a data node time relationship graph according to the time relationship and the spatiotemporal big data layer nodes through preset graph construction rules.

[0085] It should be noted that the data node time relationship map is used to represent the time relationship between spatiotemporal big data nodes. In actual operation, according to the user's relationship map generation instruction, when the spatiotemporal big data layer node relationship is determined to be a time relationship, a layer node is determined, and the layer node is used as the main body. The time sequence relationship is associated with other business links, and the corresponding relationship between adjacent spatiotemporal big data layer nodes is associated in this set through the time sequence relationship.

[0086] Furthermore, in order to construct a data node time relationship graph, the specific steps include: obtaining the time information of the spatiotemporal big data layer nodes;

[0087] Classify the spatiotemporal big data layer nodes according to the spatiotemporal big data layer node time information to obtain an initial time relationship graph;

[0088] The initial time relationship graph is annotated based on the time relationship to obtain a data node time relationship graph.

[0089] It can be understood that the time information of data image nodes is used to construct an initial time relationship map in combination with the spatiotemporal big data layer nodes, for example: land use status and control detailed planning are managed according to annual versions, etc.

[0090] In the specific implementation, the business process needs to consider the relationship between business logic, so the data needs to have time attributes, and the data before and after the business link is filtered through the time attribute to avoid logical confusion caused by data quality problems. The time filtering condition for each business link to be traced back is: the time is earlier than or equal to the current time, and the time filtering condition for each business link to be traced back is: the time is later than or equal to the current time.

[0091] In addition, the step S30 also includes: when the relationship between the nodes of the spatiotemporal big data layer is an attribute relationship, constructing a data node attribute relationship graph according to the attribute relationship and the spatiotemporal big data layer nodes through preset graph construction rules.

[0092] It should be noted that the data node attribute relationship graph is used to represent the attribute relationship between spatiotemporal big data nodes.

[0093] Furthermore, in order to obtain a data node attribute relationship graph, the specific steps include:

[0094] Obtaining spatiotemporal big data layer node attribute information and identification information corresponding to the spatiotemporal big data layer node;

[0095] Attribute classification is performed on the spatiotemporal big data layer nodes according to the spatiotemporal big data layer node attribute information and the identification information to obtain an initial attribute relationship graph;

[0096] The initial attribute relationship graph is annotated based on the attribute relationship to obtain a data node attribute relationship graph.

[0097] The data image node attribute information is used to construct an initial attribute relationship map in combination with the identification information corresponding to the spatiotemporal big data layer node and the spatiotemporal big data layer node, such as: land use approval, land reserve, land transfer planning conditions, land supply, state-owned construction land use right registration, construction land planning permit, construction project planning permit, construction permit, completion plan verification, real estate registration, etc. Since the initial attribute relationship map at this time is only a node-edge-node connection model, it is also necessary to annotate the initial attribute relationship map to determine the specific direction of the spatiotemporal big data layer node relationship, obtain the data node attribute relationship map, and perform attribute management through project identification information to generate a data node attribute relationship map.

[0098] The embodiment of the present invention obtains target entity data from a preset big data database through a relationship graph generation instruction, and constructs a data node relationship graph based on the relationship between the spatiotemporal big data layer nodes and the connection between the spatiotemporal big data layer nodes to achieve management of various types of big data. The spatiotemporal big data layer node relationship includes attribute relationship, spatial relationship and time relationship, which can realize spatiotemporal big data node spatial relationship graph, data node time relationship graph and data node attribute relationship graph, and manage big data in the form of multiple graphs, avoiding the difficulty in efficiently managing the relationship between various types of spatiotemporal big data, as well as the difficulty in analyzing and mining spatiotemporal big data, resulting in technical problems such as low user experience, and improving the management efficiency of various types of big data.

[0099] refer to Figure 5 , Figure 5 It is a flow chart of the third embodiment of a method for generating a spatiotemporal big data relationship graph according to the present invention.

[0100] Based on the above second embodiment, in this embodiment, after step S30, the following steps are included:

[0101] Step S40: The data node relationship graph is stored accordingly through a preset graph database.

[0102] It should be noted that the spatiotemporal big data node spatial relationship graph, data node time relationship graph and data node attribute relationship graph generated by the above embodiments are created and managed through a graph library, that is, after generating a data association relationship model of data node (point)-relationship (edge)-data node (edge), the data relationship model is stored using a graph database.

[0103] It is easy to understand that the preset graph database is used to store the data relationship model of data node (point)-relationship (edge)-data node (edge) so that users can call and query from the preset graph database.

[0104] Step S50: upon receiving a query instruction from the user, determining target spatiotemporal big data layer node information according to the query instruction.

[0105] It can be understood that the query instruction is used to query the data relationship graph required by the user, determine the target spatiotemporal big data layer node information through the query instruction, and then query the data relationship graph containing the target spatiotemporal big data layer node information.

[0106] Step S60: calling the target data node relationship graph based on the target spatiotemporal big data layer node information, and displaying the target data node relationship graph.

[0107] In a specific implementation, the target data node relationship graph may be one or more combinations of a spatiotemporal big data node spatial relationship graph, a data node time relationship graph, and a data node attribute relationship graph, and this embodiment does not impose any specific restrictions.

[0108] In addition, taking the construction land approval business of the natural resources industry as an example, when displaying the map, the land use status and control detailed planning links are displayed separately, and no connecting lines are used to link with other links. Since the planning / status data of three years (versions) are extracted, only one version is displayed in the land use status and control detailed planning links, and it is linked to the land approval node. When the land approval node is clicked, the land use status and control detailed planning links are switched to the corresponding content respectively.

[0109] The embodiment of the present invention obtains target entity data from a preset big data database through a relationship graph generation instruction, and constructs a data node relationship graph based on the relationship between the spatiotemporal big data layer nodes and the connection between the spatiotemporal big data layer nodes to achieve management of various types of big data, and the spatiotemporal big data layer node relationship includes attribute relationship, spatial relationship and time relationship, which can realize spatiotemporal big data node spatial relationship graph, data node time relationship graph and data node attribute relationship graph, and manage big data in the form of multiple graphs. After the graph is established, it can provide query services to users and display the corresponding data node relationship graph according to the user's query instructions, avoiding the difficulty in efficiently managing the relationship between various types of spatiotemporal big data, as well as the difficulty in analyzing and mining spatiotemporal big data, which leads to technical problems of low user experience, improves the management efficiency of various types of big data, and improves the user experience.

[0110] In addition, an embodiment of the present invention further proposes a storage medium, on which a spatiotemporal big data relationship graph generation program is stored. When the spatiotemporal big data relationship graph generation program is executed by a processor, the steps of the spatiotemporal big data relationship graph generation method as described above are implemented.

[0111] Since the storage medium adopts all the technical solutions of all the above embodiments, it at least has all the beneficial effects brought by the technical solutions of the above embodiments, which will not be described one by one here.

[0112] Reference Figure 6 , Figure 6 This is a structural block diagram of the first embodiment of the device for generating a spatiotemporal big data relationship graph according to the present invention.

[0113] like Figure 6 As shown, the spatiotemporal big data relationship graph generation device proposed in the embodiment of the present invention includes:

[0114] The data acquisition module 10 is used to acquire the target spatiotemporal big data layer metadata from a preset big data database according to the relationship map generation instruction when receiving the relationship map generation instruction;

[0115] The node confirmation module 20 is used to generate a spatiotemporal big data layer node based on the target spatiotemporal big data layer metadata, and determine the corresponding spatiotemporal big data layer node relationship according to the relationship map generation instruction, wherein the spatiotemporal big data layer node relationship includes: attribute relationship, spatial relationship and time relationship;

[0116] The graph generation module 30 is used to construct a data node relationship graph according to the node relationship of the spatiotemporal big data layer and the nodes of the spatiotemporal big data layer through preset graph construction rules, and perform data management according to the data node relationship graph.

[0117] This embodiment, when receiving a relationship map generation instruction, obtains target spatiotemporal big data layer metadata from a preset big database according to the relationship map generation instruction, generates spatiotemporal big data layer nodes based on the target spatiotemporal big data layer metadata, and determines the corresponding spatiotemporal big data layer node relationship according to the relationship map generation instruction, the spatiotemporal big data layer node relationship includes attribute relationship, spatial relationship and time relationship, and constructs a data node relationship map according to the spatiotemporal big data layer node relationship and the spatiotemporal big data layer node through a preset map construction rule, and performs data management according to the data node relationship map. This embodiment obtains target entity data from a preset big database through a relationship map generation instruction, and constructs a data node relationship map according to the relationship between the spatiotemporal big data layer node relationship and the spatiotemporal big data layer node, so as to realize the management of various types of big data, and the spatiotemporal big data layer node relationship includes attribute relationship, spatial relationship and time relationship, which can realize the establishment of relationships among multiple types of big data, avoids the difficulty in efficiently managing the relationships between various types of spatiotemporal big data, and the difficulty in analyzing and mining spatiotemporal big data, resulting in a low user experience technical problem, and improves the management efficiency of various types of big data.

[0118] In one embodiment, the graph generation module 30 is also used to construct a spatiotemporal big data node spatial relationship graph based on the spatial relationship and the spatiotemporal big data layer nodes through preset graph construction rules when the spatiotemporal big data layer node relationship is a spatial relationship; to construct a data node time relationship graph based on the time relationship and the spatiotemporal big data layer nodes through preset graph construction rules when the spatiotemporal big data layer node relationship is a temporal relationship; and to construct a data node attribute relationship graph based on the attribute relationship and the spatiotemporal big data layer nodes through preset graph construction rules when the spatiotemporal big data layer node relationship is an attribute relationship.

[0119] In one embodiment, the graph generation module 30 is also used to obtain spatial information of spatiotemporal big data layer nodes; traverse and analyze the spatiotemporal big data layer nodes according to the spatial information of the spatiotemporal big data layer nodes to obtain an initial spatial relationship graph; and annotate the initial spatial relationship graph based on the spatial relationship to obtain a spatiotemporal big data node spatial relationship graph.

[0120] In one embodiment, the graph generation module 30 is also used to obtain time information of the spatiotemporal big data layer nodes; classify the spatiotemporal big data layer nodes according to the time information of the spatiotemporal big data layer nodes to obtain an initial time relationship graph; and annotate the initial time relationship graph based on the time relationship to obtain a data node time relationship graph.

[0121] In one embodiment, the graph generation module 30 is also used to obtain the attribute information of the spatiotemporal big data layer node and the identification information corresponding to the spatiotemporal big data layer node; classify the attributes of the spatiotemporal big data layer node according to the attribute information of the spatiotemporal big data layer node and the identification information to obtain an initial attribute relationship graph; and annotate the initial attribute relationship graph based on the attribute relationship to obtain a data node attribute relationship graph.

[0122] In one embodiment, the graph generation module 30 is also used to store the data node relationship graph accordingly through a preset graph database; when receiving a user's query instruction, determine the target spatiotemporal big data layer node information according to the query instruction; call the target data node relationship graph based on the target spatiotemporal big data layer node information, and display the target data node relationship graph.

[0123] In one embodiment, the data acquisition module 10 is also used to determine the corresponding data acquisition interface according to the relationship graph generation instruction; obtain initial entity metadata from a preset big data database based on the data acquisition interface; adjust the format of the initial entity metadata based on the preset big data standard to obtain the target spatiotemporal big data layer metadata.

[0124] It should be understood that the above is only an example and does not constitute any limitation on the technical solution of the present invention. In specific applications, technicians in this field can make settings as needed, and the present invention does not limit this.

[0125] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of them according to actual needs to achieve the purpose of the present embodiment, and no limitation is made here.

[0126] In addition, for technical details that are not described in detail in this embodiment, please refer to the method for generating a spatiotemporal big data relationship graph provided in any embodiment of the present invention, and will not be repeated here.

[0127] In addition, it should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0128] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0129] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory (ROM) / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0130] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for generating a spatiotemporal big data relationship graph, characterized in that: The method for generating a spatiotemporal big data relationship graph comprises: When receiving the relationship map generation instruction, obtaining the target spatiotemporal big data layer metadata from the preset spatiotemporal big data database according to the relationship map generation instruction; Generate a spatiotemporal big data layer node based on the target spatiotemporal big data layer metadata, and determine the corresponding spatiotemporal big data layer node relationship according to the relationship map generation instruction; Constructing a spatiotemporal big data layer node relationship graph according to the spatiotemporal big data layer node relationship and the spatiotemporal big data layer nodes through a preset graph construction rule, and performing data management according to the spatiotemporal big data layer node relationship graph; The step of constructing a spatiotemporal big data layer node relationship graph based on the spatiotemporal big data layer node relationship and the spatiotemporal big data layer node using a preset graph construction rule includes: When the relationship between the spatiotemporal big data layer nodes is a spatial relationship, a spatiotemporal big data node spatial relationship map is constructed according to the spatial relationship and the data layer nodes through a preset construction rule; When the relationship between the spatiotemporal big data layer nodes is a time relationship, a data node time relationship graph is constructed according to the time relationship and the data layer nodes through a preset construction rule; When the spatiotemporal big data layer node relationship is an attribute relationship, a data node attribute relationship graph is constructed according to the attribute relationship and the data layer node through a preset construction rule; The step of constructing a spatiotemporal big data node spatial relationship graph based on the spatial relationship and the data layer nodes by using preset construction rules includes: Get the spatial information of spatiotemporal big data layer nodes; Traversing and analyzing the spatiotemporal big data layer nodes according to the spatiotemporal big data layer node spatial information to obtain an initial spatial relationship map; Annotate the initial spatial relationship map based on the spatial relationship to obtain a spatiotemporal big data node spatial relationship map; After constructing a spatiotemporal big data node relationship graph according to the spatiotemporal big data layer node relationship and the spatiotemporal big data layer node through a preset graph construction rule, it also includes: The spatiotemporal big data node relationship graph is stored accordingly through a preset graph database; Upon receiving a query instruction from a user, determining target spatiotemporal big data layer node information according to the query instruction; Based on the target spatiotemporal big data layer node information, the target spatiotemporal big data node relationship map is called, and the target spatiotemporal big data node relationship map is displayed; The step of obtaining target spatiotemporal big data layer metadata from a preset big data database according to the relationship graph generation instruction includes: Determine the corresponding spatiotemporal big data acquisition interface according to the relationship graph generation instruction; Acquire initial entity metadata from a preset big database based on the spatiotemporal big data acquisition interface; The format of the initial entity metadata is adjusted based on a preset big data standard to obtain target spatiotemporal big data layer metadata.

2. The method for generating a spatiotemporal big data relationship graph according to claim 1, characterized in that: The step of constructing a data node time relationship graph based on the time relationship and the spatiotemporal big data layer nodes by using a preset graph construction rule includes: Get the time information of the spatiotemporal big data layer node; Classify the spatiotemporal big data layer nodes according to the spatiotemporal big data layer node time information to obtain an initial time relationship graph; The initial time relationship graph is annotated based on the time relationship to obtain a spatiotemporal big data node time relationship graph.

3. The method for generating a spatiotemporal big data relationship graph according to claim 1, characterized in that: The method of constructing a spatiotemporal big data node attribute relationship graph according to the attribute relationship and the spatiotemporal big data layer node by using a preset graph construction rule includes: Obtaining spatiotemporal big data layer node attribute information and identification information corresponding to the spatiotemporal big data layer node; Attribute classification is performed on the spatiotemporal big data layer nodes according to the spatiotemporal big data layer node attribute information and the identification information to obtain an initial attribute relationship graph; The initial attribute relationship graph is annotated based on the attribute relationship to obtain a spatiotemporal big data node attribute relationship graph.

4. A device for generating a spatiotemporal big data relationship graph, characterized in that: The device for generating a spatiotemporal big data relationship graph comprises: A data acquisition module, used for acquiring target spatiotemporal big data layer metadata from a preset big data database according to the relationship map generation instruction when receiving the relationship map generation instruction; A node confirmation module is used to generate a spatiotemporal big data layer node based on the target spatiotemporal big data layer metadata, and determine the corresponding spatiotemporal big data layer node relationship according to the relationship map generation instruction, wherein the spatiotemporal big data layer node relationship includes: attribute relationship, spatial relationship and time relationship; A graph generation module is used to construct a spatiotemporal big data node relationship graph according to the spatiotemporal big data layer node relationship and the spatiotemporal big data layer nodes through a preset graph construction rule, and perform data management according to the spatiotemporal big data node relationship graph; The graph generation module is also used to construct a spatial relationship graph of spatiotemporal big data nodes according to the spatial relationship and the data layer nodes through preset construction rules when the relationship between the spatiotemporal big data layer nodes is a spatial relationship; to construct a data node time relationship graph according to the time relationship and the data layer nodes through preset construction rules when the relationship between the spatiotemporal big data layer nodes is a time relationship; and to construct a data node attribute relationship graph according to the attribute relationship and the data layer nodes through preset construction rules when the relationship between the spatiotemporal big data layer nodes is an attribute relationship. The graph generation module is also used to obtain spatial information of the spatiotemporal big data layer nodes; traverse and analyze the spatiotemporal big data layer nodes according to the spatial information of the spatiotemporal big data layer nodes to obtain an initial spatial relationship graph; annotate the initial spatial relationship graph based on the spatial relationship to obtain a spatiotemporal big data node spatial relationship graph; The graph generation module is also used to store the spatiotemporal big data node relationship graph in a corresponding manner through a preset graph database; upon receiving a user's query instruction, determine the target spatiotemporal big data layer node information according to the query instruction; call the target spatiotemporal big data node relationship graph based on the target spatiotemporal big data layer node information, and display the target spatiotemporal big data node relationship graph; The graph generation module is also used to determine the corresponding spatiotemporal big data acquisition interface according to the relationship graph generation instruction; obtain initial entity metadata from a preset big data database based on the spatiotemporal big data acquisition interface; adjust the format of the initial entity metadata based on the preset big data standard to obtain the target spatiotemporal big data layer metadata.

5. A device for generating a spatiotemporal big data relationship graph, characterized in that: The spatiotemporal big data relationship graph generation device includes: a memory, a processor, and a spatiotemporal big data relationship graph generation program stored in the memory and executable on the processor, wherein the spatiotemporal big data relationship graph generation program is configured to implement the spatiotemporal big data relationship graph generation method as described in any one of claims 1 to 3.

6. A storage medium, characterized in that: The storage medium stores a spatiotemporal big data relationship graph generation program, which, when executed by a processor, implements the spatiotemporal big data relationship graph generation method as described in any one of claims 1 to 3.

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