Resource management and control system based on Internet of Things

Abstract

The invention discloses a resource management and control system based on the Internet of Things. An operation method of the system comprises the following steps: carrying out model correction control on the resource management and control system of the Internet of Things; adjusting, distributing and periodically managing the resource management and control system of the internet of things; analyzing and optimizing the Internet of Things resource management and control system; information transmission feedback control of the Internet of Things resource management and control system is carried out; the step of carrying out model correction control on the Internet of Things resource management and control system comprises the following steps: carrying out Internet of Things processing by adopting a symbol-level LoRa network model, further carrying out correction processing on the symbol-level LoRa network model, calibrating the network model and accurately representing a wireless link of LoRa by adjusting a path loss index, and when a gateway receives a data packet, carrying out the correction processing on the LoRa network model. According to the method, when the received signal strength is smaller than or equal to a set threshold value, the received signal power RSSI (Received Signal Strength Indicator) can be read, and when the received signal strength is smaller than or equal to the set threshold value, the value of the path loss index is adjusted so as to ensure that the received signal strength is larger than the set threshold value. The system has the characteristics of intelligent analysis management and high processing efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of the Internet of Things, and particularly to a resource control system based on the Internet of Things. Background Art

[0002] With the application and development of wireless sensing and communication technologies, the Internet of Things technology IoT (Internet-of-Things) is rapidly popularizing. Its basic idea is to connect various things that are commonly present around to achieve mutual interaction and communication, thereby strengthening the monitoring and control of the environment. Therefore, more and more Internet of Things devices (such as various sensors, etc.) are being widely deployed in the real environment. However, since Internet of Things devices usually have very limited storage, computing, and wireless bandwidth resources, once some Internet of Things devices in the network run out of power, the entire network may not be able to work properly. Low-power Internet of Things often senses the environment by deploying a large number of terminal devices. However, with the large increase in terminal devices, it is easy to have a huge error in the allocation of Internet of Things resources, making it difficult to meet its application requirements and seriously reducing user satisfaction. Therefore, it is necessary to design a resource control system based on the Internet of Things with high intelligent analysis management and processing efficiency. Summary of the Invention

[0003] The purpose of the present invention is to provide a resource control system based on the Internet of Things to solve the problems raised in the above background art.

[0004] To solve the above technical problems, the present invention provides the following technical solution: A resource control method based on the Internet of Things, including: Performing model correction control of the Internet of Things resource control system; Adjusting the allocation and cycle management of the Internet of Things resource control system; Performing analysis and optimization processing of the Internet of Things resource control system; Performing information transmission feedback control of the Internet of Things resource control system; The performing of model correction control of the Internet of Things resource control system includes: After performing Internet of Things processing using a symbol-level LoRa network model, further perform correction processing on it. By adjusting the path loss exponent to calibrate the network model and accurately represent the wireless link of LoRa. When the gateway receives a data packet, it can read its received signal strength RSSI (Received Signal Strength Indicator). When the received signal strength is less than or equal to the set threshold, adjust the value of the path loss exponent to ensure that the received signal strength is greater than the set threshold.

[0005] According to the above technical solution, the adjustment allocation and cycle management of the Internet of Things resource control system include: After the LoRa gateway is controlled to collect data from all terminal devices within a set time period, data is transmitted from the terminal device to the gateway, including information on its resource allocation and bit error rate. According to the collected information, the current network life cycle T (when the current resource allocation method remains unchanged) is analyzed through the central server; Set t as a percentage of the measured network life cycle, and select different set life cycles as t values for analysis respectively, so as to obtain the network performance t value (i.e., the optimal network performance value) that meets the set requirements.

[0006] According to the above technical solution, the analysis and optimization processing of the Internet of Things resource control system include: Deploy a link quality prediction algorithm based on the Recurrent Neural Network (RNN) on the Internet of Things edge server, and use the historical data collected in the corresponding low-power personal area network for prediction. First, evaluate the link quality through a set evaluation algorithm, and then predict the link quality according to the deployed RNN-based prediction algorithm.

[0007] According to the above technical solution, the analysis and optimization processing of the Internet of Things resource control system further includes: Taking the urgency and conflict situation of the path as indicators, perform Internet of Things path scheduling priority processing, and the central controller in the network dynamically adjusts according to the actual network topology, wireless environment information and set rules; Design a channel and time slot resource allocation strategy for the retransmission link. When the data transmission fails in the previous round, it serves as an alternative resource for data retransmission. If the data transmission is successful, data retransmission will not occur.

[0008] According to the above technical solution, the information transmission feedback control of the Internet of Things resource control system includes: After collecting the real-time operation data information of the Internet of Things resource control system, organize it into a table form and transmit it to the workbench for management personnel to monitor the operation status of the system; After the user gives feedback on the use of the system, send the feedback to the developers to provide a reliable reference basis for subsequent optimization.

[0009] According to the above technical solution, an Internet of Things-based resource control system includes: A control and management module for controlling and managing the Internet of Things resource control system; An analysis and processing module for optimizing and analyzing the Internet of Things resource control system; The acquisition and transmission module is used for data acquisition and transmission of the Internet of Things resource management and control system.

[0010] According to the above technical solution, the control and management module includes: The correction control module is used for correction control management of the model; The adjustment and allocation module is used for adjustment and allocation processing of Internet of Things resources; The cycle management module is used for network life cycle management.

[0011] According to the above technical solution, the analysis and processing module includes: The quality prediction module is used for prediction and analysis of the quality of Internet of Things links; The index analysis module is used for analysis and setting of Internet of Things resource allocation indexes; The retransmission control module is used for retransmission control of Internet of Things resource information.

[0012] According to the above technical solution, the acquisition and transmission module includes: The information acquisition module is used for information acquisition of the Internet of Things resource management and control system; The feedback transmission module is used for feedback transmission of user opinion information.

[0013] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: By providing a control and management module, an analysis and processing module, and an acquisition and transmission module, and through adaptive correction processing of the network model, the analysis and processing efficiency and accuracy of the Internet of Things resource management and control system can be effectively improved, the generation of analysis and processing errors can be avoided, the link quality evaluation efficiency of the Internet of Things resource management and control system can be effectively improved, and the influence of differences in link quality of wireless signals caused by channels and time slots can be effectively avoided. Description of the Drawings

[0014] The drawings are used to provide further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. In the drawings: Figure 1 is a flowchart of a resource management and control method based on the Internet of Things provided in Embodiment 1 of the present invention; Figure 2 is a module composition diagram of a resource management and control system based on the Internet of Things provided in Embodiment 2 of the present invention. Detailed Embodiments

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0016] Embodiment 1: Figure 1 It is a flowchart of a resource control method based on the Internet of Things provided in Embodiment 1 of the present invention. This embodiment can be applied to an Internet of Things resource control system. This method can be executed by a resource control system based on the Internet of Things provided in the embodiments of the present invention. The system consists of multiple software and hardware modules, such as Figure 1 As shown, the method specifically includes the following steps: S101. Perform model correction control on the Internet of Things resource control system; Exemplarily, in the embodiments of the present invention, after the Internet of Things is processed using a symbol-level LoRa network model, further correction processing is performed on it. By adjusting the path loss exponent to calibrate the network model and accurately represent the LoRa wireless link, when the gateway receives a data packet, it can read its received signal strength RSSI (Received Signal Strength Indicator). When the received signal strength is less than or equal to the set threshold, the value of the path loss exponent 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 during actual operation, resulting in low dynamic resource allocation efficiency. Therefore, through this step, by performing adaptive correction processing on the network model, the analysis and processing efficiency and accuracy of the Internet of Things resource control system can be effectively improved, and the generation of analysis and processing errors can be avoided.

[0017] S102. Perform adjustment allocation and cycle management on the Internet of Things resource control system; Exemplarily, in the embodiments of the present invention, after the LoRa gateway is controlled to collect data from all terminal devices within a set time period, the terminal devices transmit data to the gateway, including information on its resource allocation and bit error rate. According to the collected information, the current network lifetime T (when the current resource allocation method remains unchanged) is analyzed through the central server; in this step, the terminal devices are sorted according to the delay of receiving data. Starting from the terminal device with the shortest receiving time, the network lifetime T1 under different resource allocation methods is estimated. If T1 is larger than T and exceeds the threshold X, the server sends the new resource allocation method to this terminal device. Otherwise, the terminal device or set of terminal devices with the second lowest delay is selected, and the same process is repeated until a resource allocation method where T1 is larger than T and exceeds the threshold X is obtained, making the Internet of Things resource allocation processing more comprehensive and accurate.

[0018] Set t as a percentage of the measured network lifetime, and select different set lifetimes as t values for analysis respectively, so as to obtain the network performance t value that meets the set requirements (i.e., the optimal network performance value).

[0019] S103. Perform analysis and optimization processing on the Internet of Things resource management and control system; Exemplarily, in the embodiments of the present invention, a link quality prediction algorithm based on the Recurrent Neural Network (RNN) is deployed on the Internet of Things edge server, and historical data collected in the corresponding low-power personal area network is used for prediction. First, the link quality is evaluated through a set evaluation algorithm, and then the link quality is predicted according to the deployed RNN-based prediction algorithm; in this step, RNN is a type of neural network that models sequential data and is derived from the forward feedback network. Due to its internal storage function, it can record important information related to the input, enabling it to very accurately predict the next state. At the same time, considering that the change of link quality over time is continuous, it aims to capture the short-term change law of link quality and make full use of this short-term law for fine-grained link scheduling. For example, the receiving device of the data packet is adjusted in real time to select a more reliable link for data transmission in the short term. Although the RNN-based prediction algorithm can learn the short-term change of link quality from the historical record of link quality, in a low-power personal area network using TDMA, it is not necessary to make adaptive topology changes according to the short-term link quality change. Therefore, through this step, only the overall link quality of the selected link within the corresponding time (time slot) needs to be estimated, because the resource allocation is based on time slots, and the short-term link quality change has been included in the link quality estimation within the time slot, which can effectively improve the link quality evaluation efficiency of the Internet of Things resource management and control system and effectively avoid the influence of the difference in link quality of wireless signals caused by channels and time slots.

[0020] By taking the urgency and conflict of the path as indicators, the IoT path scheduling priority processing is performed, and the central controller in the network makes dynamic adjustments according to the actual network topology, wireless environment information and set rules; in this step, the path with a shorter deadline and more links has a higher urgency and needs to be arranged for resource allocation earlier to avoid the situation where channel resources and time slot resources are occupied by non-urgent paths and the available resources are insufficient and difficult to allocate. Due to the complexity of the network topology, different paths contain different numbers and locations of wireless links, and therefore conflict with different numbers of other links. Paths with more possible conflicts need to be allocated resources earlier because they have relatively fewer available resources. Therefore, through this step, the IoT path scheduling priority processing analysis can be made more efficient and accurate, and more in line with the actual situation of the IoT.

[0021] The channel and time slot resource allocation strategy of the retransmission link is designed. When the data fails to be transmitted in the previous round, it is used as an alternative resource for data retransmission. If the data transmission is successful, data retransmission will not occur. In this step, when the data retransmission is processed, the link set that allows retransmission on each time slot and channel is obtained: for each time slot and channel, if the resource is allocated to a link, it will not conflict with the allocated link and meet the front and back packet sending order of the link in the path where it is located, then it will be added to the retransmission link set of the time slot and channel resources. The retransmission link set constitutes multiple different conflict-free subsets (links in the same subset will not conflict with each other), and then the best subset is selected according to the set requirements, and the wireless links in it are arranged in the time slot and channel for data retransmission. By arranging the channel and time slot resources for retransmission, the packet receiving rate of the path is maximized, and when data retransmission does not occur, resources are not consumed, which can avoid unnecessary energy waste.

[0022] S104, performing information transmission feedback control of the Internet of Things resource management and control system; Exemplarily, in an embodiment of the present invention, after real-time operation data information of the IoT resource management and control system is collected, it is organized into a table format and transmitted to a workbench for management personnel to monitor the operation status of the system; After users provide feedback on the use of the system, the feedback will be sent to the developers to provide a reliable reference for subsequent optimization.

[0023] Embodiment 2: Embodiment 2 of the present invention provides a resource management and control system based on the Internet of Things. Figure 2 A schematic diagram of the module structure of a resource management and control system based on the Internet of Things provided in the second embodiment, such as Figure 2 As shown, the system includes: The control and management module is used for the control and management of the Internet of Things resource control system; The analysis and processing module is used for the optimization analysis and processing of the Internet of Things resource control system; The acquisition and transmission module is used for the data acquisition and transmission of the Internet of Things resource control system.

[0024] In some embodiments of the present invention, the control and management module includes: The correction control module is used for the correction control and management of the model; The adjustment and allocation module is used for the adjustment and allocation processing of Internet of Things resources; The cycle management module is used for the network life cycle management.

[0025] In some embodiments of the present invention, the analysis and processing module includes: The quality prediction module is used for the prediction and analysis of the Internet of Things link quality; The index analysis module is used for the analysis and setting of the Internet of Things resource allocation index; The retransmission control module is used for the retransmission control of the Internet of Things resource information.

[0026] In some embodiments of the present invention, the acquisition and transmission module includes: The information acquisition module is used for the information acquisition of the Internet of Things resource control system; The feedback transmission module is used for the feedback transmission of user opinion information.

[0027] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0028] Finally, it should be noted that: the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A resource management and control method based on the Internet of Things, characterized in that: include: Conduct model correction control of IoT resource management and control systems; Adjustment allocation and cycle management of IoT resource control system; Conduct analysis and optimization of IoT resource management and control systems; Conduct information transmission feedback control of the IoT resource management and control system; The model correction control of the Internet of Things resource management and control 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 gateway receives a data packet, it can read its received signal power RSSI (Received Signal Strength Indicator). When the received signal strength is less than or equal to the set threshold, the path loss index value is adjusted to ensure that the received signal strength is greater than the set threshold.

2. The resource management and control method based on the Internet of Things according to claim 1 is characterized in that: The adjustment allocation and cycle management of the IoT resource control system includes: After controlling the LoRa gateway to collect data from all terminal devices within the set time period, the terminal devices transmit data to the gateway, which contains information about its resource allocation and bit error rate. Based on the collected information, the central server analyzes and obtains the current network life cycle T (while keeping the current resource allocation method unchanged); Set t as a percentage of the measured network life cycle, and select different set life cycles as t values ​​for analysis, so as to obtain the network performance t value (i.e., the optimal network performance value) that meets the set requirements.

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

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

5. The resource management and control method based on the Internet of Things according to claim 1 is characterized in that: The information transmission feedback control of the Internet of Things resource management and control system includes: After collecting the real-time operation data information of the IoT resource management and control system, it is organized into a table and transmitted to the workbench for management personnel to monitor the operation status of the system; After users provide feedback on the use of the system, the feedback will be sent to the developers to provide a reliable reference for subsequent optimization.

6. A resource management and control system based on the Internet of Things, characterized by: include: Control management module, used for control and management of IoT resource management and control system; Analysis and processing module, used for optimizing analysis and processing of IoT resource management and control systems; The collection and transmission module is used for data collection and transmission of the Internet of Things resource management and control system.

7. The resource management and control system based on the Internet of Things according to claim 6 is characterized in that: The control management module includes: Correction control module, used for correction control management of the model; An adjustment and allocation module is used to adjust and allocate IoT resources; The lifecycle management module is used to manage the network lifecycle.

8. The resource management and control system based on the Internet of Things according to claim 6 is characterized in that: The analysis and processing module comprises: Quality prediction module, used for predicting and analyzing IoT link quality; Index analysis module, used to analyze and set the indexes for resource allocation in IoT; The retransmission control module is used to perform retransmission control of IoT resource information.

9. The resource management and control system based on the Internet of Things according to claim 6, characterized in that: The acquisition and transmission module comprises: Information collection module, used for information collection of IoT resource management and control system; The feedback transmission module is used to transmit feedback of user opinion information.

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