A method and system for dynamically adjusting a service area of an internet of things device and a terminal

Abstract

This invention discloses a method, system, and terminal for dynamically adjusting the service area of ​​IoT devices. The method includes: acquiring data corresponding to all IoT devices within the service area; preprocessing the data to obtain target data; generating and merging buffers for each IoT device based on the target data to obtain a joint service coverage area; performing hotspot analysis on the service area to identify hotspot areas and performing path analysis to obtain service performance; determining whether there are service blind spots or overlapping coverage areas within the joint service coverage area or hotspot areas, and determining whether the service performance meets preset requirements. If service blind spots or overlapping coverage areas exist, or if the service performance does not meet preset requirements, then the IoT devices in the service area are dynamically adjusted based on the target data using an optimization algorithm. This invention continuously optimizes the service area of ​​the devices during operation, improving the system's response speed and service quality.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) data analysis technology, and in particular to a method, system, terminal, and computer-readable storage medium for dynamically adjusting the service area of ​​an IoT device. Background Technology

[0002] The Internet of Things (IoT) integrates previously isolated terminals into a network system, creating numerous new application and business models. It also connects various physical devices, sensors, vehicles, and more via the internet, aggregating data and providing entirely new application scenarios and business opportunities. By deploying sensors and monitoring equipment, the IoT enables data collection, transmission, storage, processing, and analysis, bringing greater convenience and efficiency to people's lives and work.

[0003] Dynamic adjustment technology, based on real-time data collection and processing, can continuously optimize the service area of ​​devices during operation, improving system response speed and service quality. However, currently, the division of service areas between devices in the Internet of Things (IoT) is fixed. In different scenarios, dynamic adjustment of services cannot be achieved, and effective service coverage and optimized resource utilization cannot be ensured.

[0004] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0005] The main objective of this invention is to provide a method, system, terminal, and computer-readable storage medium for dynamically adjusting the service area of ​​Internet of Things (IoT) devices. This invention aims to solve the problems in the prior art where the division of service areas between various IoT devices is fixed, lacks dynamic adjustment schemes, cannot ensure effective service coverage, and has low resource utilization.

[0006] To achieve the above objectives, the present invention provides a method for dynamically adjusting the service area of ​​an Internet of Things (IoT) device, the method comprising the following steps:

[0007] Data corresponding to all IoT devices within the service area is acquired, and the data is denoised and standardized to obtain target data.

[0008] A buffer corresponding to each IoT device is generated based on the target data, and multiple buffers are merged to obtain a joint service coverage area;

[0009] Hotspot analysis is performed on the service area to identify hotspot areas, and path analysis is performed on the service area to obtain the service effect under different traffic conditions;

[0010] Determine whether there are service blind spots or overlapping coverage areas in the joint service coverage area or the hotspot area, and determine whether the service effect meets the preset requirements. If there are service blind spots or overlapping coverage areas, or if the service effect does not meet the preset requirements, then dynamically adjust the IoT devices in the service area according to the target data through an optimization algorithm.

[0011] Optionally, in the method for dynamically adjusting the service area of ​​the Internet of Things device, the data includes sensor data and network data;

[0012] The sensor data includes: device function, device type, application scenario, device status parameters, and environmental data;

[0013] The network data includes: floor, room number, longitude, latitude, and device movement trajectory information.

[0014] Optionally, the method for dynamically adjusting the service area of ​​IoT devices, wherein generating a buffer corresponding to each IoT device based on the target data specifically includes:

[0015] The service radius of each IoT device is determined based on the device function and the device type, and the coordinates of each IoT device are determined based on the network data.

[0016] The shape of the buffer model is obtained according to the application scenario. If the application scenario is a planar layout, a circular buffer is generated with the coordinates as the center and the service radius as the radius of the circle.

[0017] If the application scenario is a three-dimensional layout, a spherical buffer is generated with the coordinates as the center of the sphere and the service radius as the radius of the sphere.

[0018] Optionally, the method for dynamically adjusting the service area of ​​the IoT device, wherein performing hotspot analysis on the service area to identify hotspot areas and performing path analysis on the service area to obtain the service effect under different traffic conditions, specifically includes:

[0019] The hotspot influencing factors of the service area are obtained, and based on the hotspot influencing factors, an independent hotspot analysis is performed on the service area using a clustering algorithm to obtain hotspot areas;

[0020] The traffic network data of the service area is acquired, and the traffic network data is evaluated using the shortest path algorithm and traffic flow analysis method to obtain the service effect under different traffic conditions.

[0021] Optionally, the method for dynamically adjusting the service area of ​​IoT devices, wherein the step of dynamically adjusting the IoT devices in the service area based on the target data and using an optimization algorithm, further includes:

[0022] The analysis results are generated by analyzing the coverage area of ​​the joint service, the hotspot areas, and the service performance.

[0023] The analysis results are visualized in the form of charts or maps to facilitate decision-making and management by managers.

[0024] Optionally, the method for dynamically adjusting the service area of ​​IoT devices, wherein if there are service blind spots or overlapping coverage areas, the method dynamically adjusts the IoT devices in the service area based on the target data and through an optimization algorithm, specifically includes:

[0025] If the joint service coverage area has service blind spots or overlapping coverage areas, then the joint service coverage area is defined as the optimization target. If the hotspot area has service blind spots or overlapping coverage areas... ， The hotspot region is then defined as the optimization target;

[0026] Using device movement restrictions and service radius restrictions as constraints, a genetic algorithm or particle swarm optimization algorithm is used to calculate the optimal service range and optimal location of each IoT device of the optimization target based on the target data, and the service radius and location of each IoT device of the optimization target are adjusted according to the optimal service range and optimal location.

[0027] Optionally, the method for dynamically adjusting the service area of ​​IoT devices, wherein if the service effect does not meet the preset requirements, the method for dynamically adjusting the IoT devices in the service area based on the target data and through an optimization algorithm, specifically includes:

[0028] If the number of service requests does not reach the first preset threshold, or the service response time does not reach the second preset threshold, then a genetic algorithm or a particle swarm optimization algorithm is used to calculate the optimal service range and optimal location of each IoT device in the service area based on the target data.

[0029] The service radius and location of each IoT device in the service area are adjusted according to the optimal service range and optimal location.

[0030] Furthermore, to achieve the above objectives, the present invention also provides a dynamic adjustment system for the service area of ​​an Internet of Things (IoT) device, wherein the dynamic adjustment system for the service area of ​​an IoT device includes:

[0031] The data acquisition module is used to acquire data corresponding to all IoT devices within the service area. The data includes sensor data and network data. The data is then denoised and standardized to obtain target data.

[0032] The joint region generation module is used to generate a buffer corresponding to each of the IoT devices based on the target data, and merge multiple buffers to obtain a joint service coverage area;

[0033] The hotspot area generation module is used to perform hotspot analysis on the service area, identify hotspot areas, and perform path analysis on the service area to obtain the service effect under different traffic conditions.

[0034] The regional dynamic adjustment module is used to determine whether there are service blind spots or overlapping coverage areas in the joint service coverage area or the hotspot area, and to determine whether the service effect meets the preset requirements. If there are service blind spots or overlapping coverage areas, or if the service effect does not meet the preset requirements, the IoT devices in the service area are dynamically adjusted according to the target data through an optimization algorithm.

[0035] In addition, to achieve the above objectives, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and an IoT device service area dynamic adjustment program stored in the memory and executable on the processor, wherein when the IoT device service area dynamic adjustment program is executed by the processor, it implements the steps of the IoT device service area dynamic adjustment method as described above.

[0036] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a dynamic adjustment program for the service area of ​​an Internet of Things (IoT) device, and when the dynamic adjustment program for the service area of ​​an IoT device is executed by a processor, it implements the steps of the dynamic adjustment method for the service area of ​​an IoT device as described above.

[0037] In this invention, data corresponding to all IoT devices within a service area is acquired, and the data is preprocessed to obtain target data. A buffer corresponding to each IoT device is generated based on the target data and merged to obtain a joint service coverage area. Hotspot analysis is performed on the service area to identify hotspot areas, and path analysis is performed to obtain service effectiveness. It is determined whether there are service blind spots or overlapping coverage areas within the joint service coverage area or hotspot areas, and whether the service effectiveness meets preset requirements. If service blind spots or overlapping coverage areas exist, or if the service effectiveness does not meet preset requirements, the IoT devices in the service area are dynamically adjusted based on the target data using an optimization algorithm. This invention, based on real-time data acquisition and processing, continuously optimizes the service area of ​​the devices during operation, improving the system's response speed and service quality. Attached Figure Description

[0038] Figure 1 This is a flowchart of a preferred embodiment of the method for dynamically adjusting the service area of ​​IoT devices according to the present invention;

[0039] Figure 2 This is a structural diagram of a preferred embodiment of the dynamic adjustment system for the service area of ​​IoT devices of the present invention;

[0040] Figure 3 This is a schematic diagram of the operating environment of a preferred embodiment of the terminal of the present invention. Detailed Implementation

[0041] This application provides a method and related equipment for dynamically adjusting the service area of ​​an Internet of Things (IoT) device. To make the purpose, technical solution, and effects of this application clearer and more explicit, the following detailed description is provided with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining this application and are not intended to limit this application.

[0042] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0043] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0044] The preferred embodiment of the present invention describes a method for dynamically adjusting the service area of ​​an IoT device, such as... Figure 1 As shown, the method for dynamically adjusting the service area of ​​the IoT device includes the following steps:

[0045] Step S10: Obtain data corresponding to all IoT devices within the service area, and perform noise reduction and standardization processing on the data to obtain target data.

[0046] Specifically, the data includes sensor data and network data; the sensor data includes: device function, device type, application scenario, device status parameters, and environmental data; the network data includes: floor, room number, longitude, latitude, and device movement trajectory information.

[0047] The sensor data is collected by IoT sensors and uploaded via network protocols (such as MQTT, HTTP / HTTPS). The network data is obtained in real time by network monitoring equipment and is transmitted and processed in a timely manner via real-time data transmission protocols (such as WebSocket).

[0048] Floor and room number data usually come from Building Information Modeling (BIM) systems or Geographic Information System (GIS) data providers.

[0049] Furthermore, the data is denoised and standardized. Specifically, the collected data is first denoised to filter out invalid data and noise, resulting in denoised data. Then, the denoised data from different sources (from different IoT devices) is standardized to ensure data consistency and comparability.

[0050] Understandably, standardization refers to unifying data from different units of different sensors, specifically including:

[0051] Unit Conversion: Convert data from different units to a unified standard unit. For example, convert Fahrenheit temperature to Celsius temperature. Data Format Standardization: Ensure all sensor data follows the same format, such as timestamps, device IDs, and data values. Coordinate System 1: Convert all location information to a unified spatial coordinate system, such as the WGS84 geographic coordinate system, for easier subsequent processing and analysis.

[0052] Step S20: Generate a buffer corresponding to each IoT device based on the target data, and merge multiple buffers to obtain a joint service coverage area.

[0053] In this embodiment, the buffer corresponding to each IoT device is calculated through geometric operations. The service coverage of the corresponding IoT device can be determined through the buffer. Buffer analysis is usually used to assess the scope of influence or service radius.

[0054] Specifically, the service radius of each IoT device is determined based on the device function and the device type, and the coordinates of each IoT device are determined based on the network data.

[0055] Understandably, different devices have different service radii, depending on the type and function of the device. Factors affecting the service radius include: Device function: For example, the service radius of a Wi-Fi router depends on signal strength and antenna configuration, while the service radius of an environmental sensor depends on the sensor's sensing range. Environmental conditions: Physical obstacles, building structures, and other environmental factors also affect the effective service radius of the device. Device configuration: The service radius of the device can be adjusted according to actual needs to optimize coverage and resource utilization.

[0056] Furthermore, based on the application scenario, the shape of the buffer model is obtained (the application scenario is obtained from the floors in the network data; the role of the floors is to define different service planes, ensuring that the service area is within a specific floor; the floors provide height information in three-dimensional space, so that the service area covers a specific floor or multiple floors). If the application scenario is a planar layout, a circular buffer is generated with the coordinates as the center and the service radius as the radius of the circle; if the application scenario is a three-dimensional layout, a spherical buffer is generated with the coordinates as the center and the service radius as the radius of the sphere.

[0057] Understandably, when defining the service area, the specific shape of the buffer model depends on the application scenario. For planar layout scenarios (two-dimensional space), such as the device service area within a specific floor, a circular buffer model is used; for multi-story buildings or three-dimensional layout scenarios (three-dimensional space), a spherical buffer model is used. When generating a circular or spherical buffer, the longitude and latitude in the network data determine the center coordinates of the sphere or circle. In two-dimensional space, a circular buffer is generated using the service radius with the device coordinates as the center; in three-dimensional space, a spherical buffer is generated using the service radius with the device coordinates as the center. The generated buffers are then stored for subsequent service area analysis.

[0058] Furthermore, the multiple buffers are merged to obtain a unified service coverage area. Specifically, multiple buffer polygons are merged to generate a unified service coverage area. The polygon merging is achieved through the union operation in geometric operations. The merging operation is based on Boolean operations, which unites multiple polygons to form a complete unified service coverage area.

[0059] Step S30: Perform hotspot analysis on the service area to identify hotspot areas, and perform path analysis on the service area to obtain the service effect under different traffic conditions.

[0060] Specifically, hotspot influencing factors of the service area are obtained, and based on the hotspot influencing factors, independent hotspot analysis is performed on the service area using a clustering algorithm to obtain hotspot areas.

[0061] Understandably, hotspot analysis typically involves statistical and cluster analysis of spatial data to identify high-density or high-demand areas. For example, analyzing historical data can determine which areas have the highest service demand, allowing for adjustments to equipment deployment or service coverage. Hotspot areas are usually identified through independent hotspot analysis and applied in multiple scenarios, including optimizing resource allocation and service deployment.

[0062] It should be noted that, depending on the actual situation, hotspot analysis can also be based on the joint service coverage area (for example, identifying high-demand hotspot areas within the joint service coverage area). Hotspot areas can be obtained independently of the joint service coverage area analysis through direct analysis of raw data (such as device location, user demand, traffic data, etc.).

[0063] It's important to note that identifying hotspot areas serves several purposes: Optimizing service coverage: Identifying areas with concentrated equipment allows for optimized equipment deployment and service coverage, avoiding resource waste and service blind spots. Achieving load balancing: Adding equipment or adjusting the service radius of existing equipment in high-density areas helps achieve load balancing and prevents individual devices from overloading. Improving service quality: By identifying high-demand areas, services in these areas can be prioritized for optimization, ensuring service quality and response speed. Therefore, the results of high-density area identification can be used to adjust equipment deployment and optimize service strategies, improving the overall system's service efficiency and quality.

[0064] Furthermore, traffic network data of the service area is acquired, and the traffic network data is evaluated using the shortest path algorithm and traffic flow analysis method to obtain the service effect under different traffic conditions.

[0065] Network analysis is typically used to evaluate the shortest or optimal path from one point to another within a traffic or service network. For example, in the service area of ​​a super device, network analysis can be used to determine the optimal route for communication between devices or for resource allocation. In this embodiment, network analysis is used to evaluate the impact of the traffic network on the service area, assessing service effectiveness under different traffic conditions using shortest path algorithms and traffic analysis methods.

[0066] The shortest path, in a transportation network, refers to the path with the shortest distance or time from one node (device or user location) to another. The specific steps of the shortest path algorithm include: Input data: Reading transportation network data, including node and edge information. Each node represents a location, and each edge represents a connection between two nodes, with weights (such as distance or time) on the edges. Initialization: Selecting a starting node, setting its distance to 0, and setting the distances of all other nodes to infinity, marking all nodes as unvisited. Node selection: Selecting the node with the smallest current distance from the unvisited nodes as the current node. Distance update: For each neighbor node of the current node, calculating the distance from the starting node through the current node to the neighbor node; if this distance is less than the current distance of the neighbor node, updating the neighbor node's distance. Marking visited: Marking the current node as visited. Node selection continues until all nodes have been visited, or the shortest path to the target node is determined. Output: The shortest path to the target node and its corresponding distance.

[0067] Step S40: Determine whether there are service blind spots or overlapping coverage areas in the joint service coverage area or the hotspot area, and determine whether the service effect meets the preset requirements. If there are service blind spots or overlapping coverage areas, or if the service effect does not meet the preset requirements, then dynamically adjust the IoT devices in the service area according to the target data through an optimization algorithm.

[0068] Specifically, it is determined whether there are service blind spots or overlapping coverage areas in the joint service coverage area or the hotspot area, and whether the service effect meets the preset requirements. If service blind spots or overlapping coverage areas exist, or if the service effect does not meet the preset requirements, the IoT devices in the service area need to be dynamically adjusted using a service range optimization algorithm. The service range optimization algorithm analyzes the service coverage and current needs of the devices using real-time and historical data, and dynamically adjusts the service range of the devices, such as adjusting the service radius and the location of mobile devices. This algorithm aims to optimize the service area of ​​the devices to maximize the coverage of the target area while ensuring efficient use of resources.

[0069] In this embodiment, if there are service blind spots or overlapping coverage areas in the joint service coverage area or the hotspot area, the joint service coverage area or the hotspot area is defined as the optimization target, with the aim of maximizing service coverage, minimizing resource waste, and balancing the load.

[0070] Using device movement restrictions and service radius restrictions as constraints, a genetic algorithm or particle swarm optimization algorithm is used to calculate the optimal service range and optimal location of each IoT device of the optimization target based on the target data, and the service radius and location of each IoT device of the optimization target are adjusted according to the optimal service range and optimal location.

[0071] Furthermore, if there are no service blind spots or overlapping coverage areas in the joint service coverage area or the hotspot area, but the number of service requests has not reached the first preset threshold, or the service response time has not reached the second preset threshold, then a genetic algorithm or a particle swarm optimization algorithm is used to calculate the optimal service range and optimal location of each IoT device in the service area based on the target data.

[0072] The service radius and location of each IoT device in the service area are adjusted based on the optimal service range and optimal location. Simultaneously, the adjusted service coverage area is recalculated based on the service radius and location, and updated to the management platform or system.

[0073] Furthermore, the step of dynamically adjusting the IoT devices in the service area based on the target data using an optimization algorithm also includes:

[0074] The analysis results are generated by analyzing the coverage area, hotspot areas, and service effectiveness of the joint service. The analysis results are then visualized in the form of charts or maps for management personnel to make decisions and manage the system.

[0075] It is understandable that the joint service coverage area, the hotspot area, and the service effect are analyzed and statistically analyzed to calculate indicators such as the number of people covered and traffic flow. Based on these indicators, analysis results are generated and visualized using visualization tools to facilitate decision-making and management. After obtaining the visualization results in the form of charts or maps, the visualization results are displayed on the management platform to facilitate evaluation and adjustment by decision-makers or managers.

[0076] As can be seen, this invention dynamically adjusts the service range of the equipment through real-time monitoring and data analysis to ensure effective service coverage and optimize resource utilization; and generates joint service coverage areas and hotspot areas (high-density areas) based on real-time data. By adjusting equipment deployment and optimizing service strategies, the joint service coverage areas and hotspot areas can be optimized, thereby improving the overall system's service efficiency and quality. It can continuously optimize the service area of ​​the equipment during operation, thereby improving the system's response speed and service quality.

[0077] Furthermore, such as Figure 2 As shown, based on the above-described method for dynamically adjusting the service area of ​​IoT devices, the present invention also provides a system for dynamically adjusting the service area of ​​IoT devices, wherein the system includes:

[0078] Data acquisition module 41 is used to acquire data corresponding to all IoT devices within the service area, and to perform noise reduction and standardization processing on the data to obtain target data;

[0079] The joint region generation module 42 is used to generate a buffer corresponding to each IoT device based on the target data, and merge multiple buffers to obtain a joint service coverage area;

[0080] Hotspot area generation module 43 is used to perform hotspot analysis on the service area, identify hotspot areas, and perform path analysis on the service area to obtain service effects under different traffic conditions.

[0081] The regional dynamic adjustment module 44 is used to determine whether there are service blind spots or overlapping coverage areas in the joint service coverage area or the hotspot area, and to determine whether the service effect meets the preset requirements. If there are service blind spots or overlapping coverage areas, or if the service effect does not meet the preset requirements, the IoT devices in the service area are dynamically adjusted according to the target data through an optimization algorithm.

[0082] Furthermore, such as Figure 3 As shown, based on the above-mentioned method and system for dynamically adjusting the service area of ​​IoT devices, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 3 Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0083] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard drive or memory. In other embodiments, the memory 20 may be an external storage device of the terminal, such as a plug-in hard drive, smart media card (SMC), secure digital card (SD), flash card, etc. Further, the memory 20 may include both internal and external storage devices. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code installed on the terminal. The memory 20 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 20 stores a dynamic adjustment program 40 for the service area of ​​an IoT device, which can be executed by the processor 10 to implement the dynamic adjustment method for the service area of ​​an IoT device in this application.

[0084] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in the memory 20 or process data, such as executing a dynamic adjustment method for the service area of ​​the Internet of Things device.

[0085] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 30 is used to display information on the terminal and to display a visual user interface. The components 10-30 of the terminal communicate with each other via a system bus.

[0086] In one embodiment, when the processor 10 executes the dynamic adjustment program 40 for the IoT device service area in the memory 20, the following steps are performed:

[0087] Data corresponding to all IoT devices within the service area is acquired, and the data is denoised and standardized to obtain target data.

[0088] A buffer corresponding to each IoT device is generated based on the target data, and multiple buffers are merged to obtain a joint service coverage area;

[0089] Hotspot analysis is performed on the service area to identify hotspot areas, and path analysis is performed on the service area to obtain the service effect under different traffic conditions;

[0090] Determine whether there are service blind spots or overlapping coverage areas in the joint service coverage area or the hotspot area, and determine whether the service effect meets the preset requirements. If there are service blind spots or overlapping coverage areas, or if the service effect does not meet the preset requirements, then dynamically adjust the IoT devices in the service area according to the target data through an optimization algorithm.

[0091] The data includes sensor data and network data;

[0092] The sensor data includes: device function, device type, application scenario, device status parameters, and environmental data;

[0093] The network data includes: floor, room number, longitude, latitude, and device movement trajectory information.

[0094] Specifically, generating a buffer corresponding to each IoT device based on the target data includes:

[0095] The service radius of each IoT device is determined based on the device function and the device type, and the coordinates of each IoT device are determined based on the network data.

[0096] The shape of the buffer model is obtained according to the application scenario. If the application scenario is a planar layout, a circular buffer is generated with the coordinates as the center and the service radius as the radius of the circle.

[0097] If the application scenario is a three-dimensional layout, a spherical buffer is generated with the coordinates as the center of the sphere and the service radius as the radius of the sphere.

[0098] Specifically, the process of performing hotspot analysis on the service area to identify hotspot areas and performing path analysis on the service area to obtain service effectiveness under different traffic conditions includes:

[0099] The hotspot influencing factors of the service area are obtained, and based on the hotspot influencing factors, an independent hotspot analysis is performed on the service area using a clustering algorithm to obtain hotspot areas;

[0100] The traffic network data of the service area is acquired, and the traffic network data is evaluated using the shortest path algorithm and traffic flow analysis method to obtain the service effect under different traffic conditions.

[0101] The step of dynamically adjusting the IoT devices in the service area based on the target data using an optimization algorithm further includes:

[0102] The analysis results are generated by analyzing the coverage area of ​​the joint service, the hotspot areas, and the service performance.

[0103] The analysis results are visualized in the form of charts or maps to facilitate decision-making and management by managers.

[0104] Wherein, if service blind spots or overlapping coverage areas exist, the IoT devices in the service area are dynamically adjusted based on the target data using an optimization algorithm, specifically including:

[0105] If the joint service coverage area has service blind spots or overlapping coverage areas, then the joint service coverage area is defined as the optimization target. If the hotspot area has service blind spots or overlapping coverage areas... ， The hotspot region is then defined as the optimization target;

[0106] Using device movement restrictions and service radius restrictions as constraints, a genetic algorithm or particle swarm optimization algorithm is used to calculate the optimal service range and optimal location of each IoT device of the optimization target based on the target data, and the service radius and location of each IoT device of the optimization target are adjusted according to the optimal service range and optimal location.

[0107] Wherein, if the service effect does not meet the preset requirements, the IoT devices in the service area are dynamically adjusted according to the target data through an optimization algorithm, specifically including:

[0108] If the number of service requests does not reach the first preset threshold, or the service response time does not reach the second preset threshold, then a genetic algorithm or a particle swarm optimization algorithm is used to calculate the optimal service range and optimal location of each IoT device in the service area based on the target data.

[0109] The service radius and location of each IoT device in the service area are adjusted according to the optimal service range and optimal location.

[0110] In summary, this invention provides a method, system, and terminal for dynamically adjusting the service area of ​​IoT devices. The method includes: acquiring data corresponding to all IoT devices within the service area; performing noise reduction and standardization on the data to obtain target data; generating a buffer corresponding to each IoT device based on the target data; merging multiple buffers to obtain a joint service coverage area; performing hotspot analysis on the service area to identify hotspot areas; and performing path analysis on the service area to obtain service effects under different traffic conditions; determining whether there are service blind spots or overlapping coverage areas in the joint service coverage area or the hotspot areas, and determining whether the service effect meets preset requirements. If service blind spots or overlapping coverage areas exist, or if the service effect does not meet preset requirements, then based on the target data, the IoT devices in the service area are dynamically adjusted using an optimization algorithm. This invention can continuously optimize the service area of ​​devices during operation, improving the system's response speed and service quality.

[0111] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.

[0112] Of course, those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0113] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for dynamically adjusting the service area of ​​an Internet of Things (IoT) device, characterized in that, The method for dynamically adjusting the service area of ​​IoT devices includes: Data corresponding to all IoT devices within the service area is acquired, and the data is denoised and standardized to obtain target data. The data includes sensor data and network data; The sensor data includes: device function, device type, application scenario, device status parameters, and environmental data; The network data includes: floor, room number, longitude, latitude, and device movement trajectory information; A buffer corresponding to each IoT device is generated based on the target data, and multiple buffers are merged to obtain a joint service coverage area; The step of generating a buffer corresponding to each IoT device based on the target data specifically includes: The service radius of each IoT device is determined based on the device function and the device type, and the coordinates of each IoT device are determined based on the network data. The shape of the buffer model is obtained according to the application scenario. If the application scenario is a planar layout, a circular buffer is generated with the coordinates as the center and the service radius as the radius of the circle. If the application scenario is a three-dimensional layout, then a spherical buffer is generated with the coordinates as the center of the sphere and the service radius as the radius of the sphere; The application scenario is derived from the floors in the network data. The role of the floors is to define different service planes, ensuring that the service area is within a specific floor. The floors provide height information in three-dimensional space, so that the service area covers a specific floor or multiple floors. Hotspot analysis is performed on the service area to identify hotspot areas, and path analysis is performed on the service area to obtain the service effect under different traffic conditions; Determine whether there are service blind spots or overlapping coverage areas in the joint service coverage area or the hotspot area, and determine whether the service effect meets the preset requirements. If there are service blind spots or overlapping coverage areas, or if the service effect does not meet the preset requirements, then dynamically adjust the IoT devices in the service area according to the target data through an optimization algorithm.

2. The method for dynamically adjusting the service area of ​​an IoT device according to claim 1, characterized in that, The process of performing hotspot analysis on the service area to identify hotspot areas, and performing path analysis on the service area to obtain service effectiveness under different traffic conditions, specifically includes: The hotspot influencing factors of the service area are obtained, and based on the hotspot influencing factors, an independent hotspot analysis is performed on the service area using a clustering algorithm to obtain hotspot areas; The traffic network data of the service area is acquired, and the traffic network data is evaluated using the shortest path algorithm and traffic flow analysis method to obtain the service effect under different traffic conditions.

3. The method for dynamically adjusting the service area of ​​an IoT device according to claim 1, characterized in that, The step of dynamically adjusting the IoT devices in the service area based on the target data and using an optimization algorithm also includes: The analysis results are generated by analyzing the coverage area of ​​the joint service, the hotspot areas, and the service performance. The analysis results are visualized in the form of charts or maps to facilitate decision-making and management by managers.

4. The method for dynamically adjusting the service area of ​​an IoT device according to claim 1, characterized in that, If service blind spots or overlapping coverage areas exist, then based on the target data, the IoT devices in the service area are dynamically adjusted using an optimization algorithm, specifically including: If the joint service coverage area has service blind spots or overlapping coverage areas, then the joint service coverage area is defined as the optimization target; if the hotspot area has service blind spots or overlapping coverage areas, then the hotspot area is defined as the optimization target. Using device movement restrictions and service radius restrictions as constraints, a genetic algorithm or particle swarm optimization algorithm is used to calculate the optimal service range and optimal location of each IoT device of the optimization target based on the target data, and the service radius and location of each IoT device of the optimization target are adjusted according to the optimal service range and optimal location.

5. The method for dynamically adjusting the service area of ​​an IoT device according to claim 1, characterized in that, If the service effect does not meet the preset requirements, then based on the target data, the IoT devices in the service area are dynamically adjusted using an optimization algorithm, specifically including: If the number of service requests does not reach the first preset threshold, or the service response time does not reach the second preset threshold, then a genetic algorithm or a particle swarm optimization algorithm is used to calculate the optimal service range and optimal location of each IoT device in the service area based on the target data. The service radius and location of each IoT device in the service area are adjusted according to the optimal service range and optimal location.

6. A dynamic adjustment system for the service area of ​​an Internet of Things (IoT) device, characterized in that, The dynamic adjustment system for the service area of ​​IoT devices is used to implement the dynamic adjustment method for the service area of ​​IoT devices according to any one of claims 1-5, wherein the dynamic adjustment system for the service area of ​​IoT devices includes: The data acquisition module is used to acquire data corresponding to all IoT devices within the service area, and to perform noise reduction and standardization processing on the data to obtain target data; The joint region generation module is used to generate a buffer corresponding to each of the IoT devices based on the target data, and merge multiple buffers to obtain a joint service coverage area; The hotspot area generation module is used to perform hotspot analysis on the service area, identify hotspot areas, and perform path analysis on the service area to obtain the service effect under different traffic conditions. The regional dynamic adjustment module is used to determine whether there are service blind spots or overlapping coverage areas in the joint service coverage area or the hotspot area, and to determine whether the service effect meets the preset requirements. If there are service blind spots or overlapping coverage areas, or if the service effect does not meet the preset requirements, the IoT devices in the service area are dynamically adjusted according to the target data through an optimization algorithm.

7. A terminal, characterized in that, The terminal includes: a memory, a processor, and a dynamic adjustment program for the service area of ​​an Internet of Things (IoT) device stored in the memory and executable on the processor. When the processor executes the dynamic adjustment program for the service area of ​​an IoT device, it implements the steps of the dynamic adjustment method for the service area of ​​an IoT device as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a dynamic adjustment program for the service area of ​​an Internet of Things (IoT) device, which, when executed by a processor, implements the steps of the dynamic adjustment method for the service area of ​​an IoT device as described in any one of claims 1-5.

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