A method, device, equipment and medium for matching an Internet of Things device with a spatial object based on location information

Abstract

This invention discloses a method, apparatus, device, and medium for associating and matching IoT devices with spatial objects based on location information. The method includes: extracting the name and coordinate location information of spatial objects from a spatial model; obtaining the location information of a target device, including coordinates and a textual location description; constructing a knowledge graph based on the name and coordinate location information of the spatial objects and the location information of the target device; the nodes of the knowledge graph are device objects and spatial objects, and the edges of the knowledge graph represent positional relationships; analyzing and calculating the association relationship between the target device and spatial object nodes based on the target device's location information, and storing the calculation results in the knowledge graph as edges between the target device nodes and spatial object nodes. This technical solution can quickly establish the association between IoT platform device objects and spatial model objects on the urban information model platform, reducing the time cost of manually establishing associations.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, and in particular to a method, apparatus, device, and medium for associating and matching IoT devices with spatial objects based on location information. Background Technology

[0002] With the acceleration of urbanization and the development of urban information modeling (IAM) technology, the construction of smart cities is in full swing across the country. Smart cities are a new theory and model that utilizes next-generation information integration technologies such as the Internet of Things (IoT), cloud computing, big data, and spatial geographic information integration to promote the intelligentization of urban planning, construction, management, and services. In smart city applications, the integration of IoT device sensing data with static urban physical models is fundamental to many downstream applications. To achieve data fusion, it is essential to establish a connection between IoT platform device objects and the spatial model of the urban information model's underlying platform. How to quickly establish this connection between IoT platform device objects and the spatial model objects of the urban information model's underlying platform is a problem that urgently needs to be solved in smart city construction.

[0003] Currently, the main solution involves business personnel manually establishing the association between IoT platform devices and the spatial model of the city information model's underlying platform. However, when the number of devices is large, manual operation is inefficient and requires significant manpower and time costs. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and medium for associating and matching IoT devices and spatial objects based on location information. It can quickly establish the association between IoT platform device objects and the spatial model of the urban information model basic platform, reducing the time cost of manually establishing the association.

[0005] According to one aspect of the present invention, a method for associating and matching IoT devices with spatial objects based on location information is provided, the method comprising:

[0006] Extract the names and coordinates of spatial objects from the spatial model;

[0007] Obtain the location information of the target device; wherein, the location information of the target device includes coordinates and a text location description;

[0008] A knowledge graph is constructed based on the name and coordinate location information of the spatial object and the location information of the target device; wherein, the nodes of the knowledge graph are device objects and spatial objects, and the edges of the knowledge graph are positional relationships;

[0009] Based on the target device location information, the association between the target device and the spatial object node is analyzed and calculated, and the calculation results are stored in the knowledge graph in the form of edges between the target device node and the spatial object node.

[0010] Optionally, the step of extracting the spatial object name and spatial object coordinate location information from the spatial model includes:

[0011] Obtain layer information from the pre-obtained spatial model file, and extract the names and coordinates of spatial objects from the layers;

[0012] And / or,

[0013] Obtain the name and coordinates of spatial objects from external geographic information data sources.

[0014] Optionally, the method further includes:

[0015] If the spatial object exists in both the spatial model file and the external geographic information data source, then entity alignment technology is used to align the different data sources of the spatial object to obtain the name and coordinate location of the spatial object.

[0016] Optionally, obtaining the location information of the target device includes:

[0017] Attribute data of the target device is obtained from the target data management platform through a preset data interface;

[0018] Read the location information of the target device from the attribute data.

[0019] Optionally, the location information of the target device includes a textual location description and a coordinate location description;

[0020] If the location is described in text, the spatial object entity is extracted using a natural language processing model.

[0021] If the description is a coordinate position, then a uniform transformation of the coordinate system is performed.

[0022] Optionally, the natural language processing model is obtained by fine-tuning a general pre-trained natural language processing model on a geospatial dataset.

[0023] Optionally, the step of analyzing and calculating the association between the target device and spatial object nodes based on the target device location information, and storing the calculation results in the knowledge graph in the form of edges between the target device nodes and spatial object nodes, includes:

[0024] If the location information of the target device includes both text location description and coordinate location description, then the location information obtained from the text location description and the location information obtained from the coordinate location description are respectively associated with the nodes in the knowledge graph to obtain the first association relationship and the second association relationship;

[0025] The first association relationship and the second association relationship are weighted to obtain the association probability value between the target device and the spatial object;

[0026] Based on the aforementioned association probability value, an edge is added between the target device node and the spatial object node in the knowledge graph.

[0027] According to another aspect of the present invention, a location-based IoT device and spatial object association matching device is provided, comprising:

[0028] The module for obtaining spatial object names and coordinates is used to extract the names and coordinates of spatial objects from the spatial model.

[0029] A target device location information acquisition module is used to acquire the location information of the target device; wherein, the location information of the target device includes coordinates and a text location description;

[0030] The knowledge graph construction module constructs a knowledge graph based on the name and coordinate location information of the spatial object and the location information of the target device; wherein, the nodes of the knowledge graph are device objects and spatial objects, and the edges of the knowledge graph are positional relationships;

[0031] The target device matching and association module analyzes and calculates the association relationship between the target device and the spatial object node based on the target device location information, and stores the calculation results in the knowledge graph in the form of edges between the target device node and the spatial object node.

[0032] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0033] At least one processor; and

[0034] A memory communicatively connected to the at least one processor; wherein,

[0035] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the location-based IoT device and spatial object association matching method according to any embodiment of the present invention.

[0036] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the location-information-based IoT device and spatial object association matching method according to any embodiment of the present invention.

[0037] The technical solution of this invention extracts the name and coordinate location information of spatial objects from a spatial model; obtains the location information of a target device, including coordinates and a textual location description; constructs a knowledge graph based on the name and coordinate location information of the spatial objects and the location information of the target device; the nodes of the knowledge graph are device objects and spatial objects, and the edges of the knowledge graph represent positional relationships; the association between the target device and spatial object nodes is analyzed and calculated based on the target device location information, and the calculation results are stored in the knowledge graph in the form of edges between the target device nodes and spatial object nodes. This technical solution can quickly establish the association between IoT platform device objects and the spatial model of the urban information model basic platform, reducing the time cost of manually establishing associations.

[0038] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of a method for associating and matching IoT devices with spatial objects based on location information, according to Embodiment 1 of the present invention;

[0041] Figure 2 This is a flowchart of calculating the association relationship according to Embodiment 1 of the present invention;

[0042] Figure 3 This is a schematic diagram of a location-based IoT device and spatial object association matching device according to Embodiment 2 of the present invention;

[0043] Figure 4 This is a schematic diagram of the structure of an electronic device that implements a location-based IoT device and spatial object association matching method according to an embodiment of the present invention. Detailed Implementation

[0044] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0045] It should be noted that the terms "first," "second," "target," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0046] Example 1

[0047] Figure 1 This invention provides a flowchart of a method for associating and matching IoT devices with spatial objects based on location information, according to Embodiment 1. This embodiment is applicable to the association of IoT platform device objects and urban information model basic platform spatial models. This method can be executed by a location-based IoT device and spatial object association and matching device, which can be implemented in hardware and / or software and can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0048] S110, Extract the name and coordinate location information of the spatial object from the spatial model.

[0049] Spatial objects can be objects within the spatial model of a city information modeling platform. The names of spatial objects can be determined based on the names of their corresponding geographic entities in the real world; for example, a spatial object could be named a hospital, a school, or a building. Coordinate location information can describe the location of the spatial object within its spatial distribution. For example, the coordinates of a hospital are (116.227704, 40.122215). The spatial object name and coordinate location information can be obtained based on the names of their corresponding objects in the real world and geographic data.

[0050] In this embodiment, extracting the name and coordinate location information of spatial objects from the spatial model includes: obtaining layer information from a pre-obtained spatial model file and extracting the name and coordinate location of spatial objects from the layers; and / or obtaining the name and coordinate location of spatial objects from an external geographic information data source.

[0051] In this context, digital cities require initial modeling, with pre-obtained spatial model files acquired from a city information modeling platform. Layers can be like films containing text or graphics, stacked sequentially to form the final page effect. Layers are the most basic component of a map, representing a collection of spatial data in graphical or image form of actual phenomena. In this embodiment, layer information can be a collection of information including the names and coordinates of multiple spatial objects. Extracting the names and coordinate descriptions of spatial objects from each layer can involve first determining the target layer based on preset conditions, and then extracting the names and coordinate information of spatial objects from the target layer. Preset conditions can include layer numbers, etc. External geographic information data sources can be collections of geographic data with definite spatial coordinates representing geographic entities and their characteristics, used to supplement missing object data in the spatial model. Geographic information data can be data representing geographic entities and their location characteristics, with definite spatial coordinates.

[0052] In this embodiment, if the spatial object exists in both the spatial model file and the external geographic information data source, entity alignment technology is used to align the different data sources of the spatial object to obtain the name and coordinate location of the spatial object.

[0053] Entity alignment involves identifying entities in a heterogeneous data source knowledge base that belong to the same entity in the real world. Entity alignment is used to determine whether two or more entities from different information sources refer to the same object in the real world. If multiple entities represent the same object, alignment relationships are established between these entities, and the information contained within the entities is fused and aggregated. In this embodiment, by performing entity alignment on spatial object information in spatial model files and external geographic information data sources, the accuracy of spatial object names and location coordinates can be improved.

[0054] S120, Obtain the location information of the target device; wherein, the location information of the target device includes coordinates and a text location description.

[0055] The target device can be a real-world Internet of Things (IoT) device. An IoT device can be any device that connects to the internet via information sensing devices and according to agreed-upon protocols to exchange and communicate information. The location information of the target device can be information used to define various spatial relationships between geographical features. The location information of the target device can be obtained from a target data management platform. This platform can be an IoT device management platform that provides IoT device management and access services. The platform stores the target device's attribute information and communicates with the target device through agreed-upon protocols to obtain device monitoring data. The platform typically provides external data interfaces to support querying device attribute information.

[0056] In this embodiment, optionally, obtaining the location information of the target device includes: obtaining the attribute data of the target device from the target data management platform through a preset data interface; and reading the location information of the target device from the attribute data.

[0057] The preset data interface can be a channel through which two independent programs transmit data and exchange information. The interface type is typically either HTTP or MQTT. The HTTP interface uses the HTTP protocol for data push. The MQTT interface uses message queue software (such as Kafka) to support publish / subscribe data transmission based on the MQTT protocol. The MQTT protocol is a lightweight transmission protocol specifically developed for the Internet of Things (IoT). It is specially optimized for low-bandwidth networks and devices with low computing power, making it suitable for various IoT application scenarios.

[0058] In this embodiment, the preset data interface can be a channel for data transmission between the target device and the target data management platform. The target data management platform can be an IoT device management platform. Obtaining the target device's attribute data from the target data management platform allows the target device and the target data management platform to communicate normally while adhering to the interface standard. Attribute data can be data categorized by a non-numerical feature. Attribute data can be features describing spatial elements. For example, attribute data can be name, type, characteristics, quantity, and level. In this embodiment, attribute data can be data describing the target device's name, location, etc.

[0059] In this embodiment, attribute data of the target device is obtained from the target data management platform through a preset data interface, and location information is read from the attribute data; this can improve the efficiency and accuracy of obtaining the target device's location information.

[0060] In this embodiment, the location information of the target device includes a text location description and a coordinate location description; if it is a text location description, the spatial object entity is extracted through a natural language processing model; if it is a coordinate location description, the coordinate system is uniformly transformed.

[0061] The textual location description can be information describing the spatial location of the target device using natural text information. For example, the device is located in room 301 on the 3rd floor of a building. The natural language processing model can be a model that processes the textual location description of the target device. Identifying the location information of the target device through the natural language processing model can be based on extracting geospatial object entity and attribute information from the textual location description, such as the entity corresponding to the building and its floor and room number. The coordinate location description can be information describing the geographical location characteristics of the target device using two-dimensional or three-dimensional coordinate data. For example, the device coordinates are (106.7109159, 29.56271428, 204.94). Coordinate system transformation involves unifying all coordinates into the CGCS2000 national geodetic coordinate system, achieved by establishing a one-to-one correspondence between the two coordinate systems. The 2000 National Geodetic Coordinate System uses the center of mass of the entire Earth, including the ocean and atmosphere, as its origin. The Z-axis points to the agreed polar direction as defined by BIH 1984.0 (BIH International Time Bureau), the X-axis points to the intersection of the zero meridian plane as defined by BIH 1984.0 and the agreed equator, and the Y-axis is a geocentric geodetic coordinate system determined by the right-hand coordinate system.

[0062] In this embodiment, by recognizing textual location descriptions through a natural language processing model and by performing a unified transformation of the coordinate system to recognize coordinate location descriptions, the location information of the target device can be obtained from different types of location information, increasing the ways to obtain the location of the target device and improving the accuracy of the obtained location of the target device.

[0063] In this embodiment, the natural language processing model is obtained by fine-tuning a general pre-trained natural language processing model on a geospatial dataset.

[0064] The natural language processing (NLP) model is a model obtained by training a general pre-trained NLP model on a geospatial dataset using NLP technology. In this embodiment, the general pre-trained NLP model is fine-tuned and trained using a geospatial dataset to obtain the NLP model, which can identify geographic information entities and location attributes in textual location descriptions, and accurately depict the location of the target device as much as possible.

[0065] S130, a knowledge graph is constructed based on the name and coordinate location information of the spatial object and the location information of the target device; wherein, the nodes of the knowledge graph are device objects and spatial objects, and the edges of the knowledge graph are positional relationships.

[0066] Knowledge graphs are a data organization form that represents things in the real physical world and their interrelationships, facilitating the querying and visualization of relationships. In this embodiment, knowledge graph nodes are spatial objects and device objects, and edges represent positional relationships. By constructing a knowledge graph, the positional relationships between spatial objects can be revealed based on location information.

[0067] S140, based on the location information of the target device, analyze and calculate the association between the target device and the spatial object node, and store the calculation results in the knowledge graph in the form of edges between the target device node and the spatial object node.

[0068] There are three types of relationships: matching, containment, and attachment. A matching relationship means that the device object and the spatial object representing the device are the same object. A containment relationship means that the device object is contained within an object representing a spatial area (such as a building or road); the coordinate representation of these objects is usually a region or line. An attachment relationship means that the device object is attached to a spatial object representing a facility, such as a light pole. Based on manually defined rules, the possible relationship types between device objects and spatial objects can be selected by the type of the spatial object, and then the existence of that type of relationship can be further calculated.

[0069] The calculation of the association relationship is divided into two parts: (1) the first association relationship: calculation based on coordinate position description, applicable to the case where both the device object and the spatial object have coordinate positions; and (2) the second association relationship: calculation based on text position description, applicable to the case where both the device object and the spatial object have text description information. The overall process is as follows: Figure 2 As shown. For matching and attachment relationships, method (1) calculates the distance between the device object and the spatial object to be matched, and obtains the association relationship that minimizes the total distance through an optimization algorithm. For inclusion relationships, method (1) calculates the distance between the device object and the corresponding spatial object, and considers the inclusion relationship to exist if the distance is less than a given threshold. Method (2) establishes the association relationship based on manually set rules by extracting the spatial object text from the device object's location description. Each calculated association relationship corresponds to an association probability value to describe the degree of association. The final association probability value between the device object and the spatial object is obtained by weighting the association probability values ​​calculated by method (1) and method (2). The weights can be set according to requirements. Based on the association probability value, an association edge of the corresponding type is added between the target device node and the spatial object node in the knowledge graph.

[0070] This solution extracts the names and coordinates of spatial objects from a spatial model; obtains the location information of the target device, including coordinates and a textual description of its location; constructs a knowledge graph based on the names and coordinates of the spatial objects and the location information of the target device; the nodes of the knowledge graph are device objects and spatial objects, and the edges represent positional relationships; and analyzes and calculates the associations between the target device and spatial object nodes based on the target device's location information, storing the calculation results in the knowledge graph as edges between the target device nodes and spatial object nodes. This technical solution can quickly establish associations between IoT platform device objects and the spatial model of the city information model platform, reducing the time cost of manually establishing associations.

[0071] Example 2

[0072] Figure 3 This is a schematic diagram of a location-based IoT device and spatial object association matching device provided in Embodiment 2 of the present invention. This device can execute the location-based IoT device and spatial object association matching method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. For example... Figure 3 As shown, the device includes:

[0073] The spatial object name and coordinate location acquisition module 310 is used to extract the name and coordinate location information of spatial objects from the spatial model.

[0074] The target device location information acquisition module 320 is used to acquire the location information of the target device; wherein, the location information of the target device includes coordinates and a text location description;

[0075] The knowledge graph construction module 330 is used to construct a knowledge graph based on the name and coordinate location information of the spatial object and the location information of the target device; wherein, the nodes of the knowledge graph are device objects and spatial objects, and the edges of the knowledge graph are positional relationships;

[0076] The target device matching and association module 340 is used to analyze and calculate the association relationship between the target device and the spatial object node based on the target device location information, and save the calculation results in the knowledge graph in the form of edges between the target device node and the spatial object node.

[0077] Optionally, the spatial object name and coordinate location acquisition module includes:

[0078] The spatial model file submodule is used to obtain layer information from a pre-obtained spatial model file and extract the names and coordinates of spatial objects from the layers.

[0079] And / or,

[0080] The Geographic Information Data Source submodule is used to obtain the names and coordinates of spatial objects from external geographic information data sources.

[0081] Optionally, the device further includes: an alignment module, used for:

[0082] If the spatial object exists in both the spatial model file and the external geographic information data source, then entity alignment technology is used to align the different data sources of the spatial object to obtain the name and coordinate location of the spatial object.

[0083] Optionally, the target device location information acquisition module includes:

[0084] The attribute data acquisition submodule is used to acquire the attribute data of the target device from the target data management platform through a preset data interface;

[0085] The location information reading submodule is used to read the location information of the target device from the attribute data.

[0086] Optionally, the location information of the target device includes a textual location description and a coordinate location description; the location information reading submodule includes:

[0087] The natural language recognition model recognition unit is used to extract spatial object entities through a natural language processing model if the description is a text location.

[0088] The coordinate transformation recognition unit is used to perform a unified transformation of the coordinate system if the description is a coordinate position.

[0089] Optionally, the location information reading submodule further includes a training unit, used for:

[0090] The natural language processing model is obtained by fine-tuning a general pre-trained natural language processing model on a geospatial dataset.

[0091] Optionally, the target device association module includes:

[0092] The association submodule is used to associate the location information obtained from the text location description and the location information obtained from the coordinate location description with the nodes in the knowledge graph if the location information of the target device includes both text location description and coordinate location description, respectively, to obtain a first association relationship and a second association relationship.

[0093] The weighting submodule is used to perform weighted processing on the first association relationship and the second association relationship to obtain the association probability value between the target device and the spatial object;

[0094] Add a submodule for adding edges between target device nodes and spatial object nodes in the knowledge graph based on the association probability value.

[0095] The location-based IoT device and spatial object association matching device provided in the embodiments of the present invention can execute the location-based IoT device and spatial object association matching method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0096] Example 3

[0097] Figure 4 This is a schematic diagram of an electronic device implementing a location-based IoT device and spatial object association matching method according to an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, 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 merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0098] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0099] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0100] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with 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 special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as location-based IoT device and spatial object association matching methods.

[0101] In some embodiments, the location-based IoT device and spatial object association matching method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the location-based IoT device and spatial object association matching method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the location-based IoT device and spatial object association matching method by any other suitable means (e.g., by means of firmware).

[0102] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0103] Computer programs used to implement 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, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0104] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0105] To provide interaction with a user, the systems and techniques described herein 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 the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the 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 sound input, voice input, or tactile input).

[0106] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0107] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0108] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0109] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for associating and matching IoT devices with spatial objects based on location information, comprising: Extract the names and coordinates of spatial objects from the spatial model; Obtain the location information of the target device; wherein, the location information of the target device includes coordinates and a text location description; A knowledge graph is constructed based on the name and coordinate location information of the spatial object and the location information of the target device; wherein, the nodes of the knowledge graph are device objects and spatial objects, and the edges of the knowledge graph are positional relationships; Based on the target device location information, the association between the target device and the spatial object node is analyzed and calculated, and the calculation results are stored in the knowledge graph in the form of edges between the target device node and the spatial object node. Specifically, the step of analyzing and calculating the association between the target device and spatial object nodes based on the target device location information, and storing the calculation results in the knowledge graph in the form of edges between the target device nodes and spatial object nodes, includes: If the location information of the target device includes both text location description and coordinate location description, then the location information obtained from the text location description and the location information obtained from the coordinate location description are respectively associated with the nodes in the knowledge graph to obtain the first association relationship and the second association relationship; The first association relationship and the second association relationship are weighted to obtain the association probability value between the target device and the spatial object; Based on the aforementioned association probability value, an edge is added between the target device node and the spatial object node in the knowledge graph.

2. The method according to claim 1, characterized in that, The extraction of spatial object names and coordinate location information from the spatial model includes: Obtain layer information from the pre-obtained spatial model file, and extract the names and coordinates of spatial objects from the layers; And / or, Obtain the name and coordinates of spatial objects from external geographic information data sources.

3. The method according to claim 2, characterized in that, The method further includes: If the spatial object exists in both the spatial model file and the external geographic information data source, then entity alignment technology is used to align the different data sources of the spatial object to obtain the name and coordinate location of the spatial object.

4. The method according to claim 1, characterized in that, The acquisition of the target device's location information includes: Attribute data of the target device is obtained from the target data management platform through a preset data interface; Read the location information of the target device from the attribute data.

5. The method according to claim 4, characterized in that, The location information of the target device includes a textual location description and a coordinate location description; If the location is described in text, the spatial object entity is extracted using a natural language processing model. If the description is a coordinate position, then a uniform transformation of the coordinate system is performed.

6. The method according to claim 5, characterized in that, The natural language processing model is obtained by fine-tuning a general pre-trained natural language processing model on a geospatial dataset.

7. A location-based IoT device for associating and matching spatial objects, characterized in that, include: The module for obtaining spatial object names and coordinates is used to extract the names and coordinates of spatial objects from the spatial model. A target device location information acquisition module is used to acquire the location information of the target device; wherein, the location information of the target device includes coordinates and a text location description; The knowledge graph construction module constructs a knowledge graph based on the name and coordinate location information of the spatial object and the location information of the target device; wherein, the nodes of the knowledge graph are device objects and spatial objects, and the edges of the knowledge graph are positional relationships; The target device matching and association module analyzes and calculates the association relationship between the target device and the spatial object node based on the target device location information, and stores the calculation results in the knowledge graph in the form of edges between the target device node and the spatial object node; The target device association module includes: The association submodule is used to associate the location information obtained from the text location description and the location information obtained from the coordinate location description with the nodes in the knowledge graph if the location information of the target device includes both text location description and coordinate location description, respectively, to obtain a first association relationship and a second association relationship. The weighting submodule is used to perform weighted processing on the first association relationship and the second association relationship to obtain the association probability value between the target device and the spatial object; Add a submodule for adding edges between target device nodes and spatial object nodes in the knowledge graph based on the association probability value.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the location-based IoT device and spatial object association matching method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the location-based IoT device and spatial object association matching method as described in any one of claims 1-6.

Images