CN115687587A - Internet of things equipment and space object association matching method, device, equipment and medium based on position information - Google Patents
Internet of things equipment and space object association matching method, device, equipment and medium based on position information Download PDFInfo
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Abstract
The embodiment of the invention discloses a method, a device, equipment and a medium for matching correlation between an internet of things device and a space object based on position information. The method comprises the following steps: extracting the name and coordinate position information of the space object from the space model; acquiring position information of target equipment, including coordinates and text position description; constructing a knowledge graph based on the name and coordinate position information of the space object and the position information of the target equipment; the nodes of the knowledge graph are equipment objects and space objects, and the edges of the knowledge graph are in a position relation; and analyzing and calculating the incidence relation between the target equipment and the space object node based on the position information of the target equipment, and storing the calculation result in a knowledge graph in the form of edges between the target equipment node and the space object node. According to the technical scheme, the association between the equipment object of the Internet of things platform and the space model object of the urban information model basic platform can be established quickly, and the time cost for establishing the association manually is reduced.
Description
Technical Field
The invention relates to the technical field of Internet of things, in particular to a method, a device, equipment and a medium for matching correlation between an Internet of things device and a space object based on position information.
Background
With the acceleration of the urbanization process and the development of urban information model technology, the construction of smart cities in various places is vigorous. The smart city is a new theory and a new mode for promoting the intellectualization of city planning, construction, management and service by applying new generation information integration technologies such as internet of things, cloud computing, big data, space geographic information integration and the like. In smart city applications, the fusion of internet of things device perception data and a static city physical model is the basis of many downstream applications. For data fusion, the association between the equipment object of the internet of things platform and the space model of the basic platform of the urban information model must be established. How to quickly establish the association between an equipment object of an internet of things platform and a space model object of a basic platform of a city information model is a problem to be solved urgently in smart city construction.
At present, the main scheme adopts the manual operation of service personnel to establish the association between an equipment object of an internet of things platform and a space model of a basic platform of a city information model. However, when the number of devices is large, the manual operation efficiency is low, and a large amount of labor and time cost are required.
Disclosure of Invention
The invention provides a method, a device, equipment and a medium for matching the association between an internet of things equipment and a space object based on position information, which can quickly establish the association between an internet of things platform equipment object and a city information model basic platform space model and reduce the time cost for manually establishing the association.
According to an aspect of the invention, a method for matching the association of an internet of things device and a space object based on position information is provided, and the method comprises the following steps:
extracting space object names and space object coordinate position information from the space model;
acquiring position information of target equipment; the position information of the target equipment comprises coordinates and text position description;
constructing a knowledge graph based on the name and coordinate position information of the space object and the position information of the target equipment; the nodes of the knowledge graph are equipment objects and space objects, and the edges of the knowledge graph are in a position relation;
and analyzing and calculating the incidence relation between the target equipment and the space object node based on the position information of the target equipment, and storing the calculation result in a knowledge graph in the form of edges between the target equipment node and the space object node.
Optionally, the extracting the name of the spatial object and the coordinate and position information of the spatial object from the spatial model includes:
obtaining layer information from a space model file obtained in advance, and extracting the name and the coordinate position of a space object from a layer;
and/or the presence of a gas in the atmosphere,
and acquiring the name and the coordinate position of the space object from an external geographic information data source.
Optionally, the method further includes:
and if the space object exists in the space model file and the external geographic information data source at the same time, aligning different data sources of the space object by adopting an entity alignment technology to obtain the name and the coordinate position of the space object.
Optionally, the obtaining the location information of the target device includes:
acquiring attribute data of target equipment from a target data management platform through a preset data interface;
and reading the position information of the target device from the attribute data.
Optionally, the location information of the target device includes a text location description and a coordinate location description;
if the description is the text position description, extracting a space object entity through a natural language processing model;
if the coordinate position description is adopted, the coordinate system is uniformly converted.
Optionally, the natural language processing model is obtained after fine tuning training performed on a geospatial data set based on a general pre-training natural language processing model.
Optionally, analyzing and calculating an association relationship between the target device and the spatial object node based on the target device location information, and storing a calculation result in a knowledge graph in a form of an edge between the target device node and the spatial object node, where the method includes:
if the position information of the target equipment comprises the character position description and the coordinate position description, associating the position information obtained by the character position description and the position information obtained by the coordinate position description with the nodes in the knowledge graph respectively to obtain a first association relation and a second association relation;
weighting the first incidence relation and the second incidence relation to obtain an incidence probability value of the target equipment and the space object;
adding an edge between a target device node and a spatial object node in the knowledge-graph based on the association probability value.
According to another aspect of the present invention, there is provided an apparatus for matching an association between an internet of things device and a spatial object based on location information, including:
the space object name and coordinate position acquisition module is used for extracting the name and coordinate position information of the space object from the space model;
the target equipment position information acquisition module is used for acquiring the position information of the target equipment; the position information of the target equipment comprises coordinates and text position description;
the knowledge map building module builds a knowledge map based on the name and coordinate position information of the space object and the position information of the target equipment; the nodes of the knowledge graph are equipment objects and space objects, and the edges of the knowledge graph are in a position relation;
and the target equipment matching correlation module is used for analyzing and calculating the correlation between the target equipment and the space object node based on the position information of the target equipment and storing the calculation result in a knowledge graph in the form of edges between the target equipment node and the space object node.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the method for matching association between an internet of things device and a space object based on location information according to any embodiment of the present invention.
According to another aspect of the present invention, a computer-readable storage medium is provided, where computer instructions are stored, and the computer instructions are configured to enable a processor to implement the method for matching an association between an internet of things device and a spatial object based on location information according to any embodiment of the present invention when executed.
According to the technical scheme of the embodiment of the invention, the name and coordinate position information of the space object are extracted from the space model; acquiring position information of target equipment, including coordinates and text position description; constructing a knowledge graph based on the name and coordinate position information of the space object and the position information of the target equipment; the nodes of the knowledge graph are equipment objects and space objects, and the edges of the knowledge graph are in a position relation; and analyzing and calculating the association relation between the target equipment and the space object node based on the position information of the target equipment, and storing the calculation result in a knowledge graph in the form of edges between the target equipment node and the space object node. According to the technical scheme, the association between the equipment object of the Internet of things platform and the space model of the basic platform of the urban information model can be established quickly, and the time cost for establishing the association manually is reduced.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a method for matching an association between an internet of things device and a spatial object based on location information according to an embodiment of the present invention;
FIG. 2 is a flowchart for calculating an association relationship according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an apparatus for matching association between an internet of things device and a space object based on location information according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing a method for matching an association between an internet of things device and a space object based on location information according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," "object," and the like in the description and claims of the invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of a method for matching an association between an internet of things device and a space object based on location information according to an embodiment of the present invention, where the embodiment is applicable to a case where an internet of things platform device object is associated with a space model of an urban information model basic platform, the method may be executed by an association matching apparatus for an internet of things device and a space object based on location information, the association matching apparatus for an internet of things device and a space object based on location information may be implemented in a hardware and/or software manner, and the association matching apparatus for an internet of things device and a space object based on location information may be configured in an electronic device with data processing capability. As shown in fig. 1, the method includes:
and S110, extracting the spatial object name and the spatial object coordinate position information from the spatial model.
The space object can be an object in a city information model platform space model. The name of the spatial object may be determined according to the name of the corresponding geographic entity object in the real world, for example, the name of the spatial object may be a certain hospital, a certain school, a certain building, etc. The coordinate location information may be coordinate information describing a position in which the spatial object is located in the spatial distribution. For example, the location coordinates of a certain hospital are (116.227704, 40.122215). The spatial object name and coordinate location information may be obtained from real-world corresponding object names and geographic data.
In this embodiment, extracting the spatial object name and the spatial object coordinate position information from the spatial model includes: acquiring layer information from a space model file acquired in advance, and extracting the name and the coordinate position of a space object from a layer; and/or acquiring the name and the coordinate position of the space object from an external geographic information data source.
The digital city needs to be modeled first, and the space model file obtained in advance can be obtained from a city information model base platform. The layers may be films containing elements such as text or graphics that are stacked in sequence one on top of the other to form the final effect of the page. Layers are the most basic components of a map, representing a collection of spatial data in the form of graphics or images of an actual phenomenon. In this embodiment, the layer information may be an information set including a plurality of spatial object names and coordinate positions. The extracting of the name and the coordinate position description of the spatial object in each layer information may be determining a target layer according to a preset condition, and extracting the name and the coordinate position information of the spatial object from the target layer. The preset condition may be a layer number or the like. The external geographic information data source may be a collection of geographic data with determined spatial coordinates of data representing geographic entities and their features, for supplementing missing portions of object data in the spatial model. The geographic information data may be data representing a geographic entity and its location characteristics, with determined spatial coordinates.
In this embodiment, if the spatial object exists in the spatial model file and the external geographic information data source at the same time, an entity alignment technology is adopted to align different data sources of the spatial object, so as to obtain the name and the coordinate position of the spatial object.
The entity alignment may be to find out the same entity belonging to the real world for each entity in the heterogeneous data source knowledge base. Entity alignment is used to determine whether two or more entities from different sources point to the same object in the real world. If a plurality of entities represent the same object, an alignment relation is constructed among the entities, and meanwhile information contained in the entities is fused and aggregated. In this embodiment, the accuracy of the name and position coordinate information of the spatial object can be improved by performing entity alignment on the spatial object information in the spatial model file and in the external geographic information data source.
S120, acquiring the position information of the target equipment; the position information of the target device comprises coordinates and text position description.
The target device may be an internet of things device in the real world. The internet of things equipment can be any equipment which is connected with the internet according to an agreed protocol through information sensing equipment to exchange and communicate information. The location information of the target device may be information used to define various spatial relationships between geographic objects. The location information of the target device may be obtained from the target data management platform. The target data management platform can be an internet of things equipment management platform, and the platform provides internet of things equipment management and internet of things equipment access services. The platform stores the attribute information of the target equipment, communicates with the target equipment through an agreed protocol and acquires equipment monitoring data. The platform generally provides an external data interface to support querying the device attribute information.
In this embodiment, optionally, the obtaining of the location information of the target device includes: acquiring attribute data of target equipment from a target data management platform through a preset data interface; and reading the position information of the target device from the attribute data.
The preset data interface can be a channel, and two mutually independent programs realize data transmission and information exchange through the interface channel. The interface type is typically an HTTP interface or an MQTT interface. The HTTP interface realizes data push based on HTTP protocol. The MQTT interface supports publish/subscribe data transmission based on MQTT protocol through message queue software (e.g., kafka, etc.). The MQTT protocol is a lightweight transmission protocol developed specially for the Internet of things, and the protocol is specially optimized for equipment with low bandwidth network and low computing power, so that the protocol is suitable for various Internet of things application scenes.
In this embodiment, the preset data interface may be a channel for data transmission between the target device and the target data management platform. The target data management platform can be an internet of things device management platform. The attribute data of the target device is acquired from the target data management platform, so that the target device and the target data management platform commonly comply with an interface standard and perform normal communication. The attribute data may be different classes of data distinguished by some non-numeric feature. The attribute data may be a feature describing a spatial element. For example, the attribute data may be name, type, attribute, quantity, and rating. In this embodiment, the attribute data may be data describing a name, a location, and the like of the target device.
In this embodiment, attribute data of a target device is acquired from a target data management platform through a preset data interface, and position information is read from the attribute data; the efficiency and accuracy of obtaining the position information of the target device can be improved.
In this embodiment, the location information of the target device includes a text location description and a coordinate location description; if the description is the text position description, extracting a space object entity through a natural language processing model; if the coordinate position description is adopted, the coordinate system is uniformly converted.
The text position description may be information describing a spatial position of the target device through natural text information. For example, the equipment is located in a floor 3 room of a building. The natural language processing model may be a model that processes word location descriptions of the target device. The location information of the target device identified by the natural language processing model may be a geospatial object entity and attribute information, such as an entity and a floor corresponding to the building, a room number, etc., extracted from the textual location description based on the natural language processing model. The coordinate location description may be information characterizing the geographic location of the target device by two-dimensional or three-dimensional coordinate data. For example, the device coordinates are (106.7109159, 29.56271428, 204.94). The coordinate system unified transformation is to convert all coordinates into a 2000 national geodetic coordinate system (CGCS 2000) uniformly, and is realized by establishing a one-to-one correspondence relationship between the two coordinate systems. The 2000 national geodetic coordinate system is a geocentric geodetic coordinate system which takes the mass center of the whole earth including the ocean and the atmosphere as an origin, the Z axis points to the polar direction (BIH International time office) of the protocol defined by BIH1984.0, the X axis points to the intersection point of the meridian plane zero defined by BIH1984.0 and the equator of the protocol, and the Y axis is determined according to the right-hand coordinate system.
In this embodiment, the character position description is recognized through the natural language processing model, and the coordinate position description is recognized through the unified transformation of the coordinate system, so that the position information of the target device can be acquired from different position information types, the position acquisition mode of the target device is increased, and the accuracy of the acquired position of the target device is improved.
In this embodiment, the natural language processing model is obtained after fine tuning training performed on a geospatial data set based on a general pre-trained natural language processing model.
The natural language processing model is obtained by training a general pre-training natural language processing model on a geographic space data set based on a natural language processing technology. In this embodiment, the universal pre-trained natural language processing model is subjected to fine tuning training through the geospatial data set to obtain the natural language processing model, so that the geographic information entity and the location attribute in the text location description can be identified, and the location of the target device can be depicted as accurately as possible.
S130, constructing a knowledge graph based on the name and coordinate position information of the space object and the position information of the target equipment; the nodes of the knowledge graph are equipment objects and space objects, and the edges of the knowledge graph are in position relation.
The knowledge graph is a data organization form for representing objects in the real physical world and mutual association thereof, and is convenient for association relation query and visual display. In this embodiment, the nodes of the knowledge graph are position relationships between the space objects and the equipment objects, and the edges are position relationships, and the position relationships between the space objects can be revealed according to the position information by constructing the knowledge graph.
S140, analyzing and calculating the incidence relation between the target equipment and the space object node based on the position information of the target equipment, and storing the calculation result in a knowledge graph in the form of edges between the target equipment node and the space object node.
The association relationship types include matching, including and attaching. The matching relationship means that the device object and the space object representing the device are the same object. An inclusion relationship refers to the inclusion of device objects into objects (e.g., buildings, roads) representing a spatial region, the coordinate representation of which is typically an area or line. The attachment relationship means that the equipment object is attached to a space object representing a facility, such as a lamp post or the like. Based on the manually set rule, the type of the relationship possibly existing between the equipment object and the space object can be selected according to the type of the space object, and whether the type relationship exists is further calculated.
The calculation of the association relationship is divided into two parts: (1) first association relation: the calculation based on the coordinate position description is suitable for the situation that the equipment object and the space object have coordinate positions; and (2) a second association relationship: the calculation based on the text position description is suitable for the situation that the equipment object and the space object have text description information. The whole flow is shown in fig. 2. For the matching relation and the attachment relation, the distance between the equipment object and the space object to be matched is calculated in a mode (1), and the association relation enabling the total distance to be minimum is obtained through an optimization algorithm. For containment relationships, manner (1) calculates the device object distance from the corresponding spatial object, a containment relationship deemed to exist for distances less than a given threshold. In the mode (2), the association relationship is established based on the manually set rule through the space object text extracted from the device object position description. Each calculated association relationship corresponds to an association probability value for describing the association degree. The final association probability value between the device object and the space object is obtained by weighting the association probability values calculated in the above modes (1) and (2), and the weight can be set according to requirements. And adding corresponding types of associated edges between the target equipment nodes and the space object nodes in the knowledge graph based on the associated probability values.
The scheme extracts the name and coordinate position information of a space object from a space model; acquiring position information of target equipment, including coordinates and text position description; constructing a knowledge graph based on the name and coordinate position information of the space object and the position information of the target equipment; the nodes of the knowledge graph are equipment objects and space objects, and the edges of the knowledge graph are in a position relation; and analyzing and calculating the incidence relation between the target equipment and the space object node based on the position information of the target equipment, and storing the calculation result in a knowledge graph in the form of edges between the target equipment node and the space object node. According to the technical scheme, the association between the equipment object of the Internet of things platform and the space model of the basic platform of the urban information model can be established quickly, and the time cost for establishing the association manually is reduced.
Example two
Fig. 3 is a schematic structural diagram of a device for matching an internet of things device and a space object based on location information according to a second embodiment of the present invention, where the device is capable of executing a method for matching an internet of things device and a space object based on location information according to any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method. As shown in fig. 3, the apparatus includes:
a spatial object name and coordinate position obtaining module 310, configured to extract a name and coordinate position information of a spatial object from a spatial model;
a target device location information obtaining module 320, configured to obtain location information of a target device; the position information of the target equipment comprises coordinates and text position description;
a knowledge graph construction module 330, configured to construct a knowledge graph based on the name and coordinate location information of the space object and the location information of the target device; the nodes of the knowledge graph are equipment objects and space objects, and the edges of the knowledge graph are in a position relation;
and the target device matching association module 340 is configured to analyze and calculate an association relationship between the target device and the spatial object node based on the target device location information, and store a calculation result in the form of an edge between the target device node and the spatial object node in the knowledge graph.
Optionally, the space object name and coordinate position obtaining module includes:
the space model file submodule is used for acquiring layer information from a space model file acquired in advance and extracting the name and the coordinate position of a space object from a layer;
and/or the presence of a gas in the gas,
and the geographic information data source submodule is used for acquiring the name and the coordinate position of the space object from an external geographic information data source.
Optionally, the apparatus further comprises: an alignment module to:
and if the space object exists in the space model file and the external geographic information data source at the same time, aligning different data sources of the space object by adopting an entity alignment technology to obtain the name and the coordinate position of the space object.
Optionally, the target device location information obtaining module includes:
the attribute data acquisition submodule is used for acquiring the attribute data of the target equipment from the target data management platform through a preset data interface;
and the position information reading submodule is used for reading the position information of the target equipment from the attribute data.
Optionally, the location information of the target device includes a text location description and a coordinate location description; the position information reading submodule includes:
the natural language identification model identification unit is used for extracting the space object entity through the natural language processing model if the character position description is carried out;
and the coordinate conversion identification unit is used for uniformly converting the coordinate system if the coordinate position description is adopted.
Optionally, the position information reading sub-module further includes a training unit, configured to:
the natural language processing model is obtained after fine tuning training is carried out on a geographic space data set based on a general pre-training natural language processing model.
Optionally, the target device association module includes:
the association submodule is used for associating the position information obtained by the character position description and the position information obtained by the coordinate position description with the nodes in the knowledge graph respectively to obtain a first association relation and a second association relation if the position information of the target equipment simultaneously comprises the character position description and the coordinate position description;
the weighting submodule is used for weighting the first incidence relation and the second incidence relation to obtain an incidence probability value of the target equipment and the space object;
and the adding submodule is used for adding edges between the target equipment nodes and the space object nodes in the knowledge graph based on the association probability values.
The device for matching the association of the internet of things equipment and the space object based on the position information, provided by the embodiment of the invention, can execute the method for matching the association of the internet of things equipment and the space object based on the position information, provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
EXAMPLE III
Fig. 4 is a schematic structural diagram of an electronic device implementing a method for matching an association between an internet of things device and a space object based on location information according to an embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as the location information based approach to matching the association of an equipment with a spatial object.
In some embodiments, the location information based method for matching the association of an internet of things device with a spatial object may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM12 and/or the communication unit 19. When loaded into RAM 13 and executed by processor 11, the computer program may perform one or more steps of the above-described location information-based method for matching an association of an equipment with a spatial object. Alternatively, in other embodiments, the processor 11 may be configured to perform the location information based method of matching an association of an equipment with a spatial object by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage 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. Alternatively, the computer readable storage medium may be a machine readable signal medium. 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 compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for matching correlation between an Internet of things device and a space object based on position information comprises the following steps:
extracting space object names and space object coordinate position information from the space model;
acquiring position information of target equipment; the position information of the target equipment comprises coordinates and text position description;
constructing a knowledge graph based on the name and coordinate position information of the space object and the position information of the target equipment; the nodes of the knowledge graph are equipment objects and space objects, and the edges of the knowledge graph are in a position relation;
and analyzing and calculating the incidence relation between the target equipment and the space object node based on the position information of the target equipment, and storing the calculation result in a knowledge graph in the form of edges between the target equipment node and the space object node.
2. The method of claim 1, wherein extracting spatial object name and spatial object coordinate location information from the spatial model comprises:
obtaining layer information from a space model file obtained in advance, and extracting the name and the coordinate position of a space object from a layer;
and/or the presence of a gas in the gas,
and acquiring the name and the coordinate position of the space object from an external geographic information data source.
3. The method of claim 2, further comprising:
and if the space object exists in the space model file and the external geographic information data source at the same time, aligning different data sources of the space object by adopting an entity alignment technology to obtain the name and the coordinate position of the space object.
4. The method of claim 1, wherein the obtaining the location information of the target device comprises:
acquiring attribute data of target equipment from a target data management platform through a preset data interface;
and reading the position information of the target device from the attribute data.
5. The method of claim 4, wherein the location information of the target device comprises a textual location description and a coordinate location description;
if the description is the text position description, extracting a space object entity through a natural language processing model;
if the coordinate position description is adopted, the coordinate system is uniformly converted.
6. The method of claim 5, wherein the natural language processing model is derived after a fine-tuning training on a geospatial data set based on a generic pre-trained natural language processing model.
7. The method according to claim 1, wherein analyzing the association relationship between the computation target device and the spatial object node based on the target device location information, and storing the computation result in the form of an edge between the target device node and the spatial object node in the knowledge graph comprises:
if the position information of the target equipment comprises the character position description and the coordinate position description, associating the position information obtained by the character position description and the position information obtained by the coordinate position description with the nodes in the knowledge graph respectively to obtain a first association relation and a second association relation;
weighting the first incidence relation and the second incidence relation to obtain an incidence probability value of the target equipment and the space object;
adding an edge between a target device node and a spatial object node in the knowledge-graph based on the association probability value.
8. An equipment of things allies oneself with equipment and space object correlation matching device based on positional information, its characterized in that includes:
the space object name and coordinate position acquisition module is used for extracting the name and coordinate position information of the space object from the space model;
the target equipment position information acquisition module is used for acquiring the position information of the target equipment; the position information of the target equipment comprises coordinates and text position description;
the knowledge map building module builds a knowledge map based on the name and coordinate position information of the space object and the position information of the target equipment; the nodes of the knowledge graph are equipment objects and space objects, and the edges of the knowledge graph are in a position relation;
and the target equipment matching correlation module is used for analyzing and calculating the correlation between the target equipment and the space object node based on the position information of the target equipment and storing the calculation result in a knowledge graph in the form of edges between the target equipment node and the space object node.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7 for associating and matching a location-based equipment with a spatial object.
10. A computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions for causing a processor to implement the method for matching association between an internet of things and a spatial object based on location information according to any one of claims 1 to 7.
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| CN116137629A (en) * | 2023-02-17 | 2023-05-19 | 软通动力信息技术(集团)股份有限公司 | Method, device, equipment and medium for matching sensing data of Internet of things with space model |
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