Graph-based multi-storage form spatial big data integrated management method and device

Abstract

The present invention discloses a graph-based multi-storage form spatial big data integrated management method and device. It includes: when obtaining the associated storage instruction for the target spatial big data set, determining the source spatial data of the target spatial big data set; obtaining the source graph element node information from the initial graph element data according to the label information and storage form information of the source spatial data; generating the target graph element node information according to the source graph element node information and the target spatial big data set; constructing the association relationship information between the target graph element node information and the source graph element node information; updating the graph element data according to the target graph element node information and the association relationship information. The present invention constructs an effective association between the multi-form storage information of spatial big data, breaks the logical discrete state between each storage form, supports the organic combination of the management, computational analysis, visualization and other scenes of spatiotemporal big data, and has strong scalability, and can accommodate more spatial big data associated storage forms.

Description

Technical Field

[0001] The present invention relates to the field of spatial information technology, and in particular to a graph-based multi-storage form spatial big data integrated management method and device. Background Art

[0002] Polymorphic storage of spatial big data solves the data storage performance requirements of diversified application scenarios, but it also brings about the problem of data integration, that is, the same spatial data set comes from different storage forms when used in data management scenarios, computing and analysis scenarios, visualization scenarios, etc., and the various spatial data storage forms are discrete and fail to be logically unified, resulting in the separation of spatial big data management, computing, visualization and other services. In the existing spatial big data polymorphic storage methods or technologies, the correlation between the same data in different forms is weak, and it cannot be logically expressed as "one" data object to the outside world, and fails to effectively support the organic combination of management, computing and analysis, visualization and other scenarios. The traditional spatial big data polymorphic storage model is a structure that is highly generalized and abstracted based on professional cognition. It is not intuitive, natural, direct and efficient. It is mainly based on structured or semi-structured models. Each metadata needs to be customized. The scalability is insufficient, and the traceability capability of the entire life cycle of the data is insufficient.

[0003] 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

[0004] The main purpose of the present invention is to provide a graph-based integrated management method and device for multi-storage-form spatial big data, aiming to solve the technical problem that the existing multi-form storage methods of spatial big data have weak correlations between the storage forms, fail to achieve logical unification, and lead to the separation of management, computing, visualization and other services.

[0005] To achieve the above object, the present invention provides a graph-based multi-storage form spatial big data integrated management method, the method comprising the following steps:

[0006] When an associated storage instruction for a target spatial large data set is obtained, a source spatial data set corresponding to the target spatial large data set is determined;

[0007] According to the label information and storage form information corresponding to the source spatial data set, the source primitive node information is queried from the initial primitive metadata;

[0008] Generate corresponding target graph element node information according to the source graph element node information and the target spatial large data set;

[0009] Constructing association relationship information between the target primitive node information and the source primitive node information based on the graph;

[0010] Update the graphic element data according to the target graphic element node information and the association relationship information.

[0011] Optionally, the source graph element node information includes the sequence number, time information, label information, storage form information, storage format, metadata and usage information of the spatial big data set.

[0012] Optionally, generating corresponding target graph element node information according to the source graph element node information and the target spatial large data set includes:

[0013] Automatically generate target serial number and target time information;

[0014] Determine target label information corresponding to a target primitive node according to label information in the source primitive node information, wherein the target label information is consistent with the label information;

[0015] Acquire target storage form information, target storage format and target metadata corresponding to the target primitive node from the target spatial big data set;

[0016] Determine the target usage information corresponding to the target primitive node according to the usage scenario information input by the user;

[0017] The target primitive node information corresponding to the target primitive node is constituted according to the target sequence number, the target time information, the target tag information, the target storage form information, the target storage format, the target metadata and the target usage information.

[0018] Optionally, the association relationship information includes data flow direction, data flow method and data flow rules of the spatial big data set in multiple storage forms.

[0019] Optionally, after updating the primitive data according to the target primitive node information and the association relationship information, the method further includes:

[0020] Get query instructions;

[0021] According to the query instruction, the final primitive node information that meets the requirements is searched from the updated primitive data.

[0022] Optionally, searching for final primitive node information that meets the requirements from the updated primitive data according to the query instruction includes:

[0023] Determine the corresponding constraint conditions according to the query instruction, and obtain the information of the primitive node to be queried;

[0024] Determine a set of association relationship information related to the to-be-queried primitive node information from the updated primitive metadata;

[0025] Determine a set of associated primitive nodes according to the set of associated relationship information;

[0026] Obtaining the associated primitive node information corresponding to each primitive node in the associated primitive node set;

[0027] The associated primitive node information is screened according to the constraint conditions to obtain final primitive node information that meets the requirements.

[0028] Optionally, the associated primitive node information is screened according to the constraint condition to obtain final primitive node information that meets the requirements, including:

[0029] When the current primitive node information meeting the requirement is not obtained, performing iterative search with each primitive node in the associated primitive node set as the initial node to obtain the target associated primitive node set;

[0030] Obtain target-associated primitive node information corresponding to each primitive node in the target-associated primitive node set;

[0031] The target associated primitive node information is screened according to the constraint conditions to obtain final primitive node information that meets the requirements.

[0032] In addition, to achieve the above-mentioned purpose, the present invention also proposes a graph-based multi-storage form spatial big data integrated management device, the graph-based multi-storage form spatial big data integrated management device comprising:

[0033] A determination module, configured to determine a source spatial data set corresponding to the target spatial large data set when an associated storage instruction for the target spatial large data set is obtained;

[0034] An acquisition module, used for querying and acquiring source primitive node information from the initial primitive metadata according to the label information and storage form information corresponding to the source spatial dataset;

[0035] A node generation module, used to generate corresponding target graph element node information according to the source graph element node information and the target spatial large data set;

[0036] A construction module, used for constructing the association relationship information between the target primitive node information and the source primitive node information based on the graph;

[0037] A graph generation module is used to update the graph metadata according to the target graph node information and the association relationship information.

[0038] In addition, to achieve the above-mentioned objectives, the present invention also proposes a graph-based multi-storage form spatial big data integrated management device, the graph-based multi-storage form spatial big data integrated management device comprising: a memory, a processor, and a graph-based multi-storage form spatial big data integrated management program stored in the memory and runnable on the processor, the graph-based multi-storage form spatial big data integrated management program being configured to implement the graph-based multi-storage form spatial big data integrated management method as described above.

[0039] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which a graph-based multi-storage form spatial big data integrated management program is stored. When the graph-based multi-storage form spatial big data integrated management program is executed by a processor, the graph-based multi-storage form spatial big data integrated management method as described above is implemented.

[0040] The present invention determines the source spatial data set corresponding to the target spatial big data set when the associated storage instruction for the target spatial big data set is obtained; the source graph element node information is queried from the initial graph element data according to the label information and storage form information corresponding to the source spatial data set; the corresponding target graph element node information is generated according to the source graph element node information and the target spatial big data set; the association relationship information between the target graph element node information and the source graph element node information is constructed based on the graph; and the graph element data is updated according to the target graph element node information and the association relationship information. In the above manner, spatial data of different storage forms are stored through the nodes of the graph element data, and the association information between the nodes is constructed, so as to construct an effective association between the multi-form storage information of the spatial big data set, break the logical discrete state between each storage form, support the organic combination of the management, computational analysis, visualization and other scenes of spatiotemporal big data, and have strong scalability, and can accommodate more spatial big data associated storage forms. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a structural schematic diagram of a graph-based multi-storage form space big data integrated management device in a hardware operating environment involved in an embodiment of the present invention;

[0042] Figure 2 It is a flowchart of the first embodiment of the graph-based multi-storage form spatial big data integrated management method of the present invention;

[0043] Figure 3 It is a schematic diagram of the structure of graph metadata of an embodiment of a graph-based multi-storage form spatial big data integrated management method of the present invention;

[0044] Figure 4It is a flowchart of the second embodiment of the graph-based multi-storage form spatial big data integrated management method of the present invention;

[0045] Figure 5 It is a data query reference graph of an embodiment of a graph-based multi-storage form spatial big data integrated management method of the present invention;

[0046] Figure 6 It is a structural block diagram of the first embodiment of the graph-based multi-storage form spatial big data integrated management device of 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 It is a schematic diagram of the structure of a graph-based multi-storage form spatial big data integrated management device in the hardware operating environment involved in the embodiment of the present invention.

[0050] like Figure 1 As shown, the graph-based multi-storage form spatial big data integrated management 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 graph-based multi-storage modality spatial big data integrated management 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 graph-based multi-storage form spatial big data integrated management program.

[0053] exist Figure 1 In the graph-based multi-storage form spatial big data integrated management 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 graph-based multi-storage form spatial big data integrated management device of the present invention can be set in the graph-based multi-storage form spatial big data integrated management device, and the graph-based multi-storage form spatial big data integrated management device calls the graph-based multi-storage form spatial big data integrated management program stored in the memory 1005 through the processor 1001, and executes the graph-based multi-storage form spatial big data integrated management method provided by the embodiment of the present invention.

[0054] The embodiment of the present invention provides a graph-based multi-storage form spatial big data integrated management method, referring to Figure 2 , Figure 2 It is a flow chart of the first embodiment of the graph-based multi-storage form spatial big data integrated management method of the present invention.

[0055] In this embodiment, the graph-based multi-storage form spatial big data integrated management method includes the following steps:

[0056] Step S10: when the associated storage instruction for the target spatial big data set is obtained, the source spatial data set corresponding to the target spatial big data set is determined.

[0057] It can be understood that the executor of this embodiment is a graph-based multi-storage form spatial big data integrated management device, which can be a computer, server and other devices, or other devices with the same or similar functions, and this embodiment does not limit this.

[0058] It should be noted that the same spatial big data set (layer) corresponds to different storage forms when used in different scenes. When it is necessary to store the target spatial big data set corresponding to a certain scene, the associated storage instruction for the target spatial big data set input by the user is received, and the source spatial data set corresponding to the target spatial big data set is determined. For example, the target spatial big data set B is a file storage form obtained by converting the spatial data set A, and the source spatial data set corresponding to the target spatial big data set B is the spatial data set A.

[0059] Step S20: querying and acquiring source primitive node information from the initial primitive data according to the label information and storage form information corresponding to the source spatial data set.

[0060] It can be understood that in this embodiment, "graph" is a data structure that describes a relationship network, which is composed of two basic elements: nodes and edges. A node represents an entity, and an edge represents the relationship between two nodes. This embodiment uses graph theory to intuitively model the multi-modal storage, and logically expresses it as "one" data object to the outside world, supporting the organic combination of management, computational analysis, visualization and other scenarios. This method is very intuitive, natural, direct and efficient in expressing the relationship between different storage forms, and does not require conversion and processing in the middle. The initial graph metadata is a data structure that is constructed in advance and contains at least a number of initial nodes. Figure 3 , Figure 3 This is a schematic diagram of the graph metadata structure of an embodiment of the graph-based multi-storage form spatial big data integrated management method of the present invention. The graph metadata is represented by G(V,E), which consists of a graph node set V = {v1, v2, v3, ..., v n}, edge (relation) set E = {e1(v a →v b ),e2(v c →v d ),…,e m (v e →v f )}, where a, b, c, d, e, f∈[1, n], and a≠b, c≠d, e≠f. In this embodiment, the storage form of the spatial large data set is v i , different storage forms of the same data can be expressed as V = {v1,v2,v3,…,v n}, the relationship between different storage forms is E = {e1(v a →v b ),e2(v c →v d ),…,e m (v e →v f )}, that is, the different storage forms of the same spatial big data set and the relationship between different storage forms can be expressed through the metadata G(V,E).

[0061] Specifically, the source graph element node information includes the sequence number, time information, label information, storage form information, storage format, metadata and usage information of the spatial big data set.

[0062] It should be noted that each primitive node v in the primitive data G(V,E) i The information contained is expressed as a seven-tuple vi (I i ,T i ,L i ,S i ,F i ,M i ,U i ), respectively, serial number I i , time information T i , label information L i , storage form information S i 、Storage format F i 、Metadata M i And usage information i , based on the label information L of the source spatial dataset i And storage form information S i Get source element node information from the initial element data for the query condition.

[0063] Step S30: Generate corresponding target graph element node information according to the source graph element node information and the target spatial big data set.

[0064] Specifically, the step S30 includes: automatically generating a target serial number and a target time information; determining the target label information corresponding to the target primitive node according to the label information in the source primitive node information, wherein the target label information is consistent with the label information; obtaining the target storage form information, the target storage format and the target metadata corresponding to the target primitive node from the target spatial big data set; determining the target usage information corresponding to the target primitive node according to the usage scenario information input by the user; and constructing the target primitive node information corresponding to the target primitive node according to the target serial number, the target time information, the target label information, the target storage form information, the target storage format, the target metadata and the target usage information.

[0065] It can be understood that according to the source element node information v i (I i ,T i ,L i ,S i ,F i ,M i ,U i ) Create target element node information v i+1 (I j ,T j ,L i ,S j ,F j ,M j ,U j ), where target number I jAnd the target time information T j Automatically generated, label information L i Keep consistent with the source element node information, and obtain the target storage form information S from the existing information of the target space large data set j 、Target storage format F j and the target metadata M j , target usage information U j The user is required to input according to the usage scenario.

[0066] Step S40: constructing association relationship information between the target primitive node information and the source primitive node information based on the graph.

[0067] Specifically, the association relationship information includes the data flow direction, data flow method and data flow rules of the spatial large data set in multiple storage forms.

[0068] It should be noted that the relationship information j Contains the data flow direction D i , data flow method H i And the data flow rule R i (layer filter condition, attribute filter condition), expressed as a triple e j (D i ,H i ,R i ), create the association relationship information between the target element node B and the source element node A j (D i ,H i ,R i ), for example, the flow direction is D i A→B, the way or method used from A to B H i To extract a layer in A, method H i The rule R used i The extracted layer number and total amount, etc.

[0069] It can be understood that the relationship between the primitive nodes can be used to express the relationship between data in different storage forms, mainly including the following four types:

[0070] 1) Single flow: The conversion relationship between different data forms is irreversible, see Figure 3 For example, the original data stored in a relational database can be converted into visual tile data through the slicing method, but the tile data cannot be converted into the original data.

[0071] 2) Dual flow: The conversion relationship between different data forms can be reversed or partially reversed, refer to Figure 3In v1→v2∪v2→v1, for example, the original data stored in the relational database can be converted into a file storage form, and vice versa, the file storage form can be restored to the relational database.

[0072] 3) One-stream-multiple: expresses that one data form is converted into multiple other storage forms, or it can be expressed that the same storage form is converted into multiple storage formats, see Figure 3 v1→v2∪v1→v3 in .

[0073] 4) Multi-stream one: It means that data from multiple storage forms are merged into a data set in one storage form. It can also mean that multiple data in the same storage form are merged into one data. Figure 3 v5→v7∪v4→v7∪v6→v7 in .

[0074] Step S50: updating the graphic element data according to the target graphic element node information and the association relationship information.

[0075] It should be understood that the target primitive node information v is added to the node set V of the initial primitive data G(V,E) i+1 , and add the association information e in the association set E j (v i →v i+1 ), thereby establishing an association between the target primitive node and the source primitive node.

[0076] This embodiment determines the source spatial data set corresponding to the target spatial big data set when the associated storage instruction for the target spatial big data set is obtained; queries and obtains the source graph element node information from the initial graph element data according to the label information and storage form information corresponding to the source spatial data set; generates the corresponding target graph element node information according to the source graph element node information and the target spatial big data set; constructs the association relationship information between the target graph element node information and the source graph element node information based on the graph; and updates the graph element data according to the target graph element node information and the association relationship information. In the above manner, spatial data of different storage forms are stored through the nodes of the graph element data, and the association information between the nodes is constructed, so as to construct an effective association between the multi-form storage information of the spatial big data set, break the logical discrete state between each storage form, support the organic combination of the management, computational analysis, visualization and other scenarios of spatiotemporal big data, and has strong scalability, and can accommodate more spatial big data associated storage forms.

[0077] refer to Figure 4 , Figure 4 It is a flow chart of the second embodiment of the graph-based multi-storage form spatial big data integrated management method of the present invention.

[0078] Based on the first embodiment described above, the graph-based multi-storage form spatial big data integrated management method of this embodiment further includes, after step S50:

[0079] Step S501: Obtain a query instruction.

[0080] It can be understood that there are two ways of data query in this implementation. One is to query data through spatial range based on spatial index, and the other is to query through existing attribute information (such as label information, storage form and other attributes).

[0081] Step S502: searching for final primitive node information that meets the requirements from the updated primitive metadata according to the query instruction.

[0082] It should be noted that in this embodiment, the primitive node of the scene data is used as the starting point, and the updated primitive metadata is traversed based on the association relationship information (edge), and the primitive node information is filtered according to the constraint conditions corresponding to the query instruction to find the final primitive node information that meets the requirements.

[0083] Specifically, step S502 includes: determining corresponding constraints according to the query instruction, and obtaining the primitive node information to be queried; determining a set of association relationship information related to the primitive node information to be queried from the updated primitive data; determining a set of associated primitive nodes according to the set of association relationship information; obtaining associated primitive node information corresponding to each primitive node in the associated primitive node set; filtering the associated primitive node information according to the constraints to obtain final primitive node information that meets the requirements.

[0084] It can be understood that obtaining the scene data to be queried element node v i , and obtain the association relationship information set {e1,e2,e3,…,e m}, and through the association relationship information set {e1,e2,e3,…,e m}Query the associated element node set {v j+1 ,v j+2 ,v j+3 ,…,v j+m}, obtain the element node information of each associated element node, and filter the element nodes according to the constraint condition f to obtain the element nodes that meet the constraint condition. For example, the constraint condition corresponding to the query instruction is that the data flow rule in the association relationship information is full extraction, and the use information in the element node information is analysis and calculation.

[0085] Furthermore, the associated primitive node information is filtered according to the constraint conditions to obtain final primitive node information that meets the requirements, including: when current primitive node information that meets the requirements is not obtained, iteratively searching with each primitive node in the associated primitive node set as the initial node to obtain a target associated primitive node set; obtaining target associated primitive node information corresponding to each primitive node in the target associated primitive node set; filtering the target associated primitive node information according to the constraint conditions to obtain final primitive node information that meets the requirements.

[0086] It should be noted that if the association node set {v j+1 ,v j+2 ,v j+3 ,…,v j+m}, if there is no associated primitive node that meets the constraint conditions, then continue with {v j+1 ,v j+2 ,v j+3 ,…,v j+m} is the initial node and an iterative search is performed using graph theory. Figure 5 , Figure 5 This is a data query reference graph for an embodiment of the graph-based multi-storage form spatial big data integrated management method of the present invention. With v1 as the initial node, the first search results are v2, v3, and v4, the second search results are v5, v6, and v7, and the third search result is the target node v9. Iteratively search for associated primitive nodes from the graph metadata until the graph search is completed or the target primitive node that meets the requirements is found, and the data information of the target primitive node is obtained. In this way, the "past and present" of big data in the entire cycle can be restored on the entire link.

[0087] This embodiment determines the source spatial data set corresponding to each target spatial big data set in the target spatial big data set when the associated storage instruction for the target spatial big data set is obtained; queries and obtains the source primitive node information from the initial primitive data set according to the label information and storage form information corresponding to the source spatial data set; generates the corresponding target primitive node information according to the source primitive node information and the target spatial big data set; constructs the association relationship information between the target primitive node information and the source primitive node information; updates the primitive data according to the target primitive node information and the association relationship information; obtains the query instruction; and searches for the final primitive node information that meets the requirements from the updated primitive data according to the query instruction. In the above manner, an effective association between multi-form storage information of spatial big data is constructed, and primitive node information that meets the requirements is quickly found from the constructed primitive data, realizing the query and tracing of the entire life cycle or link between different storage forms of spatial big data, breaking the logical discrete state between the storage forms, and supporting the organic combination of management, computational analysis, visualization and other scenarios of spatiotemporal big data.

[0088] In addition, an embodiment of the present invention also proposes a storage medium, on which a graph-based multi-storage form spatial big data integrated management program is stored. When the graph-based multi-storage form spatial big data integrated management program is executed by a processor, the graph-based multi-storage form spatial big data integrated management method as described above is implemented.

[0089] Since the storage medium adopts all the technical solutions of all the above embodiments, it has at least all the beneficial effects brought by the technical solutions of the above embodiments, which will not be described one by one here.

[0090] Reference Figure 6 , Figure 6 It is a structural block diagram of the first embodiment of the graph-based multi-storage form spatial big data integrated management device of the present invention.

[0091] like Figure 6 As shown, the graph-based multi-storage form spatial big data integrated management device proposed in the embodiment of the present invention includes:

[0092] The determination module 10 is used to determine the source spatial data set corresponding to the target spatial big data set when the associated storage instruction for the target spatial big data set is obtained.

[0093] The acquisition module 20 is used to query and acquire source primitive node information from the initial primitive data according to the label information and storage form information corresponding to the source spatial data set.

[0094] The node generation module 30 is used to generate corresponding target primitive node information according to the source primitive node information and the target spatial large data set.

[0095] The construction module 40 is used to construct the association relationship information between the target primitive node information and the source primitive node information based on the graph.

[0096] The graph generation module 50 is used to update the graph metadata according to the target graph node information and the association relationship information.

[0097] 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.

[0098] This embodiment determines the source spatial data set corresponding to the target spatial big data set when the associated storage instruction for the target spatial big data set is obtained; queries and obtains the source graph element node information from the initial graph element data according to the label information and storage form information corresponding to the source spatial data set; generates the corresponding target graph element node information according to the source graph element node information and the target spatial big data set; constructs the association relationship information between the target graph element node information and the source graph element node information based on the graph; and updates the graph element data according to the target graph element node information and the association relationship information. In the above manner, spatial data of different storage forms are stored through the nodes of the graph element data, and the association information between the nodes is constructed, so as to construct an effective association between the multi-form storage information of the spatial big data set, break the logical discrete state between each storage form, support the organic combination of the management, computational analysis, visualization and other scenarios of spatiotemporal big data, and has strong scalability, and can accommodate more spatial big data associated storage forms.

[0099] 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.

[0100] In addition, for technical details not fully described in this embodiment, please refer to the graph-based multi-storage form spatial big data integrated management method provided in any embodiment of the present invention, which will not be repeated here.

[0101] In one embodiment, the source primitive node information includes the sequence number, time information, label information, storage form information, storage format, metadata and usage information of the spatial big data set.

[0102] In one embodiment, the node generation module 30 is also used to automatically generate a target serial number and target time information; determine the target label information corresponding to the target primitive node based on the label information in the source primitive node information, wherein the target label information is consistent with the label information; obtain the target storage form information, target storage format and target metadata corresponding to the target primitive node from the target spatial big data set; determine the target usage information corresponding to the target primitive node based on the usage scenario information input by the user; and construct the target primitive node information corresponding to the target primitive node based on the target serial number, the target time information, the target label information, the target storage form information, the target storage format, the target metadata and the target usage information.

[0103] In one embodiment, the association relationship information includes the data flow direction, data flow method and data flow rule of the spatial big data set in multiple storage forms.

[0104] In one embodiment, the graph-based multi-storage form spatial big data integrated management device further includes a query module;

[0105] The query module is used to obtain a query instruction; and search for final primitive node information that meets the requirements from the updated primitive data according to the query instruction.

[0106] In one embodiment, the query module is also used to determine the corresponding constraint conditions according to the query instruction, and obtain the primitive node information to be queried; determine the association relationship information set related to the primitive node information to be queried from the updated primitive data; determine the associated primitive node set according to the association relationship information set; obtain the associated primitive node information corresponding to each primitive node in the associated primitive node set; filter the associated primitive node information according to the constraint conditions to obtain the final primitive node information that meets the requirements.

[0107] In one embodiment, the query module is also used to, when current primitive node information that meets the requirements is not obtained, perform an iterative search with each primitive node in the associated primitive node set as the initial node to obtain a target associated primitive node set; obtain target associated primitive node information corresponding to each primitive node in the target associated primitive node set; and filter the target associated primitive node information according to the constraint condition to obtain final primitive node information that meets the requirements.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 graph-based multi-storage form spatial big data integrated management method, characterized in that: The graph-based multi-storage form spatial big data integrated management method includes: When an associated storage instruction for a target spatial large data set is obtained, a source spatial data set corresponding to the target spatial large data set is determined; According to the label information and storage form information corresponding to the source spatial data set, the source graphic element node information is queried from the initial graphic element data, wherein the source graphic element node information includes the sequence number, time information, label information, storage form information, storage format, metadata and usage information of the spatial large data set; Generate corresponding target graph element node information according to the source graph element node information and the target spatial large data set; The generating corresponding target graph element node information according to the source graph element node information and the target spatial large data set includes: Automatically generate target serial number and target time information; Determine target label information corresponding to a target primitive node according to label information in the source primitive node information, wherein the target label information is consistent with the label information; Acquire target storage form information, target storage format and target metadata corresponding to the target primitive node from the target spatial big data set; Determine the target usage information corresponding to the target primitive node according to the usage scenario information input by the user; The target primitive node information corresponding to the target primitive node is formed according to the target sequence number, the target time information, the target tag information, the target storage form information, the target storage format, the target metadata and the target usage information; Constructing association relationship information between the target primitive node information and the source primitive node information based on the graph; Update the graphic element data according to the target graphic element node information and the association relationship information.

2. The graph-based multi-storage form spatial big data integrated management method according to claim 1, characterized in that: The association relationship information includes the data flow direction, data flow method and data flow rules of the spatial large data set in multiple storage forms.

3. The graph-based multi-storage form spatial big data integrated management method according to claim 1, characterized in that: After updating the image data according to the target image node information and the association relationship information, the method further includes: Get query instructions; According to the query instruction, the final primitive node information that meets the requirements is searched from the updated primitive data.

4. The graph-based multi-storage form spatial big data integrated management method according to claim 3, characterized in that: According to the query instruction, the final element node information that meets the requirements is searched from the updated element data, including: Determine the corresponding constraint conditions according to the query instruction, and obtain the information of the primitive node to be queried; Determine a set of association relationship information related to the to-be-queried primitive node information from the updated primitive metadata; Determine a set of associated primitive nodes according to the set of associated relationship information; Obtaining the associated primitive node information corresponding to each primitive node in the associated primitive node set; The associated primitive node information is screened according to the constraint conditions to obtain final primitive node information that meets the requirements.

5. The graph-based multi-storage form spatial big data integrated management method according to claim 4, characterized in that: The associated primitive node information is screened according to the constraint condition to obtain final primitive node information that meets the requirements, including: When the current primitive node information meeting the requirement is not obtained, performing iterative search with each primitive node in the associated primitive node set as the initial node to obtain the target associated primitive node set; Obtain target-associated primitive node information corresponding to each primitive node in the target-associated primitive node set; The target associated primitive node information is screened according to the constraint conditions to obtain final primitive node information that meets the requirements.

6. A graph-based multi-storage form spatial big data integrated management device, characterized in that: The graph-based multi-storage form spatial big data integrated management device comprises: A determination module, configured to determine a source spatial data set corresponding to the target spatial large data set when an associated storage instruction for the target spatial large data set is obtained; An acquisition module is used to query and acquire source primitive node information from the initial primitive data according to the label information and storage form information corresponding to the source spatial dataset, wherein the source primitive node information includes the sequence number, time information, label information, storage form information, storage format, metadata and usage information of the spatial large dataset; A node generation module, used to generate corresponding target graph element node information according to the source graph element node information and the target spatial large data set; The node generation module is also used to automatically generate a target serial number and target time information; determine the target label information corresponding to the target primitive node according to the label information in the source primitive node information, wherein the target label information is consistent with the label information; obtain the target storage form information, target storage format and target metadata corresponding to the target primitive node from the target spatial big data set; determine the target usage information corresponding to the target primitive node according to the usage scenario information input by the user; construct the target primitive node information corresponding to the target primitive node according to the target serial number, the target time information, the target label information, the target storage form information, the target storage format, the target metadata and the target usage information; A construction module, used for constructing the association relationship information between the target primitive node information and the source primitive node information based on the graph; A graph generation module is used to update the graph metadata according to the target graph node information and the association relationship information.

7. A graph-based multi-storage form spatial big data integrated management device, characterized in that: The device includes: a memory, a processor, and a graph-based multi-storage form spatial big data integration management program stored in the memory and executable on the processor, wherein the graph-based multi-storage form spatial big data integration management program is configured to implement the graph-based multi-storage form spatial big data integration management method as described in any one of claims 1 to 5.

8. A storage medium, characterized in that: The storage medium stores a graph-based multi-storage form spatial big data integrated management program, and when the graph-based multi-storage form spatial big data integrated management program is executed by the processor, it implements the graph-based multi-storage form spatial big data integrated management method as described in any one of claims 1 to 5.

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