CN112800144B - Method and device for generating multi-granularity space-time object - Google Patents
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Abstract
Embodiments of the present disclosure provide a method, apparatus, device, and computer-readable storage medium for generating multi-granularity spatiotemporal objects. The method includes importing raw data; the original data comprises one or more of vector data, image data and model data; processing and analyzing the original data; reorganizing the processed and parsed data according to a preset conversion rule; and storing the space-time object of the recombined data to generate a multi-granularity space-time object. In this way, different types of original data can be generated into multi-granularity space-time objects, the complex real world can be better described, and the problems of access, processing, description, expression, analysis, application and the like of various space-time entity information in the real world are solved.
Description
Technical Field
Embodiments of the present disclosure relate generally to the field of geographic information systems and, more particularly, relate to methods, apparatuses, devices, and computer-readable storage media for generating multi-granularity spatiotemporal objects.
Background
At present, a modeling mode which is widely applied in a geographic information system is to abstract things and phenomena in the real world into geometric characteristic elements such as points, lines, planes and the like, and organize and manage the geometric characteristic elements in a layer mode. However, the static hierarchical modeling mode expresses that people recognize a map, but not recognize the real world, and is difficult to support the relation description among a plurality of spatial entities, the interaction of different scale data of the same spatial entity and the integrated organization of multiple data.
Disclosure of Invention
According to an embodiment of the present disclosure, a generation scheme for multi-granularity spatiotemporal objects is provided.
In a first aspect of the present disclosure, a method of generating multi-granularity spatiotemporal objects is provided. The method comprises the following steps: importing original data; the original data comprises one or more of vector data, image data and model data; processing and analyzing the original data; reorganizing the processed and parsed data according to a preset conversion rule; and storing the space-time object of the recombined data to generate a multi-granularity space-time object.
Aspects and any one of the possible implementations as described above, further provide an implementation, where the importing the raw data further includes: and preprocessing the original data, wherein the preprocessing comprises unified space-time reference and picture splicing.
In the aspects and any possible implementation manners as described above, further providing an implementation manner, performing processing parsing on the data includes: and performing primitive traversal, analysis and filtration and data extraction on the preprocessed original data.
In accordance with aspects and any one of the possible implementations described above, there is further provided an implementation, the parsing filtering including obtaining an expected entity from the data, generating a data document table; the data extraction includes generating an entity object name table from entity object names in the data document table.
In the foregoing aspect and any possible implementation manner, further providing an implementation manner, the reorganizing the data after the processing and parsing according to a preset conversion rule includes: performing classification code conversion on the data document table; generating an entity object relation table through entity field mapping and materialization; establishing an entity unique code according to the entity object relation table; and establishing the object association relation of the entity in the entity object relation table.
In aspects and any one of the possible implementations described above, there is further provided an implementation, generating, by entity field mapping and materialization, an entity object relationship table includes: mapping a name field in the entity object name table to a name field of the entity object relation table; placing element codes of the same entity into a member field of the entity; the union of the entity containing elements is formed based on the remaining field mapping content of the entity.
In accordance with aspects and any one of the possible implementations described above, there is further provided an implementation, wherein establishing an entity unique code from the entity-object relationship table includes: establishing unique codes for entity objects according to the coding formats of the geographic entities, wherein the coding formats of the geographic entities are as follows: classification code + sequential code + positioning code.
In a second aspect of the present disclosure, a generation apparatus for multi-granularity spatiotemporal objects is provided. The device comprises: the importing module is used for importing the original data; the original data comprises one or more of vector data, image data and model data; the processing analysis module is used for processing and analyzing the original data; the reorganization module is used for reorganizing the processed and parsed data according to a preset conversion rule; and the storage module is used for storing the space-time object of the recombined data to generate a multi-granularity space-time object.
In a third aspect of the present disclosure, an electronic device is provided. The electronic device includes: a memory and a processor, the memory having stored thereon a computer program, the processor implementing the method as described above when executing the program.
In a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method as according to the first and/or second aspects of the present disclosure.
It should be understood that what is described in this summary is not intended to limit the critical or essential features of the embodiments of the disclosure nor to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, wherein like or similar reference numerals denote like or similar elements, in which:
FIG. 1 illustrates a flow chart of a method of generating multi-granularity spatiotemporal objects in accordance with an embodiment of the disclosure;
FIG. 2 illustrates a flow chart of reorganizing process parsed data according to an embodiment of the present disclosure;
FIG. 3 illustrates a geoentity encoding format schematic diagram in accordance with an embodiment of the present disclosure;
FIG. 4 illustrates a block diagram of a generating device of multi-granularity spatiotemporal objects according to an embodiment of the disclosure;
fig. 5 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments in this disclosure without inventive faculty, are intended to be within the scope of this disclosure.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
FIG. 1 illustrates a flow chart of a method 100 of generating multi-granularity spatiotemporal objects, as illustrated in FIG. 1 of the drawings, the method 100 comprising:
at block 110, raw data is imported;
in some embodiments, the raw data includes one or more of vector data, image data, and model data.
In some embodiments, the data is preprocessed, including performing spatial-temporal basis unification, frame stitching, etc. of different types of data.
Processing parsing the raw data at block 120;
in some embodiments, the preprocessed original data is processed and parsed, and the object information content described by the original data is obtained.
In some embodiments, the processing and parsing includes primitive traversal, parse filtering, and data extraction. Traversing each graphic element in the data, analyzing and filtering the data to obtain an expected entity, and generating a data document table. The data extraction includes: and sequentially scanning a data document table, extracting all entities contained in the data according to the entity names and the corresponding category codes in the data document table in a category sequence, and sequentially listing the entity object names in the form of an entity object name table.
In some embodiments, the entities include one or more entities of survey control points, industrial and agricultural socio-cultural facilities, residential and accessory facilities, land traffic, pipelines, body of water, seafloor terrain, reefs, sunken ships, obstacles, hydrology, land terrain and soil properties, vegetation, navigation equipment and channels, maritime regional boundaries, aviation elements, military regions, and the like.
At block 130, reorganizing the processed and parsed data according to a preset conversion rule;
in some embodiments, the conversion rule includes: space-time reference definition, original data classification coding setting, space-time object classification coding setting, conversion mapping rule definition, attribute description extension and other rules.
In some embodiments, the reorganization is the conversion of data formats such as vector data, image data, model data, etc., into space-time object formats, essentially the conversion between data models.
In some embodiments, fig. 2 shows a flow chart of reorganizing process parsed data according to an embodiment of the present disclosure, as shown in fig. 2 of the drawings, the reorganization generation includes three sub-steps:
at block 210, performing a classification transcoding on the data document table;
in some embodiments, a data dictionary table is established based on a preset geographic entity class code, wherein the data dictionary table comprises class codes of the entities. The classification codes of the primitives may be converted based on the classification codes of the entities in the dictionary table. That is, the class code in the data document table is converted into the class code of the entity in the dictionary.
At block 220, an entity object relationship table is generated by format conversion:
in some embodiments, format conversion includes field mapping and materialization, generating an entity object relationship table. Wherein,
the field mapping refers to mapping the field content of the processed and parsed data to corresponding fields in the established entity object relation table by constructing a field mapping model.
The materialization process is to merge the constituent primitives of the same entity. If the scale is 1: in a map of 5 ten thousand, a certain river is formed by combining a plurality of surfaces, and in materialization of the river, primitives named as the river need to be combined, namely unique codes of the primitives are sequentially filled in a member field of a river name entity.
In some embodiments, the specific flow of formatting conversion includes: firstly, mapping a name field in an entity object name table to a name field of an entity object relation table, and ensuring that all entities in data are extracted; then carrying out materialization processing, and placing element codes of the same entity into a member field of the entity; and finally, mapping the content based on the residual fields of the entity to form a union set of the elements contained in the entity.
Establishing an entity unique code according to the entity object relationship table at block 230;
in some embodiments, the entity classification codes in the object entity relationship table and all the space coordinates of the entity containing elements are read, and a unique code is established for the entity object according to a geoentity coding scheme, wherein the geoentity coding format is as follows: classification code (major class-middle class-minor class) +sequential code+location code, as shown in FIG. 3 of the drawings. Wherein, the classification code in the geographic entity code is consistent with the classification code in the entity object relation table, and the fixed classification code is extracted according to the object classification;
the sequential code serves as a supplemental code to distinguish objects where the prefix code (classification code) is identical to the suffix code (positioning code).
The determination of the positioning code is based on all spatial coordinates of the entity containing element; and extracting a minimum bounding box based on the central line and the outer contour of the object to serve as a spatial feature representation of an entity, and performing Geohash geogrid coding on the feature representation to serve as a positioning code.
At block 240, establishing an object association of the entity in the entity object relationship table;
in some embodiments, a home relationship, such as a parent-child relationship, is established for the entity according to the entity-object relationship in the entity-object relationship table, e.g., jinan City is affiliated with Shandong province.
Through the above steps, octaves of multi-granularity spatiotemporal objects are generated.
At block 140, the reorganized data is subjected to spatiotemporal object storage to generate multi-granularity spatiotemporal objects.
In some embodiments, the reorganized data is serialized with a classification code, and each constructed object is stored based on a multi-granularity spatiotemporal object storage table to generate multi-granularity spatiotemporal objects.
In some embodiments, the multi-granularity spatiotemporal object includes a spatiotemporal reference, a spatial location, a spatial morphology, a composition structure, an associative relationship, a cognitive ability, a behavioral ability, an attribute feature. Wherein,
the spatiotemporal reference is used to support "absolute position, relative position, local position". Rules: absolute space-time frame + movable frame.
The spatial locations are used to achieve "absolute spatiotemporal location, relative spatiotemporal location, local spatiotemporal location" and spatiotemporal relational expressions (full spatial spatiotemporal topology). Rules: serialized coordinates + poses (orientations) corresponding to the spatiotemporal references.
The spatial morphology is used to implement a visual-oriented description. Spatial morphology describes the form and shape of a spatiotemporal object that is presented in space, described in different ways, depending on the morphology of the empty entity in the real world. Rules: geometry (points, lines, facets, patches), matrix, streaming media, equations (fields).
The composition structure is used for describing and operating the complex object structure relation by utilizing the characteristics and operation of the collection. Rules are set (cross, merge, complement, or) expression and operation.
The association relation is used for describing association and action relation between the operation objects by using a graph model (node + edge). Various interactions between spatiotemporal objects are described. Mainly comprises attribute association, space association, comprehensive association and the like. Rules: network (graph) model representation and operation.
In the cognitive ability, each class of cognition is defined as a function (parameterized expression, inferred expression, decision regression, neural network …, input and output), and collectively constitutes a functional. Cognitive description and modeling is achieved through functional operations. Rules: functional expression and manipulation.
In the behavior capability, each class of behavior defines a set, and the set groups are formed in a whole. Behavior description and modeling is achieved through cluster operations. Rules: collection (Family of set) expression and manipulation.
The attribute features are dynamic attributes (hierarchies: abstractions and refinements) corresponding to the knowledge hierarchy. Rules: tag data.
In some embodiments, the multi-granularity spatiotemporal object storage table includes the following sub-tables: time zone, spatial reference, object classification, data type, spatial morphology, spatio-temporal object; the method comprises the following steps:
(1) Time zone timezone-names
(2) Spatial reference: spatial_ref_sys
(3) Object class table
| Name of the name | Type(s) | Description of the invention | Remarks |
| Id | Integer | ID | |
| ClassType | Varchar(32) | Door type | |
| Big | Varchar(12) | General class of | |
| Middle | Varchar(12) | Middle class | |
| Small | Varchar(12) | Subclass of | |
| Code | Varchar(12) | Encoding | |
| Name | Varchar(64) | Name of the name | |
| Remark | Varchar(256) | Remarks |
(4) Data type classification SourceType
| Name of the name | Type(s) | Description of the invention | Remarks |
| Id | integer | Id | |
| Name | Varchar(64) | Data name | |
| Scale | Varchar(32) | Proportional scale | |
| Srid | integer | Spatial reference | |
| Description | Varchar(256) | Description of the invention |
(5) Space morphology GeomType
(6) Space-time object table Objects
According to the embodiment of the disclosure, the following technical effects are achieved: the method generates different types of original data into multi-granularity space-time objects, can better describe the complex real world, and solves the problems of access, processing, description, expression, analysis, application and the like of various space-time entity information in the real world.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present disclosure is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present disclosure. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all alternative embodiments, and that the acts and modules referred to are not necessarily required by the present disclosure.
The foregoing is a description of embodiments of the method, and the following further describes embodiments of the present disclosure through examples of apparatus.
Fig. 4 illustrates a block diagram of an apparatus 400 for generating multi-granularity spatiotemporal objects according to an embodiment of the disclosure. As shown in fig. 4, the apparatus 400 includes:
an importing module 410, configured to import the original data; the original data comprises one or more of vector data, image data and model data;
the processing analysis module 420 is configured to process and analyze the raw data;
the reorganization module 430 is configured to reorganize the processed and parsed data according to a preset conversion rule;
and the storage module 440 is configured to store the space-time object of the reorganized data, and generate a multi-granularity space-time object.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the described modules may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again.
Fig. 5 shows a schematic block diagram of an electronic device 500 that may be used to implement embodiments of the present disclosure. As shown, the device 500 includes a Central Processing Unit (CPU) 501 that may perform various suitable actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM) 502 or loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The CPU 501, ROM 502, and RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Various components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, etc.; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508 such as a magnetic disk, an optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processing unit 501 performs the various methods and processes described above, such as methods 100, 200. For example, in some embodiments, the methods 100, 200 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into RAM 503 and executed by CPU 501, one or more of the steps of the methods 100, 200 described above may be performed. Alternatively, in other embodiments, CPU 501 may be configured to perform methods 100, 200 by any other suitable means (e.g., by means of firmware).
The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), etc.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Moreover, although operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.
Claims (4)
1. A method for generating multi-granularity spatiotemporal objects, comprising:
importing original data; the original data comprises one or more of vector data, image data and model data; the importing the original data further includes: preprocessing the original data, including space-time reference unification and picture splicing;
processing and analyzing the original data, including: performing primitive traversal, analysis and filtration and data extraction on the preprocessed original data; the parsing and filtering includes: obtaining expected entities from the data, and generating a data document table; the data extraction includes: generating an entity object name table according to the entity object names in the data document table;
and reorganizing the processed and parsed data according to a preset conversion rule, wherein the reorganizing comprises the following steps: performing classification code conversion on the data document table; generating an entity object relation table through entity field mapping and materialization; establishing an entity unique code according to the entity object relation table; establishing an object association relation of an entity in an entity object relation table; the establishing entity unique codes according to the entity object relation table comprises the following steps: establishing unique codes for entity objects according to the coding formats of the geographic entities, wherein the coding formats of the geographic entities are as follows: classification code + sequential code + positioning code;
storing the space-time object of the recombined data to generate a multi-granularity space-time object, which comprises the following steps: serializing the recombined data by using a classification code, and storing each constructed object based on a multi-granularity space-time object storage table to generate a multi-granularity space-time object; the multi-granularity spatiotemporal object comprises: space-time reference, spatial position, spatial morphology, composition structure, association relationship, cognitive ability, behavioral ability, and attribute characteristics; the multi-granularity space-time object storage table comprises the following sub-tables: time zone, spatial reference, object classification, data type, spatial morphology, and spatio-temporal object.
2. The method of claim 1, wherein generating the entity-object-relationship table by entity field mapping and materialization comprises:
mapping a name field in the entity object name table to a name field of the entity object relation table;
placing element codes of the same entity into a member field of the entity;
the union of the entity containing elements is formed based on the remaining field mapping content of the entity.
3. A device for generating multi-granularity space-time objects, comprising:
the importing module is used for importing the original data; the original data comprises one or more of vector data, image data and model data; the importing the original data further includes: preprocessing the original data, including space-time reference unification and picture splicing;
the processing analysis module is used for processing and analyzing the original data and comprises the following steps: performing primitive traversal, analysis and filtration and data extraction on the preprocessed original data; the parsing and filtering includes: obtaining expected entities from the data, and generating a data document table; the data extraction includes: generating an entity object name table according to the entity object names in the data document table;
the reorganization module is used for reorganizing the processed and parsed data according to a preset conversion rule, and comprises the following steps: performing classification code conversion on the data document table; generating an entity object relation table through entity field mapping and materialization; establishing an entity unique code according to the entity object relation table; establishing an object association relation of an entity in an entity object relation table; the establishing entity unique codes according to the entity object relation table comprises the following steps: establishing unique codes for entity objects according to the coding formats of the geographic entities, wherein the coding formats of the geographic entities are as follows: classification code + sequential code + positioning code;
the storage module is used for storing the space-time object of the recombined data to generate a multi-granularity space-time object, and comprises the following steps: serializing the recombined data by using a classification code, and storing each constructed object based on a multi-granularity space-time object storage table to generate a multi-granularity space-time object; the multi-granularity spatiotemporal object comprises: space-time reference, spatial position, spatial morphology, composition structure, association relationship, cognitive ability, behavioral ability, and attribute characteristics; the multi-granularity space-time object storage table comprises the following sub-tables: time zone, spatial reference, object classification, data type, spatial morphology, and spatio-temporal object.
4. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, characterized in that the processor, when executing the program, implements the method according to any of claims 1-2.
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| CN116521805A (en) * | 2023-03-27 | 2023-08-01 | 山东省国土空间数据和遥感技术研究院(山东省海域动态监视监测中心) | Space track storage, query and generation method based on subdivision grid |
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