Efficient reasoning method, device and equipment for spatial relationship of large-scale geographic elements

Abstract

The invention relates to the technical field of geographic reasoning, in particular to a large-scale geographic element space relation efficient reasoning method, device and equipment, and the method comprises the steps: obtaining a static vector boundary and a dynamic change vector boundary of a geographic element in a target region; performing the same type of grid expression on the static vector boundary and the dynamic change vector boundary to obtain a grid expression set of a static space range and a grid expression set of a dynamic change range; constructing a geographic knowledge graph according to the grid expression set in the static space range, and updating the geographic knowledge graph according to the grid expression set in the dynamic change range; and reasoning the spatial relationship between each geographic element and the dynamic change range based on the updated geographic knowledge graph. Therefore, the problems that in the prior art, data quality is inconsistent, the automation degree is low, the real-time requirement is difficult to meet, expansibility and performance optimization challenges exist, and complex knowledge representation and processing are difficult are solved.

Description

Technical Field

[0001] The present invention relates to the field of geographic reasoning technology, and in particular to a method, device and equipment for efficiently reasoning about the spatial relationship of large-scale geographic elements. Background Art

[0002] In recent years, knowledge graphs have formed advantages in the operability and computability of knowledge acquisition, knowledge fusion, knowledge expression, knowledge search and knowledge reasoning due to their large scale, rich semantics and friendly structure, and have achieved results in tasks such as web search, intelligent recommendation, and knowledge question answering. As a form of knowledge organization based on semantic networks, knowledge graphs associate knowledge extracted from data in a directed graph to express entities and their semantic relationships. The nodes of the graph represent entities and concepts, and the edges represent the attributes of entities and concepts or the semantic relationships between them.

[0003] However, based on this, the geographic knowledge graph has expanded the concepts, entities and relationships of knowledge according to the characteristics of geographic knowledge itself. For example, the geographic knowledge graph in the form of "process-relationship" representation models the temporal and spatial characteristics, which can reflect the evolution of geographical phenomena and the characteristics of geological mechanisms. With the introduction of the Web service standards and data sharing principles of the Open Geospatial Alliance, the amount of open geographic data with spatial characteristics on the Web is also increasing. After the knowledge extracted from these data is organized and associated using the geographic knowledge graph, other geographic knowledge implicit in them can be inferred.

[0004] The spatial relationship between geographic elements is an important kind of geographic knowledge. Geographic elements are entities with certain geographic spatial locations in the real world. The spatial locations of geographic elements are generally expressed in the form of coordinates. When reasoning about spatial relationships based on coordinate expressions, the number of calculations will increase exponentially with the number of geographic elements, which will reduce the computational efficiency when reasoning about large-scale spatial relationships of geographic elements and increase the response time of reasoning. In addition, in scenarios with high real-time requirements, knowledge graphs need to be updated frequently, and high reasoning delays may cause the reasoning results to be inconsistent with the actual status. Summary of the invention

[0005] The present invention provides a method, device and equipment for efficient reasoning of large-scale spatial relationships of geographic elements, so as to solve the problems in the prior art such as how to improve the accuracy and efficiency of reasoning of large-scale spatial relationships of geographic elements.

[0006] The first aspect of the present invention provides an efficient reasoning method for spatial relationships of large-scale geographic elements, including the following steps: obtaining static vector boundaries and dynamically changing vector boundaries of geographic elements in a target area; performing the same type of grid expression on the static vector boundaries and the dynamically changing vector boundaries, respectively, to obtain a grid expression set of a static spatial range and a grid expression set of a dynamic changing range; constructing a geographic knowledge graph based on the grid expression set of the static spatial range, and updating the geographic knowledge graph based on the grid expression set of the dynamic changing range; inferring the spatial relationship between each geographic element and the dynamic changing range based on the updated geographic knowledge graph, wherein the spatial relationship includes geographic elements within the dynamic changing range, geographic elements intersecting the dynamic changing range, and geographic elements outside the dynamic changing range.

[0007] Optionally, the same type of grid expression is performed on the static vector boundary and the dynamically changing vector boundary respectively to obtain a grid expression set of the static spatial range and a grid expression set of the dynamic changing range, including: obtaining the spatial grids for dividing the static spatial range and the dynamic changing range of the geographic element; classifying the spatial grids where the static vector boundary and the dynamically changing vector boundary intersect respectively; and determining the grid expression set of the static spatial range and the grid expression set of the dynamic changing range according to the classification results.

[0008] Optionally, the nodes in the geographic knowledge graph ontology include at least one of the following: geographic element nodes, grid expression set nodes of the static spatial range of geographic elements, included grid set nodes of the static spatial range of geographic elements, intersecting grid set nodes of the static spatial range of geographic elements, grid unit nodes, and the node relationships include the relationships between grid units in the grid expression set.

[0009] Optionally, updating the geographic knowledge graph according to the grid expression set with a dynamically changing range includes: performing at least one update action of adding, deleting, merging and modifying nodes and relationships in the geographic knowledge graph according to the grid expression set with a dynamically changing range.

[0010] Optionally, at least one update action of adding, deleting, merging and modifying nodes and relationships in the geographic knowledge graph is performed according to the grid expression set of the dynamic changing range, including: adding at least one of a dynamic changing range node, a dynamic changing range grid expression set node, a dynamic changing range containing grid set node and a dynamic changing range intersecting grid set node in the geographic knowledge graph; updating the relationship between grid unit nodes and grid units in the geographic knowledge graph, merging identical nodes, and establishing a relationship between the grid expression set of the dynamic changing range and the grid expression set of the static spatial range of geographic elements.

[0011] Optionally, the spatial relationship between each geographic element and the dynamic change range is inferred based on the updated geographic knowledge graph, including: based on the updated geographic knowledge graph, the spatial relationship between each geographic element and the dynamic change range is inferred using an inheritance reasoning method.

[0012] The second aspect of the present invention provides an efficient reasoning device for spatial relationships of large-scale geographic elements, including: an acquisition module, used to obtain static vector boundaries and dynamically changing vector boundaries of geographic elements in a target area; an expression module, used to perform the same type of grid expression on the static vector boundaries and the dynamically changing vector boundaries, respectively, to obtain a grid expression set of a static spatial range and a grid expression set of a dynamic changing range; an update module, used to construct a geographic knowledge graph based on the grid expression set of the static spatial range, and to update the geographic knowledge graph based on the grid expression set of the dynamic changing range; an inference module, used to infer the spatial relationship between each geographic element and the dynamic changing range based on the updated geographic knowledge graph, wherein the spatial relationship includes geographic elements within the dynamic changing range, geographic elements intersecting the dynamic changing range, and geographic elements outside the dynamic changing range.

[0013] The third aspect of the present invention provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement an efficient reasoning method for large-scale spatial relationships of geographic elements as in the above-mentioned embodiment.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement an efficient method for reasoning about spatial relationships of large-scale geographic elements as in the above-mentioned embodiment.

[0015] A fifth aspect of the present invention provides a computer program product, including a computer program or instructions, which, when executed, are used to implement an efficient method for reasoning about spatial relationships of large-scale geographic elements as in the above-mentioned embodiment.

[0016] Thus, the present invention includes the following beneficial effects: The embodiments of the present invention can simplify the complexity of data processing, improve computing efficiency, and construct a dynamically updated geographic knowledge graph by obtaining the static and dynamic vector boundaries of geographic elements and expressing them in a grid format, thereby meeting real-time requirements and achieving powerful spatial relationship reasoning capabilities. The embodiments of the present invention can be widely used in multiple fields such as urban planning, environmental protection, and disaster warning, making full use of structured information in the graph, reducing information redundancy, and improving the accuracy and efficiency of reasoning. Thus, the technical problems of how to improve the accuracy and efficiency of large-scale geographic element spatial relationship reasoning in the prior art are solved.

[0017] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 A flowchart of an efficient reasoning method for large-scale spatial relationships of geographic elements provided according to an embodiment of the present invention; Figure 2 A flowchart of an efficient reasoning method for large-scale spatial relationships of geographic elements according to an embodiment of the present invention; Figure 3 A diagram of the geographic knowledge graph ontology structure provided according to an embodiment of the present invention; Figure 4 A schematic diagram of a geographical knowledge graph obtained after updating according to an embodiment of the present invention; Figure 5 An example diagram of a large-scale geographic element spatial relationship efficient reasoning device according to an embodiment of the present invention; Figure 6 FIG. 4 is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0020] The following describes the efficient reasoning method, device and equipment for large-scale spatial relationship of geographic elements of the embodiments of the present invention with reference to the accompanying drawings. In view of the problem of inaccurate or delayed reasoning mentioned in the above background technology, the present invention provides a method for efficient reasoning of large-scale spatial relationship of geographic elements. In this method, by obtaining the static and dynamic vector boundaries of geographic elements and expressing them in a grid, the complexity of data processing can be simplified, the computing efficiency can be improved, and a dynamically updated geographic knowledge graph can be constructed to meet real-time requirements, thereby achieving powerful spatial relationship reasoning capabilities and being widely used in multiple fields such as urban planning, environmental protection and disaster warning. As a result, the problems of inconsistent data quality, low degree of automation, difficulty in meeting real-time requirements, scalability and performance optimization challenges, and complex knowledge representation and processing difficulties in the prior art are solved.

[0021] Specifically, Figure 1 The flowchart is a method for efficiently reasoning about large-scale spatial relationships of geographic elements according to an embodiment of the present invention.

[0022] like Figure 1 As shown, the efficient reasoning method for large-scale spatial relationships of geographic elements includes the following steps: In step S101, the static vector boundaries and the dynamically changing vector boundaries of the geographic elements in the target area are obtained.

[0023] Among them, the static vector boundary can be the spatial position and shape of the geographical elements in the target area that remain unchanged at a specific time point or time period, and the dynamically changing vector boundary can be the boundary of the geographical elements in the target area that changes over time.

[0024] It can be understood that the embodiments of the present invention can achieve accurate and dynamic description of geographic elements by obtaining static vector boundaries and dynamically changing vector boundaries of geographic elements in the target area, thereby facilitating subsequent spatial analysis.

[0025] In step S102, the same type of grid expression is performed on the static vector boundary and the dynamically changing vector boundary respectively, so as to obtain a grid expression set of the static space range and a grid expression set of the dynamically changing range.

[0026] Among them, grid expression can be a data representation method that divides the geographic space into a series of regular or irregular grid units.

[0027] It can be understood that the embodiment of the present invention realizes the unification of data format and discretization of spatial information by expressing static vector boundaries and dynamically changing vector boundaries in the same type of grid, thereby improving data processing efficiency and spatial analysis capabilities. At the same time, the information in the grid unit can be updated in real time or regularly to realize real-time monitoring and early warning of dynamic changes in geographic elements.

[0028] In an embodiment of the present invention, the same type of grid expression is performed on static vector boundaries and dynamically changing vector boundaries respectively to obtain a grid expression set of a static spatial range and a grid expression set of a dynamic changing range, including: obtaining spatial grids for dividing the static spatial range and the dynamic changing range of geographic elements; classifying the spatial grids where the static vector boundaries and the dynamically changing vector boundaries intersect; and determining the grid expression set of the static spatial range and the grid expression set of the dynamic changing range according to the classification results.

[0029] Among them, the spatial grid can be a system that divides the geographic space into a series of regular or irregular grid units, and the grid classification can be based on certain rules or standards, dividing the spatial grids that intersect at static vector boundaries or dynamically changing vector boundaries into different categories or groups.

[0030] It can be understood that the embodiments of the present invention can divide the static and dynamic spatial ranges of geographic elements into spatial grids and classify these grids, thereby determining the grid expression sets of static and dynamic spatial ranges, improving data processing efficiency, enhancing spatial analysis capabilities, promoting data sharing and integration, improving decision support capabilities, and supporting real-time dynamic monitoring, providing strong support for the comprehensive application of geographic spatial data.

[0031] Specifically, the geographic coordinates of the vertices in the vector boundary of the geographic element are input into the spatial grid partitioning function, and the input parameters required by the function are set, where the input parameters may include the maximum partitioning level, the minimum partitioning level, and the maximum number of grids. The spatial grid partitioning function outputs the original grid expression set of the static spatial range of the geographic element { , … }; the original grid expression set of the output geographic element static spatial range is input into the grid expression set optimization function, and the grid expression set optimization function outputs the grid expression set of the static spatial range of the geographic element; wherein, the grid expression set optimization function adopts the RCC5 regional calculation model to determine the spatial relationship between the vector boundary of the geographic element and the grid cells in the original grid expression set of the static spatial range of the geographic element.

[0032] Get the geographic coordinates of the vertices in the vector boundary of the geographic element, and form a coordinate expression set of the static spatial range of the geographic element, recorded as { , … }; Traverse the original grid expression set of the static spatial range of geographic elements { , … }, get the current grid cell ID, input it into the grid coordinate calculation function, the grid coordinate calculation function outputs the vertex coordinates of the current grid and the current original grid cell coordinate expression set, recorded as .

[0033] The coordinate expression set of the static spatial range of geographic elements and the original grid cell coordinates of the static spatial extent of the geographic feature Input into the RCC5 regional calculation function, which outputs a coordinate expression set of the static spatial range of geographic elements. and the original grid cells of the static spatial extent of geographic features The spatial relationship between them.

[0034] If the output spatial relationship is Contains, the original grid cells of the static spatial extent of the geographic feature are added to the collection If the output spatial relationship is intersect, the original grid cells of the static spatial extent of the geographic feature are added to the collection middle.

[0035] Repeat the above process until the original grid expression set of the static spatial range of the geographic element is { , … } Each grid in the traversal is completed. Get the collection , denoted as { , … }; Get the collection , denoted as . Will gather and collection Add to the empty collection in sequence, and output the grid expression collection of the static spatial range of geographic features {{ , … }, { , … }}.

[0036] In step S103, a geographic knowledge graph is constructed based on a grid expression set of a static spatial range, and the geographic knowledge graph is updated based on a grid expression set of a dynamically changing range.

[0037] Among them, the geographic knowledge graph can be a graph-based data structure used to represent the complex relationships between geographic entities.

[0038] It can be understood that the embodiments of the present invention achieve deep integration and efficient use of complex geographic data by constructing a geographic knowledge graph; at the same time, through a set of grid expressions with a dynamically changing range, geographic data can be updated in real time or periodically to ensure that the information in the geographic knowledge graph is always kept up to date. For application scenarios of real-time decision-making, accurate and timely geographic information services can be provided to users.

[0039] In an embodiment of the present invention, updating the geographic knowledge graph according to a grid expression set with a dynamically changing range includes: performing at least one update action of adding, deleting, merging and modifying nodes and relationships in the geographic knowledge graph according to the grid expression set with a dynamically changing range.

[0040] Among them, the nodes in the geographic knowledge graph may include at least one of the following: geographic element nodes, grid expression set nodes of the static spatial range of geographic elements, included grid set nodes of the static spatial range of geographic elements, intersecting grid set nodes of the static spatial range of geographic elements, and grid unit nodes. The node relationship includes the relationship between grid units in the grid expression set.

[0041] It can be understood that the embodiments of the present invention can ensure the timeliness and accuracy of the geographic knowledge graph by dynamically updating the nodes and relationships in the graph, support complex geographic spatial analysis and decision-making, promote the sharing and collaboration of geographic information, and promote the development and application of geographic information technology.

[0042] In an embodiment of the present invention, at least one update action of adding, deleting, merging and modifying nodes and relationships in a geographic knowledge graph is performed according to a grid expression set of a dynamic changing range, including: adding at least one of a dynamic changing range node, a grid expression set node of a dynamic changing range, a grid set node containing a dynamic changing range and an intersecting grid set node of a dynamic changing range to the geographic knowledge graph; updating the relationship between grid unit nodes and grid units of the geographic knowledge graph, merging identical nodes, and establishing a relationship between a grid expression set of a dynamic changing range and a grid expression set of a static spatial range of geographic elements.

[0043] The dynamically changing range node may be used to represent a geographical area or object that changes with time or conditions.

[0044] It is understandable that by adding dynamically changing range nodes and their related set nodes, the geographic knowledge graph can capture the dynamic changes of geographic entities in real time or regularly, which are accurately recorded in the form of grid expression sets, and establish a clear relationship with the static spatial range grid expression set of geographic elements, thereby clearly depicting the complex spatial relationship between geographic entities. In addition, by updating grid unit nodes and their relationships and merging duplicate nodes, the embodiment of the present invention not only reduces data redundancy, but also significantly improves data consistency and query efficiency.

[0045] Specifically, a geographic knowledge graph is constructed with geographic elements, a grid expression set of the static spatial range of geographic elements, and grid cells in the grid expression set of the static spatial range of geographic elements as nodes; the grid cells in the grid expression set of the static spatial range of geographic elements are traversed to obtain the current grid cell ID, and the current grid cell ID is input into the grid relationship acquisition function, which outputs the child grid ID and parent grid ID of the current grid cell. The child grid node and parent grid node of the current grid cell are generated and added to the geographic knowledge graph; the above process is repeated until every grid cell in the grid expression set of the static spatial range of geographic elements is traversed.

[0046] The grid cells in the grid expression set of the static spatial range of all geographic elements are compared in pairs. By comparing the grid cell ID, grid cell sub-grid ID, and grid cell parent grid ID, the grid cell nodes with the same ID in the geographic knowledge graph are merged and the pre-built geographic knowledge graph is output.

[0047] In step S104, the spatial relationship between each geographic element and the dynamic change range is inferred based on the updated geographic knowledge graph.

[0048] Among them, the spatial relationship may include geographic elements within the dynamic change range, geographic elements that intersect the dynamic change range, and geographic elements outside the dynamic change range.

[0049] It can be understood that the embodiments of the present invention can significantly improve the accuracy of understanding geographic spatial changes, enhance the scientific nature and timeliness of decision-making, and optimize data management efficiency by inferring the spatial relationship between geographic elements and dynamically changing ranges based on the updated geographic knowledge graph.

[0050] In an embodiment of the present invention, the spatial relationship between each geographical element and the dynamic change range is inferred based on the updated geographical knowledge graph, including: based on the updated geographical knowledge graph, the spatial relationship between each geographical element and the dynamic change range is inferred using the inheritance reasoning method.

[0051] It can be understood that the embodiments of the present invention can use the inheritance reasoning method to infer the spatial relationship between geographic elements and dynamically changing ranges, make full use of the structured information in the map, reduce information redundancy, and improve the accuracy and efficiency of reasoning.

[0052] Specifically, the embodiment of the present invention can query the grid set containing the dynamic range and the grid set intersecting the dynamic range from the updated geographic knowledge graph; wherein the grid set containing the dynamic range R is recorded as { , … }, the set of intersecting grids with dynamic range R is recorded as { , … }.

[0053] Traverse the dynamically changing range of the containing grid collection { , … }, query the current grid cell from the updated geographic knowledge graph All sub-grid units of the output sub-grid set { , … }.

[0054] Traverse the subgrid collection { , … }, query whether the current grid cell is included in the spatial grid expression set of a certain geographic element. If so, output the grid expression set of the static spatial range of the geographic element containing the current sub-grid cell { , … }.

[0055] Traverse the grid expression set of the static spatial range of geographic elements { , … }, if the current grid cell or the parent grid cell of the current grid cell is contained in { , … }, output 1 and add it to the output set K. If the current grid cell or the parent grid cell of the current grid cell is included in { , … }, output 0.5 and add it to the output set K. If the current grid cell or the parent grid cell of the current grid cell is not included in {{ , … }, { , … }}, output 0 and add it to the output set K; repeat this process until the grid expression set { , … }, all grid cells in the set are traversed. If all elements in the set K are 1, it is determined that the current geographic element is included in the dynamic change range. Otherwise, it is determined that the current geographic element intersects with the dynamic change range.

[0056] Repeat the above process until the dynamically changing range of the grid set { , … } The grid cells in the traversal are completed. All geographic features included in the real-time dynamic change range and the geographic features intersecting with the real-time dynamic change range are output.

[0057] The efficient reasoning method for large-scale spatial relationships of geographic elements proposed in an embodiment of the present invention can simplify the complexity of data processing, improve computing efficiency, and construct a dynamically updated geographic knowledge graph by obtaining the static and dynamic vector boundaries of geographic elements and expressing them in a gridded manner. It can meet real-time requirements and thus achieve powerful spatial relationship reasoning capabilities. It can be widely used in many fields such as urban planning, environmental protection, and disaster warning.

[0058] The following will use a specific example to explain the efficient reasoning method for large-scale spatial relationships of geographic elements. Take the flooding scene in Wuhan as an example. Figure 2 As shown, the specific steps include: S1: Obtain OSM (Open Street Map) data of the target city's AOI (Area of ​​Interest), and extract the vector boundaries of surface geographic features such as residential areas, ring roads, parks, and buildings, recorded as: P={A,B…Z},A= , B= …Z= .

[0059] The acquired OSM data is preprocessed, including: clearing the data items with missing spatial location fields, feature type fields, and feature names; clearing the data with excessive spatial location data deviation; and removing redundancy from similar and repeated data.

[0060] S2: respectively set the three parameters of the maximum grid division level, the minimum grid division level, and the maximum number of grids required for dividing residential areas, ring roads, and flood areas, as shown in Table 1, which is a parameter setting table.

[0061] Table 1

[0062] After multiple adjustments, the above parameters can ensure that the spatial location of geographic elements is efficiently covered while the grid coverage error at the boundaries of geographic elements is as small as possible. According to the parameter settings, the vector boundaries of the acquired geographic elements are input, the spatial location of the geographic elements is divided, and the grid expression set of residential areas, ring roads, and flood ranges is obtained; Among them, the grid expression set of residential areas is: {31da180b54,31da180ca7,31da180cac,31da180cb4,31da186d8c,31da186dc2c,31da186dd4,31da186ddc,31da186de4,31da186dec,31da186df4,31da186dfc,31da186e04,31da186e0c,31da186e14,31da186e1c,31da186e24,31da186e2c,31da186e34,31da186e3c, 31da186e404,31da186e6c,31da186e74,31da187187f,31da18718c,31da1871dc,31da1871e4,31da1871ec,31da1871f4,31da1871fc,31da18 7204,31da18720c,31da187214,31da18721c,31da187224,31da18722c,31da187234,31da18723c,31da187244,31da18724c,31da187254,31d a18725c,31da187264,31da18726c,31da187274,31da18727c,31da18728b,31da1872e4,31da1872efc,31da1872f4,31da1872fc,31da187304 ,31da18730c,31da187314,31da18731c,31da187324,31da187334,31da18733c,31da187344,31da18734c,31da187354,31da18735c,31da187 364,31da18736c,31da187374,31da18737c,31da187384,31da187394,31da18739c,31da1873a4,31da1873ac,31da1873b4,31da1873bc,31da 1873c4,31da1873cc,31da1873d3,31da18747f,31da187484,31da18748c,31da187494,31da18749c,31da1874a4,31da1874ac,31da1874bd}.

[0063] Set of grid representations of the circular road: {31da22962c,31da22978c,31da229794,31da2297c4,31da2297cf,31da2297d4,31da2297dc,31da2297e4,31da2297ec,31da2297f4,31da2297fc,31da22a2ad5,31da22a2b4,31da22a2cc,31da22a2d4,31da22a32c,31da22a334,31da22a33c,31da22a344,31da22a3481,31da22a35d,31da22a364,31da22a3684,31da22a454,31da22a45c,31da22a484,31da22a48c,31da22a49c,31da22a4f4,31da22a4f9,31da22a584,31da22a59c,31da22a5a4,31da22a5ac,31da22a954,31da22abfc,31da22ac04,31da22ac0fc,31da22ac11,31da22ac17,31da22ac1c,31da22ac22c,31da22ac34,31da22ac3c,31da22ac4c,31da22ad84,31da22ad8c,31da22ad91,31da22ad97,31da22ad9c,31da22ada2b,31da22adb4,31da22adbc,31da22adcc,31da22aeab5,31da22aead,31da22aeb4,31da22aecc,31da22aed4,31da22af155,31da22af25,31da22af2c,31da22af301,31da22af3c,31da22af41,31da22af64,31da22af6c,31da22af7c,31da22b255,31da22b257,31da22b25c,31da22b264,31da22b27c,31da22b28ac,31da22b2f4,31da22b2fc,31da22b304,31da22b30c,31da22b364,31da22b374,31da22b37c,31da22b3801,31da22b3a7,31da22b3a9,31da22b3ab,31da22b404,31da22b40c,31da22b474,<h2 style=";text-align:left;direction:ltr">31da22b47c,31da22b484,31da22b49d,31da22b6a4,31da22b6ac,31da22b6b4,31da22b6b9,31da22b6bb,31da22b6c4,31da22b6c9,31da22b6dc,31da22b724,31da22b73c,31da22b744,31da22b75c,31da22b76 4,31da22b7684,31da22b90c,31da22b914,31da22b91c,31da22b923,31da22b925,31da22b92c,31da22b931,31da22b93b,31da22b93d,31da22b9a4,31da22b9bc,31da22b9c4,31da22b9dc,31da22b9e1,31da22b 9e7,31da22b9ec,31da22bd2c,31da22bd34,31da22bd4c,31da22bd54,31da22bd62c,31da22bd7c,31da22bd84,31da22bd8c,31da22bd94,31da22bde4,31da22bdec,31da22bdf4,31da22bdfb,31da22be0c,31da 22be14,31da22be2c,31da22be31,31da22be3c,31da22be72d,31da22bed4,31da22bedc,31da22bee4,31da22befc,31da22bf04,31da22bf1d,31da22bf1f,31da22bfac,31da22bfb4,31da22bfcc,31da22bfd3}。,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0064] The grid expression set of flood range: {31da17f9,31da17fc4,31da17fdc,31da17ff,31da1804,31da1809,31da180b,31da180c4,31da180cc,31da180dc,31da180f,31da18714,31da18734,31da1873c,31da1875,31da18764,31da1876c,31da18774,31da2295c,31da22964,31da22974,31da2297c,31da2299,31da229c4,31da188 a229dc,31da229f,31da22b,31da22c1,31da22c34,31da22c3c,31da22c5,31da22c7,31da22cc,31da22d4,31da22d9,31da22db,31da22dc4,31da22d cc,31da22dec,31da2325c,31da23264,31da23274,31da2327c,31da232c,31da23324,31da2332c,31da3cc14,31da18764,31da1876c,31da18774,31d a2295c,31da22964,31da22974,31da2297c,31da2299,31da3cc2c,31da3cc34,31da3cc3c,31da3cc5,31da3cc7,31da3cc84,31da3cc8c,31da3cc94, 31da3ccd,31da3ccf,31da3cd4,31da3cdc,31da3ce1,31da3ce3,31da3ce44,31da3ce4c,31da3ce7,31da18764,31da1876c,31da18774,31da2295c,3 1da22964,31da22974,31da2297c,31da2299,31da3d17c,31da3d19,31da3d1bc,31da3d1c4,31da3d1d4,31da3d1dc,31da3d1f,31da3d24,31da3d2c, 31da3d34,31da3d39,31da3d3b,31da3d3d,31da3d3ec,31da3d454,31da3d45c,31da3d464,31da3d47c,31da3d4c,31da3d54,31da3d5a4,31da3d5ac}.

[0065] S3: The spatial location grid expression set of non-dynamically changing geographic elements such as residential areas and ring roads, according to Figure 3 The ontology structure shown is used to construct a geographic knowledge graph.

[0066] S4: According to the spatial location grid expression set of the flood range, according to the ontological structure diagram of the geographic knowledge graph, add, delete, merge, and modify the nodes and relationships in the constructed geographic knowledge graph, and establish the relationship between the grid expression set of the flood range and the grid expression set of the static spatial range of the geographic elements. The updated geographic knowledge graph is as follows: Figure 4 shown.

[0067] S5: Traverse the grid cells in the grid expression set of the flood range, and query whether the current grid cell has a parent grid or a child grid in the updated geographic knowledge graph.

[0068] Specifically, the grid with ID 31da2297c in the spatial grid expression set of the flood range is queried, and 10 sub-grids are queried in the geographic knowledge graph, as shown in Table 2, which is a list of sub-grids.

[0069] Table 2

[0070] The 10 subgrid IDs are: 31da22978c, 31da229794, 31da2297c4, 31da2297cf, 31da2297d4, 31da2297dc, ​​31da2297e4, 31da2297ec, 31da2297f4, 31da2297fc; all of these grids belong to the spatial grid expression set of the ring road. That is, the spatial location of the ring road must overlap with the flood range.

[0071] The ring road is located, and the grid cells in the spatial grid expression set of the ring road are traversed. The parent grid of the current grid cell is queried in the updated geographic knowledge graph. Specifically, the grid with ID 31da22bd54 in the spatial grid expression set of the ring road is queried, and one parent grid 31da22b is found. This grid is in the spatial grid expression set of the flood range.

[0072] Continue to query the grid with ID 31da22a2b4 in the spatial grid expression set of the ring road, and find a parent grid 31da22b, which is located in the spatial grid expression set of the flood range... until the grid cells in the spatial grid expression set of the ring road are traversed, as shown in Table 3, which is a parent grid list.

[0073] Table 3

[0074] Therefore, the parent grids of all grid cells in the spatial grid expression set of the ring road belong to the spatial grid expression set of the flood zone.

[0075] Repeat step S5 for the residential area, and obtain that the ring road is included in the flood range, and the residential area intersects with the flood range.

[0076] In summary, the embodiments of the present invention can obtain the vector boundary of static geographic elements and the vector boundary of dynamic geographic elements respectively, input them into the spatial position compression expression module, use the spatial grid division method, and output the grid expression set of the static spatial range and the dynamic change range of the geographic elements; input the grid expression set of the static spatial range of the geographic elements into the geographic knowledge graph construction module, and output the pre-constructed geographic knowledge graph containing all geographic element nodes and grid unit nodes; input the pre-constructed geographic knowledge graph and the grid expression set of the dynamic change range into the geographic knowledge graph update module, update the pre-constructed geographic knowledge graph, establish the relationship between the grid expression set of the dynamic change range and the grid expression set of the static spatial range of the geographic elements, and output the updated geographic knowledge graph; finally, the spatial relationship reasoning module uses the inheritance reasoning method according to the updated geographic knowledge graph to infer the geographic elements contained in the dynamic change range or intersecting with the dynamic change range. At the same time, the structural characteristics of the spatial division grid and the geographic knowledge graph are utilized to improve the performance of large-scale spatial relationship reasoning of geographic elements, which is helpful to provide support for the timely identification of dynamic change scenes, such as flood disasters.

[0077] Next, the efficient inference device for large-scale spatial relationship of geographic elements proposed in accordance with an embodiment of the present invention will be described with reference to the accompanying drawings.

[0078] Figure 5 It is a block diagram of an efficient device for reasoning about large-scale spatial relationships of geographic elements according to an embodiment of the present invention.

[0079] like Figure 5 As shown, the large-scale geographic element spatial relationship efficient reasoning device 10 includes: an acquisition module 100, an expression module 200, an update module 300 and a reasoning module 400.

[0080] Among them, the acquisition module 100 is used to obtain the static vector boundaries and dynamically changing vector boundaries of the geographic elements in the target area; the expression module 200 is used to perform the same type of grid expression on the static vector boundaries and the dynamically changing vector boundaries, respectively, to obtain a grid expression set of the static spatial range and a grid expression set of the dynamic changing range; the update module 300 is used to construct a geographic knowledge graph based on the grid expression set of the static spatial range, and to update the geographic knowledge graph based on the grid expression set of the dynamic changing range; the reasoning module 400 is used to infer the spatial relationship between each geographic element and the dynamic changing range based on the updated geographic knowledge graph, wherein the spatial relationship includes geographic elements within the dynamic changing range, geographic elements intersecting the dynamic changing range, and geographic elements outside the dynamic changing range.

[0081] It should be noted that the aforementioned explanation of the embodiment of the method for efficient reasoning of spatial relationships of large-scale geographic elements is also applicable to the efficient device for efficient reasoning of spatial relationships of large-scale geographic elements of this embodiment, and will not be repeated here.

[0082] According to the large-scale efficient reasoning device for spatial relations of geographic elements proposed in the embodiment of the present invention, by obtaining the static and dynamic vector boundaries of geographic elements and expressing them in a gridded manner, the complexity of data processing can be simplified, the computing efficiency can be improved, and a dynamically updated geographic knowledge graph can be constructed to meet real-time requirements, thereby achieving a powerful spatial relationship reasoning capability and being widely used in multiple fields such as urban planning, environmental protection, and disaster warning. Thus, the problems of inconsistent data quality, low degree of automation, difficulty in meeting real-time requirements, challenges in scalability and performance optimization, and difficulties in complex knowledge representation and processing in the prior art are solved.

[0083] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include: A memory 601 , a processor 602 , and a computer program stored in the memory 601 and executable on the processor 602 .

[0084] When the processor 602 executes the program, the efficient reasoning method for large-scale spatial relationships of geographic elements provided in the above-mentioned embodiment is implemented.

[0085] Furthermore, the electronic device further comprises: The communication interface 603 is used for communication between the memory 601 and the processor 602 .

[0086] The memory 601 is used to store computer programs that can be executed on the processor 602 .

[0087] The memory 601 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0088] If the memory 601, the processor 602 and the communication interface 603 are implemented independently, the communication interface 603, the memory 601 and the processor 602 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0089] Optionally, in a specific implementation, if the memory 601, the processor 602 and the communication interface 603 are integrated on a chip, the memory 601, the processor 602 and the communication interface 603 can communicate with each other through an internal interface.

[0090] The processor 602 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0091] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned efficient reasoning method for spatial relationships of large-scale geographic elements.

[0092] An embodiment of the present invention also provides a computer program product, including a computer program or instructions, which, when executed, implements the above-mentioned efficient reasoning method for spatial relationships of large-scale geographic elements.

[0093] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0094] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0095] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0096] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, the steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0097] A person of ordinary skill in the art may understand that all or part of the steps carried by the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the above-mentioned program may be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0098] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. An efficient reasoning method for large-scale spatial relationships of geographic elements, characterized by: The following steps are involved: Obtain static vector boundaries and dynamically changing vector boundaries of geographic elements in the target area; Performing the same type of grid expression on the static vector boundary and the dynamically changing vector boundary respectively, to obtain a grid expression set of a static spatial range and a grid expression set of a dynamically changing range; Constructing a geographic knowledge graph according to the grid expression set of the static spatial range, and updating the geographic knowledge graph according to the grid expression set of the dynamically changing range; The spatial relationship between each geographic element and the dynamic change range is inferred based on the updated geographic knowledge graph, wherein the spatial relationship includes the geographic elements within the dynamic change range, the geographic elements intersecting the dynamic change range, and the geographic elements outside the dynamic change range.

2. The efficient reasoning method for large-scale spatial relationships of geographic elements according to claim 1 is characterized in that: The same type of grid expression is performed on the static vector boundary and the dynamically changing vector boundary to obtain a grid expression set of a static space range and a grid expression set of a dynamically changing range, including: Obtain the spatial grids for dividing the static spatial range and dynamic range of geographic elements; Classifying spatial grids where the static vector boundary and the dynamically changing vector boundary intersect each other; According to the classification results, a grid expression set of a static spatial range and a grid expression set of a dynamic change range are determined.

3. The efficient reasoning method for large-scale spatial relationships of geographic elements according to claim 1 is characterized in that: The updating of the geographic knowledge graph according to the grid expression set of the dynamic change range includes: At least one update action of adding, deleting, merging and modifying nodes and relationships in the geographic knowledge graph is performed according to the grid expression set of the dynamic change range.

4. The efficient reasoning method for large-scale spatial relationships of geographic elements according to claim 3 is characterized in that: The updating action of adding, deleting, merging and modifying nodes and relationships in the geographic knowledge graph according to the grid expression set of the dynamic change range includes: Adding at least one of a dynamically changing range node, a dynamically changing range grid expression set node, a dynamically changing range containing grid set node, and a dynamically changing range intersecting grid set node to the geographic knowledge graph; Update the relationship between the grid unit nodes and grid units of the geographic knowledge graph, merge the same nodes, and establish the relationship between the grid expression set of the dynamic change range and the grid expression set of the static spatial range of the geographic elements.

5. The efficient reasoning method for large-scale spatial relationships of geographic elements according to claim 3 is characterized in that: The nodes in the geographic knowledge graph include at least one of the following: a geographic element node, a grid expression set node of a static spatial range of a geographic element, a contained grid set node of a static spatial range of a geographic element, an intersecting grid set node of a static spatial range of a geographic element, and a grid unit node. The node relationship includes the relationship between the grid units in the grid expression set.

6. The efficient reasoning method for large-scale spatial relationships of geographic elements according to claim 1 is characterized in that: The inference of the spatial relationship between each geographic element and the dynamic change range based on the updated geographic knowledge graph includes: Based on the updated geographic knowledge graph, the inheritance reasoning method is used to infer the spatial relationship between each geographic element and the dynamic change range.

7. An efficient reasoning device for large-scale spatial relations of geographic elements, characterized in that: include: An acquisition module, used for acquiring static vector boundaries and dynamically changing vector boundaries of geographic elements in a target area; An expression module, used to perform the same type of grid expression on the static vector boundary and the dynamically changing vector boundary respectively, to obtain a grid expression set of a static space range and a grid expression set of a dynamically changing range; An updating module, configured to construct a geographic knowledge graph according to the grid expression set of the static spatial range, and to update the geographic knowledge graph according to the grid expression set of the dynamically changing range; An inference module is used to infer the spatial relationship between each geographical element and the dynamic change range based on the updated geographical knowledge graph, wherein the spatial relationship includes the geographical elements within the dynamic change range, the geographical elements intersecting the dynamic change range, and the geographical elements outside the dynamic change range.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the efficient reasoning method for large-scale spatial relationships of geographic elements as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed, the efficient reasoning method for large-scale spatial relationships of geographic elements as described in any one of claims 1-6 is implemented.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed, the efficient reasoning method for large-scale spatial relationships of geographic elements as described in any one of claims 1-6 is implemented.

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