Internet of Things data intelligent transmission method and system

Abstract

The invention relates to the technical field of Internet of Things communication transmission, and discloses an Internet of Things data intelligent transmission method and system, and the method comprises the steps: abstracting the performance of a multi-source heterogeneous link through a software defined network controller, dynamically generating a slice template, carrying out the traffic marking and priority scheduling, constructing a multi-path target constraint optimization model, and carrying out the optimization of the multi-path target constraint optimization model. And in combination with a network function virtualization mechanism, multi-level intelligent transmission is implemented at edge nodes, and a final transmission control instruction is generated. In the prior art, in a heterogeneous multi-network environment, low-delay, high-reliability and high-bandwidth transmission cannot be guaranteed at the same time, and especially in an industrial automation scene, technical problems that a control instruction is easy to delay and a monitoring data bandwidth is limited exist. According to the invention, the overall transmission reliability and bandwidth utilization efficiency are improved through the dynamic priority traffic scheduling based on network slices, the multipath reliability product model and the dynamic batch processing and progressive compression technology based on real-time link feedback.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things communication transmission technology, and in particular to an Internet of Things data intelligent transmission method and system. Background Art

[0002] Currently, IoT technology is widely used in various scenarios, including industrial automation, smart manufacturing, and smart factories, enabling remote device control, anomaly detection, energy optimization, and environmental monitoring. However, existing IIoT communication solutions often suffer from the following shortcomings when operating in multi-source, heterogeneous network environments (including cellular networks, low-power wide area networks (LPWANs), and Wi-Fi Mesh). For example, traditional solutions typically employ fixed paths or single-link transmission mechanisms, failing to dynamically select the optimal path based on real-time link quality. This can lead to excessive data transmission delays, reduced reliability, and even the potential safety hazard of critical control command failures when faced with link congestion, interference, or bandwidth fluctuations.

[0003] Furthermore, industrial automation scenarios often require meeting the differentiated QoS requirements of multiple service types simultaneously. For example, remote robotic arm control commands require millisecond-level latency and extremely high reliability, while high-definition video surveillance prioritizes bandwidth security and continuity. Periodic environmental monitoring data requires low energy consumption and a certain degree of latency tolerance. Traditional Industrial IoT solutions often lack effective dynamic slicing mechanisms and cross-network intelligent scheduling to handle these concurrent, differentiated, and multi-priority data flows. This results in a failure to ensure tiered quality of service, easily leading to link conflicts and resource waste.

[0004] Therefore, there is an urgent need for an IoT intelligent transmission method that can dynamically allocate slices and optimize multi-path selection based on real-time link performance status and service priority in a complex multi-network convergence environment, and combine multi-level transmission mechanisms such as real-time batch processing, adaptive compression, and redundant error correction to improve the transmission reliability, real-time performance, and bandwidth utilization efficiency of multi-service data in industrial automation scenarios, and achieve highly secure, stable, and adaptive industrial informatization support capabilities. Summary of the Invention

[0005] In response to the above-mentioned technical deficiencies, the purpose of the present invention is to propose an intelligent data transmission method for the Internet of Things, aiming to solve the technical problems in the existing technology that are unable to simultaneously guarantee low latency, high reliability and high bandwidth transmission in a heterogeneous multi-network environment, especially in industrial automation scenarios where control instructions are easily delayed and monitoring data bandwidth is limited.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides an Internet of Things data intelligent transmission method, The method for intelligently transmitting IoT data includes: Step S10: Centrally abstract the multi-source heterogeneous network links through the software-defined network controller, extract the real-time link performance parameters of each link, and establish a global link performance status database in the network controller based on the real-time link performance parameters of the link ; Step S20: pre-set the first network slice template and use the global link performance status database Dynamically adjust the slice ratio of the first network slice template to obtain a second network slice template; Step S30: Obtain IoT data stream , based on the second network slice template, IoT data flow Perform real-time traffic marking and priority label binding, and output priority transmission schedule T; Step S40: Based on the output priority transmission schedule T, The multi-path links in the selection and allocation are optimized, and the target constraint conditions are optimized to obtain the optimal path subset. ; Step S50: Based on the optimal path subset Multi-level intelligent transmission deployment is carried out at the edge nodes of the Internet of Things through the network function virtualization mechanism, including dynamic batch processing, adaptive compression processing and redundant error correction processing, and the final optimized transmission control instructions are generated.

[0007] Preferably, in step S10, the multi-source heterogeneous network links include cellular networks, low-power wide area networks, and Wi-Fi networks; and the real-time link performance parameters of the links include available bandwidth parameters, delay parameters, packet loss rate parameters, and usage costs.

[0008] Preferably, in step S20, the second network slice template is defined as , where k is the slice index number, which is used to distinguish the slice strategies corresponding to different application scenarios; is the kth network slice; Network slicing The corresponding end-to-end delay; Network slicing The lower bound of the probability that the corresponding data is delivered correctly; Network slicing The corresponding minimum bandwidth requirement; Network slicing The corresponding priority.

[0009] Preferably, in step S40, the optimal path subset The objective constraints of the optimization solution include the introduction of a reliability product model and the combination of a cost function, specifically satisfying:

[0010] in, Indicates the Real-time delay of the link; Indicates the Reliability indicators of links; Indicates the The current available bandwidth of the link; Indicates a link The comprehensive cost of , including energy consumption, bandwidth cost and congestion index, This means that the link The constraint of minimum comprehensive cost; 、 、 Derived from the second network slice template.

[0011] Preferably, in step S30, before the step of outputting the priority transmission schedule table T, the method further includes: Classify and get the first priority slice , second priority slice and the third priority slice .

[0012] Preferably, in step S50, dynamic batch processing specifically adopts a dynamic remaining bandwidth perception adjustment method, which performs buffer batch scheduling by dynamically calculating a batch factor by real-time detection of the remaining available bandwidth of the link, estimating future load and the current cached data volume.

[0013] Preferably, in step S50, the adaptive compression processing specifically includes: combining the IoT data stream The compression ratio is dynamically calculated based on the priority of the slice to which it belongs, and the data is graded according to the multi-layer progressive compression mode, giving priority to the minimum compression task of high-priority data and reducing the bit rate of low-priority data.

[0014] The present invention also provides an Internet of Things data intelligent transmission system comprising: The link performance abstraction module is used to perform centralized abstraction processing on multi-source heterogeneous network links through a software-defined network controller, extract the real-time link performance parameters of each link, and establish a global link performance status database in the network controller based on the real-time link performance parameters of the link. ; Dynamic slicing adjustment module, used to pre-set the first network slicing template and adjust the network slicing template according to the global link performance status database Dynamically adjust the slice ratio of the first network slice template to obtain a second network slice template; Traffic marking and scheduling table generation module for obtaining IoT data streams , based on the second network slice template, IoT data flow Perform real-time traffic marking and priority label binding, and output priority transmission schedule T; The multi-path optimization selection module is used to select the optimal path for IoT data streams based on the output priority transmission schedule table T. The multi-path links in the selection and allocation are optimized, and the target constraint conditions are optimized to obtain the optimal path subset. ; Multi-level intelligent transmission deployment module for optimal path subsets Multi-level intelligent transmission deployment is carried out at the edge nodes of the Internet of Things through the network function virtualization mechanism, including dynamic batch processing, adaptive compression processing and redundant error correction processing, and the final optimized transmission control instructions are generated.

[0015] The present invention also provides an Internet of Things data intelligent transmission device, comprising: a memory, a processor, and an Internet of Things data intelligent transmission program stored in the memory and runnable on the processor. When the Internet of Things data intelligent transmission program is executed by the processor, an Internet of Things data intelligent transmission method is implemented.

[0016] The present invention also provides a computer program product, including an Internet of Things data intelligent transmission program, which implements the Internet of Things data intelligent transmission method when executed by a processor.

[0017] The beneficial effects of the present invention are as follows: the present invention effectively ensures the extremely high reliability and low-latency transmission of key services such as remote control instructions in complex multi-network environments in industrial automation scenarios through a multi-path reliability product optimization model based on dynamic slicing templates, combined with priority-aware link selection and parallel redundancy strategies, thereby avoiding the risk of control failure caused by a single path bottleneck in traditional solutions.

[0018] The present invention utilizes a batch adjustment method based on dynamic remaining bandwidth perception and a multi-layer progressive adaptive compression method to achieve hierarchical dynamic processing of data of different priorities, reduce the instantaneous impact of non-real-time data on the link, significantly improve the overall network resource utilization, and reduce data packet loss and power consumption problems caused by bandwidth shortages in industrial scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 This is a flow chart of a first embodiment of a method for intelligently transmitting IoT data according to the present invention.

[0021] Figure 2 This is a schematic diagram of equipment for an intelligent data transmission method for the Internet of Things of the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] Example 1: Figure 1 FIG. 1 is a flow chart of the first embodiment of the method for intelligently transmitting data of the Internet of Things according to the present invention, and provides the first embodiment of the method for intelligently transmitting data of the Internet of Things according to the present invention.

[0024] In a first embodiment, the method for intelligently transmitting IoT data includes: Step S10: Centrally abstract the multi-source heterogeneous network links through the software-defined network controller, extract the real-time link performance parameters of each link, and establish a global link performance status database in the network controller based on the real-time link performance parameters of the link ; It should be noted that the real-time link performance parameters for each link refer to a multi-dimensional set of indicators that dynamically characterize the link's transmission capacity, stability, and resource consumption. These include, but are not limited to, the link's instantaneous latency, packet loss rate (or reliability), available bandwidth, current utilization, instantaneous power consumption, historical load trends, and short-term predicted bandwidth availability. These parameters collectively characterize the link's multi-level transmission performance under varying loads and time periods, comprehensively reflecting the link's actual dynamic transmission characteristics. The global link performance status database is a comprehensive link performance information repository maintained and updated in real time by the network controller. It contains not only a snapshot of each link's current performance parameters, but also auxiliary scheduling information such as load trend curves generated from historical data, performance prediction models, abnormal event records, and link priority recommendations. This database serves as the core basis for subsequent slice template adjustments, path optimization, and traffic priority scheduling, ensuring adaptive transmission decisions in complex industrial automation scenarios.

[0025] It's understandable that link performance parameters aren't isolated and static; rather, they reflect the dynamic evolution of network operations. By continuously collecting and storing data in real time, the controller not only understands the current instantaneous link status but also identifies performance fluctuations caused by various factors, such as surrounding interference, load peaks, and environmental changes. This identification provides a basis for targeted adjustments in subsequent slice configuration and dynamic scheduling. For example, based on historical trends, the controller can predict in advance that a link is about to enter a congested state, thereby dynamically adjusting slice bandwidth allocation and redundant link reservation strategies.

[0026] It should be understood that unlike traditional solutions that rely solely on fixed bandwidth estimation or link selection based on static signal strength indicators, the present invention transforms network scheduling from "passive response" to "active prediction" by introducing multi-dimensional real-time link performance parameters and a database based on the fusion of historical and real-time information. This addresses the technical shortcomings of traditional methods, such as their inability to promptly perceive link jitter, sudden delay increases, and burst packet loss in complex environments. Through more detailed performance profiling, the network controller can respond intelligently at the millisecond level, effectively ensuring the immediate accessibility and ultra-high reliability of high-priority industrial control data, while also rationally scheduling non-real-time data to avoid link resource conflicts.

[0027] For example, in a verification test in an industrial workshop, the high-priority link used for robotic arm motion control was continuously monitored through the link performance status database constructed by the present invention. When the link detected that the background camera was uploading high-definition video at the same time, it predicted in advance that the bandwidth might drop by about 50%, and completed the priority switching and backup link activation within 200ms, ensuring that the end-to-end transmission delay of the robotic arm control signaling was always less than 40ms.

[0028] Step S20: pre-set the first network slice template and use the global link performance status database Dynamically adjust the slice ratio of the first network slice template to obtain a second network slice template; It should be noted that the first network slice template refers to a predefined virtual network resource partitioning strategy based on the needs of different types of IoT services in industrial automation scenarios. This strategy ensures that different data flows receive the quality of service (QoS) requirements that match their mission characteristics during transmission. Each slice template contains at least three key elements: maximum allowable latency, minimum reliability requirements, and minimum bandwidth requirements. For example, for industrial control instructions, strict latency and reliability requirements can be preset; for surveillance video streams, bandwidth and continuity can be prioritized; and for periodic environmental data, higher latency can be tolerated while maintaining energy efficiency. The global link performance status database provides real-time link capacity, load, and forecast information. This information is compared and analyzed with the pre-set first network slice template to generate the second network slice template. Dynamic slice allocation refers to the real-time optimization of bandwidth, priority, and resource ratios within the pre-set slices based on the actual available performance of the current links. This ensures that the slice configuration is more closely aligned with the current network conditions and avoids resource waste or service quality degradation caused by static allocation.

[0029] Understandably, different IoT services have vastly different demands for network resources within the same timeframe. In industrial automation scenarios, production line control signaling requires millisecond-level latency, while equipment status monitoring data only needs to arrive periodically. By incorporating real-time information from a global link performance status database and dynamically adjusting the bandwidth weight, priority, and allowed redundancy ratio of slices, multiple logical slices can coexist and dynamically change on the same physical network. For example, if a sudden drop in available bandwidth on a link is detected, the bandwidth share of lower-priority slices can be promptly reduced to prioritize the normal operation of higher-priority slices, ensuring control security at the industrial site.

[0030] It should be understood that compared with the traditional IoT solution that uses fixed slice configuration or relies solely on historical average load information, the dynamically adjusted second network slice template in the present invention achieves adaptive scheduling capabilities by integrating real-time link status. This mechanism not only ensures that high-priority tasks take priority in occupying resources when the quality of the sudden link deteriorates, but also flexibly expands the resources of low-priority tasks when the network status is good, thereby improving overall bandwidth utilization and service experience. In this way, the present invention overcomes the problems of priority confusion and uncontrollable delays that are prone to occur in traditional static slicing technology when multiple services conflict and resource preemption occur.

[0031] Step S30: Obtain IoT data stream , based on the second network slice template, IoT data flow Perform real-time traffic marking and priority label binding, and output priority transmission schedule T; It should be noted that IoT data flows typically include control command flows, high-definition video monitoring flows, periodic status reporting flows, and other auxiliary information flows. Each data flow requires differentiated processing based on its source, purpose, and requirements for real-time performance, reliability, and bandwidth. Real-time traffic marking automatically appends a set of identifiers to each data flow based on the configuration parameters in the second network slice template. These identifiers indicate the preferred network resources, target transmission path, and acceptable performance thresholds (such as maximum allowable latency and minimum reliability). Priority label binding assigns a dynamic priority factor to each data flow based on the marking, ensuring appropriate transmission guarantees or resource allocation during network scheduling. The priority transmission schedule table T, compiled based on the marking results and priority labels, details the distribution order, path selection plan, and redundancy requirements for each data flow.

[0032] Understandably, in IoT scenarios, different data streams enter the network simultaneously, with varying degrees of importance and network dependency. By introducing the second network slice template into the marking and labeling process, the first round of "traffic classification" can be completed before the data stream enters the network, eliminating the need for repeated link status queries or re-evaluation of priorities during subsequent scheduling. This mechanism effectively reduces the computational burden and decision-making latency during scheduling, improving the response speed of high-priority tasks and ensuring the safe arrival of critical control instructions. It also ensures that low-priority data can be smoothly transmitted when network resources are sufficient, avoiding congestion and backlogs.

[0033] It should be understood that, unlike the extensive scheduling schemes based on fixed ports or static service identifiers commonly used in traditional IoT networks, the present invention deeply combines slice templates with the real-time characteristics of data streams to achieve dynamic marking and priority configuration accurate to each data stream. This not only solves the problem that "fixed priority" or "single traffic marking" in traditional methods cannot be flexibly adjusted according to network conditions, but also significantly improves the support capability for sudden tasks. For example, when the network status suddenly changes, the system can quickly and dynamically increase the priority of a key flow according to the current template without having to rearrange all scheduling queues as a whole, thereby ensuring critical services more efficiently and stably.

[0034] For example, in a smart factory environment, a controller simultaneously receives three types of data streams from multiple production lines: robotic arm motion commands, high-definition video surveillance streams, and periodic equipment temperature reporting streams. Based on the second network slice template, the system automatically tags the robotic arm command stream with an "ultra-high priority" tag, allowing a maximum latency of no more than 50ms. The video surveillance stream is marked as "medium priority," prioritizing bandwidth continuity but tolerating a latency of 200ms. The periodic reporting stream is marked as "low priority," which can be cached in batches and sent later, allowing for a latency of 1 second or even higher. The final output schedule T specifies that the command stream must occupy the main 5G link path, the monitoring stream is shared between Wi-Fi Mesh and cellular links, and the periodic data is buffered and transmitted via LPWAN during idle time. In this way, even under simulated network congestion, the command stream maintains a stable latency of less than 40ms. Although video surveillance quality is slightly degraded, it remains continuous, and the automatic postponement of periodic data does not affect system security.

[0035] Step S40: Based on the output priority transmission schedule T, The multi-path links in the selection and allocation are optimized, and the target constraint conditions are optimized to obtain the optimal path subset. ; It should be noted that a multipath link refers to multiple optional transmission paths from a source node to a destination node. These paths may span different types of networks (e.g., cellular links, Wi-Fi Mesh, LPWAN, etc.), and each path exhibits different performance characteristics at the same time, such as latency, reliability, bandwidth, and energy consumption. For each IoT data stream, the controller comprehensively evaluates all available paths based on the marking information (including priority, maximum latency, minimum reliability, and minimum bandwidth) in the scheduling table T generated in step S30, and constructs a set of target constraints to select the optimal path combination that meets business requirements. The optimal path subset here refers to the set of paths selected after comprehensively considering bandwidth utilization, link cost, and overall network load, while meeting various performance requirements. The goal is to simultaneously improve transmission efficiency, ensure business quality, and reduce resource waste.

[0036] It is understandable that multi-path selection is not simply about finding the shortest path or the path with the largest bandwidth. In actual industrial automation scenarios, if a single shortest path is congested or interfered with, its actual performance may be far worse than expected. Therefore, this step comprehensively considers the immediate performance of each path, the historical performance of the link, and the current network load, and sorts and scores the paths in multiple dimensions to more accurately meet the needs of different types of data streams. For example, high-reliability control instructions need to give priority to a set of paths with low latency and high redundancy, while monitoring video streams pay more attention to the allocation of total bandwidth. Multiple paths with large bandwidth but acceptable latency can be used for parallel load sharing.

[0037] It should be understood that unlike traditional solutions that select primary and backup links based solely on static path priority or link signal strength, the present invention introduces multi-dimensional real-time constraints into the path selection process, such as maximum allowable latency, reliability floor, minimum bandwidth requirement, and link cost limits, to form an overall optimization objective. This mechanism enables path selection to focus not only on a single performance metric but also to balance multiple objectives based on service characteristics, ensuring the secure and timely transmission of critical tasks, maximizing the use of available link resources, and avoiding issues such as link overload or vacancy, thereby improving overall system robustness and stability.

[0038] Step S50: Based on the optimal path subset Multi-level intelligent transmission deployment is carried out at the edge nodes of the Internet of Things through the network function virtualization mechanism, including dynamic batch processing, adaptive compression processing and redundant error correction processing, and the final optimized transmission control instructions are generated.

[0039] It's important to note that network function virtualization (NFV) involves dynamically loading and executing network function modules on general-purpose computing platforms through software, rather than relying on traditional fixed hardware logic. These modules include dynamic batch processing, adaptive compression, and redundant error correction. Each module configures policy parameters based on the optimal path subset selection and the service characteristics of the corresponding data flow, collaboratively optimizing final transmission performance. The resulting transmission control instructions not only guide link-level data transmission but also provide feedback to update the link performance status database, forming an adaptive closed-loop control system.

[0040] It is understandable that dynamic batching aggregates, buffers, and sends data streams in batches based on the network's instantaneous bandwidth margin, current load conditions, and service priorities, thereby reducing the risk of instantaneous network congestion and improving overall energy efficiency. Adaptive compression dynamically reduces the bit rate, resolution, or streamlines data content based on the link's current available bandwidth and priority, thereby maintaining business continuity in bandwidth-constrained environments. The redundant error correction module dynamically configures the redundant check ratio to enhance the data stream's ability to resist packet loss in multi-path transmission, ensuring that data with high reliability requirements can still be fully received under extreme circumstances. These three functional modules work together to achieve multi-level, fine-grained intelligent optimization from an end-to-end perspective.

[0041] It should be understood that unlike traditional solutions that rely solely on single-link encoding, static compression strategies, or fixed batch processing strategies, the present invention flexibly integrates multifunctional network virtualization modules at the edge nodes, enabling dynamic policy switching based on real-time link conditions and service priorities. For example, upon detecting a sudden drop in available link bandwidth, the batch processing module automatically increases the aggregation threshold, the compression module immediately reduces the data encoding rate, and the redundancy module automatically increases the checksum ratio, forming a multi-level responsive optimization mechanism. This mechanism not only ensures the stability and timeliness of high-priority tasks (such as control instructions), but also improves overall network resource utilization, reduces energy consumption and conflicts, and adapts to the highly dynamic and complex network environments of industrial automation sites.

[0042] For example, in an IoT edge node deployment at a smart factory, batch processing is disabled for remote robotic arm control command streams (to ensure real-time performance), and the compression ratio is maintained at the highest precision (near 1). A high checksum ratio is set in the redundant error correction module to mitigate packet loss. For HD video surveillance streams, when network bandwidth drops by 50%, the system automatically adjusts the batching threshold from every 1 frame to every 5 frames. The compression module immediately reduces the resolution from 1080p to 720p, and the redundancy module's checksum ratio is reduced to conserve bandwidth. For periodic environmental monitoring data, the system significantly increases the batching threshold and allows for higher latency, while simultaneously adopting a high compression ratio and minimal redundancy strategy to conserve link resources for high-priority tasks. Field verification has shown that the end-to-end latency of the robotic arm command stream remains below 50ms at all times. Surveillance video, while experiencing some degradation in quality, remains smooth. Periodic monitoring data is smoothly buffered and uploaded in a centralized manner, improving overall system transmission stability and resource utilization by approximately 45%.

[0043] Embodiment 2: Furthermore, the present invention provides an intelligent IoT data transmission system that employs the method for intelligent IoT data transmission described in the aforementioned embodiment, thereby resolving the technical problem of intelligent IoT data transmission. Compared to the prior art, the beneficial effects of the intelligent IoT data transmission system provided by the present invention are the same as those of the method for intelligent IoT data transmission described in the aforementioned embodiment. Other technical features of the intelligent IoT data transmission system are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0044] Example 3: The present invention provides an IoT data intelligent transmission device, please refer to Figure 2An IoT data intelligent transmission device includes: at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the IoT data intelligent transmission method described in the first embodiment. The IoT data intelligent transmission device in this embodiment of the present invention may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. The IoT data intelligent transmission device is merely an example and should not limit the functionality or scope of use of the embodiments of the present invention. The IoT data intelligent transmission device may include a processing device 1001 that can perform various appropriate actions and processes based on programs stored in a read-only memory 1002 or programs loaded from a storage device 1003 into a random access memory 1004. Random access memory 1004 also stores various programs and data required for the operation of the IoT data intelligent transmission device. The processing device 1001, read-only memory 1002, and random access memory 1004 are interconnected via a bus 1005. An I / O interface 1006 is also connected to the bus. Typically, the following systems may be connected to the I / O interface 1006: an input device 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003; and a communication device 1009. The communication device 1009 may allow an IoT data intelligent transmission device to communicate with other devices wirelessly or wired to exchange data. While the figure illustrates an IoT data intelligent transmission device with various systems, it should be understood that implementation or presence of all illustrated systems is not required. More or fewer systems may alternatively be implemented or present.

[0045] Example 4: The present invention also provides a computer program product, comprising a computer program. When executed by a processor, the computer program implements the steps of the aforementioned method for intelligent IoT data transmission. The computer program product provided by the present invention can solve the technical problem of intelligent IoT data transmission. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as those of the method for intelligent IoT data transmission provided by the aforementioned embodiment, and are not further elaborated here.

[0046] In particular, according to the embodiments disclosed in the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present invention are performed.

[0047] It should be understood that the various parts disclosed in the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any appropriate manner in any one or more embodiments or examples.

[0048] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for intelligent transmission of Internet of Things data, characterized in that: Methods include: Step S10: Centrally abstract the multi-source heterogeneous network links through the software-defined network controller, extract the real-time link performance parameters of each link, and establish a global link performance status database in the network controller based on the real-time link performance parameters of the link ; Step S20: pre-set the first network slice template and use the global link performance status database Dynamically adjust the slice ratio of the first network slice template to obtain a second network slice template; Step S30: Obtain IoT data stream , based on the second network slice template, IoT data flow Perform real-time traffic marking and priority label binding, and output priority transmission schedule T; Step S40: Based on the output priority transmission schedule T, The multi-path links in the selection and allocation are optimized, and the target constraint conditions are optimized to obtain the optimal path subset. ; Step S50: Based on the optimal path subset Multi-level intelligent transmission deployment is carried out at the edge nodes of the Internet of Things through the network function virtualization mechanism, including dynamic batch processing, adaptive compression processing and redundant error correction processing, and the final optimized transmission control instructions are generated.

2. The method for intelligent data transmission of Internet of Things according to claim 1, wherein: In step S10, the multi-source heterogeneous network links include cellular networks, low-power wide area networks, and Wi-Fi networks; the real-time link performance parameters of the links include available bandwidth parameters, delay parameters, packet loss rate parameters, and usage costs.

3. The method for intelligently transmitting Internet of Things data according to claim 1, wherein: In step S20, the second network slice template is defined as , where k is the slice index number, which is used to distinguish the slice strategies corresponding to different application scenarios; is the kth network slice; Network slicing The corresponding end-to-end delay; Network slicing The lower bound of the probability that the corresponding data is delivered correctly; Network slicing The corresponding minimum bandwidth requirement; Network slicing The corresponding priority.

4. The method for intelligently transmitting Internet of Things data according to claim 3, wherein: In step S40, the optimal path subset The objective constraints of the optimization solution include the introduction of a reliability product model and the combination of a cost function, specifically satisfying: in, Indicates the Real-time delay of the link; Indicates the Reliability indicators of links; Indicates the The current available bandwidth of the link; Indicates a link The comprehensive cost of the network, including energy consumption, bandwidth costs and congestion indicators; Indicates that the link The constraint of minimum comprehensive cost; 、 、 Derived from the second network slice template.

5. The method for intelligent data transmission of Internet of Things according to claim 1, wherein: In step S30, before the step of outputting the priority transmission schedule table T, the method further includes: Classify and get the first priority slice , second priority slice and the third priority slice .

6. The method for intelligently transmitting Internet of Things data according to claim 1, wherein: In step S50, dynamic batch processing specifically adopts a dynamic remaining bandwidth awareness adjustment method, which performs buffer batch scheduling by dynamically calculating the batch factor by real-time detection of the remaining available bandwidth of the link, estimating the future load and the current cached data volume.

7. The method for intelligently transmitting Internet of Things data according to claim 1, wherein: In step S50, the adaptive compression process specifically includes: combining the IoT data stream The compression ratio is dynamically calculated based on the priority of the slice to which it belongs, and the data is graded according to the multi-layer progressive compression mode, giving priority to the minimum compression task of high-priority data and reducing the bit rate of low-priority data.

8. An Internet of Things data intelligent transmission system, applied to an Internet of Things data intelligent transmission method according to any one of claims 1 to 7, characterized in that: The IoT data intelligent transmission system includes: The link performance abstraction module is used to perform centralized abstraction processing on multi-source heterogeneous network links through a software-defined network controller, extract the real-time link performance parameters of each link, and establish a global link performance status database in the network controller based on the real-time link performance parameters of the link. ; Dynamic slicing adjustment module, used to pre-set the first network slicing template and adjust the network slicing template according to the global link performance status database Dynamically adjust the slice ratio of the first network slice template to obtain a second network slice template; Traffic marking and scheduling table generation module for obtaining IoT data streams , based on the second network slice template, IoT data flow Perform real-time traffic marking and priority label binding, and output priority transmission schedule T; The multi-path optimization selection module is used to select the optimal path for IoT data streams based on the output priority transmission schedule table T. The multi-path links in the selection and allocation are optimized, and the target constraint conditions are optimized to obtain the optimal path subset. ; Multi-level intelligent transmission deployment module for optimal path subsets Multi-level intelligent transmission deployment is carried out at the edge nodes of the Internet of Things through the network function virtualization mechanism, including dynamic batch processing, adaptive compression processing and redundant error correction processing, and the final optimized transmission control instructions are generated.

9. An IoT data intelligent transmission device, characterized in that: The IoT data intelligent transmission device includes: a memory, a processor, and an IoT data intelligent transmission program stored in the memory and runnable on the processor. When the IoT data intelligent transmission program is executed by the processor, an IoT data intelligent transmission method according to any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that The computer program product includes an Internet of Things data intelligent transmission program, and when the Internet of Things data intelligent transmission program is executed by a processor, it implements an Internet of Things data intelligent transmission method according to any one of claims 1 to 7.

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