A method and device for calculating the matching degree between Internet of Things perception data and spatial model

Abstract

The present invention discloses a method and device for calculating the degree of matching between IoT perception data and a spatial model. The method includes: determining first position information of the IoT perception data and second position information of the spatial model; the first and second position information are located within the same coordinate system; determining the position information of a target sphere associated with the IoT perception data based on the first and second position information; determining the spatial topological relationship between the target sphere position information and the spatial model; the spatial topological relationship includes being separated or inseparable; and determining the degree of matching between the IoT perception data and the spatial model based on the spatial topological relationship. By implementing this solution, a more accurate degree of matching can be determined, which is conducive to determining a more reliable matching relationship and solves the problem of low reliability and unclear matching degree calculation in related technologies.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and device for calculating the matching degree between Internet of Things perception data and a spatial model. Background Art

[0002] The operation of digital twin cities and factories generates massive amounts of IoT data. Linking IoT sensor data with component-, system-, and system-level spatial models of cities and factories, and synchronizing them in real time, enables comprehensive integration of this massive data, unlocking its true potential and building a new management model for digital twin cities and factories.

[0003] Related art solutions for calculating the matching degree between IoT sensing data and spatial models rely on technicians calculating the matching degree based on the type and name of the IoT sensing device and the name of the spatial model, resulting in low manual matching efficiency. Methods for quickly calculating the matching degree between IoT sensing data and spatial models by directly calculating the minimum distance relationship based on location coordinate information are meaningless when IoT sensing data points are at the boundary or within the spatial model, leading to deviations in the matching results and low matching reliability. Summary of the Invention

[0004] The present invention provides a method and device for calculating the matching degree between IoT perception data and spatial models, which can achieve more accurate matching degree determination, facilitate determining more reliable matching relationships, and solve the problems of low reliability and unclear matching degree calculation in related technologies.

[0005] According to one aspect of the present invention, a method for calculating the matching degree between IoT perception data and a spatial model is provided, the method comprising:

[0006] Determining first location information of the IoT sensing data and determining second location information of the spatial model; the first location information and the second location information are located in the same coordinate system;

[0007] Determine the target sphere position information associated with the IoT sensing data based on the first position information and the second position information;

[0008] Determining a spatial topological relationship between the target sphere position information and the spatial model; the spatial topological relationship includes being separated or not separated;

[0009] A degree of matching between the IoT perception data and the spatial model is determined based on the spatial topological relationship.

[0010] According to another aspect of the present invention, a device for calculating the degree of matching between IoT perception data and a spatial model is provided, the device comprising:

[0011] a location information determination module, configured to determine first location information of IoT sensing data and second location information of the spatial model; the first location information and the second location information being located in the same coordinate system;

[0012] a sphere position information determination module, configured to determine the target sphere position information associated with the IoT sensing data based on the first position information and the second position information;

[0013] A spatial topological relationship determination module is used to determine the spatial topological relationship between the target sphere position information and the spatial model; the spatial topological relationship includes being separated or not separated;

[0014] A matching degree determination module is used to determine the matching degree between the IoT perception data and the spatial model based on the spatial topological relationship.

[0015] According to another aspect of the present invention, an electronic device is provided, comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for calculating the degree of matching between IoT perception data and a spatial model as described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for calculating the degree of matching between IoT perception data and a spatial model according to any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention determines the first position information of the IoT perception data and the second position information of the spatial model; the first position information and the second position information are located in the same coordinate system; the position information of the target sphere associated with the IoT perception data is determined based on the first position information and the second position information; the spatial topological relationship between the target sphere position information and the spatial model is determined; the spatial topological relationship includes being separated or not separated; and the matching degree between the IoT perception data and the spatial model is determined based on the spatial topological relationship. By implementing the technical solution provided by the embodiment of the present invention, a more accurate matching degree can be determined, which is conducive to determining a more reliable matching relationship and solves the problem of low reliability and unclear matching degree calculation in related technologies.

[0021] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 This is a flow chart of a method for calculating the degree of matching between IoT perception data and a spatial model provided by an embodiment of the present invention;

[0024] Figure 2 This is a flowchart of another method for calculating the degree of matching between IoT perception data and a spatial model provided by an embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram of the structure of a device for calculating the degree of matching between IoT perception data and a spatial model provided by an embodiment of the present invention;

[0026] Figure 4 This is a structural diagram of an electronic device that implements the method for calculating the degree of matching between IoT perception data and a spatial model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] It is understandable that before using the technical solutions disclosed in the embodiments of the present invention, the type, scope of application, and usage scenarios of the personal information involved in the present invention should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0030] For example, in response to a user's active request, a prompt message is sent to the user to clearly inform the user that the operation requested will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the electronic device, application, server, storage medium, or other software or hardware that performs the operation of the technical solution of the present invention based on the prompt message.

[0031] As an optional but non-limiting implementation, in response to receiving a user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. Furthermore, the pop-up window may also contain a selection control for the user to select "agree" or "disagree" to provide personal information to the electronic device.

[0032] It is understandable that the above notification and user authorization process is merely illustrative and does not limit the implementation of the present invention. Other methods that comply with relevant laws and regulations may also be applied to the implementation of the present invention.

[0033] It is understandable that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) must comply with the requirements of relevant laws, regulations and relevant provisions.

[0034] One application scenario of this solution is that there are multiple IoT perception data and multiple spatial models. The matching degree between any IoT perception data and any spatial model is calculated to determine the matching relationship between the IoT perception data and the spatial model.

[0035] Figure 1This is a flow chart of a method for calculating the degree of matching between IoT perception data and a spatial model provided by an embodiment of the present invention. This embodiment is applicable to situations where the degree of matching between IoT perception data and a spatial model is calculated. This method can be executed by a device for calculating the degree of matching between IoT perception data and a spatial model. The device can be implemented in the form of hardware and / or software. The device can be configured in an electronic device for calculating the degree of matching between IoT perception data and a spatial model. Figure 1 As shown, the method includes:

[0036] S110: Determine first location information of IoT perception data and determine second location information of the spatial model.

[0037] The first position information and the second position information are located in the same coordinate system.

[0038] Specifically, IoT perception data can be collected through the IoT and sensors or perception terminals, and can be time-stamped and reflect the state of the perceived object. IoT perception data types include, but are not limited to, ambient environment data, operational status data, and structural deformation data. Ambient environment data can include monitoring data on the natural and safety environment of the perceived object, such as wind speed and direction, temperature and humidity, and the concentrations of combustible gases and pollutants. Operational status data can include state data on the accessibility, capacity, availability, and controllability of the perceived object during operation, such as pressure and energy consumption. Structural deformation data can include data on changes in deformation, movement, and speed during operation, such as tilt, subsidence, vibration, and stress. This solution can extract IoT perception data point coordinate information, accuracy, IoT perception data source, and name based on source data such as IoT perception data monitoring information ledgers and collection equipment ledgers. It then performs operations such as unique data identification and spatial and temporal reference conversion to the 2000 National Geodetic Coordinate System, achieving structured data processing and attribute mapping. The resulting output is an IoT perception data table that determines the primary location information of the IoT perception data. The first position information is spatial position coordinate information.

[0039] Spatial model data can be a digital expression of a physical space object, reflecting information such as the spatial position, geometric shape, texture and attributes of the object. Spatial model data types include but are not limited to geometric shape types and description object types. Among them, geometric shape types include but are not limited to point, line, surface and body. Description object types include but are not limited to equipment models, production line models, workshop models, factory models, building models, transportation facility models, water system models, vegetation models and site models. This solution can extract the source and name, precision, spatial model geometric type and object type of spatial model data based on source data such as CIM, BIM, modeling technology documents related to spatial model data, perform data unique identification, space-time benchmark conversion to the 2000 National Geodetic Coordinate System, spatial model monomerization and other operations, output spatial model data table, and associate with the spatial model to determine the second position information of the spatial model. The second position information is spatial position coordinate information.

[0040] S120: Determine the target sphere position information associated with the IoT perception data based on the first position information and the second position information.

[0041] Among them, this solution can determine the position information of a sphere with the first position information of the Internet of Things perception data as the center and the length determined according to specific rules as the radius based on the first position information and the second position information, that is, the target sphere position information associated with the Internet of Things perception data.

[0042] S130: Determine the spatial topological relationship between the target sphere position information and the spatial model.

[0043] The spatial topological relationship includes being separated or not separated.

[0044] Specifically, spatial topological relationships refer to topological invariants under topological transformations, which are used to describe spatial topological relationships between spatial entities, including containment, intersection, connection, overlap, coverage, and separation. Except for separation relationships, other relationships can be understood as inseparable.

[0045] S140: Determine a degree of matching between the IoT perception data and the spatial model based on the spatial topological relationship.

[0046] The matching degree is a measure of the conformity of the matching results obtained by constructing matching rules to match IoT sensor data with the spatial model. A higher matching degree indicates a more reliable match. This solution can determine the matching degree between IoT sensor data and the spatial model based on whether the spatial topology of the target sphere's position information and the spatial model are separated or inseparable.

[0047] The technical solution of the embodiment of the present invention determines the first position information of the IoT perception data and the second position information of the spatial model; the first position information and the second position information are located in the same coordinate system; the position information of the target sphere associated with the IoT perception data is determined based on the first position information and the second position information; the spatial topological relationship between the target sphere position information and the spatial model is determined; the spatial topological relationship includes being separated or not separated; and the matching degree between the IoT perception data and the spatial model is determined based on the spatial topological relationship. By implementing the technical solution provided by the embodiment of the present invention, a more accurate matching degree can be determined, which is conducive to determining a more reliable matching relationship and solves the problem of low reliability and unclear matching degree calculation in related technologies.

[0048] Figure 2 This is a flow chart of a method for calculating the degree of matching between IoT perception data and spatial models provided by an embodiment of the present invention. This embodiment is optimized based on the above embodiment. Figure 2 As shown, the method for calculating the matching degree between IoT perception data and spatial model in the embodiment of the present invention may include:

[0049] S210: Determine first location information of IoT perception data and determine second location information of the spatial model.

[0050] S220: Determine a target radius associated with the IoT perception data based on the first location information and the second location information.

[0051] Among them, this solution can determine the radius of the sphere associated with the Internet of Things perception data, that is, the target radius, with the first location information of the Internet of Things perception data as the center of the sphere based on the first location information and the second location information.

[0052] S230: Determine the target sphere position information according to the first position information and the target radius.

[0053] Among them, this solution can determine the target sphere position information with the first position information as the sphere center and the target radius as the sphere radius.

[0054] S240: Determine the spatial topological relationship between the target sphere position information and the spatial model.

[0055] S250: Determine a degree of matching between the IoT perception data and the spatial model based on the spatial topological relationship.

[0056] In this embodiment, optionally, determining a target radius associated with the Internet of Things perception data based on the first location information and the second location information includes: determining first precision information based on the first location information; determining second precision information based on the second location information; and determining the target radius associated with the Internet of Things perception data based on the sum of the first precision information and the second precision information.

[0057] This solution can determine the location accuracy of the IoT sensing data point based on the first location information, i.e., the first accuracy information, and the location accuracy of the spatial model based on the second location information, i.e., the second accuracy information. The sum of the first and second accuracy information is used as the target radius. This can reduce the impact of matching errors caused by accuracy.

[0058] For example, taking the fourth level of accuracy in the table of geometric accuracy levels of coordinate information corresponding to the first accuracy information and the second accuracy information (as shown in Table 1) as an example, the first accuracy information is 0.2m~0.5m, and the CIM4 level functional model is 1:200~1:500 or LOD1.0, that is, within 0.2m~0.5m, then the target radius = 0.5+0.5=1m.

[0059] Table 1

[0060]

[0061] In this embodiment, optionally, determining the spatial topological relationship between the target sphere position information and the spatial model includes: processing the target sphere position information and the second position information based on a nine-intersection model to obtain the spatial topological relationship between the target sphere position information and the spatial model.

[0062] Among them, this solution can use graphics algorithms such as computational geometry algorithms to describe the spatial topological relationship between the target sphere position information and the spatial model based on the nine-intersection model.

[0063] In this embodiment, optionally, determining the degree of matching between the IoT perception data and the spatial model based on the spatial topological relationship includes: if the spatial topological relationship is inseparable, the degree of matching between the IoT perception data and the spatial model is 100%.

[0064] If the position information of the target sphere centered on the IoT sensor data and the spatial model have an inseparable spatial topology, such as being contained within, intersecting, connecting, overlapping, or covering, the IoT sensor data and the spatial model have a reliable match, with a 100% match degree. This allows for accurate determination of the match degree and reliable match, providing support for automatic matching of IoT sensor data and spatial models.

[0065] In a feasible embodiment, optionally, determining the degree of matching between the IoT perception data and the spatial model based on the spatial topological relationship further includes: if the spatial topological relationship is separated, determining the spatial distance between the IoT perception data and the spatial model; and determining the degree of matching between the IoT perception data and the spatial model based on the spatial distance and the target radius.

[0066] If this solution determines the spatial topology relationship as separated, it means the target sphere is not connected to the spatial model. Therefore, the spatial distance between the IoT perception data and the spatial model must be determined. The matching degree between the IoT perception data and the spatial model is then determined based on the ratio of the target radius to this spatial distance. The larger the spatial distance, the lower the likelihood that the target sphere matches the spatial model. This method allows for accurate matching and reliable matching relationships, providing support for the automatic matching of IoT perception data and spatial models.

[0067] In another feasible implementation, optionally, determining the spatial distance between the Internet of Things perception data and the spatial model includes: taking the minimum Euclidean distance between the first position information and the second position information as the spatial distance between the Internet of Things perception data and the spatial model.

[0068] The process of determining the minimum Euclidean distance may refer to related technologies.

[0069] The technical solution of the embodiment of the present invention determines the first position information of the IoT perception data and determines the second position information of the spatial model; the first position information and the second position information are located in the same coordinate system; the target radius associated with the IoT perception data is determined based on the first position information and the second position information; the target sphere position information is determined based on the first position information and the target radius; the spatial topological relationship between the target sphere position information and the spatial model is determined; the spatial topological relationship includes being separated or not separated; and the matching degree between the IoT perception data and the spatial model is determined based on the spatial topological relationship. By implementing the technical solution provided by the embodiment of the present invention, a more accurate matching degree can be determined, which is conducive to determining a more reliable matching relationship and solves the problem of low reliability and unclear matching degree calculation in related technologies.

[0070] Figure 3 This is a schematic diagram of the structure of the device for calculating the degree of matching between IoT perception data and spatial model provided by an embodiment of the present invention. Figure 3 As shown, the device includes:

[0071] The location information determination module 310 is used to determine the first location information of the IoT sensing data and determine the second location information of the spatial model; the first location information and the second location information are located in the same coordinate system;

[0072] A sphere position information determination module 320 is configured to determine the target sphere position information associated with the IoT sensing data based on the first position information and the second position information;

[0073] A spatial topological relationship determination module 330 is configured to determine a spatial topological relationship between the target sphere position information and the spatial model; the spatial topological relationship may include being separated or not separated;

[0074] The matching degree determination module 340 is configured to determine the matching degree between the IoT perception data and the spatial model based on the spatial topological relationship.

[0075] Optionally, the sphere position information determination module 320 includes a target radius determination unit, which is used to determine the target radius associated with the Internet of Things perception data based on the first position information and the second position information; and a sphere position information determination unit, which is used to determine the target sphere position information based on the first position information and the target radius.

[0076] Optionally, the target radius determination unit is specifically used to determine first precision information based on the first location information; determine second precision information based on the second location information; and determine the target radius associated with the Internet of Things perception data based on the sum of the first precision information and the second precision information.

[0077] Optionally, the matching degree determination module 340 is specifically configured to determine that if the spatial topological relationship is inseparable, the matching degree between the IoT perception data and the spatial model is 100%.

[0078] Optionally, the matching degree determination module 340 is specifically used to determine the spatial distance between the IoT perception data and the spatial model if the spatial topological relationship is separated; and determine the matching degree between the IoT perception data and the spatial model based on the spatial distance and the target radius.

[0079] Optionally, the matching degree determination module 340 is specifically configured to use the minimum Euclidean distance between the first location information and the second location information as the spatial distance between the IoT perception data and the spatial model.

[0080] Optionally, a target radius determination module is specifically used to determine first precision information of the first location information based on the first location information; determine second precision information of the second location information based on the second location information; and determine the target radius of the Internet of Things perception data based on the sum of the first precision information and the second precision information.

[0081] Optionally, the spatial distance determination unit is specifically configured to use the minimum Euclidean distance between the first location information and the second location information as the spatial distance between the Internet of Things perception data and the spatial model.

[0082] The device for calculating the degree of matching between IoT perception data and spatial models provided in an embodiment of the present invention can execute the method for calculating the degree of matching between IoT perception data and spatial models provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0083] Figure 4 A schematic diagram of the structure of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. 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 processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0084] like Figure 4 As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., which is communicatively connected to the at least one processor 41. The memory stores a computer program that can be executed by the at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 into the random access memory (RAM) 43. Various programs and data required for the operation of the electronic device 40 can also be stored in the RAM 43. The processor 41, ROM 42, and RAM 43 are connected to each other via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0085] Multiple components in the electronic device 40 are connected to the I / O interface 45, including an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a magnetic disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0086] Processor 41 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 41 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, digital signal processors (DSPs), and any other suitable processors, controllers, microcontrollers, etc. Processor 41 executes the various methods and processes described above, such as the method for calculating the degree of match between IoT perception data and a spatial model.

[0087] In some embodiments, the method for calculating the degree of match between IoT perception data and a spatial model may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the method for calculating the degree of match between IoT perception data and a spatial model described above may be performed. Alternatively, in other embodiments, the processor 41 may be configured to execute the method for calculating the degree of match between IoT perception data and a spatial model by any other appropriate means (e.g., by means of firmware).

[0088] Various embodiments of the systems and techniques described 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), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0089] Computer programs 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, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0090] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0091] 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 can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the 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 input, voice input, or tactile input).

[0092] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, 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: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0093] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0094] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0095] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for calculating the matching degree between IoT perception data and spatial model, characterized in that: include: Determine first location information of the IoT sensing data and determine second location information of the spatial model; The first position information and the second position information are located in the same coordinate system; Determine the target sphere position information associated with the IoT sensing data based on the first position information and the second position information; Determining a spatial topological relationship between the target sphere position information and the spatial model; The spatial topological relationship includes being separated or not separated; A degree of matching between the IoT perception data and the spatial model is determined based on the spatial topological relationship.

2. The method according to claim 1, characterized in that Determining the target sphere position information associated with the IoT sensing data according to the first position information and the second position information includes: Determine a target radius associated with the IoT sensing data based on the first location information and the second location information; The target sphere position information is determined according to the first position information and the target radius.

3. The method according to claim 2, characterized in that Determining a target radius associated with the IoT sensing data according to the first location information and the second location information includes: determining first accuracy information according to the first position information; determining second accuracy information according to the second position information; Determine a target radius associated with the Internet of Things perception data according to the sum of the first precision information and the second precision information.

4. The method according to claim 3, characterized in that Determining the spatial topological relationship between the target sphere position information and the spatial model includes: The target sphere position information and the second position information are processed based on the nine-intersection model to obtain a spatial topological relationship between the target sphere position information and a spatial model.

5. The method according to claim 4, characterized in that Determining a degree of matching between the IoT perception data and the spatial model based on the spatial topological relationship includes: If the spatial topological relationship is inseparable, the matching degree between the IoT perception data and the spatial model is 100%.

6. The method according to claim 5, characterized in that Determining a matching degree between the IoT perception data and the spatial model based on the spatial topological relationship further includes: If the spatial topological relationship is separated, determining the spatial distance between the IoT sensing data and the spatial model; A degree of matching between the IoT perception data and the spatial model is determined based on the spatial distance and the target radius.

7. The method according to claim 6, wherein determining the spatial distance between the IoT sensing data and the spatial model comprises: The minimum Euclidean distance between the first location information and the second location information is used as the spatial distance between the Internet of Things perception data and the spatial model.

8. A device for calculating the matching degree between IoT perception data and spatial model, characterized in that: include: a location information determination module, configured to determine first location information of IoT perception data and second location information of the spatial model; The first position information and the second position information are located in the same coordinate system; a sphere position information determination module, configured to determine the target sphere position information associated with the IoT sensing data based on the first position information and the second position information; A spatial topological relationship determination module, configured to determine the spatial topological relationship between the target sphere position information and the spatial model; The spatial topological relationship includes being separated or not separated; A matching degree determination module is used to determine the matching degree between the IoT perception data and the spatial model based on the spatial topological relationship.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected 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 so that the at least one processor can execute the method for calculating the degree of matching between IoT perception data and a spatial model according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for calculating the degree of matching between IoT perception data and a spatial model according to any one of claims 1 to 7 when executed.

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