Route selection method and device for Internet of Things platform data and electronic equipment

Abstract

The invention relates to the technical field of electronic equipment, in particular to an Internet of Things platform data routing method and device and electronic equipment. The method comprises the following steps: acquiring a current network state of to-be-accessed side end equipment in an Internet of Things platform, topological structure information of the Internet of Things platform, and current network performance data and current resource utilization data of each service node in the Internet of Things platform; calculating the comprehensive weight of each service node according to the current network state, the current network performance data, the current resource utilization data and the topological structure information; sorting all service nodes in the Internet of Things platform according to the comprehensive weight, and obtaining a target service node according to a sorting result; and the address of the target service node is sent to the to-be-accessed edge equipment, so that the to-be-accessed edge equipment is accessed to the Internet of Things platform according to the target service node, and the problems of data bypassing, uneven load, access congestion and the like caused by traditional static configuration are effectively avoided.

Description

Technical Field

[0001] This invention relates to the field of electronic equipment technology, and more specifically to a method, apparatus, and electronic equipment for routing data on an Internet of Things (IoT) platform. Background Technology

[0002] In existing IoT architectures, edge devices typically employ static configuration, pre-setting the address of the service node and consistently reporting data to that node. While this approach is simple to implement and suitable for small-scale, stable network environments, it has numerous limitations in practical applications. As the scale of device deployment expands and application scenarios become more complex, static configuration is no longer sufficient to meet the efficiency, reliability, and security requirements of modern IoT.

[0003] First, when devices are located in different geographical locations due to movement or changes in the network environment, the original fixed node address may cause data to be routed, increasing network latency and reducing communication efficiency. For example, if a mobile vehicle device is still connected to the original service node at a distant location, it not only wastes bandwidth but may also result in data loss due to network instability. Second, because all devices are hard-bound to specific nodes, some nodes are prone to overload while other nodes are idle, creating a serious load imbalance problem. This can lead to slow response or even crashes of critical nodes, affecting the stable operation of the entire system.

[0004] Furthermore, static configuration has significant shortcomings in terms of security. Once the authentication information and communication addresses between devices and service nodes are fixed, it is difficult to dynamically adjust them according to new security threats. If a node is attacked or its keys are leaked, the system cannot automatically switch or isolate the risk, which can easily lead to security incidents such as data breaches or man-in-the-middle attacks. At the same time, the matching between devices and nodes lacks intelligent judgment and cannot select the optimal path based on real-time network quality, processing capacity, and business needs, resulting in low data transmission efficiency and difficulty in guaranteeing service quality.

[0005] A more prominent problem is that static configuration heavily relies on manual intervention. Every time a new device is added, a node is replaced, or a system upgrade is performed, maintenance personnel must manually modify the configuration, which is not only time-consuming and labor-intensive but also prone to errors, significantly increasing system maintenance costs and management complexity. Especially in large-scale deployment scenarios, this model severely restricts the system's scalability and flexibility. Therefore, modern IoT is gradually shifting towards a dynamic and intelligent architecture, using mechanisms such as service discovery, load balancing, automatic configuration, and dynamic updates of security policies to achieve adaptive matching between devices and service nodes, thereby improving the overall system's responsiveness, security, and operational efficiency. Summary of the Invention

[0006] This invention provides a method, apparatus, and electronic device for routing data on an Internet of Things (IoT) platform, in order to solve problems such as low communication efficiency, unbalanced load, poor security, and high operation and maintenance costs caused by traditional static configuration.

[0007] In a first aspect, the present invention provides a method for routing data on an Internet of Things (IoT) platform. The method includes the following steps: obtaining the current network status of the edge device to be connected in the IoT platform, the topology information of the IoT platform, and the current network performance data and current resource utilization data of each service node in the IoT platform; calculating the comprehensive weight of each service node based on the current network status, current network performance data, current resource utilization data, and topology information; sorting all service nodes in the IoT platform according to the comprehensive weight, and obtaining the target service node based on the sorting result; and sending the address of the target service node to the edge device to be connected, so that the edge device to be connected can access the IoT platform according to the target service node.

[0008] The IoT platform data routing method provided by this invention comprehensively considers the current network status of edge devices, platform topology, network performance and resource utilization of each service node, dynamically calculates the comprehensive weight of service nodes and selects the optimal target node, realizing intelligent decision-making for data routing. This effectively avoids problems such as data detours, uneven load and access congestion caused by traditional static configuration, significantly improves communication efficiency, system stability and resource utilization, and enhances the scalability and adaptability of the IoT platform.

[0009] In some optional implementations, calculating the comprehensive weight of each service node based on the current network status, current network performance data, current resource utilization data, and topology information includes: calculating the transmission cost factor between the edge device to be accessed and each service node based on the topology information and the current network status; calculating the network health factor between the edge device to be accessed and each service node based on the current network performance data of each service node in the IoT platform; calculating the load processing factor of each service node based on the current resource utilization data of each service node in the IoT platform; calculating the service-related factor of each service node based on the historical service data, user preference information, preset service policies, and current resource utilization data of each service node in the IoT platform; and obtaining the comprehensive weight of each service node based on the transmission cost factor, network health factor, load processing factor, and service-related factor.

[0010] This implementation method introduces transmission cost factors, network health factors, load processing factors, and service-related factors to quantify and integrate multi-dimensional information such as network topology, real-time network status, service node performance, and resource load. This enables a refined evaluation of service node selection, taking into account not only physical distance and network quality but also node processing capabilities and service compatibility. This makes routing decisions more comprehensive, intelligent, and closer to actual operational needs, effectively improving access stability, response speed, and overall system load balancing.

[0011] In some optional implementations, the current network status of the edge device to be accessed includes the network connection quality and network congestion coefficient of the edge device to be accessed; calculating the transmission cost factor between the edge device to be accessed and each service node based on the topology information and the current network status includes: calculating the physical distance between the edge device to be accessed and the IoT platform based on the topology information; and inputting the physical distance, network connection quality, and network congestion coefficient into a preset transmission cost function to obtain the transmission cost factor between the edge device to be accessed and each service node.

[0012] This implementation combines topology information with the network connection quality and network congestion coefficient of edge devices, and uses physical distance and real-time network status to jointly quantify the transmission cost factor. This can more accurately reflect the actual overhead of data transmission, avoid inefficient routing caused by relying solely on fixed addresses or a single indicator, effectively reduce latency and packet loss risks, and improve communication reliability and network resource utilization.

[0013] In some optional implementations, calculating the network health factor between the edge device to be connected and each service node based on the current network performance data of each service node in the IoT platform includes: inputting the current network performance data into a trained neural network model to obtain the network health factor between the edge device to be connected and each service node; wherein the current network performance data of the service node includes the current network bandwidth, current transmission delay, current packet loss rate and current network jitter of the service node.

[0014] This implementation method inputs performance data such as the current network bandwidth, transmission delay, packet loss rate, and network jitter of the service node into a trained neural network model. By utilizing the model's nonlinear fitting and comprehensive judgment capabilities for multidimensional network indicators, it achieves intelligent and dynamic evaluation of network health factors. Compared with traditional threshold or weighted methods, it is more accurate and adaptive, effectively identifying potential communication risks in complex network environments and improving the reliability of routing selection and the overall communication quality of the system.

[0015] In some optional implementations, calculating the load processing factor of each service node based on the current resource utilization data of each service node in the IoT platform includes: obtaining the current resource utilization data and processing capacity limit of each service node respectively; wherein, the current network performance data of the service node includes CPU utilization and / or memory occupancy; determining the remaining capacity of each service node based on the current usage and processing capacity limit of each service node respectively; inputting the current usage and remaining capacity into a preset evolutionary strategy algorithm model to obtain the predicted response speed value and the comprehensive remaining processing capacity score of each service node; and obtaining the load processing factor of each service node based on the predicted response speed value and the comprehensive remaining processing capacity score of each service node.

[0016] This implementation combines current resource utilization data such as CPU utilization and memory usage of service nodes with their processing capacity limits to quantify their remaining capacity. It also uses a preset evolutionary strategy algorithm model to predict response speed and evaluate overall remaining processing capacity, thus achieving dynamic and intelligent analysis of node load status. This can more accurately reflect the actual service capacity of nodes, avoid overload or resource idleness, and improve the rationality of routing selection, system stability, and overall resource utilization efficiency.

[0017] In one optional implementation, the business-related factors for each service node are calculated based on its historical business data, user preference information, preset business strategies, and current resource utilization data within the IoT platform. This includes: acquiring feature data obtained by statistically analyzing historical business data for each service node, including service response time and service stability metrics; acquiring user preference information and preset business strategies for each service node; inputting the service response time, service stability, user preference, and business strategies into a trained random forest model to obtain a basic business relevance score for each service node; and adjusting the basic business relevance score based on current resource utilization data to obtain the business-related factors for each service node.

[0018] This implementation method integrates historical business data, user preferences, business strategies, and real-time resource status, and uses a trained random forest model to accurately quantify the business relevance of service nodes. It also dynamically adjusts the system based on current resource utilization, which not only improves the intelligence and accuracy of service scheduling but also effectively enhances the system's response efficiency, stability, and resource utilization.

[0019] Secondly, the present invention also provides a routing device for IoT platform data. The device includes an acquisition module, a comprehensive weight determination module, a target service node determination module, and a sending module. The acquisition module is used to acquire the current network status of the edge device to be accessed in the IoT platform, the topology information of the IoT platform, and the current network performance data and current resource utilization data of each service node in the IoT platform. The comprehensive weight determination module is used to calculate the comprehensive weight of each service node based on the current network status, current network performance data, current resource utilization data, and topology information. The target service node determination module is used to sort all service nodes in the IoT platform according to the comprehensive weight, and obtain the target service node based on the sorting result. The sending module is used to send the address of the target service node to the edge device to be accessed, so that the edge device to be accessed can access the IoT platform according to the target service node.

[0020] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the IoT platform data routing method described in the first aspect or any corresponding embodiment thereof.

[0021] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the Internet of Things platform data routing method described in the first aspect or any corresponding embodiment thereof.

[0022] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the Internet of Things platform data routing method described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0023] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a first flowchart of an IoT platform data routing selection method according to an embodiment of the present invention; Figure 2 This is a second flowchart of the IoT platform data routing selection method according to an embodiment of the present invention; Figure 3This is a structural block diagram of an IoT platform data routing device according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0027] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0028] As an optional application scenario of this invention, such as Figure 1 As shown, the structural components of the product are introduced in conjunction with specific scenarios.

[0029] According to an embodiment of the present invention, an embodiment of a routing method for IoT platform data is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0030] This embodiment provides a method for routing data on an IoT platform, which can be used in the aforementioned IoT platform. Figure 1 This is a flowchart of the first type of data routing selection method for an IoT platform according to an embodiment of the present invention, as follows: Figure 1 As shown, the process includes the following steps: Step S101: Obtain the current network status of the edge devices to be connected in the IoT platform, the topology information of the IoT platform, and the current network performance data and current resource utilization data of each service node in the IoT platform.

[0031] The current network status refers to the real-time network environment of the edge device to be accessed, such as the network type currently connected to the device, the assigned IP address, network connection quality indicators, the device's geographical location and time zone information, as well as real-time parameters reflecting communication performance, such as network latency, jitter, and packet loss rate.

[0032] Topology information refers to the logical or physical connection relationships between each service node and the edge device, such as the connection path between each service node, the number of hops or routing distance between nodes, and the hierarchical structure of the network.

[0033] Current network performance data is used to characterize the current network communication capabilities of each service node, such as available network bandwidth (B), real-time packet loss rate (P), network latency (D), jitter (J), historical network fault records or stability scores, and security-related indicators, such as whether it has been subjected to network attacks and whether it supports security mechanisms such as end-to-end encryption.

[0034] Current resource utilization data reflects the real-time load status of each service node in terms of computing and storage, such as CPU utilization, memory usage, disk I / O load, the number of concurrent requests, the remaining processing capacity relative to the preset limit, and the service response time (RT) that reflects service capabilities.

[0035] Step S102: Calculate the comprehensive weight of each service node based on the current network status, current network performance data, current resource utilization data, and topology information.

[0036] Step S103: Sort all service nodes in the IoT platform according to the comprehensive weight, and obtain the target service node based on the sorting result.

[0037] Step S104: Send the address of the target service node to the edge device to be connected, so that the edge device to be connected can access the IoT platform according to the target service node.

[0038] In other words, the target service node address is sent to the edge device, so that it can dynamically and automatically connect to the service node that best suits the current network and business needs without relying on preset or fixed configurations, thereby improving access efficiency, reducing latency, and enhancing the system's adaptability and scalability.

[0039] The IoT platform data routing method provided in this embodiment comprehensively considers the current network status of edge devices, platform topology, network performance and resource utilization of each service node, dynamically calculates the comprehensive weight of service nodes and selects the optimal target node, realizing intelligent decision-making for data routing. This effectively avoids problems such as data detours, uneven load and access congestion caused by traditional static configuration, significantly improves communication efficiency, system stability and resource utilization, and enhances the scalability and adaptability of the IoT platform.

[0040] This embodiment provides a method for routing data on an IoT platform, which can be used in the aforementioned IoT platform. Figure 2 This is a second flowchart of the IoT platform data routing selection method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Obtain the current network status of the edge devices to be connected in the IoT platform, the topology information of the IoT platform, and the current network performance data and current resource utilization data of each service node in the IoT platform.

[0041] Step S202: Calculate the comprehensive weight of each service node based on the current network status, current network performance data, current resource utilization data, and topology information.

[0042] In some optional implementations, calculating the comprehensive weight of each service node based on the current network status, current network performance data, current resource utilization data, and topology information includes the following steps: Step S2021: Calculate the transmission cost factor between the edge device to be accessed and each service node based on the topology information and the current network status.

[0043] The current network status of the edge device to be connected includes the network connection quality and network congestion coefficient of the edge device to be connected.

[0044] Specifically, calculating the transmission cost factor between the edge device to be connected and each service node based on the topology information and the current network status includes: calculating the physical distance between the edge device to be connected and the IoT platform based on the topology information; and inputting the physical distance, network connection quality, and network congestion coefficient into a preset transmission cost function to obtain the transmission cost factor between the edge device to be connected and each service node.

[0045] For example, the transmission cost function is as follows:

[0046] in, d q represents physical distance, and q represents network connection quality. This represents the network congestion coefficient. α , β and γ These are weighting coefficients that can be flexibly adjusted according to the actual deployment environment.

[0047] Step S2022: Calculate the network health factor between the edge device to be connected and each service node based on the current network performance data of each service node in the IoT platform.

[0048] Specifically, calculating the network health factor between the edge device to be connected and each service node based on the current network performance data of each service node in the IoT platform includes: inputting the current network performance data into a trained neural network model to obtain the network health factor between the edge device to be connected and each service node; wherein the current network performance data of the service node includes the current network bandwidth, current transmission delay, current packet loss rate and current network jitter of the service node.

[0049] For example, the training method for a neural network model includes: obtaining a training dataset, which includes multiple training data points, each of which includes historical network performance data and historical network health assessment indicators corresponding to the historical network performance data; and training the neural network using the training dataset to obtain a trained neural network model.

[0050] For example, the neural network model can specifically employ deep learning structures such as multilayer perceptrons (MLP) or convolutional neural networks (CNN) to effectively capture the nonlinear correlations between network performance metrics. Regarding feature input, in addition to current network bandwidth, latency, packet loss rate, and jitter, historical network fault records of the service node can be integrated as supplementary features reflecting its long-term stability, thereby improving the comprehensiveness of the health assessment.

[0051] Step S2023: Calculate the load processing factor of each service node based on the current resource utilization data of each service node in the IoT platform.

[0052] Specifically, the load processing factor of each service node is calculated based on the current resource utilization data of each service node in the IoT platform, including: obtaining the current resource utilization data and processing capacity limit of each service node; wherein, the current network performance data of the service node includes CPU utilization and / or memory usage; determining the remaining capacity of each service node based on its current usage and processing capacity limit; inputting the current usage and remaining capacity into a preset evolutionary strategy algorithm model to obtain the predicted response speed value and comprehensive remaining processing capacity score of each service node; and obtaining the load processing factor of each service node based on the predicted response speed value and comprehensive remaining processing capacity score.

[0053] For example, when calculating the load processing factor, the evolutionary strategy algorithm model not only considers basic resource indicators such as the current CPU utilization and memory usage of the service node, but also further introduces dimensions such as disk I / O load, concurrent request count, and task queue length to more comprehensively depict its operating status. The upper limit of processing capacity can be dynamically set according to the node hardware specifications or system configuration, and adaptively adjusted in combination with historical load fluctuation trends.

[0054] Step S2024: Calculate the business-related factors for each service node based on its historical business data, user preference information, preset business strategies, and current resource utilization data.

[0055] Specifically, based on the historical business data, user preference information, preset business strategies, and current resource utilization data of each service node in the IoT platform, the business-related factors for each service node are calculated as follows: Feature data obtained by statistically analyzing the historical business data of each service node, including service response time and service stability indicators; user preference information and preset business strategies of each service node; inputting the service response time, service stability, user preference, and business strategies into a trained random forest model to obtain a basic business relevance score for each service node; and adjusting the basic business relevance score based on current resource utilization data to obtain the business-related factors for each service node.

[0056] For example, when generating basic business relevance scores, the trained random forest model can further integrate derived features such as the node's historical service success rate, fault recovery time, and business type matching degree to enhance the characterization of the service node's business adaptability.

[0057] User preference information may include end-users' preferred configurations for low latency, high reliability, or nodes in specific regions, while preset business policies may cover operational rules such as prioritizing high-value customer traffic and ensuring critical business links.

[0058] When dynamically adjusting the basic score based on current resource utilization data, the system introduces a decay factor based on the real-time load level of CPU and memory. When node resources are scarce, the business-related factors are appropriately reduced to avoid service quality degradation due to excessive allocation of business requests. Conversely, when resources are abundant, the weight can be increased to fully leverage the business carrying advantages of high-performance nodes, thereby achieving coordinated optimization of business needs and system status.

[0059] Step S2025: Obtain the comprehensive weight of each service node based on the transmission cost factor, network health factor, load processing factor, and business-related factors.

[0060] For example, the calculation of the comprehensive weight can adopt a feature fusion strategy, which normalizes the transmission cost factor, network health factor, load processing factor, and service-related factor, and then fuses them through methods such as weighted summation, weighted average, or principal component analysis (PCA). The weight of each factor can be dynamically configured according to the actual business scenario. For example, in latency-sensitive applications, the proportion of transmission cost factor and network health factor can be increased, while in scenarios with high reliability requirements, the influence of load processing factor and service-related factor can be enhanced.

[0061] Step S203: Sort all service nodes in the IoT platform according to the comprehensive weight, and obtain the target service node based on the sorting result.

[0062] For example, when sorting service nodes by comprehensive weight, a descending order can be used to prioritize the node with the highest comprehensive weight as the target service node. If the weight difference of multiple nodes is within a preset threshold range, auxiliary criteria such as geographical proximity, security level, or historical connection success rate can be introduced for secondary screening to improve decision robustness.

[0063] Step S204: Send the address of the target service node to the edge device to be connected, so that the edge device to be connected can access the IoT platform according to the target service node.

[0064] The IoT platform data routing method provided in this embodiment solves problems such as inaccurate device-node matching, unbalanced load, insufficient security, and complex management in existing technologies through steps such as device attribute reporting, node status collection, weight calculation, optimal node selection, and dynamic adjustment and optimization. This method can improve the overall performance and reliability of IoT systems, reduce maintenance costs, adapt to constantly changing network environments, and enhance system security and flexibility.

[0065] This embodiment also provides a routing device for IoT platform data, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0066] This embodiment provides a routing device for IoT platform data, such as... Figure 3 As shown, it includes: The acquisition module 301 is used to acquire the current network status of the edge devices to be connected in the IoT platform, the topology information of the IoT platform, and the current network performance data and current resource utilization data of each service node in the IoT platform.

[0067] The comprehensive weight determination module 302 is used to calculate the comprehensive weight of each service node based on the current network status, current network performance data, current resource utilization data and topology information.

[0068] The target service node determination module 303 is used to sort all service nodes in the IoT platform according to the comprehensive weight, and obtain the target service node based on the sorting result.

[0069] The sending module 304 is used to send the address of the target service node to the edge device to be connected, so that the edge device to be connected can access the Internet of Things platform according to the target service node.

[0070] In some optional implementations, the comprehensive weight determination module 302 includes a transmission cost factor determination unit, a network health factor determination unit, a load processing factor determination unit, a service-related factor determination unit, and a comprehensive processing unit. Specifically, the transmission cost factor determination unit calculates the transmission cost factor between the access edge device and each service node based on topology information and the current network status; the network health factor determination unit calculates the network health factor between the access edge device and each service node based on the current network performance data of each service node in the IoT platform; the load processing factor determination unit calculates the load processing factor of each service node based on the current resource utilization data of each service node in the IoT platform; the service-related factor determination unit calculates the service-related factor of each service node based on historical service data, user preference information, preset service policies, and current resource utilization data of each service node in the IoT platform; and the comprehensive processing unit obtains the comprehensive weight of each service node based on the transmission cost factor, network health factor, load processing factor, and service-related factor.

[0071] In some optional implementations, the transmission cost factor determination unit is specifically used to: calculate the physical distance between the edge device to be accessed and the IoT platform based on the topology information; and input the physical distance, network connection quality, and network congestion coefficient into a preset transmission cost function to obtain the transmission cost factor between the edge device to be accessed and each service node.

[0072] In some optional implementations, the network health factor determination unit is specifically used to: input current network performance data into a trained neural network model to obtain the network health factor between the edge device to be accessed and each service node; wherein the current network performance data of the service node includes the current network bandwidth, current transmission latency, current packet loss rate, and current network jitter of the service node. In some optional implementations, the load processing factor determination unit is specifically used to: obtain the current resource utilization data and processing capacity limit of each service node; wherein the current network performance data of the service node includes CPU utilization and / or memory usage; determine the remaining capacity of each service node based on its current usage and processing capacity limit; input the current usage and remaining capacity into a preset evolutionary strategy algorithm model to obtain the predicted response speed value and comprehensive remaining processing capacity score of each service node; and obtain the load processing factor of each service node based on its predicted response speed value and comprehensive remaining processing capacity score.

[0073] In some optional implementations, the business relevance factor determination unit is specifically used to: acquire feature data obtained by statistically analyzing historical business data for each service node, including service response time indicators and service stability indicators; acquire user preference information and preset business strategies for each service node; input the service response time indicators, service stability indicators, user preference information, and business strategies into the trained random forest model to obtain a basic business relevance score for each service node; and adjust the basic business relevance score based on current resource utilization data to obtain the business relevance factor for each service node.

[0074] The IoT platform data routing method provided in this embodiment of the invention can execute the IoT platform data routing method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the various modules and units described above are the same as in the corresponding embodiments described above, and will not be repeated here.

[0075] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0076] The following is a detailed reference. Figure 4This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 402 or a program loaded from memory 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device. The processor 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0077] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0078] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 409, or installed from a memory 408, or installed from a ROM 402. When the computer program is executed by the processor 401, it performs the functions defined in the IoT platform data routing method of the embodiments of the present invention.

[0079] Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0080] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the data routing method for the IoT platform shown in the above embodiments is implemented.

[0081] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0082] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for routing data on an Internet of Things (IoT) platform, characterized in that, The method includes: Obtain the current network status of the edge devices to be connected in the IoT platform, the topology information of the IoT platform, and the current network performance data and current resource utilization data of each service node in the IoT platform; The comprehensive weight of each service node is calculated based on the current network status, the current network performance data, the current resource utilization data, and the topology information. All service nodes in the IoT platform are sorted according to the comprehensive weight, and the target service node is obtained based on the sorting result. The address of the target service node is sent to the edge device to be connected, so that the edge device to be connected can access the Internet of Things platform according to the target service node.

2. The method according to claim 1, characterized in that, The calculation of the comprehensive weight of each service node based on the current network status, the current network performance data, the current resource utilization data, and the topology information includes: Calculate the transmission cost factor between the edge device to be accessed and each of the service nodes based on the topology information and the current network status; The network health factor between the edge device to be connected and each of the service nodes is calculated based on the current network performance data of each service node in the IoT platform. The load processing factor of each service node is calculated based on the current resource utilization data of each service node in the IoT platform. Based on the historical business data, user preference information, preset business strategies, and current resource utilization data of each service node in the IoT platform, the business-related factors of each service node are calculated. The comprehensive weight of each service node is obtained based on the transmission cost factor, the network health factor, the load handling factor, and the service-related factor.

3. The method according to claim 2, characterized in that, The current network status of the edge device to be accessed includes the network connection quality and network congestion coefficient of the edge device to be accessed; the calculation of the transmission cost factor between the edge device to be accessed and each of the service nodes based on the topology information and the current network status includes: Calculate the physical distance between the edge device to be connected and the IoT platform based on the topology information; The physical distance, network connection quality, and network congestion coefficient are input into a preset transmission cost function to obtain the transmission cost factor between the edge device to be accessed and each of the service nodes.

4. The method according to claim 2, characterized in that, The calculation of the network health factor between the edge device to be connected and each of the service nodes based on the current network performance data of each service node in the IoT platform includes: The current network performance data is input into a trained neural network model to obtain the network health factors between the edge device to be connected and each of the service nodes; wherein the current network performance data of the service node includes the current network bandwidth, current transmission delay, current packet loss rate and current network jitter of the service node.

5. The method according to claim 2, characterized in that, The load processing factor for each service node is calculated based on the current resource utilization data of each service node in the IoT platform, including: Obtain the current resource utilization data and processing capacity limit of each service node; wherein, the current network performance data of the service node includes CPU utilization and / or memory usage. The remaining capacity of each service node is determined based on its current usage and maximum processing capacity. The current usage and remaining capacity are input into a preset evolutionary strategy algorithm model to obtain the predicted response speed and comprehensive remaining processing capacity score for each service node. The load processing factor of each service node is obtained based on the predicted response speed and the overall remaining processing capacity score.

6. The method according to claim 1, characterized in that, The calculation of the business-related factors for each service node based on its historical business data, user preference information, preset business strategies, and current resource utilization data in the IoT platform includes: Obtain feature data from each service node's statistical analysis of historical business data, including service response time metrics and service stability metrics. Obtain user preference information and preset business strategies for each of the service nodes; The service response time metric, the service stability metric, the user preference information, and the business strategy are input into the trained random forest model to obtain the basic business relevance score for each service node. The business relevance score of the basic business is adjusted based on the current resource utilization data to obtain the business relevance factor of each service node.

7. A data routing device for an Internet of Things (IoT) platform, characterized in that, The device includes: The acquisition module is used to acquire the current network status of the edge devices to be connected in the IoT platform, the topology information of the IoT platform, and the current network performance data and current resource utilization data of each service node in the IoT platform. The comprehensive weight determination module is used to calculate the comprehensive weight of each service node based on the current network status, the current network performance data, the current resource utilization data, and the topology information. The target service node determination module is used to sort all service nodes in the IoT platform according to the comprehensive weight, and obtain the target service node based on the sorting result; The sending module is used to send the address of the target service node to the edge device to be connected, so that the edge device to be connected can access the Internet of Things platform according to the target service node.

8. An electronic device, characterized in that, include: A memory and a processor are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the data routing method for the Internet of Things platform as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the routing method for IoT platform data as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes computer instructions for causing a computer to perform the routing method for IoT platform data as described in any one of claims 1 to 6.

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