A resource management and control system based on the Internet of Things

Abstract

The application discloses a resource management and control system based on Internet of Things, and a running method of the system, which comprises the following steps: model correction control of the Internet of Things resource management and control system; adjustment distribution and periodical management of the Internet of Things resource management and control system; analysis optimization processing of the Internet of Things resource management and control system; information transmission feedback control of the Internet of Things resource management and control system; the model correction control of the Internet of Things resource management and control system comprises the following steps: after the Internet of Things processing is performed by using a symbol level LoRa network model, the processing is further corrected, the network model is calibrated by adjusting a path loss index, and the wireless link of the LoRa is accurately represented; when a gateway receives a data packet, the gateway can read a received signal strength indicator (RSSI); when the received signal strength is less than or equal to a set threshold value, the value of the path loss index is adjusted, so that the received signal strength is greater than the set threshold value. The application has the characteristics of intelligent analysis management and high processing efficiency.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, specifically to a resource management system based on IoT. Background Technology

[0002] With the application and development of wireless sensing and communication technologies, the Internet of Things (IoT) is rapidly gaining popularity. Its basic idea is to connect various ubiquitous things in our surroundings, enabling them to interact and communicate, thereby strengthening environmental monitoring and control. Therefore, an increasing number of IoT devices (such as various sensors) are being deployed in real-world environments. However, because IoT devices typically have very limited storage, computing, and wireless bandwidth resources, if some IoT devices in the network run out of power, the entire network may cease to function properly. Low-power IoT often senses the environment by deploying a large number of terminal devices, but with the massive increase in terminal devices, the allocation of IoT resources is prone to significant errors, making it difficult to meet application requirements and severely reducing user satisfaction. Therefore, it is essential to design an IoT-based resource management system with high intelligent analysis, management, and processing efficiency. Summary of the Invention

[0003] The purpose of this invention is to provide a resource management system based on the Internet of Things (IoT) to solve the problems mentioned in the background section.

[0004] To address the aforementioned technical problems, this invention provides the following technical solution: a resource management method based on the Internet of Things, comprising:

[0005] Model correction control for IoT resource management systems;

[0006] Adjustment, allocation, and periodic management of IoT resource management systems;

[0007] Analyze and optimize the IoT resource management system;

[0008] To perform information transmission and feedback control for the Internet of Things (IoT) resource management system;

[0009] The model correction control for the IoT resource management system includes:

[0010] After using the symbol-level LoRa network model for IoT processing, it is further corrected by adjusting the path loss index to calibrate the network model and accurately represent the LoRa wireless link. When the gateway receives a data packet, it can read the received signal strength (RSSI). If the received signal strength is less than or equal to a set threshold, the value of the path loss index is adjusted to ensure that the received signal strength is greater than the set threshold.

[0011] According to the above technical solution, the adjustment, allocation, and periodic management of the IoT resource management system include:

[0012] After controlling the LoRa gateway to collect data from all terminal devices within a set time period, the terminal devices transmit data to the gateway, which includes information on their resource allocation and bit error rate. Based on the collected information, the central server analyzes the data to obtain the current network lifecycle T (while keeping the current resource allocation method unchanged).

[0013] Set t to the percentage of the measured network lifetime, and select different set lifetimes as t values ​​for analysis to obtain the network performance t value that meets the set requirements (i.e. the optimal network performance value).

[0014] According to the above technical solution, the analysis and optimization process of the IoT resource management system includes:

[0015] A link quality prediction algorithm based on Recurrent Neural Network (RNN) is deployed on the IoT edge server. Historical data collected from the corresponding low-power personal area network is used for prediction. First, the link quality is evaluated by a set evaluation algorithm, and then the link quality is predicted according to the deployed RNN-based prediction algorithm.

[0016] According to the above technical solution, the analysis and optimization process of the IoT resource management system further includes:

[0017] The IoT path scheduling priority is handled based on the urgency and conflict of the path, and the central controller in the network makes dynamic adjustments based on the actual network topology, wireless environment information and set rules.

[0018] Design a channel and time slot resource allocation strategy for the retransmission link. When data fails in the previous round of transmission, it serves as a backup resource for data retransmission. If the data transmission is successful, data retransmission will not occur.

[0019] According to the above technical solution, the information transmission feedback control of the IoT resource management system includes:

[0020] After collecting real-time operational data from the IoT resource management system, the data is organized into tables and transmitted to the workbench for administrators to monitor the system's operational status.

[0021] After users provide feedback on how they use the system, they send their feedback to the developers, providing a reliable reference for future optimizations.

[0022] According to the above technical solution, a resource management and control system based on the Internet of Things includes:

[0023] The control and management module is used for the control and management of the IoT resource management system.

[0024] The analysis and processing module is used for optimization analysis and processing of the IoT resource management system.

[0025] The data acquisition and transmission module is used for data acquisition and transmission in the Internet of Things (IoT) resource management system.

[0026] According to the above technical solution, the control and management module includes:

[0027] The correction control module is used for correction control management of the model;

[0028] The allocation module is used for adjusting and allocating IoT resources.

[0029] The lifecycle management module is used for network lifecycle management.

[0030] According to the above technical solution, the analysis and processing module includes:

[0031] The quality prediction module is used for predictive analysis of IoT link quality.

[0032] The indicator analysis module is used to set and analyze indicators for IoT resource allocation.

[0033] The retransmission control module is used to control the retransmission of IoT resource information.

[0034] According to the above technical solution, the acquisition and transmission module includes:

[0035] The information acquisition module is used to collect information for the Internet of Things (IoT) resource management system.

[0036] The feedback transmission module is used to transmit user feedback information.

[0037] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: The present invention, by setting up a control management module, an analysis and processing module, and an acquisition and transmission module, and by adaptively correcting the network model, can effectively improve the analysis and processing efficiency and accuracy of the IoT resource management system, avoid the generation of analysis and processing errors, and effectively improve the link quality assessment efficiency of the IoT resource management system, effectively avoiding the impact of differences in wireless signal link quality caused by channels and time slots. Attached Figure Description

[0038] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0039] Figure 1 This is a flowchart of a resource management method based on the Internet of Things provided in Embodiment 1 of the present invention;

[0040] Figure 2 This is a module configuration diagram of a resource management system based on the Internet of Things provided in Embodiment 2 of the present invention. Detailed Implementation

[0041] 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, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Example 1: Figure 1 This is a flowchart illustrating a resource management method based on the Internet of Things (IoT) according to Embodiment 1 of the present invention. This embodiment can be applied to an IoT resource management system. The method can be executed by an IoT-based resource management system provided in this embodiment, which consists of multiple software and hardware modules, such as... Figure 1 As shown, the method specifically includes the following steps:

[0043] S101, Perform model correction control for the Internet of Things resource management system;

[0044] For example, in this embodiment of the invention, after using a symbol-level LoRa network model for IoT processing, it is further corrected by adjusting the path loss index to calibrate the network model and accurately represent the LoRa wireless link. When the gateway receives a data packet, it can read the received signal strength (RSSI). If the received signal strength is less than or equal to a set threshold, the value of the path loss index is adjusted to ensure that the received signal strength is greater than the set threshold. Due to the dynamic nature of the wireless environment, the network model may become inaccurate in actual operation, resulting in low efficiency of dynamic resource allocation. Therefore, through this step, by adaptively correcting the network model, the analysis and processing efficiency and accuracy of the IoT resource management system can be effectively improved, and the generation of analysis and processing errors can be avoided.

[0045] S102. Adjustment, allocation, and periodic management of IoT resource management system;

[0046] For example, in this embodiment of the invention, after controlling the LoRa gateway to collect data from all terminal devices within a set time period, the terminal devices transmit data to the gateway, which includes information on their resource allocation and bit error rate. Based on the collected information, the central server analyzes and obtains the current network lifetime T (while keeping the current resource allocation method unchanged). In this step, the terminal devices are sorted according to the latency of receiving data. Starting from the terminal device with the shortest reception time, the network lifetime T1 under different resource allocation methods is estimated. If T1 is greater than T and exceeds the threshold X, the server sends the new resource allocation method to that terminal device. Otherwise, the same process is repeated for the terminal device or set of terminal devices with the second lowest latency, to obtain the resource allocation method where T1 is greater than T and exceeds the threshold X, making the IoT resource allocation processing more comprehensive and accurate.

[0047] Set t to the percentage of the measured network lifetime, and select different set lifetimes as t values ​​for analysis to obtain the network performance t value that meets the set requirements (i.e. the optimal network performance value).

[0048] S103. Perform analysis and optimization of the IoT resource management system;

[0049] For example, in this embodiment of the invention, a link quality prediction algorithm based on a Recurrent Neural Network (RNN) is deployed on an IoT edge server. Historical data collected from the corresponding low-power personal area network (LPA) is used for prediction. First, the link quality is evaluated using a pre-defined evaluation algorithm, and then the deployed RNN-based prediction algorithm is used to predict the link quality. In this step, RNN is a type of neural network that models sequential data, originating from feedforward networks. Due to its internal storage function, it can record important information related to the input, enabling it to predict the next state very accurately. Considering that link quality changes continuously over time, the algorithm aims to capture the short-term variation patterns of link quality and fully utilize these short-term patterns for fine-grained link scheduling, such as adjusting the receiving device of data packets in real time to select a more reliable link for data transmission in the short term. Although the RNN-based prediction algorithm can learn short-term changes in link quality from historical link quality records, it is still necessary to consider the impact of TDMA on link quality. In low-power personal area networks, there is no need for adaptive topology changes based on short-term link quality variations. Therefore, this step only requires estimating the overall link quality of the selected link within the corresponding time slot. Since resource allocation is based on time slots, short-term link quality variations are already included in the link quality estimation within the time slot. This can effectively improve the link quality assessment efficiency of IoT resource management systems and effectively avoid the impact of differences in wireless signal link quality caused by channels and time slots.

[0050] By prioritizing IoT path scheduling based on path urgency and conflict levels, the central controller in the network dynamically adjusts the process according to the actual network topology, wireless environment information, and set rules. In this step, paths with shorter deadlines and more links have higher urgency and need to be allocated resources earlier to prevent channel and time slot resources from being occupied by less urgent paths, or insufficient available resources for allocation. Furthermore, due to the complexity of the network topology, different paths contain varying numbers and locations of wireless links, thus conflicting with different numbers of other links. Paths with a higher potential for conflict need to be allocated resources earlier because they have relatively fewer available resources. Therefore, this step makes IoT path scheduling priority analysis more efficient, accurate, and more consistent with the actual situation of the IoT.

[0051] The design of the retransmission link channel and time slot resource allocation strategy is as follows: when data fails in the previous round of transmission, it serves as a backup resource for data retransmission; if the data transmission is successful, data retransmission will not occur. In this step, when performing data retransmission processing, the set of links allowed for retransmission on each time slot and channel is obtained: for each time slot and channel, if allocating the resource to a link will not conflict with the already allocated links and satisfies the packet transmission order of the links in its path, then it is added to the retransmission link set of that time slot and channel resource. The retransmission link set constitutes multiple different conflict-free subsets (links within the same subset will not conflict with each other). Then, according to the set requirements, the best subset is selected, and the wireless links within it are arranged for data retransmission on that time slot and channel. By arranging the channel and time slot resources for retransmission, the packet reception rate of the path is maximized, and resources are not consumed when no data retransmission occurs, thus avoiding unnecessary energy waste.

[0052] S104. Perform information transmission feedback control for the Internet of Things resource management system;

[0053] For example, in this embodiment of the invention, after collecting the real-time operating data information of the Internet of Things resource management system, it is organized into a table format and transmitted to the workbench for managers to monitor the operating status of the system.

[0054] After users provide feedback on how they use the system, they send their feedback to the developers, providing a reliable reference for future optimizations.

[0055] Example 2: Example 2 of the present invention provides a resource management and control system based on the Internet of Things. Figure 2 This is a schematic diagram of the module structure of a resource management system based on the Internet of Things provided in Embodiment 2, as shown below. Figure 2 As shown, the system includes:

[0056] The control and management module is used for the control and management of the IoT resource management system.

[0057] The analysis and processing module is used for optimization analysis and processing of the IoT resource management system.

[0058] The data acquisition and transmission module is used for data acquisition and transmission in the Internet of Things (IoT) resource management system.

[0059] In some embodiments of the present invention, the control and management module includes:

[0060] The correction control module is used for correction control management of the model;

[0061] The allocation module is used for adjusting and allocating IoT resources.

[0062] The lifecycle management module is used for network lifecycle management.

[0063] In some embodiments of the present invention, the analysis and processing module includes:

[0064] The quality prediction module is used for predictive analysis of IoT link quality.

[0065] The indicator analysis module is used to set and analyze indicators for IoT resource allocation.

[0066] The retransmission control module is used to control the retransmission of IoT resource information.

[0067] In some embodiments of the present invention, the acquisition and transmission module includes:

[0068] The information acquisition module is used to collect information for the Internet of Things (IoT) resource management system.

[0069] The feedback transmission module is used to transmit user feedback information.

[0070] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0071] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A resource management method based on the Internet of Things, characterized in that: include: Model correction control for IoT resource management systems; Adjustment, allocation, and periodic management of IoT resource management systems; Analyze and optimize the IoT resource management system; To perform information transmission and feedback control for the Internet of Things (IoT) resource management system; The model correction control for the IoT resource management system includes: After using the symbol-level LoRa network model for IoT processing, it is further corrected by adjusting the path loss index to calibrate the network model and accurately represent the LoRa wireless link. When the LoRa gateway receives a data packet, it reads the Received Signal Strength Indicator (RSSI). If the received signal strength is less than or equal to the set threshold, the value of the path loss index is adjusted to ensure that the received signal strength is greater than the set threshold. The adjustment, allocation, and periodic management of the IoT resource management system include: After the LoRa gateway collects data from all terminal devices within a set time period, the terminal devices transmit data to the LoRa gateway, which includes information on their resource allocation and bit error rate. The central server analyzes the collected information to obtain the current network lifetime T while keeping the current resource allocation method unchanged. The terminal devices are sorted according to their data reception latency. Starting from the terminal device with the shortest reception time, the network lifetime T1 under different resource allocation methods is estimated. If T1 is greater than T and exceeds the threshold X, the central server sends the new resource allocation method to that terminal device. Otherwise, the same process is repeated for the terminal device or set of terminal devices with the second lowest latency to obtain the resource allocation method where T1 is greater than T and exceeds the threshold X. Set t as a percentage of the measured network lifetime T, and analyze the t values ​​corresponding to different set lifetimes T1 to obtain network performance t values ​​that meet the set requirements.

2. The resource management method based on the Internet of Things according to claim 1, characterized in that: The analysis and optimization process for the IoT resource management system includes: A link quality prediction algorithm based on Recurrent Neural Network (RNN) is deployed on the IoT edge server. Historical data collected from the corresponding low-power personal area network is used for prediction. First, the link quality is evaluated by a set evaluation algorithm, and then the link quality is predicted according to the deployed RNN-based prediction algorithm.

3. The resource management method based on the Internet of Things according to claim 1, characterized in that: The analysis and optimization process for the IoT resource management system further includes: The IoT path scheduling priority is handled based on the urgency and conflict of the path, and the central controller in the network makes dynamic adjustments based on the actual network topology, wireless environment information and set rules. Design a channel and time slot resource allocation strategy for the retransmission link. When data fails in the previous round of transmission, it serves as a backup resource for data retransmission. If the data transmission is successful, data retransmission will not occur.

4. The resource management method based on the Internet of Things according to claim 1, characterized in that: The information transmission feedback control of the Internet of Things resource management system includes: After collecting real-time operational data from the IoT resource management system, the data is organized into tables and transmitted to the workbench for administrators to monitor the system's operational status. After users provide feedback on how they use the system, they send their feedback to the developers, providing a reliable reference for future optimizations.

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