Efficient Reasoning Method, Apparatus and Equipment for Spatial Relationships of Large-Scale Geographic Elements

Abstract

The present invention relates to the field of geographical reasoning technology, and particularly relates to an efficient reasoning method, device and equipment for the spatial relationship of large-scale geographical elements. The method includes: obtaining the static vector boundary and the dynamic change vector boundary of geographical elements in a target area; respectively 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 the static spatial range and a grid expression set of the dynamic change range; constructing a geographical knowledge graph according to the grid expression set of the static spatial range, and updating the geographical knowledge graph according to the grid expression set of the dynamic change range; reasoning about the spatial relationship between each geographical element and the dynamic change range based on the updated geographical knowledge graph. Thereby, the problems in the prior art such as inconsistent data quality, low automation degree, difficulty in meeting real-time requirements, challenges in scalability and performance optimization, and problems of complex knowledge representation and processing are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of geographical reasoning, and particularly relates to an efficient reasoning method, device and equipment for spatial relationships of large-scale geographical elements. Background Art

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

[0003] However, on this basis, geographical knowledge graphs have expanded the concepts, entities and relationships of knowledge according to the characteristics of geographical knowledge itself. For example, a geographical knowledge graph in the form of "process-relationship" models time and space characteristics and can reflect the evolution process of geographical phenomena and the characteristics of geoscience mechanisms. With the proposal of principles such as the Web service standard and data sharing of the Open Geospatial Consortium, the open geographical data with spatial characteristics on the Web is also increasing. After organizing and associating the knowledge extracted from these data with a geographical knowledge graph, other implicit geographical knowledge can be inferred.

[0004] The spatial relationship between geographical elements is an important geographical knowledge, and geographical elements are entities with certain geographical spatial positions in the real world. The spatial positions of geographical elements are generally expressed in the form of coordinates. When reasoning about spatial relationships based on the coordinate expression form, the number of calculations increases exponentially with the increase in the number of geographical elements, which will reduce the operation efficiency when reasoning about the spatial relationships of large-scale geographical elements and increase the response time of reasoning. In addition, in scenarios with high real-time requirements, the knowledge graph needs to be updated frequently, and a high reasoning delay may lead to inconsistent reasoning results with the actual state. Summary of the Invention

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

[0006] An embodiment of the first aspect of the present invention provides an efficient inference method for the spatial relationship of large-scale geographical elements, including the following steps: obtaining the static vector boundary and dynamic change vector boundary of geographical elements within a target area; performing the same type of grid representation on the static vector boundary and dynamic change vector boundary respectively to obtain a grid representation set of the static spatial range and a grid representation set of the dynamic change range; constructing a geographical knowledge graph based on the grid representation set of the static spatial range, and updating the geographical knowledge graph according to the grid representation set of the dynamic change range; inferring the spatial relationship between each geographical element and the dynamic change range based on the updated geographical knowledge graph, where the spatial relationship includes geographical elements within the dynamic change range, geographical elements intersecting the dynamic change range, and geographical elements outside the dynamic change range.

[0007] Optionally, performing the same type of grid representation on the static vector boundary and dynamic change vector boundary respectively to obtain a grid representation set of the static spatial range and a grid representation set of the dynamic change range includes: obtaining the spatial grid divided by the static spatial range and dynamic change range of geographical elements; classifying the spatial grids intersected by the static vector boundary and dynamic change vector boundary respectively; determining the grid representation set of the static spatial range and the grid representation set of the dynamic change range according to the classification result.

[0008] Optionally, the nodes in the geographical knowledge graph ontology include at least one of the following: geographical element node, grid representation set node of the static spatial range of geographical elements, included grid set node of the static spatial range of geographical elements, intersecting grid set node of the static spatial range of geographical elements, grid cell node, and the node relationship is the relationship of grid cells in the grid representation set.

[0009] Optionally, updating the geographical knowledge graph according to the grid representation set of the dynamic change range includes: performing at least one update action of adding, deleting, merging, and modifying on the nodes and relationships in the geographical knowledge graph according to the grid representation set of the dynamic change range.

[0010] Optionally, performing at least one update action of adding, deleting, merging, and modifying on the nodes and relationships in the geographical knowledge graph according to the grid representation set of the dynamic change range includes: adding at least one of the dynamic change range node, grid representation set node of the dynamic change range, included grid set node of the dynamic change range, and intersecting grid set node of the dynamic change range to the geographical knowledge graph; updating the grid cell nodes and the relationships of grid cells in the geographical knowledge graph, merging the same nodes, and establishing the relationship between the grid representation set of the dynamic change range and the grid representation set of the static spatial range of geographical elements.

[0011] Optionally, based on the updated geographical knowledge graph, infer the spatial relationships between various geographical elements and the dynamic change range, including: based on the updated geographical knowledge graph, adopt an inheritance inference method to infer the spatial relationships between various geographical elements and the dynamic change range.

[0012] In the second aspect of the embodiments of the present invention, an efficient inference device for the spatial relationships of large-scale geographical elements is provided, including: an acquisition module, configured to acquire the static vector boundaries and dynamic change vector boundaries of geographical elements within a target area; an expression module, configured to perform the same type of grid expression on the static vector boundaries and dynamic change vector boundaries respectively, to obtain a grid expression set of the static spatial range and a grid expression set of the dynamic change range; an update module, configured to construct a geographical knowledge graph according to the grid expression set of the static spatial range, and update the geographical knowledge graph according to the grid expression set of the dynamic change range; an inference module, configured to infer the spatial relationships between various geographical elements and the dynamic change range based on the updated geographical knowledge graph, where the spatial relationships include geographical elements within the dynamic change range, geographical elements intersecting the dynamic change range, and geographical elements outside the dynamic change range.

[0013] In the third aspect of the embodiments of the present invention, an electronic device is provided, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the efficient inference method for the spatial relationships of large-scale geographical elements as in the above embodiments.

[0014] In the fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and the program is executed by a processor to implement the efficient inference method for the spatial relationships of large-scale geographical elements as in the above embodiments.

[0015] In the fifth aspect of the embodiments of the present invention, a computer program product is provided, including a computer program or instruction, and when the computer program or instruction is executed, it is used to implement the efficient inference method for the spatial relationships of large-scale geographical elements as in the above embodiments.

[0016] Therefore, the present invention includes the following beneficial effects:

[0017] In the embodiments of the present invention, by acquiring the static and dynamic vector boundaries of geographical elements and performing grid expression, the complexity of data processing can be simplified, the calculation efficiency can be improved, and a dynamically updated geographical knowledge graph can be constructed to meet real-time requirements, thereby realizing a powerful spatial relationship inference ability, which can be widely applied to multiple fields such as urban planning, environmental protection, and disaster warning, making full use of the structured information in the graph, reducing information redundancy, and improving the accuracy and efficiency of inference. Thus, the technical problems in the prior art such as how to improve the accuracy and efficiency of inferring the spatial relationships of large-scale geographical elements are solved.

[0018] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and / or additional aspects and advantages of the present invention will become apparent and be readily understood from the following description of embodiments in conjunction with the accompanying drawings, in which:

[0020] Figure 1 A flowchart of an efficient reasoning method for large-scale geographical element spatial relationships according to an embodiment of the present invention;

[0021] Figure 2 A flowchart of an efficient reasoning method for large-scale geographical element spatial relationships according to an embodiment of the present invention;

[0022] Figure 3 An ontology structure diagram of a geographical knowledge graph according to an embodiment of the present invention;

[0023] Figure 4 A schematic diagram of an updated geographical knowledge graph according to an embodiment of the present invention;

[0024] Figure 5 An example diagram of an efficient reasoning device for large-scale geographical element spatial relationships according to an embodiment of the present invention;

[0025] Figure 6 A schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] Embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as a limitation to the present invention.

[0027] The following describes the efficient inference method, device, and equipment for the spatial relationship of large-scale geographical elements in the embodiments of the present invention. Aiming at the problems of inaccurate or delayed inference mentioned in the above background technology, the present invention provides an efficient inference method for the spatial relationship of large-scale geographical elements. In this method, by obtaining the static and dynamic vector boundaries of geographical elements and performing grid-based representation, the complexity of data processing can be simplified, the calculation efficiency can be improved, and a dynamically updated geographical knowledge graph can be constructed to meet real-time requirements, thereby achieving a powerful spatial relationship inference ability and being widely applied in multiple fields such as urban planning, environmental protection, and disaster warning. Thus, the problems in the prior art such as inconsistent data quality, low automation level, difficulty in meeting real-time requirements, challenges in scalability and performance optimization, and problems in complex knowledge representation and processing are solved.

[0028] Specifically, Figure 1 is a flowchart of the efficient inference method for the spatial relationship of large-scale geographical elements in the embodiments of the present invention.

[0029] As Figure 1 shown, the efficient inference method for the spatial relationship of large-scale geographical elements includes the following steps:

[0030] In step S101, obtain the static vector boundary and the dynamic change vector boundary of geographical elements in the target area.

[0031] Among them, the static vector boundary can be the spatial position and shape of geographical elements in the target area that remain unchanged at a certain specific time point or time period, and the dynamic change vector boundary can be the boundary of geographical elements that change over time in the target area.

[0032] It can be understood that the embodiments of the present invention can achieve an accurate and dynamic description of geographical elements by obtaining the static vector boundary and the dynamic change vector boundary of geographical elements in the target area, which is convenient for subsequent spatial analysis.

[0033] In step S102, perform the same type of grid representation on the static vector boundary and the dynamic change vector boundary respectively to obtain a set of grid representations of the static spatial range and a set of grid representations of the dynamic change range.

[0034] Among them, the grid representation can be a data representation method that divides the geographical space into a series of regular or irregular grid cells.

[0035] It can be understood that the embodiments of the present invention achieve the unification of data formats and the discretization of spatial information by performing the same type of grid representation on the static vector boundary and the dynamic change vector boundary, improving the data processing efficiency and spatial analysis ability. At the same time, the information in the grid cells can be updated in real time or regularly to achieve real-time monitoring and warning of the dynamic changes of geographical elements.

[0036] In an embodiment of the present invention, the static vector boundary and the dynamically changing vector boundary are respectively expressed by the same type of grid, and a grid expression set for the static spatial range and a grid expression set for the dynamically changing range are obtained, including: obtaining spatial grids divided by the static spatial range and the dynamically changing range of geographical features; classifying the spatial grids intersected by the static vector boundary and the dynamically changing vector boundary respectively; and determining the grid expression set for the static spatial range and the grid expression set for the dynamically changing range according to the classification results.

[0037] Among them, the spatial grid can be a system that divides the geographical space into a series of regular or irregular grid cells, and the grid classification can be to divide the spatial grids intersected by the static vector boundary or the dynamically changing vector boundary into different categories or groups according to certain rules or criteria.

[0038] It can be understood that in an embodiment of the present invention, by dividing the static and dynamic spatial ranges of geographical features into spatial grids and classifying these grids, the grid expression sets for the static and dynamic spatial ranges are determined, which improves the data processing efficiency, enhances the spatial analysis ability, promotes data sharing and integration, improves the decision support ability, and supports real-time dynamic monitoring, providing strong support for the comprehensive application of geographical space data.

[0039] Specifically, the geographical coordinates of the vertices in the vector boundary of the geographical feature are input into the spatial grid division function, and the input parameters required by the function are set. Among them, the input parameters can include the maximum division level, the minimum division level, and the maximum number of grids. The spatial grid division function outputs the original grid expression set { , … } of the static spatial range of the geographical feature; the original grid expression set of the static spatial range of the output geographical feature 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 geographical feature; among them, the grid expression set optimization function uses the RCC5 region calculus model to judge the spatial relationship between the vector boundary of the geographical feature and the grid cells in the original grid expression set of the static spatial range of the geographical feature.

[0040] Obtain the geographical coordinates of the vertices in the vector boundary of the geographical feature, and form the coordinate expression set of the static spatial range of the geographical feature, denoted as { , … }; traverse the original grid expression set { , … The grid cells in}, obtain the current grid cell ID, and input it into the grid coordinate calculation function. The grid coordinate calculation function outputs the vertex coordinates of the current grid and the set of current original grid cell coordinate expressions, denoted as .

[0041] Input the set of coordinate expressions of the static spatial range of the geographical feature and the set of original grid cell coordinate expressions of the static spatial range of the geographical feature into the RCC5 region calculus function. The RCC5 region calculus function outputs the set of coordinate expressions of the static spatial range of the geographical feature and the spatial relationship between the original grid cells of the static spatial range of the geographical feature .

[0042] If the output spatial relationship is containment, add the original grid cells of the static spatial range of the geographical feature to the set ; if the output spatial relationship is intersection, add the original grid cells of the static spatial range of the geographical feature to the set .

[0043] Repeat the above process until each grid in the set of original grid expressions { , … } of the static spatial range of the geographical feature is traversed. Obtain the set , denoted as { , … }; obtain the set , denoted as . Add the set and the set to the empty set in sequence, and output the grid expression set of the static spatial range of the geographical feature {{ , … }, { , … }}.

[0044] In step S103, construct a geographical knowledge graph based on the grid expression set of the static spatial range, and update the geographical knowledge graph according to the grid expression set of the dynamic change range.

[0045] Among them, the geographical knowledge graph can be a graph-based data structure used to represent the complex relationships between geographical entities.

[0046] It can be understood that in the embodiments of the present invention, by constructing a geographical knowledge graph, the deep integration and efficient utilization of complex geographical data are achieved; at the same time, through the grid expression set with a dynamic range of changes, geographical data can be updated in real time or regularly, ensuring that the information in the geographical knowledge graph always remains up-to-date. For application scenarios of real-time decision-making, accurate and timely geographical information services can be provided to users.

[0047] In the embodiments of the present invention, updating the geographical knowledge graph according to the grid expression set with a dynamic range of changes includes: performing at least one update action of adding, deleting, merging, and modifying nodes and relationships in the geographical knowledge graph according to the grid expression set with a dynamic range of changes.

[0048] Among them, the nodes in the geographical knowledge graph may include at least one of the following: geographical feature nodes, grid expression set nodes of the static spatial range of geographical features, inclusion grid set nodes of the static spatial range of geographical features, intersection grid set nodes of the static spatial range of geographical features, grid cell nodes, and the node relationships are the relationships of grid cells in the grid expression set.

[0049] It can be understood that in the embodiments of the present invention, by dynamically updating the nodes and relationships in the geographical knowledge graph, the timeliness and accuracy of the graph are ensured, complex geographical space analysis and decision-making are supported, the sharing and collaboration of geographical information are promoted, and the development and application of geographical information technology are driven.

[0050] In the embodiments of the present invention, performing at least one update action of adding, deleting, merging, and modifying nodes and relationships in the geographical knowledge graph according to the grid expression set with a dynamic range of changes includes: adding at least one of the dynamic range of change nodes, grid expression set nodes of the dynamic range of changes, inclusion grid set nodes of the dynamic range of changes, and intersection grid set nodes of the dynamic range of changes in the geographical knowledge graph; updating the grid cell nodes and the relationships of grid cells in the geographical knowledge graph, merging the same nodes, and establishing the relationship between the grid expression set with a dynamic range of changes and the grid expression set of the static spatial range of geographical features.

[0051] Among them, the dynamic range of change nodes can be used to represent geographical regions or objects that change over time or conditions.

[0052] It can be understood that in the embodiments of the present invention, by adding dynamic range nodes and their related set nodes, the geographical knowledge graph can capture the dynamic changes of geographical entities in real time or regularly, which are accurately recorded in the form of grids representing sets, and a clear relationship is established with the grid representation set of the static spatial range of geographical features, thus clearly depicting the complex spatial relationships between geographical entities. In addition, in the embodiments of the present invention, by updating the grid cell nodes and their relationships and merging duplicate nodes, not only data redundancy is reduced, but also the consistency and query efficiency of data are significantly improved.

[0053] Specifically, construct a geographical knowledge graph with geographical features, the grid representation set of the static spatial range of geographical features, and the grid cells in the grid representation set of the static spatial range of geographical features as nodes; traverse the grid cells in the grid representation set of the static spatial range of geographical features, obtain the current grid cell ID, input the current grid cell ID into the grid relationship acquisition function, and the grid relationship acquisition function outputs the sub-grid ID and parent-grid ID of the current grid cell. Generate the sub-grid node and parent-grid node of the current grid cell and add them to the geographical knowledge graph; repeat the above process until each grid cell in the grid representation set of the static spatial range of geographical features is traversed.

[0054] Pairwise compare all the grid cells in the grid representation set of the static spatial range of geographical features, and by comparing the grid cell ID, the sub-grid ID of the grid cell, and the parent-grid ID of the grid cell, merge the grid cell nodes with the same ID in the geographical knowledge graph, and output the pre-constructed geographical knowledge graph.

[0055] In step S104, infer the spatial relationships between each geographical feature and the dynamic range based on the updated geographical knowledge graph. <Q

[0056] Among them, the spatial relationships may include geographical features within the dynamic range, geographical features intersecting the dynamic range, and geographical features outside the dynamic range.

[0057] It can be understood that in the embodiments of the present invention, inferring the spatial relationships between geographical features and the dynamic range based on the updated geographical knowledge graph can significantly improve the understanding accuracy of geographical spatial changes, enhance the scientificity and timeliness of decision-making, and optimize the data management efficiency. <Q

[0058] In the embodiments of the present invention, inferring the spatial relationships between each geographical feature and the dynamic range based on the updated geographical knowledge graph includes: based on the updated geographical knowledge graph, using the inheritance inference method to infer the spatial relationships between each geographical feature and the dynamic range.

[0059] It is understandable that the embodiments of the present invention can adopt an inheritance reasoning method to reason about the spatial relationship between geographical elements and the dynamic change range, make full use of the structured information in the atlas, reduce information redundancy, and improve the accuracy and efficiency of reasoning.

[0060] Specifically, the embodiments of the present invention can query the inclusion grid set of the dynamic change range and the intersection grid set of the dynamic change range from the updated geographical knowledge graph; where the inclusion grid set of the dynamic change range R is denoted as { , … }, and the intersection grid set of the dynamic change range R is denoted as { , … }.

[0061] Traverse the grid cells in the inclusion grid set { , … } of the dynamic change range, and query all the sub-grid cells of the current grid cell from the updated geographical knowledge graph, and output the sub-grid set { , , … }.

[0062] Traverse the grid cells in the sub-grid set { , … }, and query whether the current grid cell is included in the spatial grid expression set of a certain geographical element. If so, output the grid expression set { , … } of the static spatial range of the geographical element containing the current sub-grid cell.

[0063] Traverse the grid cells in the grid expression set { , … } of the static spatial range of the geographical element. If the current grid cell or the parent grid cell of the current grid cell is included 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 {{ , … }, { , … In {}, output 0 and add it to the output set K; repeat this process until all grid cells in the grid expression set { , … } are traversed. If all elements in set K are 1, it is determined that the current geographical feature is included in the dynamic change range. Otherwise, it is determined that the current geographical feature intersects with the dynamic change range.

[0064] Repeat the above process until all grid cells in the inclusion grid set of the dynamic change range { , … } are traversed. Output all geographical features included in the real-time dynamic change range and geographical features that intersect with the real-time dynamic change range.

[0065] According to the efficient reasoning method for large-scale geographical feature spatial relationships proposed in the embodiments of the present invention, by obtaining the static and dynamic vector boundaries of geographical features and performing grid-based representation, the complexity of data processing can be simplified, the calculation efficiency can be improved, and a dynamically updated geographical knowledge graph can be constructed to meet real-time requirements, thereby realizing a powerful spatial relationship reasoning ability and can be widely applied to multiple fields such as urban planning, environmental protection, and disaster warning.

[0066] Next, a specific embodiment will be used to specifically illustrate the efficient reasoning method for large-scale geographical feature spatial relationships. Taking the Wuhan flood inundation scenario as an example, as Figure 2 shown, the specific steps include:

[0067] S1: Obtain the OSM (Open Street Map) data of the AOI (Area of Interest) of the target city, and extract the vector boundaries of area geographical features such as residential areas, circular roads, parks, and buildings, denoted as:

[0068] P = {A, B…Z}, A = ,

[0069] B = …Z = .

[0070] Preprocess the obtained OSM data, including: clearing data items with missing spatial location fields, feature type fields, and feature names; clearing data with overly large spatial location data deviations; and removing redundant data for similar duplicates.

[0071] S2: Set the three parameters of the maximum division level, minimum division level, and maximum number of grids required for dividing residential areas, circular roads, and flood areas respectively. As shown in the example in Table 1, Table 1 is a parameter setting table.

[0072] Table 1

[0073]

[0074] After multiple debuggings of the above parameters, it can ensure that while the spatial positions of geographical elements are efficiently covered, the grid coverage error at the boundaries of geographical elements is as small as possible. According to the parameter settings, input the vector boundaries of the obtained geographical elements, and divide the spatial positions of the geographical elements to obtain the grid expression sets of residential areas, circular roads, and flood areas;

[0075] Among them, the set of grid expressions in the residential area: {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, 31da187204, 31da18720c, 31da187214, 31da18721c, 31da187224, 31da18722c, 31da187234, 31da18723c, 31da187244, 31da18724c, 31da187254, 31da18725c, 31da187264, 31da18726c, 31da187274, 31da18727c, 31da18728b, 31da1872e4, 31da1872efc, 31da1872f4, 31da1872fc, 31da187304, 31da18730c, 31da187314, 31da18731c, 31da187324, 31da187334, 31da18733c, 31da187344, 31da18734c, 31da187354, 31da18735c, 31da187364, 31da18736c, 31da187374, 31da18737c, �1da187384, 31da187394, 31da18739c, 31da1873a4, 31da1873ac, 31da1873b4, 31da1873bc, 31da1873c4, 31da1873cc, 31da1873d3, 31da18747f, 31da18748c, 31da187494, 31da18749c, 31da1874a4, 31da1874ac, 31da1874bd}.

[0076] 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,31da22b474,31da22b47c,31da22b484,31da22b49d,31da22b6a4,31da22b6ac,31da22b6b4,31da22b6b9,31da22b6bb,31da22b6c4,31da22b6c9,31da22b6dc,31da22b724,31da22b73c,31da22b744,31da22b75c,31da22b764,31da22b7684,31da22b90c,31da22b914,31da22b91c,31da22b923,31da22b925,31da22b92c,31da22b931,31da22b93b,31da22b93d,31da22b9a4,31da22b9bc,31da22b9c4,31da22b9dc,31da22b9e1,31da22b9e7,31da22b9ec,31da22bd2c,31da22bd34,31da22bd4c,31da22bd54,31da22bd62c,31da22bd7c,31da22bd84,31da22bd8c,31da22bd94,31da22bde4,31da22bdec,31da22bdf4,31da22bdfb,31da22be0c,31da22be14,31da22be2c,31da22be31,31da22be3c,31da22be72d,31da22bed4,31da22bedc,31da22bee4,31da22befc,31da22bf04,31da22bf1d,31da22bf1f,31da22bfac,31da22bfb4,31da22bfcc,31da22bfd3}。,

[0077] Grid expression set of flood areas: {31da17f9, 31da17fc4, 31da17fdc, 31da17ff, 31da1804, 31da1809, 31da180b, 31da180c4, 31da180cc, 31da180dc, 31da180f, 31da18714, 31da18734, 31da1873c, 31da1875, 31da18764, 31da1876c, 31da18774, 31da2295c, 31da22964, 31da22974, 31da2297c, 31da2299, 31da229c4, 31da229dc, 31da229f, 31da22b, 31da22c1, 31da22c34, 31da22c3c, 31da22c5, 31da22c7, 31da22cc, 31da22d4, 31da22d9, 31da22db, 31da22dc4, 31da22dcc, 31da22dec, 31da2325c, 31da23264, 31da23274, 31da2327c, 31da232c, 31da23324, 31da2332c, 31da3cc14, 31da18764, 31da1876c, 31da18774, 31da2295c, 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, 31da22964, 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}.

[0078] S3: For the set of grid expressions of the spatial positions of non-dynamically changing geographical elements such as residential areas and circular roads, construct a geographical knowledge graph according to the Figure 3 ontology structure shown.

[0079] S4: According to the set of grid expressions of the spatial positions of the flood range, add, delete, merge, and modify the nodes and relationships in the constructed geographical knowledge graph according to the ontology structure diagram of the geographical knowledge graph, and establish the relationship between the set of grid expressions of the flood range and the set of grid expressions of the static spatial range of geographical elements. The updated geographical knowledge graph is as Figure 4 shown.

[0080] S5: Traverse the grid cells in the set of grid expressions of the flood range, and query whether the current grid cell has a parent grid or a child grid in the updated geographical knowledge graph.

[0081] Specifically, query the grid with the ID of 31da2297c in the set of spatial grid expressions of the flood range. 10 child grids are found in the geographical knowledge graph, as shown in Table 2. Table 2 is the list of child grids.

[0082] Table 2

[0083]

[0084] The 10 child grid IDs are: 31da22978c, 31da229794, 31da2297c4, 31da2297cf, 31da2297d4, 31da2297dc, 31da2297e4, 31da2297ec, 31da2297f4, 31da2297fc; all of these grids belong to the set of spatial grid expressions of the circular road. That is, there must be an overlapping part between the spatial position of the circular road and the flood range.

[0085] Locate the circular road and start traversing the grid cells in the set of spatial grid expressions of the circular road, and query whether the current grid cell has a parent grid in the updated geographical knowledge graph; specifically, query the grid with the ID of 3-1da22bd54 in the set of spatial grid expressions of the circular road. 1 parent grid 31da22b is found, and this grid is in the set of spatial grid expressions of the flood range.

[0086] Continue to query the grid with the ID of 31da22a2b4 in the set of spatial grid expressions of the circular road. 1 parent grid 31da22b is found, and this grid is in the set of spatial grid expressions of the flood range... until all the grid cells in the set of spatial grid expressions of the circular road are traversed, as shown in Table 3. Table 3 is the list of parent grids.

[0087] Table 3

[0088]

[0089] Therefore, the parent grids of all grid cells in the spatial grid expression set of the circular road belong to the spatial grid expression set of the flood area.

[0090] Repeat step S5 for the residential area, and it is obtained that the circular road is included in the flood range and the residential area intersects with the flood range.

[0091] In summary, the embodiments of the present invention can respectively obtain the vector boundaries of static geographic elements and dynamic geographic elements, input them into the spatial position compression expression module, use the spatial grid division method, and output the grid expression sets 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 including all geographic element nodes and grid cell 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 reason about the geographic elements included in or intersecting with the dynamic change range. At the same time, by utilizing the structural characteristics of the spatial division grid and the geographic knowledge graph, the performance of reasoning about the spatial relationships of large-scale geographic elements can be improved, which helps to provide support for timely discrimination of dynamic change scenarios, such as flood disasters.

[0092] Secondly, a high-efficiency reasoning device for spatial relationships of large-scale geographic elements according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0093] Figure 5 It is a block diagram of a high-efficiency reasoning device for spatial relationships of large-scale geographic elements according to an embodiment of the present invention.

[0094] As Figure 5 shown, the high-efficiency reasoning device 10 for spatial relationships of large-scale geographic elements includes: an acquisition module 100, an expression module 200, an update module 300, and a reasoning module 400.

[0095] Among them, the acquisition module 100 is used to acquire the static vector boundary and the dynamic change vector boundary of geographical elements in the target area; the expression module 200 is used to perform the same type of grid expression on the static vector boundary and the dynamic change vector boundary respectively, and obtain the grid expression set of the static spatial range and the grid expression set of the dynamic change range; the update module 300 is used to construct a geographical knowledge graph according to the grid expression set of the static spatial range, and update the geographical knowledge graph according to the grid expression set of the dynamic change range; the reasoning module 400 is used to reason about the spatial relationship between each geographical element and the dynamic change range based on the updated geographical knowledge graph, where the spatial relationship includes geographical elements within the dynamic change range, geographical elements intersecting the dynamic change range, and geographical elements outside the dynamic change range.

[0096] It should be noted that the foregoing explanation of the embodiments of the efficient reasoning method for the spatial relationship of large-scale geographical elements is also applicable to the efficient reasoning device for the spatial relationship of large-scale geographical elements in this embodiment, and will not be elaborated here.

[0097] According to the efficient reasoning device for the spatial relationship of large-scale geographical elements proposed in the embodiments of the present invention, by acquiring the static and dynamic vector boundaries of geographical elements and performing grid expression, the complexity of data processing can be simplified, the calculation efficiency can be improved, and a dynamically updated geographical knowledge graph can be constructed, which can meet the real-time requirements, thereby realizing a powerful spatial relationship reasoning ability and being widely applied to multiple fields such as urban planning, environmental protection, and disaster warning. Thus, the problems in the prior art such as inconsistent data quality, low automation degree, difficulty in meeting real-time requirements, challenges in scalability and performance optimization, and problems of complex knowledge representation and processing are solved.

[0098] Figure 6 It is a schematic structural diagram of an electronic device provided in the embodiments of the present invention. The electronic device may include:

[0099] A memory 601, a processor 602, and a computer program stored on the memory 601 and executable on the processor 602.

[0100] When the processor 602 executes the program, it implements the efficient reasoning method for the spatial relationship of large-scale geographical elements provided in the above embodiments.

[0101] Furthermore, the electronic device further includes:

[0102] A communication interface 603 for communication between the memory 601 and the processor 602.

[0103] The memory 601 is used to store a computer program executable on the processor 602.

[0104] The memory 601 may include a high-speed RAM (Random Access Memory) memory and may also include non-volatile memory, such as at least one disk memory.

[0105] 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 interconnected via a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, 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 the sake of representation, Figure 6 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0106] Optionally, in a specific implementation, if the memory 601, the processor 602, and the communication interface 603 are integrated on a single chip, the memory 601, the processor 602, and the communication interface 603 can communicate with each other via an internal interface.

[0107] 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.

[0108] The embodiments of the present invention also provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the efficient inference method for large-scale geographical feature spatial relationships as described above is implemented.

[0109] [[ID=!17]]The embodiments of the present invention also provide a computer program product, including a computer program or instructions, and when the program or instructions are executed, the efficient inference method for large-scale geographical feature spatial relationships as described above is implemented.

[0110] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection 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 can be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0111] In addition, the terms "first" and "second" are used only for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of these features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise explicitly and specifically defined.

[0112] Any process or method description shown in a flowchart or described in other ways herein can be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0113] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above 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, any one or a combination of the following well-known technologies in the art can be used: discrete logic circuits having logic gate circuits for implementing logic functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays, field-programmable gate arrays, etc.

[0114] Those of ordinary skill in the art in this technical field can understand that all or part of the steps carried by the methods for implementing the above embodiments can be completed by instructing relevant hardware through a program, and the above program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0115] 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 should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to 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 the static vector boundaries and dynamic 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 to obtain a grid expression set of the static spatial range and a grid expression set of the dynamically changing range, including: obtaining spatial grids for dividing the static spatial range and the dynamic range of the geographic element, classifying spatial grids where the static vector boundary and the dynamically changing vector boundary intersect, and determining the grid expression set of the static spatial range and the grid expression set of the dynamically changing range according to the classification results; 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 dynamically changing range, wherein updating the geographic knowledge graph based on the grid expression set of the dynamically changing range includes: performing at least one update action of adding, deleting, merging, and modifying nodes and relationships in the geographic knowledge graph based on 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 updating action of adding, deleting, merging and modifying nodes and relationships in the geographic knowledge graph according to the grid expression set of the dynamically changing 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 dynamically changing range and the grid expression set of the static spatial range of the geographic elements.

3. The efficient reasoning method for large-scale spatial relationships of geographic elements according to claim 2 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 geographic element static spatial range, a contained grid set node of a geographic element static spatial range, an intersecting grid set node of a geographic element static spatial range, and a grid unit node. The node relationship includes the relationship between the grid units in the grid expression set.

4. The efficient reasoning method for large-scale spatial relationships of geographic elements according to claim 1 is characterized in that: The reasoning 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.

5. An efficient reasoning device for large-scale spatial relationships of geographic elements, characterized by: include: An acquisition module, used to acquire the static vector boundaries and dynamically changing vector boundaries of geographic elements in the target area; an expression module, configured 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 the static spatial range and a grid expression set of the dynamically changing range, comprising: obtaining spatial grids for dividing the static spatial range and the dynamic range of the geographic element, classifying spatial grids where the static vector boundary and the dynamically changing vector boundary intersect, and determining a grid expression set of the static spatial range and a grid expression set of the dynamically changing range based on the classification results; An updating module is configured to construct a geographic knowledge graph based on the grid expression set of the static spatial range, and update the geographic knowledge graph based on the grid expression set of the dynamically changing range, wherein the updating of the geographic knowledge graph based on the grid expression set of the dynamically changing range includes: performing at least one update action of adding, deleting, merging, and modifying nodes and relationships in the geographic knowledge graph based on the grid expression set of the dynamically changing range; An inference module is used to infer the spatial relationship between each geographic element and the dynamic change range 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.

6. 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 4.

7. 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 to 4 is implemented.

8. 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 to 4 is implemented.

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